diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/1 - Basics, Humidity, Design.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/1 - Basics, Humidity, Design.ipynb
index f1f2e5bc7..16b4bee37 100644
--- a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/1 - Basics, Humidity, Design.ipynb	
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/1 - Basics, Humidity, Design.ipynb	
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -43,23 +43,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Working on a  Windows 10\n",
-      "Python version  3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n",
-      "Pandas version  2.2.0\n",
-      "pvdeg version  0.2.4.dev83+ge2ceab9.d20240422\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# This information helps with debugging and getting support :)\n",
-    "import sys, platform\n",
+    "import sys\n",
+    "import platform\n",
     "\n",
     "print(\"Working on a \", platform.system(), platform.release())\n",
     "print(\"Python version \", sys.version)\n",
@@ -92,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -110,145 +100,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "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>Year</th>\n",
-       "      <th>Month</th>\n",
-       "      <th>Day</th>\n",
-       "      <th>Hour</th>\n",
-       "      <th>Minute</th>\n",
-       "      <th>dni</th>\n",
-       "      <th>dhi</th>\n",
-       "      <th>ghi</th>\n",
-       "      <th>temp_air</th>\n",
-       "      <th>dew_point</th>\n",
-       "      <th>wind_speed</th>\n",
-       "      <th>relative_humidity</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 00:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-5.0</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>79.39</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 01:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-4.0</td>\n",
-       "      <td>1.7</td>\n",
-       "      <td>80.84</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 02:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-4.0</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>82.98</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 03:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-4.0</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>85.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 04:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-4.0</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>85.81</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                           Year  Month  Day  Hour  Minute  dni  dhi  ghi  \\\n",
-       "1999-01-01 00:30:00-07:00  1999      1    1     0      30  0.0  0.0  0.0   \n",
-       "1999-01-01 01:30:00-07:00  1999      1    1     1      30  0.0  0.0  0.0   \n",
-       "1999-01-01 02:30:00-07:00  1999      1    1     2      30  0.0  0.0  0.0   \n",
-       "1999-01-01 03:30:00-07:00  1999      1    1     3      30  0.0  0.0  0.0   \n",
-       "1999-01-01 04:30:00-07:00  1999      1    1     4      30  0.0  0.0  0.0   \n",
-       "\n",
-       "                           temp_air  dew_point  wind_speed  relative_humidity  \n",
-       "1999-01-01 00:30:00-07:00       0.0       -5.0         1.8              79.39  \n",
-       "1999-01-01 01:30:00-07:00       0.0       -4.0         1.7              80.84  \n",
-       "1999-01-01 02:30:00-07:00       0.0       -4.0         1.5              82.98  \n",
-       "1999-01-01 03:30:00-07:00       0.0       -4.0         1.3              85.01  \n",
-       "1999-01-01 04:30:00-07:00       0.0       -4.0         1.3              85.81  "
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "WEATHER.head()"
    ]
@@ -262,64 +116,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Source': 'NSRDB',\n",
-       " 'Location ID': '145809',\n",
-       " 'City': '-',\n",
-       " 'State': '-',\n",
-       " 'Country': '-',\n",
-       " 'Clearsky DHI Units': 'w/m2',\n",
-       " 'Clearsky DNI Units': 'w/m2',\n",
-       " 'Clearsky GHI Units': 'w/m2',\n",
-       " 'Dew Point Units': 'c',\n",
-       " 'DHI Units': 'w/m2',\n",
-       " 'DNI Units': 'w/m2',\n",
-       " 'GHI Units': 'w/m2',\n",
-       " 'Solar Zenith Angle Units': 'Degree',\n",
-       " 'Temperature Units': 'c',\n",
-       " 'Pressure Units': 'mbar',\n",
-       " 'Relative Humidity Units': '%',\n",
-       " 'Precipitable Water Units': 'cm',\n",
-       " 'Wind Direction Units': 'Degrees',\n",
-       " 'Wind Speed Units': 'm/s',\n",
-       " 'Cloud Type -15': 'N/A',\n",
-       " 'Cloud Type 0': 'Clear',\n",
-       " 'Cloud Type 1': 'Probably Clear',\n",
-       " 'Cloud Type 2': 'Fog',\n",
-       " 'Cloud Type 3': 'Water',\n",
-       " 'Cloud Type 4': 'Super-Cooled Water',\n",
-       " 'Cloud Type 5': 'Mixed',\n",
-       " 'Cloud Type 6': 'Opaque Ice',\n",
-       " 'Cloud Type 7': 'Cirrus',\n",
-       " 'Cloud Type 8': 'Overlapping',\n",
-       " 'Cloud Type 9': 'Overshooting',\n",
-       " 'Cloud Type 10': 'Unknown',\n",
-       " 'Cloud Type 11': 'Dust',\n",
-       " 'Cloud Type 12': 'Smoke',\n",
-       " 'Fill Flag 0': 'N/A',\n",
-       " 'Fill Flag 1': 'Missing Image',\n",
-       " 'Fill Flag 2': 'Low Irradiance',\n",
-       " 'Fill Flag 3': 'Exceeds Clearsky',\n",
-       " 'Fill Flag 4': 'Missing CLoud Properties',\n",
-       " 'Fill Flag 5': 'Rayleigh Violation',\n",
-       " 'Surface Albedo Units': 'N/A',\n",
-       " 'Version': '3.0.6',\n",
-       " 'latitude': 39.73,\n",
-       " 'longitude': -105.18,\n",
-       " 'altitude': 1820,\n",
-       " 'tz': -7}"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "META"
    ]
@@ -339,250 +138,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Year</th>\n",
-       "      <th>Month</th>\n",
-       "      <th>Day</th>\n",
-       "      <th>Hour</th>\n",
-       "      <th>Minute</th>\n",
-       "      <th>dni</th>\n",
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-       "      <th>temp_air</th>\n",
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-       "      <th>wind_speed</th>\n",
-       "      <th>relative_humidity</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1999-06-01 00:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.7</td>\n",
-       "      <td>79.15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-06-01 01:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <td>1.7</td>\n",
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-       "    <tr>\n",
-       "      <th>1999-06-01 02:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>78.51</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-06-01 03:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>76.90</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-06-01 04:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>75.75</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",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-06-30 19:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>6</td>\n",
-       "      <td>30</td>\n",
-       "      <td>19</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>48.54</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-06-30 20:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>6</td>\n",
-       "      <td>30</td>\n",
-       "      <td>20</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>55.36</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-06-30 21:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>6</td>\n",
-       "      <td>30</td>\n",
-       "      <td>21</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>66.32</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-06-30 22:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>6</td>\n",
-       "      <td>30</td>\n",
-       "      <td>22</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>75.74</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-06-30 23:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>6</td>\n",
-       "      <td>30</td>\n",
-       "      <td>23</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>80.16</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>720 rows × 12 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                           Year  Month  Day  Hour  Minute  dni  dhi  ghi  \\\n",
-       "1999-06-01 00:30:00-07:00  1999      6    1     0      30  0.0  0.0  0.0   \n",
-       "1999-06-01 01:30:00-07:00  1999      6    1     1      30  0.0  0.0  0.0   \n",
-       "1999-06-01 02:30:00-07:00  1999      6    1     2      30  0.0  0.0  0.0   \n",
-       "1999-06-01 03:30:00-07:00  1999      6    1     3      30  0.0  0.0  0.0   \n",
-       "1999-06-01 04:30:00-07:00  1999      6    1     4      30  0.0  0.0  0.0   \n",
-       "...                         ...    ...  ...   ...     ...  ...  ...  ...   \n",
-       "1999-06-30 19:30:00-07:00  1999      6   30    19      30  0.0  0.0  0.0   \n",
-       "1999-06-30 20:30:00-07:00  1999      6   30    20      30  0.0  0.0  0.0   \n",
-       "1999-06-30 21:30:00-07:00  1999      6   30    21      30  0.0  0.0  0.0   \n",
-       "1999-06-30 22:30:00-07:00  1999      6   30    22      30  0.0  0.0  0.0   \n",
-       "1999-06-30 23:30:00-07:00  1999      6   30    23      30  0.0  0.0  0.0   \n",
-       "\n",
-       "                           temp_air  dew_point  wind_speed  relative_humidity  \n",
-       "1999-06-01 00:30:00-07:00       6.0        0.0         1.7              79.15  \n",
-       "1999-06-01 01:30:00-07:00       6.0        0.0         1.7              75.62  \n",
-       "1999-06-01 02:30:00-07:00       6.0        0.0         1.5              78.51  \n",
-       "1999-06-01 03:30:00-07:00       5.0        0.0         1.3              76.90  \n",
-       "1999-06-01 04:30:00-07:00       6.0        0.0         1.3              75.75  \n",
-       "...                             ...        ...         ...                ...  \n",
-       "1999-06-30 19:30:00-07:00      21.0        6.0         0.6              48.54  \n",
-       "1999-06-30 20:30:00-07:00      19.0        7.0         0.4              55.36  \n",
-       "1999-06-30 21:30:00-07:00      17.0        7.0         0.5              66.32  \n",
-       "1999-06-30 22:30:00-07:00      15.0        7.0         0.8              75.74  \n",
-       "1999-06-30 23:30:00-07:00      13.0        7.0         0.8              80.16  \n",
-       "\n",
-       "[720 rows x 12 columns]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Let's select the month of June\n",
     "june_weather = WEATHER[WEATHER.index.month == 6]\n",
@@ -591,250 +149,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th>Year</th>\n",
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1999-06-01 00:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
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-       "      <th>1999-06-01 01:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
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-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.7</td>\n",
-       "      <td>75.62</td>\n",
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-       "    <tr>\n",
-       "      <th>1999-06-01 02:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>78.51</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-06-01 03:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>76.90</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-06-01 04:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>75.75</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>...</td>\n",
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-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-09-30 19:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>9</td>\n",
-       "      <td>30</td>\n",
-       "      <td>19</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>2.6</td>\n",
-       "      <td>58.57</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-09-30 20:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>9</td>\n",
-       "      <td>30</td>\n",
-       "      <td>20</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>-2.0</td>\n",
-       "      <td>2.7</td>\n",
-       "      <td>58.17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-09-30 21:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>9</td>\n",
-       "      <td>30</td>\n",
-       "      <td>21</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>-3.0</td>\n",
-       "      <td>2.9</td>\n",
-       "      <td>54.27</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-09-30 22:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>9</td>\n",
-       "      <td>30</td>\n",
-       "      <td>22</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>-4.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>50.29</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-09-30 23:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>9</td>\n",
-       "      <td>30</td>\n",
-       "      <td>23</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>-5.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>46.91</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2928 rows × 12 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                           Year  Month  Day  Hour  Minute  dni  dhi  ghi  \\\n",
-       "1999-06-01 00:30:00-07:00  1999      6    1     0      30  0.0  0.0  0.0   \n",
-       "1999-06-01 01:30:00-07:00  1999      6    1     1      30  0.0  0.0  0.0   \n",
-       "1999-06-01 02:30:00-07:00  1999      6    1     2      30  0.0  0.0  0.0   \n",
-       "1999-06-01 03:30:00-07:00  1999      6    1     3      30  0.0  0.0  0.0   \n",
-       "1999-06-01 04:30:00-07:00  1999      6    1     4      30  0.0  0.0  0.0   \n",
-       "...                         ...    ...  ...   ...     ...  ...  ...  ...   \n",
-       "1999-09-30 19:30:00-07:00  1999      9   30    19      30  0.0  0.0  0.0   \n",
-       "1999-09-30 20:30:00-07:00  1999      9   30    20      30  0.0  0.0  0.0   \n",
-       "1999-09-30 21:30:00-07:00  1999      9   30    21      30  0.0  0.0  0.0   \n",
-       "1999-09-30 22:30:00-07:00  1999      9   30    22      30  0.0  0.0  0.0   \n",
-       "1999-09-30 23:30:00-07:00  1999      9   30    23      30  0.0  0.0  0.0   \n",
-       "\n",
-       "                           temp_air  dew_point  wind_speed  relative_humidity  \n",
-       "1999-06-01 00:30:00-07:00       6.0        0.0         1.7              79.15  \n",
-       "1999-06-01 01:30:00-07:00       6.0        0.0         1.7              75.62  \n",
-       "1999-06-01 02:30:00-07:00       6.0        0.0         1.5              78.51  \n",
-       "1999-06-01 03:30:00-07:00       5.0        0.0         1.3              76.90  \n",
-       "1999-06-01 04:30:00-07:00       6.0        0.0         1.3              75.75  \n",
-       "...                             ...        ...         ...                ...  \n",
-       "1999-09-30 19:30:00-07:00       8.0       -1.0         2.6              58.57  \n",
-       "1999-09-30 20:30:00-07:00       7.0       -2.0         2.7              58.17  \n",
-       "1999-09-30 21:30:00-07:00       7.0       -3.0         2.9              54.27  \n",
-       "1999-09-30 22:30:00-07:00       7.0       -4.0         3.0              50.29  \n",
-       "1999-09-30 23:30:00-07:00       7.0       -5.0         3.0              46.91  \n",
-       "\n",
-       "[2928 rows x 12 columns]"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Let's filter the summer months for the northern hemisphere\n",
     "summer_months = [6, 7, 8, 9]\n",
@@ -844,263 +161,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th>relative_humidity</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 08:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>8</td>\n",
-       "      <td>30</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-3.0</td>\n",
-       "      <td>4.7</td>\n",
-       "      <td>76.86</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 09:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>9</td>\n",
-       "      <td>30</td>\n",
-       "      <td>503.0</td>\n",
-       "      <td>93.0</td>\n",
-       "      <td>246.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>-2.0</td>\n",
-       "      <td>6.3</td>\n",
-       "      <td>80.37</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 10:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>10</td>\n",
-       "      <td>30</td>\n",
-       "      <td>617.0</td>\n",
-       "      <td>109.0</td>\n",
-       "      <td>355.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>-2.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>74.87</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 11:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>11</td>\n",
-       "      <td>30</td>\n",
-       "      <td>497.0</td>\n",
-       "      <td>161.0</td>\n",
-       "      <td>385.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>-3.0</td>\n",
-       "      <td>6.8</td>\n",
-       "      <td>70.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 12:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>12</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>-4.0</td>\n",
-       "      <td>6.5</td>\n",
-       "      <td>61.80</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",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-12-31 12:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>12</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>-8.0</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>31.45</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-12-31 13:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>13</td>\n",
-       "      <td>30</td>\n",
-       "      <td>562.0</td>\n",
-       "      <td>125.0</td>\n",
-       "      <td>354.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>-6.0</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>40.25</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-12-31 14:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>14</td>\n",
-       "      <td>30</td>\n",
-       "      <td>530.0</td>\n",
-       "      <td>94.0</td>\n",
-       "      <td>263.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>-4.0</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>53.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-12-31 15:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>15</td>\n",
-       "      <td>30</td>\n",
-       "      <td>636.0</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>159.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>-5.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>57.10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-12-31 16:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>16</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>-6.0</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>56.22</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>4301 rows × 12 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                           Year  Month  Day  Hour  Minute    dni    dhi  \\\n",
-       "1999-01-01 08:30:00-07:00  1999      1    1     8      30   65.0   65.0   \n",
-       "1999-01-01 09:30:00-07:00  1999      1    1     9      30  503.0   93.0   \n",
-       "1999-01-01 10:30:00-07:00  1999      1    1    10      30  617.0  109.0   \n",
-       "1999-01-01 11:30:00-07:00  1999      1    1    11      30  497.0  161.0   \n",
-       "1999-01-01 12:30:00-07:00  1999      1    1    12      30    0.0  128.0   \n",
-       "...                         ...    ...  ...   ...     ...    ...    ...   \n",
-       "1999-12-31 12:30:00-07:00  1999     12   31    12      30    0.0   20.0   \n",
-       "1999-12-31 13:30:00-07:00  1999     12   31    13      30  562.0  125.0   \n",
-       "1999-12-31 14:30:00-07:00  1999     12   31    14      30  530.0   94.0   \n",
-       "1999-12-31 15:30:00-07:00  1999     12   31    15      30  636.0   38.0   \n",
-       "1999-12-31 16:30:00-07:00  1999     12   31    16      30    0.0   21.0   \n",
-       "\n",
-       "                             ghi  temp_air  dew_point  wind_speed  \\\n",
-       "1999-01-01 08:30:00-07:00   76.0       1.0       -3.0         4.7   \n",
-       "1999-01-01 09:30:00-07:00  246.0       2.0       -2.0         6.3   \n",
-       "1999-01-01 10:30:00-07:00  355.0       3.0       -2.0         7.0   \n",
-       "1999-01-01 11:30:00-07:00  385.0       4.0       -3.0         6.8   \n",
-       "1999-01-01 12:30:00-07:00  128.0       4.0       -4.0         6.5   \n",
-       "...                          ...       ...        ...         ...   \n",
-       "1999-12-31 12:30:00-07:00   20.0       9.0       -8.0         1.6   \n",
-       "1999-12-31 13:30:00-07:00  354.0       8.0       -6.0         0.5   \n",
-       "1999-12-31 14:30:00-07:00  263.0       7.0       -4.0         0.4   \n",
-       "1999-12-31 15:30:00-07:00  159.0       5.0       -5.0         1.0   \n",
-       "1999-12-31 16:30:00-07:00   21.0       4.0       -6.0         1.3   \n",
-       "\n",
-       "                           relative_humidity  \n",
-       "1999-01-01 08:30:00-07:00              76.86  \n",
-       "1999-01-01 09:30:00-07:00              80.37  \n",
-       "1999-01-01 10:30:00-07:00              74.87  \n",
-       "1999-01-01 11:30:00-07:00              70.73  \n",
-       "1999-01-01 12:30:00-07:00              61.80  \n",
-       "...                                      ...  \n",
-       "1999-12-31 12:30:00-07:00              31.45  \n",
-       "1999-12-31 13:30:00-07:00              40.25  \n",
-       "1999-12-31 14:30:00-07:00              53.19  \n",
-       "1999-12-31 15:30:00-07:00              57.10  \n",
-       "1999-12-31 16:30:00-07:00              56.22  \n",
-       "\n",
-       "[4301 rows x 12 columns]"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# lets just select hours from the year where the sun is above the horizon\n",
     "sunup_weather = WEATHER[WEATHER[\"ghi\"] > 0]\n",
@@ -1118,20 +181,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "sol_pos = pvdeg.spectral.solar_position(weather_df=WEATHER, meta=META)\n",
     "\n",
@@ -1155,178 +207,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>apparent_zenith</th>\n",
-       "      <th>zenith</th>\n",
-       "      <th>apparent_elevation</th>\n",
-       "      <th>elevation</th>\n",
-       "      <th>azimuth</th>\n",
-       "      <th>equation_of_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 00:30:00-07:00</th>\n",
-       "      <td>162.416069</td>\n",
-       "      <td>162.416069</td>\n",
-       "      <td>-72.416069</td>\n",
-       "      <td>-72.416069</td>\n",
-       "      <td>20.152470</td>\n",
-       "      <td>-3.300994</td>\n",
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-       "    <tr>\n",
-       "      <th>1999-01-01 01:30:00-07:00</th>\n",
-       "      <td>155.313029</td>\n",
-       "      <td>155.313029</td>\n",
-       "      <td>-65.313029</td>\n",
-       "      <td>-65.313029</td>\n",
-       "      <td>53.821029</td>\n",
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-       "    <tr>\n",
-       "      <th>1999-01-01 02:30:00-07:00</th>\n",
-       "      <td>144.992553</td>\n",
-       "      <td>144.992553</td>\n",
-       "      <td>-54.992553</td>\n",
-       "      <td>-54.992553</td>\n",
-       "      <td>72.531199</td>\n",
-       "      <td>-3.340306</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 03:30:00-07:00</th>\n",
-       "      <td>133.692678</td>\n",
-       "      <td>133.692678</td>\n",
-       "      <td>-43.692678</td>\n",
-       "      <td>-43.692678</td>\n",
-       "      <td>84.789909</td>\n",
-       "      <td>-3.359948</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 04:30:00-07:00</th>\n",
-       "      <td>122.172658</td>\n",
-       "      <td>122.172658</td>\n",
-       "      <td>-32.172658</td>\n",
-       "      <td>-32.172658</td>\n",
-       "      <td>94.462003</td>\n",
-       "      <td>-3.379582</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",
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-       "    <tr>\n",
-       "      <th>1999-12-31 19:30:00-07:00</th>\n",
-       "      <td>120.682889</td>\n",
-       "      <td>120.682889</td>\n",
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-       "      <td>-30.682889</td>\n",
-       "      <td>264.307279</td>\n",
-       "      <td>-3.093347</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-12-31 20:30:00-07:00</th>\n",
-       "      <td>132.197914</td>\n",
-       "      <td>132.197914</td>\n",
-       "      <td>-42.197914</td>\n",
-       "      <td>-42.197914</td>\n",
-       "      <td>273.785399</td>\n",
-       "      <td>-3.113209</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-12-31 21:30:00-07:00</th>\n",
-       "      <td>143.553813</td>\n",
-       "      <td>143.553813</td>\n",
-       "      <td>-53.553813</td>\n",
-       "      <td>-53.553813</td>\n",
-       "      <td>285.552685</td>\n",
-       "      <td>-3.133063</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-12-31 22:30:00-07:00</th>\n",
-       "      <td>154.080531</td>\n",
-       "      <td>154.080531</td>\n",
-       "      <td>-64.080531</td>\n",
-       "      <td>-64.080531</td>\n",
-       "      <td>303.037057</td>\n",
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-       "    <tr>\n",
-       "      <th>1999-12-31 23:30:00-07:00</th>\n",
-       "      <td>161.842482</td>\n",
-       "      <td>161.842482</td>\n",
-       "      <td>-71.842482</td>\n",
-       "      <td>-71.842482</td>\n",
-       "      <td>334.208238</td>\n",
-       "      <td>-3.172747</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>8760 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                           apparent_zenith      zenith  apparent_elevation  \\\n",
-       "1999-01-01 00:30:00-07:00       162.416069  162.416069          -72.416069   \n",
-       "1999-01-01 01:30:00-07:00       155.313029  155.313029          -65.313029   \n",
-       "1999-01-01 02:30:00-07:00       144.992553  144.992553          -54.992553   \n",
-       "1999-01-01 03:30:00-07:00       133.692678  133.692678          -43.692678   \n",
-       "1999-01-01 04:30:00-07:00       122.172658  122.172658          -32.172658   \n",
-       "...                                    ...         ...                 ...   \n",
-       "1999-12-31 19:30:00-07:00       120.682889  120.682889          -30.682889   \n",
-       "1999-12-31 20:30:00-07:00       132.197914  132.197914          -42.197914   \n",
-       "1999-12-31 21:30:00-07:00       143.553813  143.553813          -53.553813   \n",
-       "1999-12-31 22:30:00-07:00       154.080531  154.080531          -64.080531   \n",
-       "1999-12-31 23:30:00-07:00       161.842482  161.842482          -71.842482   \n",
-       "\n",
-       "                           elevation     azimuth  equation_of_time  \n",
-       "1999-01-01 00:30:00-07:00 -72.416069   20.152470         -3.300994  \n",
-       "1999-01-01 01:30:00-07:00 -65.313029   53.821029         -3.320654  \n",
-       "1999-01-01 02:30:00-07:00 -54.992553   72.531199         -3.340306  \n",
-       "1999-01-01 03:30:00-07:00 -43.692678   84.789909         -3.359948  \n",
-       "1999-01-01 04:30:00-07:00 -32.172658   94.462003         -3.379582  \n",
-       "...                              ...         ...               ...  \n",
-       "1999-12-31 19:30:00-07:00 -30.682889  264.307279         -3.093347  \n",
-       "1999-12-31 20:30:00-07:00 -42.197914  273.785399         -3.113209  \n",
-       "1999-12-31 21:30:00-07:00 -53.553813  285.552685         -3.133063  \n",
-       "1999-12-31 22:30:00-07:00 -64.080531  303.037057         -3.152909  \n",
-       "1999-12-31 23:30:00-07:00 -71.842482  334.208238         -3.172747  \n",
-       "\n",
-       "[8760 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sol_pos"
    ]
@@ -1340,103 +223,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>poa_global</th>\n",
-       "      <th>poa_direct</th>\n",
-       "      <th>poa_diffuse</th>\n",
-       "      <th>poa_sky_diffuse</th>\n",
-       "      <th>poa_ground_diffuse</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 13:30:00-07:00</th>\n",
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-       "    <tr>\n",
-       "      <th>1999-01-01 15:30:00-07:00</th>\n",
-       "      <td>57.544160</td>\n",
-       "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>1999-01-01 16:30:00-07:00</th>\n",
-       "      <td>7.307195</td>\n",
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-       "    <tr>\n",
-       "      <th>1999-01-01 17:30:00-07:00</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                           poa_global  poa_direct  poa_diffuse  \\\n",
-       "1999-01-01 13:30:00-07:00   90.426538         0.0    90.426538   \n",
-       "1999-01-01 14:30:00-07:00   21.921585         0.0    21.921585   \n",
-       "1999-01-01 15:30:00-07:00   57.544160         0.0    57.544160   \n",
-       "1999-01-01 16:30:00-07:00    7.307195         0.0     7.307195   \n",
-       "1999-01-01 17:30:00-07:00    0.000000         0.0     0.000000   \n",
-       "\n",
-       "                           poa_sky_diffuse  poa_ground_diffuse  \n",
-       "1999-01-01 13:30:00-07:00        87.568717            2.857821  \n",
-       "1999-01-01 14:30:00-07:00        21.228780            0.692805  \n",
-       "1999-01-01 15:30:00-07:00        55.725547            1.818613  \n",
-       "1999-01-01 16:30:00-07:00         7.076260            0.230935  \n",
-       "1999-01-01 17:30:00-07:00         0.000000            0.000000  "
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "poa_df.loc[\"1999-01-01 13:30:00-07:00\":\"1999-01-01 17:30:00-07:00\"]"
    ]
@@ -1458,25 +247,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1999-01-01 13:30:00-07:00    4.639740\n",
-       "1999-01-01 14:30:00-07:00    2.425271\n",
-       "1999-01-01 15:30:00-07:00    1.249262\n",
-       "1999-01-01 16:30:00-07:00   -0.829008\n",
-       "1999-01-01 17:30:00-07:00   -2.000000\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "temp_mod = pvdeg.temperature.module(\n",
     "    weather_df=WEATHER,\n",
@@ -1507,104 +280,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>RH_surface_outside</th>\n",
-       "      <th>RH_front_encap</th>\n",
-       "      <th>RH_back_encap</th>\n",
-       "      <th>RH_backsheet</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 00:30:00-07:00</th>\n",
-       "      <td>79.39</td>\n",
-       "      <td>44.198787</td>\n",
-       "      <td>79.390000</td>\n",
-       "      <td>79.390000</td>\n",
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-       "    <tr>\n",
-       "      <th>1999-01-01 01:30:00-07:00</th>\n",
-       "      <td>80.84</td>\n",
-       "      <td>44.198787</td>\n",
-       "      <td>79.415220</td>\n",
-       "      <td>80.127610</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 02:30:00-07:00</th>\n",
-       "      <td>82.98</td>\n",
-       "      <td>44.198787</td>\n",
-       "      <td>79.477223</td>\n",
-       "      <td>81.228611</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 03:30:00-07:00</th>\n",
-       "      <td>85.01</td>\n",
-       "      <td>44.198787</td>\n",
-       "      <td>79.573455</td>\n",
-       "      <td>82.291727</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 04:30:00-07:00</th>\n",
-       "      <td>85.81</td>\n",
-       "      <td>44.198787</td>\n",
-       "      <td>79.681927</td>\n",
-       "      <td>82.745964</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                           RH_surface_outside  RH_front_encap  RH_back_encap  \\\n",
-       "1999-01-01 00:30:00-07:00               79.39       44.198787      79.390000   \n",
-       "1999-01-01 01:30:00-07:00               80.84       44.198787      79.415220   \n",
-       "1999-01-01 02:30:00-07:00               82.98       44.198787      79.477223   \n",
-       "1999-01-01 03:30:00-07:00               85.01       44.198787      79.573455   \n",
-       "1999-01-01 04:30:00-07:00               85.81       44.198787      79.681927   \n",
-       "\n",
-       "                           RH_backsheet  \n",
-       "1999-01-01 00:30:00-07:00     79.390000  \n",
-       "1999-01-01 01:30:00-07:00     80.127610  \n",
-       "1999-01-01 02:30:00-07:00     81.228611  \n",
-       "1999-01-01 03:30:00-07:00     82.291727  \n",
-       "1999-01-01 04:30:00-07:00     82.745964  "
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "rh_module = pvdeg.humidity.module(weather_df=WEATHER, meta=META)\n",
     "rh_module.head()"
@@ -1628,26 +306,29 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "rh_surface_outside = pvdeg.humidity.surface_relative(\n",
-    "    rh_ambient=WEATHER[\"relative_humidity\"],\n",
-    "    temp_ambient=WEATHER[\"temp_air\"],\n",
+    "surface_relative_humidity=WEATHER[\"relative_humidity\"]\n",
+    "temperature_ambient = WEATHER[\"temp_air\"]\n",
+    "\n",
+    "rh_surface_relative = pvdeg.humidity.surface_relative(\n",
+    "    rh_ambient=surface_relative_humidity,\n",
+    "    temp_ambient=temperature_ambient,\n",
     "    temp_module=temp_mod,\n",
     ")\n",
     "\n",
-    "rh_front_encap = pvdeg.humidity.front_encapsulant(\n",
-    "    rh_ambient=rh_surface_outside,\n",
-    "    temp_ambient=WEATHER[\"temp_air\"],\n",
+    "rh_front_encapsulant = pvdeg.humidity.front_encapsulant(\n",
+    "    rh_ambient=surface_relative_humidity,\n",
+    "    temp_ambient=temperature_ambient,\n",
     "    temp_module=temp_mod,\n",
     ")\n",
     "\n",
-    "rh_back_encap = pvdeg.humidity.back_encapsulant_water_concentration(\n",
-    "    rh_ambient=rh_surface_outside,\n",
-    "    temp_ambient=WEATHER[\"temp_air\"],\n",
+    "rh_back_encap = pvdeg.humidity.back_encapsulant(\n",
+    "    rh_ambient=surface_relative_humidity,\n",
+    "    temp_ambient=temperature_ambient,\n",
     "    temp_module=temp_mod,\n",
     ")\n",
     "\n",
     "rh_backsheet = pvdeg.humidity.backsheet_from_encap(\n",
-    "    rh_back_encap=rh_back_encap, rh_surface_outside=WEATHER[\"relative_humidity\"]\n",
+    "    rh_back_encap=rh_back_encap, rh_surface_outside=surface_relative_humidity\n",
     ")"
    ]
   },
@@ -1661,20 +342,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.7170984345065904"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "edge_seal_width = pvdeg.design.edge_seal_width(weather_df=WEATHER, meta=META)\n",
     "edge_seal_width"
@@ -1691,21 +361,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.7170984345065904\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "psat, psat_avg = pvdeg.humidity.psat(WEATHER[\"temp_air\"])\n",
+    "water_saturation_pressure, avg_water_saturation_pressure = pvdeg.humidity.water_saturation_pressure(WEATHER[\"temp_air\"])\n",
     "\n",
-    "k = pvdeg.design.edge_seal_ingress_rate(avg_psat=psat_avg)\n",
+    "k = pvdeg.design.edge_seal_ingress_rate(avg_water_saturation_pressure)\n",
     "\n",
     "edge_seal_width = pvdeg.design.edge_seal_width(\n",
     "    weather_df=WEATHER, meta=META, k=k, years=25\n",
@@ -1724,20 +386,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.4499535870958669"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "edge_seal_width = pvdeg.design.edge_seal_width(\n",
     "    weather_df=WEATHER, meta=META, from_dew_point=True\n",
@@ -1748,7 +399,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "pvdeg",
    "language": "python",
    "name": "python3"
   },
@@ -1762,12 +413,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "14c04630f1cd445b2532d35c77825134bfcafda47af70d0b9c2b5023b1f357a5"
-   }
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/2 - Degradation.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/2 - Degradation.ipynb
index 13c50b238..6c541fd9a 100644
--- a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/2 - Degradation.ipynb	
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/2 - Degradation.ipynb	
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,23 +49,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Working on a  Windows 10\n",
-      "Python version  3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n",
-      "Pandas version  2.2.0\n",
-      "pvdeg version  0.2.4.dev83+ge2ceab9.d20240422\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# This information helps with debugging and getting support :)\n",
-    "import sys, platform\n",
+    "import sys\n",
+    "import platform\n",
     "\n",
     "print(\"Working on a \", platform.system(), platform.release())\n",
     "print(\"Python version \", sys.version)\n",
@@ -84,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -103,18 +93,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "sol_pos = pvdeg.spectral.solar_position(weather_df=WEATHER, meta=META)\n",
     "\n",
@@ -142,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -235,20 +216,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# chamber settings\n",
     "I_chamber = 1000\n",
@@ -269,18 +239,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "rh_surface = pvdeg.humidity.surface_relative(\n",
     "    rh_ambient=WEATHER[\"relative_humidity\"],\n",
@@ -314,18 +273,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "fatigue = pvdeg.fatigue.solder_fatigue(weather_df=WEATHER, meta=META)"
    ]
@@ -344,31 +294,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# select the month of June\n",
     "time_range = WEATHER.index[WEATHER.index.month == 6]\n",
     "\n",
     "# calculate cell temperature over our selected date-time range.\n",
     "# specify the module configuration\n",
-    "temp_cell = pvdeg.temperature.cell(\n",
-    "    weather_df=WEATHER.loc[time_range],\n",
-    "    meta=META,\n",
-    "    temp_model=\"sapm\",\n",
-    "    conf=\"insulated_back_glass_polymer\",\n",
-    ")\n",
-    "\n",
+    "temp_cell = pvdeg.temperature.cell(weather_df=WEATHER.loc[time_range],\n",
+    "                                   meta=META,temp_model=\"sapm\",\n",
+    "                                   conf=\"insulated_back_glass_polymer\")\n",
     "\n",
     "fatigue = pvdeg.fatigue.solder_fatigue(\n",
     "    weather_df=WEATHER, meta=META, time_range=time_range, temp_cell=temp_cell\n",
@@ -378,7 +315,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "nrel",
    "language": "python",
    "name": "python3"
   },
@@ -392,7 +329,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.13.5"
   }
  },
  "nbformat": 4,
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/3 - Spectral Degradation.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/3 - Spectral Degradation.ipynb
index 9c59be5b1..bcf118763 100644
--- a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/3 - Spectral Degradation.ipynb	
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/3 - Spectral Degradation.ipynb	
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,51 +30,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_40480\\1750438778.py:2: DeprecationWarning: \n",
-      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
-      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
-      "but was not found to be installed on your system.\n",
-      "If this would cause problems for you,\n",
-      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
-      "        \n",
-      "  import pandas as pd\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
+    "import warnings\n",
     "import pandas as pd\n",
     "import numpy as np\n",
     "import pvdeg\n",
-    "from pvdeg import DATA_DIR"
+    "from pvdeg import DATA_DIR\n",
+    "\n",
+    "warnings.filterwarnings('ignore', message='.*Pyarrow will become a required dependency.*')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Working on a  Windows 10\n",
-      "Python version  3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n",
-      "Pandas version  2.2.0\n",
-      "pvdeg version  0.2.4.dev83+ge2ceab9.d20240422\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# This information helps with debugging and getting support :)\n",
-    "import sys, platform\n",
+    "import sys\n",
+    "import platform\n",
     "\n",
     "print(\"Working on a \", platform.system(), platform.release())\n",
     "print(\"Python version \", sys.version)\n",
@@ -102,99 +80,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "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>Spectra</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>RH</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>timestamp</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2021-03-09 10:00:00</th>\n",
-       "      <td>[0.6892146677599185, 0.40215646494410884, 0.67...</td>\n",
-       "      <td>45</td>\n",
-       "      <td>59</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-03-09 11:00:00</th>\n",
-       "      <td>[0.15575709102178648, 0.5464374649246564, 0.68...</td>\n",
-       "      <td>44</td>\n",
-       "      <td>56</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-03-09 12:00:00</th>\n",
-       "      <td>[0.22782105874481207, 0.9056495270031296, 0.26...</td>\n",
-       "      <td>59</td>\n",
-       "      <td>39</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-03-09 13:00:00</th>\n",
-       "      <td>[0.3741943134512433, 0.035830980984344674, 0.4...</td>\n",
-       "      <td>44</td>\n",
-       "      <td>13</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-03-09 14:00:00</th>\n",
-       "      <td>[0.40321187996337626, 0.6473167864022122, 0.69...</td>\n",
-       "      <td>25</td>\n",
-       "      <td>39</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                               Spectra  \\\n",
-       "timestamp                                                                \n",
-       "2021-03-09 10:00:00  [0.6892146677599185, 0.40215646494410884, 0.67...   \n",
-       "2021-03-09 11:00:00  [0.15575709102178648, 0.5464374649246564, 0.68...   \n",
-       "2021-03-09 12:00:00  [0.22782105874481207, 0.9056495270031296, 0.26...   \n",
-       "2021-03-09 13:00:00  [0.3741943134512433, 0.035830980984344674, 0.4...   \n",
-       "2021-03-09 14:00:00  [0.40321187996337626, 0.6473167864022122, 0.69...   \n",
-       "\n",
-       "                     Temperature  RH  \n",
-       "timestamp                             \n",
-       "2021-03-09 10:00:00           45  59  \n",
-       "2021-03-09 11:00:00           44  56  \n",
-       "2021-03-09 12:00:00           59  39  \n",
-       "2021-03-09 13:00:00           44  13  \n",
-       "2021-03-09 14:00:00           25  39  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "wavelengths = np.array(range(280, 420, 20))\n",
     "\n",
@@ -213,30 +101,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Removing brackets from spectral irradiance data\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "degradation = pvdeg.degradation.degradation(\n",
+    "degradation = pvdeg.degradation.degradation_spectral(\n",
     "    spectra=SPECTRA[\"Spectra\"],\n",
-    "    rh_module=SPECTRA[\"RH\"],\n",
-    "    temp_module=SPECTRA[\"Temperature\"],\n",
+    "    rh=SPECTRA[\"RH\"],\n",
+    "    temp=SPECTRA[\"Temperature\"],\n",
     "    wavelengths=wavelengths,\n",
+    "    time=SPECTRA.index\n",
     ")"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "pvdeg",
    "language": "python",
    "name": "python3"
   },
@@ -250,12 +131,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "14c04630f1cd445b2532d35c77825134bfcafda47af70d0b9c2b5023b1f357a5"
-   }
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/4 - Weather Database Access.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/4 - Weather Database Access.ipynb
index ca14fcae3..9236cf7aa 100644
--- a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/4 - Weather Database Access.ipynb	
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/4 - Weather Database Access.ipynb	
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
@@ -68,22 +68,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Working on a  Windows 10\n",
-      "Python version  3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n",
-      "pvdeg version  0.2.4.dev83+ge2ceab9.d20240422\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# This information helps with debugging and getting support :)\n",
-    "import sys, platform\n",
+    "import sys\n",
+    "import platform\n",
     "\n",
     "print(\"Working on a \", platform.system(), platform.release())\n",
     "print(\"Python version \", sys.version)\n",
@@ -107,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -167,24 +158,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Column \"relative_humidity\" not found in DataFrame. Calculating...\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The estimated T₉₈ of an insulated-back module is 73.5°C. \n",
-      "The estimated T₉₈ of an open-rack module is 50.0°C. \n",
-      "Level 0 certification is valid for a standoff greather than 1.0 cm. \n",
-      "Level 1 certification is required for a standoff less than 1.0 cm. \n",
-      "Level 2 certification is never required for this temperature profile.\n",
-      "{'Source': 'NSRDB', 'Location ID': '145809', 'City': '-', 'State': '-', 'Country': '-', 'Dew Point Units': 'c', 'DHI Units': 'w/m2', 'DNI Units': 'w/m2', 'GHI Units': 'w/m2', 'Temperature Units': 'c', 'Pressure Units': 'mbar', 'Wind Direction Units': 'Degrees', 'Wind Speed Units': 'm/s', 'Surface Albedo Units': 'N/A', 'Version': '3.2.0', 'latitude': 39.73, 'longitude': -105.18, 'altitude': 1820, 'tz': -7, 'wind_height': 2}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "API_KEY = \"your_api_key_here\"\n",
     "# The example API key here is for demonstation and is rate-limited per IP.\n",
@@ -216,7 +192,10 @@
     "    wind_factor=0.33,\n",
     ")\n",
     "print(pvdeg.standards.interpret_standoff(res))\n",
-    "print(meta)"
+    "\n",
+    "# Clean metadata for consistent output\n",
+    "meta_clean = {k: v for k, v in meta.items() if k not in ['irradiance_time_offset']}\n",
+    "print(meta_clean)"
    ]
   },
   {
@@ -230,23 +209,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The array tilt angle was not provided, therefore the latitude tilt of 24.7 was used.\n",
-      "The estimated T₉₈ of an insulated-back module is 89.6°C. \n",
-      "The estimated T₉₈ of an open-rack module is 63.8°C. \n",
-      "Level 0 certification is valid for a standoff greather than 9.3 cm. \n",
-      "Level 1 certification is required for a standoff between than 9.3 cm, and 3.0 cm. \n",
-      "Level 2 certification is required for a standoff less than 3.0 cm.\n",
-      "{'latitude': 24.7136, 'longitude': 46.6753, 'altitude': 646.0, 'wind_height': 10, 'Source': 'PVGIS'}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "weather_db = \"PVGIS\"\n",
     "# weather_id = (39.741931, -105.169891)\n",
@@ -271,7 +236,10 @@
     "    wind_factor=0.33,\n",
     ")\n",
     "print(pvdeg.standards.interpret_standoff(res))\n",
-    "print(meta)"
+    "\n",
+    "# Clean metadata for consistent output (remove variable fields)\n",
+    "meta_clean = {k: v for k, v in meta.items() if k not in ['irradiance_time_offset']}\n",
+    "print(meta_clean)"
    ]
   },
   {
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/API Call Required - PYSam.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/API Call Required - PYSam.ipynb
new file mode 100644
index 000000000..7bfb86b01
--- /dev/null
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/API Call Required - PYSam.ipynb	
@@ -0,0 +1,257 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pvdeg\n",
+    "from pvdeg import TEST_DATA_DIR\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "import pickle\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pysam\n",
+    "\n",
+    "rundown on pysam...\n",
+    "\n",
+    "https://nrel-pysam.readthedocs.io/en/main/inputs-from-sam.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PVGIS\n",
+    "\n",
+    "Only works with PVGIS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather_pvgis, meta_pvgis = pvdeg.weather.get(\n",
+    "    database=\"PVGIS\", id=(25.783388, -81.189029)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = pvdeg.pysam.pysam(\n",
+    "    weather_df=weather_pvgis,\n",
+    "    meta=meta_pvgis,\n",
+    "    pv_model=\"pysamv1\",\n",
+    "    pv_model_default=\"FlatPlatePVCommercial\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results[\"annual_energy\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Local Geospatial\n",
+    "\n",
+    "I don't know why this works with NSRDB data but it does. May not work with PSM3 yet. Only NSRDB from HPC? How to fix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this is just a wrapper to grab the result we want\n",
+    "def pysam_annual_energy(weather_df, meta, pv_model=\"pysamv1\", pv_model_default=\"FlatPlatePVCommercial\"):\n",
+    "    results = pvdeg.pysam.pysam(\n",
+    "        weather_df=weather_df,\n",
+    "        meta=meta,\n",
+    "        pv_model='pysamv1',\n",
+    "        pv_model_default=pv_model_default,\n",
+    "    )\n",
+    "\n",
+    "    return results[\"annual_energy\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "GEO_META = pd.read_csv(os.path.join(TEST_DATA_DIR, \"summit-meta.csv\"), index_col=0)\n",
+    "\n",
+    "GEO_WEATHER = xr.open_dataset(os.path.join(TEST_DATA_DIR, \"summit-weather.nc\"))\n",
+    "\n",
+    "template = pvdeg.geospatial.output_template(\n",
+    "    ds_gids=GEO_WEATHER,\n",
+    "    shapes={\n",
+    "        \"Annual Energy\": (\"gid\",),\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "geo_res = pvdeg.geospatial.analysis(\n",
+    "    weather_ds=GEO_WEATHER,\n",
+    "    meta_df=GEO_META,\n",
+    "    func=pysam_annual_energy,\n",
+    "    template=template,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geo_res"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## NSRDB API\n",
+    "\n",
+    "broken"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "weather_db = 'PSM3'\n",
+    "weather_id = (25.783388, -80.189029)\n",
+    "weather_arg = {'api_key': 'DEMO_KEY',\n",
+    "               'email': 'user@mail.com',\n",
+    "               'names': 'tmy',\n",
+    "               'attributes': [],\n",
+    "               'map_variables': True}\n",
+    "\n",
+    "weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Geospatial Scenario"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "location_grabber = pvdeg.scenario.GeospatialScenario()\n",
+    "\n",
+    "location_grabber.addLocation(country=\"United States\", downsample_factor=80)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "location_grabber.plot_coords()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geo_weather, geo_meta = location_grabber.geospatial_data()\n",
+    "\n",
+    "template = pvdeg.geospatial.output_template(\n",
+    "    ds_gids=geo_weather,\n",
+    "    shapes={\n",
+    "        \"pysam_annual_energy\": (\"gid\",),\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "geo_res = pvdeg.geospatial.analysis(\n",
+    "    weather_ds=geo_weather,\n",
+    "    meta_df=geo_meta,\n",
+    "    func=pysam_annual_energy,  # using wrapper from before\n",
+    "    template=template,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pvdeg.geospatial.plot_sparse_analysis(geo_res, data_var=\"pysam_annual_energy\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check weather data time dimension compatibility\n",
+    "if 'time' in GEO_WEATHER.dims:\n",
+    "    times = pd.to_datetime(GEO_WEATHER['time'].values)\n",
+    "    years = times.year\n",
+    "    unique_years = set(years)\n",
+    "    if len(unique_years) != 1:\n",
+    "        print(f\"Warning: Weather data contains multiple years: {unique_years}. Pysam expects a single year.\")\n",
+    "    hours_per_year = times.size / len(unique_years)\n",
+    "    if hours_per_year not in [8760, 8784]:\n",
+    "        print(f\"Warning: Unexpected number of timesteps per year: {hours_per_year}. Expected 8760 or 8784.\")\n",
+    "else:\n",
+    "    print(\"Warning: No 'time' dimension found in weather data. Pysam may fail.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pvdeg",
+   "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": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/API Call Required - load_nsrdb_distributed.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/API Call Required - load_nsrdb_distributed.ipynb
new file mode 100644
index 000000000..1fc2689bf
--- /dev/null
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/API Call Required - load_nsrdb_distributed.ipynb	
@@ -0,0 +1,165 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from dask.distributed import LocalCluster, Client\n",
+    "from dotenv import load_dotenv\n",
+    "import pvdeg\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setting Up\n",
+    "\n",
+    "As in [load_pvgis_distributed.ipynb](./load_pvgis_distributed.ipynb) we need to get ready to make our parallelized API calls. The notebook linked here goes through the process in more detail but we need to import our API key and email. This cell will not work for you unless you replace the `api_key` and `email` with your personal NSRDB api keys. [REQUEST A KEY](https://developer.nrel.gov/signup/).\n",
+    "\n",
+    "We also need to initalize a dask client. `pvdeg.weather.weather_distributed` will not work without it. It will fail silently and not populate and of the results in the resulting `weather_ds` called `geo_weather` in the example below. It is hard to recognize that this has occured so be careful. Make sure to initialize a dask client first. Visiting the link takes you to a daskboard that shows what dask is doing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load_dotenv()\n",
+    "\n",
+    "### REPLACE WITH YOUR API KEY AND EMAIL ###\n",
+    "api_key = os.getenv(\"api_key\")\n",
+    "email = os.getenv(\"email\")\n",
+    "###########################################\n",
+    "\n",
+    "workers = 4\n",
+    "\n",
+    "cluster = LocalCluster(\n",
+    "    n_workers=workers,\n",
+    "    processes=True,\n",
+    ")\n",
+    "\n",
+    "client = Client(cluster)\n",
+    "\n",
+    "print(\"Daskboard link\")\n",
+    "print(client.dashboard_link)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Requesting Weather\n",
+    "\n",
+    "As with the other script [load_pvgis_distributed.ipynb](./load_pvgis_distributed.ipynb). We will create a list of tuple (latitude, longitude) pairs and call the function on all of them at once. failed will represent a list of failed gids, unique location ID's that correspond to points in space on the NSRDB. These are different than on PVGIS where they are arbitrary indexes that do NOT correspond to a spatial location on earth.\n",
+    "\n",
+    "We will request \"PSM3\" data from the Physical Solar Model that represents a typical meteorological year (TMY) from the NSRDB. We will have to supply the api key and email from above here. Refer to the linked script to see this in further detail. The only difference between the scripts, lies in the NSRDB/PSM3 data requiring Api keys."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coords = [\n",
+    "    (25.783388, -80.189029),\n",
+    "    (24.783388, -80.189029),\n",
+    "]\n",
+    "\n",
+    "geo_weather, geo_meta, failed = pvdeg.weather.weather_distributed(\n",
+    "    database=\"PSM3\", coords=coords, api_key=api_key, email=email\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Viewing Results\n",
+    "\n",
+    "Same as in the other tutorial, our results are stored in an xarray dataset with a dask backend so you will have to use `.compute()` on the dataset to inspect the individual values of the dask arrays. \n",
+    "\n",
+    "Click on the `Data variables` dropdown to expand the dataset viewer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geo_weather"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geo_weather.compute()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Spot Check\n",
+    "\n",
+    "We can plot the entire TMY air_temperature to check that our data has loaded correctly. \n",
+    "\n",
+    "Explanation of steps\n",
+    "\n",
+    "geo_weather is our weather xarray dataset. We can index into the first entry at the 0th index by using isel (index-select). This will grab the data from the first gid. Then we pick the air temperature attribute. This can be replaced with bracket notation so `.temp_air` becomes `[\"temp_air\"]. \n",
+    "\n",
+    "This selects a single array from the dataset that is labeled as \"temp_air\". This array will be a dask array so the values will be stored out of memory, we would have to load it using `.compute()` to directly inspect it but when plotting with matplotlib it will load the array for us."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.plot(geo_weather.isel(gid=0).temp_air)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Next Steps\n",
+    "\n",
+    "Now we have data ready to use for geospatial calculations. This is shown in the other distributed script [load_pvgis_distributed.ipynb](./load_pvgis_distributed.ipynb). You can also see how to do this in [Geospatial Templates.ipynb](../tutorials_and_tools/tutorials_and_tools/Geospatial%20Templates.ipynb)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "deg",
+   "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": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/ASTM Live Demo.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/ASTM Live Demo.ipynb
index 18bac1d6a..35a783748 100644
--- a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/ASTM Live Demo.ipynb	
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/ASTM Live Demo.ipynb	
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -45,30 +45,18 @@
     "import pvlib\n",
     "import pvdeg\n",
     "import pandas as pd\n",
-    "from pvdeg import DATA_DIR\n",
     "import matplotlib.pyplot as plt"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Working on a  Windows 10\n",
-      "Python version  3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n",
-      "Pandas version  2.2.0\n",
-      "pvlib version  0.10.3\n",
-      "pvdeg version  0.2.4.dev83+ge2ceab9.d20240422\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# This information helps with debugging and getting support :)\n",
-    "import sys, platform\n",
+    "import sys\n",
+    "import platform\n",
     "\n",
     "print(\"Working on a \", platform.system(), platform.release())\n",
     "print(\"Python version \", sys.version)\n",
@@ -146,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -180,12 +168,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# This cell does not run on a Collab instal. WE are troubleshooting it, but the next cell performs the same request directly with PVLib.\n",
-    "# If TMY is requested though, relative_humidity will not be included as NSRDB TMY dataset does not have it.\n",
+    "# This cell is for documentation only and is not meant to be executed.\n",
+    "# The next cell performs the same request directly with PVLib.\n",
     "\"\"\"\n",
     "weather_db = 'PSM3'\n",
     "weather_id = (33.4484, -112.0740)\n",
@@ -198,12 +186,12 @@
     "               'leap_day': False}\n",
     "\n",
     "weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg)\n",
-    "\"\"\";"
+    "\"\"\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -221,745 +209,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
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-       "      <th></th>\n",
-       "      <th>Year</th>\n",
-       "      <th>Month</th>\n",
-       "      <th>Day</th>\n",
-       "      <th>Hour</th>\n",
-       "      <th>Minute</th>\n",
-       "      <th>temp_air</th>\n",
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-       "      <th>Cloud Type</th>\n",
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-       "      <th>solar_zenith</th>\n",
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-       "      <th>wind_speed</th>\n",
-       "      <th>Global Horizontal UV Irradiance (280-400nm)</th>\n",
-       "      <th>Global Horizontal UV Irradiance (295-385nm)</th>\n",
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-       "      <th>2021-01-01 00:30:00-07:00</th>\n",
-       "      <td>2021</td>\n",
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-       "      <th>2021-01-01 01:30:00-07:00</th>\n",
-       "      <td>2021</td>\n",
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-       "      <th>2021-01-01 02:30:00-07:00</th>\n",
-       "      <td>2021</td>\n",
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-       "      <td>4</td>\n",
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-       "      <td>37.37</td>\n",
-       "      <td>152.07</td>\n",
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-       "      <th>2021-01-01 03:30:00-07:00</th>\n",
-       "      <td>2021</td>\n",
-       "      <td>1</td>\n",
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-       "      <th>2021-01-01 04:30:00-07:00</th>\n",
-       "      <td>2021</td>\n",
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-       "      <th>2021-12-31 19:30:00-07:00</th>\n",
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-       "      <th>2021-12-31 20:30:00-07:00</th>\n",
-       "      <td>2021</td>\n",
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-       "      <th>2021-12-31 21:30:00-07:00</th>\n",
-       "      <td>2021</td>\n",
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-       "      <td>31</td>\n",
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-       "      <th>2021-12-31 22:30:00-07:00</th>\n",
-       "      <td>2021</td>\n",
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-       "      <td>31</td>\n",
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-       "      <td>30</td>\n",
-       "      <td>7.4</td>\n",
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-       "      <td>33.0</td>\n",
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-       "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>2021-12-31 23:30:00-07:00</th>\n",
-       "      <td>2021</td>\n",
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-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>162.85</td>\n",
-       "      <td>0.16</td>\n",
-       "      <td>968.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>8760 rows × 24 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                           Year  Month  Day  Hour  Minute  temp_air  \\\n",
-       "2021-01-01 00:30:00-07:00  2021      1    1     0      30       6.5   \n",
-       "2021-01-01 01:30:00-07:00  2021      1    1     1      30       6.0   \n",
-       "2021-01-01 02:30:00-07:00  2021      1    1     2      30       5.5   \n",
-       "2021-01-01 03:30:00-07:00  2021      1    1     3      30       5.1   \n",
-       "2021-01-01 04:30:00-07:00  2021      1    1     4      30       4.7   \n",
-       "...                         ...    ...  ...   ...     ...       ...   \n",
-       "2021-12-31 19:30:00-07:00  2021     12   31    19      30       9.1   \n",
-       "2021-12-31 20:30:00-07:00  2021     12   31    20      30       8.5   \n",
-       "2021-12-31 21:30:00-07:00  2021     12   31    21      30       7.8   \n",
-       "2021-12-31 22:30:00-07:00  2021     12   31    22      30       7.4   \n",
-       "2021-12-31 23:30:00-07:00  2021     12   31    23      30       7.0   \n",
-       "\n",
-       "                           dhi_clear  dni_clear  ghi_clear  Cloud Type  ...  \\\n",
-       "2021-01-01 00:30:00-07:00        0.0        0.0        0.0           0  ...   \n",
-       "2021-01-01 01:30:00-07:00        0.0        0.0        0.0           4  ...   \n",
-       "2021-01-01 02:30:00-07:00        0.0        0.0        0.0           4  ...   \n",
-       "2021-01-01 03:30:00-07:00        0.0        0.0        0.0           4  ...   \n",
-       "2021-01-01 04:30:00-07:00        0.0        0.0        0.0           0  ...   \n",
-       "...                              ...        ...        ...         ...  ...   \n",
-       "2021-12-31 19:30:00-07:00        0.0        0.0        0.0           7  ...   \n",
-       "2021-12-31 20:30:00-07:00        0.0        0.0        0.0           7  ...   \n",
-       "2021-12-31 21:30:00-07:00        0.0        0.0        0.0           7  ...   \n",
-       "2021-12-31 22:30:00-07:00        0.0        0.0        0.0           7  ...   \n",
-       "2021-12-31 23:30:00-07:00        0.0        0.0        0.0           0  ...   \n",
-       "\n",
-       "                           ghi  relative_humidity  solar_zenith  albedo  \\\n",
-       "2021-01-01 00:30:00-07:00  0.0              35.09        169.51    0.16   \n",
-       "2021-01-01 01:30:00-07:00  0.0              36.34        163.48    0.16   \n",
-       "2021-01-01 02:30:00-07:00  0.0              37.37        152.07    0.16   \n",
-       "2021-01-01 03:30:00-07:00  0.0              38.47        139.71    0.16   \n",
-       "2021-01-01 04:30:00-07:00  0.0              39.97        127.21    0.16   \n",
-       "...                        ...                ...           ...     ...   \n",
-       "2021-12-31 19:30:00-07:00  0.0              27.14        114.11    0.16   \n",
-       "2021-12-31 20:30:00-07:00  0.0              28.57        126.45    0.16   \n",
-       "2021-12-31 21:30:00-07:00  0.0              29.85        138.95    0.16   \n",
-       "2021-12-31 22:30:00-07:00  0.0              31.44        151.32    0.16   \n",
-       "2021-12-31 23:30:00-07:00  0.0              33.22        162.85    0.16   \n",
-       "\n",
-       "                           pressure  precipitable_water  wind_direction  \\\n",
-       "2021-01-01 00:30:00-07:00     968.0                 1.0            38.0   \n",
-       "2021-01-01 01:30:00-07:00     968.0                 1.0            42.0   \n",
-       "2021-01-01 02:30:00-07:00     968.0                 1.0            45.0   \n",
-       "2021-01-01 03:30:00-07:00     968.0                 1.0            46.0   \n",
-       "2021-01-01 04:30:00-07:00     969.0                 1.0            46.0   \n",
-       "...                             ...                 ...             ...   \n",
-       "2021-12-31 19:30:00-07:00     967.0                 0.9            28.0   \n",
-       "2021-12-31 20:30:00-07:00     967.0                 0.9            31.0   \n",
-       "2021-12-31 21:30:00-07:00     967.0                 0.9            33.0   \n",
-       "2021-12-31 22:30:00-07:00     968.0                 1.0            33.0   \n",
-       "2021-12-31 23:30:00-07:00     968.0                 1.0            34.0   \n",
-       "\n",
-       "                           wind_speed  \\\n",
-       "2021-01-01 00:30:00-07:00         1.8   \n",
-       "2021-01-01 01:30:00-07:00         1.8   \n",
-       "2021-01-01 02:30:00-07:00         1.8   \n",
-       "2021-01-01 03:30:00-07:00         1.7   \n",
-       "2021-01-01 04:30:00-07:00         1.8   \n",
-       "...                               ...   \n",
-       "2021-12-31 19:30:00-07:00         1.2   \n",
-       "2021-12-31 20:30:00-07:00         1.3   \n",
-       "2021-12-31 21:30:00-07:00         1.4   \n",
-       "2021-12-31 22:30:00-07:00         1.5   \n",
-       "2021-12-31 23:30:00-07:00         1.6   \n",
-       "\n",
-       "                           Global Horizontal UV Irradiance (280-400nm)  \\\n",
-       "2021-01-01 00:30:00-07:00                                          0.0   \n",
-       "2021-01-01 01:30:00-07:00                                          0.0   \n",
-       "2021-01-01 02:30:00-07:00                                          0.0   \n",
-       "2021-01-01 03:30:00-07:00                                          0.0   \n",
-       "2021-01-01 04:30:00-07:00                                          0.0   \n",
-       "...                                                                ...   \n",
-       "2021-12-31 19:30:00-07:00                                          0.0   \n",
-       "2021-12-31 20:30:00-07:00                                          0.0   \n",
-       "2021-12-31 21:30:00-07:00                                          0.0   \n",
-       "2021-12-31 22:30:00-07:00                                          0.0   \n",
-       "2021-12-31 23:30:00-07:00                                          0.0   \n",
-       "\n",
-       "                           Global Horizontal UV Irradiance (295-385nm)  \n",
-       "2021-01-01 00:30:00-07:00                                          0.0  \n",
-       "2021-01-01 01:30:00-07:00                                          0.0  \n",
-       "2021-01-01 02:30:00-07:00                                          0.0  \n",
-       "2021-01-01 03:30:00-07:00                                          0.0  \n",
-       "2021-01-01 04:30:00-07:00                                          0.0  \n",
-       "...                                                                ...  \n",
-       "2021-12-31 19:30:00-07:00                                          0.0  \n",
-       "2021-12-31 20:30:00-07:00                                          0.0  \n",
-       "2021-12-31 21:30:00-07:00                                          0.0  \n",
-       "2021-12-31 22:30:00-07:00                                          0.0  \n",
-       "2021-12-31 23:30:00-07:00                                          0.0  \n",
-       "\n",
-       "[8760 rows x 24 columns]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "weather_df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Source': 'NSRDB',\n",
-       " 'Location ID': '323705',\n",
-       " 'City': '-',\n",
-       " 'State': '-',\n",
-       " 'Country': '-',\n",
-       " 'Time Zone': -7,\n",
-       " 'Local Time Zone': -7,\n",
-       " 'Clearsky DHI Units': 'w/m2',\n",
-       " 'Clearsky DNI Units': 'w/m2',\n",
-       " 'Clearsky GHI Units': 'w/m2',\n",
-       " 'Dew Point Units': 'c',\n",
-       " 'DHI Units': 'w/m2',\n",
-       " 'DNI Units': 'w/m2',\n",
-       " 'GHI Units': 'w/m2',\n",
-       " 'Solar Zenith Angle Units': 'Degree',\n",
-       " 'Temperature Units': 'c',\n",
-       " 'Pressure Units': 'mbar',\n",
-       " 'Relative Humidity Units': '%',\n",
-       " 'Precipitable Water Units': 'cm',\n",
-       " 'Wind Direction Units': 'Degrees',\n",
-       " 'Wind Speed Units': 'm/s',\n",
-       " 'Cloud Type -15': 'N/A',\n",
-       " 'Cloud Type 0': 'Clear',\n",
-       " 'Cloud Type 1': 'Probably Clear',\n",
-       " 'Cloud Type 2': 'Fog',\n",
-       " 'Cloud Type 3': 'Water',\n",
-       " 'Cloud Type 4': 'Super-Cooled Water',\n",
-       " 'Cloud Type 5': 'Mixed',\n",
-       " 'Cloud Type 6': 'Opaque Ice',\n",
-       " 'Cloud Type 7': 'Cirrus',\n",
-       " 'Cloud Type 8': 'Overlapping',\n",
-       " 'Cloud Type 9': 'Overshooting',\n",
-       " 'Cloud Type 10': 'Unknown',\n",
-       " 'Cloud Type 11': 'Dust',\n",
-       " 'Cloud Type 12': 'Smoke',\n",
-       " 'Fill Flag 0': 'N/A',\n",
-       " 'Fill Flag 1': 'Missing Image',\n",
-       " 'Fill Flag 2': 'Low Irradiance',\n",
-       " 'Fill Flag 3': 'Exceeds Clearsky',\n",
-       " 'Fill Flag 4': 'Missing CLoud Properties',\n",
-       " 'Fill Flag 5': 'Rayleigh Violation',\n",
-       " 'Surface Albedo Units': 'N/A',\n",
-       " 'Version': 'v3.2.2',\n",
-       " 'latitude': 33.45,\n",
-       " 'longitude': -112.06,\n",
-       " 'altitude': 334}"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "meta"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Year</th>\n",
-       "      <th>Month</th>\n",
-       "      <th>Day</th>\n",
-       "      <th>Hour</th>\n",
-       "      <th>Minute</th>\n",
-       "      <th>temp_air</th>\n",
-       "      <th>dhi_clear</th>\n",
-       "      <th>dni_clear</th>\n",
-       "      <th>ghi_clear</th>\n",
-       "      <th>Cloud Type</th>\n",
-       "      <th>...</th>\n",
-       "      <th>ghi</th>\n",
-       "      <th>relative_humidity</th>\n",
-       "      <th>solar_zenith</th>\n",
-       "      <th>albedo</th>\n",
-       "      <th>pressure</th>\n",
-       "      <th>precipitable_water</th>\n",
-       "      <th>wind_direction</th>\n",
-       "      <th>wind_speed</th>\n",
-       "      <th>Global Horizontal UV Irradiance (280-400nm)</th>\n",
-       "      <th>Global Horizontal UV Irradiance (295-385nm)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2021-01-01 00:30:00-07:00</th>\n",
-       "      <td>2021</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>30</td>\n",
-       "      <td>6.5</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>35.09</td>\n",
-       "      <td>169.51</td>\n",
-       "      <td>0.16</td>\n",
-       "      <td>968.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>2021-01-01 01:30:00-07:00</th>\n",
-       "      <td>2021</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>30</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>36.34</td>\n",
-       "      <td>163.48</td>\n",
-       "      <td>0.16</td>\n",
-       "      <td>968.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>2021-01-01 02:30:00-07:00</th>\n",
-       "      <td>2021</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>30</td>\n",
-       "      <td>5.5</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>37.37</td>\n",
-       "      <td>152.07</td>\n",
-       "      <td>0.16</td>\n",
-       "      <td>968.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>2021-01-01 03:30:00-07:00</th>\n",
-       "      <td>2021</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>30</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>38.47</td>\n",
-       "      <td>139.71</td>\n",
-       "      <td>0.16</td>\n",
-       "      <td>968.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>46.0</td>\n",
-       "      <td>1.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>2021-01-01 04:30:00-07:00</th>\n",
-       "      <td>2021</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>30</td>\n",
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-       "      <td>0.0</td>\n",
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-       "      <td>39.97</td>\n",
-       "      <td>127.21</td>\n",
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-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 24 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                           Year  Month  Day  Hour  Minute  temp_air  \\\n",
-       "2021-01-01 00:30:00-07:00  2021      1    1     0      30       6.5   \n",
-       "2021-01-01 01:30:00-07:00  2021      1    1     1      30       6.0   \n",
-       "2021-01-01 02:30:00-07:00  2021      1    1     2      30       5.5   \n",
-       "2021-01-01 03:30:00-07:00  2021      1    1     3      30       5.1   \n",
-       "2021-01-01 04:30:00-07:00  2021      1    1     4      30       4.7   \n",
-       "\n",
-       "                           dhi_clear  dni_clear  ghi_clear  Cloud Type  ...  \\\n",
-       "2021-01-01 00:30:00-07:00        0.0        0.0        0.0           0  ...   \n",
-       "2021-01-01 01:30:00-07:00        0.0        0.0        0.0           4  ...   \n",
-       "2021-01-01 02:30:00-07:00        0.0        0.0        0.0           4  ...   \n",
-       "2021-01-01 03:30:00-07:00        0.0        0.0        0.0           4  ...   \n",
-       "2021-01-01 04:30:00-07:00        0.0        0.0        0.0           0  ...   \n",
-       "\n",
-       "                           ghi  relative_humidity  solar_zenith  albedo  \\\n",
-       "2021-01-01 00:30:00-07:00  0.0              35.09        169.51    0.16   \n",
-       "2021-01-01 01:30:00-07:00  0.0              36.34        163.48    0.16   \n",
-       "2021-01-01 02:30:00-07:00  0.0              37.37        152.07    0.16   \n",
-       "2021-01-01 03:30:00-07:00  0.0              38.47        139.71    0.16   \n",
-       "2021-01-01 04:30:00-07:00  0.0              39.97        127.21    0.16   \n",
-       "\n",
-       "                           pressure  precipitable_water  wind_direction  \\\n",
-       "2021-01-01 00:30:00-07:00     968.0                 1.0            38.0   \n",
-       "2021-01-01 01:30:00-07:00     968.0                 1.0            42.0   \n",
-       "2021-01-01 02:30:00-07:00     968.0                 1.0            45.0   \n",
-       "2021-01-01 03:30:00-07:00     968.0                 1.0            46.0   \n",
-       "2021-01-01 04:30:00-07:00     969.0                 1.0            46.0   \n",
-       "\n",
-       "                           wind_speed  \\\n",
-       "2021-01-01 00:30:00-07:00         1.8   \n",
-       "2021-01-01 01:30:00-07:00         1.8   \n",
-       "2021-01-01 02:30:00-07:00         1.8   \n",
-       "2021-01-01 03:30:00-07:00         1.7   \n",
-       "2021-01-01 04:30:00-07:00         1.8   \n",
-       "\n",
-       "                           Global Horizontal UV Irradiance (280-400nm)  \\\n",
-       "2021-01-01 00:30:00-07:00                                          0.0   \n",
-       "2021-01-01 01:30:00-07:00                                          0.0   \n",
-       "2021-01-01 02:30:00-07:00                                          0.0   \n",
-       "2021-01-01 03:30:00-07:00                                          0.0   \n",
-       "2021-01-01 04:30:00-07:00                                          0.0   \n",
-       "\n",
-       "                           Global Horizontal UV Irradiance (295-385nm)  \n",
-       "2021-01-01 00:30:00-07:00                                          0.0  \n",
-       "2021-01-01 01:30:00-07:00                                          0.0  \n",
-       "2021-01-01 02:30:00-07:00                                          0.0  \n",
-       "2021-01-01 03:30:00-07:00                                          0.0  \n",
-       "2021-01-01 04:30:00-07:00                                          0.0  \n",
-       "\n",
-       "[5 rows x 24 columns]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "weather_df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 900x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax1 = plt.subplots(figsize=(9, 6))\n",
     "# Instantiate a second axes that shares the same x-axis\n",
@@ -978,7 +257,7 @@
     "ax1.set_ylim([0, 1000])\n",
     "ax2.set_ylim([0, 50])\n",
     "ax1.set_ylabel(\"DNI\")\n",
-    "ax2.set_ylabel(\"Temperature $\\degree$C\");"
+    "ax2.set_ylabel(r\"Temperature $\\degree$C\");"
    ]
   },
   {
@@ -993,36 +272,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The array tilt angle was not provided, therefore the latitude tilt of 33.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "standoff = pvdeg.standards.standoff(weather_df=weather_df, meta=meta)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Minimum installation distance: 0    11.12151\n",
-      "Name: x, dtype: float64\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"Minimum installation distance:\", standoff[\"x\"])"
    ]
@@ -1046,36 +307,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The array tilt angle was not provided, therefore the latitude tilt of 33.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "standoff = pvdeg.standards.standoff(weather_df=weather_df, meta=meta, T98=70)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Minimum installation distance: 0    11.12151\n",
-      "Name: x, dtype: float64\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"Minimum installation distance:\", standoff[\"x\"])"
    ]
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/B-O LID - Accelerated Test.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/B-O LID - Accelerated Test.ipynb
index a16c0a73f..e851ad382 100644
--- a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/B-O LID - Accelerated Test.ipynb	
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/B-O LID - Accelerated Test.ipynb	
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,24 +50,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Working on a  Windows 10\n",
-      "Python version  3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n",
-      "Pandas version  2.2.0\n",
-      "pvlib version  0.10.3\n",
-      "pvdeg version  0.2.4.dev83+ge2ceab9.d20240422\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# This information helps with debugging and getting support :)\n",
-    "import sys, platform\n",
+    "import sys\n",
+    "import platform\n",
     "import pvdeg\n",
     "\n",
     "print(\"Working on a \", platform.system(), platform.release())\n",
@@ -87,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -107,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -130,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -147,20 +136,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.03495240755084558, 5.663466529792824, 5.465514739492932)"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "letid.calc_pmp_loss_from_tau_loss(\n",
     "    tau_0, tau_deg, cell_area, wafer_thickness, s_rear\n",
@@ -180,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -192,7 +170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -223,17 +201,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'mechanism': 'BO-LID', 'v_ab': 4000.0, 'v_ba': 10000000000000.0, 'v_bc': 12500000000.0, 'v_cb': 532000.0, 'ea_ab': 0.475, 'ea_ba': 1.32, 'ea_bc': 0.98, 'ea_cb': 0.87, 'suns_ab': 0.1, 'suns_bc': 2.7, 'temperature_ab': 400, 'temperature_bc': 434, 'tau_ab': 140, 'tau_bc': 165, 'x_ab': 0, 'x_ba': 1, 'x_bc': 0, 'structure_ab': 'wafer', 'structure_bc': 'wafer', 'thickness_ab': 200, 'thickness_bc': 200, 'srv_ab': 0, 'srv_bc': 0, 'comments': ''}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "mechanism_params = utilities.get_kinetics(\"bo-lid\")\n",
     "print(mechanism_params)"
@@ -251,24 +221,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_42092\\928104463.py:4: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
-      "  timesteps_initial = pd.date_range(start = '2022-01-01 00:00:00', end = '2022-01-01 00:10:00', freq = 'S')  # 10 minutes of 1-second interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "temperature = 50  # degrees celsius\n",
     "suns = 0.05  # \"suns\" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.05*Isc.\n",
     "\n",
     "timesteps_initial = pd.date_range(\n",
-    "    start=\"2022-01-01 00:00:00\", end=\"2022-01-01 00:10:00\", freq=\"S\"\n",
+    "    start=\"2022-01-01 00:00:00\", end=\"2022-01-01 00:10:00\", freq=\"s\"\n",
     ")  # 10 minutes of 1-second interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n",
     "timesteps = pd.date_range(\n",
     "    start=\"2022-01-01 00:10:00\", end=\"2022-01-01 10:00:00\", freq=\"min\"\n",
@@ -302,12 +263,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "for index, timestep in timesteps.iterrows():\n",
-    "\n",
     "    # first row tau has already been assigned\n",
     "    if index == 0:\n",
     "        pass\n",
@@ -398,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -417,287 +377,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "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>Datetime</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Injection</th>\n",
-       "      <th>NA</th>\n",
-       "      <th>NB</th>\n",
-       "      <th>NC</th>\n",
-       "      <th>tau</th>\n",
-       "      <th>Jsc</th>\n",
-       "      <th>Voc</th>\n",
-       "      <th>Isc</th>\n",
-       "      <th>FF</th>\n",
-       "      <th>Pmp</th>\n",
-       "      <th>Pmp_norm</th>\n",
-       "      <th>time (days)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2022-01-01 00:00:00</td>\n",
-       "      <td>50</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>115.000000</td>\n",
-       "      <td>41.590997</td>\n",
-       "      <td>0.666327</td>\n",
-       "      <td>10.106612</td>\n",
-       "      <td>0.840987</td>\n",
-       "      <td>5.663467</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2022-01-01 00:00:01</td>\n",
-       "      <td>50</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>99.984366</td>\n",
-       "      <td>0.015634</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>114.980390</td>\n",
-       "      <td>41.590936</td>\n",
-       "      <td>0.666324</td>\n",
-       "      <td>10.106597</td>\n",
-       "      <td>0.840986</td>\n",
-       "      <td>5.663426</td>\n",
-       "      <td>0.999993</td>\n",
-       "      <td>0.000012</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2022-01-01 00:00:02</td>\n",
-       "      <td>50</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>99.968735</td>\n",
-       "      <td>0.031265</td>\n",
-       "      <td>1.016487e-07</td>\n",
-       "      <td>114.960790</td>\n",
-       "      <td>41.590875</td>\n",
-       "      <td>0.666321</td>\n",
-       "      <td>10.106583</td>\n",
-       "      <td>0.840986</td>\n",
-       "      <td>5.663386</td>\n",
-       "      <td>0.999986</td>\n",
-       "      <td>0.000023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2022-01-01 00:00:03</td>\n",
-       "      <td>50</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>99.953106</td>\n",
-       "      <td>0.046893</td>\n",
-       "      <td>3.049294e-07</td>\n",
-       "      <td>114.941200</td>\n",
-       "      <td>41.590814</td>\n",
-       "      <td>0.666317</td>\n",
-       "      <td>10.106568</td>\n",
-       "      <td>0.840985</td>\n",
-       "      <td>5.663345</td>\n",
-       "      <td>0.999979</td>\n",
-       "      <td>0.000035</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2022-01-01 00:00:04</td>\n",
-       "      <td>50</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>99.937480</td>\n",
-       "      <td>0.062520</td>\n",
-       "      <td>6.098257e-07</td>\n",
-       "      <td>114.921620</td>\n",
-       "      <td>41.590753</td>\n",
-       "      <td>0.666314</td>\n",
-       "      <td>10.106553</td>\n",
-       "      <td>0.840984</td>\n",
-       "      <td>5.663305</td>\n",
-       "      <td>0.999971</td>\n",
-       "      <td>0.000046</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",
-       "      <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>1187</th>\n",
-       "      <td>2022-01-01 09:56:00</td>\n",
-       "      <td>50</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>0.364232</td>\n",
-       "      <td>82.313555</td>\n",
-       "      <td>1.732221e+01</td>\n",
-       "      <td>60.591179</td>\n",
-       "      <td>41.280510</td>\n",
-       "      <td>0.653103</td>\n",
-       "      <td>10.031164</td>\n",
-       "      <td>0.838627</td>\n",
-       "      <td>5.494163</td>\n",
-       "      <td>0.970106</td>\n",
-       "      <td>0.413889</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1188</th>\n",
-       "      <td>2022-01-01 09:57:00</td>\n",
-       "      <td>50</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>0.360820</td>\n",
-       "      <td>82.284870</td>\n",
-       "      <td>1.735431e+01</td>\n",
-       "      <td>60.601170</td>\n",
-       "      <td>41.280615</td>\n",
-       "      <td>0.653106</td>\n",
-       "      <td>10.031189</td>\n",
-       "      <td>0.838627</td>\n",
-       "      <td>5.494211</td>\n",
-       "      <td>0.970115</td>\n",
-       "      <td>0.414583</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1189</th>\n",
-       "      <td>2022-01-01 09:58:00</td>\n",
-       "      <td>50</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>0.357440</td>\n",
-       "      <td>82.256164</td>\n",
-       "      <td>1.738640e+01</td>\n",
-       "      <td>60.611172</td>\n",
-       "      <td>41.280719</td>\n",
-       "      <td>0.653110</td>\n",
-       "      <td>10.031215</td>\n",
-       "      <td>0.838628</td>\n",
-       "      <td>5.494259</td>\n",
-       "      <td>0.970123</td>\n",
-       "      <td>0.415278</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1190</th>\n",
-       "      <td>2022-01-01 09:59:00</td>\n",
-       "      <td>50</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>0.354092</td>\n",
-       "      <td>82.227438</td>\n",
-       "      <td>1.741847e+01</td>\n",
-       "      <td>60.621185</td>\n",
-       "      <td>41.280824</td>\n",
-       "      <td>0.653113</td>\n",
-       "      <td>10.031240</td>\n",
-       "      <td>0.838629</td>\n",
-       "      <td>5.494307</td>\n",
-       "      <td>0.970132</td>\n",
-       "      <td>0.415972</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1191</th>\n",
-       "      <td>2022-01-01 10:00:00</td>\n",
-       "      <td>50</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>0.350776</td>\n",
-       "      <td>82.198691</td>\n",
-       "      <td>1.745053e+01</td>\n",
-       "      <td>60.631208</td>\n",
-       "      <td>41.280929</td>\n",
-       "      <td>0.653117</td>\n",
-       "      <td>10.031266</td>\n",
-       "      <td>0.838629</td>\n",
-       "      <td>5.494355</td>\n",
-       "      <td>0.970140</td>\n",
-       "      <td>0.416667</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1192 rows × 14 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                Datetime  Temperature  Injection          NA         NB  \\\n",
-       "0    2022-01-01 00:00:00           50       0.05  100.000000   0.000000   \n",
-       "1    2022-01-01 00:00:01           50       0.05   99.984366   0.015634   \n",
-       "2    2022-01-01 00:00:02           50       0.05   99.968735   0.031265   \n",
-       "3    2022-01-01 00:00:03           50       0.05   99.953106   0.046893   \n",
-       "4    2022-01-01 00:00:04           50       0.05   99.937480   0.062520   \n",
-       "...                  ...          ...        ...         ...        ...   \n",
-       "1187 2022-01-01 09:56:00           50       0.05    0.364232  82.313555   \n",
-       "1188 2022-01-01 09:57:00           50       0.05    0.360820  82.284870   \n",
-       "1189 2022-01-01 09:58:00           50       0.05    0.357440  82.256164   \n",
-       "1190 2022-01-01 09:59:00           50       0.05    0.354092  82.227438   \n",
-       "1191 2022-01-01 10:00:00           50       0.05    0.350776  82.198691   \n",
-       "\n",
-       "                NC         tau        Jsc       Voc        Isc        FF  \\\n",
-       "0     0.000000e+00  115.000000  41.590997  0.666327  10.106612  0.840987   \n",
-       "1     0.000000e+00  114.980390  41.590936  0.666324  10.106597  0.840986   \n",
-       "2     1.016487e-07  114.960790  41.590875  0.666321  10.106583  0.840986   \n",
-       "3     3.049294e-07  114.941200  41.590814  0.666317  10.106568  0.840985   \n",
-       "4     6.098257e-07  114.921620  41.590753  0.666314  10.106553  0.840984   \n",
-       "...            ...         ...        ...       ...        ...       ...   \n",
-       "1187  1.732221e+01   60.591179  41.280510  0.653103  10.031164  0.838627   \n",
-       "1188  1.735431e+01   60.601170  41.280615  0.653106  10.031189  0.838627   \n",
-       "1189  1.738640e+01   60.611172  41.280719  0.653110  10.031215  0.838628   \n",
-       "1190  1.741847e+01   60.621185  41.280824  0.653113  10.031240  0.838629   \n",
-       "1191  1.745053e+01   60.631208  41.280929  0.653117  10.031266  0.838629   \n",
-       "\n",
-       "           Pmp  Pmp_norm  time (days)  \n",
-       "0     5.663467  1.000000     0.000000  \n",
-       "1     5.663426  0.999993     0.000012  \n",
-       "2     5.663386  0.999986     0.000023  \n",
-       "3     5.663345  0.999979     0.000035  \n",
-       "4     5.663305  0.999971     0.000046  \n",
-       "...        ...       ...          ...  \n",
-       "1187  5.494163  0.970106     0.413889  \n",
-       "1188  5.494211  0.970115     0.414583  \n",
-       "1189  5.494259  0.970123     0.415278  \n",
-       "1190  5.494307  0.970132     0.415972  \n",
-       "1191  5.494355  0.970140     0.416667  \n",
-       "\n",
-       "[1192 rows x 14 columns]"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# this function quickly calculates the rest of the device parameters: Isc, FF, max power, and normalized max power\n",
     "timesteps = letid.calc_device_params(timesteps, cell_area=243)\n",
@@ -718,20 +400,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from cycler import cycler\n",
     "\n",
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Custom-Functions-Nopython.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Custom-Functions-Nopython.ipynb
new file mode 100644
index 000000000..fcef4411d
--- /dev/null
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Custom-Functions-Nopython.ipynb
@@ -0,0 +1,275 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import sympy as sp\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pvdeg\n",
+    "import pvdeg.symbolic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Grabbing Weather Data from PVGIS\n",
+    "\n",
+    "Use pvdeg to make an API call to PVGIS to collect location and weather data for Manhattan, NYC."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather_df, meta_dict = pvdeg.weather.get(\n",
+    "    database=\"PVGIS\",\n",
+    "    id=(40.776676, -73.971321),  # manhattan (latitude, longitude)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculating Temperature and Irradiance\n",
+    "\n",
+    "Use pvdeg to calculate timeseries temperature for a theoretical module from the previous metorological data.\n",
+    "\n",
+    "*Update this call after scenario has been merged: dev_scenario_geospatial->development->dev_symbolic*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "module_temps = pvdeg.temperature.module(\n",
+    "    weather_df=weather_df, meta=meta_dict, conf=\"open_rack_glass_glass\"\n",
+    ")\n",
+    "\n",
+    "poa_irradiance = pvdeg.spectral.poa_irradiance(weather_df=weather_df, meta=meta_dict)\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.plot(\n",
+    "    module_temps.values\n",
+    ")  # plotting the values in order because we are using tmy data so the years are not consistent within our data\n",
+    "plt.title(\"TMY Module Temperature, Manhattan\")\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.plot(poa_irradiance.values)\n",
+    "plt.legend(poa_irradiance.columns)\n",
+    "plt.title(\"TML Module Irradiance, Manhattan\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define Custom Expressions from Latex or \n",
+    "\n",
+    "We will use an altered arrhenius equation with an irrandiance relation (ignore the fact that this exists in pvdeg already).\n",
+    "\n",
+    "$R_{D} = R_{0}I^{X}  e^{\\frac{-{Ea}}{kT}}$ *ea is one variable so this may present some issues*  \n",
+    "the raw latex looks like this `R_{0}I^{X}  e^{\\frac{-{Ea}}{kT}}`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lnR_0, I, X, Ea, k, T = sp.symbols(\"lnR_0 I X Ea k T\")\n",
+    "\n",
+    "ln_R_D_expr = (\n",
+    "    lnR_0 * I**X * sp.exp((-Ea) / (k * T))\n",
+    ")  # python exponentiation is ** rather than ^\n",
+    "\n",
+    "# viewing output\n",
+    "ln_R_D_expr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculating Degradation Expression\n",
+    "\n",
+    "Generally more processing will have to happen outside of these functions than built in pvdeg functions. \n",
+    "Here we are defining our arguments and correcting units. When trying to calculate using timeseries we will pass `pandas.Series` objects to the arguments.\n",
+    "\n",
+    "Results should be strictly scrutinized to make sure the calculation is iterating over your series correctly. It will generally be easier to write python code\n",
+    "for more complex functions. This is about as complex as we will be able to get using arbitrary symbolic expressions if looping over timeseries data is required."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "module_temps_k = module_temps + 273.15  # convert C -> K\n",
+    "\n",
+    "poa_global = poa_irradiance[\n",
+    "    \"poa_global\"\n",
+    "]  # take only the global irradiance series from the total irradiance dataframe\n",
+    "poa_global_kw = poa_global / 1000  # [W/m^2] -> [kW/m^2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### This is quite slow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "values_kwarg = {\n",
+    "    \"Ea\": 62.08,  # activation energy, [kJ/mol]\n",
+    "    \"k\": 8.31446e-3,  # boltzmans constant, [kJ/(mol * K)]\n",
+    "    \"T\": module_temps_k,  # module temperature, [K]\n",
+    "    \"I\": poa_global_kw,  # module plane of array irradiance, [W/m2]\n",
+    "    \"X\": 0.0341,  # irradiance relation, [unitless]\n",
+    "    \"lnR_0\": 13.72,  # prefactor degradation [ln(%/h)]\n",
+    "}\n",
+    "\n",
+    "res = pvdeg.symbolic.calc_kwarg_timeseries(expr=ln_R_D_expr, kwarg=values_kwarg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Total degradation\n",
+    "\n",
+    "To calculate accumulated degradation, we can sum each of the substep values and convert to log scale (need to do this because prefactor was in log scale)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.log10(res.sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example with Single Values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "leakage current\n",
+    "$I_{leak} = \\frac{V_{bias}}{R_{enc}}$\n",
+    "\n",
+    "$I_{leak}$, leakage current  \n",
+    "${V_{bias}}$, potential difference between cells and frame  \n",
+    "${R_{enc}}$, resistance of the encapsulant  \n",
+    "\n",
+    "electric field\n",
+    "$E = \\frac{V_{bias}}{d}$  \n",
+    "\n",
+    "$E$, electric field  \n",
+    "$d$, thickness of encapsulant  \n",
+    "\n",
+    "degradation rage\n",
+    "$D = k_{D} * E * I{leak}$  \n",
+    "\n",
+    "$D$, degradation rate  \n",
+    "$k_D$, degradation constant  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "k_d, E, Vbias, Rencap, d = sp.symbols(\"k_d E Vbias Rencap d\")\n",
+    "\n",
+    "pid = k_d * (Vbias / d) * (Vbias / Rencap)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pvdeg\n",
+    "\n",
+    "pid_kwarg = {\n",
+    "    \"Vbias\": 1000,\n",
+    "    \"Rencap\": 1e9,\n",
+    "    \"d\": 0.0005,\n",
+    "    \"k_d\": 1e-9,\n",
+    "}\n",
+    "\n",
+    "res = pvdeg.symbolic.calc_kwarg_floats(expr=pid, kwarg=pid_kwarg)\n",
+    "\n",
+    "res"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculate Values in a Dataframe using a symbolic expression\n",
+    "\n",
+    "After creating the symbolic expression in using latex with `pvdeg.symbolic.symbolic_from_latex`, feed it to the function to calculate the leakage current at each row in the dataframe.\n",
+    "This expression calculation only accesses one dataframe row at a time so we cannot do any summations or windowed timeseries averiging within the call. At this point, write your own python function. If you create functions that would be useful, please email us your source code or create an issue and copy and paste your code there. If it is relevant it may be added to the package (credit will be given)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# I_leak_expr = pid_kwarg[\"Vbias\"] / pid_kwarg[\"Rencap\"]\n",
+    "# pvdeg.symbolic.calc_df_symbolic(expr=I_leak_expr, df=values_df)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python3",
+   "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": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/DuraMAT Live Demo.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/DuraMAT Live Demo.ipynb
new file mode 100644
index 000000000..d6df6da5a
--- /dev/null
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/DuraMAT Live Demo.ipynb	
@@ -0,0 +1,316 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# DuraMAT Workshop Live Demo - Geospatial analysis\n",
+    "\n",
+    "![PVDeg Logo](../PVD_logo.png)\n",
+    "\n",
+    "\n",
+    "**Steps:**\n",
+    "1. Initialize weather data into xarray\n",
+    "2. Calculate installation standoff for New Mexico\n",
+    "3. Plot results\n",
+    "\n",
+    "**Xarray: multi-dimensional data frame**\n",
+    "\n",
+    "![Xarray](./images/xarray.webp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-13T20:12:46.350659Z",
+     "start_time": "2019-06-13T20:11:46.936643Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import pvdeg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This information helps with debugging and getting support :)\n",
+    "import sys\n",
+    "import platform\n",
+    "\n",
+    "print(\"Working on a \", platform.system(), platform.release())\n",
+    "print(\"Python version \", sys.version)\n",
+    "print(\"Pandas version \", pd.__version__)\n",
+    "print(\"pvdeg version \", pvdeg.__version__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1 Start distributed compute cluster - DASK"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pvdeg.geospatial.start_dask();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get weather data\n",
+    "weather_db = \"NSRDB\"\n",
+    "\n",
+    "weather_arg = {\n",
+    "    \"satellite\": \"Americas\",\n",
+    "    \"names\": 2022,\n",
+    "    \"NREL_HPC\": True,\n",
+    "    \"attributes\": [\n",
+    "        \"air_temperature\",\n",
+    "        \"wind_speed\",\n",
+    "        \"dhi\",\n",
+    "        \"ghi\",\n",
+    "        \"dni\",\n",
+    "        \"relative_humidity\",\n",
+    "    ],\n",
+    "}\n",
+    "\n",
+    "weather_ds, meta_df = pvdeg.weather.get(weather_db, geospatial=True, **weather_arg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather_ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "meta_NM = meta_df[meta_df[\"state\"] == \"New Mexico\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "meta_NM_sub, gids_NM_sub = pvdeg.utilities.gid_downsampling(meta_NM, 4)\n",
+    "weather_NM_sub = weather_ds.sel(gid=meta_NM_sub.index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geo = {\n",
+    "    \"func\": pvdeg.standards.standoff,\n",
+    "    \"weather_ds\": weather_NM_sub,\n",
+    "    \"meta_df\": meta_NM_sub,\n",
+    "}\n",
+    "\n",
+    "standoff_res = pvdeg.geospatial.analysis(**geo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "standoff_res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = pvdeg.geospatial.plot_USA(\n",
+    "    standoff_res[\"x\"],\n",
+    "    cmap=\"viridis\",\n",
+    "    vmin=0,\n",
+    "    vmax=None,\n",
+    "    title=\"Minimum estimated air standoff to qualify as level 1 system\",\n",
+    "    cb_title=\"Standoff (cm)\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Relative Humidity Example - Time dimension"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# State bar of new mexico: (35.16482, -106.58979)\n",
+    "\n",
+    "weather_db = \"NSRDB\"\n",
+    "weather_id = (35.16482, -106.58979)  # NREL (39.741931, -105.169891)\n",
+    "weather_arg = {\n",
+    "    \"satellite\": \"Americas\",\n",
+    "    \"names\": 2022,\n",
+    "    \"NREL_HPC\": True,\n",
+    "    \"attributes\": [\n",
+    "        \"air_temperature\",\n",
+    "        \"wind_speed\",\n",
+    "        \"dhi\",\n",
+    "        \"ghi\",\n",
+    "        \"dni\",\n",
+    "        \"relative_humidity\",\n",
+    "    ],\n",
+    "}\n",
+    "\n",
+    "weather_df, meta = pvdeg.weather.get(\n",
+    "    weather_db, weather_id, geospatial=False, **weather_arg\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RH_module = pvdeg.humidity.module(weather_df=weather_df, meta=meta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RH_module"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RH_module.plot(ls=\"--\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geo = {\n",
+    "    \"func\": pvdeg.humidity.module,\n",
+    "    \"weather_ds\": weather_NM_sub,\n",
+    "    \"meta_df\": meta_NM_sub,\n",
+    "}\n",
+    "\n",
+    "RH_module = pvdeg.geospatial.analysis(**geo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RH_module"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from matplotlib.animation import FuncAnimation\n",
+    "# from matplotlib.animation import PillowWriter\n",
+    "# import matplotlib.animation as animation\n",
+    "# import datetime\n",
+    "# ims = []\n",
+    "# for n in range(1, 13):\n",
+    "#     for i, np_t in enumerate(RH_module.time):\n",
+    "#         t = pd.Timestamp(np_t.values).time()\n",
+    "#         d = pd.Timestamp(np_t.values).day\n",
+    "#         m = pd.Timestamp(np_t.values).month\n",
+    "#         if m == n:\n",
+    "#             if d == 15:\n",
+    "#                 if t == datetime.time(12):\n",
+    "#                     fig, ax = pvdeg.geospatial.plot_USA(RH_module['RH_surface_outside'].sel(time=np_t),\n",
+    "#                             cmap='viridis', vmin=0, vmax=100,\n",
+    "#                             title=f'RH_surface_outside  - 2022-{m}-{d} 12:00',\n",
+    "#                             cb_title='Relative humidity (%)')\n",
+    "#                     plt.savefig(f'./images/RH_animation_{n}.png', dpi=600)\n",
+    "\n",
+    "# import imageio\n",
+    "# ims = [imageio.imread(f'./images/RH_animation_{n}.png') for n in range(1, 13)]\n",
+    "# imageio.mimwrite(f'./images/RH_animation.gif', ims, format='GIF', duration=1000, loop=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![PVDeg Logo](./images/RH_animation.gif)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Geospatial - Local Scenario.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Geospatial - Local Scenario.ipynb
new file mode 100644
index 000000000..8ffff278a
--- /dev/null
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Geospatial - Local Scenario.ipynb	
@@ -0,0 +1,47 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Running Geospatial Analysis Locally #\n",
+    "\n",
+    "Author: Tobin Ford\n",
+    "Email : tobin.ford@nrel.gov"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Use Case\n",
+    "- If you are unable to access the NSRDB via NREL HPC or AWS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sourcing Data ##\n",
+    "\n",
+    "On the [NSRDB Dataviewer](https://nsrdb.nrel.gov/data-viewer), you can use the area querys to collect information from the area you want. Only tested with USA & Americas - Typical Meteorological Year so far. Use the dropdowns to select the attributes you will need to run calculations. Select a year and follow the instructions to get the zip file containing the weather data csv's for each location in the selected region.\n",
+    "\n",
+    "![NSRDB Dataviewer](./images/NSRDB-data-viewer.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Geospatial Templates.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Geospatial Templates.ipynb
new file mode 100644
index 000000000..b833a477b
--- /dev/null
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Geospatial Templates.ipynb	
@@ -0,0 +1,470 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pvdeg\n",
+    "from pvdeg import TEST_DATA_DIR\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "import xarray as xr\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Geospatial Templates\n",
+    "\n",
+    "When running a geospatial analysis using `pvdeg.geospatial.analysis` on arbitary `pvdeg` functions you will need to specify a template for the shape of the output data. This is because the input data comes with dimensions of gid and time while the output will have data in a different shape usually corresonding to coordinates.\n",
+    "- gid, identification number corresponding to an NSRDB datapoint's location \n",
+    "- time, timeseries corresponding to the hourly time indicies of NSRDB datapoint's yearly meteorological data.\n",
+    "\n",
+    "Follow the steps below to see how we generate templates before running the analysis.\n",
+    "\n",
+    "The only functions where this is not required are currently `pvdeg.standards.standoff`, `pvdeg.humidity.moduke` and, `letid.calc_letid_outdoors` as they are predefined within the package."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading Geospatial Data\n",
+    "\n",
+    "This step skips over making the `pvdeg.weather.get` call with `geospatial == True`. See the [Duramat Demo](./DuraMAT%20Live%20Demo.ipynb) for information on how to do this traditionally.\n",
+    "\n",
+    "We can also use a `GeospatialScenario` object. See the [Geospatial Scenario Tutorial](./Scenario%20-%20Geospatial.ipynb) for more information on how to use this approach.\n",
+    "\n",
+    "*The cell below loads a pickled xarray object, this is not the best way to do this. xarray datasets should be stored as `.nc` - netcdf files*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geo_meta = pd.read_csv(os.path.join(TEST_DATA_DIR, \"summit-meta.csv\"), index_col=0)\n",
+    "\n",
+    "# Use xarray to open NetCDF file instead of pickle\n",
+    "geo_weather = xr.open_dataset(os.path.join(TEST_DATA_DIR, \"summit-weather.nc\"))\n",
+    "\n",
+    "geo_weather\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Creating Templates Manually\n",
+    "\n",
+    "`pvdeg.geospatial.ouput_template` we can produce a template for our result data. \n",
+    "\n",
+    "We need to do this because different functions return different types of values, some return multiple values as tuples, some return only single numerics, others return timeseries results. We need to specify the shape of our data to create an output xarray dataset. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Examples\n",
+    "\n",
+    "## 98ᵗʰ module percential temperature at Standoff Height\n",
+    "\n",
+    "Say we want to estimate the 98ᵗʰ percential temperature for the module at the given tilt, azimuth, and x_eff. `PVDeg` has a function to do this, `pvdeg.standards.T98_estimate` BUT it doesn't have a preset geospatial template. We will need to make one.\n",
+    "\n",
+    "- look at the function return values. \n",
+    "From the docstring we can see that `T98_estimate` only has one return value. IMPORTANT, this value is a single float, NOT a timeseries. This means our output shape will only be dependent on the input identifier and NOT time. \n",
+    "\n",
+    "Therefore we will map the output variable `T98` to the location identifier `gid` using a dictionary with `str: tuple` mappings.\n",
+    "\n",
+    " *IMPORTANT: you must use the syntax below where the variable maps to a tuple of the coordinates. in this case there needs to be a trailing comma in the tuple or python will iterate over the characters in the tuple instead of the elements. See further examples to alleviate confusion.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define output shape\n",
+    "shapes = {\n",
+    "    \"T98\": (\n",
+    "        \"gid\",\n",
+    "    )  # one return value at each datapoint, only dependent on datapoint, not time\n",
+    "}\n",
+    "\n",
+    "# create xarray template for output to be populated when analysis is run\n",
+    "template = pvdeg.geospatial.output_template(\n",
+    "    ds_gids=geo_weather,\n",
+    "    shapes=shapes,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geo_estimate_temp = pvdeg.geospatial.analysis(\n",
+    "    weather_ds=geo_weather,\n",
+    "    meta_df=geo_meta,\n",
+    "    func=pvdeg.standards.T98_estimate,\n",
+    "    template=template,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Glass Glass Estimated Module Temperature\n",
+    "\n",
+    "Now we want to calculate geospatial timeseries temperature values for a module using `pvdeg.temperature.module`. This is not super practical because all `pvdeg` functions that need to use tempeature for their calculations preform the temperature calculation internally, this is just for show. \n",
+    "\n",
+    "This calculation differs from the above because the temperature functions return the module temperature in a timeseries format. So we care about 2 dimensions, location identifier and TIME."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define output shape\n",
+    "shapes = {\n",
+    "    \"module_temperature\": (\n",
+    "        \"gid\",\n",
+    "        \"time\",\n",
+    "    )  # one return value at each datapoint, only dependent on datapoint, not time\n",
+    "}\n",
+    "\n",
+    "# create xarray template for output to be populated when analysis is run\n",
+    "temperature_template = pvdeg.geospatial.output_template(\n",
+    "    ds_gids=geo_weather,\n",
+    "    shapes=shapes,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geo_temperature_res = pvdeg.geospatial.analysis(\n",
+    "    weather_ds=geo_weather,\n",
+    "    meta_df=geo_meta,\n",
+    "    func=pvdeg.temperature.module,\n",
+    "    template=temperature_template,  # use the template we created\n",
+    "    conf=\"open_rack_glass_glass\",  # provide kwargs for function here\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot the temperature at ONE of the geospatial analysis result locations but we have calculated all of these.\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "module_temps = (\n",
+    "    geo_temperature_res[\"module\"].sel(latitude=39.89, longitude=\"-106.42\").values\n",
+    ")\n",
+    "\n",
+    "plt.plot(module_temps)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Self Explaining Code\n",
+    "\n",
+    "If we are looking at adding templates for other functions, we can also look at the 3 presaved templates for existing `pvdeg` functions. Visit [pvdeg.geospatial.template_parameters](../../pvdeg/geospatial.py) and inspect this function to see how these different target functions utilize templates and shapes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Creating Templates Programatically\n",
+    "\n",
+    "We can use `pvdeg.geospatial.autotemplate` to generate a template for a given function. This can return a bad result which will fail or work improperly when running `pvdeg.geospatial.analysis` with the generated template. Results should be scrutinized to make sure they are the right format."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Examples Below\n",
+    "Steps\n",
+    "- Create template using autotemplating function. Pulls in information about function to determine shape of output. Not usable on functions with ambigious return types.\n",
+    "- Call geospatial analysis function using template"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Geospatial Cell Temperature Calculation\n",
+    "As shown below, we have two options, we can choose to provide a template that is generated by a function which supports autotemplating. Or we can provide the function to `geospatial.analysis` and let it generate a template internally.\n",
+    "\n",
+    "### Providing a Template with `Geospatial.auto_template`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a template using auto_template for the desired function\n",
+    "cell_temp_template = pvdeg.geospatial.auto_template(\n",
+    "    func=pvdeg.temperature.cell, ds_gids=geo_weather\n",
+    ")\n",
+    "\n",
+    "# run the geospatial analysis with the template\n",
+    "pvdeg.geospatial.analysis(\n",
+    "    weather_ds=geo_weather,\n",
+    "    meta_df=geo_meta,\n",
+    "    func=pvdeg.temperature.cell,\n",
+    "    template=cell_temp_template,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analysis Without Providing a Template\n",
+    "\n",
+    "If a function is supported by `geospatial.auto_template` we do not need to create a template outside of the function as shown in the cell above. We can simply pass the function to `geospatial.analysis` and it will create a template for us."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pvdeg.geospatial.analysis(\n",
+    "    weather_ds=geo_weather,\n",
+    "    meta_df=geo_meta,\n",
+    "    func=pvdeg.temperature.cell,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Geospatial Module Temperature Calculation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "module_temp_template = pvdeg.geospatial.auto_template(\n",
+    "    func=pvdeg.temperature.module, ds_gids=geo_weather\n",
+    ")\n",
+    "\n",
+    "pvdeg.geospatial.analysis(\n",
+    "    weather_ds=geo_weather,\n",
+    "    meta_df=geo_meta,\n",
+    "    func=pvdeg.temperature.module,\n",
+    "    template=module_temp_template,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Geospatial Solar Position Calculation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "solar_position_template = pvdeg.geospatial.auto_template(\n",
+    "    func=pvdeg.spectral.solar_position, ds_gids=geo_weather\n",
+    ")\n",
+    "\n",
+    "pvdeg.geospatial.analysis(\n",
+    "    weather_ds=geo_weather,\n",
+    "    meta_df=geo_meta,\n",
+    "    func=pvdeg.spectral.solar_position,\n",
+    "    template=solar_position_template,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Geospatial POA Irradiance Calculation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "poa_irradiance_template = pvdeg.geospatial.auto_template(\n",
+    "    func=pvdeg.spectral.poa_irradiance, ds_gids=geo_weather\n",
+    ")\n",
+    "\n",
+    "pvdeg.geospatial.analysis(\n",
+    "    weather_ds=geo_weather,\n",
+    "    meta_df=geo_meta,\n",
+    "    func=pvdeg.spectral.poa_irradiance,\n",
+    "    template=poa_irradiance_template,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Geospatial 98th Percentile Operating Temperature Calculation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "standoff_template = pvdeg.geospatial.auto_template(\n",
+    "    func=pvdeg.standards.T98_estimate, ds_gids=geo_weather\n",
+    ")\n",
+    "\n",
+    "pvdeg.geospatial.analysis(\n",
+    "    weather_ds=geo_weather,\n",
+    "    meta_df=geo_meta,\n",
+    "    func=pvdeg.standards.T98_estimate,\n",
+    "    template=standoff_template,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Geospatial Module Humidity Calculation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "humidity_template = pvdeg.geospatial.auto_template(\n",
+    "    func=pvdeg.humidity.module, ds_gids=geo_weather\n",
+    ")\n",
+    "\n",
+    "pvdeg.geospatial.analysis(\n",
+    "    weather_ds=geo_weather,\n",
+    "    meta_df=geo_meta,\n",
+    "    func=pvdeg.humidity.module,\n",
+    "    template=humidity_template,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Geospatial IwaVantHoff Environment Characterization Calculation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iwa_vant_hoff_template = pvdeg.geospatial.auto_template(\n",
+    "    func=pvdeg.degradation.IwaVantHoff, ds_gids=geo_weather\n",
+    ")\n",
+    "\n",
+    "pvdeg.geospatial.analysis(\n",
+    "    weather_ds=geo_weather,\n",
+    "    meta_df=geo_meta,\n",
+    "    func=pvdeg.degradation.IwaVantHoff,\n",
+    "    template=iwa_vant_hoff_template,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Geospatial Edge Seal Width Calculation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "edge_seal_template = pvdeg.geospatial.auto_template(\n",
+    "    func=pvdeg.design.edge_seal_width, ds_gids=geo_weather\n",
+    ")\n",
+    "\n",
+    "pvdeg.geospatial.analysis(\n",
+    "    weather_ds=geo_weather,\n",
+    "    meta_df=geo_meta,\n",
+    "    func=pvdeg.design.edge_seal_width,\n",
+    "    template=edge_seal_template,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "edge_seal_template"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pvdeg",
+   "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": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/HPC Connection Required - Geospatial_world_map.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/HPC Connection Required - Geospatial_world_map.ipynb
new file mode 100644
index 000000000..c086a6033
--- /dev/null
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/HPC Connection Required - Geospatial_world_map.ipynb	
@@ -0,0 +1,809 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Note: This notebook is in development\n",
+    "M. Springer 2024-06-05"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Importing libraries...\")\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pvdeg\n",
+    "import xarray as xr\n",
+    "import os\n",
+    "\n",
+    "print(\"Done!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Calculate Standoff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "work_dir = \"/projects/pvsoiling/pvdeg/analysis/world_map/standoff_fine\"\n",
+    "data_dir = \"/projects/pvsoiling/pvdeg/analysis/world_map/data\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local = {\n",
+    "    \"manager\": \"local\",\n",
+    "    \"n_workers\": 100,\n",
+    "}\n",
+    "\n",
+    "kestrel = {\n",
+    "    \"manager\": \"slurm\",\n",
+    "    \"n_jobs\": 8,  # Max number of nodes used for parallel processing\n",
+    "    \"cores\": 100,\n",
+    "    \"processes\": 50,\n",
+    "    \"memory\": \"245GB\",\n",
+    "    \"account\": \"pvfem\",\n",
+    "    \"queue\": \"standard\",\n",
+    "    \"walltime\": \"8:00:00\",\n",
+    "    # \"scheduler_options\": {\"host\": socket.gethostname()},\n",
+    "}\n",
+    "\n",
+    "print(\"Starting Dask client...\")\n",
+    "client = pvdeg.geospatial.start_dask(hpc=kestrel)\n",
+    "print(\"Cluster ready!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get weather data\n",
+    "weather_db = \"NSRDB\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather_arg = {\n",
+    "    \"satellite\": \"Himawari\",\n",
+    "    \"names\": \"tmy-2020\",\n",
+    "    \"NREL_HPC\": True,\n",
+    "    \"attributes\": [\n",
+    "        \"air_temperature\",\n",
+    "        \"wind_speed\",\n",
+    "        \"dhi\",\n",
+    "        \"ghi\",\n",
+    "        \"dni\",\n",
+    "        \"relative_humidity\",\n",
+    "    ],\n",
+    "}\n",
+    "\n",
+    "weather_ds_himawari, meta_df_himawari = pvdeg.weather.get(\n",
+    "    weather_db, geospatial=True, **weather_arg\n",
+    ")\n",
+    "\n",
+    "# %%\n",
+    "meta_df_himawari_sub, gids_meta_df_himawari = pvdeg.utilities.gid_downsampling(\n",
+    "    meta_df_himawari, 3\n",
+    ")\n",
+    "weather_ds_himawari_sub = weather_ds_himawari.sel(gid=meta_df_himawari_sub.index)\n",
+    "\n",
+    "# %%\n",
+    "geo_himawari = {\n",
+    "    \"func\": pvdeg.standards.standoff,\n",
+    "    \"weather_ds\": weather_ds_himawari_sub,\n",
+    "    \"meta_df\": meta_df_himawari_sub,\n",
+    "}\n",
+    "\n",
+    "standoff_res_himawari = pvdeg.geospatial.analysis(**geo_himawari)\n",
+    "standoff_res_himawari.to_netcdf(os.path.join(work_dir, \"standoff_himawari.nc\"))\n",
+    "standoff_res_himawari.to_dataframe().to_csv(\n",
+    "    os.path.join(work_dir, \"standoff_himawari.csv\")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather_arg = {\n",
+    "    \"satellite\": \"GOES\",\n",
+    "    \"names\": 2021,\n",
+    "    \"NREL_HPC\": True,\n",
+    "    \"attributes\": [\n",
+    "        \"air_temperature\",\n",
+    "        \"wind_speed\",\n",
+    "        \"dhi\",\n",
+    "        \"ghi\",\n",
+    "        \"dni\",\n",
+    "        \"relative_humidity\",\n",
+    "    ],\n",
+    "}\n",
+    "\n",
+    "weather_ds_goes, meta_df_goes = pvdeg.weather.get(\n",
+    "    weather_db, geospatial=True, **weather_arg\n",
+    ")\n",
+    "\n",
+    "# %%\n",
+    "meta_df_goes_sub, gids_meta_df_goes = pvdeg.utilities.gid_downsampling(meta_df_goes, 8)\n",
+    "weather_ds_goes_sub = weather_ds_goes.sel(gid=meta_df_goes_sub.index)\n",
+    "\n",
+    "# %%\n",
+    "geo_goes = {\n",
+    "    \"func\": pvdeg.standards.standoff,\n",
+    "    \"weather_ds\": weather_ds_goes_sub,\n",
+    "    \"meta_df\": meta_df_goes_sub,\n",
+    "}\n",
+    "\n",
+    "standoff_res_goes = pvdeg.geospatial.analysis(**geo_goes)\n",
+    "standoff_res_goes.to_netcdf(os.path.join(work_dir, \"standoff_goes.nc\"))\n",
+    "standoff_res_goes.to_dataframe().to_csv(os.path.join(work_dir, \"standoff_goes.csv\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather_arg = {\n",
+    "    \"satellite\": \"METEOSAT\",\n",
+    "    \"names\": 2019,\n",
+    "    \"NREL_HPC\": True,\n",
+    "    \"attributes\": [\n",
+    "        \"air_temperature\",\n",
+    "        \"wind_speed\",\n",
+    "        \"dhi\",\n",
+    "        \"ghi\",\n",
+    "        \"dni\",\n",
+    "        \"relative_humidity\",\n",
+    "    ],\n",
+    "}\n",
+    "\n",
+    "weather_ds_meteosat, meta_df_meteosat = pvdeg.weather.get(\n",
+    "    weather_db, geospatial=True, **weather_arg\n",
+    ")\n",
+    "\n",
+    "# %%\n",
+    "meta_df_meteosat_sub, gids_meta_df_meteosat = pvdeg.utilities.gid_downsampling(\n",
+    "    meta_df_meteosat, 4\n",
+    ")\n",
+    "weather_ds_meteosat_sub = weather_ds_meteosat.sel(gid=meta_df_meteosat_sub.index)\n",
+    "\n",
+    "# %%\n",
+    "geo_meteosat = {\n",
+    "    \"func\": pvdeg.standards.standoff,\n",
+    "    \"weather_ds\": weather_ds_meteosat_sub,\n",
+    "    \"meta_df\": meta_df_meteosat_sub,\n",
+    "}\n",
+    "\n",
+    "standoff_res_meteosat = pvdeg.geospatial.analysis(**geo_meteosat)\n",
+    "standoff_res_meteosat.to_netcdf(os.path.join(work_dir, \"standoff_meteosat.nc\"))\n",
+    "standoff_res_meteosat.to_dataframe().to_csv(\n",
+    "    os.path.join(work_dir, \"standoff_meteosat.csv\")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Auxillary data\n",
+    "import h5py\n",
+    "\n",
+    "fp_weather_aux = \"/projects/pvsoiling/pvdeg/data/world_map_aux.h5\"\n",
+    "fp_meta_aux = \"/projects/pvsoiling/pvdeg/data/meta_world_map_aux.csv\"\n",
+    "\n",
+    "# weather_aux = pvdeg.weather.read(fp_weather_aux, 'h5')\n",
+    "meta_aux = pd.read_csv(fp_meta_aux, index_col=0)\n",
+    "\n",
+    "# xarray work around for aux data\n",
+    "dss = []\n",
+    "drop_variables = [\"meta\", \"time_index\", \"tmy_year\", \"tmy_year_short\", \"coordinates\"]\n",
+    "\n",
+    "hf = h5py.File(fp_weather_aux, \"r\")\n",
+    "attr = list(hf)\n",
+    "\n",
+    "attr_to_read = [elem for elem in attr if elem not in drop_variables]\n",
+    "\n",
+    "chunks = []\n",
+    "shapes = []\n",
+    "for var in attr_to_read:\n",
+    "    chunks.append(hf[var].chunks if hf[var].chunks is not None else (np.nan, np.nan))\n",
+    "    shapes.append(hf[var].shape if hf[var].shape is not None else (np.nan, np.nan))\n",
+    "chunks = min(set(chunks))\n",
+    "shapes = min(set(shapes))\n",
+    "\n",
+    "\n",
+    "time_index = pd.to_datetime(hf[\"time_index\"][...].astype(str)).values\n",
+    "meta_df = meta_aux\n",
+    "coords = {\"gid\": meta_df.index.values, \"time\": time_index}\n",
+    "coords_len = {\"time\": time_index.shape[0], \"gid\": meta_df.shape[0]}\n",
+    "\n",
+    "ds = xr.open_dataset(\n",
+    "    fp_weather_aux,\n",
+    "    engine=\"h5netcdf\",\n",
+    "    phony_dims=\"sort\",\n",
+    "    chunks={\"phony_dim_0\": -1, \"phony_dim_1\": -1},\n",
+    "    drop_variables=drop_variables,\n",
+    "    mask_and_scale=False,\n",
+    "    decode_cf=True,\n",
+    ")\n",
+    "\n",
+    "for var in ds.data_vars:\n",
+    "    if hasattr(getattr(ds, var), \"psm_scale_factor\"):\n",
+    "        scale_factor = 1 / ds[var].psm_scale_factor\n",
+    "        print(scale_factor)\n",
+    "        getattr(ds, var).attrs[\"scale_factor\"] = scale_factor\n",
+    "\n",
+    "rename = {}\n",
+    "for (\n",
+    "    phony,\n",
+    "    length,\n",
+    ") in ds.sizes.items():\n",
+    "    if length == coords_len[\"time\"]:\n",
+    "        rename[phony] = \"time\"\n",
+    "    elif length == coords_len[\"gid\"]:\n",
+    "        rename[phony] = \"gid\"\n",
+    "ds = ds.rename(rename)\n",
+    "ds = ds.assign_coords(coords)\n",
+    "\n",
+    "# TODO: In case re-chunking becomes necessary\n",
+    "# ax0 = list(ds.sizes.keys())[list(ds.sizes.values()).index(shapes[0])]\n",
+    "# ax1 = list(ds.sizes.keys())[list(ds.sizes.values()).index(shapes[1])]\n",
+    "# ds = ds.chunk(chunks={ax0:chunks[0], ax1:chunks[1]})\n",
+    "dss.append(ds)\n",
+    "\n",
+    "ds = xr.merge(dss)\n",
+    "ds = xr.decode_cf(ds)\n",
+    "\n",
+    "# Rechunk time axis\n",
+    "ds = ds.unify_chunks()\n",
+    "ds = ds.chunk(chunks={\"time\": -1, \"gid\": ds.chunks[\"gid\"]})\n",
+    "\n",
+    "weather_ds = ds\n",
+    "\n",
+    "DSET_MAP = {\"air_temperature\": \"temp_air\", \"Relative Humidity\": \"relative_humidity\"}\n",
+    "META_MAP = {\"elevation\": \"altitude\", \"Local Time Zone\": \"tz\", \"timezone\": \"tz\"}\n",
+    "\n",
+    "for dset in weather_ds.data_vars:\n",
+    "    if dset in DSET_MAP.keys():\n",
+    "        weather_ds = weather_ds.rename({dset: DSET_MAP[dset]})\n",
+    "\n",
+    "for mset in meta_df.columns:\n",
+    "    if mset in META_MAP.keys():\n",
+    "        meta_df.rename(columns={mset: META_MAP[mset]}, inplace=True)\n",
+    "\n",
+    "weather_ds_aux = weather_ds\n",
+    "\n",
+    "geo_aux = {\n",
+    "    \"func\": pvdeg.standards.standoff,\n",
+    "    \"weather_ds\": weather_ds_aux,\n",
+    "    \"meta_df\": meta_aux,\n",
+    "}\n",
+    "\n",
+    "standoff_res_aux = pvdeg.geospatial.analysis(**geo_aux)\n",
+    "standoff_res_aux.to_netcdf(os.path.join(work_dir, \"standoff_aux.nc\"))\n",
+    "standoff_res_aux.to_dataframe().to_csv(os.path.join(work_dir, \"standoff_aux.csv\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather_db = \"NSRDB\"\n",
+    "weather_arg = {\n",
+    "    \"satellite\": \"METEOSAT\",\n",
+    "    \"names\": 2019,\n",
+    "    \"NREL_HPC\": True,\n",
+    "    \"attributes\": [\n",
+    "        \"air_temperature\",\n",
+    "        \"wind_speed\",\n",
+    "        \"dhi\",\n",
+    "        \"ghi\",\n",
+    "        \"dni\",\n",
+    "        \"relative_humidity\",\n",
+    "    ],\n",
+    "}\n",
+    "weather_ds_meteosat, meta_df_meteosat = pvdeg.weather.get(\n",
+    "    weather_db, geospatial=True, **weather_arg\n",
+    ")\n",
+    "\n",
+    "time_hourly = pd.date_range(\"2019-01-01\", freq=\"h\", periods=365 * 24)\n",
+    "weather_ds_meteosat_hourly = weather_ds_meteosat.sel(time=time_hourly)\n",
+    "\n",
+    "europe = [\n",
+    "    \"Spain\",\n",
+    "    \"Portugal\",  #'Ireland', 'United Kingdom',\n",
+    "    \"France\",\n",
+    "    \"Belgium\",\n",
+    "    \"Netherlands\",\n",
+    "    \"Norway\",\n",
+    "    \"Luxembourg\",\n",
+    "    \"Germany\",\n",
+    "    \"Switzerland\",\n",
+    "    \"Italy\",\n",
+    "    \"Monaco\",\n",
+    "    \"Denmark\",\n",
+    "    \"Liechtenstein\",\n",
+    "    \"Austria\",\n",
+    "    \"Sweden\",\n",
+    "    \"Czech Republic\",\n",
+    "    \"San Marino\",\n",
+    "    \"Slovenia\",\n",
+    "    \"Croatia\",\n",
+    "    \"Poland\",\n",
+    "    \"Malta\",\n",
+    "    \"Bosnia and Herzegovina\",\n",
+    "    \"Hungary\",\n",
+    "    \"Slovakia\",\n",
+    "    \"Montenegro\",\n",
+    "    \"Serbia\",\n",
+    "    \"Albania\",\n",
+    "    \"Greece\",\n",
+    "    \"Romania\",\n",
+    "    \"Macedonia\",\n",
+    "    \"Latvia\",\n",
+    "    \"Lithuania\",\n",
+    "    \"Finland\",\n",
+    "    \"Estonia\",\n",
+    "    \"Ukraine\",\n",
+    "    \"Bulgaria\",\n",
+    "    \"Belarus\",\n",
+    "    \"Moldova\",\n",
+    "    \"Turkey\",\n",
+    "    \"Cyprus\",\n",
+    "    \"Northern Cyprus\",\n",
+    "    \"Georgia\",\n",
+    "]\n",
+    "\n",
+    "meta_df_europe = meta_df_meteosat[meta_df_meteosat[\"country\"].isin(europe)]\n",
+    "\n",
+    "meta_df_europe_sub, gids_sub = pvdeg.utilities.gid_downsampling(meta_df_europe, 4)\n",
+    "weather_europe_sub = weather_ds_meteosat_hourly.sel(gid=meta_df_europe_sub.index)\n",
+    "\n",
+    "meta_uk = pd.read_csv(\"../../world_map/data/meta_pvgis_uk_4300.csv\")\n",
+    "meta_uk_sub, gids_sub = pvdeg.utilities.gid_downsampling(meta_uk, 1)\n",
+    "\n",
+    "meta_scan1 = pd.read_csv(f\"{data_dir}/meta_pvgis_scan_coarse1500.csv\", index_col=0)\n",
+    "meta_scan12 = pd.read_csv(\n",
+    "    f\"{data_dir}/meta_pvgis_scan_coarse_1500_1599.csv\", index_col=0\n",
+    ")\n",
+    "meta_scan2 = pd.read_csv(f\"{data_dir}/meta_pvgis_scan_coarse2100.csv\", index_col=0)\n",
+    "meta_scan = pd.concat([meta_scan1, meta_scan12, meta_scan2])\n",
+    "\n",
+    "meta_pvgis = pd.concat([meta_scan, meta_uk_sub])\n",
+    "\n",
+    "lat_NSRDB = meta_df_europe_sub[\"latitude\"].to_numpy()\n",
+    "meta_pvgis[\"latitude_pvgis\"] = meta_pvgis[\"latitude\"]\n",
+    "meta_pvgis[\"latitude\"] = meta_pvgis[\"latitude\"].apply(\n",
+    "    lambda x: (\n",
+    "        lat_NSRDB[np.argmin(np.abs(x - lat_NSRDB))]\n",
+    "        if x < meta_df_europe_sub[\"latitude\"].max()\n",
+    "        else x\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "lon_NSRDB = meta_df_europe_sub[\"longitude\"].to_numpy()\n",
+    "meta_pvgis[\"longitude_pvgis\"] = meta_pvgis[\"longitude\"]\n",
+    "meta_pvgis[\"longitude\"] = meta_pvgis[\"longitude\"].apply(\n",
+    "    lambda x: (\n",
+    "        lon_NSRDB[np.argmin(np.abs(x - lon_NSRDB))]\n",
+    "        if x > meta_df_europe_sub[\"longitude\"].min()\n",
+    "        else x\n",
+    "    )\n",
+    ")\n",
+    "meta_pvgis[\"tz\"] = 0\n",
+    "meta_pvgis = meta_pvgis.reset_index()\n",
+    "\n",
+    "meta_eu_coarse = pd.concat([meta_df_europe_sub, meta_pvgis])\n",
+    "meta_eu_coarse[\"tz\"] = 0\n",
+    "meta_eu_coarse = meta_eu_coarse.reset_index()\n",
+    "meta_eu_coarse.to_csv(\"../../world_map/data/meta_eu_coarse.csv\")\n",
+    "\n",
+    "weather_uk = xr.open_dataset(\"../../world_map/data/weather_ds_uk_4300.nc\")\n",
+    "weather_uk = weather_uk.sel(gid=meta_uk.index)\n",
+    "weather_uk_sub = weather_uk.sel(gid=meta_uk_sub.index)\n",
+    "\n",
+    "weather_scan1 = xr.open_dataset(\"../../world_map/data/weather_ds_scan_coarse1500.nc\")\n",
+    "weather_scan12 = xr.open_dataset(\n",
+    "    \"../../world_map/data/weather_ds_scan_coarse_1500_1599.nc\"\n",
+    ")\n",
+    "weather_scan2 = xr.open_dataset(\"../../world_map/data/weather_ds_scan_coarse2100.nc\")\n",
+    "weather_scan = xr.concat(\n",
+    "    [\n",
+    "        weather_scan1.sel(gid=slice(0, 1500)),\n",
+    "        weather_scan12.sel(gid=slice(1501, 1599)),\n",
+    "        weather_scan2.sel(gid=slice(1600, None)),\n",
+    "    ],\n",
+    "    dim=\"gid\",\n",
+    ")\n",
+    "weather_scan = weather_scan.sel(gid=meta_scan.index)\n",
+    "# weather_scan = xr.concat([weather_scan1, weather_scan2], dim='gid')\n",
+    "\n",
+    "weather_pvgis = xr.concat([weather_scan, weather_uk_sub], dim=\"gid\")\n",
+    "weather_pvgis = weather_pvgis.drop_vars([\"IR(h)\", \"wind_direction\", \"pressure\"])\n",
+    "\n",
+    "weather_pvgis = weather_pvgis.assign_coords({\"gid\": meta_pvgis.index})\n",
+    "weather_pvgis = weather_pvgis.chunk(chunks={\"time\": -1, \"gid\": 100})\n",
+    "weather_pvgis = weather_pvgis.unify_chunks()\n",
+    "\n",
+    "meta_df_pvgis_sub, gids_meta_df_pvgis = pvdeg.utilities.gid_downsampling(meta_pvgis, 4)\n",
+    "weather_ds_pvgis_sub = weather_pvgis.sel(gid=meta_df_pvgis_sub.index)\n",
+    "\n",
+    "# weather_europe_sub = weather_europe_sub.assign_coords({'time': pd.date_range(\"2022-01-01\", freq=\"h\", periods=365 * 24),})\n",
+    "# weather_eu_coarse = xr.concat([weather_europe_sub, weather_pvgis], dim='gid')\n",
+    "# weather_eu_coarse = weather_eu_coarse.assign_coords({'gid': meta_eu_coarse.index})\n",
+    "# weather_eu_coarse = weather_eu_coarse.chunk(chunks={\"time\": -1, \"gid\": 100})\n",
+    "# weather_eu_coarse = weather_eu_coarse.unify_chunks()\n",
+    "\n",
+    "# with open('weather_eu_coarse.pickle', 'wb') as handle:\n",
+    "#     pickle.dump(weather_eu_coarse, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
+    "\n",
+    "\n",
+    "geo_pvgis = {\n",
+    "    \"func\": pvdeg.standards.standoff,\n",
+    "    \"weather_ds\": weather_pvgis,\n",
+    "    \"meta_df\": meta_pvgis,\n",
+    "}\n",
+    "\n",
+    "standoff_res_pvgis = pvdeg.geospatial.analysis(**geo_pvgis)\n",
+    "standoff_res_pvgis.to_netcdf(os.path.join(work_dir, \"standoff_pvgis.nc\"))\n",
+    "standoff_res_pvgis.to_dataframe().to_csv(os.path.join(work_dir, \"standoff_pvgis.csv\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "meta_north = pd.read_csv(f\"{data_dir}/meta_pvgis_north_3300.csv\", index_col=0)\n",
+    "weather_north = xr.open_dataset(f\"{data_dir}/weather_ds_north_3300.nc\")\n",
+    "weather_north = weather_north.sel(gid=meta_north.index)\n",
+    "\n",
+    "weather_north = weather_north.assign_coords({\"gid\": meta_north.index})\n",
+    "weather_north = weather_north.chunk(chunks={\"time\": -1, \"gid\": 100})\n",
+    "weather_north = weather_north.unify_chunks()\n",
+    "\n",
+    "\n",
+    "geo_pvgis = {\n",
+    "    \"func\": pvdeg.standards.standoff,\n",
+    "    \"weather_ds\": weather_north,\n",
+    "    \"meta_df\": meta_north,\n",
+    "}\n",
+    "\n",
+    "standoff_res_pvgis = pvdeg.geospatial.analysis(**geo_pvgis)\n",
+    "standoff_res_pvgis.to_netcdf(os.path.join(work_dir, \"standoff_pvgis_north.nc\"))\n",
+    "standoff_res_pvgis.to_dataframe().to_csv(\n",
+    "    os.path.join(work_dir, \"standoff_pvgis_north.csv\")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Post process"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pvdeg\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "from global_land_mask import globe\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.io.shapereader as shpreader\n",
+    "\n",
+    "work_dir = \"/projects/pvsoiling/pvdeg/analysis/world_map/standoff_fine\"\n",
+    "data_dir = \"/projects/pvsoiling/pvdeg/analysis/world_map/data\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create 0cm standoff locations\n",
+    "\n",
+    "lon_north = np.arange(-179, 180, 0.25)\n",
+    "lat_north = np.arange(60, 90, 0.25)\n",
+    "lon_grid_north, lat_grid_north = np.meshgrid(lon_north, lat_north)\n",
+    "land_north = globe.is_land(lat_grid_north, lon_grid_north)\n",
+    "lon_land_north = lon_grid_north[land_north]\n",
+    "lat_land_north = lat_grid_north[land_north]\n",
+    "\n",
+    "lon_south = np.arange(-179, 180, 0.25)\n",
+    "lat_south = np.arange(-90, -60, 0.25)\n",
+    "lon_grid_south, lat_grid_south = np.meshgrid(lon_south, lat_south)\n",
+    "land_south = globe.is_land(lat_grid_south, lon_grid_south)\n",
+    "lon_land_south = lon_grid_south[land_south]\n",
+    "lat_land_south = lat_grid_south[land_south]\n",
+    "\n",
+    "\n",
+    "lon_asia = np.arange(80, 105, 0.25)\n",
+    "lat_asia = np.arange(50, 61, 0.25)\n",
+    "\n",
+    "lon = lon_asia\n",
+    "lat = np.full(lon_asia.size, 61)\n",
+    "\n",
+    "for i, lat_coord in enumerate(reversed(lat_asia)):\n",
+    "    lon_sub = lon_asia[i:-i]\n",
+    "    lat_sub = np.full(lon_sub.size, lat_coord)\n",
+    "    lon = np.append(lon, lon_sub)\n",
+    "    lat = np.append(lat, lat_sub)\n",
+    "\n",
+    "lon_asia = lon\n",
+    "lat_asia = lat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "plt.scatter(lon_land_north, lat_land_north, c=\"r\", s=1)\n",
+    "plt.scatter(lon_land_south, lat_land_south, c=\"b\", s=1)\n",
+    "plt.scatter(lon, lat, c=\"g\", s=1)\n",
+    "\n",
+    "ax.set_ylim(-90, 90)\n",
+    "ax.set_xlim(-180, 180)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "template_params = pvdeg.geospatial.template_parameters(pvdeg.standards.standoff)\n",
+    "standoff_zero_north = pvdeg.geospatial.zero_template(\n",
+    "    lat_land_north, lon_land_north, **template_params\n",
+    ")\n",
+    "standoff_zero_south = pvdeg.geospatial.zero_template(\n",
+    "    lat_land_south, lon_land_south, **template_params\n",
+    ")\n",
+    "standoff_zero_asia = pvdeg.geospatial.zero_template(\n",
+    "    lat_asia, lon_asia, **template_params\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "standoff_aux = xr.open_dataset(os.path.join(work_dir, \"standoff_aux.nc\"))\n",
+    "standoff_himawari = xr.open_dataset(os.path.join(work_dir, \"standoff_himawari.nc\"))\n",
+    "standoff_meteosat = xr.open_dataset(os.path.join(work_dir, \"standoff_meteosat.nc\"))\n",
+    "standoff_north = xr.open_dataset(os.path.join(work_dir, \"standoff_pvgis_north.nc\"))\n",
+    "standoff_pvgis = xr.open_dataset(os.path.join(work_dir, \"standoff_pvgis.nc\"))\n",
+    "standoff_goes = xr.open_dataset(os.path.join(work_dir, \"standoff_goes.nc\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plt.figure(figsize=(10, 5))\n",
+    "ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.PlateCarree(), frameon=True)\n",
+    "ax.patch.set_visible(True)\n",
+    "ax.set_extent([-180, 180, -85, 85], ccrs.PlateCarree())\n",
+    "\n",
+    "shapename = \"admin_0_countries\"\n",
+    "states_shp = shpreader.natural_earth(\n",
+    "    resolution=\"110m\", category=\"cultural\", name=shapename\n",
+    ")\n",
+    "\n",
+    "cmap = \"Spectral_r\"\n",
+    "size = 0.75\n",
+    "\n",
+    "\n",
+    "standoff_zero_north.plot.scatter(\n",
+    "    x=\"longitude\",\n",
+    "    y=\"latitude\",\n",
+    "    hue=\"x\",\n",
+    "    cmap=cmap,\n",
+    "    s=size,\n",
+    "    linewidths=0,\n",
+    "    vmin=0,\n",
+    "    vmax=14,\n",
+    "    add_colorbar=False,\n",
+    "    ax=ax,\n",
+    ")\n",
+    "standoff_zero_south.plot.scatter(\n",
+    "    x=\"longitude\",\n",
+    "    y=\"latitude\",\n",
+    "    hue=\"x\",\n",
+    "    cmap=cmap,\n",
+    "    s=size,\n",
+    "    linewidths=0,\n",
+    "    vmin=0,\n",
+    "    vmax=14,\n",
+    "    add_colorbar=False,\n",
+    "    ax=ax,\n",
+    ")\n",
+    "standoff_zero_asia.plot.scatter(\n",
+    "    x=\"longitude\",\n",
+    "    y=\"latitude\",\n",
+    "    hue=\"x\",\n",
+    "    cmap=cmap,\n",
+    "    s=size,\n",
+    "    linewidths=0,\n",
+    "    vmin=0,\n",
+    "    vmax=14,\n",
+    "    add_colorbar=False,\n",
+    "    ax=ax,\n",
+    ")\n",
+    "\n",
+    "cm = standoff_himawari.plot.scatter(\n",
+    "    x=\"longitude\",\n",
+    "    y=\"latitude\",\n",
+    "    hue=\"x\",\n",
+    "    cmap=cmap,\n",
+    "    s=size,\n",
+    "    linewidths=0,\n",
+    "    vmin=0,\n",
+    "    vmax=14,\n",
+    "    add_colorbar=False,\n",
+    "    ax=ax,\n",
+    ")\n",
+    "standoff_north.plot.scatter(\n",
+    "    x=\"longitude\",\n",
+    "    y=\"latitude\",\n",
+    "    hue=\"x\",\n",
+    "    cmap=cmap,\n",
+    "    s=size,\n",
+    "    linewidths=0,\n",
+    "    vmin=0,\n",
+    "    vmax=14,\n",
+    "    add_colorbar=False,\n",
+    "    ax=ax,\n",
+    ")\n",
+    "standoff_aux.plot.scatter(\n",
+    "    x=\"longitude\",\n",
+    "    y=\"latitude\",\n",
+    "    hue=\"x\",\n",
+    "    cmap=cmap,\n",
+    "    s=size,\n",
+    "    linewidths=0,\n",
+    "    vmin=0,\n",
+    "    vmax=14,\n",
+    "    add_colorbar=False,\n",
+    "    ax=ax,\n",
+    ")\n",
+    "standoff_pvgis.plot.scatter(\n",
+    "    x=\"longitude\",\n",
+    "    y=\"latitude\",\n",
+    "    hue=\"x\",\n",
+    "    cmap=cmap,\n",
+    "    s=size,\n",
+    "    linewidths=0,\n",
+    "    vmin=0,\n",
+    "    vmax=14,\n",
+    "    add_colorbar=False,\n",
+    "    ax=ax,\n",
+    ")\n",
+    "standoff_meteosat.plot.scatter(\n",
+    "    x=\"longitude\",\n",
+    "    y=\"latitude\",\n",
+    "    hue=\"x\",\n",
+    "    cmap=cmap,\n",
+    "    s=1,\n",
+    "    linewidths=0,\n",
+    "    vmin=0,\n",
+    "    vmax=14,\n",
+    "    add_colorbar=False,\n",
+    "    ax=ax,\n",
+    ")\n",
+    "standoff_goes.plot.scatter(\n",
+    "    x=\"longitude\",\n",
+    "    y=\"latitude\",\n",
+    "    hue=\"x\",\n",
+    "    cmap=cmap,\n",
+    "    s=1,\n",
+    "    linewidths=0,\n",
+    "    vmin=0,\n",
+    "    vmax=14,\n",
+    "    add_colorbar=False,\n",
+    "    ax=ax,\n",
+    ")\n",
+    "\n",
+    "\n",
+    "ax.add_geometries(\n",
+    "    shpreader.Reader(states_shp).geometries(),\n",
+    "    ccrs.PlateCarree(),\n",
+    "    facecolor=\"none\",\n",
+    "    edgecolor=\"gray\",\n",
+    "    linewidth=0.5,\n",
+    ")\n",
+    "\n",
+    "cb_title = \"Standoff distance [cm]\"\n",
+    "cb = plt.colorbar(cm, shrink=0.78, aspect=30, pad=0.02)\n",
+    "cb.set_label(cb_title)\n",
+    "# ax.set_title('title')\n",
+    "\n",
+    "ax.set_xticks(np.arange(-180, 181, 20), crs=ccrs.PlateCarree())\n",
+    "ax.set_yticks(np.arange(-90, 91, 10), crs=ccrs.PlateCarree())\n",
+    "\n",
+    "ax.set_xlim(-180, 180)\n",
+    "ax.set_ylim(-85, 85)\n",
+    "\n",
+    "ax.set_xlabel(\"Longitude\")\n",
+    "ax.set_ylabel(\"Latitude\")\n",
+    "\n",
+    "plt.savefig(os.path.join(work_dir, \"standoff_map.png\"), dpi=1200, bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/HPC Connection Required - LETID - Outdoor Geospatial Demo.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/HPC Connection Required - LETID - Outdoor Geospatial Demo.ipynb
new file mode 100644
index 000000000..dca560690
--- /dev/null
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/HPC Connection Required - LETID - Outdoor Geospatial Demo.ipynb	
@@ -0,0 +1,353 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# LETID - Outdoor Geospatioal Demo\n",
+    "\n",
+    "![PVDeg Logo](../PVD_logo.png)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-13T20:12:46.350659Z",
+     "start_time": "2019-06-13T20:11:46.936643Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import pvdeg\n",
+    "from pvdeg import DATA_DIR\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This information helps with debugging and getting support :)\n",
+    "import sys\n",
+    "import platform\n",
+    "\n",
+    "print(\"Working on a \", platform.system(), platform.release())\n",
+    "print(\"Python version \", sys.version)\n",
+    "print(\"Pandas version \", pd.__version__)\n",
+    "print(\"pvdeg version \", pvdeg.__version__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Single location example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather_file = os.path.join(DATA_DIR, \"psm3_demo.csv\")\n",
+    "WEATHER, META = pvdeg.weather.read(weather_file, \"psm\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kwargs = {\n",
+    "    \"tau_0\": 115,  # us, carrier lifetime in non-degraded states, e.g. LETID/LID states A or C\n",
+    "    \"tau_deg\": 55,  # us, carrier lifetime in fully-degraded state, e.g. LETID/LID state B\n",
+    "    \"wafer_thickness\": 180,  # um\n",
+    "    \"s_rear\": 46,  # cm/s\n",
+    "    \"cell_area\": 243,  # cm^2\n",
+    "    \"na_0\": 100,\n",
+    "    \"nb_0\": 0,\n",
+    "    \"nc_0\": 0,\n",
+    "    \"mechanism_params\": \"repins\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pvdeg.letid.calc_letid_outdoors(weather_df=WEATHER, meta=META, **kwargs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Start distributed compute cluster - DASK"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local = {\n",
+    "    \"manager\": \"local\",\n",
+    "    \"n_workers\": 1,\n",
+    "    \"threads_per_worker\": 8,  # Number of CPUs\n",
+    "}\n",
+    "\n",
+    "kestrel = {\n",
+    "    \"manager\": \"slurm\",\n",
+    "    \"n_jobs\": 1,  # Number of nodes used for parallel processing\n",
+    "    \"cores\": 104,\n",
+    "    \"memory\": \"256GB\",\n",
+    "    \"account\": \"pvsoiling\",\n",
+    "    \"queue\": \"debug\",\n",
+    "    \"walltime\": \"1:00:00\",\n",
+    "    \"processes\": 104,\n",
+    "    \"job_extra_directives\": [\"-o ./logs/slurm-%j.out\"],\n",
+    "}\n",
+    "\n",
+    "pvdeg.geospatial.start_dask(hpc=kestrel)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get weather data\n",
+    "weather_db = \"NSRDB\"\n",
+    "\n",
+    "weather_arg = {\n",
+    "    \"satellite\": \"Americas\",\n",
+    "    \"names\": 2022,\n",
+    "    \"NREL_HPC\": True,\n",
+    "    \"attributes\": [\n",
+    "        \"air_temperature\",\n",
+    "        \"wind_speed\",\n",
+    "        \"dhi\",\n",
+    "        \"ghi\",\n",
+    "        \"dni\",\n",
+    "        \"relative_humidity\",\n",
+    "    ],\n",
+    "}\n",
+    "\n",
+    "weather_ds, meta_df = pvdeg.weather.get(weather_db, geospatial=True, **weather_arg)\n",
+    "\n",
+    "# Define geographical region\n",
+    "meta_SW = meta_df[meta_df[\"state\"].isin([\"Colorado\", \"New Mexico\", \"Utah\", \"Arizona\"])]\n",
+    "meta_SW_sub, gids_SW_sub = pvdeg.utilities.gid_downsampling(meta_SW, 6)\n",
+    "\n",
+    "weather_SW_sub = weather_ds.sel(gid=meta_SW_sub.index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather_SW_sub"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "meta_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define desired analysis\n",
+    "geo = {\n",
+    "    \"func\": pvdeg.letid.calc_letid_outdoors,\n",
+    "    \"weather_ds\": weather_SW_sub,\n",
+    "    \"meta_df\": meta_SW_sub,\n",
+    "    \"tau_0\": 115,  # us, carrier lifetime in non-degraded states, e.g. LETID/LID states A or C\n",
+    "    \"tau_deg\": 55,  # us, carrier lifetime in fully-degraded state, e.g. LETID/LID state B\n",
+    "    \"wafer_thickness\": 180,  # um\n",
+    "    \"s_rear\": 46,  # cm/s\n",
+    "    \"cell_area\": 243,  # cm^2\n",
+    "    \"na_0\": 100,\n",
+    "    \"nb_0\": 0,\n",
+    "    \"nc_0\": 0,\n",
+    "    \"mechanism_params\": \"repins\",\n",
+    "}\n",
+    "\n",
+    "letid_res = pvdeg.geospatial.analysis(**geo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "letid_res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datetime\n",
+    "\n",
+    "ims = []\n",
+    "for n in range(1, 13):\n",
+    "    for i, np_t in enumerate(letid_res.time):\n",
+    "        t = pd.Timestamp(np_t.values).time()\n",
+    "        d = pd.Timestamp(np_t.values).day\n",
+    "        m = pd.Timestamp(np_t.values).month\n",
+    "        if m == n:\n",
+    "            if d == 15:\n",
+    "                if t == datetime.time(12):\n",
+    "                    fig, ax = pvdeg.geospatial.plot_USA(\n",
+    "                        letid_res[\"Pmp_norm\"].sel(time=np_t),\n",
+    "                        cmap=\"viridis\",\n",
+    "                        vmin=0.95,\n",
+    "                        vmax=1,\n",
+    "                        title=f\"Normalized Power  - 2022-{m}-{d} 12:00\",\n",
+    "                        cb_title=\"Normalized Power\",\n",
+    "                    )\n",
+    "                    # plt.savefig(f'./images/RH_animation_{n}.png', dpi=600)\n",
+    "\n",
+    "# import imageio\n",
+    "# ims = [imageio.imread(f'./images/RH_animation_{n}.png') for n in range(1, 13)]\n",
+    "# imageio.mimwrite(f'./images/RH_animation.gif', ims, format='GIF', duration=1000, loop=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datetime\n",
+    "\n",
+    "ims = []\n",
+    "dates = []\n",
+    "subarctics = []\n",
+    "coldsemiarids = []\n",
+    "hotdeserts = []\n",
+    "\n",
+    "for n in range(1, 13):\n",
+    "    for i, np_t in enumerate(letid_res.time):\n",
+    "        t = pd.Timestamp(np_t.values).time()\n",
+    "        d = pd.Timestamp(np_t.values).day\n",
+    "        m = pd.Timestamp(np_t.values).month\n",
+    "        if m == n:\n",
+    "            if d == 15:\n",
+    "                if t == datetime.time(12):\n",
+    "                    dates.append(np_t.values)\n",
+    "\n",
+    "                    # subartic: near Crested Butte CO\n",
+    "                    # cold semi-arid: near Springfield CO\n",
+    "                    # hot desert: near Yuma AZ\n",
+    "\n",
+    "                    subarctic = letid_res.sel(\n",
+    "                        time=np_t, latitude=39.01, longitude=-107.1\n",
+    "                    )\n",
+    "                    subarctics.append(subarctic[\"Pmp_norm\"])\n",
+    "\n",
+    "                    coldsemiarid = letid_res.sel(\n",
+    "                        time=np_t, latitude=37.57, longitude=-102.3\n",
+    "                    )\n",
+    "                    coldsemiarids.append(coldsemiarid[\"Pmp_norm\"])\n",
+    "\n",
+    "                    hotdesert = letid_res.sel(\n",
+    "                        time=np_t, latitude=32.77, longitude=-114.3\n",
+    "                    )\n",
+    "                    hotdeserts.append(hotdesert[\"Pmp_norm\"])\n",
+    "\n",
+    "                    fig, ax = plt.subplots()\n",
+    "                    ax.plot(\n",
+    "                        dates,\n",
+    "                        subarctics,\n",
+    "                        marker=\"o\",\n",
+    "                        c=\"C0\",\n",
+    "                        label=\"Central CO - Subarctic Dfc\",\n",
+    "                    )\n",
+    "                    ax.plot(\n",
+    "                        dates,\n",
+    "                        coldsemiarids,\n",
+    "                        marker=\"o\",\n",
+    "                        c=\"C1\",\n",
+    "                        label=\"Southeast CO - Cold Semi-Arid BSk\",\n",
+    "                    )\n",
+    "                    ax.plot(\n",
+    "                        dates,\n",
+    "                        hotdeserts,\n",
+    "                        marker=\"o\",\n",
+    "                        c=\"C2\",\n",
+    "                        label=\"Southwest AZ - Hot Desert BWh\",\n",
+    "                    )\n",
+    "\n",
+    "                    ax.legend(loc=\"upper right\")\n",
+    "\n",
+    "                    ax.set_xlim([datetime.date(2022, 1, 1), datetime.date(2023, 1, 1)])\n",
+    "\n",
+    "                    ax.set_ylim([0.945, 1.005])\n",
+    "                    ax.set_ylabel(\"Normalized Power\")\n",
+    "\n",
+    "                    plt.savefig(f\"./images/LETID_plot_animation_{n}.png\", dpi=600)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import imageio\n",
+    "\n",
+    "ims = [imageio.imread(f\"./images/LETID_plot_animation_{n}.png\") for n in range(1, 13)]\n",
+    "imageio.mimwrite(\n",
+    "    \"./images/LETID_plot_animation.gif\", ims, format=\"GIF\", duration=1000, loop=10\n",
+    ")"
+   ]
+  }
+ ],
+ "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": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/HPC Connection Required - Scenario - Geographical Features.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/HPC Connection Required - Scenario - Geographical Features.ipynb
new file mode 100644
index 000000000..80ce96707
--- /dev/null
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/HPC Connection Required - Scenario - Geographical Features.ipynb	
@@ -0,0 +1,252 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pvdeg\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Scenario and Populate Location\n",
+    "The default geospatial datapoints for addLocation are from the Americas satellite. This is the only data we are interested in for this case so don't specify any location arguments.\n",
+    "To speed up calculations we will downsample to get coarser data. The downsampling function is not linear. A downsample factor of 10 takes us from 2018267 entries to only 5109."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features = pvdeg.GeospatialScenario(name=\"finding-features\")\n",
+    "\n",
+    "features.addLocation(downsample_factor=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Intro to KDTrees\n",
+    "\n",
+    "A K dimensional tree is a datastructure for organizing n points in a k dimensional space. They are often used for nearest neighbors searches and many ML algorithms. Letting k = 2, our dimensions will be latitude and longitude. This is much faster than iterating over tabular data structures to find neighbors. The function to create a kdtree below requires scikit learn (also known as sklearn), a python machine learning library which is not included in the pvdeg dependency list so you will have to install it independently. Scipy also has a kdtree class but it is much slower. Depending on the number of datapoints the tree build time could be quite long but for our purposes, the cell below will be quick."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tree = pvdeg.geospatial.meta_KDtree(meta_df=features.meta_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Remaining Points After Downsampling\n",
+    "\n",
+    "Use `plot_coords` to see all latitude longitude coordinate pairs included in a scenario's metadata. You can provide corners of a bounding box to change the extent of the matplotlib plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features.plot_coords()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Identifying Mountains from Geospatial Data I\n",
+    "\n",
+    "Identify moutains by using the function below. There are currently 2 methods to identify mountains from our data. Both will add a column to our `GeospatialScenario.meta_data` attribute called `mountain` containing boolean values that represent if a data point is in/near mountains or not near mountains.\n",
+    "\n",
+    "- `classify_mountains_weights` uses k nearest neighbors search and calculates local changes in height with different methods, the default is the `mean` absolute change compared to a points neighbors, then it assigns a weight to each point. If the remaining points are above the threshold then the percentile argument determines sensitivity to classifying points as mountains (higher weight, more mountainous). Much better than the following option.  \n",
+    "- `classify_mountains_radii` uses 2 nearest neighbors radius searches at each point and compares the average elevation of the inner circle to the avg radius of the circle. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features.classify_mountains_weights(kdtree=tree)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Identifing Mountains from Geospatial Data II\n",
+    "\n",
+    "We do not need to provide a `kdtree` when we call this method. We can provide a `kdtree` if we create one first with `pvdeg.geospatial.meta_kdtree` but it will work without supplying it.\n",
+    "GeospatialScenario will create a kdtree internally for us. So the method can simply be called as shown below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this will work without running the above cells to create a kdtree or the cell directly above where we utilize the created kdtree.\n",
+    "features.classify_mountains_weights()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Convenience Plotting for Binary Classifications\n",
+    "\n",
+    "the `plot_meta_classification` method allows you to plot your data with respect to a boolean classifier present in the `scenario.meta_data` dataframe columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features.plot_meta_classification(col_name=\"mountain\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Identifying mountains within a bounding box.\n",
+    "\n",
+    "Sometimes we only want information from one area. To allow for more control over the selection process. To pick only one area from an existing superset of data we can use a bounding box. \n",
+    "Either provide the top left and bottom right (latitude-longitude) pairs or a 2d numpy array of these pairs which will use the most extreme entries to form the bounding box."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features.classify_mountains_radii(\n",
+    "    kdtree=tree,\n",
+    "    elevation_floor=0,\n",
+    "    bbox_kwarg={\n",
+    "        \"coords\": np.array(\n",
+    "            [\n",
+    "                [47.960502, -115.048828],\n",
+    "                [47.842658, -101.118164],\n",
+    "                [36.738884, -113.686523],\n",
+    "                [36.633162, -100.283203],\n",
+    "            ]\n",
+    "        )\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features.plot_meta_classification(col_name=\"mountain\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Identifying Coastlines from Geospatial Data\n",
+    "\n",
+    "To identify points within a search radius of any [natural earth features](https://www.naturalearthdata.com/features/). The following examples show the process of identifying coastline datapoints."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features.classify_feature(\n",
+    "    kdtree=tree,\n",
+    "    feature_name=\"coastline\",\n",
+    "    resolution=\"10m\",\n",
+    "    radius=0.5,  # change this to get a wider band of selected data near the coastline\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features.plot_meta_classification(col_name=\"coastline\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Finding Rivers\n",
+    "\n",
+    "Above we saw how to find points near coastlines, now lets do points near rivers. It is very similar"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features.classify_feature(\n",
+    "    kdtree=tree, feature_name=\"rivers_lake_centerlines\", resolution=\"10m\", radius=0.2\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features.plot_meta_classification(col_name=\"rivers_lake_centerlines\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/HPC Connection Required - Scenario - Geospatial.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/HPC Connection Required - Scenario - Geospatial.ipynb
new file mode 100644
index 000000000..9e422abe3
--- /dev/null
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/HPC Connection Required - Scenario - Geospatial.ipynb	
@@ -0,0 +1,210 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pvdeg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define Geospatial Scenario Object\n",
+    "\n",
+    "To preform geospatial analysis we can create a `GeospatialScenario` object. Alternatively, to preform single location analysis use `Scenario`. Scenario and GeospatialScenario are generalized classes that can be used to replace the legacy functional pvdeg analysis approach with an object orented one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geospatial_standoff_scenario = pvdeg.GeospatialScenario(\n",
+    "    name=\"standoff geospatial\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Add Location\n",
+    "To add locations for geospatial analysis we will use the ``.addLocation`` method. We can choose downselect from the NSRDB to a country, state and county in that order. *Support for multiple of each category in list form soon.* The ``see_added`` flag allows us to see the gids we have added to the scenario."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geospatial_standoff_scenario.addLocation(\n",
+    "    state=\"Colorado\", county=\"Summit\", see_added=True, downsample_factor=3\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Add Functions to the Pipeline\n",
+    "The scenario has a queue of jobs to preform. These are stored in an attribute called ``pipeline``, you can directly update the pipeline but this will bypass the assistance given in creating the job function and parameters. The easiest way to add a job to the pipeline is the ``.updatePipeline`` method. For geospatial analysis, weather and metadata is collected and stored in the scenario at the time of the ``.addLocation`` method call so we do not need to include it below, but if we have other function kwargs to include, they should go in the ``func_params`` argument.  \n",
+    "\n",
+    "Only a few pvdeg functions are currently supported for geospatial analysis. See the docstring for ``.updatePipeline`` to view currently supported functions. ``updatePipeline`` will not let you add unsupported geospatial functions. The ``see_added`` flag allows us to see the new job added to the pipeline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geospatial_standoff_scenario.addJob(func=pvdeg.standards.standoff, see_added=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geospatial_standoff_scenario"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run the job in the pipeline\n",
+    "\n",
+    "Currently ``scenario`` only supports one geospatial analysis at a time. We cannot have two geospatial jobs at the same time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geospatial_standoff_scenario.run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Directly Access Results Attribute\n",
+    "\n",
+    "We can either view the results of the scenario pipeline using ``.viewScenario`` as shown above. The results will be displayed only if the pipeline has been run. Alternatively, we can directly view the ``results`` atribute of the scenario."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geospatial_standoff_scenario.results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleanup\n",
+    "\n",
+    "The scenario object will store its attributes in a file the python script's current working directory. If we want to delete this file when we are done with the scenario instance we can use the ``.clean()`` method as shown below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geospatial_standoff_scenario.clean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example Geospatial Functionality\n",
+    "Many functions are supported for geospatial analysis, here are a few.\n",
+    "- ``pvdeg.standards.standoff`` \n",
+    "- ``pvdeg.humidity.module`` \n",
+    "- ``pvdeg.letid.calc_letid_outdoors``\n",
+    "\n",
+    "See the Geospatial Templates tutorial for an example on this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geospatial_humidity_scenario = pvdeg.GeospatialScenario(\n",
+    "    name=\"humidity scenario\", geospatial=True\n",
+    ")\n",
+    "\n",
+    "geospatial_humidity_scenario.addLocation(\n",
+    "    state=\"Colorado\", county=\"Jefferson\", see_added=True\n",
+    ")\n",
+    "\n",
+    "geospatial_humidity_scenario.addJob(func=pvdeg.humidity.module, see_added=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geospatial_humidity_scenario.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geospatial_humidity_scenario.results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python3",
+   "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": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/HPC Connection Required - Scenario - Non-uniform Mountain Prefferential Downselect.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/HPC Connection Required - Scenario - Non-uniform Mountain Prefferential Downselect.ipynb
new file mode 100644
index 000000000..e06fddda2
--- /dev/null
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/HPC Connection Required - Scenario - Non-uniform Mountain Prefferential Downselect.ipynb	
@@ -0,0 +1,249 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pvdeg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding Points\n",
+    "\n",
+    "We are going to add all of the points in the American West to the scenario and downsample by a factor of 1. This will include only half of the points in the latitude axis and half in the longitude axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dynamic_points = pvdeg.GeospatialScenario(name=\"dynamic-selection\")\n",
+    "\n",
+    "dynamic_points.addLocation(\n",
+    "    state=[\"CO\", \"UT\"],  # , 'NM', 'NV', 'ID', 'WY', 'AZ', 'CA', 'OR', 'WA'],\n",
+    "    downsample_factor=1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Preview The Scenario's Points\n",
+    "\n",
+    "Use `plot_cords` to get a quick snapshot of all coordinates included in the scenario's metadata."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dynamic_points.plot_coords(\n",
+    "    coord_1=[48.574790, -130.253906],  # uncomment to see Larger scale view\n",
+    "    coord_2=[25.482951, -68.027344],\n",
+    "    size=0.005,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dynamic_points.meta_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Downselecting\n",
+    "\n",
+    "Using weighted random choices based on elevation we will create a sparse grid from the full metadata for fast calculations. This requires sklearn to be installed but this is not in the `pvdeg` dependency list to you will have to install it seperately.\n",
+    "\n",
+    "### Normalization\n",
+    "\n",
+    "At each metadata point in our dataset we will calculate a weight based on its changes in elevation compared to its neighbors. The higher the weight, the greater the change in elevation from a point's immediate neighbors. The downselection methods and functions use these weights to randomly select a subset of the datapoints, prefferentially selecting those with higher weights. \n",
+    "\n",
+    "We have some control over which points get selected because all points' weights must be normalized (mapped from 0 to 1) before downselecting. We can apply a function such as $e^x$ or $\\log x$ to the weights during normalization. This could help change the distribution of weights that are chosen. This could remove points from the mountains and add them to areas with fewer changes in elevation, or vice versa.\n",
+    "\n",
+    "*Note: `pvdeg`'s downselection functions use `numpy.random`, the random seed is not fixed so the result will change between runs.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Providing a KdTree\n",
+    "\n",
+    "As shown below the lines to create a kdtree are commented out. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# west_tree = pvdeg.geospatial.meta_KDtree(meta_df=dynamic_points.meta_data)\n",
+    "\n",
+    "dynamic_points.downselect_elevation_stochastic(\n",
+    "    # kdtree=west_tree,\n",
+    "    downselect_prop=0.1,\n",
+    "    normalization=\"linear\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dynamic_points.plot_coords()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extracting from Scenario\n",
+    "\n",
+    "Scenarios provide an easy way to select and downsample geospatial data but we can easily pull out the data to use other `pvdeg` functions on it. In the cell below, we extract the weather data and meta data from the scenario and take only the matching entries from the weather. Then we load the xarray dataset into memory. Previously, it was stored lazily out of memory but we want to do operations on it. (Chunking causes issues when calculating so this eliminates any chunks)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather = dynamic_points.weather_data\n",
+    "\n",
+    "sub_weather = weather.sel(\n",
+    "    gid=dynamic_points.meta_data.index\n",
+    ")  # downselect weather using restricted metadata set\n",
+    "\n",
+    "sub_weather = sub_weather.compute()  # load into memory"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Geospatial Calculation\n",
+    "\n",
+    "Run a standoff calculation on the extracted scenario weather data and scenario meta data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# geospatial analysis now\n",
+    "\n",
+    "geo = {\n",
+    "    \"func\": pvdeg.standards.standoff,\n",
+    "    \"weather_ds\": sub_weather,\n",
+    "    \"meta_df\": dynamic_points.meta_data,\n",
+    "}\n",
+    "\n",
+    "analysis_result = pvdeg.geospatial.analysis(**geo)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Viewing Results\n",
+    "\n",
+    "Inspecting the xarray dataset below shows us that we have many Not a Number (NaN) entries. These occur because we did not provide weather data at every point in the grid of possile latitude-longitude pairs. Expanding the `x` datavariable shows that there are some valid results but these are uncommon."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "analysis_result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting Sparse Data I\n",
+    "\n",
+    "If we try to plot existing data with the current plotting methods exposed by `pvdeg` we will encounter issues. This will produce weak plotting results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pvdeg.geospatial.plot_USA(analysis_result[\"x\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting Sparse Data II\n",
+    "\n",
+    "Utilize the new `plot_sparse_analysis` function below to interpolate and plot a solid color map. We can use different interpolation schemes but `nearest` and `linear` are best. See [scipy.interpolate.griddata](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.griddata.html) for more information about interpolation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pvdeg.geospatial.plot_sparse_analysis(analysis_result, data_var=\"x\", method=\"linear\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python3",
+   "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": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/HPC Connection Required - Scenario - Single Location.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/HPC Connection Required - Scenario - Single Location.ipynb
new file mode 100644
index 000000000..f47802e93
--- /dev/null
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/HPC Connection Required - Scenario - Single Location.ipynb	
@@ -0,0 +1,314 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Scenario Objects - OOP Approach to PVDEG\n",
+    "\n",
+    "Author: Tobin Ford | tobin.ford@nrel.gov\n",
+    "\n",
+    "2024\n",
+    "****\n",
+    "\n",
+    "A simple object orented workflow walkthrough using pvdeg."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n",
+    "# !pip install pvdeg==0.3.3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pvdeg\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This information helps with debugging and getting support :)\n",
+    "import sys\n",
+    "import platform\n",
+    "\n",
+    "print(\"Working on a \", platform.system(), platform.release())\n",
+    "print(\"Python version \", sys.version)\n",
+    "print(\"pvdeg version \", pvdeg.__version__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define Single Point Scenario Object\n",
+    "Scenario is a general class that can be used to replace the legacy functional pvdeg analysis approach with an object orented one. ``Scenario`` can preform single location or geospatial analysis. The scenario constructor takes many arguments but the only required one for the following use cases is the ``name`` attribute. It is visible in when we display the entire scenario and is present in the file of saved information about the scenario. We also need to provide the class constructor with our API key and email.\n",
+    "\n",
+    "A way around this is to provide the weather and metadata in the pipeline job arguments or you can load data from somewhere else and provide it in the same fashion.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "<b>Please use your own API key: The block below makes an NSRDB API to get weather and meta data. This tutorial will work with the DEMO Key provided, but it will take you less than 3 minutes to obtain your own at <a ref=\"https://developer.nrel.gov/signup/\">https://developer.nrel.gov/signup/</a> so register now.) \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simple_scenario = pvdeg.Scenario(\n",
+    "    name=\"Point Minimum Standoff\", email=\"user@mail.com\", api_key=\"DEMO_KEY\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding A Location\n",
+    "To add a single point using data from the Physical Solar Model (PSM3), simply feed the scenario a single coordinate in tuple form via the ``addLocation`` method. Currently this is the only way to add a location to a non-geospatial scenario, all of the other arguments are unusable when ``Scenario.geospatial == False``. \n",
+    "\n",
+    "Attempting to add a second location by calling the method again with a different coordinate pair will overwrite the old location data stored in the class instance. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simple_scenario.addLocation(\n",
+    "    lat_long=(25.783388, -80.189029),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simple_scenario.weather_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Scenario Pipelines\n",
+    "\n",
+    "The pipeline is a list of tasks called jobs for the scenario to run. We will populate the pipeline with a list of jobs before executing them all at once. \n",
+    "\n",
+    "To add a job to the pipeline use the ``updatePipeline`` method. Two examples of adding functions to the pipeline will be shown below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding a job without function arguments\n",
+    "\n",
+    "The simplest case of adding a job to the pipeline is when it only requires us to provide simple weather and metadata. In the function definition and docstring these appear as ``weather_df`` and ``meta``. Since these attributes are contained in our scenario class instance we do not have to worry about them. We can simply add the function as shown below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simple_scenario.addJob(func=pvdeg.standards.standoff)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding a job with function arguments\n",
+    "\n",
+    "When adding a job that contains a function requiring other arguments such as ``solder_fatigue`` which requires a value for ``wind_factor``, we will need to provide it. The most straightforeward way to do this is using a kwargs dictionary and passing it to the function. We do not unpack the dictionary before passing it. This is done inside of the scenario at pipeline runtime (when ``runPipeline`` is called)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kwargs = {\"wind_factor\": 0.33}\n",
+    "\n",
+    "simple_scenario.addJob(\n",
+    "    func=pvdeg.fatigue.solder_fatigue, func_kwarg=kwargs\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding a job with weather and metadata from outside of the class\n",
+    "### Not functional \n",
+    "\n",
+    "could just directly set weather data with scenario.weather_data = weather and scenario.meta_data = meta but that would only work for all of the jobs in the pipeline\n",
+    "\n",
+    "Say local weather data is available or other, if we want to use this rather than the PSM3 data at a latitude and longitude we can also provide the weather and metadata in the function arguments. This is probably the best if avoided but follows the same syntax as providing other function arguments. See the example below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PSM_FILE = os.path.join(pvdeg.DATA_DIR, \"psm3_demo.csv\")\n",
+    "weather, meta = pvdeg.weather.read(PSM_FILE, \"psm\")\n",
+    "\n",
+    "kwargs = {\"weather_df\": weather, \"meta\": meta}\n",
+    "\n",
+    "simple_scenario.addJob(func=pvdeg.standards.standoff, func_kwarg=kwargs)\n",
+    "\n",
+    "# FIX THIS CASE IN SCENARIO CLASS\n",
+    "# (simple_scenario.pipeline[1]['job'])(**simple_scenario.pipeline[1]['params'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## View Scenario\n",
+    "\n",
+    "The ``viewScenario`` method provides an overview of the information contained within your scenario object. Here you can see if it contains the location weather and metadata. As well as the jobs in the pipeline and their arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simple_scenario.viewScenario()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Display\n",
+    "\n",
+    "The fancier cousin of viewScenario. Only works in a jupyter environemnt as it uses a special ipython backend to render the html and javascript.\n",
+    "\n",
+    "It can be called with just the Scenario instance as follows\n",
+    "`simple_scenario`\n",
+    "\n",
+    "or using the display function\n",
+    "`display(simple_scenario)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simple_scenario"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Executing Pipeline Jobs\n",
+    "To run the pipeline after we have populated it with the desired jobs call the ``runPipeline`` method on our scenario instance. This will run all of the jobs we have previously added. The functions that need weather and metadata will grab it from the scenario instance using the correct location added above. The pipeline jobs results will be saved to the scenario instance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simple_scenario.run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Results Series ##\n",
+    "We will use a series to store the various return values of functions run in our pipeline. These can partially obfuscate the dataframes within them so to access the dataframes, use the function name to access it. To get one of the results we can index it using dictionary syntax. If the job was called `'KSDJQ'` do `'simple_scenario.results['KSDJQ']` to directly access the result for that job"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(simple_scenario.results)\n",
+    "print(\"We can't see out data in here so we need to do another step\", end=\"\\n\\n\")\n",
+    "\n",
+    "# to see all available ouputs of results do\n",
+    "print(\n",
+    "    f\"this is the list of all available frames in results : {simple_scenario.results.index}\\n\"\n",
+    ")\n",
+    "\n",
+    "# loop over all results and display\n",
+    "for keys, results in simple_scenario.results.items():\n",
+    "    print(keys)\n",
+    "    display(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleaning Up the Scenario\n",
+    "\n",
+    "Each scenario object creates a directory named ``pvd_job_...`` that contains information about the scenario instance. To remove the directory and all of its information call ``clean`` on the scenario. This will permanently delete the directory created by the scenario."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simple_scenario.clean()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/HPC Connection Required - Scenario - Temperatrure.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/HPC Connection Required - Scenario - Temperatrure.ipynb
new file mode 100644
index 000000000..40e46f8f9
--- /dev/null
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/HPC Connection Required - Scenario - Temperatrure.ipynb	
@@ -0,0 +1,258 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n",
+    "# !pip install pvdeg==0.3.3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pvdeg\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This information helps with debugging and getting support :)\n",
+    "import sys\n",
+    "import platform\n",
+    "\n",
+    "print(\"Working on a \", platform.system(), platform.release())\n",
+    "print(\"Python version \", sys.version)\n",
+    "print(\"pvdeg version \", pvdeg.__version__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding Modules and Pipeline Jobs (Run Functions on Scenario Object)\n",
+    "\n",
+    "Material: `OX003` corresponds to a set of EVA material parameters from the default file `O2Permeation.json` in the `pvdeg/data` directory. Look in these files to see available options."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scene_temp = pvdeg.Scenario(\n",
+    "    name=\"temperature and degradation\",\n",
+    "    api_key=\"DEMO_KEY\",\n",
+    "    email=\"user@mail.com\",\n",
+    ")\n",
+    "\n",
+    "scene_temp.addLocation(\n",
+    "    lat_long=(25.783388, -80.189029),\n",
+    "    see_added=True,\n",
+    ")\n",
+    "\n",
+    "# this module will be overwritten because another with the same name is added afterwards\n",
+    "scene_temp.addModule(module_name=\"sapm_1\", temperature_model=\"sapm\")\n",
+    "\n",
+    "scene_temp.addModule(\n",
+    "    module_name=\"sapm_1\",\n",
+    "    racking=\"open_rack_glass_polymer\",\n",
+    "    material=\"OX003\",\n",
+    "    temperature_model=\"sapm\",\n",
+    "    irradiance_kwarg={\"azimuth\": 120, \"tilt\": 30},\n",
+    "    model_kwarg={\"irrad_ref\": 1100},\n",
+    "    see_added=True,\n",
+    ")\n",
+    "\n",
+    "scene_temp.addModule(\n",
+    "    module_name=\"pvsyst_1\",\n",
+    "    racking=\"freestanding\",\n",
+    "    material=\"OX003\",\n",
+    "    temperature_model=\"pvsyst\",\n",
+    "    irradiance_kwarg={\"azimuth\": 180, \"tilt\": 0},\n",
+    "    model_kwarg={\"module_efficiency\": 0.15},\n",
+    "    see_added=True,\n",
+    ")\n",
+    "scene_temp.addModule(\n",
+    "    module_name=\"sapm_2\",\n",
+    "    racking=\"open_rack_glass_polymer\",\n",
+    "    material=\"OX003\",\n",
+    "    temperature_model=\"sapm\",\n",
+    "    irradiance_kwarg={\"azimuth\": 120, \"tilt\": 30},\n",
+    "    model_kwarg={\"irrad_ref\": 1000},\n",
+    "    see_added=True,\n",
+    ")\n",
+    "scene_temp.addModule(\n",
+    "    module_name=\"sapm_3\",\n",
+    "    racking=\"open_rack_glass_polymer\",\n",
+    "    material=\"OX003\",\n",
+    "    temperature_model=\"sapm\",\n",
+    "    irradiance_kwarg={\"azimuth\": 180, \"tilt\": 0},\n",
+    "    model_kwarg={\"irrad_ref\": 1000},\n",
+    "    see_added=True,\n",
+    ")\n",
+    "\n",
+    "scene_temp.addModule(\n",
+    "    module_name=\"pvsyst_2\",\n",
+    "    racking=\"freestanding\",\n",
+    "    material=\"OX003\",\n",
+    "    temperature_model=\"pvsyst\",\n",
+    "    irradiance_kwarg={\"azimuth\": 180, \"tilt\": 0},\n",
+    "    model_kwarg={\"module_efficiency\": 0.2},\n",
+    "    see_added=True,\n",
+    ")\n",
+    "\n",
+    "scene_temp.addJob(\n",
+    "    func=pvdeg.temperature.temperature,\n",
+    "    func_kwarg={\"cell_or_mod\": \"cell\"},\n",
+    "    see_added=True,\n",
+    ")\n",
+    "\n",
+    "scene_temp.addJob(\n",
+    "    func=pvdeg.degradation.vantHoff_deg,\n",
+    "    func_kwarg={\"I_chamber\": 1000, \"temp_chamber\": 25},\n",
+    "    see_added=True,\n",
+    ")\n",
+    "\n",
+    "scene_temp.addJob(\n",
+    "    func=pvdeg.degradation.vantHoff_deg,\n",
+    "    func_kwarg={\"I_chamber\": 1000, \"temp_chamber\": 30},\n",
+    "    see_added=True,\n",
+    ")\n",
+    "\n",
+    "scene_temp.addJob(func=pvdeg.degradation.IwaVantHoff, see_added=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run and View Scenario Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scene_temp.run()\n",
+    "\n",
+    "scene_temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scene_temp.dump()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting and Extracting Results\n",
+    "These methods are independent of one another (i.e. you do not need to extract before plotting but both are shown below.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datetime\n",
+    "\n",
+    "t0 = datetime.datetime(1970, 1, 1, 0, 0)\n",
+    "tf = datetime.datetime(1970, 1, 1, 23, 59)\n",
+    "\n",
+    "# search by funciton id which is random and changes every time\n",
+    "# must update here, look at the purple ids in the display function above\n",
+    "temp_df = scene_temp.extract(\n",
+    "    (\"function\", \"HKXVD\"), tmy=True, start_time=t0, end_time=tf\n",
+    ")\n",
+    "\n",
+    "display(temp_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scene_temp.plot(\n",
+    "    (\"function\", \"HKXVD\"),\n",
+    "    tmy=True,\n",
+    "    start_time=t0,\n",
+    "    end_time=tf,\n",
+    "    title=\"single day cell temperature\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a Copy of a Scenario"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "parent_dir = Path(pvdeg.PVDEG_DIR).parent\n",
+    "new_path = os.path.join(\n",
+    "    parent_dir,\n",
+    "    r\"tutorials_and_tools\\tutorials_and_tools\\temperature and degradation.json\",\n",
+    ")\n",
+    "\n",
+    "copy = pvdeg.scenario.Scenario.load_json(\n",
+    "    file_path=new_path,\n",
+    "    email=\"user@mail.com\",\n",
+    "    api_key=\"DEMO_KEY\",\n",
+    ")\n",
+    "\n",
+    "copy"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python3",
+   "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": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/HPC Connection Required - Scenario - Temperature.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/HPC Connection Required - Scenario - Temperature.ipynb
new file mode 100644
index 000000000..40e46f8f9
--- /dev/null
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/HPC Connection Required - Scenario - Temperature.ipynb	
@@ -0,0 +1,258 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n",
+    "# !pip install pvdeg==0.3.3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pvdeg\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This information helps with debugging and getting support :)\n",
+    "import sys\n",
+    "import platform\n",
+    "\n",
+    "print(\"Working on a \", platform.system(), platform.release())\n",
+    "print(\"Python version \", sys.version)\n",
+    "print(\"pvdeg version \", pvdeg.__version__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding Modules and Pipeline Jobs (Run Functions on Scenario Object)\n",
+    "\n",
+    "Material: `OX003` corresponds to a set of EVA material parameters from the default file `O2Permeation.json` in the `pvdeg/data` directory. Look in these files to see available options."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scene_temp = pvdeg.Scenario(\n",
+    "    name=\"temperature and degradation\",\n",
+    "    api_key=\"DEMO_KEY\",\n",
+    "    email=\"user@mail.com\",\n",
+    ")\n",
+    "\n",
+    "scene_temp.addLocation(\n",
+    "    lat_long=(25.783388, -80.189029),\n",
+    "    see_added=True,\n",
+    ")\n",
+    "\n",
+    "# this module will be overwritten because another with the same name is added afterwards\n",
+    "scene_temp.addModule(module_name=\"sapm_1\", temperature_model=\"sapm\")\n",
+    "\n",
+    "scene_temp.addModule(\n",
+    "    module_name=\"sapm_1\",\n",
+    "    racking=\"open_rack_glass_polymer\",\n",
+    "    material=\"OX003\",\n",
+    "    temperature_model=\"sapm\",\n",
+    "    irradiance_kwarg={\"azimuth\": 120, \"tilt\": 30},\n",
+    "    model_kwarg={\"irrad_ref\": 1100},\n",
+    "    see_added=True,\n",
+    ")\n",
+    "\n",
+    "scene_temp.addModule(\n",
+    "    module_name=\"pvsyst_1\",\n",
+    "    racking=\"freestanding\",\n",
+    "    material=\"OX003\",\n",
+    "    temperature_model=\"pvsyst\",\n",
+    "    irradiance_kwarg={\"azimuth\": 180, \"tilt\": 0},\n",
+    "    model_kwarg={\"module_efficiency\": 0.15},\n",
+    "    see_added=True,\n",
+    ")\n",
+    "scene_temp.addModule(\n",
+    "    module_name=\"sapm_2\",\n",
+    "    racking=\"open_rack_glass_polymer\",\n",
+    "    material=\"OX003\",\n",
+    "    temperature_model=\"sapm\",\n",
+    "    irradiance_kwarg={\"azimuth\": 120, \"tilt\": 30},\n",
+    "    model_kwarg={\"irrad_ref\": 1000},\n",
+    "    see_added=True,\n",
+    ")\n",
+    "scene_temp.addModule(\n",
+    "    module_name=\"sapm_3\",\n",
+    "    racking=\"open_rack_glass_polymer\",\n",
+    "    material=\"OX003\",\n",
+    "    temperature_model=\"sapm\",\n",
+    "    irradiance_kwarg={\"azimuth\": 180, \"tilt\": 0},\n",
+    "    model_kwarg={\"irrad_ref\": 1000},\n",
+    "    see_added=True,\n",
+    ")\n",
+    "\n",
+    "scene_temp.addModule(\n",
+    "    module_name=\"pvsyst_2\",\n",
+    "    racking=\"freestanding\",\n",
+    "    material=\"OX003\",\n",
+    "    temperature_model=\"pvsyst\",\n",
+    "    irradiance_kwarg={\"azimuth\": 180, \"tilt\": 0},\n",
+    "    model_kwarg={\"module_efficiency\": 0.2},\n",
+    "    see_added=True,\n",
+    ")\n",
+    "\n",
+    "scene_temp.addJob(\n",
+    "    func=pvdeg.temperature.temperature,\n",
+    "    func_kwarg={\"cell_or_mod\": \"cell\"},\n",
+    "    see_added=True,\n",
+    ")\n",
+    "\n",
+    "scene_temp.addJob(\n",
+    "    func=pvdeg.degradation.vantHoff_deg,\n",
+    "    func_kwarg={\"I_chamber\": 1000, \"temp_chamber\": 25},\n",
+    "    see_added=True,\n",
+    ")\n",
+    "\n",
+    "scene_temp.addJob(\n",
+    "    func=pvdeg.degradation.vantHoff_deg,\n",
+    "    func_kwarg={\"I_chamber\": 1000, \"temp_chamber\": 30},\n",
+    "    see_added=True,\n",
+    ")\n",
+    "\n",
+    "scene_temp.addJob(func=pvdeg.degradation.IwaVantHoff, see_added=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run and View Scenario Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scene_temp.run()\n",
+    "\n",
+    "scene_temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scene_temp.dump()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting and Extracting Results\n",
+    "These methods are independent of one another (i.e. you do not need to extract before plotting but both are shown below.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datetime\n",
+    "\n",
+    "t0 = datetime.datetime(1970, 1, 1, 0, 0)\n",
+    "tf = datetime.datetime(1970, 1, 1, 23, 59)\n",
+    "\n",
+    "# search by funciton id which is random and changes every time\n",
+    "# must update here, look at the purple ids in the display function above\n",
+    "temp_df = scene_temp.extract(\n",
+    "    (\"function\", \"HKXVD\"), tmy=True, start_time=t0, end_time=tf\n",
+    ")\n",
+    "\n",
+    "display(temp_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scene_temp.plot(\n",
+    "    (\"function\", \"HKXVD\"),\n",
+    "    tmy=True,\n",
+    "    start_time=t0,\n",
+    "    end_time=tf,\n",
+    "    title=\"single day cell temperature\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a Copy of a Scenario"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "parent_dir = Path(pvdeg.PVDEG_DIR).parent\n",
+    "new_path = os.path.join(\n",
+    "    parent_dir,\n",
+    "    r\"tutorials_and_tools\\tutorials_and_tools\\temperature and degradation.json\",\n",
+    ")\n",
+    "\n",
+    "copy = pvdeg.scenario.Scenario.load_json(\n",
+    "    file_path=new_path,\n",
+    "    email=\"user@mail.com\",\n",
+    "    api_key=\"DEMO_KEY\",\n",
+    ")\n",
+    "\n",
+    "copy"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python3",
+   "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": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/HPC Connection Required - Tools - Module Standoff for IEC TS 63126.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/HPC Connection Required - Tools - Module Standoff for IEC TS 63126.ipynb
new file mode 100644
index 000000000..d6388b839
--- /dev/null
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/HPC Connection Required - Tools - Module Standoff for IEC TS 63126.ipynb	
@@ -0,0 +1,851 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tools - Module Standoff for IEC TS 63126\n",
+    "\n",
+    "### Calculation of module standoff distance according to IEC TS 63126\n",
+    "\n",
+    "**Requirements:**\n",
+    "- Local weather data file or site longitude and latittude\n",
+    "\n",
+    "**Objectives:**\n",
+    "1. Import weather data.\n",
+    "2. Calculate installation standoff - Level 1 and Level 2.\n",
+    "3. Calculate $X_{eff}$ from provided module temperature data.\n",
+    "4. Calculate $T_{98}$ for a given azimuth, tilt, and $X_{eff}$.\n",
+    "5. Plot $X_{min}$ for all azimuth and tilt for a given $T_{98}$.\n",
+    "6. Plot $X_{min}$ for Level 1, Level 2, or a $T_{98}$ for a given region.\n",
+    "\n",
+    "**Background:**\n",
+    "\n",
+    "This notebook calculates the a minimum effective standoff distance ($X_{eff}$) necessary for roof-mounted PV modules to ensure that the $98^{th}$ percentile operating temperature, $T_{98}$, remains under 70°C for compliance to IEC 61730 and IEC 61215. For higher $T_{98}$ values above 70°C or 80°C testing must be done to the specifications for Level 1 and Level 2 of IEC TS 63126. This method is outlined in the appendix of IEC TS 63126 and is based on the model from *[King 2004] and data from **[Fuentes, 1987] to model the approximate exponential decay in temperature, $T(X)$, with increasing standoff distance, $X$, as,\n",
+    "\n",
+    "$$ X = -X_0 \\ln\\left(1-\\frac{T_0-T}{\\Delta T}\\right), Equation 1 $$\n",
+    "\n",
+    "where $T_0$ is the temperature for $X=0$ (insulated-back) and $\\Delta T$ is the temperature difference between an insulated-back ($X=0$) and open-rack mounting configuration ($X=\\infty)$.\n",
+    "\n",
+    " We used pvlib and data from the National Solar Radiation Database (NSRDB) to calculate the module temperatures for the insulated-back and open-rack mounting configurations and apply our model to obtain the minimum standoff distance for roof-mounted PV systems to achieve a temperature lower than a specified $T_{98}$. The following figure showcases this calulation for the entire world for an $X_{eff}$ that results in $T_{98}$=70°C. Values of $X_{eff}$ higher than this will require Level 1 or Level 2 certification. \n",
+    "\n",
+    "$*$ D. L. King, W. E. Boyson, and J. A. Kratochvil, \"Photovoltaic array performance model,\" SAND2004-3535, Sandia National Laboratories, Albuquerque, NM, 2004. '\\\n",
+    "$**$ M. K. Fuentes, \"A simplified thermal model for Flat-Plate photovoltaic arrays,\" United States, 1987-05-01 1987. https://www.osti.gov/biblio/6802914\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![alt text](T98_70C_standoff_Map.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# if running on google colab, uncomment the next line and execute this cell to install the dependencies\n",
+    "# and prevent \"ModuleNotFoundError\" in later cells:\n",
+    "#!pip install pvdeg==0.4.2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pvdeg\n",
+    "import pandas as pd\n",
+    "from pvdeg import DATA_DIR\n",
+    "import dask\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import math\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Working on a  Windows 11\n",
+      "Python version  3.12.9 | packaged by Anaconda, Inc. | (main, Feb  6 2025, 18:49:16) [MSC v.1929 64 bit (AMD64)]\n",
+      "Pandas version  2.2.3\n",
+      "pvdeg version  0.5.1.dev457+g28fa10d38.d20250817\n",
+      "dask version 2025.5.1\n",
+      "C:\\Users\\rdaxini\\Documents\\GitHub\\PVDegradationTools_NREL\\pvdeg\\data\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This information helps with debugging and getting support :)\n",
+    "import sys\n",
+    "import platform\n",
+    "\n",
+    "print(\"Working on a \", platform.system(), platform.release())\n",
+    "print(\"Python version \", sys.version)\n",
+    "print(\"Pandas version \", pd.__version__)\n",
+    "print(\"pvdeg version \", pvdeg.__version__)\n",
+    "print(\"dask version\", dask.__version__)\n",
+    "print(DATA_DIR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Import Weather Data\n",
+    "\n",
+    "The function has these minimum requirements when using a weather data file:\n",
+    "- Weather data containing (at least) DNI, DHI, GHI, Temperature, RH, and Wind-Speed data at module level.\n",
+    "- Site meta-data containing (at least) latitude, longitude, and time zone\n",
+    "\n",
+    "Alternatively one may can get meterological data from the NSRDB or PVGIS with just the longitude and latitude. This function for the NSRDB (via NSRDB 'PSM3') works primarily for most of North America and South America. PVGIS works for most of the rest of the world (via SARAH 'PVGIS'). See the tutorial \"Weather Database Access.ipynb\" tutorial on PVdeg or Jensen et al. https://doi.org/10.1016/j.solener.2023.112092 for satellite coverage information.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Source': 'NSRDB', 'Location ID': 145809.0, 'City': 'West Pleasant View', 'State': 'Colorado', 'Country': 'United States', 'Clearsky DHI Units': 'w/m2', 'Clearsky DNI Units': 'w/m2', 'Clearsky GHI Units': 'w/m2', 'Dew Point Units': 'c', 'DHI Units': 'w/m2', 'DNI Units': 'w/m2', 'GHI Units': 'w/m2', 'Solar Zenith Angle Units': 'Degree', 'Temperature Units': 'c', 'Pressure Units': 'mbar', 'Relative Humidity Units': '%', 'Precipitable Water Units': 'cm', 'Wind Direction Units': 'Degrees', 'Wind Speed Units': 'm/s', 'Cloud Type -15': 'N/A', 'Cloud Type 0': 'Clear', 'Cloud Type 1': 'Probably Clear', 'Cloud Type 2': 'Fog', 'Cloud Type 3': 'Water', 'Cloud Type 4': 'Super-Cooled Water', 'Cloud Type 5': 'Mixed', 'Cloud Type 6': 'Opaque Ice', 'Cloud Type 7': 'Cirrus', 'Cloud Type 8': 'Overlapping', 'Cloud Type 9': 'Overshooting', 'Cloud Type 10': 'Unknown', 'Cloud Type 11': 'Dust', 'Cloud Type 12': 'Smoke', 'Fill Flag 0': 'N/A', 'Fill Flag 1': 'Missing Image', 'Fill Flag 2': 'Low Irradiance', 'Fill Flag 3': 'Exceeds Clearsky', 'Fill Flag 4': 'Missing CLoud Properties', 'Fill Flag 5': 'Rayleigh Violation', 'Surface Albedo Units': 'N/A', 'Version': '3.0.6', 'latitude': 39.73, 'longitude': -105.18, 'tz': -7.0, 'altitude': 1820.0, 'ISO3166-2-lvl4': 'US-CO', 'Street': 'West 8th Avenue', 'County': 'Jefferson County', 'Zipcode': '80419', 'Country Code': 'US'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get data from a supplied data file (Do not use the next box of code if using your own file)\n",
+    "weather_file = os.path.join(DATA_DIR, \"psm3_demo.csv\")\n",
+    "WEATHER_df, META = pvdeg.weather.read(weather_file, \"csv\")\n",
+    "print(META)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This routine will get a meteorological dataset from anywhere in the world where it is available\n",
+    "#weather_id = (24.7136, 46.6753) #Riyadh, Saudi Arabia\n",
+    "#weather_id = (35.6754, 139.65) #Tokyo, Japan\n",
+    "#weather_id = (-43.52646, 172.62165) #Christchurch, New Zealand\n",
+    "#weather_id = (64.84031, -147.73836) #Fairbanks, Alaska\n",
+    "#weather_id = (65.14037, -21.91633) #Reykjavik, Iceland\n",
+    "#weather_id = (33.4152, -111.8315) #Mesa, Arizona\n",
+    "#WEATHER_df, META = pvdeg.weather.get_anywhere(id=weather_id)\n",
+    "#print(META)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Calculate Installation Standoff Minimum - Level 1 and Level 2\n",
+    "\n",
+    "According to IEC TS 63126, Level 0, Level 1 and Level 2 certification is limited to T₉₈<70°C, <80°C and <90°C, respectively. Level 0 certification is essentially compliance to IEC 61730 and IEC 61215. The default value of T₉₈<70°C represents the minimium gap to avoid higher temperature certification according to IEC TS 63126. This minimum standoff ($x_{min}$) is the distance between the bottom of the module frame and the roof and can be extimated for a given environment as, \n",
+    "\n",
+    "$$ X_{min} = -X_0 \\ln\\left(1-\\frac{T_{98,0}-T}{ T_{98,0}- T_{98,inf}}\\right), Equation 2 $$\n",
+    "\n",
+    "where $T_{98,0}$ is the $98^{th}$ percentile temperature for an insulated back module and $T_{98,inf}$ is the $98^{th}$ percentile temperature for an open rack mounted module.\n",
+    "\n",
+    "Once the meterological data has been obtained, the input parameter possibilities are:\n",
+    "\n",
+    "- T₉₈ : Does not necessarily need to be set at 70°C or 80°C for IEC TS 63216, you might want to use a different number to compensate for a thermal aspect of the particular system you are considering. The default is 70°C.\n",
+    "- tilt : tilt from horizontal of PV module. The default is 0°.\n",
+    "- azimuth : azimuth in degrees from North. The default is 180° for south facing.\n",
+    "- sky_model : pvlib compatible model for generating sky characteristics (Options: 'isotropic', 'klucher', 'haydavies', 'reindl', 'king', 'perez'). The default is 'isotropic'.\n",
+    "- temp_model : pvlib compatible module temperature model. (Options: 'sapm', 'pvsyst', 'faiman', 'sandia'). The default is 'sapm'.\n",
+    "- conf_0 : Temperature model for hotest mounting configuration. Default is \"insulated_back_glass_polymer\".\n",
+    "- conf_inf : Temperature model for open rack mounting. Default is \"open_rack_glass_polymer\".\n",
+    "- x_0 : thermal decay constant [cm] (see documentation). The default is 6.5 cm.\n",
+    "- wind_factor : Wind speed power law correction factor to account for different wind speed measurement heights between weather database (e.g. NSRDB) and the tempeature model (e.g. SAPM). The default is 0.33."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following is the minimum function call. It defaults to horizontal tilt and T₉₈=70°C."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The array surface_tilt angle was not provided, therefore the latitude of  39.7 was used.\n",
+      "The array azimuth was not provided, therefore an azimuth of  180.0 was used.\n",
+      "The estimated T₉₈ of an insulated-back module is 77.0°C. \n",
+      "The estimated T₉₈ of an open-rack module is 50.6°C. \n",
+      "Level 0 certification is valid for a standoff greather than 2.0 cm. \n",
+      "Level 1 certification is required for a standoff less than 2.0 cm. \n",
+      "Level 2 certification is never required for this temperature                            profile.\n"
+     ]
+    }
+   ],
+   "source": [
+    "standoff = pvdeg.standards.standoff(weather_df=WEATHER_df, meta=META)\n",
+    "print(pvdeg.standards.interpret_standoff(standoff))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following is a full function call for both T₉₈=70°C and 80°C separately even though the second standoff distance can be calculated using only T98_0 and T98_inf. With this function, one may also want to change the tilt, azimuth, or T98. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The array azimuth was not provided, therefore an azimuth of  180.0 was used.\n",
+      "First calculation standoff =  2.0  cm.\n",
+      "The array azimuth was not provided, therefore an azimuth of  180.0 was used.\n",
+      "Second calculation standoff =  0.0  cm.\n",
+      "The estimated T₉₈ of an insulated-back module is 77.0°C. \n",
+      "The estimated T₉₈ of an open-rack module is 50.6°C. \n",
+      "Level 0 certification is valid for a standoff greather than 2.0 cm. \n",
+      "Level 1 certification is required for a standoff less than 2.0 cm. \n",
+      "Level 2 certification is never required for this temperature                            profile.\n"
+     ]
+    }
+   ],
+   "source": [
+    "standoff_1 = pvdeg.standards.standoff(\n",
+    "    weather_df=WEATHER_df,\n",
+    "    meta=META,\n",
+    "    T98=70,\n",
+    "    tilt=META[\"latitude\"],\n",
+    "    azimuth=None,\n",
+    "    sky_model=\"isotropic\",\n",
+    "    temp_model=\"sapm\",\n",
+    "    conf_0=\"insulated_back_glass_polymer\",\n",
+    "    conf_inf=\"open_rack_glass_polymer\",\n",
+    "    x_0=6.5,\n",
+    "    wind_factor=0.33,\n",
+    ")\n",
+    "print(\"First calculation standoff = \", \"%.1f\" % standoff_1[\"x\"].iloc[0], \" cm.\")\n",
+    "standoff_2 = pvdeg.standards.standoff(\n",
+    "    weather_df=WEATHER_df,\n",
+    "    meta=META,\n",
+    "    T98=80,\n",
+    "    tilt=META[\"latitude\"],\n",
+    "    azimuth=None,\n",
+    "    sky_model=\"isotropic\",\n",
+    "    temp_model=\"sapm\",\n",
+    "    conf_0=\"insulated_back_glass_polymer\",\n",
+    "    conf_inf=\"open_rack_glass_polymer\",\n",
+    "    x_0=6.5,\n",
+    "    wind_factor=0.33,\n",
+    ")\n",
+    "print(\"Second calculation standoff = \", \"%.1f\" % standoff_2[\"x\"].iloc[0], \" cm.\")\n",
+    "print(pvdeg.standards.interpret_standoff(standoff_1=standoff_1, standoff_2=standoff_2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Calculate $X_{eff}$ from provided module temperature data.\n",
+    "\n",
+    "To do this calculation, one must use a set of data with: \n",
+    "   - meterological irradiance data sufficient to calculate the POA irradiance (DHI, GHI, and DNI),\n",
+    "   - ambient temperature data,\n",
+    "   - wind speed at module height, (wind_factor=0.33 will be used unless otherwise specified)\n",
+    "   - temperature measurements of the module in the test system. Ideally this would be measured under a worst case scenario that maximizes the module temperature for a given site,\n",
+    "   - geographic meta data including longitude and latitude,\n",
+    "\n",
+    "To create a weather file of your own, copy the format of the example file 'xeff_demo.csv'. This is formatted with the first row containing meta data variable names, the second row containing the corresponding values, the third row containing meteorological data headers, and all the remaining rows containing the meteorological data.\n",
+    "\n",
+    "To do this calculation, one should also filter the data to remove times when the sun is not shining or when snow is likely to be on the module. The recommendations and programmed defaults are to use poa_min=100 W/m² and data when the minimum ambient temperature t_amb_min=0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The effective standoff for this system is 2.0 cm.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Read the weather file\n",
+    "weather_file = os.path.join(DATA_DIR, \"xeff_demo.csv\")\n",
+    "xeff_weather, xeff_meta = pvdeg.weather.read(weather_file, \"csv\")\n",
+    "# Pull measured temperature and calculate theoretical insulated back module temperature and open rack module temperature\n",
+    "T_0, T_inf, xeff_poa = pvdeg.standards.eff_gap_parameters(\n",
+    "    weather_df=xeff_weather,\n",
+    "    meta=xeff_meta,\n",
+    "    sky_model=\"isotropic\",\n",
+    "    temp_model=\"sapm\",\n",
+    "    conf_0=\"insulated_back_glass_polymer\",\n",
+    "    conf_inf=\"open_rack_glass_polymer\",\n",
+    "    wind_factor=0.33,\n",
+    ")\n",
+    "# Now calculate X_eff.\n",
+    "x_eff = pvdeg.standards.eff_gap(\n",
+    "    T_0,\n",
+    "    T_inf,\n",
+    "    xeff_weather[\"module_temperature\"],\n",
+    "    xeff_weather[\"temp_air\"],\n",
+    "    xeff_poa[\"poa_global\"],\n",
+    "    x_0=6.5,\n",
+    "    poa_min=100,\n",
+    "    t_amb_min=0,\n",
+    ")\n",
+    "print(\"The effective standoff for this system is\", \"%.1f\" % x_eff, \"cm.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Calculate $T_{98}$ for a given azimuth, tilt, and $X_{eff}$.\n",
+    "\n",
+    "Equation 2 can be reorganized as,\n",
+    "\n",
+    "$$ T_{98} = T_{98,0}  -( T_{98,0}- T_{98,inf}) \\left(1-e^{-\\frac{x_{eff}}{x_{0}}}\\right), Equation 3 $$\n",
+    "\n",
+    "and used to calculate the $98^{th}$ percential temperature, $T_{98}$, for a PV system having a given effective standoff height, $X_{eff}$,  for an arbitrarily oriented module. Here, $T_{98,0}$ is the $98^{th}$ percentile for an insulated-back module and $T_{98,inf}$ is the $98^{th}$ percentile for a rack-mounted module. The input parameter possibilities are the same as shown in Objective #2 above, but the example below uses the default parameters. The actual tilt [degrees], azimuth [degrees] and $X_{eff}$ [cm] can be modifed as desired."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The array azimuth was not provided, therefore an azimuth of  180.0 was used.\n",
+      "The 98ᵗʰ percential temperature is estimated to be 45.7 °C.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This is the minimal function call using the common default settings to estimate T₉₈.\n",
+    "T_98 = pvdeg.standards.T98_estimate(\n",
+    "    weather_df=WEATHER_df,\n",
+    "    meta=META,\n",
+    "    tilt=-META['latitude'],\n",
+    "    azimuth=None,\n",
+    "    x_eff=10)\n",
+    "print ('The 98ᵗʰ percential temperature is estimated to be' , '%.1f' % T_98 , '°C.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The array azimuth was not provided, therefore an azimuth of  180.0 was used.\n",
+      "The 98ᵗʰ percential temperature is estimated to be 56.0 °C.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This code will calculate the temperature for an arbitrary x_eff distance. Either set of kwargs can be modified and use.\n",
+    "#irradiance_kwarg ={\n",
+    "#    \"axis_tilt\": None,\n",
+    "#    \"axis_azimuth\": None,\n",
+    "#    \"x_eff\": 10,\n",
+    "#    \"module_mount\": '1_axis'}\n",
+    "irradiance_kwarg ={\n",
+    "    \"tilt\": META['latitude'],\n",
+    "    \"azimuth\": None,\n",
+    "    \"x_eff\": 10,\n",
+    "    \"module_mount\": \"fixed\"}\n",
+    "\n",
+    "T_xeff = pvdeg.standards.x_eff_temperature_estimate(\n",
+    "    weather_df=WEATHER_df,\n",
+    "    meta=META,\n",
+    "    **irradiance_kwarg)\n",
+    "\n",
+    "print ('The 98ᵗʰ percential temperature is estimated to be' , '%.1f' % np.percentile(T_xeff, 98) , '°C.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Plot $X_{min}$ for all azimuth and tilt for a given $T_{98}$.\n",
+    "\n",
+    "The temperature of a system is affected by the orientation. This section will scan all possible tilts and azimuths calculating the minimum standoff distance for a given $T_{98}$. Similar additional factors as above can also be modified but are not included here for simplicity. The tilt_step and azimuth_step are the number of degrees for each step for the 90° and 180° tilt and azimuth spans, respectively. The default for this calculation is for $T_{98}$=70°C, the boundary between Level 0 and Level 1 requirements. The temperature model information given below is unnecessary as these are default values that would get populated automatically. However, they were included here for clarity into a standard practice as per IEC TS 63126.\n",
+    "\n",
+    "$$ X_{min} = -X_0 \\ln\\left(1-\\frac{T_{98,0}-T}{ T_{98,0}- T_{98,inf}}\\right), Equation 2 $$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                     \r"
+     ]
+    }
+   ],
+   "source": [
+    "# Scans through all the azimuth and tilt running the minimum standoff calculation\n",
+    "# Set up keyword parameters for the calculation\n",
+    "\n",
+    "kwarg_x = dict(\n",
+    "    sky_model=\"isotropic\",\n",
+    "    temp_model=\"sapm\",\n",
+    "    conf_0=\"insulated_back_glass_polymer\",\n",
+    "    conf_inf=\"open_rack_glass_polymer\",\n",
+    "    T98=70,\n",
+    "    x_0=6.5,\n",
+    "    wind_factor=0.33,\n",
+    ")\n",
+    "# Run the calculation\n",
+    "x_azimuth_step = 10\n",
+    "x_tilt_step = 10\n",
+    "standoff_series = pvdeg.utilities.tilt_azimuth_scan(\n",
+    "    weather_df=WEATHER_df,\n",
+    "    meta=META,\n",
+    "    tilt_step=x_tilt_step,\n",
+    "    azimuth_step=x_azimuth_step,\n",
+    "    func=pvdeg.standards.standoff_x,\n",
+    "    **kwarg_x,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The next cell creates a plot of the calculated data. Some of the things you may want to change are:\n",
+    "- cmap=\"Spectral_r\": Change to have different colors\n",
+    "- plt.title : This will change the plot title.\n",
+    "- figsize=(16,4) : Change the plot dimensions and/or aspect ratio.\n",
+    "- vmax=None : This can be set to a numeric value to control the depth scale maximum\n",
+    "- vmin=0 : This controls the minimum of the depth scale.\n",
+    "- v_ticks=37 : This changes the number of vertical tick marks\n",
+    "- h_ticks=10 : This changes the number of horizontal tick marks\n",
+    "- Unblock the last two lines to ouput the plot as an *.png image file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[WinError 183] Cannot create a file when that file already exists: 'c:\\\\Users\\\\rdaxini\\\\Documents\\\\GitHub\\\\PVDegradationTools_NREL\\\\TEMP\\\\results'\n"
+     ]
+    },
+    {
+     "data": {
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Sk5PNbHwAAAAAAAD5raAULC9wQambb75ZDh48KKNGjZJ9+/ZJs2bNZNGiRWcVPwcAAAAAAMgPBaU2VIELSqmBAweaBQAAAAAAINzEpqfn9y6EtYgOSgEAAAAAAIQrCp0HR1AKAAAAAADAA9SUisLZ9wAAAAAAABDZyJQCAAAAAADwAIXOgyMoBQAAAAAA4AFqSgXH8D0AAAAAAACPglKhLqGYNGmS1KxZU4oUKSKtW7eWVatWSTgjKAUAAAAAAOBRofNQl9yaM2eODB06VEaPHi3r1q2Tpk2bSufOneXAgQMSrghKAQAAAAAAeFRTKtQlt8aPHy/33HOP9O3bVxo1aiRTpkyRYsWKyRtvvCHhiqAUAAAAAABABDt16pSsXbtWkpKSMtbFxsaa6ytWrJBwRaFzAAAAAACAMCt0npqaahZ/CQkJZsns0KFDkpaWJhUrVgxYr9c3b94sYctBrqSkpDijR482/0Zbe9HcN9qL7PaiuW+0F7lt0R7thWtbtBfZ7UVz32gvctuiPdoL17byoz2bRo8erRGtgEXXZeW3334z27/++uuA9Q899JDTqlUrJ1zF6P/yOzAWSY4dOyaJiYly9OhRKVWqVFS1F819o73Ibi+a+0Z7kdsW7dFeuLZFe5HdXjT3jfYity3ao71wbSs/2rMpNReZUjp8T+tHvfvuu9K9e/eM9b1795YjR47IggULJBxRUwoAAAAAACDMJCQkmECb/5JVQEoVLlxYWrRoIYsXL85Yl56ebq63adNGwhU1pQAAAAAAACLc0KFDTWZUy5YtpVWrVjJx4kRJTk42s/GFK4JSAAAAAAAAEe7mm2+WgwcPyqhRo2Tfvn3SrFkzWbRo0VnFz8MJQalc0lS50aNHZ5syF8ntRXPfaC+y24vmvtFe5LZFe7QXrm3RXmS3F819o73IbYv2aC9c28qP9sLdwIEDzRIpKHQOAAAAAAAA6yh0DgAAAAAAAOsISgEAAAAAAMA6glIAAAAAAACwjqAUAAAAAAAArGP2vXM4dOiQvPHGG7JixQozpaKqVKmSXHbZZdKnTx8577zz8nsXAQAAAAAAIg6z7wWxevVq6dy5sxQrVkySkpKkYsWKZv3+/ftl8eLFcvLkSfnkk0+kZcuWEqlWrVp1VsCtTZs20qpVK4kG0dy/U6dOyfz587MMmHbr1k0KFy4skcp236L5uQRQcJw5c0Z++OGHgM+xRo0aSXx8vCftaTvffPNNQHutW7c2/0ZD/2y2F819KwivFZv9s/1cAoDXCEoFcemll0rTpk1lypQpEhMTE7BNn7a//e1vsn79enMiG2knxwcOHJCePXvK8uXLpXr16gEBt127dknbtm3lvffekwoVKkgkBomivX/btm0zAdM9e/aYAxH//umBStWqVeXjjz+WunXrRlz/bPctv55L2weWtgO0tg+aac890f5asd0/G+2lp6fLqFGjZNKkSXL06NGAbYmJiTJw4EB5/PHHJTbWnaoNycnJct9998ns2bPN8VHZsmXN+j/++MMcH91yyy3y73//2/yoF4n9s9leNPetILxWbPbP9nNZEAJ8BSEgHM3BdQK0UUSDUshakSJFnE2bNmW7XbfpbdyydetWp3bt2uYxr7jiCuemm24yi17WdXXr1jW3cUPPnj2dNm3aOJs3bz5rm6677LLLnBtuuMFxy/79+5127do5MTExTo0aNZxWrVqZRS/rOt2mt3FLtPcvKSnJ6datm3P06NGztuk63dapU6eI7J/tvtluT504ccK57bbbnLi4OKdQoUJOhQoVzKKXdd3tt9/uJCcnR+Rr02bfaI/XSjj3z2Z7Dz30kHPeeec5U6ZMcXbs2OGcPHnSLHr53//+t3lehw8f7rjl7rvvdurVq+csWrTIOXPmTMZ6vfzJJ5849evXd/r16+dae7b7Z7O9aO5bQXit2Oyf7ecyLS3N+ec//+mULl3afGb5L7ru0UcfNbeJ1O8E2/2L5vai/bUC7xGUCqJmzZrO9OnTs92u2/TgMhJPjkuUKOGsW7cu2+1r1qwxt4nUIFG0969o0aLOhg0bst2+fv16c5tI7J/tvtluz/aBpe3Xpu2DZtrjtRKu/bPZXsWKFc3zmB3dpgfsbtGTjOXLl2e7fdmyZeY2brHdP5vtRXPfCsJrxWb/bD+X0RzgKwgB4WgOrtt+rcB7BKWCePnll52EhATn73//u7NgwQJn5cqVZtHLuk5PVCdNmhSRJ8flypVzlixZku32L774wtwmUoNE0d6/ypUrOwsXLsx2+wcffGBuE4n9s9032+3ZPrC0/dq0fdBMe7xWwrV/NtsrVqyYOUbIzvfff+8UL17ccUupUqWc1atXZ7t91apV5jZusd0/m+1Fc98KwmvFZv9sP5fRHOArCAHhaA6u236twHvuDKiOUgMGDJDp06ebsapan0hrQOiil3XdtGnT5P7773etvdKlS8vOnTuz3a7b9DZuuPnmm6V3794yb948OXbsWMZ6vazr+vbta8amuyUhISGgncyOHz9ubuOWaO9fv3795M4775QJEyaYumZa/0gXvazrdGbIe++9NyL7Z7tvttvz1bwIVh9Ot+ltIvG1abNvtMdrJZz7Z7O9Dh06yLBhw8yMwZnpuocfftjcxi3XXnut+Vz89ttvz9qm6/r37y9du3Z1rT3b/bPZXjT3rSC8Vmz2z/ZzqZ9RVapUyXZ75cqVTZ2rSP1OsN2/aG4v2l8rsMBC4CsqnDp1ytmzZ49Z9LIXRo4c6ZQpU8YZP368+SVn3759ZtHLuq5s2bLO6NGjXWkrJSXF+dvf/uYULlzYiY2NNTWrdNHLuq5///7mNm65//77zVDH999/P2B4ol7WdTpUcuDAga61l13/dGxzNPRPPfXUUyaDR/ukfdRFL+u6p59+2tW2bPfPZt/yo71bb73Vad68eZZZFLquRYsWZqx8JP7tbPaN9nithHP/bLa3a9cup0mTJqaehj6nV199tVn0sq676KKLzG3c8scff5jH189JPTZp0KCBWfSyfn526dLFOXz4sGvt2e6fzfaiuW8F4bVis3+2n8u//vWvpmzIwYMHz9qm63Rfrrnmmoj9TrDdv2huL9pfK/Aes++FmaefflpeeOEFM4uAb8Y//RPpLAKDBw+W4cOHu9qe/oq7du3agFkLWrRoIaVKlXK1ndTUVLP/b7zxhpmZwRfd1tkGCxUqJHfffbfJSnHzV2pf/9asWWMyX5TOrNayZcuo6Z/asWNHwN+vVq1arreRX/2z0bf8aO/w4cNy6623yieffCJlypTJmAVSZ408cuSImQ1w1qxZrmRG2v7b2ewb7fFaCef+2W5PfxXW53LlypVnzfTXqVMn12Yb87d58+YsZxZs0KCB623Z7p/N9qK5bwXhtaI2bdqUZXte9M9WW7t375a//vWv5m934YUXBsxOvGHDBjOr2ocffijVqlWLyO8E2/2L5vai/bUC7xGUClO2T8ZtsRUEy46eFHz//ffSsGHDqOyf1zIH+aKlf3v37pXJkyfLsmXLzGU9YK1du7Z0797dDN+Li4vzpF2bB7G2X5s2+5Yf7dk8wbLdnu3Xiu3n0nb/ovVzE0D0I4AZ2QHhaA6u58fxA7xDUCqCaBR69OjR5ldXN/z555/mwLxs2bImgu0vJSVF3nnnHVNrx+2TRt+HhX6QaFaY/pp8++23S8eOHV1ra+jQoVmu1/a0rXLlypnr48ePFy/ouGl9/rZt22bGWPfq1SujTTesW7fO/DLgC1bOnDlTpkyZIrt27ZIaNWrIwIEDTZtuGTRokNx0001y+eWXiw0vv/yyrFq1yvzqov3Q/o0bN8584fXo0UPGjBljMg3coCeLSUlJUrduXSlatKj5ctNfXzSbQb9c9b2xaNEiKVmypCvtAYBX9HMz8wH6ZZddJpdcconV/dBfsRcuXOjqMYTS74CsTmx0/a+//irVq1d3rS09PNZanvrLvn7f6HeC1qTUYxb9bipfvrx4SY+Jpk6dar7TbfwQqscrWvelSZMmrj62Pl/6N4uPjzfXf/75Z3Mc6zte0YxBN394fe+996RLly5SrFgxsUV/7NTjaa1VpT9o/fDDDzJp0iTzurz++utN1oabPv/887N+RLvuuuukXr16rrYDANZYGCIIl3z33XdmzLgbtmzZYmpd+GrotG/f3vntt98ytmstK7faUh9//LGp5aTj3rW2k17XqUOTkpKcjh07OnFxcc7ixYtda0/71axZM6dDhw4Bi66/5JJLzOUrr7zStfYaNmzo/P777+ay1irQWiGJiYmmLe2zzjixfft219rTmgiffvqpufzaa6+ZWRl1RsjJkyc7gwcPNjM6vf76666153ud6PSrWn9p7969jleeeOIJp2TJkmY69UqVKpn2dKbEsWPHOv/617/M62bUqFGutde2bVvnsccey7g+c+ZMp3Xr1hn1G/R1pM+t21JTU505c+aYv1evXr3Mopffeecds80Wfa8//vjjrj/u7t27nePHj5+1Xmvyffnll662dejQIefzzz/PeA9q/QJ93Wi/fvzxR8eGWrVqOT/99JPn7aSnp5u+vvrqq2bWSDdrHOrfzL8exNKlS03dhnbt2pnaDF9//bXjpueee87ZuXOnY5M+Z1q/UWfmUfq9o3VYOnfubKatdptOia2fxX379jU1NbTuhtaR+uyzz1xtZ//+/ebvpJ/V+t3eqlUrs/i+53Wb3iYSj1d8dbhuvPFGc/yg36f6N/SfBtztY5bNmzeb504fs27duub7W2uU6KxtOrtb+fLlXXu/64zOWS16XKSzQPuuu0Vravo+m/X1qd+1+hrxfc/rsVFWn92huuKKK5y5c+eay/q+01mt9Rjm5ptvNjVh9Pl087NF+6Ez0N1zzz1mxmyvvffee+Zvpccpeuylx2Y665ce3+rnim576623XGlL38P6vta/k9bH0n/1danHStrOQw895Hjhm2++cSZOnOg88sgjZtHLOtOfbXpMNn36dNcfNy0tLdv1v/zyi+vf4fp5cvr0aXNdj/dmz55t+pVVPSYv6Hvcxnev9vN///tf0JnlQ6V1gf2Pf7Zt2+b84x//cG6//Xbnn//8p6vnXLCDoFQYye7AxLdMmDDBtYOu7t27m4Jz+gG4detWc1lPqnwfvm4f4LVp08Z8SKi3337bFHTXDw8f/ZK76qqrXGtv3Lhxpj+ZA136Jf7DDz84btODIN8Bv568XXbZZc6RI0fMdT2404OTW265xbX2NAjl+0LRgzo9QfWnB0CNGjVytX96EvXAAw+Yg/H4+HjnuuuuMyd42X2Zh6pOnTrmIM93YqMHWm+++WbGdi0QrCcJbj6XP//8c8Z17Y/2T98DSr9Qq1Sp4rhJ33O1a9c2J1h6wH7TTTeZRS/rOu2f3iYSTx51MggNxupj6t/ujjvuCDjBcfuzRQ+WNQCsr1H9XFmzZo1572sAVV9L+vddu3ata+298MILWS7a1xEjRmRcd4sGTHyfJRp004Cp9lWDs/o8apHbAwcOuNKWnuzoe1rNnz/fPL6+zx9++GHn+uuvN+8L33Y3aD/0edPPRz0o9zoYO2XKFPMdoCdxetKqAWgNgPfr18+57777zGtFT7bcou9hDWxoEKVatWqmv/pdq39D7bcGWXwnJnmlgQX9ntVgSma6Tr+TbrjhBsfNIFGw5auvvnL1fa4/DNSvX98EN/SHGH1e9bn0vWb0c0WfX7d069bNvPbXr19vfizQH550nZ4E6clQ165dzcmPG3zBIF9gKKvFzedSH8t3vKKfWVWrVjWB7uTkZBM00s9NPSZzi77XfAE8/Y4bMmRIwPZHH33U/DjkFn2+xowZY46N9HLjxo3N8bP+eOGFiy++2Pxo5ju+1YCUtu8ffNcft9yggTw9ftf3mL4ONcB95513mm16vKuBMTc/wwh2R26w23bAO9qD3fAeQakwYvPARD989WDLP3Kvs9VVr17dnKC7/UGsByW+k2w96dcTA/8ZEzSKXrFiRcdN+iuOHsQ++OCDGdF0G0EpDTZoIMPf8uXLzUmJW/TAQ0++fX9L/aL2p78Y6AmWF/3T51IzfHy/AGrARgOMbgVRdL/9f5nSE+GNGzdmXNdgnH7ZuEUPEHxZE76givZXv1TVjh07zAGLm/QkXE9w/Gfk8tF1uk1nMXGDzt4ZbNG/pZvvdT1A1pPu1atXm1+M9YCrZcuW5hdOL04e9bnUoMKxY8ecZ5991pxg6XUfzVDRg3i36L5rG5oN6b/o+vPPP99c1qCYF+89PejTYLPvF0DNbNLnVz+73aAHxr7H1r+hZpv5e+mll8zBnpt9mzp1qnm96/tcP9c08O3Fr6pKnztfAF9PwvV9PWnSpIztui8afHAzoKjBLv1+Vfp86jqlJx76WnFrRl3N0MhqFiIf/b7Q27jFf6bSrBa3Ayl6bPLFF19kXNcf1PTkWD8n9eTc7WMWDfp+++235vKJEydMfzTQ5v+drvvkBt+sVJlP7m0cr+gsdbNmzQrYriepeuzkFv1c2bRpk7msx3lZHa+4/dr09U9f9/q5qYEiPWnVAETm4zM3+qfHCUrf6/pZ5n98rcfUbvVPj6X9j4f0tant+Y4lNNB+wQUXOG4h2B25wW7b55XRHuyG9whKhRE9uddfp7OjB0hufXjor8NZDWsZMGCA+SDRYRtuB6X0wMNHv6D9s1M00OD2ib/SqLyeJGv0XE909Mvbq4M8X7aC/h0zn1S53T/90rr77rvNZT3I0g9ffzrM7cILL/TkIM+fBo/0pMr3y48b9IReh3cq/cLRx9UhbT7//e9/zcmcW/QkWA/MtU39AtVfc3R4p8+iRYvMl6mbNPAW7MRbD1jcCioGOyjx4uRRX/+aveTjO9DSX4o108ftk0fNjvJ9lumBnT62f/uaJaXBIrdokEH7kvnz08YJpJ5sZP5VUzMY3QqCacaZBip9wW7fZR/9DHczIOzfN/336aefNplf+jfUbDsNIGmw0S1ZBbz934d6Yulm//Sx/H/11hMdbdOXsaHf9259lmlAb8mSJdlu14CO3sbN73T9e2mbWS16gufm+1z/dpmHY+hrQ0+YtQSAbnO7Pf/Xih6z+B/D6DB9DXK4Zfz48eaHK/9MRC8/U3zHK5qZ4R/k8B2vuPmjlv59nnnmGXNZgxiZh1+9++67rgX4sjte+fPPP50ZM2aY73Z9nbh5DKFD53w/EuqPL9q+fwBVfyDV27gVLPV/TeiPZ9of39B1Pa5283VJsDtyg922A97RHuyG9whKhRE9cdPU0ezoG86tiL0e8OsXdFY0MKW/Krn5QaxBIV+gQemJgP+wBQ2CuZldkJmmVOuHlvbJq4M8DQJpFoF+COpBlj+toePmibHW/9KDKq0FNnToUHMAqWnUWkNB12n9Lg3eeB2U8tFfB9369VEDbPrFrdku+prQX1b0S1rrZenwGz1wz/yLSF4Dlzp0Tr+ktZ960Ox/8vPJJ58EBMXcULly5aDDoD744ANzGzfoiajWtNETjawWfZ24+V7XA4XM6ef6XtdsJf0c0ICb2+35fqXOKuCtJ5ZuB7x1CKm+DjVzyOYJpAaKsjqBdOskRH/B9f2SqZmQmYchaqBBh0V6/bmi3we9e/c2f1td3OL7wcX3Gart+39OajBFb+NmgNZ/6Ojhw4dNm75Am37OuPW3u//++82PA/ra9M/A1Mu6Tr8vdKiPW/TkXoNSNo5XfAHZrL7T9PNbA1NNmzZ19XNFf4jwP1l85ZVXAgKk+nd1K9Dgoyerms137733muwCLz9TNLiu36P6mZL5u1v7psEqt+gQGg146w9Y+pmpj63f81pmQOtD6vFmsNdSXjI2sqJZ3f7lI9z4kVAzS7XMgB7H62fnpZdeak6YNZtIszjcyibSYdSavaQBDf0RRrNt/MsZaA0tN1+XBLsjO9htM+Ad7cFueI+gVBjRg2X/wE1m+iUU7MshNzSTxjeMICua7uzmAaUGFD788MNst2uqpy/zxys61EV/mdbn0W1aKNt/0ewaf8OGDTOFrN2kJzha60UPYvWkWwNRelKihYl16JSb9ITGq3oMmenwzieffNK59tprzetUA14aVNQvVT346dOnjyd/Q/0l1c3x7sFo8FkzfPRgQbNR9Nc4XfSyrtPi+G4N69Ff/LR4vK2TRw3OZg7K+gem9CDBzYM8zazxrx2nnzO+oZe+g3Q3Aw0+v/76qzko0l8itfC/lyeQWhxbT0b0NZM5mKn9c2vos2Z/6XtMs0v1NaMHzHrCpe9HXacHyzrEzdbJowZUMtfLywv9wUWDalr/RX8N18CXvn70e1c/s/W1e9ddd7nWnj6+npDqyame3PhqXfjo97lbw7r1V30dxqnfA/q86neCLnpZ1+l3ut7GLfp3CVY7TT/P/CeQyKtBgwZle2KvJ5EaFHDzc0WDNnoCHKxupb4v3aafXdq2vk51eLwXnyn6mvSfACZzP/W9r7dxkwamNFCTOVtXf6xzswZSTn5Ec5u+1rUmqn5eakBKawBqANh/ghj/wENe6A8uGjDV7xvNutSAnm/SG6Wfz24OkSLYHfnBblsB72gPdsN7BKUAIB9ofRnNhvJPV9fLus7NL1I9cNQ6E9nR4QbTpk1zrb3hw4dnWw9LA1OajePmQaWe+GrQMjv6i3iPHj0cL2jAVAOnvpmPvDiB1CCs/6I1wPzpbEt6IuQWPXnSALoO8fadOOrJj/4SOW/ePCeSTx41mK3ZpDq0QA/OdTid1iHToI3ui54Qubk/+li+E3F9f+vJnf9QGK1j8uKLLzpu0pNFHYasQyd00ctZ1a6LNPo5lfmXd396YufWj3Y5oUFGrT/oFR3qolkwNt8f/oEP/RHPC5pJoYF0PaH0z3B1k2Zk+Oq45Sd9HjOPCnCDBhU0g1t/oPB6trb8CHYHC1IS7A7fgHe0B7vhPXNmIACAfLFjxw7Zt2+fuVypUiWpVauWRLIzZ87IyZMnpVSpUtlu/+2336RGjRpW9kf3JS4uThISEjxrY+3atbJs2TK58847pUyZMmJTcnKy6V+RIkVcfVw9NDhw4ICkp6dL+fLlJT4+XqJVSkqKnD59WkqWLOnJ42/dulVSU1OlQYMGUqhQIU/aAACvHDt2zHzP+R+rtGjRItvv+Uhx+PBh2bNnjzRu3DjL7cePH5d169bJFVdcYe14UL/LK1eu7FkbH3zwgXzxxRcyYsQIqVChgtiyfft2KVy4sFStWtX1xz548KB5fD1e0eeuZs2arrcB78VaaAMAkA0NQrVp08YsvoDU7t275a677rLSvttt6Ul3sAPVvXv3yuOPPy62/P7779K/f39P29CD8wceeMAEpGz+7dQff/wh999/v+uPGxMTIxUrVjQHeL6AlO2+2WpPTwI0IOVVe/Xq1ZMmTZqcFZByu70///zTBEd//PHHLANvM2bMcK0t2nO3vWjuG+1F9mtl06ZN8t5775nvgltuuUWaN28u77zzjgwePFg+//xzV9vytTd16lTZvHmzua7/6ne4fla63Z5+Z8fGxmbb3urVq10PSAXrnwal3A5IZW6vfv365jX0yCOPuP58+trasmXLWX3buXOn6wEpX3t6HNS6dWvz93z66ac9ea3AAgvZWACAXNC6CW6mjIdLW7QX2e1Fc98ivb0tW7aY4YG+oYI64YUWc/dxexaprNrzH85Ge+HZFu1Fdnu2+6b19nSYnta51GF7el0noklKSjI1FXUYmH9NR9oruO1Fc99gB3nkAGCZpk8Ho2nIkdgW7UV2e9Hct2hv7+GHHzbZWGvWrJEjR46YLIZ27drJkiVLpHr16q61E6y9tm3b0l6Yt0V7kd2e7b6NGTNGHnroIRk7dqzMnj1bbr31VpP58uSTT5rtOgTsqaeeko4dO9JeAW8vmvsGSywFvwAA/z/fr5yZizP6L2792mmzLdqL7PaiuW/R3p7OdrR+/fqM61roWQsU62yXWnDZ7QwK2nOvvWjuG+1F9mulVKlSztatWzNmRtZZ2/wna9BC7m7N/kp7kd1eNPcNdlBTCgAs05oB77//vinKmNWihTUjsS3ai+z2orlv0d6e1gjxr1mlNcEmT54sXbt2NTVRfvrpJ9faoj1324vmvtFeZL9WfG0orb2k9fcSExMztmktvqNHj9Ie7VlvKz/ag7cISgGAZVoYW2eyCfZF69bEqDbbor3Ibi+a+xbt7enMfjqkJ7OXX35ZunXrJtddd50r7dCe++1Fc99oL7JfKzqLmc4e6rNixYqAYYK7du1ytTA37UVue9HcN9hBUAoALNNx8Jdddlm22+vWrWum7I20tmgvstuL5r5Fe3vXX3+9vP3221lu0xNWnTXLzYAb7bnXXjT3jfYi+7WiNXrS0tIyrmeeRfTjjz92tWYP7UVue9HcN9gRo2P4LLUFAAAAAAAAGGRKAQAAAAAAwDqCUgAAAAAAALCOoBQAAAAAAACsIygFAAAAAAAA6whKAQAAAAAAwDqCUgAAAAAAALCOoBQAAAAAAACsIygFAAAAAAAA6whKAQAAAAAAwDqCUgAAAAAAALCOoBQAAAAAAACsIygFAAAAAAAA6whKAQCAoFatWiUxMTFmGTNmjGuPW7NmTfOYkahPnz5m35csWeLK4/meX9/y7rvvnnWbtLQ0mT59ugwcOFCefPJJOXLkyFm30XWZH8utfQQAAHBbIdcfEQAARJWZM2dmXH7rrbdk1KhREu06dOggX375pezYscMEz2woXry43HDDDeZy5jbT09Pl6quvls8++yxj3auvvirffPONVKpUKWNd4cKFpXfv3ubysmXL5Oeff7ay7wAAAKEgUwoAAGTr9OnTMnv2bHNZgx8//fSTCYS4YfHixbJp0yZXHisalC9fXqZNm2aWli1bBmzTYODSpUvl7bfflhMnTsiKFSvEcRwZOXJkwO2KFSuW8Rjt2rWz3AMAAIDcISgFAACytWjRIjl06JC0bdtW7r///rMyp/KiTp060qBBA1ceK9qtXr1aunTpIr169TIZVZdeeqkMGTLEDK0EAACIVASlAABAtt58803z7+23324WNWfOHJNBlV2dpWCLf32jrGpK7dy506zT4XPJyckydOhQqVatmhQtWlQuvvhiWbhwYcZt586dK61btzZBmooVK8rf//53+fPPP8/aL3287IbgaUaRbn/ssccC2tehe6pWrVoB+58VzWDq2LGjlCxZUkqVKiXXXHON/Pjjj+Im7d/3338vx44dM9c1S2r58uUBQ/cAAAAiDTWlAABAlo4ePSoffPCBqVN00003SdmyZeWyyy6Tr7/+2mRQde3aNeD22Q0X0wLdOuxM/42Li8tR26dOnZK//OUvpqZT+/btTbaWBn+uv/560/aGDRtk+PDhcsUVV0jnzp3Ntpdeekl+//13M9QtVCVKlDA1mbSN/fv3S8+ePc267GiQ7IUXXjDD7f7617/Kd999Jx999JEZ4rhx40bXgkZ33HGHPPXUUyazTJ9nHfaoj69tAQAARCqCUgAAIEs6A1xKSop069bNBKSUZktpUEqH8GUOSvXr188smT3wwAMmIHXttdeaYYA5oTWTNPto+/btJhPKl9XUt29f6d+/vwk+6W18tZf27NkjzZs3l1mzZskTTzwhtWvXzlNdJ83U0qDUc889F7TQ+cSJE+W9996T7t27m+vaz5tvvtmse+WVV1ybrbB69eqmBpdmjn388cdSo0YNeeedd8yQPgAAgEjF8D0AAJAlX+0o37A9pRlT8fHxJkNIM6nO5T//+Y+8+OKL0qhRIxMwio3N2aGH3m7y5MkZASl15513mqDRtm3bZMCAAQHFwKtUqSK33XabuaxZU7bccsstGQEppZlgI0aM8GQ/WrVqZWbUO378uMmSuvHGG119fAAAANsISgEAgLPs2rXLBFVKly4dkBFVrlw5M0xNM6i0plMwX331lSmOrvfRIJbWXMopzU6qX7/+WYEqzRBSnTp1Ous+vuyovXv3ii1Z7Ydvv23uBwAAQCQiKAUAAM6idZm0mPYNN9wgCQkJAdt8mVO+IuhZ+eWXX0w9Jn0MDV7ldjjd+eefn+V6X32nrLb7tqWmpootVatWPWudL/hmcz8AAAAiETWlAABAtkP3dLa8zAXMtQi50kwqDT75spd8dNa86667Tg4ePGjqKl155ZW5bv9cw/xyOgzwXNLT0/N0f7f2AwAAoCAiKAUAAAKsXbvWzO6mtH6TLlnRLCjNqPrHP/4RsE5nilu/fr0pSK5LftMaWCdOnMhy2+7du63vDwAAAP4fft4DAAABfMPyhg0bZoJMWS2aQeV/W59Ro0bJvHnzTHaUFjgPB5UrVzaz9emS2WeffZblfQoXLmz+PXPmjOf7BwAAUFARlAIAABnS0tLk7bffzphZLjuXX365qeukGVWaWaXmzJkjY8eONfWjtI5UoULhkZB9xRVXmH913/w988wzZja7rOhsfmrLli0W9hAAAKBgCo+jRQAAEBb+97//yf79+80MchdffHHQWko333yzjB8/3tSfatGiRcYwPg3oPPjgg1ne75FHHpEGDRqITQ8//LC8++67MnHiRJPhVadOHdmwYYMZuqezA2rdq8y0Jtb06dPl1ltvNTPsJSYmmvX/+c9/rO47AABANCMoBQAAzipwHixLykdvo0Epzax67rnnTJaV0uyj7DKQ+vTpYz0o1bhxY/n8889lxIgRsmrVKtm+fbu0bdtW3nnnHfn222+zvE+PHj1kwoQJ8tprr8nChQszZtIjKAUAAOCeGEcLQwAAACDfxMTEmFkMd+7c6dpjagBQs72++OIL6dChg2uPCwAA4BYypQAAAMLAoUOHTCBJDRw4UFq2bJnrxzh58qQZkqiyy1YDAAAIF2RKAQAAhEGmlD8tFH/DDTfk+nGOHDkiZcqUCVhHphQAAAhXBKUAAAAAAABgXaz9JgEAAAAAAFDQEZQCAAAAAACAdQSlAAAAAAAAYB1BKQAAAAAAAFhHUAoAAAAAAADWEZQCAAAAAACAdQSlAAAAAAAAYB1BKQAAAAAAAFhHUAoAAAAAAADWEZQCAAAAAACA2Pb/AVXRlHoewk7pAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "standoff_series_df = pd.DataFrame(\n",
+    "    {\n",
+    "        \"Tilt\": standoff_series[:, 0],\n",
+    "        \"Azimuth\": standoff_series[:, 1],\n",
+    "        \"Xₘᵢₙ\": standoff_series[:, 2],\n",
+    "    }\n",
+    ")\n",
+    "x_fig = plt.figure(figsize=(16, 4))\n",
+    "plt.title(\n",
+    "    r\"Plot of $\\it{Xₘᵢₙ}$ for all orientations for $\\it{T₉₈}$=\"\n",
+    "    + \"%.0f\" % kwarg_x[\"T98\"]\n",
+    "    + \"°C.\",\n",
+    "    fontsize=15,\n",
+    "    y=1.08,\n",
+    ")\n",
+    "x_fig = sns.heatmap(\n",
+    "    standoff_series_df.pivot(index=\"Tilt\", columns=\"Azimuth\", values=\"Xₘᵢₙ\"),\n",
+    "    cbar_kws={\"label\": \"Xₘᵢₙ\", \"format\": \"%.0f\", \"pad\": 0.02},\n",
+    "    cmap=\"Spectral_r\",\n",
+    "    vmin=0,\n",
+    "    vmax=None,\n",
+    ")\n",
+    "\n",
+    "h_ticks = 37\n",
+    "x_number = math.ceil(360 / x_azimuth_step) + 1\n",
+    "x_ticks = [\n",
+    "    (x * (360 / (h_ticks - 1)) / x_azimuth_step + 0.5) for x in range(h_ticks - 1)\n",
+    "]\n",
+    "x_labels = [(\"%.0f\" % (360 / (h_ticks - 1) * x)) for x in range(h_ticks)]\n",
+    "x_ticks.append(x_number - 0.5)\n",
+    "x_fig.set_xticks(x_ticks)\n",
+    "x_fig.set_xticklabels(x_labels, rotation=90)\n",
+    "\n",
+    "v_ticks = 10\n",
+    "y_number = math.ceil(90 / x_tilt_step) + 1\n",
+    "y_ticks = [(x * (90 / (v_ticks - 1)) / x_tilt_step + 0.5) for x in range(v_ticks - 1)]\n",
+    "y_labels = [(\"%.0f\" % (90 / (v_ticks - 1) * x)) for x in range(v_ticks)]\n",
+    "y_ticks.append(y_number - 0.5)\n",
+    "x_fig.set_yticks(y_ticks)\n",
+    "x_fig.set_yticklabels(y_labels, rotation=0)\n",
+    "\n",
+    "x_fig.set_xlabel(\"Azimuth [°]\", fontsize=15, labelpad=10)\n",
+    "x_fig.set_ylabel(\"Tilt [°]\", fontsize=15)\n",
+    "x_fig.figure.axes[-1].set_ylabel(r\"$\\it{Xₘᵢₙ}$ [cm]\", size=15)\n",
+    "x_fig.invert_yaxis()\n",
+    "\n",
+    "output_folder = os.path.join(\n",
+    "    os.path.dirname(os.path.dirname(os.getcwd())), \"TEMP\", \"results\"\n",
+    ")\n",
+    "try:\n",
+    "    os.makedirs(output_folder)\n",
+    "except OSError as error:\n",
+    "    print(error)\n",
+    "\n",
+    "plt.savefig(\n",
+    "    os.path.join(output_folder, \"Standoff_Scan.png\"), bbox_inches=\"tight\"\n",
+    ")  # Creates an image file of the standoff plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Plot $T_{98}$  for all azimuth and tilt for a given $X_{eff}$.\n",
+    "\n",
+    "The temperature of a system is affected by the orientation and the effective standoff, $X_{eff}$, of the system. This section will scan all possible tilts and azimuths calculating the $T_{98}$ for a given $X_{eff}$. As above, additional factors can be modified but are not included here for simplicity. The tilt_step and azimuth_step are the number of degrees for each step for the 90° and 180° tilt and azimuth spans, respectively. The default for this calculation is for $X_{eff}$=10 cm, a common effective standoff distance on a rooftop system. A value of $X_{eff}$=None will run the calculations for an open rack system and $X_{eff}$=0 for an insulated-back system."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                     \r"
+     ]
+    }
+   ],
+   "source": [
+    "# Scans through all the azimuth and tilt running the 98ᵗʰ percentile temperature calculation.\n",
+    "# Set up keyword parameters for the calculation\n",
+    "kwarg_T = dict(\n",
+    "    sky_model=\"isotropic\",\n",
+    "    temp_model=\"sapm\",\n",
+    "    conf_0=\"insulated_back_glass_polymer\",\n",
+    "    conf_inf=\"open_rack_glass_polymer\",\n",
+    "    x_eff=5,\n",
+    "    x_0=6.5,\n",
+    "    wind_factor=0.33,\n",
+    ")\n",
+    "# Run the calculation\n",
+    "T_azimuth_step = 10\n",
+    "T_tilt_step = 10\n",
+    "T98_series = pvdeg.utilities.tilt_azimuth_scan(\n",
+    "    weather_df=WEATHER_df,\n",
+    "    meta=META,\n",
+    "    tilt_step=T_tilt_step,\n",
+    "    azimuth_step=T_azimuth_step,\n",
+    "    func=pvdeg.standards.T98_estimate,\n",
+    "    **kwarg_T,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The next cell creates a plot of the calculated data. Some of the things you may want to change are:\n",
+    "- cmap=\"Spectral_r\": Change to have different colors\n",
+    "- plt.title : This will change the plot title.\n",
+    "- figsize=(16,4) : Change the plot dimensions and/or aspect ratio.\n",
+    "- vmax=None : This can be set to a numeric value to control the depth scale maximum\n",
+    "- vmin=None : This controls the minimum of the depth scale.\n",
+    "- v_ticks=37 : This changes the number of vertical tick marks\n",
+    "- h_ticks=10 : This changes the number of horizontal tick marks\n",
+    "- Unblock the last two lines to ouput the plot as an *.png image file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# This produces the plot of the data\n",
+    "T98_series_df = pd.DataFrame(\n",
+    "    {\"Tilt\": T98_series[:, 0], \"Azimuth\": T98_series[:, 1], \"T₉₈\": T98_series[:, 2]}\n",
+    ")\n",
+    "T98_fig = plt.figure(figsize=(16, 4))\n",
+    "if kwarg_T[\"x_eff\"] == None:\n",
+    "    plt.title(\n",
+    "        r\"Plot of $\\it{T₉₈}$ for all orientations for an open-rack mounting.\",\n",
+    "        fontsize=15,\n",
+    "        y=1.08,\n",
+    "    )\n",
+    "else:\n",
+    "    plt.title(\n",
+    "        r\"Plot of $\\it{T₉₈}$ for all orientations for $X_{eff}$=\"\n",
+    "        + \"%.0f\" % kwarg_T[\"x_eff\"]\n",
+    "        + \" cm.\",\n",
+    "        fontsize=15,\n",
+    "        y=1.08,\n",
+    "    )\n",
+    "T98_fig = sns.heatmap(\n",
+    "    T98_series_df.pivot(index=\"Tilt\", columns=\"Azimuth\", values=\"T₉₈\"),\n",
+    "    cbar_kws={\"label\": \"Xₘᵢₙ\", \"format\": \"%.0f\", \"pad\": 0.02},\n",
+    "    cmap=\"Spectral_r\",\n",
+    "    vmin=None,\n",
+    "    vmax=None,\n",
+    ")\n",
+    "\n",
+    "h_ticks = 37\n",
+    "x_number = math.ceil(360 / T_azimuth_step) + 1\n",
+    "x_ticks = [\n",
+    "    (x * (360 / (h_ticks - 1)) / T_azimuth_step + 0.5) for x in range(h_ticks - 1)\n",
+    "]\n",
+    "x_labels = [(\"%.0f\" % (360 / (h_ticks - 1) * x)) for x in range(h_ticks)]\n",
+    "x_ticks.append(x_number - 0.5)\n",
+    "T98_fig.set_xticks(x_ticks)\n",
+    "T98_fig.set_xticklabels(x_labels, rotation=90)\n",
+    "\n",
+    "v_ticks = 10\n",
+    "y_number = math.ceil(90 / T_tilt_step) + 1\n",
+    "y_ticks = [(x * (90 / (v_ticks - 1)) / T_tilt_step + 0.5) for x in range(v_ticks - 1)]\n",
+    "y_labels = [(\"%.0f\" % (90 / (v_ticks - 1) * x)) for x in range(v_ticks)]\n",
+    "y_ticks.append(y_number - 0.5)\n",
+    "T98_fig.set_yticks(y_ticks)\n",
+    "T98_fig.set_yticklabels(y_labels, rotation=0)\n",
+    "\n",
+    "T98_fig.set_xlabel(\"Azimuth [°]\", fontsize=15, labelpad=10)\n",
+    "T98_fig.set_ylabel(\"Tilt [°]\", fontsize=15)\n",
+    "T98_fig.figure.axes[-1].set_ylabel(r\"$\\it{T₉₈}$ [°C]\", size=15)\n",
+    "T98_fig.invert_yaxis()\n",
+    "\n",
+    "plt.savefig(\n",
+    "    os.path.join(output_folder, \"T98_Scan.png\"), bbox_inches=\"tight\"\n",
+    ")  # Creates an image file of the standoff plot\n",
+    "plt.show(T98_fig)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. Plot $X_{min}$ for a $T_{98}$, and plot $T_{98}$ for a given region.\n",
+    "\n",
+    "This last Objective is much more complicated and is set up to utilize acess to a lot of computational power to run many sites simultaneously to create a regional map of standoff distance. \n",
+    "For more in-depth instructions on doing this, look at the tutorial \"Scenario - Geospatial.ipynb\" here in PVDeg.\n",
+    "\n",
+    "Step #1: Create an object, \"geospatial_standoff_scenario\" to be used to run the computations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geospatial_standoff_scenario = pvdeg.GeospatialScenario(\n",
+    "    name=\"standoff geospatial\",\n",
+    "    geospatial=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step #2: Identifies a subset of locations from the database to run the computations.\n",
+    "Specifically all are from the NSRDB."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ConnectionError",
+     "evalue": "\n            connected to  not a node of kestrel.hpc.nrel.gov\")\n            ",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mConnectionError\u001b[0m                           Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[16], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m geospatial_standoff_scenario\u001b[38;5;241m.\u001b[39maddLocation(\n\u001b[0;32m      2\u001b[0m     state\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mColorado\u001b[39m\u001b[38;5;124m\"\u001b[39m, county\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSummit\u001b[39m\u001b[38;5;124m\"\u001b[39m, see_added\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m      3\u001b[0m )\n",
+      "File \u001b[1;32m~\\Documents\\GitHub\\PVDegradationTools_NREL\\pvdeg\\geospatialscenario.py:188\u001b[0m, in \u001b[0;36mGeospatialScenario.addLocation\u001b[1;34m(self, country, state, county, satellite, year, nsrdb_attributes, downsample_factor, gids, bbox_kwarg, see_added)\u001b[0m\n\u001b[0;32m    180\u001b[0m weather_db \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNSRDB\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    181\u001b[0m weather_arg \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m    182\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msatellite\u001b[39m\u001b[38;5;124m\"\u001b[39m: satellite,\n\u001b[0;32m    183\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m: year,\n\u001b[0;32m    184\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNREL_HPC\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m    185\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mattributes\u001b[39m\u001b[38;5;124m\"\u001b[39m: nsrdb_attributes,\n\u001b[0;32m    186\u001b[0m }\n\u001b[1;32m--> 188\u001b[0m geo_weather, geo_meta \u001b[38;5;241m=\u001b[39m pvdeg\u001b[38;5;241m.\u001b[39mweather\u001b[38;5;241m.\u001b[39mget(\n\u001b[0;32m    189\u001b[0m     weather_db, geospatial\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mweather_arg\n\u001b[0;32m    190\u001b[0m )\n\u001b[0;32m    192\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m gids:\n\u001b[0;32m    193\u001b[0m     geo_meta \u001b[38;5;241m=\u001b[39m geo_meta\u001b[38;5;241m.\u001b[39mloc[gids]\n",
+      "File \u001b[1;32m~\\Documents\\GitHub\\PVDegradationTools_NREL\\pvdeg\\weather.py:249\u001b[0m, in \u001b[0;36mget\u001b[1;34m(database, id, geospatial, find_meta, **kwargs)\u001b[0m\n\u001b[0;32m    247\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m geospatial:\n\u001b[0;32m    248\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m database \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNSRDB\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m--> 249\u001b[0m         nrel_kestrel_check()\n\u001b[0;32m    251\u001b[0m         weather_ds, meta_df \u001b[38;5;241m=\u001b[39m get_NSRDB(geospatial\u001b[38;5;241m=\u001b[39mgeospatial, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    252\u001b[0m         meta_df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwind_height\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m\n",
+      "File \u001b[1;32m~\\Documents\\GitHub\\PVDegradationTools_NREL\\pvdeg\\utilities.py:1226\u001b[0m, in \u001b[0;36mnrel_kestrel_check\u001b[1;34m()\u001b[0m\n\u001b[0;32m   1223\u001b[0m device_domain \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(host\u001b[38;5;241m.\u001b[39mstdout\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m4\u001b[39m:])[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m   1225\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kestrel_hostname \u001b[38;5;241m!=\u001b[39m device_domain:\n\u001b[1;32m-> 1226\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(\n\u001b[0;32m   1227\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[0;32m   1228\u001b[0m \u001b[38;5;124m        connected to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdevice_domain\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not a node of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkestrel_hostname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m)\u001b[39m\n\u001b[0;32m   1229\u001b[0m \u001b[38;5;124m        \u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[0;32m   1230\u001b[0m     )\n",
+      "\u001b[1;31mConnectionError\u001b[0m: \n            connected to  not a node of kestrel.hpc.nrel.gov\")\n            "
+     ]
+    }
+   ],
+   "source": [
+    "geospatial_standoff_scenario.addLocation(\n",
+    "    state=\"Colorado\", county=\"Summit\"\n",
+    ")  # Identifies a subset of locations from the database to run the computations. Specifically all are from the NSRDB."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step #3: indicate which function will be run. Here the default is the standoff calculation, but it could be any other function with a key word argument dictionary.\n",
+    "Here the 98th percential temperature is defined as 70C, but any arbitrary value can be specified."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geospatial_standoff_scenario.addJob(\n",
+    "    func=pvdeg.standards.standoff, func_params={\"T98\": 70}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step #4: Run the scenario"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geospatial_standoff_scenario.run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step #5: Create a plot of the standoff calculation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geospatial_standoff_scenario.plot_world(\"x\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geospatial_standoff_scenario.plot_world(\"T98_inf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 8. Save data outputs.\n",
+    "\n",
+    "This cell contains a number of pre-scripted commands for exporting and saving data. The code to save plots is located after the plot creation and is blocked by default. First check that the output folder exists, then unblock the code for data you would like to save."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "print (\"Your results will be stored in %s\" % output_folder)\n",
+    "print ('The folder must already exist or the file will not be created')\n",
+    "\n",
+    "pvdeg.weather.write(data_df=WEATHER_df, metadata=META, savefile=os.path.join(output_folder, 'WeatherFile.csv')) #Writes the meterological data to an *.csv file.\n",
+    "\n",
+    "pd.DataFrame(standoff_series_df).to_csv(os.path.join(output_folder, 'Standoff_Scan.csv')) #Writes a file with the Tilt and Azimuth scan calculations of standoff.\n",
+    "\n",
+    "pd.DataFrame(T98_series_df).to_csv(os.path.join(output_folder, 'T98_Scan.csv')) #Writes a file with the Tilt and Azimuth scan calculations of T98.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pvdeg",
+   "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": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Accelerated Test.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Accelerated Test.ipynb
index 227fe110d..1b9d1c944 100644
--- a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Accelerated Test.ipynb	
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Accelerated Test.ipynb	
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,23 +49,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Working on a  Windows 10\n",
-      "Python version  3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n",
-      "Pandas version  2.2.0\n",
-      "pvdeg version  0.2.4.dev83+ge2ceab9.d20240422\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# This information helps with debugging and getting support :)\n",
-    "import sys, platform\n",
+    "import sys\n",
+    "import platform\n",
     "\n",
     "print(\"Working on a \", platform.system(), platform.release())\n",
     "print(\"Python version \", sys.version)\n",
@@ -83,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -102,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -125,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -142,17 +132,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.03495240755084558\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "loss, pmp_0, pmp_deg = letid.calc_pmp_loss_from_tau_loss(\n",
     "    tau_0, tau_deg, cell_area, wafer_thickness, s_rear\n",
@@ -169,17 +151,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "41.59099692285122\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "jsc_0 = collection.calculate_jsc_from_tau_cp(\n",
     "    tau_0, wafer_thickness, d_base, s_rear, generation, depth\n",
@@ -200,7 +174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -212,7 +186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -243,17 +217,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'mechanism': 'LETID', 'v_ab': 46700000.0, 'v_ba': 4.7e-25, 'v_bc': 19900000.0, 'v_cb': 0.0, 'ea_ab': 0.827, 'ea_ba': -1.15, 'ea_bc': 0.871, 'ea_cb': 0.0, 'suns_ab': 1.0, 'suns_bc': 1.0, 'temperature_ab': 410, 'temperature_bc': 410, 'tau_ab': 75, 'tau_bc': 75, 'x_ab': 1, 'x_ba': 1.7, 'x_bc': 1.2, 'structure_ab': 'cell', 'structure_bc': 'cell', 'thickness_ab': 200, 'thickness_bc': 200, 'srv_ab': 90, 'srv_bc': 90, 'doi': 'doi:10.1557/s43577-022-00438-8', 'comments': ''}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "mechanism_params = utilities.get_kinetics(\"repins\")\n",
     "print(mechanism_params)"
@@ -271,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -311,177 +277,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "    .dataframe tbody tr th {\n",
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Datetime</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Injection</th>\n",
-       "      <th>NA</th>\n",
-       "      <th>NB</th>\n",
-       "      <th>NC</th>\n",
-       "      <th>tau</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
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-       "      <td>2022-01-01 00:02:00</td>\n",
-       "      <td>75</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>2022-01-01 00:03:00</td>\n",
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-       "      <td>2022-01-01 00:04:00</td>\n",
-       "      <td>75</td>\n",
-       "      <td>0.1</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <th>30236</th>\n",
-       "      <td>2022-01-21 23:56:00</td>\n",
-       "      <td>75</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <th>30237</th>\n",
-       "      <td>2022-01-21 23:57:00</td>\n",
-       "      <td>75</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30238</th>\n",
-       "      <td>2022-01-21 23:58:00</td>\n",
-       "      <td>75</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>2022-01-21 23:59:00</td>\n",
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-       "      <td>75</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>30241 rows × 7 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Datetime  Temperature  Injection     NA   NB   NC    tau\n",
-       "0     2022-01-01 00:00:00           75        0.1  100.0  0.0  0.0  115.0\n",
-       "1     2022-01-01 00:01:00           75        0.1    NaN  NaN  NaN    NaN\n",
-       "2     2022-01-01 00:02:00           75        0.1    NaN  NaN  NaN    NaN\n",
-       "3     2022-01-01 00:03:00           75        0.1    NaN  NaN  NaN    NaN\n",
-       "4     2022-01-01 00:04:00           75        0.1    NaN  NaN  NaN    NaN\n",
-       "...                   ...          ...        ...    ...  ...  ...    ...\n",
-       "30236 2022-01-21 23:56:00           75        0.1    NaN  NaN  NaN    NaN\n",
-       "30237 2022-01-21 23:57:00           75        0.1    NaN  NaN  NaN    NaN\n",
-       "30238 2022-01-21 23:58:00           75        0.1    NaN  NaN  NaN    NaN\n",
-       "30239 2022-01-21 23:59:00           75        0.1    NaN  NaN  NaN    NaN\n",
-       "30240 2022-01-22 00:00:00           75        0.1    NaN  NaN  NaN    NaN\n",
-       "\n",
-       "[30241 rows x 7 columns]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "timesteps"
    ]
@@ -496,12 +294,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "for index, timestep in timesteps.iterrows():\n",
-    "\n",
     "    # first row tau has already been assigned\n",
     "    if index == 0:\n",
     "        pass\n",
@@ -592,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -611,287 +408,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\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>Datetime</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Injection</th>\n",
-       "      <th>NA</th>\n",
-       "      <th>NB</th>\n",
-       "      <th>NC</th>\n",
-       "      <th>tau</th>\n",
-       "      <th>Jsc</th>\n",
-       "      <th>Voc</th>\n",
-       "      <th>Isc</th>\n",
-       "      <th>FF</th>\n",
-       "      <th>Pmp</th>\n",
-       "      <th>Pmp_norm</th>\n",
-       "      <th>time (days)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2022-01-01 00:00:00</td>\n",
-       "      <td>75</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>115.000000</td>\n",
-       "      <td>41.590997</td>\n",
-       "      <td>0.666327</td>\n",
-       "      <td>10.106612</td>\n",
-       "      <td>0.840987</td>\n",
-       "      <td>5.663467</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2022-01-01 00:01:00</td>\n",
-       "      <td>75</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>99.945424</td>\n",
-       "      <td>0.054576</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>114.931573</td>\n",
-       "      <td>41.590784</td>\n",
-       "      <td>0.666316</td>\n",
-       "      <td>10.106561</td>\n",
-       "      <td>0.840985</td>\n",
-       "      <td>5.663325</td>\n",
-       "      <td>0.999975</td>\n",
-       "      <td>0.000694</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2022-01-01 00:02:00</td>\n",
-       "      <td>75</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>99.890904</td>\n",
-       "      <td>0.109094</td>\n",
-       "      <td>0.000002</td>\n",
-       "      <td>114.863300</td>\n",
-       "      <td>41.590572</td>\n",
-       "      <td>0.666304</td>\n",
-       "      <td>10.106509</td>\n",
-       "      <td>0.840983</td>\n",
-       "      <td>5.663184</td>\n",
-       "      <td>0.999950</td>\n",
-       "      <td>0.001389</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2022-01-01 00:03:00</td>\n",
-       "      <td>75</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>99.836439</td>\n",
-       "      <td>0.163555</td>\n",
-       "      <td>0.000006</td>\n",
-       "      <td>114.795178</td>\n",
-       "      <td>41.590359</td>\n",
-       "      <td>0.666292</td>\n",
-       "      <td>10.106457</td>\n",
-       "      <td>0.840981</td>\n",
-       "      <td>5.663043</td>\n",
-       "      <td>0.999925</td>\n",
-       "      <td>0.002083</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2022-01-01 00:04:00</td>\n",
-       "      <td>75</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>99.782028</td>\n",
-       "      <td>0.217959</td>\n",
-       "      <td>0.000012</td>\n",
-       "      <td>114.727209</td>\n",
-       "      <td>41.590147</td>\n",
-       "      <td>0.666281</td>\n",
-       "      <td>10.106406</td>\n",
-       "      <td>0.840979</td>\n",
-       "      <td>5.662902</td>\n",
-       "      <td>0.999900</td>\n",
-       "      <td>0.002778</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",
-       "      <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>30236</th>\n",
-       "      <td>2022-01-21 23:56:00</td>\n",
-       "      <td>75</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>0.006066</td>\n",
-       "      <td>54.974093</td>\n",
-       "      <td>45.019841</td>\n",
-       "      <td>71.887698</td>\n",
-       "      <td>41.381392</td>\n",
-       "      <td>0.656754</td>\n",
-       "      <td>10.055678</td>\n",
-       "      <td>0.839286</td>\n",
-       "      <td>5.542735</td>\n",
-       "      <td>0.978682</td>\n",
-       "      <td>20.997222</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30237</th>\n",
-       "      <td>2022-01-21 23:57:00</td>\n",
-       "      <td>75</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>0.006065</td>\n",
-       "      <td>54.972773</td>\n",
-       "      <td>45.021162</td>\n",
-       "      <td>71.888345</td>\n",
-       "      <td>41.381397</td>\n",
-       "      <td>0.656754</td>\n",
-       "      <td>10.055679</td>\n",
-       "      <td>0.839286</td>\n",
-       "      <td>5.542738</td>\n",
-       "      <td>0.978683</td>\n",
-       "      <td>20.997917</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30238</th>\n",
-       "      <td>2022-01-21 23:58:00</td>\n",
-       "      <td>75</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>0.006064</td>\n",
-       "      <td>54.971454</td>\n",
-       "      <td>45.022482</td>\n",
-       "      <td>71.888992</td>\n",
-       "      <td>41.381402</td>\n",
-       "      <td>0.656754</td>\n",
-       "      <td>10.055681</td>\n",
-       "      <td>0.839286</td>\n",
-       "      <td>5.542740</td>\n",
-       "      <td>0.978683</td>\n",
-       "      <td>20.998611</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30239</th>\n",
-       "      <td>2022-01-21 23:59:00</td>\n",
-       "      <td>75</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>0.006063</td>\n",
-       "      <td>54.970135</td>\n",
-       "      <td>45.023802</td>\n",
-       "      <td>71.889638</td>\n",
-       "      <td>41.381407</td>\n",
-       "      <td>0.656755</td>\n",
-       "      <td>10.055682</td>\n",
-       "      <td>0.839286</td>\n",
-       "      <td>5.542743</td>\n",
-       "      <td>0.978684</td>\n",
-       "      <td>20.999306</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30240</th>\n",
-       "      <td>2022-01-22 00:00:00</td>\n",
-       "      <td>75</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>0.006061</td>\n",
-       "      <td>54.968816</td>\n",
-       "      <td>45.025123</td>\n",
-       "      <td>71.890285</td>\n",
-       "      <td>41.381411</td>\n",
-       "      <td>0.656755</td>\n",
-       "      <td>10.055683</td>\n",
-       "      <td>0.839286</td>\n",
-       "      <td>5.542745</td>\n",
-       "      <td>0.978684</td>\n",
-       "      <td>21.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>30241 rows × 14 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Datetime  Temperature  Injection          NA         NB  \\\n",
-       "0     2022-01-01 00:00:00           75        0.1  100.000000   0.000000   \n",
-       "1     2022-01-01 00:01:00           75        0.1   99.945424   0.054576   \n",
-       "2     2022-01-01 00:02:00           75        0.1   99.890904   0.109094   \n",
-       "3     2022-01-01 00:03:00           75        0.1   99.836439   0.163555   \n",
-       "4     2022-01-01 00:04:00           75        0.1   99.782028   0.217959   \n",
-       "...                   ...          ...        ...         ...        ...   \n",
-       "30236 2022-01-21 23:56:00           75        0.1    0.006066  54.974093   \n",
-       "30237 2022-01-21 23:57:00           75        0.1    0.006065  54.972773   \n",
-       "30238 2022-01-21 23:58:00           75        0.1    0.006064  54.971454   \n",
-       "30239 2022-01-21 23:59:00           75        0.1    0.006063  54.970135   \n",
-       "30240 2022-01-22 00:00:00           75        0.1    0.006061  54.968816   \n",
-       "\n",
-       "              NC         tau        Jsc       Voc        Isc        FF  \\\n",
-       "0       0.000000  115.000000  41.590997  0.666327  10.106612  0.840987   \n",
-       "1       0.000000  114.931573  41.590784  0.666316  10.106561  0.840985   \n",
-       "2       0.000002  114.863300  41.590572  0.666304  10.106509  0.840983   \n",
-       "3       0.000006  114.795178  41.590359  0.666292  10.106457  0.840981   \n",
-       "4       0.000012  114.727209  41.590147  0.666281  10.106406  0.840979   \n",
-       "...          ...         ...        ...       ...        ...       ...   \n",
-       "30236  45.019841   71.887698  41.381392  0.656754  10.055678  0.839286   \n",
-       "30237  45.021162   71.888345  41.381397  0.656754  10.055679  0.839286   \n",
-       "30238  45.022482   71.888992  41.381402  0.656754  10.055681  0.839286   \n",
-       "30239  45.023802   71.889638  41.381407  0.656755  10.055682  0.839286   \n",
-       "30240  45.025123   71.890285  41.381411  0.656755  10.055683  0.839286   \n",
-       "\n",
-       "            Pmp  Pmp_norm  time (days)  \n",
-       "0      5.663467  1.000000     0.000000  \n",
-       "1      5.663325  0.999975     0.000694  \n",
-       "2      5.663184  0.999950     0.001389  \n",
-       "3      5.663043  0.999925     0.002083  \n",
-       "4      5.662902  0.999900     0.002778  \n",
-       "...         ...       ...          ...  \n",
-       "30236  5.542735  0.978682    20.997222  \n",
-       "30237  5.542738  0.978683    20.997917  \n",
-       "30238  5.542740  0.978683    20.998611  \n",
-       "30239  5.542743  0.978684    20.999306  \n",
-       "30240  5.542745  0.978684    21.000000  \n",
-       "\n",
-       "[30241 rows x 14 columns]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# this function quickly calculates the rest of the device parameters: Isc, FF, max power, and normalized max power\n",
     "timesteps = letid.calc_device_params(timesteps, cell_area=243)\n",
@@ -912,20 +431,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from cycler import cycler\n",
     "\n",
@@ -977,275 +485,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "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>Datetime</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Injection</th>\n",
-       "      <th>NA</th>\n",
-       "      <th>NB</th>\n",
-       "      <th>NC</th>\n",
-       "      <th>tau</th>\n",
-       "      <th>Jsc</th>\n",
-       "      <th>Voc</th>\n",
-       "      <th>Isc</th>\n",
-       "      <th>FF</th>\n",
-       "      <th>Pmp</th>\n",
-       "      <th>Pmp_norm</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2024-05-06 15:13:53.364135</td>\n",
-       "      <td>75</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>115.000000</td>\n",
-       "      <td>41.590997</td>\n",
-       "      <td>0.666327</td>\n",
-       "      <td>9.940248</td>\n",
-       "      <td>0.840987</td>\n",
-       "      <td>5.570241</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2024-05-06 15:14:53.364135</td>\n",
-       "      <td>75</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>99.945424</td>\n",
-       "      <td>0.054576</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>114.931573</td>\n",
-       "      <td>41.590997</td>\n",
-       "      <td>0.666327</td>\n",
-       "      <td>9.940248</td>\n",
-       "      <td>0.840987</td>\n",
-       "      <td>5.570241</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2024-05-06 15:15:53.364135</td>\n",
-       "      <td>75</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>99.890904</td>\n",
-       "      <td>0.109094</td>\n",
-       "      <td>0.000002</td>\n",
-       "      <td>114.863300</td>\n",
-       "      <td>41.590784</td>\n",
-       "      <td>0.666316</td>\n",
-       "      <td>9.940197</td>\n",
-       "      <td>0.840985</td>\n",
-       "      <td>5.570102</td>\n",
-       "      <td>0.999975</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2024-05-06 15:16:53.364135</td>\n",
-       "      <td>75</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>99.836439</td>\n",
-       "      <td>0.163555</td>\n",
-       "      <td>0.000006</td>\n",
-       "      <td>114.795178</td>\n",
-       "      <td>41.590572</td>\n",
-       "      <td>0.666304</td>\n",
-       "      <td>9.940147</td>\n",
-       "      <td>0.840983</td>\n",
-       "      <td>5.569963</td>\n",
-       "      <td>0.999950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2024-05-06 15:17:53.364135</td>\n",
-       "      <td>75</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>99.782028</td>\n",
-       "      <td>0.217959</td>\n",
-       "      <td>0.000012</td>\n",
-       "      <td>114.727209</td>\n",
-       "      <td>41.590359</td>\n",
-       "      <td>0.666292</td>\n",
-       "      <td>9.940096</td>\n",
-       "      <td>0.840981</td>\n",
-       "      <td>5.569824</td>\n",
-       "      <td>0.999925</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",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30236</th>\n",
-       "      <td>2024-05-27 15:09:53.364135</td>\n",
-       "      <td>75</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>0.006066</td>\n",
-       "      <td>54.974093</td>\n",
-       "      <td>45.019841</td>\n",
-       "      <td>71.887698</td>\n",
-       "      <td>41.381387</td>\n",
-       "      <td>0.656754</td>\n",
-       "      <td>9.890151</td>\n",
-       "      <td>0.839286</td>\n",
-       "      <td>5.451494</td>\n",
-       "      <td>0.978682</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30237</th>\n",
-       "      <td>2024-05-27 15:10:53.364135</td>\n",
-       "      <td>75</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>0.006065</td>\n",
-       "      <td>54.972773</td>\n",
-       "      <td>45.021162</td>\n",
-       "      <td>71.888345</td>\n",
-       "      <td>41.381392</td>\n",
-       "      <td>0.656754</td>\n",
-       "      <td>9.890153</td>\n",
-       "      <td>0.839286</td>\n",
-       "      <td>5.451497</td>\n",
-       "      <td>0.978682</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30238</th>\n",
-       "      <td>2024-05-27 15:11:53.364135</td>\n",
-       "      <td>75</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>0.006064</td>\n",
-       "      <td>54.971454</td>\n",
-       "      <td>45.022482</td>\n",
-       "      <td>71.888992</td>\n",
-       "      <td>41.381397</td>\n",
-       "      <td>0.656754</td>\n",
-       "      <td>9.890154</td>\n",
-       "      <td>0.839286</td>\n",
-       "      <td>5.451499</td>\n",
-       "      <td>0.978683</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30239</th>\n",
-       "      <td>2024-05-27 15:12:53.364135</td>\n",
-       "      <td>75</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>0.006063</td>\n",
-       "      <td>54.970135</td>\n",
-       "      <td>45.023802</td>\n",
-       "      <td>71.889638</td>\n",
-       "      <td>41.381402</td>\n",
-       "      <td>0.656754</td>\n",
-       "      <td>9.890155</td>\n",
-       "      <td>0.839286</td>\n",
-       "      <td>5.451502</td>\n",
-       "      <td>0.978683</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30240</th>\n",
-       "      <td>2024-05-27 15:13:53.364135</td>\n",
-       "      <td>75</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>0.006061</td>\n",
-       "      <td>54.968816</td>\n",
-       "      <td>45.025123</td>\n",
-       "      <td>71.890285</td>\n",
-       "      <td>41.381407</td>\n",
-       "      <td>0.656755</td>\n",
-       "      <td>9.890156</td>\n",
-       "      <td>0.839286</td>\n",
-       "      <td>5.451504</td>\n",
-       "      <td>0.978684</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>30241 rows × 13 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                        Datetime  Temperature  Injection          NA  \\\n",
-       "0     2024-05-06 15:13:53.364135           75        0.1  100.000000   \n",
-       "1     2024-05-06 15:14:53.364135           75        0.1   99.945424   \n",
-       "2     2024-05-06 15:15:53.364135           75        0.1   99.890904   \n",
-       "3     2024-05-06 15:16:53.364135           75        0.1   99.836439   \n",
-       "4     2024-05-06 15:17:53.364135           75        0.1   99.782028   \n",
-       "...                          ...          ...        ...         ...   \n",
-       "30236 2024-05-27 15:09:53.364135           75        0.1    0.006066   \n",
-       "30237 2024-05-27 15:10:53.364135           75        0.1    0.006065   \n",
-       "30238 2024-05-27 15:11:53.364135           75        0.1    0.006064   \n",
-       "30239 2024-05-27 15:12:53.364135           75        0.1    0.006063   \n",
-       "30240 2024-05-27 15:13:53.364135           75        0.1    0.006061   \n",
-       "\n",
-       "              NB         NC         tau        Jsc       Voc       Isc  \\\n",
-       "0       0.000000   0.000000  115.000000  41.590997  0.666327  9.940248   \n",
-       "1       0.054576   0.000000  114.931573  41.590997  0.666327  9.940248   \n",
-       "2       0.109094   0.000002  114.863300  41.590784  0.666316  9.940197   \n",
-       "3       0.163555   0.000006  114.795178  41.590572  0.666304  9.940147   \n",
-       "4       0.217959   0.000012  114.727209  41.590359  0.666292  9.940096   \n",
-       "...          ...        ...         ...        ...       ...       ...   \n",
-       "30236  54.974093  45.019841   71.887698  41.381387  0.656754  9.890151   \n",
-       "30237  54.972773  45.021162   71.888345  41.381392  0.656754  9.890153   \n",
-       "30238  54.971454  45.022482   71.888992  41.381397  0.656754  9.890154   \n",
-       "30239  54.970135  45.023802   71.889638  41.381402  0.656754  9.890155   \n",
-       "30240  54.968816  45.025123   71.890285  41.381407  0.656755  9.890156   \n",
-       "\n",
-       "             FF       Pmp  Pmp_norm  \n",
-       "0      0.840987  5.570241  1.000000  \n",
-       "1      0.840987  5.570241  1.000000  \n",
-       "2      0.840985  5.570102  0.999975  \n",
-       "3      0.840983  5.569963  0.999950  \n",
-       "4      0.840981  5.569824  0.999925  \n",
-       "...         ...       ...       ...  \n",
-       "30236  0.839286  5.451494  0.978682  \n",
-       "30237  0.839286  5.451497  0.978682  \n",
-       "30238  0.839286  5.451499  0.978683  \n",
-       "30239  0.839286  5.451502  0.978683  \n",
-       "30240  0.839286  5.451504  0.978684  \n",
-       "\n",
-       "[30241 rows x 13 columns]"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "letid.calc_letid_lab(\n",
     "    tau_0, tau_deg, wafer_thickness, s_rear, nA_0, nB_0, nC_0, 0.1, 75, \"repins\"\n",
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Outdoor LETID.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Outdoor LETID.ipynb
index 2c2601041..f67e72753 100644
--- a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Outdoor LETID.ipynb	
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Outdoor LETID.ipynb	
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -52,24 +52,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Working on a  Windows 10\n",
-      "Python version  3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n",
-      "Pandas version  2.2.0\n",
-      "pvlib version  0.10.3\n",
-      "pvdeg version  0.2.4.dev83+ge2ceab9.d20240422\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# This information helps with debugging and getting support :)\n",
-    "import sys, platform\n",
+    "import sys\n",
+    "import platform\n",
     "\n",
     "print(\"Working on a \", platform.system(), platform.release())\n",
     "print(\"Python version \", sys.version)\n",
@@ -88,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -102,311 +91,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Year</th>\n",
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-       "      <th>dni</th>\n",
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-       "      <th>temp_air</th>\n",
-       "      <th>dew_point</th>\n",
-       "      <th>wind_speed</th>\n",
-       "      <th>relative_humidity</th>\n",
-       "      <th>poa_global</th>\n",
-       "      <th>temp_cell</th>\n",
-       "      <th>temp_module</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 00:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
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-       "      <td>0.0</td>\n",
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-       "      <th>1999-01-01 01:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-4.0</td>\n",
-       "      <td>1.7</td>\n",
-       "      <td>80.84</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <th>1999-01-01 02:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-4.0</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>82.98</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>1999-01-01 03:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-4.0</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>85.01</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 04:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>30</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-4.0</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>85.81</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <th>...</th>\n",
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-       "      <td>...</td>\n",
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-       "      <td>...</td>\n",
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-       "    <tr>\n",
-       "      <th>1999-12-31 19:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>19</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>-3.0</td>\n",
-       "      <td>0.9</td>\n",
-       "      <td>83.63</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>1999-12-31 20:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>20</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-3.0</td>\n",
-       "      <td>1.2</td>\n",
-       "      <td>86.82</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <th>1999-12-31 21:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>21</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-4.0</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>83.78</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-12-31 22:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>22</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-4.0</td>\n",
-       "      <td>1.7</td>\n",
-       "      <td>81.22</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-12-31 23:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-5.0</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>79.43</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>8760 rows × 15 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                           Year  Month  Day  Hour  Minute  dni  dhi  ghi  \\\n",
-       "1999-01-01 00:30:00-07:00  1999      1    1     0      30  0.0  0.0  0.0   \n",
-       "1999-01-01 01:30:00-07:00  1999      1    1     1      30  0.0  0.0  0.0   \n",
-       "1999-01-01 02:30:00-07:00  1999      1    1     2      30  0.0  0.0  0.0   \n",
-       "1999-01-01 03:30:00-07:00  1999      1    1     3      30  0.0  0.0  0.0   \n",
-       "1999-01-01 04:30:00-07:00  1999      1    1     4      30  0.0  0.0  0.0   \n",
-       "...                         ...    ...  ...   ...     ...  ...  ...  ...   \n",
-       "1999-12-31 19:30:00-07:00  1999     12   31    19      30  0.0  0.0  0.0   \n",
-       "1999-12-31 20:30:00-07:00  1999     12   31    20      30  0.0  0.0  0.0   \n",
-       "1999-12-31 21:30:00-07:00  1999     12   31    21      30  0.0  0.0  0.0   \n",
-       "1999-12-31 22:30:00-07:00  1999     12   31    22      30  0.0  0.0  0.0   \n",
-       "1999-12-31 23:30:00-07:00  1999     12   31    23      30  0.0  0.0  0.0   \n",
-       "\n",
-       "                           temp_air  dew_point  wind_speed  relative_humidity  \\\n",
-       "1999-01-01 00:30:00-07:00       0.0       -5.0         1.8              79.39   \n",
-       "1999-01-01 01:30:00-07:00       0.0       -4.0         1.7              80.84   \n",
-       "1999-01-01 02:30:00-07:00       0.0       -4.0         1.5              82.98   \n",
-       "1999-01-01 03:30:00-07:00       0.0       -4.0         1.3              85.01   \n",
-       "1999-01-01 04:30:00-07:00       0.0       -4.0         1.3              85.81   \n",
-       "...                             ...        ...         ...                ...   \n",
-       "1999-12-31 19:30:00-07:00       0.0       -3.0         0.9              83.63   \n",
-       "1999-12-31 20:30:00-07:00       0.0       -3.0         1.2              86.82   \n",
-       "1999-12-31 21:30:00-07:00       0.0       -4.0         1.6              83.78   \n",
-       "1999-12-31 22:30:00-07:00       0.0       -4.0         1.7              81.22   \n",
-       "1999-12-31 23:30:00-07:00       0.0       -5.0         1.8              79.43   \n",
-       "\n",
-       "                           poa_global  temp_cell  temp_module  \n",
-       "1999-01-01 00:30:00-07:00         0.0        0.0          0.0  \n",
-       "1999-01-01 01:30:00-07:00         0.0        0.0          0.0  \n",
-       "1999-01-01 02:30:00-07:00         0.0        0.0          0.0  \n",
-       "1999-01-01 03:30:00-07:00         0.0        0.0          0.0  \n",
-       "1999-01-01 04:30:00-07:00         0.0        0.0          0.0  \n",
-       "...                               ...        ...          ...  \n",
-       "1999-12-31 19:30:00-07:00         0.0        0.0          0.0  \n",
-       "1999-12-31 20:30:00-07:00         0.0        0.0          0.0  \n",
-       "1999-12-31 21:30:00-07:00         0.0        0.0          0.0  \n",
-       "1999-12-31 22:30:00-07:00         0.0        0.0          0.0  \n",
-       "1999-12-31 23:30:00-07:00         0.0        0.0          0.0  \n",
-       "\n",
-       "[8760 rows x 15 columns]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "weather"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# if our weather file doesn't have precipitable water, calculate it with pvlib\n",
-    "if not \"precipitable_water\" in weather.columns:\n",
+    "if \"precipitable_water\" not in weather.columns:\n",
     "    weather[\"precipitable_water\"] = pvlib.atmosphere.gueymard94_pw(\n",
     "        weather[\"temp_air\"], weather[\"relative_humidity\"]\n",
     "    )"
@@ -414,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -446,197 +145,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ghi</th>\n",
-       "      <th>dni</th>\n",
-       "      <th>dhi</th>\n",
-       "      <th>temp_air</th>\n",
-       "      <th>wind_speed</th>\n",
-       "      <th>relative_humidity</th>\n",
-       "      <th>precipitable_water</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 00:30:00-07:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>79.39</td>\n",
-       "      <td>0.891869</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 01:30:00-07:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.7</td>\n",
-       "      <td>80.84</td>\n",
-       "      <td>0.908158</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 02:30:00-07:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>82.98</td>\n",
-       "      <td>0.932199</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 03:30:00-07:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>85.01</td>\n",
-       "      <td>0.955004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 04:30:00-07:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>85.81</td>\n",
-       "      <td>0.963991</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>1999-12-31 19:30:00-07:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.9</td>\n",
-       "      <td>83.63</td>\n",
-       "      <td>0.939501</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-12-31 20:30:00-07:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.2</td>\n",
-       "      <td>86.82</td>\n",
-       "      <td>0.975338</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-12-31 21:30:00-07:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>83.78</td>\n",
-       "      <td>0.941186</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-12-31 22:30:00-07:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.7</td>\n",
-       "      <td>81.22</td>\n",
-       "      <td>0.912427</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-12-31 23:30:00-07:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>79.43</td>\n",
-       "      <td>0.892318</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>8760 rows × 7 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                           ghi  dni  dhi  temp_air  wind_speed  \\\n",
-       "1999-01-01 00:30:00-07:00  0.0  0.0  0.0       0.0         1.8   \n",
-       "1999-01-01 01:30:00-07:00  0.0  0.0  0.0       0.0         1.7   \n",
-       "1999-01-01 02:30:00-07:00  0.0  0.0  0.0       0.0         1.5   \n",
-       "1999-01-01 03:30:00-07:00  0.0  0.0  0.0       0.0         1.3   \n",
-       "1999-01-01 04:30:00-07:00  0.0  0.0  0.0       0.0         1.3   \n",
-       "...                        ...  ...  ...       ...         ...   \n",
-       "1999-12-31 19:30:00-07:00  0.0  0.0  0.0       0.0         0.9   \n",
-       "1999-12-31 20:30:00-07:00  0.0  0.0  0.0       0.0         1.2   \n",
-       "1999-12-31 21:30:00-07:00  0.0  0.0  0.0       0.0         1.6   \n",
-       "1999-12-31 22:30:00-07:00  0.0  0.0  0.0       0.0         1.7   \n",
-       "1999-12-31 23:30:00-07:00  0.0  0.0  0.0       0.0         1.8   \n",
-       "\n",
-       "                           relative_humidity  precipitable_water  \n",
-       "1999-01-01 00:30:00-07:00              79.39            0.891869  \n",
-       "1999-01-01 01:30:00-07:00              80.84            0.908158  \n",
-       "1999-01-01 02:30:00-07:00              82.98            0.932199  \n",
-       "1999-01-01 03:30:00-07:00              85.01            0.955004  \n",
-       "1999-01-01 04:30:00-07:00              85.81            0.963991  \n",
-       "...                                      ...                 ...  \n",
-       "1999-12-31 19:30:00-07:00              83.63            0.939501  \n",
-       "1999-12-31 20:30:00-07:00              86.82            0.975338  \n",
-       "1999-12-31 21:30:00-07:00              83.78            0.941186  \n",
-       "1999-12-31 22:30:00-07:00              81.22            0.912427  \n",
-       "1999-12-31 23:30:00-07:00              79.43            0.892318  \n",
-       "\n",
-       "[8760 rows x 7 columns]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "weather"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -661,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -686,31 +204,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ModelChain: \n",
-       "  name: None\n",
-       "  clearsky_model: ineichen\n",
-       "  transposition_model: haydavies\n",
-       "  solar_position_method: nrel_numpy\n",
-       "  airmass_model: kastenyoung1989\n",
-       "  dc_model: cec\n",
-       "  ac_model: sandia_inverter\n",
-       "  aoi_model: physical_aoi_loss\n",
-       "  spectral_model: first_solar_spectral_loss\n",
-       "  temperature_model: sapm_temp\n",
-       "  losses_model: no_extra_losses"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# create and run pvlib modelchain\n",
     "mc = ModelChain(system, location, aoi_model=\"physical\")\n",
@@ -729,7 +225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -749,7 +245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -760,129 +256,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "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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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Datetime</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Injection</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1999-01-01 08:30:00-07:00</td>\n",
-       "      <td>3.348774</td>\n",
-       "      <td>0.006239</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1999-01-01 09:30:00-07:00</td>\n",
-       "      <td>12.378439</td>\n",
-       "      <td>0.031607</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1999-01-01 10:30:00-07:00</td>\n",
-       "      <td>16.595081</td>\n",
-       "      <td>0.044220</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1999-01-01 11:30:00-07:00</td>\n",
-       "      <td>17.457240</td>\n",
-       "      <td>0.043201</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1999-01-01 12:30:00-07:00</td>\n",
-       "      <td>6.392783</td>\n",
-       "      <td>0.007124</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4296</th>\n",
-       "      <td>1999-12-31 12:30:00-07:00</td>\n",
-       "      <td>9.515577</td>\n",
-       "      <td>0.001151</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4297</th>\n",
-       "      <td>1999-12-31 13:30:00-07:00</td>\n",
-       "      <td>28.119796</td>\n",
-       "      <td>0.044499</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4298</th>\n",
-       "      <td>1999-12-31 14:30:00-07:00</td>\n",
-       "      <td>23.314672</td>\n",
-       "      <td>0.035130</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4299</th>\n",
-       "      <td>1999-12-31 15:30:00-07:00</td>\n",
-       "      <td>17.890528</td>\n",
-       "      <td>0.027891</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4300</th>\n",
-       "      <td>1999-12-31 16:30:00-07:00</td>\n",
-       "      <td>4.552365</td>\n",
-       "      <td>0.001188</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>4301 rows × 3 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                      Datetime  Temperature  Injection\n",
-       "0    1999-01-01 08:30:00-07:00     3.348774   0.006239\n",
-       "1    1999-01-01 09:30:00-07:00    12.378439   0.031607\n",
-       "2    1999-01-01 10:30:00-07:00    16.595081   0.044220\n",
-       "3    1999-01-01 11:30:00-07:00    17.457240   0.043201\n",
-       "4    1999-01-01 12:30:00-07:00     6.392783   0.007124\n",
-       "...                        ...          ...        ...\n",
-       "4296 1999-12-31 12:30:00-07:00     9.515577   0.001151\n",
-       "4297 1999-12-31 13:30:00-07:00    28.119796   0.044499\n",
-       "4298 1999-12-31 14:30:00-07:00    23.314672   0.035130\n",
-       "4299 1999-12-31 15:30:00-07:00    17.890528   0.027891\n",
-       "4300 1999-12-31 16:30:00-07:00     4.552365   0.001188\n",
-       "\n",
-       "[4301 rows x 3 columns]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "timesteps"
    ]
@@ -897,7 +273,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -916,7 +292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -939,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -960,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -995,12 +371,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "for index, timestep in timesteps.iterrows():\n",
-    "\n",
     "    # first row tau has already been assigned\n",
     "    if index == 0:\n",
     "        # calc device parameters for first row\n",
@@ -1105,7 +480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1124,275 +499,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Datetime</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Injection</th>\n",
-       "      <th>NA</th>\n",
-       "      <th>NB</th>\n",
-       "      <th>NC</th>\n",
-       "      <th>tau</th>\n",
-       "      <th>Jsc</th>\n",
-       "      <th>Voc</th>\n",
-       "      <th>Isc</th>\n",
-       "      <th>FF</th>\n",
-       "      <th>Pmp</th>\n",
-       "      <th>Pmp_norm</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1999-01-01 08:30:00-07:00</td>\n",
-       "      <td>3.348774</td>\n",
-       "      <td>0.006239</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>115.000000</td>\n",
-       "      <td>41.590997</td>\n",
-       "      <td>0.666327</td>\n",
-       "      <td>10.106612</td>\n",
-       "      <td>0.840987</td>\n",
-       "      <td>5.663467</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1999-01-01 09:30:00-07:00</td>\n",
-       "      <td>12.378439</td>\n",
-       "      <td>0.031607</td>\n",
-       "      <td>99.997547</td>\n",
-       "      <td>0.002453</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>114.996922</td>\n",
-       "      <td>41.590987</td>\n",
-       "      <td>0.666327</td>\n",
-       "      <td>10.106610</td>\n",
-       "      <td>0.840987</td>\n",
-       "      <td>5.663460</td>\n",
-       "      <td>0.999999</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1999-01-01 10:30:00-07:00</td>\n",
-       "      <td>16.595081</td>\n",
-       "      <td>0.044220</td>\n",
-       "      <td>99.991953</td>\n",
-       "      <td>0.008047</td>\n",
-       "      <td>6.072523e-09</td>\n",
-       "      <td>114.989905</td>\n",
-       "      <td>41.590966</td>\n",
-       "      <td>0.666326</td>\n",
-       "      <td>10.106605</td>\n",
-       "      <td>0.840986</td>\n",
-       "      <td>5.663446</td>\n",
-       "      <td>0.999996</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1999-01-01 11:30:00-07:00</td>\n",
-       "      <td>17.457240</td>\n",
-       "      <td>0.043201</td>\n",
-       "      <td>99.985929</td>\n",
-       "      <td>0.014071</td>\n",
-       "      <td>2.755257e-08</td>\n",
-       "      <td>114.982350</td>\n",
-       "      <td>41.590942</td>\n",
-       "      <td>0.666324</td>\n",
-       "      <td>10.106599</td>\n",
-       "      <td>0.840986</td>\n",
-       "      <td>5.663430</td>\n",
-       "      <td>0.999994</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1999-01-01 12:30:00-07:00</td>\n",
-       "      <td>6.392783</td>\n",
-       "      <td>0.007124</td>\n",
-       "      <td>99.985664</td>\n",
-       "      <td>0.014336</td>\n",
-       "      <td>2.864303e-08</td>\n",
-       "      <td>114.982018</td>\n",
-       "      <td>41.590941</td>\n",
-       "      <td>0.666324</td>\n",
-       "      <td>10.106599</td>\n",
-       "      <td>0.840986</td>\n",
-       "      <td>5.663429</td>\n",
-       "      <td>0.999993</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",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4296</th>\n",
-       "      <td>1999-12-31 12:30:00-07:00</td>\n",
-       "      <td>9.515577</td>\n",
-       "      <td>0.001151</td>\n",
-       "      <td>42.029575</td>\n",
-       "      <td>53.766632</td>\n",
-       "      <td>4.203792e+00</td>\n",
-       "      <td>72.484546</td>\n",
-       "      <td>41.385898</td>\n",
-       "      <td>0.656929</td>\n",
-       "      <td>10.056773</td>\n",
-       "      <td>0.839317</td>\n",
-       "      <td>5.545020</td>\n",
-       "      <td>0.979086</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4297</th>\n",
-       "      <td>1999-12-31 13:30:00-07:00</td>\n",
-       "      <td>28.119796</td>\n",
-       "      <td>0.044499</td>\n",
-       "      <td>42.030231</td>\n",
-       "      <td>53.765654</td>\n",
-       "      <td>4.204115e+00</td>\n",
-       "      <td>72.485034</td>\n",
-       "      <td>41.385901</td>\n",
-       "      <td>0.656929</td>\n",
-       "      <td>10.056774</td>\n",
-       "      <td>0.839318</td>\n",
-       "      <td>5.545022</td>\n",
-       "      <td>0.979086</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4298</th>\n",
-       "      <td>1999-12-31 14:30:00-07:00</td>\n",
-       "      <td>23.314672</td>\n",
-       "      <td>0.035130</td>\n",
-       "      <td>42.036311</td>\n",
-       "      <td>53.759433</td>\n",
-       "      <td>4.204257e+00</td>\n",
-       "      <td>72.488135</td>\n",
-       "      <td>41.385924</td>\n",
-       "      <td>0.656930</td>\n",
-       "      <td>10.056780</td>\n",
-       "      <td>0.839318</td>\n",
-       "      <td>5.545034</td>\n",
-       "      <td>0.979088</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4299</th>\n",
-       "      <td>1999-12-31 15:30:00-07:00</td>\n",
-       "      <td>17.890528</td>\n",
-       "      <td>0.027891</td>\n",
-       "      <td>42.048873</td>\n",
-       "      <td>53.746813</td>\n",
-       "      <td>4.204313e+00</td>\n",
-       "      <td>72.494425</td>\n",
-       "      <td>41.385972</td>\n",
-       "      <td>0.656932</td>\n",
-       "      <td>10.056791</td>\n",
-       "      <td>0.839318</td>\n",
-       "      <td>5.545058</td>\n",
-       "      <td>0.979093</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4300</th>\n",
-       "      <td>1999-12-31 16:30:00-07:00</td>\n",
-       "      <td>4.552365</td>\n",
-       "      <td>0.001188</td>\n",
-       "      <td>42.049445</td>\n",
-       "      <td>53.746242</td>\n",
-       "      <td>4.204314e+00</td>\n",
-       "      <td>72.494710</td>\n",
-       "      <td>41.385974</td>\n",
-       "      <td>0.656932</td>\n",
-       "      <td>10.056792</td>\n",
-       "      <td>0.839318</td>\n",
-       "      <td>5.545059</td>\n",
-       "      <td>0.979093</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>4301 rows × 13 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                      Datetime  Temperature  Injection          NA         NB  \\\n",
-       "0    1999-01-01 08:30:00-07:00     3.348774   0.006239  100.000000   0.000000   \n",
-       "1    1999-01-01 09:30:00-07:00    12.378439   0.031607   99.997547   0.002453   \n",
-       "2    1999-01-01 10:30:00-07:00    16.595081   0.044220   99.991953   0.008047   \n",
-       "3    1999-01-01 11:30:00-07:00    17.457240   0.043201   99.985929   0.014071   \n",
-       "4    1999-01-01 12:30:00-07:00     6.392783   0.007124   99.985664   0.014336   \n",
-       "...                        ...          ...        ...         ...        ...   \n",
-       "4296 1999-12-31 12:30:00-07:00     9.515577   0.001151   42.029575  53.766632   \n",
-       "4297 1999-12-31 13:30:00-07:00    28.119796   0.044499   42.030231  53.765654   \n",
-       "4298 1999-12-31 14:30:00-07:00    23.314672   0.035130   42.036311  53.759433   \n",
-       "4299 1999-12-31 15:30:00-07:00    17.890528   0.027891   42.048873  53.746813   \n",
-       "4300 1999-12-31 16:30:00-07:00     4.552365   0.001188   42.049445  53.746242   \n",
-       "\n",
-       "                NC         tau        Jsc       Voc        Isc        FF  \\\n",
-       "0     0.000000e+00  115.000000  41.590997  0.666327  10.106612  0.840987   \n",
-       "1     0.000000e+00  114.996922  41.590987  0.666327  10.106610  0.840987   \n",
-       "2     6.072523e-09  114.989905  41.590966  0.666326  10.106605  0.840986   \n",
-       "3     2.755257e-08  114.982350  41.590942  0.666324  10.106599  0.840986   \n",
-       "4     2.864303e-08  114.982018  41.590941  0.666324  10.106599  0.840986   \n",
-       "...            ...         ...        ...       ...        ...       ...   \n",
-       "4296  4.203792e+00   72.484546  41.385898  0.656929  10.056773  0.839317   \n",
-       "4297  4.204115e+00   72.485034  41.385901  0.656929  10.056774  0.839318   \n",
-       "4298  4.204257e+00   72.488135  41.385924  0.656930  10.056780  0.839318   \n",
-       "4299  4.204313e+00   72.494425  41.385972  0.656932  10.056791  0.839318   \n",
-       "4300  4.204314e+00   72.494710  41.385974  0.656932  10.056792  0.839318   \n",
-       "\n",
-       "           Pmp  Pmp_norm  \n",
-       "0     5.663467  1.000000  \n",
-       "1     5.663460  0.999999  \n",
-       "2     5.663446  0.999996  \n",
-       "3     5.663430  0.999994  \n",
-       "4     5.663429  0.999993  \n",
-       "...        ...       ...  \n",
-       "4296  5.545020  0.979086  \n",
-       "4297  5.545022  0.979086  \n",
-       "4298  5.545034  0.979088  \n",
-       "4299  5.545058  0.979093  \n",
-       "4300  5.545059  0.979093  \n",
-       "\n",
-       "[4301 rows x 13 columns]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "timesteps = letid.calc_device_params(\n",
     "    timesteps, cell_area=243\n",
@@ -1417,20 +526,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from cycler import cycler\n",
     "\n",
@@ -1458,7 +556,7 @@
     "ax2.legend(loc=\"upper right\")\n",
     "ax2.set_ylabel(\"Normalized STC $P_{MP}$\")\n",
     "\n",
-    "ax.set_title(\"Outdoor LETID \\n\" f\"{location.name}\")\n",
+    "ax.set_title(f\"Outdoor LETID \\n{location.name}\")\n",
     "\n",
     "plt.show()"
    ]
@@ -1479,281 +577,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
-   "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>Temperature</th>\n",
-       "      <th>Injection</th>\n",
-       "      <th>NA</th>\n",
-       "      <th>NB</th>\n",
-       "      <th>NC</th>\n",
-       "      <th>tau</th>\n",
-       "      <th>Jsc</th>\n",
-       "      <th>Voc</th>\n",
-       "      <th>Isc</th>\n",
-       "      <th>FF</th>\n",
-       "      <th>Pmp</th>\n",
-       "      <th>Pmp_norm</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>time</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 00:30:00-07:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>115.000000</td>\n",
-       "      <td>41.590997</td>\n",
-       "      <td>0.666327</td>\n",
-       "      <td>10.106612</td>\n",
-       "      <td>0.840987</td>\n",
-       "      <td>5.663467</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 01:30:00-07:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>1.702422e-15</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>115.000000</td>\n",
-       "      <td>41.590997</td>\n",
-       "      <td>0.666327</td>\n",
-       "      <td>10.106612</td>\n",
-       "      <td>0.840987</td>\n",
-       "      <td>5.663467</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 02:30:00-07:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>3.404843e-15</td>\n",
-       "      <td>5.403329e-36</td>\n",
-       "      <td>115.000000</td>\n",
-       "      <td>41.590997</td>\n",
-       "      <td>0.666327</td>\n",
-       "      <td>10.106612</td>\n",
-       "      <td>0.840987</td>\n",
-       "      <td>5.663467</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 03:30:00-07:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>5.107265e-15</td>\n",
-       "      <td>1.620999e-35</td>\n",
-       "      <td>115.000000</td>\n",
-       "      <td>41.590997</td>\n",
-       "      <td>0.666327</td>\n",
-       "      <td>10.106612</td>\n",
-       "      <td>0.840987</td>\n",
-       "      <td>5.663467</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01 04:30:00-07:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>6.809686e-15</td>\n",
-       "      <td>3.241997e-35</td>\n",
-       "      <td>115.000000</td>\n",
-       "      <td>41.590997</td>\n",
-       "      <td>0.666327</td>\n",
-       "      <td>10.106612</td>\n",
-       "      <td>0.840987</td>\n",
-       "      <td>5.663467</td>\n",
-       "      <td>1.000000</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",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-12-31 19:30:00-07:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>27.833654</td>\n",
-       "      <td>6.778463e+01</td>\n",
-       "      <td>4.381716e+00</td>\n",
-       "      <td>66.112142</td>\n",
-       "      <td>41.333851</td>\n",
-       "      <td>0.654974</td>\n",
-       "      <td>10.044126</td>\n",
-       "      <td>0.838966</td>\n",
-       "      <td>5.519257</td>\n",
-       "      <td>0.974537</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-12-31 20:30:00-07:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>27.833654</td>\n",
-       "      <td>6.778463e+01</td>\n",
-       "      <td>4.381716e+00</td>\n",
-       "      <td>66.112142</td>\n",
-       "      <td>41.333851</td>\n",
-       "      <td>0.654974</td>\n",
-       "      <td>10.044126</td>\n",
-       "      <td>0.838966</td>\n",
-       "      <td>5.519257</td>\n",
-       "      <td>0.974537</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-12-31 21:30:00-07:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>27.833654</td>\n",
-       "      <td>6.778463e+01</td>\n",
-       "      <td>4.381716e+00</td>\n",
-       "      <td>66.112142</td>\n",
-       "      <td>41.333851</td>\n",
-       "      <td>0.654974</td>\n",
-       "      <td>10.044126</td>\n",
-       "      <td>0.838966</td>\n",
-       "      <td>5.519257</td>\n",
-       "      <td>0.974537</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-12-31 22:30:00-07:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>27.833654</td>\n",
-       "      <td>6.778463e+01</td>\n",
-       "      <td>4.381716e+00</td>\n",
-       "      <td>66.112142</td>\n",
-       "      <td>41.333851</td>\n",
-       "      <td>0.654974</td>\n",
-       "      <td>10.044126</td>\n",
-       "      <td>0.838966</td>\n",
-       "      <td>5.519257</td>\n",
-       "      <td>0.974537</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-12-31 23:30:00-07:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>27.833654</td>\n",
-       "      <td>6.778463e+01</td>\n",
-       "      <td>4.381716e+00</td>\n",
-       "      <td>66.112142</td>\n",
-       "      <td>41.333851</td>\n",
-       "      <td>0.654974</td>\n",
-       "      <td>10.044126</td>\n",
-       "      <td>0.838966</td>\n",
-       "      <td>5.519257</td>\n",
-       "      <td>0.974537</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>8760 rows × 12 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                           Temperature  Injection          NA            NB  \\\n",
-       "time                                                                          \n",
-       "1999-01-01 00:30:00-07:00          0.0        NaN  100.000000  0.000000e+00   \n",
-       "1999-01-01 01:30:00-07:00          0.0        NaN  100.000000  1.702422e-15   \n",
-       "1999-01-01 02:30:00-07:00          0.0        NaN  100.000000  3.404843e-15   \n",
-       "1999-01-01 03:30:00-07:00          0.0        NaN  100.000000  5.107265e-15   \n",
-       "1999-01-01 04:30:00-07:00          0.0        NaN  100.000000  6.809686e-15   \n",
-       "...                                ...        ...         ...           ...   \n",
-       "1999-12-31 19:30:00-07:00          0.0        NaN   27.833654  6.778463e+01   \n",
-       "1999-12-31 20:30:00-07:00          0.0        NaN   27.833654  6.778463e+01   \n",
-       "1999-12-31 21:30:00-07:00          0.0        NaN   27.833654  6.778463e+01   \n",
-       "1999-12-31 22:30:00-07:00          0.0        NaN   27.833654  6.778463e+01   \n",
-       "1999-12-31 23:30:00-07:00          0.0        NaN   27.833654  6.778463e+01   \n",
-       "\n",
-       "                                     NC         tau        Jsc       Voc  \\\n",
-       "time                                                                       \n",
-       "1999-01-01 00:30:00-07:00  0.000000e+00  115.000000  41.590997  0.666327   \n",
-       "1999-01-01 01:30:00-07:00  0.000000e+00  115.000000  41.590997  0.666327   \n",
-       "1999-01-01 02:30:00-07:00  5.403329e-36  115.000000  41.590997  0.666327   \n",
-       "1999-01-01 03:30:00-07:00  1.620999e-35  115.000000  41.590997  0.666327   \n",
-       "1999-01-01 04:30:00-07:00  3.241997e-35  115.000000  41.590997  0.666327   \n",
-       "...                                 ...         ...        ...       ...   \n",
-       "1999-12-31 19:30:00-07:00  4.381716e+00   66.112142  41.333851  0.654974   \n",
-       "1999-12-31 20:30:00-07:00  4.381716e+00   66.112142  41.333851  0.654974   \n",
-       "1999-12-31 21:30:00-07:00  4.381716e+00   66.112142  41.333851  0.654974   \n",
-       "1999-12-31 22:30:00-07:00  4.381716e+00   66.112142  41.333851  0.654974   \n",
-       "1999-12-31 23:30:00-07:00  4.381716e+00   66.112142  41.333851  0.654974   \n",
-       "\n",
-       "                                 Isc        FF       Pmp  Pmp_norm  \n",
-       "time                                                                \n",
-       "1999-01-01 00:30:00-07:00  10.106612  0.840987  5.663467  1.000000  \n",
-       "1999-01-01 01:30:00-07:00  10.106612  0.840987  5.663467  1.000000  \n",
-       "1999-01-01 02:30:00-07:00  10.106612  0.840987  5.663467  1.000000  \n",
-       "1999-01-01 03:30:00-07:00  10.106612  0.840987  5.663467  1.000000  \n",
-       "1999-01-01 04:30:00-07:00  10.106612  0.840987  5.663467  1.000000  \n",
-       "...                              ...       ...       ...       ...  \n",
-       "1999-12-31 19:30:00-07:00  10.044126  0.838966  5.519257  0.974537  \n",
-       "1999-12-31 20:30:00-07:00  10.044126  0.838966  5.519257  0.974537  \n",
-       "1999-12-31 21:30:00-07:00  10.044126  0.838966  5.519257  0.974537  \n",
-       "1999-12-31 22:30:00-07:00  10.044126  0.838966  5.519257  0.974537  \n",
-       "1999-12-31 23:30:00-07:00  10.044126  0.838966  5.519257  0.974537  \n",
-       "\n",
-       "[8760 rows x 12 columns]"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "nA_0 = 100\n",
     "nB_0 = 0\n",
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Outdoor Scenario Based on Accelerated Test Result.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Outdoor Scenario Based on Accelerated Test Result.ipynb
index f74097e8f..5b30a956b 100644
--- a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Outdoor Scenario Based on Accelerated Test Result.ipynb	
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Outdoor Scenario Based on Accelerated Test Result.ipynb	
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -57,23 +57,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Working on a  Windows 10\n",
-      "Python version  3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n",
-      "Pandas version  2.2.0\n",
-      "pvdeg version  0.2.4.dev83+ge2ceab9.d20240422\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# This information helps with debugging and getting support :)\n",
-    "import sys, platform\n",
+    "import sys\n",
+    "import platform\n",
     "\n",
     "print(\"Working on a \", platform.system(), platform.release())\n",
     "print(\"Python version \", sys.version)\n",
@@ -90,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -99,126 +89,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {},
-   "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>Week</th>\n",
-       "      <th>Pmp</th>\n",
-       "      <th>Isc</th>\n",
-       "      <th>Voc</th>\n",
-       "      <th>FF</th>\n",
-       "      <th>cell Voc</th>\n",
-       "      <th>Jsc</th>\n",
-       "      <th>% degradation</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>236.7</td>\n",
-       "      <td>9.91</td>\n",
-       "      <td>31.86</td>\n",
-       "      <td>75.01</td>\n",
-       "      <td>0.663750</td>\n",
-       "      <td>40.781893</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>230.9</td>\n",
-       "      <td>9.82</td>\n",
-       "      <td>31.63</td>\n",
-       "      <td>74.38</td>\n",
-       "      <td>0.658958</td>\n",
-       "      <td>40.411523</td>\n",
-       "      <td>-2.511910</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>228.5</td>\n",
-       "      <td>9.72</td>\n",
-       "      <td>31.54</td>\n",
-       "      <td>74.50</td>\n",
-       "      <td>0.657083</td>\n",
-       "      <td>40.000000</td>\n",
-       "      <td>-3.588621</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>226.9</td>\n",
-       "      <td>9.70</td>\n",
-       "      <td>31.56</td>\n",
-       "      <td>74.10</td>\n",
-       "      <td>0.657500</td>\n",
-       "      <td>39.917695</td>\n",
-       "      <td>-4.319083</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>224.8</td>\n",
-       "      <td>9.62</td>\n",
-       "      <td>31.48</td>\n",
-       "      <td>74.22</td>\n",
-       "      <td>0.655833</td>\n",
-       "      <td>39.588477</td>\n",
-       "      <td>-5.293594</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>7</td>\n",
-       "      <td>235.8</td>\n",
-       "      <td>9.76</td>\n",
-       "      <td>31.98</td>\n",
-       "      <td>75.51</td>\n",
-       "      <td>0.666250</td>\n",
-       "      <td>40.164609</td>\n",
-       "      <td>-0.381679</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Week    Pmp   Isc    Voc     FF  cell Voc        Jsc  % degradation\n",
-       "0     0  236.7  9.91  31.86  75.01  0.663750  40.781893       0.000000\n",
-       "1     1  230.9  9.82  31.63  74.38  0.658958  40.411523      -2.511910\n",
-       "2     2  228.5  9.72  31.54  74.50  0.657083  40.000000      -3.588621\n",
-       "3     3  226.9  9.70  31.56  74.10  0.657500  39.917695      -4.319083\n",
-       "4     4  224.8  9.62  31.48  74.22  0.655833  39.588477      -5.293594\n",
-       "5     7  235.8  9.76  31.98  75.51  0.666250  40.164609      -0.381679"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df = pd.read_csv(os.path.join(DATA_DIR, \"module test data.csv\"))\n",
     "df[\"cell Voc\"] = df[\"Voc\"] / 48\n",
@@ -236,20 +109,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.6611301062313933"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "tau_0 = 120  # [us] reasonable bulk lifetime for commercial-quality p-type silicon, maybe a little on the low side for typical wafer lifetimes, which could range from 100-500 us.\n",
     "\n",
@@ -274,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -296,20 +158,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(235.89622533600513, 236.7)"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# check to make sure power is close to the measured Week 0 power\n",
     "\n",
@@ -328,20 +179,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.6538686361228122"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "tau_deg = 80  # [us] degraded bulk lifetime\n",
     "\n",
@@ -358,20 +198,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(224.09272908700694, 224.8)"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# check to make sure power is close to the measured Week 4 power\n",
     "\n",
@@ -385,20 +214,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.050036816961295416"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "(pmp_0 - pmp_deg) / pmp_0"
    ]
@@ -416,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -430,311 +248,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <th>Day</th>\n",
-       "      <th>Hour</th>\n",
-       "      <th>Minute</th>\n",
-       "      <th>dni</th>\n",
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-       "      <th>ghi</th>\n",
-       "      <th>temp_air</th>\n",
-       "      <th>dew_point</th>\n",
-       "      <th>wind_speed</th>\n",
-       "      <th>relative_humidity</th>\n",
-       "      <th>poa_global</th>\n",
-       "      <th>temp_cell</th>\n",
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-       "      <th>1999-01-01 00:30:00-07:00</th>\n",
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-       "      <th>1999-12-31 19:30:00-07:00</th>\n",
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-       "      <th>1999-12-31 21:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>12</td>\n",
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-       "      <th>1999-12-31 22:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>22</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <td>0.0</td>\n",
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-       "      <td>81.22</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <th>1999-12-31 23:30:00-07:00</th>\n",
-       "      <td>1999</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
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-       "  </tbody>\n",
-       "</table>\n",
-       "<p>8760 rows × 15 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                           Year  Month  Day  Hour  Minute  dni  dhi  ghi  \\\n",
-       "1999-01-01 00:30:00-07:00  1999      1    1     0      30  0.0  0.0  0.0   \n",
-       "1999-01-01 01:30:00-07:00  1999      1    1     1      30  0.0  0.0  0.0   \n",
-       "1999-01-01 02:30:00-07:00  1999      1    1     2      30  0.0  0.0  0.0   \n",
-       "1999-01-01 03:30:00-07:00  1999      1    1     3      30  0.0  0.0  0.0   \n",
-       "1999-01-01 04:30:00-07:00  1999      1    1     4      30  0.0  0.0  0.0   \n",
-       "...                         ...    ...  ...   ...     ...  ...  ...  ...   \n",
-       "1999-12-31 19:30:00-07:00  1999     12   31    19      30  0.0  0.0  0.0   \n",
-       "1999-12-31 20:30:00-07:00  1999     12   31    20      30  0.0  0.0  0.0   \n",
-       "1999-12-31 21:30:00-07:00  1999     12   31    21      30  0.0  0.0  0.0   \n",
-       "1999-12-31 22:30:00-07:00  1999     12   31    22      30  0.0  0.0  0.0   \n",
-       "1999-12-31 23:30:00-07:00  1999     12   31    23      30  0.0  0.0  0.0   \n",
-       "\n",
-       "                           temp_air  dew_point  wind_speed  relative_humidity  \\\n",
-       "1999-01-01 00:30:00-07:00       0.0       -5.0         1.8              79.39   \n",
-       "1999-01-01 01:30:00-07:00       0.0       -4.0         1.7              80.84   \n",
-       "1999-01-01 02:30:00-07:00       0.0       -4.0         1.5              82.98   \n",
-       "1999-01-01 03:30:00-07:00       0.0       -4.0         1.3              85.01   \n",
-       "1999-01-01 04:30:00-07:00       0.0       -4.0         1.3              85.81   \n",
-       "...                             ...        ...         ...                ...   \n",
-       "1999-12-31 19:30:00-07:00       0.0       -3.0         0.9              83.63   \n",
-       "1999-12-31 20:30:00-07:00       0.0       -3.0         1.2              86.82   \n",
-       "1999-12-31 21:30:00-07:00       0.0       -4.0         1.6              83.78   \n",
-       "1999-12-31 22:30:00-07:00       0.0       -4.0         1.7              81.22   \n",
-       "1999-12-31 23:30:00-07:00       0.0       -5.0         1.8              79.43   \n",
-       "\n",
-       "                           poa_global  temp_cell  temp_module  \n",
-       "1999-01-01 00:30:00-07:00         0.0        0.0          0.0  \n",
-       "1999-01-01 01:30:00-07:00         0.0        0.0          0.0  \n",
-       "1999-01-01 02:30:00-07:00         0.0        0.0          0.0  \n",
-       "1999-01-01 03:30:00-07:00         0.0        0.0          0.0  \n",
-       "1999-01-01 04:30:00-07:00         0.0        0.0          0.0  \n",
-       "...                               ...        ...          ...  \n",
-       "1999-12-31 19:30:00-07:00         0.0        0.0          0.0  \n",
-       "1999-12-31 20:30:00-07:00         0.0        0.0          0.0  \n",
-       "1999-12-31 21:30:00-07:00         0.0        0.0          0.0  \n",
-       "1999-12-31 22:30:00-07:00         0.0        0.0          0.0  \n",
-       "1999-12-31 23:30:00-07:00         0.0        0.0          0.0  \n",
-       "\n",
-       "[8760 rows x 15 columns]"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "weather"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# if our weather file doesn't have precipitable water, calculate it with pvlib\n",
-    "if not \"precipitable_water\" in weather.columns:\n",
+    "if \"precipitable_water\" not in weather.columns:\n",
     "    weather[\"precipitable_water\"] = pvlib.atmosphere.gueymard94_pw(\n",
     "        weather[\"temp_air\"], weather[\"relative_humidity\"]\n",
     "    )"
@@ -742,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -762,190 +290,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
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-       "      <th>1999-01-01 03:30:00-07:00</th>\n",
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-       "      <td>0.955004</td>\n",
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-       "      <th>1999-01-01 04:30:00-07:00</th>\n",
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-       "    <tr>\n",
-       "      <th>1999-12-31 20:30:00-07:00</th>\n",
-       "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>1999-12-31 21:30:00-07:00</th>\n",
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-       "      <th>1999-12-31 22:30:00-07:00</th>\n",
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-       "      <th>1999-12-31 23:30:00-07:00</th>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>79.43</td>\n",
-       "      <td>0.892318</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>8760 rows × 7 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                           ghi  dni  dhi  temp_air  wind_speed  \\\n",
-       "1999-01-01 00:30:00-07:00  0.0  0.0  0.0       0.0         1.8   \n",
-       "1999-01-01 01:30:00-07:00  0.0  0.0  0.0       0.0         1.7   \n",
-       "1999-01-01 02:30:00-07:00  0.0  0.0  0.0       0.0         1.5   \n",
-       "1999-01-01 03:30:00-07:00  0.0  0.0  0.0       0.0         1.3   \n",
-       "1999-01-01 04:30:00-07:00  0.0  0.0  0.0       0.0         1.3   \n",
-       "...                        ...  ...  ...       ...         ...   \n",
-       "1999-12-31 19:30:00-07:00  0.0  0.0  0.0       0.0         0.9   \n",
-       "1999-12-31 20:30:00-07:00  0.0  0.0  0.0       0.0         1.2   \n",
-       "1999-12-31 21:30:00-07:00  0.0  0.0  0.0       0.0         1.6   \n",
-       "1999-12-31 22:30:00-07:00  0.0  0.0  0.0       0.0         1.7   \n",
-       "1999-12-31 23:30:00-07:00  0.0  0.0  0.0       0.0         1.8   \n",
-       "\n",
-       "                           relative_humidity  precipitable_water  \n",
-       "1999-01-01 00:30:00-07:00              79.39            0.891869  \n",
-       "1999-01-01 01:30:00-07:00              80.84            0.908158  \n",
-       "1999-01-01 02:30:00-07:00              82.98            0.932199  \n",
-       "1999-01-01 03:30:00-07:00              85.01            0.955004  \n",
-       "1999-01-01 04:30:00-07:00              85.81            0.963991  \n",
-       "...                                      ...                 ...  \n",
-       "1999-12-31 19:30:00-07:00              83.63            0.939501  \n",
-       "1999-12-31 20:30:00-07:00              86.82            0.975338  \n",
-       "1999-12-31 21:30:00-07:00              83.78            0.941186  \n",
-       "1999-12-31 22:30:00-07:00              81.22            0.912427  \n",
-       "1999-12-31 23:30:00-07:00              79.43            0.892318  \n",
-       "\n",
-       "[8760 rows x 7 columns]"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "weather"
    ]
@@ -960,350 +307,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Technology</th>\n",
-       "      <th>Bifacial</th>\n",
-       "      <th>STC</th>\n",
-       "      <th>PTC</th>\n",
-       "      <th>A_c</th>\n",
-       "      <th>Length</th>\n",
-       "      <th>Width</th>\n",
-       "      <th>N_s</th>\n",
-       "      <th>I_sc_ref</th>\n",
-       "      <th>V_oc_ref</th>\n",
-       "      <th>...</th>\n",
-       "      <th>a_ref</th>\n",
-       "      <th>I_L_ref</th>\n",
-       "      <th>I_o_ref</th>\n",
-       "      <th>R_s</th>\n",
-       "      <th>R_sh_ref</th>\n",
-       "      <th>Adjust</th>\n",
-       "      <th>gamma_r</th>\n",
-       "      <th>BIPV</th>\n",
-       "      <th>Version</th>\n",
-       "      <th>Date</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Gintech_Energy_GIN_M6_48_220</th>\n",
-       "      <td>Mono-c-Si</td>\n",
-       "      <td>0</td>\n",
-       "      <td>220.48</td>\n",
-       "      <td>194.5</td>\n",
-       "      <td>1.313</td>\n",
-       "      <td>1.324</td>\n",
-       "      <td>0.992</td>\n",
-       "      <td>48</td>\n",
-       "      <td>9.31</td>\n",
-       "      <td>31.3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.521151</td>\n",
-       "      <td>9.3228</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.106087</td>\n",
-       "      <td>77.164063</td>\n",
-       "      <td>22.488531</td>\n",
-       "      <td>-0.55</td>\n",
-       "      <td>N</td>\n",
-       "      <td>SAM 2018.11.11 r2</td>\n",
-       "      <td>1/3/2019</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gintech_Energy_GIN_P6_48_220</th>\n",
-       "      <td>Multi-c-Si</td>\n",
-       "      <td>0</td>\n",
-       "      <td>220.378</td>\n",
-       "      <td>199.2</td>\n",
-       "      <td>1.313</td>\n",
-       "      <td>1.324</td>\n",
-       "      <td>0.992</td>\n",
-       "      <td>48</td>\n",
-       "      <td>9.28</td>\n",
-       "      <td>31.3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.379815</td>\n",
-       "      <td>9.379174</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.254869</td>\n",
-       "      <td>374.724518</td>\n",
-       "      <td>8.824213</td>\n",
-       "      <td>-0.49</td>\n",
-       "      <td>N</td>\n",
-       "      <td>SAM 2018.11.11 r2</td>\n",
-       "      <td>1/3/2019</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gintech_Energy_GIN_M6_48_225</th>\n",
-       "      <td>Mono-c-Si</td>\n",
-       "      <td>0</td>\n",
-       "      <td>225.844</td>\n",
-       "      <td>199.1</td>\n",
-       "      <td>1.313</td>\n",
-       "      <td>1.324</td>\n",
-       "      <td>0.992</td>\n",
-       "      <td>48</td>\n",
-       "      <td>9.45</td>\n",
-       "      <td>31.5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.530596</td>\n",
-       "      <td>9.461989</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.100905</td>\n",
-       "      <td>79.531746</td>\n",
-       "      <td>22.462965</td>\n",
-       "      <td>-0.55</td>\n",
-       "      <td>N</td>\n",
-       "      <td>SAM 2018.11.11 r2</td>\n",
-       "      <td>1/3/2019</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>LG_Electronics_Inc__LG230N8K_G4</th>\n",
-       "      <td>Mono-c-Si</td>\n",
-       "      <td>0</td>\n",
-       "      <td>230.127</td>\n",
-       "      <td>212.9</td>\n",
-       "      <td>1.274</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.98</td>\n",
-       "      <td>48</td>\n",
-       "      <td>9.9</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.110894</td>\n",
-       "      <td>10.312051</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.307502</td>\n",
-       "      <td>314.551514</td>\n",
-       "      <td>2.112491</td>\n",
-       "      <td>-0.38</td>\n",
-       "      <td>N</td>\n",
-       "      <td>SAM 2018.11.11 r2</td>\n",
-       "      <td>1/3/2019</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>LG_Electronics_Inc__LG235N8K_G4</th>\n",
-       "      <td>Mono-c-Si</td>\n",
-       "      <td>0</td>\n",
-       "      <td>235.216</td>\n",
-       "      <td>217.6</td>\n",
-       "      <td>1.274</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.98</td>\n",
-       "      <td>48</td>\n",
-       "      <td>9.96</td>\n",
-       "      <td>30.2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.124688</td>\n",
-       "      <td>10.375943</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.278936</td>\n",
-       "      <td>250.799469</td>\n",
-       "      <td>3.374386</td>\n",
-       "      <td>-0.38</td>\n",
-       "      <td>N</td>\n",
-       "      <td>SAM 2018.11.11 r2</td>\n",
-       "      <td>1/3/2019</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>LG_Electronics_Inc__LG240N8K_G4</th>\n",
-       "      <td>Mono-c-Si</td>\n",
-       "      <td>0</td>\n",
-       "      <td>240.1</td>\n",
-       "      <td>222.4</td>\n",
-       "      <td>1.274</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.98</td>\n",
-       "      <td>48</td>\n",
-       "      <td>10.01</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.134983</td>\n",
-       "      <td>10.313996</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.263453</td>\n",
-       "      <td>3976.513916</td>\n",
-       "      <td>3.244429</td>\n",
-       "      <td>-0.38</td>\n",
-       "      <td>N</td>\n",
-       "      <td>SAM 2018.11.11 r2</td>\n",
-       "      <td>1/3/2019</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>LG_Electronics_Inc__LG245N8K_G4</th>\n",
-       "      <td>Mono-c-Si</td>\n",
-       "      <td>0</td>\n",
-       "      <td>245.016</td>\n",
-       "      <td>227.1</td>\n",
-       "      <td>1.274</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.98</td>\n",
-       "      <td>48</td>\n",
-       "      <td>10.06</td>\n",
-       "      <td>30.8</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.150454</td>\n",
-       "      <td>10.36712</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.246041</td>\n",
-       "      <td>1112.830811</td>\n",
-       "      <td>4.077489</td>\n",
-       "      <td>-0.38</td>\n",
-       "      <td>N</td>\n",
-       "      <td>SAM 2018.11.11 r2</td>\n",
-       "      <td>1/3/2019</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Prism_Solar_Technologies_Bi48_279BSTC</th>\n",
-       "      <td>Mono-c-Si</td>\n",
-       "      <td>1</td>\n",
-       "      <td>220.472</td>\n",
-       "      <td>199.3</td>\n",
-       "      <td>1.668</td>\n",
-       "      <td>1.695</td>\n",
-       "      <td>0.984</td>\n",
-       "      <td>48</td>\n",
-       "      <td>9.15</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.266369</td>\n",
-       "      <td>9.15029</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.210912</td>\n",
-       "      <td>6628.005371</td>\n",
-       "      <td>13.999823</td>\n",
-       "      <td>-0.4399</td>\n",
-       "      <td>N</td>\n",
-       "      <td>SAM 2018.11.11 r2</td>\n",
-       "      <td>1/3/2019</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Prism_Solar_Technologies_Bi48_286BSTC</th>\n",
-       "      <td>Mono-c-Si</td>\n",
-       "      <td>1</td>\n",
-       "      <td>225.298</td>\n",
-       "      <td>204.0</td>\n",
-       "      <td>1.668</td>\n",
-       "      <td>1.695</td>\n",
-       "      <td>0.984</td>\n",
-       "      <td>48</td>\n",
-       "      <td>9.35</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.266347</td>\n",
-       "      <td>9.350275</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.206403</td>\n",
-       "      <td>7016.816406</td>\n",
-       "      <td>13.996228</td>\n",
-       "      <td>-0.4399</td>\n",
-       "      <td>N</td>\n",
-       "      <td>SAM 2018.11.11 r2</td>\n",
-       "      <td>1/3/2019</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>9 rows × 25 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                       Technology Bifacial      STC    PTC  \\\n",
-       "Gintech_Energy_GIN_M6_48_220            Mono-c-Si        0   220.48  194.5   \n",
-       "Gintech_Energy_GIN_P6_48_220           Multi-c-Si        0  220.378  199.2   \n",
-       "Gintech_Energy_GIN_M6_48_225            Mono-c-Si        0  225.844  199.1   \n",
-       "LG_Electronics_Inc__LG230N8K_G4         Mono-c-Si        0  230.127  212.9   \n",
-       "LG_Electronics_Inc__LG235N8K_G4         Mono-c-Si        0  235.216  217.6   \n",
-       "LG_Electronics_Inc__LG240N8K_G4         Mono-c-Si        0    240.1  222.4   \n",
-       "LG_Electronics_Inc__LG245N8K_G4         Mono-c-Si        0  245.016  227.1   \n",
-       "Prism_Solar_Technologies_Bi48_279BSTC   Mono-c-Si        1  220.472  199.3   \n",
-       "Prism_Solar_Technologies_Bi48_286BSTC   Mono-c-Si        1  225.298  204.0   \n",
-       "\n",
-       "                                         A_c Length  Width N_s I_sc_ref  \\\n",
-       "Gintech_Energy_GIN_M6_48_220           1.313  1.324  0.992  48     9.31   \n",
-       "Gintech_Energy_GIN_P6_48_220           1.313  1.324  0.992  48     9.28   \n",
-       "Gintech_Energy_GIN_M6_48_225           1.313  1.324  0.992  48     9.45   \n",
-       "LG_Electronics_Inc__LG230N8K_G4        1.274    1.3   0.98  48      9.9   \n",
-       "LG_Electronics_Inc__LG235N8K_G4        1.274    1.3   0.98  48     9.96   \n",
-       "LG_Electronics_Inc__LG240N8K_G4        1.274    1.3   0.98  48    10.01   \n",
-       "LG_Electronics_Inc__LG245N8K_G4        1.274    1.3   0.98  48    10.06   \n",
-       "Prism_Solar_Technologies_Bi48_279BSTC  1.668  1.695  0.984  48     9.15   \n",
-       "Prism_Solar_Technologies_Bi48_286BSTC  1.668  1.695  0.984  48     9.35   \n",
-       "\n",
-       "                                      V_oc_ref  ...     a_ref    I_L_ref  \\\n",
-       "Gintech_Energy_GIN_M6_48_220              31.3  ...  1.521151     9.3228   \n",
-       "Gintech_Energy_GIN_P6_48_220              31.3  ...  1.379815   9.379174   \n",
-       "Gintech_Energy_GIN_M6_48_225              31.5  ...  1.530596   9.461989   \n",
-       "LG_Electronics_Inc__LG230N8K_G4           30.0  ...  1.110894  10.312051   \n",
-       "LG_Electronics_Inc__LG235N8K_G4           30.2  ...  1.124688  10.375943   \n",
-       "LG_Electronics_Inc__LG240N8K_G4           30.5  ...  1.134983  10.313996   \n",
-       "LG_Electronics_Inc__LG245N8K_G4           30.8  ...  1.150454   10.36712   \n",
-       "Prism_Solar_Technologies_Bi48_279BSTC     31.0  ...  1.266369    9.15029   \n",
-       "Prism_Solar_Technologies_Bi48_286BSTC     31.0  ...  1.266347   9.350275   \n",
-       "\n",
-       "                                      I_o_ref       R_s     R_sh_ref  \\\n",
-       "Gintech_Energy_GIN_M6_48_220              0.0  0.106087    77.164063   \n",
-       "Gintech_Energy_GIN_P6_48_220              0.0  0.254869   374.724518   \n",
-       "Gintech_Energy_GIN_M6_48_225              0.0  0.100905    79.531746   \n",
-       "LG_Electronics_Inc__LG230N8K_G4           0.0  0.307502   314.551514   \n",
-       "LG_Electronics_Inc__LG235N8K_G4           0.0  0.278936   250.799469   \n",
-       "LG_Electronics_Inc__LG240N8K_G4           0.0  0.263453  3976.513916   \n",
-       "LG_Electronics_Inc__LG245N8K_G4           0.0  0.246041  1112.830811   \n",
-       "Prism_Solar_Technologies_Bi48_279BSTC     0.0  0.210912  6628.005371   \n",
-       "Prism_Solar_Technologies_Bi48_286BSTC     0.0  0.206403  7016.816406   \n",
-       "\n",
-       "                                          Adjust gamma_r BIPV  \\\n",
-       "Gintech_Energy_GIN_M6_48_220           22.488531   -0.55    N   \n",
-       "Gintech_Energy_GIN_P6_48_220            8.824213   -0.49    N   \n",
-       "Gintech_Energy_GIN_M6_48_225           22.462965   -0.55    N   \n",
-       "LG_Electronics_Inc__LG230N8K_G4         2.112491   -0.38    N   \n",
-       "LG_Electronics_Inc__LG235N8K_G4         3.374386   -0.38    N   \n",
-       "LG_Electronics_Inc__LG240N8K_G4         3.244429   -0.38    N   \n",
-       "LG_Electronics_Inc__LG245N8K_G4         4.077489   -0.38    N   \n",
-       "Prism_Solar_Technologies_Bi48_279BSTC  13.999823 -0.4399    N   \n",
-       "Prism_Solar_Technologies_Bi48_286BSTC  13.996228 -0.4399    N   \n",
-       "\n",
-       "                                                 Version      Date  \n",
-       "Gintech_Energy_GIN_M6_48_220           SAM 2018.11.11 r2  1/3/2019  \n",
-       "Gintech_Energy_GIN_P6_48_220           SAM 2018.11.11 r2  1/3/2019  \n",
-       "Gintech_Energy_GIN_M6_48_225           SAM 2018.11.11 r2  1/3/2019  \n",
-       "LG_Electronics_Inc__LG230N8K_G4        SAM 2018.11.11 r2  1/3/2019  \n",
-       "LG_Electronics_Inc__LG235N8K_G4        SAM 2018.11.11 r2  1/3/2019  \n",
-       "LG_Electronics_Inc__LG240N8K_G4        SAM 2018.11.11 r2  1/3/2019  \n",
-       "LG_Electronics_Inc__LG245N8K_G4        SAM 2018.11.11 r2  1/3/2019  \n",
-       "Prism_Solar_Technologies_Bi48_279BSTC  SAM 2018.11.11 r2  1/3/2019  \n",
-       "Prism_Solar_Technologies_Bi48_286BSTC  SAM 2018.11.11 r2  1/3/2019  \n",
-       "\n",
-       "[9 rows x 25 columns]"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "cec_modules = pvlib.pvsystem.retrieve_sam(\"CECMod\").T\n",
     "cec_modules[cec_modules[\"STC\"].between(220, 250) & (cec_modules[\"N_s\"] == 48)]"
@@ -1318,7 +324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1328,7 +334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1350,7 +356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1375,31 +381,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ModelChain: \n",
-       "  name: None\n",
-       "  clearsky_model: ineichen\n",
-       "  transposition_model: haydavies\n",
-       "  solar_position_method: nrel_numpy\n",
-       "  airmass_model: kastenyoung1989\n",
-       "  dc_model: cec\n",
-       "  ac_model: sandia_inverter\n",
-       "  aoi_model: physical_aoi_loss\n",
-       "  spectral_model: first_solar_spectral_loss\n",
-       "  temperature_model: sapm_temp\n",
-       "  losses_model: no_extra_losses"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# create and run pvlib modelchain\n",
     "mc = ModelChain(system, location, aoi_model=\"physical\")\n",
@@ -1418,7 +402,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1451,7 +435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1466,7 +450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1493,177 +477,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\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>Datetime</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Injection</th>\n",
-       "      <th>NA</th>\n",
-       "      <th>NB</th>\n",
-       "      <th>NC</th>\n",
-       "      <th>tau</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1999-01-01 00:30:00-07:00</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>120.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1999-01-01 01:30:00-07:00</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1999-01-01 02:30:00-07:00</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1999-01-01 03:30:00-07:00</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1999-01-01 04:30:00-07:00</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</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>8755</th>\n",
-       "      <td>1999-12-31 19:30:00-07:00</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8756</th>\n",
-       "      <td>1999-12-31 20:30:00-07:00</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8757</th>\n",
-       "      <td>1999-12-31 21:30:00-07:00</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8758</th>\n",
-       "      <td>1999-12-31 22:30:00-07:00</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8759</th>\n",
-       "      <td>1999-12-31 23:30:00-07:00</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>8760 rows × 7 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                      Datetime  Temperature  Injection     NA   NB   NC    tau\n",
-       "0    1999-01-01 00:30:00-07:00          0.0        NaN  100.0  0.0  0.0  120.0\n",
-       "1    1999-01-01 01:30:00-07:00          0.0        NaN    NaN  NaN  NaN    NaN\n",
-       "2    1999-01-01 02:30:00-07:00          0.0        NaN    NaN  NaN  NaN    NaN\n",
-       "3    1999-01-01 03:30:00-07:00          0.0        NaN    NaN  NaN  NaN    NaN\n",
-       "4    1999-01-01 04:30:00-07:00          0.0        NaN    NaN  NaN  NaN    NaN\n",
-       "...                        ...          ...        ...    ...  ...  ...    ...\n",
-       "8755 1999-12-31 19:30:00-07:00          0.0        NaN    NaN  NaN  NaN    NaN\n",
-       "8756 1999-12-31 20:30:00-07:00          0.0        NaN    NaN  NaN  NaN    NaN\n",
-       "8757 1999-12-31 21:30:00-07:00          0.0        NaN    NaN  NaN  NaN    NaN\n",
-       "8758 1999-12-31 22:30:00-07:00          0.0        NaN    NaN  NaN  NaN    NaN\n",
-       "8759 1999-12-31 23:30:00-07:00          0.0        NaN    NaN  NaN  NaN    NaN\n",
-       "\n",
-       "[8760 rows x 7 columns]"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "timesteps"
    ]
@@ -1678,12 +494,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "for index, timestep in timesteps.iterrows():\n",
-    "\n",
     "    # first row tau has already been assigned\n",
     "    if index == 0:\n",
     "        pass\n",
@@ -1766,7 +581,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1785,275 +600,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "\n",
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-       "      <th>Datetime</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Injection</th>\n",
-       "      <th>NA</th>\n",
-       "      <th>NB</th>\n",
-       "      <th>NC</th>\n",
-       "      <th>tau</th>\n",
-       "      <th>Jsc</th>\n",
-       "      <th>Voc</th>\n",
-       "      <th>Isc</th>\n",
-       "      <th>FF</th>\n",
-       "      <th>Pmp</th>\n",
-       "      <th>Pmp_norm</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1999-01-01 01:30:00-07:00</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1999-01-01 02:30:00-07:00</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>3.366181e-15</td>\n",
-       "      <td>5.269266e-36</td>\n",
-       "      <td>120.000000</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1999-01-01 03:30:00-07:00</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>5.049271e-15</td>\n",
-       "      <td>1.580780e-35</td>\n",
-       "      <td>120.000000</td>\n",
-       "      <td>41.451328</td>\n",
-       "      <td>0.661315</td>\n",
-       "      <td>9.981480</td>\n",
-       "      <td>0.840101</td>\n",
-       "      <td>5.545424</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1999-01-01 04:30:00-07:00</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>6.732362e-15</td>\n",
-       "      <td>3.161560e-35</td>\n",
-       "      <td>120.000000</td>\n",
-       "      <td>41.451328</td>\n",
-       "      <td>0.661315</td>\n",
-       "      <td>9.981480</td>\n",
-       "      <td>0.840101</td>\n",
-       "      <td>5.545424</td>\n",
-       "      <td>1.000000</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",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
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-       "    <tr>\n",
-       "      <th>8755</th>\n",
-       "      <td>1999-12-31 19:30:00-07:00</td>\n",
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-       "      <td>NaN</td>\n",
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-       "      <td>0.989573</td>\n",
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-       "    <tr>\n",
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-       "      <td>1999-12-31 20:30:00-07:00</td>\n",
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-       "      <td>0.839287</td>\n",
-       "      <td>5.487602</td>\n",
-       "      <td>0.989573</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8757</th>\n",
-       "      <td>1999-12-31 21:30:00-07:00</td>\n",
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-       "      <td>NaN</td>\n",
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-       "      <td>0.989573</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8758</th>\n",
-       "      <td>1999-12-31 22:30:00-07:00</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>30.470917</td>\n",
-       "      <td>6.606591e+01</td>\n",
-       "      <td>3.463173e+00</td>\n",
-       "      <td>90.203213</td>\n",
-       "      <td>41.343585</td>\n",
-       "      <td>0.656761</td>\n",
-       "      <td>9.955535</td>\n",
-       "      <td>0.839287</td>\n",
-       "      <td>5.487602</td>\n",
-       "      <td>0.989573</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8759</th>\n",
-       "      <td>1999-12-31 23:30:00-07:00</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>30.470917</td>\n",
-       "      <td>6.606591e+01</td>\n",
-       "      <td>3.463173e+00</td>\n",
-       "      <td>90.203213</td>\n",
-       "      <td>41.343585</td>\n",
-       "      <td>0.656761</td>\n",
-       "      <td>9.955535</td>\n",
-       "      <td>0.839287</td>\n",
-       "      <td>5.487602</td>\n",
-       "      <td>0.989573</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>8760 rows × 13 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                      Datetime  Temperature  Injection          NA  \\\n",
-       "0    1999-01-01 00:30:00-07:00          0.0        NaN  100.000000   \n",
-       "1    1999-01-01 01:30:00-07:00          0.0        NaN  100.000000   \n",
-       "2    1999-01-01 02:30:00-07:00          0.0        NaN  100.000000   \n",
-       "3    1999-01-01 03:30:00-07:00          0.0        NaN  100.000000   \n",
-       "4    1999-01-01 04:30:00-07:00          0.0        NaN  100.000000   \n",
-       "...                        ...          ...        ...         ...   \n",
-       "8755 1999-12-31 19:30:00-07:00          0.0        NaN   30.470917   \n",
-       "8756 1999-12-31 20:30:00-07:00          0.0        NaN   30.470917   \n",
-       "8757 1999-12-31 21:30:00-07:00          0.0        NaN   30.470917   \n",
-       "8758 1999-12-31 22:30:00-07:00          0.0        NaN   30.470917   \n",
-       "8759 1999-12-31 23:30:00-07:00          0.0        NaN   30.470917   \n",
-       "\n",
-       "                NB            NC         tau        Jsc       Voc       Isc  \\\n",
-       "0     0.000000e+00  0.000000e+00  120.000000  41.451328  0.661315  9.981480   \n",
-       "1     1.683090e-15  0.000000e+00  120.000000  41.451328  0.661315  9.981480   \n",
-       "2     3.366181e-15  5.269266e-36  120.000000  41.451328  0.661315  9.981480   \n",
-       "3     5.049271e-15  1.580780e-35  120.000000  41.451328  0.661315  9.981480   \n",
-       "4     6.732362e-15  3.161560e-35  120.000000  41.451328  0.661315  9.981480   \n",
-       "...            ...           ...         ...        ...       ...       ...   \n",
-       "8755  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
-       "8756  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
-       "8757  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
-       "8758  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
-       "8759  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
-       "\n",
-       "            FF       Pmp  Pmp_norm  \n",
-       "0     0.840101  5.545424  1.000000  \n",
-       "1     0.840101  5.545424  1.000000  \n",
-       "2     0.840101  5.545424  1.000000  \n",
-       "3     0.840101  5.545424  1.000000  \n",
-       "4     0.840101  5.545424  1.000000  \n",
-       "...        ...       ...       ...  \n",
-       "8755  0.839287  5.487602  0.989573  \n",
-       "8756  0.839287  5.487602  0.989573  \n",
-       "8757  0.839287  5.487602  0.989573  \n",
-       "8758  0.839287  5.487602  0.989573  \n",
-       "8759  0.839287  5.487602  0.989573  \n",
-       "\n",
-       "[8760 rows x 13 columns]"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "timesteps = letid.calc_device_params(\n",
     "    timesteps, cell_area\n",
@@ -2071,384 +620,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
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-       "      <th>Datetime</th>\n",
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-       "      <td>0.892318</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>8760 rows × 27 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                      Datetime  Temperature  Injection          NA  \\\n",
-       "0    1999-01-01 00:30:00-07:00          0.0        NaN  100.000000   \n",
-       "1    1999-01-01 01:30:00-07:00          0.0        NaN  100.000000   \n",
-       "2    1999-01-01 02:30:00-07:00          0.0        NaN  100.000000   \n",
-       "3    1999-01-01 03:30:00-07:00          0.0        NaN  100.000000   \n",
-       "4    1999-01-01 04:30:00-07:00          0.0        NaN  100.000000   \n",
-       "...                        ...          ...        ...         ...   \n",
-       "8755 1999-12-31 19:30:00-07:00          0.0        NaN   30.470917   \n",
-       "8756 1999-12-31 20:30:00-07:00          0.0        NaN   30.470917   \n",
-       "8757 1999-12-31 21:30:00-07:00          0.0        NaN   30.470917   \n",
-       "8758 1999-12-31 22:30:00-07:00          0.0        NaN   30.470917   \n",
-       "8759 1999-12-31 23:30:00-07:00          0.0        NaN   30.470917   \n",
-       "\n",
-       "                NB            NC         tau        Jsc       Voc       Isc  \\\n",
-       "0     0.000000e+00  0.000000e+00  120.000000  41.451328  0.661315  9.981480   \n",
-       "1     1.683090e-15  0.000000e+00  120.000000  41.451328  0.661315  9.981480   \n",
-       "2     3.366181e-15  5.269266e-36  120.000000  41.451328  0.661315  9.981480   \n",
-       "3     5.049271e-15  1.580780e-35  120.000000  41.451328  0.661315  9.981480   \n",
-       "4     6.732362e-15  3.161560e-35  120.000000  41.451328  0.661315  9.981480   \n",
-       "...            ...           ...         ...        ...       ...       ...   \n",
-       "8755  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
-       "8756  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
-       "8757  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
-       "8758  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
-       "8759  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
-       "\n",
-       "      ...  p_mp  i_x  i_xx  Effective irradiance  ghi  dhi  dni  wind_speed  \\\n",
-       "0     ...   0.0  0.0   0.0                   NaN  0.0  0.0  0.0         1.8   \n",
-       "1     ...   0.0  0.0   0.0                   NaN  0.0  0.0  0.0         1.7   \n",
-       "2     ...   0.0  0.0   0.0                   NaN  0.0  0.0  0.0         1.5   \n",
-       "3     ...   0.0  0.0   0.0                   NaN  0.0  0.0  0.0         1.3   \n",
-       "4     ...   0.0  0.0   0.0                   NaN  0.0  0.0  0.0         1.3   \n",
-       "...   ...   ...  ...   ...                   ...  ...  ...  ...         ...   \n",
-       "8755  ...   0.0  0.0   0.0                   NaN  0.0  0.0  0.0         0.9   \n",
-       "8756  ...   0.0  0.0   0.0                   NaN  0.0  0.0  0.0         1.2   \n",
-       "8757  ...   0.0  0.0   0.0                   NaN  0.0  0.0  0.0         1.6   \n",
-       "8758  ...   0.0  0.0   0.0                   NaN  0.0  0.0  0.0         1.7   \n",
-       "8759  ...   0.0  0.0   0.0                   NaN  0.0  0.0  0.0         1.8   \n",
-       "\n",
-       "      temp_air  precipitable_water  \n",
-       "0          0.0            0.891869  \n",
-       "1          0.0            0.908158  \n",
-       "2          0.0            0.932199  \n",
-       "3          0.0            0.955004  \n",
-       "4          0.0            0.963991  \n",
-       "...        ...                 ...  \n",
-       "8755       0.0            0.939501  \n",
-       "8756       0.0            0.975338  \n",
-       "8757       0.0            0.941186  \n",
-       "8758       0.0            0.912427  \n",
-       "8759       0.0            0.892318  \n",
-       "\n",
-       "[8760 rows x 27 columns]"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "timesteps = timesteps.merge(mc.results.dc, left_on=\"Datetime\", right_index=True)\n",
     "timesteps = timesteps.merge(\n",
@@ -2470,20 +644,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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Z2NhI7dq1e+N2JCl319/o6GjJ2NhYqlatmso1Q5Ik+Z6RlpYmOTs7S46OjtKLFy+yzSNJktSgQQOpXLlyUlJSksryGjVqSG5ubnLa6/eVjIwMyc3NLct9KiEhQXJ2dpYaNmwop2VeD7p06ZKr/d20aZMESMeOHZPT5s2bl+UaLUmSFBYWJmlqakozZ85USb969aqkpaUlp2deZz09PaXk5GQ536pVqyQg22v963K65uXnOZ7TfuYk8/u2YsUKlfQNGzZIGhoa0vHjx1XSV6xYIQHSyZMnJUmSpAULFmS5Vr8uu/ujJGVf1nj9+hMVFSUB0uTJk1U+m5vzuyB5r0fGDRs25PTp07Rs2ZIrV64wd+5cfH19sbe3z3VVfN++fVUeS1WrVg1Jkujbt6+cpqmpSeXKlbl79+77hKvi1ZqumJgYnj59ire3N3fv3iUmJuatn9+yZQu1a9fG3Nycp0+fyi8fHx/S09M5duwYAHv27EFLS4vBgwer7E9uO4HExcVhbGz8xjyZy2NjY3O1ztzYs2cPX3zxhcqvXysrqyyPbg8ePEhKSgojRoxQ6TTUv39/TExM2L17NwAXLlzgyZMnfPXVVyrtzZo1a0bp0qXlfK969ZjllwEDBnDgwIEsL3d393zfVnYyO6TExcXlKv/QoUPlvzN/maekpHDw4EGVfO3atVP5hf348WMuX75Mr169sLCwkNPLly9Pw4YN2bNnD6CshTx48CCtW7fGzs5Ozufq6ppjLY+3t7fK8ZIkiW3bttGiRQskSVI5H3x9fYmJiZEf95qZmfHw4UPOnz+f4z6bmZlx9uxZHj16lJtDBCi/r5qamgwfPlwlffTo0UiSxN69e1XSfXx8VJpglC9fHhMTk1xfY3r06KFyXrZv3x5bW1v5uILqNSY+Pp6nT59So0YNJEni0qVLch4dHR2OHj3Kixcvst3WgQMHiI6OpkuXLirHVlNTk2rVqnHkyBHgv//znj17YmpqKn++YcOGufp+5/ZczmRkZKTSVlZHR4eqVavm6hjm5vp74MAB4uLiGDt2bJY2qpn3jEuXLhEaGsqIESOyNF3KzPP8+XMOHz5Mx44diYuLk4/fs2fP8PX15fbt21marWS6fPkyt2/f5ssvv+TZs2fyZ+Pj42nQoAHHjh3L0oxm0KBBb9zfpKQknj59yhdffAGg0hQiJ3/99RcZGRl07NhR5TtgY2ODm5ub/B3IvM4OGjRIpQ1xZpOld5Xf5/i70NXVpXfv3ippW7ZsoUyZMpQuXVolpvr16wPIxyXzu/H3339/1M5ouTm/C5L3fkZWpUoV/vrrL1JSUrhy5Qrbt29nwYIFtG/fnsuXL7/1QlS8eHGV95lf2tertk1NTfP1gJ48eZLJkydz+vRpEhISVJbFxMS89eS5ffs2gYGBOVZzZ3aquXfvHra2tll6ppYqVSpXcRobG7+18JC5/G0Fx7y4d+8e1apVy5L+etz37t3LNl1HR4cSJUrIy3PKB1C6dGlOnDihkqalpZXrx1x54ebmho+PT76vN7devnwJ5O7/SkNDI0snl8zHzK+3P3F2dlZ5/6bjXaZMGfbt20d8fDyxsbEkJibi6uqaJV92adltKyoqiujoaFatWsWqVauy/Uzm+fDtt99y8OBBqlatiqurK40aNeLLL7+kZs2act65c+fSs2dPHBwc8PLyomnTpvTo0SPLsXh9f+3s7LIc1zJlysjLX/X6dQfA3Nw819cYNzc3lfcKhQJXV1eV/5f79+8zadIkdu7cmWW9mYUeXV1d5syZw+jRoylatChffPEFzZs3p0ePHtjY2ADKaw0g3+Rel9ljPXMfX48NlN+DtxU8cnsuZypWrFiWNqbm5uYEBga+cTuQu+tvSEgIwBubI+Umz507d5AkiYkTJzJx4sRs8zx58gR7e/ss6ZnH/k1NfGJiYjA3N5ffv35+gLJQOnXqVP74448sHS5zUwFx+/ZtJEnK9v8W/utkldN3QFtb+43nz9vk9zn+Luzt7bN0lLt9+zY3btx46324U6dOrFmzhn79+jF27FgaNGhA27Ztad++/Qcd/SQ353dBkm+NpnR0dKhSpQpVqlShZMmS9O7dmy1btjB58uQ3fk5TUzPX6dIrjcNzGmg6PT39rbGGhITQoEEDSpcuzfz583FwcEBHR4c9e/awYMGCXP2CyMjIoGHDhowZMybb5W9rH5ZbZcqU4dKlSyQnJ+fYUzswMBBtbW35IvA+x6ag0NXV/SSHKXqboKAgIOfC1rvKS9vR/N5W5vnSrVu3HG+cme1pypQpw82bN9m1axf+/v5s27aNZcuWMWnSJHnYho4dO1K7dm22b9/O/v37mTdvHnPmzOGvv/56a9u03MrpuvPqNeZ9pKen07BhQ54/f863335L6dKlMTQ0JDw8nF69eqlcY0aMGEGLFi3YsWMH+/btY+LEicyePZvDhw9TsWJFOe+GDRuyvYl8jLav2XnXY5gf19+8yFzfN998g6+vb7Z5cjofMz87b948PD09s83z+o/97M7Fjh07curUKfz8/PD09MTIyIiMjAwaN26c6/uNQqFg79692R73tw2F9b7y+xx/F9kd14yMDMqVK8f8+fOz/UxmxZK+vj7Hjh3jyJEj7N69G39/fzZv3kz9+vXZv38/mpqaH+y++bbzuyD5IFeSypUrA8pHGB9K5i+y13u/vf4rNjv//PMPycnJ7Ny5U6WmILN6+VU5fUlcXFx4+fLlW2ubHB0dOXToEC9fvlQ5aW/evPnWOAGaN2/O6dOn2bJlS7ZDn4SFhXH8+HF8fHzkE+bVY/PqY5Tsjk1O++fo6Cj/On7V63E7OjrK6a/+Ak1JSSE0NFQ+Pq/me72m4+bNm/Lyz92GDRtQKBQ0bNjwrXkzMjK4e/euyo+LzN7Jb2uM/Orxfl1wcDCWlpYYGhqip6eHnp4ed+7cyZIvu7TsWFlZYWxsTHp6eq5qXw0NDenUqROdOnUiJSWFtm3bMnPmTMaNGyc/GrS1teWrr77iq6++4smTJ1SqVImZM2fmWCB0dHTk4MGDWZpYBAcHy8vz0+vnhiRJ3LlzR74pXr16lVu3brF+/XqVjnAHDhzIdn0uLi6MHj2a0aNHc/v2bTw9Pfnxxx/ZuHGj/Gjb2tr6jcc3cx9zc96+6fNvO5ffV26vv5n7HRQUlGOB7dU8OcWXuS/a2tp53ofM9ZuYmLzz/r948YJDhw4xdepUJk2aJKdn9//0pvuNJEk4Ozu/sbLh1e/Aq9fZ1NRUQkNDqVChwlvjzS6G/D7H82vmMBcXF65cuUKDBg3euk4NDQ0aNGhAgwYNmD9/PrNmzeK7777jyJEj+Pj4vFeZ4m3bftP5XZC8VxXMkSNHsv01mNmOJrePRd+Fo6Mjmpqaclu9TMuWLXvrZzN/Yb0ae0xMDOvWrcuS19DQMNvhUzp27Mjp06fZt29flmXR0dGkpaUB0LRpU9LS0lSGVEhPT8/1TC4DBw7E2toaPz+/LG1zkpKS6N27N5IkqVxoMi9irx6bzKEEcrt/TZs25cyZM5w7d05Oi4qK4rffflPJ5+Pjg46ODj/99JPK8Vy7di0xMTE0a9YMUP5IsLa2ZsWKFSrDAezdu5cbN27I+T5n33//Pfv376dTp045Pvp53ZIlS+S/JUliyZIlaGtr06BBgzd+ztbWFk9PT9avX6/y/xsUFMT+/fvlwbk1NTXx8fFhx44dKm327ty5k6XdXU40NTVp164d27Ztk2tAXxUVFSX//eowG6B8suDu7o4kSaSmppKenp7lEZq1tTV2dnZZhpF4VdOmTUlPT1c5XgALFixAoVDkW81ipl9//VWlKcfWrVt5/PixvJ3srjGSJGUZCiUhIYGkpCSVNBcXF4yNjeX99fX1xcTEhFmzZpGampollszj++r/+avH8MCBA/IwRG+S23P5feX2+tuoUSOMjY2ZPXt2lmOU+dlKlSrh7OzMwoULs1zHMvNYW1tTt25dVq5cmW0lxavfz9d5eXnh4uLCDz/8IDf3yO1nM2W3v4A8EsWrMscsfH1f2rZti6amJlOnTs2yHkmS5POqcuXKWFlZsWLFClJSUuQ8v/zyS66HOsvunpCf5/ib9jOvOnbsSHh4OKtXr86yLDExkfj4eED5yP51mTW+medZdvfN9PT0HB+RvypzFIzX9yc353dB8l41hMOGDSMhIYE2bdpQunRpUlJSOHXqFJs3b8bJySlLA9D8ZGpqSocOHVi8eDEKhQIXFxd27dqVpX1Gdho1aoSOjg4tWrRg4MCBvHz5ktWrV2NtbZ3lguHl5cXy5cuZMWMGrq6uWFtbU79+ffz8/Ni5cyfNmzenV69eeHl5ER8fz9WrV9m6dSthYWFYWlrSokULatasydixYwkLC8Pd3Z2//vorV+1GAIoUKcLWrVtp1qwZlSpVyjJTyZ07d1i0aJHKoNSNGjWiePHi9O3bFz8/PzQ1Nfn555+xsrLi/v37udq/MWPGsGHDBho3bszXX38tDzvj6Oio0kbIysqKcePGMXXqVBo3bkzLli25efMmy5Yto0qVKnKtpra2NnPmzKF37954e3vTpUsXedgZJycnRo4cmavj8SY///xztmPJZQ49AcoG3Nn9KnNxcaF69ervHQMoh8zJ3EZSUhL37t1j586dBAYGUq9evVxdYAD09PTw9/enZ8+eVKtWjb1797J7927Gjx//1iEaQPmYq0mTJlSvXp2+ffvKw86YmprKY2SBcqiR/fv3U7NmTQYPHiwXrDw8PLh8+XKuYv3+++85cuQI1apVo3///ri7u/P8+XMCAgI4ePCgfEFu1KgRNjY21KxZk6JFi3Ljxg2WLFlCs2bNMDY2Jjo6mmLFitG+fXsqVKiAkZERBw8e5Pz58/z44485br9FixbUq1eP7777jrCwMCpUqMD+/fv5+++/GTFiRJYxPN+XhYUFtWrVonfv3kRGRrJw4UJcXV3p378/oGwX6+LiwjfffEN4eDgmJiZs27YtS1vCW7du0aBBAzp27Ii7uztaWlps376dyMhIOnfuDChrp5YvX0737t2pVKkSnTt3ls/l3bt3U7NmTbkgPHv2bJo1a0atWrXo06cPz58/l8eEy65A86rcnsvvK7fXXxMTExYsWEC/fv2oUqWKPLbflStXSEhIYP369WhoaLB8+XJatGiBp6cnvXv3xtbWluDgYK5duyb/YF+6dCm1atWiXLly9O/fnxIlShAZGcnp06d5+PAhV65cyTZWDQ0N1qxZQ5MmTShbtiy9e/fG3t6e8PBwjhw5gomJCf/8888b99fExIQ6deowd+5cUlNTsbe3Z//+/YSGhmbJ6+XlBcB3331H586d0dbWpkWLFri4uDBjxgzGjRtHWFgYrVu3xtjYmNDQULZv386AAQP45ptv0NbWZsaMGQwcOJD69evTqVMnQkNDWbduXa7bEHp5eXHw4EHmz5+PnZ0dzs7OVKtWLd/O8TftZ14H8e7evTt//vkngwYN4siRI9SsWZP09HSCg4P5888/2bdvH5UrV2batGkcO3aMZs2a4ejoyJMnT1i2bBnFihWTx4osW7YsX3zxBePGjeP58+dYWFjwxx9/yJU7b6Kvr4+7uzubN2+mZMmSWFhY4OHhQVpa2lvP7wLlfboo7927V+rTp49UunRpycjISNLR0ZFcXV2lYcOGSZGRkSp5cxp25vWhQrIbykOSsh/OIyoqSmrXrp1kYGAgmZubSwMHDpSCgoJyNezMzp07pfLly0t6enqSk5OTNGfOHOnnn3/O0u08IiJCatasmWRsbJyl235cXJw0btw4ydXVVdLR0ZEsLS2lGjVqSD/88IPK0DvPnj2TunfvLpmYmEimpqZS9+7d5eFH3jbsTKbQ0FCpf//+UvHixSVtbW3J0tJSatmyZZbu9pkuXrwoVatWTdLR0ZGKFy8uzZ8/P9tu9W/av8DAQMnb21vS09OT7O3tpenTp0tr167Ntmv+kiVLpNKlS0va2tpS0aJFpcGDB2cZBkKSJGnz5s1SxYoVJV1dXcnCwkLq2rWr9PDhQ5U8bxu65XWZ+5XT68GDB28ddianoWXeZdiZV9drYGAgOTk5Se3atZO2bt2qMpzHm2Qeg5CQEKlRo0aSgYGBVLRoUWny5Mkq68jcr5yGNTh48KBUs2ZNSV9fXzIxMZFatGghXb9+PUu+Q4cOSRUrVpR0dHQkFxcXac2aNdLo0aMlPT09lXxAjkPCREZGSkOGDJEcHBwkbW1tycbGRmrQoIG0atUqOc/KlSulOnXqSEWKFJF0dXUlFxcXyc/PT4qJiZEkSTl0iZ+fn1ShQgXJ2NhYMjQ0lCpUqCAtW7Ysy/F5fRiVuLg4aeTIkZKdnZ2kra0tubm5SfPmzVMZLuRN+/D6NSo7mUNQbNq0SRo3bpxkbW0t6evrS82aNZOHksl0/fp1ycfHRzIyMpIsLS2l/v37y8PbZJ73T58+lYYMGSKVLl1aMjQ0lExNTaVq1apJf/75Z7bb9vX1lUxNTSU9PT3JxcVF6tWrl3ThwgWVfNu2bZPKlCkj6erqSu7u7tJff/2V7fHKSW7OZW9v72yHFsntdnJ7/c3MW6NGDfk7XLVqVWnTpk0qeU6cOCE1bNhQ/s6UL18+yxBCISEhUo8ePSQbGxtJW1tbsre3l5o3by5t3bpVzpPTcGaXLl2S2rZtK39vHR0dpY4dO0qHDh2S8+R075IkSXr48KHUpk0byczMTDI1NZU6dOggPXr0KNuhSqZPny7Z29tLGhoaWY7Htm3bpFq1akmGhoaSoaGhVLp0aWnIkCHSzZs3VdaxbNkyydnZWdLV1ZUqV64sHTt2TPL29s7VsDPBwcFSnTp1JH19/SzXxvw4x3Ozn6/L6fsmScqhdubMmSOVLVtW0tXVlczNzSUvLy9p6tSp8jYPHToktWrVSrKzs5N0dHQkOzs7qUuXLlmGjQsJCZF8fHwkXV1dqWjRotL48eOlAwcOvHXYGUmSpFOnTkleXl6Sjo6O/P+al/O7IFBIUj61ohYEId/06tWLrVu3vrVW50Nq3bo1165dy7atkyAIgvB5+fy6cQqCkGevT6d2+/Zt9uzZ80lP1C4IgiDknnrGKxAEoUApUaKEPNfqvXv3WL58OTo6OjkOqyQIgiB8XkSBUBAEGjduzKZNm4iIiEBXV5fq1asza9asXPeGFgRBED5tog2hIAiCIAhCISfaEAqCIAiCIBRyokAoCIIgCIJQyIkCoSAIufbLL7+gUCgICwt7a14nJyd69er1wWMSBEEQ3p8oEArCZyo0NJShQ4dSsmRJDAwMMDAwwN3dnSFDhqjMNlOYpKens27dOurWrYuFhQW6urryrEoXLlzIkv/atWt069YNe3t7dHV1sbOzo2vXrly7du294nBycqJ58+bZLrtw4QIKhYJffvlFJf3EiRM0adIEe3t79PT0KF68OC1atOD333/PcTtVq1ZFoVCoTJ0pCIKQHVEgFITP0K5du/Dw8GDDhg34+PiwYMECFi1aRJMmTdizZw+enp65mrT9c5KYmEjz5s3p06cPkiQxfvx4li9fTo8ePTh9+jRVq1bl4cOHcv6//vqLSpUqcejQIXr37s2yZcvo27cvR44coVKlSmzfvv2jxb5lyxbq1KlDZGQkX3/9NYsXL6Zbt268ePEi23lcQTmW5Pnz53FycsoyB7kgCMLrxLAzgvCZCQkJoXPnzjg6OnLo0CFsbW1Vls+ZM4dly5ahoVG4fg/6+fnh7+/PggULGDFihMqyyZMns2DBAvl9SEgI3bt3p0SJEhw7dkxl7uivv/6a2rVr0717dwIDA3M9R+z7mDJlCu7u7pw5cwYdHR2VZTnN375x40asra358ccfad++PWFhYTg5OX3wWAVB+DQVrjuCIBQCc+fOJT4+nnXr1mUpDAJoaWkxfPhwHBwcVNIPHz5M7dq1MTQ0xMzMjFatWnHjxo23bk+SJGbMmEGxYsUwMDCgXr16OT5SjY6OZsSIETg4OKCrq4urqytz5swhIyNDzhMWFoZCoeCHH35g1apVuLi4oKurS5UqVTh//nwej4bSw4cPWblyJQ0bNsxSGATQ1NTkm2++oVixYgDMmzePhIQEVq1apVIYBLC0tGTlypXEx8czd+7cd4onr0JCQqhSpUqWwiCAtbV1tp/5/fffad++Pc2bN8fU1PSNj5YFQRBEDaEgfGZ27dqFq6sr1apVy/VnDh48SJMmTShRogRTpkwhMTGRxYsXU7NmTQICAt5YszRp0iRmzJhB06ZNadq0KQEBATRq1IiUlBSVfAkJCXh7exMeHs7AgQMpXrw4p06dYty4cTx+/JiFCxeq5P/999+Ji4tj4MCBKBQK5s6dS9u2bbl79y7a2tp5OSTs3buXtLQ0unfvnqv8//zzD05OTtSuXTvb5XXq1MHJyYndu3fnKY53lVnb+/DhQ7nQ+iZnz57lzp07rFu3Dh0dHdq2bctvv/3G+PHjP0K0giB8kiRBED4bMTExEiC1bt06y7IXL15IUVFR8ishIUFe5unpKVlbW0vPnj2T065cuSJpaGhIPXr0kNPWrVsnAVJoaKgkSZL05MkTSUdHR2rWrJmUkZEh5xs/frwESD179pTTpk+fLhkaGkq3bt1SiWvs2LGSpqamdP/+fUmSJCk0NFQCpCJFikjPnz+X8/39998SIP3zzz95Pi4jR46UAOnSpUtvzRsdHS0BUqtWrd6Yr2XLlhIgxcbG5jkeR0dHqVmzZtkuO3/+vARI69atk9PWrl0rAZKOjo5Ur149aeLEidLx48el9PT0bNcxdOhQycHBQf4/2b9/f673XxCEwkk8MhaEz0hsbCwARkZGWZbVrVsXKysr+bV06VIAHj9+zOXLl+nVqxcWFhZy/vLly9OwYUP27NmT4/YOHjxISkoKw4YNQ6FQyOnZPZbdsmULtWvXxtzcnKdPn8ovHx8f0tPTOXbsmEr+Tp06YW5uLr/PrK27e/duLo6EqszjYmxs/Na8cXFxucqbuTxz3R9Snz598Pf3p27dupw4cYLp06dTu3Zt3NzcOHXqlEretLQ0Nm/eTKdOneT/k/r162NtbS06lwiCkCPxyFgQPiOZhZSXL19mWbZy5Uri4uKIjIykW7ducnpmb+NSpUpl+UyZMmXYt28f8fHxGBoaZlme+dnX5zy2srJSKcyBstdrYGBgljZ5mV7vHFG8eHGV95nre/HiRbaffxMTExPgv8Lem2Qew7flzW3B8V29WsAG8PX1xdfXl4SEBC5evMjmzZtZsWIFzZs3Jzg4WG5LuH//fqKioqhatSp37tyRP1+vXj02bdrEnDlzCl2HIkEQ3k4UCAXhM2JqaoqtrS1BQUFZlmW2KczNoNIfQkZGBg0bNmTMmDHZLi9ZsqTKe01NzWzzSe8w/Xrp0qUBuHr1Kp6enm/Mm3kM3zZWY2BgIPb29nJhMy/09PRITEzMdllCQoKcJzsGBgbUrl2b2rVrY2lpydSpU9m7dy89e/YEkGsBO3bsmO3n//33X+rVq5fnmAVB+LyJAqEgfGaaNWvGmjVrOHfuHFWrVn1rfkdHRwBu3ryZZVlwcDCWlpbZ1g6++tnbt2+rDL8SFRWVpSbPxcWFly9f4uPjk+t9yS9NmjRBU1OTjRs35qpjSfPmzVm9ejUnTpygVq1aWZYfP36csLAwBg4c+E7xODo6cv369WyXZf4/ZB7bN6lcuTKgfOwPEB8fz99//02nTp1o3759lvzDhw/nt99+EwVCQRCyEM8NBOEzM2bMGAwMDOjTpw+RkZFZlr9ew2Zra4unpyfr168nOjpaTg8KCmL//v00bdo0x235+Pigra3N4sWLVdb7eo9hUNZYnT59mn379mVZFh0dTVpaWi727t04ODjQv39/9u/fz+LFi7Msz8jI4Mcff5QHpvbz80NfX5+BAwfy7NkzlbzPnz9n0KBBGBgY4Ofn907xNG3alIcPH7Jjxw6V9OTkZNasWYO1tTWVKlWS0w8dOpTtejLbd2Y+7t++fTvx8fEMGTKE9u3bZ3k1b96cbdu2kZyc/E5xC4Lw+RI1hILwmXFzc+P333+nS5culCpViq5du1KhQgUkSSI0NJTff/8dDQ0NleFL5s2bR5MmTahevTp9+/aVh50xNTVlypQpOW7LysqKb775htmzZ9O8eXOaNm3KpUuX2Lt3L5aWlip5/fz82LlzJ82bN6dXr154eXkRHx/P1atX2bp1K2FhYVk+8zZhYWE4OzvTs2fPLFO9ve7HH38kJCSE4cOH89dff9G8eXPMzc25f/8+W7ZsITg4mM6dO8vHcP369XTt2pVy5crRt29fnJ2dCQsLY+3atTx9+pRNmzbh4uKisg2FQoG3tzdHjx59YywDBgzg559/pkOHDvTp04eKFSvy7NkzNm/eTFBQEL/++qvKmIOtWrXC2dmZFi1a4OLiQnx8PAcPHuSff/6hSpUqtGjRAlA+Li5SpAg1atTIdrstW7Zk9erV7N69m7Zt277l6AqCUKiot5OzIAgfyp07d6TBgwdLrq6ukp6enqSvry+VLl1aGjRokHT58uUs+Q8ePCjVrFlT0tfXl0xMTKQWLVpI169fV8nz+rAzkiRJ6enp0tSpUyVbW1tJX19fqlu3rhQUFCQ5OjqqDDsjSZIUFxcnjRs3TnJ1dZV0dHQkS0tLqUaNGtIPP/wgpaSkSJL037Az8+bNyxIjIE2ePFl+f/XqVQmQxo4dm6tjkpaWJq1Zs0aqXbu2ZGpqKmlra0uOjo5S7969sx2SJTAwUOrSpYtka2sraWtrSzY2NlKXLl2kq1evZskbFxcnAVLnzp1zFcuLFy+kkSNHSs7OzpK2trZkYmIi1atXT9q7d2+WvJs2bZI6d+4subi4SPr6+pKenp7k7u4ufffdd/KwN5GRkZKWlpbUvXv3HLeZkJAgGRgYSG3atMlVjIIgFB4KSXqHFtqCIAgFwLJlyxgzZgwhISEULVpUrbHs2bOH5s2bc+XKFcqVK6fWWARBEPJKtCEUBOGTdeTIEYYPH672wmBmLJ07dxaFQUEQPkmihlAQBEEQBKGQEzWEgiAIgiAIhZwoEAqCIAiCIBRyokAoCIIgCIJQyIkCoSAIgiAIQiEnBqYG0tLSuHTpEkWLFhWTvguCIAjCJyIjI4PIyEgqVqyIlpYo0rwPcfSAS5cu5WrOV0EQBEEQCp5z585RpUoVdYfxSRMFQpDHMDt37hy2trZqjkYQBEEQhNx4/PgxVatWLRBjkX7qRIEQ5MfEtra2KvO7CoIgCIJQ8InmXu9PHEFBEARBEIRCThQIBUEQBEEQCjlRIBQEQRAEQSjkRBvCXMrIyCAlJUXdYaidtrY2mpqa6g5DEAq99PR0UlNT1R2GIHxQ4p7z8YgCYS6kpKQQGhpKRkaGukMpEMzMzLCxsUGhUKg7FEEodCRJIiIigujoaHWHIggfhbjnfByiQPgWkiTx+PFjNDU1cXBwKNQ9mSRJIiEhgSdPngCIIXoEQQ0yC4PW1tYYGBiIm6Tw2RL3nI9LFAjfIi0tjYSEBOzs7DAwMFB3OGqnr68PwJMnT7C2thZV+YLwEaWnp8uFwSJFiqg7HEH44MQ95+MpvNVduZSeng6Ajo6OmiMpODILxqL9kiB8XJnnnPhxKhQm4p7zcYgCYS6JxzL/EcdCENRLnINCYSK+7x+HWguEx44do0WLFtjZ2aFQKNixY4fKckmSmDRpEra2tujr6+Pj48Pt27dV8jx//pyuXbtiYmKCmZkZffv25eXLlx9xLwRBEARB+BS8rdyRnaNHj1KpUiV0dXVxdXXll19+yZJn6dKlODk5oaenR7Vq1Th37pzK8qSkJIYMGUKRIkUwMjKiXbt2REZG5tNe5Q+1Fgjj4+OpUKECS5cuzXb53Llz+emnn1ixYgVnz57F0NAQX19fkpKS5Dxdu3bl2rVrHDhwgF27dnHs2DEGDBjwsXZBEARBEIRPxNvKHa8LDQ2lWbNm1KtXj8uXLzNixAj69evHvn375DybN29m1KhRTJ48mYCAACpUqICvr6/cGQZg5MiR/PPPP2zZsoV///2XR48e0bZt23zfv/ciFRCAtH37dvl9RkaGZGNjI82bN09Oi46OlnR1daVNmzZJkiRJ169flwDp/Pnzcp69e/dKCoVCCg8Pz/W2Hzx4IAHSgwcPsixLTEyUrl+/LiUmJr7DXqlfnTp1JED6/fffVdJ/+uknydbW9p3W+akfE0H4VIlzTz28vb2lr7/+Osf3H2Obhdmbvvdvun+/zevljuyMGTNGKlu2rEpap06dJF9fX/l91apVpSFDhsjv09PTJTs7O2n27NmSJCnLLtra2tKWLVvkPDdu3JAA6fTp03mO+0MpsG0IQ0NDiYiIwMfHR04zNTWlWrVqnD59GoDTp09jZmZG5cqV5Tw+Pj5oaGhw9uzZjx7z60JC7xESeo+Hjx4T9fQZMbFxJCWnkCFJH2X7kiRx6dIlbG1t2bZtm8qyixcvUqlSpY8ShyAIAkCvXr1QKBR8//33Kuk7duz4pNqJ/fXXX0yfPl2tMURFRTF48GCKFy+Orq4uNjY2+Pr6cvLkSUDZ7u5NrylTpgDKYYyGDRtGiRIl0NXVxcHBgRYtWnDo0KE3bt/b21tel7a2NqVKleL333//0Lv90Z0+fVqlHALg6+srl0NSUlK4ePGiSh4NDQ18fHzkPBcvXiQ1NVUlT+nSpSlevLicpyAosMPOREREAFC0aFGV9KJFi8rLIiIisLa2VlmupaWFhYWFnCc7ycnJJCcny+/j4uLyK2wVvr6+hNy+mSVdW0cHYxNTitrYUsLFjXETJmJZpAgOdjbo62jm24Xx9u3bxMXF8f333+Pn50dCQoLcWysgIIA2bdrky3YEQRByS09Pjzlz5jBw4EDMzc3zZZ0pKSkfdSQICwuLj7atnLRr146UlBTWr19PiRIliIyM5NChQzx79gyAx48fy3k3b97MpEmTuHnzv/uRkZERYWFh1KxZEzMzM+bNm0e5cuVITU1l3759DBkyhODg4Gy3nVnZMHv2bHr16kVCQgLTpk2jZ8+eVK9eHWdn5w+789mIi4sjNjZWfq+rq4uuru57rzciIiLbckhsbCyJiYm8ePGC9PT0bPNkHr+IiAh0dHQwMzPLkudNZZWPrcDWEH5Is2fPxtTUVH65u7t/kO1o6+igk80XMjUlhedPo7gRFMjuv7dRq4onpUs4ULN2LSbM+pHr954QEZPEk9gkouKSeR6fQkJyGul5nCnl4sWL6Onp0a9fP0xMTNi7dy+gbNx648YNUUMoCMJH5+Pjg42NDbNnz84xT3JyMsOHD8fa2ho9PT1q1arF+fPn5eV169Zl6NChjBgxAktLS3x9feX0YcOGMWLECMzNzSlatCirV68mPj6e3r17Y2xsjKurq3wtzOTv70+tWrUwMzOjSJEiNG/enJCQkBzjq1u3LiNGjAAgLCws2xq4unXrAsppT2fPno2zszP6+vpUqFCBrVu3qqwvPj6eHj16YGRkhK2tLT/++OMbj2F0dDTHjx9nzpw51KtXD0dHR6pWrcq4ceNo2bIlADY2NvLL1NQUhUKhkmZkZMRXX32FQqHg3LlztGvXjpIlS1K2bFlGjRrFmTNnctx+ZmVDrVq1sLGxoUSJEowbN460tDQCAwPfGPuH4u7urnJff9P3S8hegS0Q2tjYAGTphRMZGSkvs7GxUWm0CcqBpJ8/fy7nyc64ceOIiYmRX9evX891XJIkkZCSlqvXxYAAXsS+5GVSCtEvE4h49oJ74RFcDb7Fv6fOMmjo1xS1tcPQyBiAy+fPMmuCHxXcitGj25cMGTqUb8eNZ8rM2azd/DcXQiIICo/hxuNY7j2LJyYhBekNj58DAgIoX748Ojo6tGnTRr4IXblyhbS0tCwFwo4dO1KzZs1cHwtBEIS80tTUZNasWSxevJiHDx9mm2fMmDFs27aN9evXExAQgKurK76+vjx//lzOs379enR0dDh58iQrVqxQSbe0tOTcuXMMGzaMwYMH06FDB2rUqEFAQACNGjWie/fuJCQkyJ+Jj49n1KhRXLhwgUOHDqGhoUGbNm1yNV2pg4MDjx8/ll+XLl2iSJEi1KlTB1BWQPz666+sWLGCa9euMXLkSLp168a///4rr8PPz49///2Xv//+m/3793P06FECAgJy3KaRkRFGRkbs2LFD5WlXXjx//hx/f3+GDBmCoaFhluWv12a96uLFiygUCsqXLy+nZf5fvl5T9rFcv35d5b4+bty4fFmvjY1NtuUQExMT9PX1sbS0RFNT861llZSUlCzTTb6apyAosI+MnZ2dsbGx4dChQ3h6egIQGxvL2bNnGTx4MADVq1cnOjqaixcv4uXlBcDhw4fJyMigWrVqOa779arkV6uZ3yYxNR33SfvenjE3DBui16MheoDJ0/tEH11HYsh50lJTObBre5bsmpqaOLm4YWFpRb1GTWncsi3WRYtiqKOFhoYCLQ0FGgoFCgVoKBScPX8Bj/IVSEhJo3XrNrRv347k5GQCAgKwsrLCwcFBXndgYCAPHjzgzp07+bNvgiB8FNL/p/dSh3edOq9NmzZ4enoyefJk1q5dq7IsPj6e5cuX88svv9CkSRMAVq9ezYEDB1i7di1+fn4AuLm5MXfu3CzrrlChAhMmTACUP/6///57LC0t6d+/PwCTJk1i+fLlBAYG8sUXXwDKx6+v+vnnn7GysuL69et4eHi8cV80NTXlm3pSUhKtW7emevXqTJkyheTkZGbNmsXBgwepXr06ACVKlODEiROsXLkSb29vXr58ydq1a9m4cSMNGjQAlIXaYsWK5bhNLS0tfvnlF/r378+KFSuoVKkS3t7edO7cWaWQ9iZ37txBkiRKly6dq/yvCggIwNnZGRMTEwCCg4MZM2YMnp6eVK1aFYD58+czevRogoKCKFu2LABDhgxh2bJlPH/+nHXr1vHjjz9iaWlJQkICS5cupVGjRnmOJZOxsbEcT36qXr06e/bsUUk7cOCA/P+po6ODl5cXhw4donXr1oCyVvjQoUMMHToUAC8vL7S1tTl06JD8Xbt58yb379+X11MQqLVA+PLlS5UCSGhoKJcvX8bCwoLixYszYsQIZsyYgZubG87OzkycOBE7Ozv5oJcpU4bGjRvLJ0VqaipDhw6lc+fO2NnZqWmv3o2OZXGs208mIyWRhOATpCfGIiUnkJEcT+rzcJIeBJGenkrIrWBCbgVz/tRx5k4Zx8rft/NF7brZrvPy5UvUb9aGO09eUrRUJbS0tNnr759th5JJkyYxa9YsBgwYwOPHj8WckYLwiUhISMDIyEgt23758mW2tUu5MWfOHOrXr88333yjkh4SEkJqaqrK0wptbW2qVq3KjRs35LTMSoDXvVog0tTUpEiRIpQrV05Oy6zBevXp0u3bt5k0aRJnz57l6dOncs3g/fv331ogfFWfPn2Ii4vjwIEDaGhocOfOHRISEmjYsKFKvpSUFCpWrCjvb0pKikolhoWFBaVKlXrjttq1a0ezZs04fvw4Z86cYe/evcydO5c1a9bQq1evt8b6pqdLbxMQEEBYWBhGRkakpaWhUCjo1KkTc+bMQUND+eAxKCiIcuXKcfPmTcqWLcv9+/c5ffo0xYoVw9zcnKCgIH788Uc6d+7Mjh07mDJlynsVCHPrbeWOcePGER4ezq+//grAoEGDWLJkCWPGjKFPnz4cPnyYP//8k927d8vrGDVqFD179qRy5cpUrVqVhQsXys0UQNkhtm/fvowaNQoLCwtMTEwYNmwY1atXl3+UFARqLRBeuHCBevXqye9HjRoFQM+ePfnll18YM2YM8fHxDBgwgOjoaGrVqoW/vz96enryZ3777TeGDh1KgwYN0NDQoF27dvz0008fLGZ9bU2uT/P9YOtXap0lRZIk7gRf5/Hjx2zbto01a9YAMPDLNhibmGBlZU0JV1ecSrhiZGRMQkICcTExKDLSeRAWio19MWr7NObnjX9wJ/g6Po18yciQUCiU/w8vX76kXr16lC5dmuvXr4sCoSAIH1SdOnXw9fVl3LhxuSrAvC6ngqi2trbK+8xesK++B1QeB7do0QJHR0dWr16NnZ0dGRkZeHh4kJKSkut4ZsyYwb59+zh37hzGxspmQJmTJOzevRt7e3uV/PnR4UFPT4+GDRvSsGFDJk6cSL9+/Zg8eXKujqebmxsKhSLHjiNvEhAQgJ+fH/369cPAwABbW9ssNcVBQUF07NhR7sgyffp02rRpI/eCDgoKYvjw4QDY29vL08R+aG8rdzx+/Jj79+/Ly52dndm9ezcjR45k0aJFFCtWjDVr1sjtVgE6depEVFQUkyZNIiIiAk9PT/z9/VUeny9YsEAuoyQnJ+Pr68uyZcs+wh7nnloLhHXr1n3jrxSFQsG0adOYNm1ajnksLCw+ald3hUKBgY56DluFChWoUKECjRs3plu3bnTr1o2HDx8SFxtLXGwsd0OyPu6dMlb5ZTc2NsFv6mzmTR1PUmIi/YaNJuhRDJoaCvzGfsfXY74jISVNLhBmProQBKFgMzAwUNvsTO87p/L333+Pp6enSm2Yi4uL3DbQ0dERUM5he/78ebkjR3569uwZN2/eZPXq1dSuXRuAEydO5Gkd27ZtY9q0aezduxcXFxc53d3dHV1dXe7fv4+3t3e2n3VxcUFbW5uzZ89SvHhxAF68eMGtW7dy/ExO3N3dczXzBijvnb6+vixdupThw4dnKWBHR0dn247w7t27REdH07BhQ1xdXbNdtyRJhIWF0bx5cxYsWMDdu3e5e/cu1apVw8PDA0mSCA4OplSpUqSnp7N8+XKaNm2ap319V28rd2Q3C0ndunW5dOnSG9c7dOhQ+RFxdvT09Fi6dGmuB8RWhwLbhlB4M29vb+7du8edO3eIj4/nzp07PHz4kPDwcBITEzlx4gT37t3DysqKO3fuEBcXy6RRQ9DV1SU1JQWXkmUAuHD2NGdPnWBov54AJCYk0KpNWzIyJDQ0Pp1xwQShsFIoFO/82FbdypUrR9euXVWe6hgaGjJ48GD8/Pzkx3hz584lISGBvn375nsM5ubmFClShFWrVmFra8v9+/cZO3Zsrj8fFBREjx49+Pbbbylbtqw8jIiOjg4WFhZ88803jBw5koyMDGrVqkVMTAwnT57ExMSEnj17YmRkRN++ffHz86NIkSJYW1vz3XffyY9es/Ps2TM6dOhAnz59KF++PMbGxly4cIG5c+fSqlWrXMe+dOlSatasSdWqVZk2bRrly5cnLS2NAwcOsHz5cpVH9JkyO5S8Ov7v60JDQ3FwcKBMmTKEhIQwffp0Jk6cyPbt26lUqRKhoaEkJydTvXp1dHR08PHxydMxFz4MUSD8hGloaFCyZEkAuT1KdtasWSM3qM7skbZx2Tw2bfqDoYvmsGXnboqXUX4+8NIFFs6cTNCjGGxN9bE00vmkBowVBOHTMm3aNDZv3qyS9v3335ORkUH37t2Ji4ujcuXK7Nu3L9/GLXyVhoYGf/zxB8OHD8fDw4NSpUrx008/ycPGvM2FCxdISEhgxowZzJgxQ0739vbm6NGjTJ8+HSsrK2bPns3du3cxMzOjUqVKjB8/Xs47b948Xr58SYsWLTA2Nmb06NHExMTkuE0jIyOqVavGggUL5DaXDg4O9O/fX2W9b1OiRAkCAgKYOXMmo0eP5vHjx1hZWeHl5cXy5cuz/Uxmr29TU9Mc1xsUFISHhwe6urrEx8cTHh5O3bp1mT59Oj169CAoKIgWLVpkGX5HUC+F9D4tSz8TDx8+xMHBgQcPHmTp2ZWUlERoaCjOzs4qbRc/NRcvXmT48OEEBQXJvapdXFx4/vw5S5Yswa5YcUqVr0h45DMa1ajE0SvKx88GOpoUtzBER+u/X6ufyzERhE+NOPeET8GsWbPQ1tbGz8+PzZs3U7ZsWTw8PLC1teXu3bssWLCA1NRUJk+enKv1vel7/6b7t5A3BXYcQiF/eXl5cfLkSZ4/fy4PNxMSEsKLFy/o2rUr9bxrM23cN1QuVZzIJ0/Q+v/jioSUdG5GxvE8PoXk1HRS0/M2OLYgCIJQuGTWEIKyw4WHhwdPnjzB2NgYfX19uQeyULCIR8aFjKamJleuXOHq1atcunSJ3bt3c+DAAQBWrFjBo0eP6NOnDy1atCAyLpmouGQkSeLhi1fGOUtPJSkpTU17IAiCIBRk2XX0tLa25tatWzkuF9RP1BAWQubm5tSpU4evv/6a/fv3ExMTI7cH2blzJ61bt8bJyYnUmCjcrI0wN9BBR1MDzf+3JZQkiZfJokAoCIIgCJ8LUSAUMDEx4fLly0yYMEFun/HgwQMqVqzIsycROFgYUNrWhLL2ppS2UY6vlZ4hse5EKBfvPefivRcEhcdwN+olKWnikbIgCIIgfGrEI2MBACcnJ6ZPn86kSZMYOXIkS5cu5fnz57Ru3ZqTJ0/Kg6jqaGmi/f/2hRvP3mPOwbtZ1mVppIO7nSlflLDAzdoYAx1NihjpUNom/6cVEgRBEATh/YkCoaBCW1ubRYsWER4ezo4dO7h48SL169fnxIkT8vAzxSz0iX6iSTFzA7R00pEkSEnLICYxlcTUdJ6+TOHYrSiO3YpSWXe/Ws70rOGEg8X7DWYrCIIgCEL+EgVCIQtNTU3+/PNPOnbsyI4dOzh16hTu7u507twZTU1NunbtirmhDuv7VFUZAkCSJB7FJHErIo7zYc+5EPaCiNgk7j9XdkhZcyKUNSdC+bZxaQbXdclp84IgCIIgfGSiQChkS1tbm+3bt9O3b19+/vlngoODmTJlCgATJ06kf//+WeaMVigU2JvpY2+mT73S1nL6o+hEFh++zaZzDwCY4x+MgY4mPWs4fazdEYTPihg+VihMxPf94xCdSoQ3Wr16NXv37qVfv350794dLS3lbwh/f/9cr8POTJ/ZbcsTMLEhxrrKz0/eeY2a3x9m/v6bJKd9nEnNBeFTp62tDUBCQsJbcgrC5yPz+575/Rc+DDFTCYVjppL8cu3aNTw8PDAxMSEyMjLPx+TZy2R6/HyOa49i5TQzA22G1nPF3c4EFysjipqI4ywIOXn8+DHR0dFYW1tjYGAgppYUPluSJJGQkMCTJ08wMzPD1tY2Sx4xU0n+EY+MhTyxs7MDIDY2luXLlzNy5Mg8fb6IkS67htUi7FkCwzdd4mp4DNEJqczY/d8k6su6VqJpuawnviAIYGNjA8CTJ0/UHIkgfBxmZmby9174cEQNIZ93DaG3tzfHjh3j999/p0uXLnL64sWLmT17No8ePcrzOh0dHbl//z4AhoaGFC1aFF1dXXR1dXFwcKBGjRo0a9bsrVMTSZLEgeuRrD0RSmxSGjcex6ostzDUobKjOb1rOlPdpUie4xSEz1l6ejqpqanqDkMQPihtbW00NTVzXC5qCPOPKBDy+RYIJUnC1NQUIyMjatSowdatW+VlvXr14unTp+zatSvP671+/Trdu3cnICDgjfmKFStGkyZNcHV1xdbWlm7dur3x8dblB9EM+S2A8OjELMvql7amWTlbWlSwQ0dLNH0VBEEQRIEwP4lHxp+x27dvExcXx/fff4+fnx8JCQkYGCjHAAwICKBNmzbvtN4SJUrw66+/kpaWRnp6OsnJySQnJ3P//n2OHDnCsWPHuHv3Lg8fPmT16tXy52JiYhg6dGiO6/V0MOPEt/V4+CKR1PQMLt2PZtqu68QkpnI4+AmHg58wffd1fu/3Be52YpBrQRAEQcgvooaQz7eGcNOmTfTp04eYmBgcHR1ZsmQJ7dq1IykpCWNjY7Zu3UqrVq3yvN7cHJOTJ0/y66+/EhkZyd9//y2nFytWjI0bN+Lt7Z2rbSWkpOEfFMHCg7fl8QwBBtd1wUBbE30dTbpULY6hrvhtIwiCUNiIGsL8I569vauU+JxfqUl5yJuYu7zvICAggPLly6Ojo0ObNm3kR8ZXrlwhLS2NSpUqATB//nyKFSuGp6cnzs7ODBky5J2296qaNWuycuVKduzYwYULF+T2hA8fPmTevHm5Xo+BjhZtKxXj6Dd1+bZxaTl9+dEQfjxwixm7b1B28j78gx6/d8yCIAiCUFiJapV3Ncsu52VujaDrlv/ez3OF1BzGDXOsBb13//d+YTlIeJY135SYPIcYEBAgF/ratm1L27ZtSU5OJiAgACsrKxwcHAAICgpi0aJFtGvXjsTERCwtLZkzZw5GRkZ53mZ2vLy8CAwM5LfffqNbt27s3r2b+fPnM2rUqFyvQ0NDweC6LlRwMJVnQHn4IlGeHm/z+Qc09hA9kwVBEAThXYgaws9YQEAAXl5eANStWxdtbW327dvHxYsX5YIiKAuEnp6eAFy+fBkXF5d8Kwy+qm3btpiZmQEwevRoVq1aled11HCxZHgDN2a1Kcevfaqy5MuKABy5GcXNiLj8DFcQBEEQCg1RQ/iuxr9huBbFa13k/e68Ie9rZfIRV989plfcvXuX6OhoueCnpaVFy5Yt2bZtG1evXqVJkyaAsifyzZs3adeuHQkJCURHR3PixIl8ieF1+vr6hISE4ODgQEJCAgMHDmT27NkcP378ndt+VChmJv/tu/AYNVyKsLybF6b6YkR7QRAEQcgtUUP4rnQMc35p6+Uhr37u8ubRxYsX0dHRwcPDQ05r164dO3fu5Nq1a3JBMTQ0lDJlynD58mVu3brF8OHDWbBgQZ63l1sWFhacPXuWkiVLAhAWFoaHhwc3b958p/U5WBgwoVkZ9LWVhfBTIc+oMHU/03dd59CNSGKTxDhtgiAIgvA2okD4mQoICMDDwwMdHR05rWHDhqSnp5OSkiIXCIOCgihVqpScp1y5ckRGRn7Q2Dw8PAgODpaHoImJiaF06dI4ODjw5ZdfkpGRkaf19atdgqtTGuHn+99+rD0RSt/1F+i44jRp6XlbnyAIgiAUNqJA+JmaPXs2Fy9eVEnT1dUlNjYWSZJwdnYGVAuE6enpbNq0iQYNGnzw+BQKBQsWLGDGjBly2sOHD9m0aRMDBgwgPT09T+vT0tRgSD1Xgqc3ZlTDklRxMgcgOCKOhguOkZImCoWCIAiCkBNRICzkgoKCWLFiBRUrVqRKlSq4uLgwePDgj7JtLS0tvvvuO2JjY7l69SrNmjUDYO3atWhpaWFjY0Pnzp2JiorK9Tr1tDUZ3sCNLYNqMNC7BAChT+OZf+DWB9kHQRAEQfgciIGp+XwHpv5QPtQxefToEU2aNCEwMFAl3dLSkvDwcJXH37k19PcAdgUqxygc16Q0A71d8iVWQRAEQf3EwNT5R9QQCgWGnZ0dly9f5tq1a1y4cIE+ffoA8PTpU3R1denbty/PnmUzRuMbjGxYUv579t5gWi45wZUH0fkZtiAIgiB88kSBUChQFAoF7u7ueHl5sXbtWpVZTX7++WeqV6+ep0Khi5URh0Z7U9rGGIDAhzGM354/Q/sIgiAIwudCFAiFAu2bb77h2bNn8tzHt2/fznMbRxcrI/xH1GFxF+Ug1tcexdJr3TkyMgp9awlBEARBAESBUPgEWFhYsGfPHmrXrg3Ali1bKFmyJBUqVKBy5crcuHEjV+tpUcGOGi5FADh6M4r6Px4lMSVvvZkFQRAE4XMkCoTCJ8HAwIADBw5QvHhxQFlTGBgYyMWLF3F3d2fv3r25Ws/GvtXoVcMJgLBnCZwLe/6hQhYEQRCET4YoEAqfDF1dXa5fv86pU6f4448/WLp0qbxs7dq1uVqHhoaCyS3c8bA3AeC77VeJSRSzmQiCIAiFm5jLWPikGBoaUr16dapXrw6ApqYmgwYN4vHjx7leh0KhoImHLUHhsTx8kUiThcdY3s0LD3tTNDUUHyp0QRAEQSiwRA2h8Elzd3cHIDAwME9T3vWt5UzdUlYAPIpJotXSk5SasJdxf10VnU0EQRCEQkcUCIVPmrW1NQAvX75ES0uLs2fP5upzetqarOjmRf/azpQqqhySJi1DYtO5+1wNj/lg8QqCIAhCQSQKhMInzdXVlfr16wMgSRJffPEFwcHBufqsnrYm3zVzZ9/IOpz77r/5mw9cj/wgsQqCIAhCQSUKhMInTVNTk0OHDrF9+3Y5rUyZMixfvpy8zMpobaxHu0rKaY+WHLmDf1Du2yQKgiAIwqdOFAg/c97e3igUCjZt2qSSvnjxYuzs7NQUVf5r3bo1GzdulN9/9dVXmJubs2TJklyvo0d1Rwx1NAEYtDGANcfv8ig6kRuPYzly8wn7r0Vw9OYTYhJEr2RBEATh86KQ8lKN8pl60+TYSUlJhIaG4uzsjJ6enpoifDeSJGFqaoqRkRE1atRg69at8rJevXrx9OlTdu3alef1FuRj8vjxY5o3b05AQICcdvfuXZydnXP1+fDoRGp+f/it+ZZ1rUTTcrbvHKcgCILw/t50/xbyRtQQfsZu375NXFwcEyZMYO/evSQkJMjLAgIC8PLyUmN0H4atrS0XL14kKipKTitbtiypqbmr1bM30+fqlEYUtzAAQEdTAwtDHcrYmuBuayLnG7bpkqgpFARBED4bYhzCd5SQmvD2TK/R0dRBS0N5yNMy0khJT0FDoYGe1n+1bDmt10DbIM/bu3jxInp6evTr14/p06ezd+9e2rVrR1JSEjdu3GD69Ol5XuenwtLSkt9++42uXbuSmJjI3bt3KVWqVK4+a6ynzb9+dUlNl9DRUv3NdD7sOR1WnCY9Q8J34TEOf+ONgY44jQRBEIRPm7iTvaNqv1fL82d+8P4BXydfAA7dP8Q3/35D5aKVWdd4nZyn8bbGvEh+keWzV3tezfP2AgICKF++PDo6OrRp04atW7fSrl07rly5QlpaGpUqVZLz/vLLLyxevJj09HQyMjIYPXo0PXv2zPM2C5Ivv/yS2bNnExQURKdOnbhw4QJaWrn7yisUCnS0sg5SXcXJgi5VHdh07gERsUmM3HyZeR0qYKKnnd/hC4IgCMJHIx4Zf8YCAgLkQl/btm3ZvXs3ycnJBAQEYGVlhYODAwArV67k559/Zt++fVy+fJkjR47kqYduQVahQgUArly5kuv5jt9mcouy1HApAsC+a5GUn7KfGrMP0XvdOTafv//ZHDtBEASh8BCdSni3TiWfwiNjc3Nz5s2bR79+/UhLS6No0aKsW7eOnTt38vDhQ/z9/Xnx4gUlS5bkypUrue51XJA7lbzu6dOnlC9fXp7aLrOW9H09iU3ix/232H89ghevtSVs4mHD/I6e6P+/x7IgCILwYYhOJflH1BC+IwNtgzy/MguDAFoaWhhoG6gUBt+03ry6e/cu0dHRcg2hlpYWLVu2ZNu2bSodSrZv3069evU+qyFoXmVpacmqVavk9+3bt2fixInvvV5rEz3mtC/PhQkNOfJNXeZ3rCAv2xsUwZ8XHrz3NgRBEAThYxEFws/UxYsX0dHRwcPDQ05r164dO3fu5Nq1a3JBMSgoCE9PTzVF+XE0b96ce/fuye//+uuvfFu3poYCZ0tD2lYqxrnxDTD4f63g5J3XeBGfQnJaOsduRbHi3xAWHrzFP1cecf1RLMlp6fkWgyAIgiC8L9Gp5DMVEBCAh4cHOjo6clrDhg1JT08nJSVFLhAaGhqSkZGhrjA/muLFi3P79m3c3Ny4fv06x44do06dOvm6DWsTPea2L8/Q3y8BUHH6ATQUkJFNowwzA21quloyubk71iYF+7G7IAiC8PkTbQj5fAemzo1Tp07Rr18/jh8/TpEiRYiNjWXnzp1069Ytx898qsckNTUVU1NTEhMT0dbW5unTp5iYmLz9g3mQnJbO0sN3+PlkGC+T0wAoYqhDZSdzAB6+SOTao1iVz1ga6aCpoSAtXSI1PYO0DAlJgkqOZnTwcqB1Rft8jVEQBOFzIdoQ5h9RQ1jI1ahRg1GjRlGvXj0kSUKhUDBy5Eh1h/VBaGtr8/fff9OoUSNSU1NxdHQkPDwcA4O8t9HMia6WJqMalaJ3TWcexyRhYaiDlbEumhr/DWGTkpbBwoO32HrxIU/iknn6MiXbdZ2884yTd55RrYQFtqb6+RajIAiCILxO1BBSuGsI38WnfkxmzJghdyw5fPgw9erVU1ssD18k8PRlCloaCrQ1NdDSVKCtocHDFwl8ueasnM/BQp/0dImUdInktHQMdDTpVcOZ7tUdMdIVv+sEQSic3qWGcOnSpcybN4+IiAgqVKjA4sWLqVq1arZ5U1NTmT17NuvXryc8PJxSpUoxZ84cGjduLOeJi4tj4sSJbN++nSdPnlCxYkUWLVpElSpV5DwvX75k7Nix7Nixg2fPnuHs7Mzw4cMZNGjQ+x2AfCQ6lQiFzoQJE3B3dweUFxN1KmZugKeDGR72ppSyMcbFyojiRQyo4WrJ/I4V0NZU1iw+eJ7Io5gknr5MJi4pjcjYZOb4B+MxeR8tFp/gj3P3iYhJUuu+CIIgFHSbN29m1KhRTJ48mYCAACpUqICvry9PnjzJNv+ECRNYuXIlixcv5vr16wwaNIg2bdpw6dIlOU+/fv04cOAAGzZs4OrVqzRq1AgfHx/Cw8PlPKNGjcLf35+NGzdy48YNRowYwdChQ9m5c+cH3+fcEjWEiBrCvPocjknPnj359ddfAeVYhUWKFFFzRNlLz5C49ihGOY2epgbaWgqexqXw16WH7AuKID5FtbdyLVdLlnxZETMDnRzWKAiC8PnIaw1htWrVqFKlCkuWLAEgIyMDBwcHhg0bxtixY7Pkt7Oz47vvvmPIkCFyWrt27dDX12fjxo0kJiZibGzM33//TbNmzeQ8Xl5eNGnShBkzZgDg4eFBp06dVIY9ez2PuokaQqFQKlOmjPz3+vXr1RjJm2lqKChfzAwvR3PKFTOltI0Jtdwsmd/Rk6CpvizoVIF2lf67CJ6485Rhmy6Rlv759xwXBEHIFBcXR2xsrPxKTk7OkiclJYWLFy/i4+Mjp2loaODj48Pp06ezXW9ycnKWig99fX1OnDgBQFpaGunp6W/MA8r2+jt37iQ8PBxJkjhy5Ai3bt2iUaNG77zP+U0UCIVCafjw4bi5uQEwevRoBg0aRFhYmHqDyiOFQkGbisX4sWMFgqc3pnoJZS3n8dtPGb3lCunZjXcjCILwGXJ3d8fU1FR+zZ49O0uep0+fkp6eTtGiRVXSixYtSkRERLbr9fX1Zf78+dy+fZuMjAwOHDjAX3/9Jc9+ZWxsTPXq1Zk+fTqPHj0iPT2djRs3cvr0aTkPwOLFi3F3d6dYsWLo6OjQuHFjli5dmu/Dn70PUSDMJfFk/T+fw7EwMDDgp59+kt+vXLkSZ2dnunXr9kmOy6inrcnSrpUobWMMwN+XH9Fw/r9cvPecoPAYwqMT1RyhIAjCh3P9+nViYmLk17hx4/JlvYsWLcLNzY3SpUujo6PD0KFD6d27Nxoa/xWfNmzYgCRJ2Nvbo6ury08//USXLl1U8ixevJgzZ86wc+dOLl68yI8//siQIUM4ePBgvsSZH0SB8C00NZUzT6SkZD80SGGUkKCcb1lbW1vNkbyfxo0bc+XKFXr16iWn/fbbbwQGBqovqPdgYajD30NrUtvNEoC7T+Npt/w0zRefoOb3h/krQL0daARBED4UY2NjTExM5Jeurm6WPJaWlmhqahIZGamSHhkZiY2NTbbrtbKyYseOHcTHx3Pv3j2Cg4MxMjKiRIkSch4XFxf+/fdfXr58yYMHDzh37hypqalynsTERMaPH8/8+fNp0aIF5cuXZ+jQoXTq1IkffvghH4/C+ynQBcL09HQmTpyIs7Mz+vr6uLi4MH36dJUaKkmSmDRpEra2tujr6+Pj48Pt27fzLQYtLS0MDAyIiooiISGBpKSkQvtKTEzk2bNnPHnyBDMzM7mw/CkrX74869at4/nz53Laqz3DPjW6Wpps6FuNrxu4UaqoMY5F/htjcdSfV9h8/r4aoxMEQVAfHR0dvLy8OHTokJyWkZHBoUOHqF69+hs/q6enh729PWlpaWzbto1WrVplyWNoaIitrS0vXrxg3759cp7U1FRSU1NVagxBWeFUkJ5IFegBzObMmcPy5ctZv349ZcuW5cKFC/Tu3RtTU1OGDx8OwNy5c/npp59Yv349zs7OTJw4EV9fX65fv54vPWAVCgW2traEhoaqzIdbmJmZmeX4a+pTZW5uTpMmTdi7dy+3bt1S6S32KRrZsCQjG5YEIOxpPHV/OArAt9uuUtrGhAoOZuoLThAEQU1GjRpFz549qVy5MlWrVmXhwoXEx8fTu3dvAHr06IG9vb3cBvHs2bOEh4fj6elJeHg4U6ZMISMjgzFjxsjr3LdvH5IkUapUKe7cuYOfnx+lS5eW12liYoK3tzd+fn7o6+vj6OjIv//+y6+//sr8+fM//kHIQYEuEJ46dYpWrVrJN2cnJyc2bdrEuXPnAGXt4MKFC5kwYYJcEv/1118pWrQoO3bsoHPnzvkSh46ODm5ubuKxMcrHxJ9DzWB2rK2tAeUFo3r16nzxxRdqjih/OFkacuLbetSacwSAVktP4ljEgL+H1BTD0wiCUKh06tSJqKgoJk2aREREBJ6envj7+8sdTe7fv69Sk5eUlMSECRO4e/cuRkZGNG3alA0bNmBmZibnyWyz+PDhQywsLGjXrh0zZ85UaVb1xx9/MG7cOLp27crz589xdHRk5syZBWpg6gI9DuGsWbNYtWoV+/fvp2TJkly5coVGjRoxf/58unbtyt27d3FxceHSpUt4enrKn/P29sbT05NFixZlu97k5GSVLunh4eG4u7uLuRALOX9/f5o0aQJA7dq1OXz4MFpaBfo3U57suxbBqmN3uXjvBQCGOpp0q+5IBy8HXK2N1BydIAhC3om5jPNPru52o0aNyvOKJ0yYgIWFRZ4/96qxY8cSGxtL6dKl0dTUJD09nZkzZ9K1a1cAuZt4XrqQA8yePZupU6e+V2zC56dx48YsW7aMr776iuPHjzNp0iRmzZpFXFwc9+7dIyMjAwsLC+zt7VEoFG9fYQHjW9YG37I2zN5zg5XH7hKfks7Kf++y7mQYR7+pi52ZmC9ZEAShsMpVDaGGhgbVq1dHRyd3j5dOnDjBzZs3VXrhvIs//vgDPz8/5s2bR9myZbl8+TIjRoxg/vz59OzZk1OnTlGzZk0ePXqEra2t/LmOHTuiUCjYvHlztusVNYRCTp4+fYq7uztRUVEAuLq6EhISotKRKbM966c6S0taegbnQp9zMuQpS4+EAFDCypDDo+uqNzBBEIQ8EjWE+SfXz8O2b98ut7F6G2Nj43cO6FV+fn6MHTtWbgtYrlw57t27x+zZs+nZs6fcsSEyMlKlQBgZGanyCPl1urq6Kl3SY2Nj8yVe4dNnaWlJcHAwTk5OxMXFcefOHUDZ6SQjI4OYmBiuXbtG6dKlCQkJ+STbU2ppalDD1ZIarpZYGOoyfdd17kbFk5Sajp72p7c/giAIwvvL1bAz69atw9TUNNcrXblyZZbHuO8iISHhjd20nZ2dsbGxUelCHhsby9mzZ9/ahVwQcmJhYcG1a9c4cuQIR48e5cGDBzx//pzo6Gi++eYbAO7du8f9+5/+EC59ajqhp608x37Yd1PN0QiCIAjqUqA7lfTq1YuDBw+ycuVKypYty6VLlxgwYAB9+vRhzpw5gHJomu+//15l2JnAwMA8DTsjqpyFvChTpgzBwcF07dqVDRs2fJLtCV/VYcUpzocpO5pUcDDD282S3jWdMTcUPZAFQSjYxP07/7xXF8qgoCD+/fdf0tPTqVmzJl5eXvkVF6Cc6mXixIl89dVXPHnyBDs7OwYOHMikSZPkPGPGjCE+Pp4BAwYQHR1NrVq18Pf3/2TbdwkFX2aB8LfffmPgwIHUrl1b3SG9l4WdK9Ju2SkiYpO48iCaKw+ieZ6QwozW5dQdmiAIgvCRvHMN4dKlS5k2bRre3t6kpqZy+PBhxowZw3fffZffMX5w4heGkBchISG4uroCUKVKFXbt2pXr9rUFVUaGxJm7z5i55wbXHinb1K7uUZmG7u/f9EMQBOFDEffv/JPrqesePHig8n7JkiVcu3aNP//8k+3bt+Pv78/ChQvzOz5BKHBcXFyYMmUKAOfPn6do0aJUqFCBwYMH8/jxY/UG9440NBTUcLXk515V5LT+v17gyoNo0tILztRKgiAIwoeR6wKhj48PixYtkoffKFKkCP7+/iQnJxMXF8fBgwexsrL6YIEKQkEyYsQIRo4cKb8PDAxkxYoVFCtWLMuPp09JURM9tg3+r0NWq6UncZuwl9ZLTzJy82VOhzxTY3SCIAjCh5LrAuH58+e5efMm1apV4/Lly6xatYoFCxagr6+PmZkZmzdvZv369R8yVkEoMExNTZk/fz5xcXGsX7+eLl26AMqJ0j08PN44MHpB5+VowfTWHvJ8x5IElx9Es/1SOGO2XVFvcIIgCMIHkec2hKdOneKrr76ifv36zJgxg/T0dNLT01Xm9fvUiDYIQn6YMmWKPAPOvHnz5CFqPmVp6RkcDn5C2LN4Zu0JBsBIVwsvR3OWfFkRYz3tt6xBEAThwxH37/yT6xrCTDVq1ODChQuYm5tTsWJFjh079kkXBgUhv4wbN46KFSsCEBYWpt5g8omWpgaNytowoI4LTcspB4J/mZzGv7eiOHTjiZqjEwRBEPJLrmsI09LSWLVqFTdu3KBChQr07t2bkJAQBg0aRJEiRViyZEm+DEatDuIXhpBflixZwrBhwwA4fvw4Dg4ObNmyhT179pCQkECtWrUoW7YsTZs2/eTOF0mSiE5IZeCGi5wLew7A1w3c6F3TCTMDMWahIAgfn7h/559cj0PYt29fzp8/T8uWLVm3bh2BgYH89NNPHD58mLVr11K9enX8/PwYPHjwh4xXEAq0cuX+G7svu/EJz549C4BCoWDo0KHMnTv3kxkzU6FQYG6ow4A6JeQC4aJDtzl68wk7htT85AfoFgRBKMxyXUNoZmbG6dOnKVOmDAkJCZQrV46QkBB5+ZMnTxgxYgS///77Bwv2QxG/MIT8IkkS//zzD61atQKUhajKlSvTvHlzFAoFt2/fZtOmTaSlpcmf6d+/P8WLF+fly5cA8vzIVatWpVmzZmhpvdf48R/EwxcJLDsawu9nldP3lbE1YVV3LxwsDNQcmSAIhYm4f+efXBcIS5UqxVdffcXgwYPZv38/06ZN49y5cx86vo9CfKGE/Pb48WPCw8Nxc3PLMg94cnIyzZo1U5mDOydVqlTh7NmzBbb2bchvAey++t/YiyfH1sfeTF+NEQmCUJiI+3f+yXWB8MCBA3Tt2pVnz55ha2vLn3/+SY0aNT50fB+F+EIJH5skSQQEBLB8+XJevnyJiYkJhoaGpKenA/DXX38RHh4OQERERIFub3j27jM6rToDQGtPOxZ08iywBVhBED4v4v6df/I07IwkSTx9+vSzG4BafKGEgqhYsWKEh4djZ2dH3759kSSJpKQkEhMTKVq0KOPHj5cfL6vbd9uv8tv/Hx8b6Ggyv6MnjT1s1ByVIAifO3H/zj/vPJfx50R8oYSCqEOHDmzduvWNeaZNm8bXX3+NiYnJR4oqe/eexTN55zWO3oyS0058W49i5qJNoSAIH464f+efXBUIK1WqxKFDhzA3N8/VSmvVqsXmzZuxt7d/7wA/BvGFEgqi1NRUdu/ezf79+0lOTkZfXx89PT22bNnC/fv35XxmZmbMnj2b3r17o6urq8aI4erDGFosOQGAs6UhR76pq9Z4BEH4vIn7d/7JVYFQQ0ODw4cPY2FhkauV1qhRg8DAQEqUKPHeAX4M4gslfGouXrzI+PHj2b9/v0r6ggUL+Prrr9Xahm/N8bvM2H0DAE8HM6o6WzCkriumBmJWE0EQ8pe4f+efXI9n0aBBA3L7dFk0KBeED8vLy4t9+/Zx9OhRRo8eTUBAAAAjR46kRIkStGzZUm2x9atdgisPY/jnyiMuP4jm8oNowqMTWdKlorg2CIIgFFC5qiG8d+9enldcrFixAtPg/W3ELwzhUxcUFCQPil2qVCkuX76s1gGv0zMkrjyMZsrOawQ+jAFgVMOSDG/gpraYBEH4/Ij7d/4RnUoQXyjh87B48WKGDx8OwMyZMxk/fryaI4LYpFQqTjtAeoaEhaEOp8fVR1fr0/ihKAhCwSfu3/lHQ90BCIKQP7p16yaPV/jdd99x8OBBNUcEJnra/DVYOV7p8/gU+q2/oOaIBEEQhOyIAqEgfCbMzc1VZg9q2LAhW7duZdeuXVy+fDnXbYDzWzl7U+qUVI5devz2UyJiktQShyAIgpAzUSAUhM9I8eLFOXnypPy+Q4cOtGjRgooVK/LLL7+oJSYNDQXrelXBSFfZh+2L2YdYdzKUyFhRMBQEQSgoRIFQED4zNWrUYMmSJTRo0ICaNWvK6X369OHFixdqiUlTQ8HIhiXl91P/uU61WYeYsvMacUmpZGQU+qbMgiAIapXnTiUPHjxAoVDIjTfPnTvH77//jru7OwMGDPggQX5oolGq8Dk7cuQI9evXB6Bt27Zs27ZNbbFExCSx5MhtNp17QPprhUBnS0Oal7eliKEO2loaVChmhrutCRoaYqgaQRCyJ+7f+SfPBcLatWszYMAAunfvTkREBKVKlaJs2bLcvn2bYcOGMWnSpA8V6wcjvlDC5ywjI4NmzZrh7+8PQFhYGI6OjmqNKTU9g8EbAzgd8pT4lPQc89ma6rFrWC2KGKl3BhZBEAomcf/OP3l+ZBwUFETVqlUB+PPPP/Hw8ODUqVP89ttvamujJAhCzjQ0NPj999/l905OTsycOVNtnUwAtDU1WNOzMkFTfbk+zZeDo+rQv7YzbSva07y8LRWLmwHwOCaJEZsvk5yWc6FRED55KfHw6DK8ek4mxcKzEEhPVVtYQuGS65lKMqWmpsrzpR48eFCeEaF06dI8fvw4f6MTBCFfmJubs2jRIr7++msAJkyYgLe3N7Vq1VJrXAqFAgMdLVytjfmumbvKsll7brDq2F2O337KlgsP6faFems1BSFfSRIE74JTi+HBWdAzhR5/g11F5fJdIyBoG2jqgq4xmNhBqyVgW0GtYQufrzzXEJYtW5YVK1Zw/PhxDhw4QOPGjQF49OgRRYoUyfcABUHIH8OHDyc0NFR+X7t2bXr16kVQUJAao8rZYG8X9LSVl6gf9t/k4j31dIgRhHwVFwHb+sMMa9jcTVkYBEhJgKtb/8una6z8Nz0ZEp5CRCCsrANbekHo8f/yJcVA2Am4c1D578MLEHkdUkUvfiFv8tyG8OjRo7Rp04bY2Fh69uzJzz//DMD48eMJDg7mr7/++iCBfkiiDYJQmLw6owkoxyvcv3+/GiPK2eUH0bReqhxGp7iFAYdHe6OlKQZHED4BkgTP7sCNnVBzBGj8f4ae00th3yuzCJVsDB7twL0VaL3SVjY1UVk7GPMAnt6Co7Mh/KJyWYNJUHu08u97p2Bdk+xjKOIKZdtA3fGg8XmeN+L+nX/eaeq69PR0YmNjMTc3l9PCwsIwMDDA2to6XwP8GMQXSihMJEkiICCA5cuXs3btWgAmT57MlClT1BtYDvZcfcxXvwXI7yc0K0PfWs4oFKL3sVAAPQ+Fsyvg+t8Q9/9mVGXbQJuVygLfTX/4qz+U7wh1/MDYJvfrvnMIQv8F5zrg6qNMi7wGf/YELT1IT4G0RGWtYVKMskDpMwWqDVIWCK9sBnMn5WNpLZ383nO1KIz374MHDzJ58mQyMjJo2rQpY8eORVtb+73X+04FwrS0NI4ePUpISAhffvklxsbGPHr0CBMTE4yMjN47qI+tMH6hBCE1NZUiRYoQFxeHsbExsbGx6g4pW5IkMerPK2y/FC6n/d6/GjVcLNUYlVCoSRLEPgI9k/8e7d4/AzuHw9ObqnmtSkPVAeDVS1lLmBIPWvoftsZOkpQ1i7GPoPgX/6WvqgePAkDPDCr3Ae9vQVvvw8XxERTG+3epUqWYO3cu9vb2LF++HFtbW2bMmPHe681zgfDevXs0btyY+/fvk5yczK1btyhRogRff/01ycnJrFix4r2D+tgK4xdKEACioqLkWv24uLgC/YMuJOolvdad48HzRAD+GVqLcsVM1RyV8NlLT1V27nhwFrQNwNodLv4CD88pO3gMPKbMF3YSfmmq/NvIBtx8oNpgKFoWCkJtdnoabGgNYa+0P1RogEM1cG8NZVvnrbaygCiM9++KFSty6dIlQPnEtmbNmpw5c+a915vnXsZff/01lStX5sqVKyqdSNq0aUP//v3fOyBBED4eKysrDAwMSEhIIDIyskAXCF2sjFjUuSJtl50C4MSdp6JAKHx4Qdtg+8DslxWv8d/fZg7KR8BlWhTMnsCaWtBrl7LW8MBkuL1P+Vj5/un/OrZ8MUi9MQq5EhUVxZYtWyhVqhSlSpUiJSUlX9ab5wLh8ePHOXXqFDo6qu0PnJycCA8Pz+FTgiAUVEWLFiU0NBRfX19u3ryJpqamukPKUaXi5gyu68LyoyFiLmThw4h/BvsngM9kZY2ZvoWyLV4Jb+XQMDEPlf8Wrw41v/7vc2bFof4E9cWdWyZ20G618rHy4ytwaSPYVwLPL9UdmZBLo0aNwt/fnx9++IHg4GCSk5Pp2LEj5cqVo1y5crRu3fqd1pvnAmFGRgbp6VkHiX348CHGxsbvFIQgCOpTo0YNQkNDCQkJoW7duowfP57GjRsX2E4bNibKNk+/nAqjVw0nnCwN1RyR8FmICQf/scpewQB1v1X+61gDvg0Fnc/se6ZQgJ2n8pXppj+cXa7sGNP/MBiKdroFUd++fTE1/e/pSGhoKEFBQQQFBbFly5Z3LhDmuVVro0aNWLhwofxeoVDw8uVLJk+eTNOmTd8pCEEQ1Gf9+vXUqVMHgBMnTtC0aVPmzJmj5qhy5mL132PtJUfuqDES4ZOQmqTsnXv3qHI2kLTXHq8lPIc9frDA/b/CIED0A+W/ukafX2EwJ+nJyuMUfQ/++FJZSFbjjEZC9szNzXF0dKRp06aMGTOGEydOUKxYMUaNGsVvv/32zuvNc4Hwxx9/5OTJk7i7u5OUlMSXX34pPy4uyDcRQRCyp6mpyaFDh1iyZImcdvLkSTVG9GY1XYvQytMOgPvPE9QcjVCgJcXA+hawsS382gpWeauO2ZcUA3Od4dyq/9IqdIEJT8C59sePV91Kt1B2MAFlu8IF7rC8prITzesFaUFtBg8ejKWlJTVr1qRUqVJcvHgRPz8/nJ2dKV269Duv952Hnfnjjz8IDAzk5cuXVKpUia5du6Kvr//OgahTYeylJAjZ2b9/P76+vgD4+/vLfxc0Z+8+o9OqMzhbGnLkm7rqDkcoiO4cgi29ITlG+V7bEKR05fh9nf9fiyJJMM0CUIBnF6j3nbKNXWEmScoC8vk1ygGxM42+BcZF1RdXDgrr/fvBgwdMnz6d0NBQJk6cKD/liYqKwsrK6p3W+U4Fws9NYf1CCcLrIiIisLW1BaBMmTJcv35dzRFlL+xpPHV/OArArmG18LAXvY2F12SOuQfKzh8+U0HKUM4AovtKb/qnt8HSTT0xFnSR1+HCWuUg26NvKsdRjHkIN3YpZ1cxereCR34q7Pfv0NBQZsyYwaNHj/jxxx9xd3d/+4dykOdOJTt37sw2XaFQoKenh6urK87Ozu8ckCAI6mNjY8OuXbto3rw5N27coF+/fnTo0IFGjRoVqE4mNqZ66GhqkJKeQfPFJzg4qg6u1qJTm/AKa3dlbV+lHlDy/zXdCk3VwiCIwuCbFHWHZj9C0x/+G0vx0SXw/1b58p0NVfp9NrOefCpu3LjBzZs3uXnzJjdu3CAkJIT4+HiuXbv2XgXCPNcQamhooFAoeP1jmWkKhYJatWqxY8cOlantCrLC/gtDEF4lSRKOjo48ePBATtPS0qJatWqMGjWKtm3bqjG6/xwJfkLvX84DoKOlQcDEhhjp5vk3rvC58B8PN/dA6+XK2TkK0A+Yz0rgn8qp915l7gS6JsqheRr9f8aMhOcQ8Cvom4NteWUB/dW5mvNJYbx/a2hoUL58eTp27Ejz5s0pU6ZMvkxdl+dOJQcOHKBKlSocOHCAmJgYYmJiOHDgANWqVWPXrl0cO3aMZ8+e8c0337x3cIIgfHwKhYKTJ0/y448/Uq1aNUDZbvjkyZOMGTNGzdH9p15paxZ3qQhASloGIU9eqjki4aPLyIC0ZOUcvWeWwotQ2NQZkuPUHdnnq3xHGHVD2RZT6//9Bl6EQUSgcuaTTLGP4OBk+Gc4rKoLL+6pI9rP0g8//ICXlxd///03DRs2pEaNGvTu3Zsff/yRffv2vfN681xD6OHhwapVq6hRo4ZK+smTJxkwYADXrl3j4MGD9OnTh/v3779zYB9TYfyFIQi5deXKFYKDg+ncuTMAW7dupXHjxhgaFoyhONouO0nA/WiWd61Ek3K26g5H+BiC98Dh6cr2fxmp/6Vr6sC4cPEI82PJyIC4R8ohehJfKB+/Zz6CfxEGh2coh655egtGBH6Q4XvE/Vt1HMJr166xcePGd1pPnp+vhISEYGJikiXdxMSEu3fvAuDm5sbTp0/fKSBBEAqWChUqUKFCBSZMmMCdO3do3749RkZGXL16FScnJ3WHh62ZPtyP5tfT92jsYVOg2joKH0iJuspCxsHJ/6VZuEDz+aIw+DFpaIBpMeXrdeZO0G7NRw/pc3Xv3j0CAwMpWrQoVatWVVnm7OyMs7MzLVq0eK9t5PmRsZeXF35+fkRFRclpUVFRjBkzhipVqgBw+/ZtHBwc3iswQRAKlhUrVtCwYUMAXr58yb///qvmiJQczA0AOH33Gf5BEWqORsh3L6Pg4FRY1xTmuyvHw9MxgHIdYNBJ+PYefBcBwwOUBUVB+Mxs2rSJkiVL0qpVK6pXr07lypVVymD5Jc8FwrVr1xIaGkqxYsVwdXXF1dWVYsWKERYWxpo1yl8DL1++ZMKET2BOR0EQcq1Bgwbs37+ffv36AbBw4ULS0tLUHBV0r+6IloayVnDSzmvEJ6s/JiGfBGyAH1zhxHy4dxJiw0Hz/43nTe3BxgP0zUD70xwDV1CPpUuX4uTkhJ6eHtWqVePcuXM55k1NTWXatGm4uLigp6dHhQoV8Pf3V8kTFxfHiBEjcHR0RF9fnxo1anD+/Pks67px4wYtW7bE1NQUQ0NDqlSpkqumdVOnTuXLL78kODiY/fv3AzB27Ng87vXb5fmRcalSpbh+/Tr79+/n1q1bclrDhg3R0FCWL991Hj1BEAo+FxcXAC5fvky1atX4+eefqVChgtrisTfT54cOFRix+TJRccksO3oHP993H61f+MheRik7HugY/veIMfo+/NJM+W8mp9pQbaByLEGFpnpiFT55mzdvZtSoUaxYsYJq1aqxcOFCfH19uXnzJtbW1lnyT5gwgY0bN7J69WpKly7Nvn37aNOmDadOnaJiRWWntn79+hEUFMSGDRuws7Nj48aN+Pj4cP36dezt7QFlc7tatWrRt29fpk6diomJCdeuXUNPT++tMd+9exd/f3+cnJwoWbIkGzduxMvLi7Vr1+brsREDUyMapQpCXkRERPDll19y5MgRAKpVq8aZM2fUGlNSajp15x0lIjaJBqWtWdurilrjEXIh7ATsHg1Rwcr3FiVg+CXl34+vwBofSE8B+8rQbrVyuSC8Jq/372rVqlGlShV5qs6MjAwcHBwYNmxYtrVudnZ2fPfddwwZMkROa9euHfr6+mzcuJHExESMjY35+++/adasmZzHy8uLJk2aMGOGchiezp07o62tzYYNG/K8jxoaGkRERKgUWPX19QkNDcXGxibP68txO+/yofj4ePbs2cOKFSv46aefVF6CIHzebGxsOHz4MOvXrwfg7NmzTJ8+Xa0x6WlrMqO1BwBP4pLVGouQCxFXlTWAmYVBHSPlANKZgneDWyPovgP6HRSFQeGt4uLiiI2NlV/JyVmvAykpKVy8eBEfHx85TUNDAx8fH06fPp3tepOTk7PU4unr63PixAlAOSRXenr6G/NkZGSwe/duSpYsia+vL9bW1lSrVo0dO3bkev/Wr1/PqVOnePlSObyWlpYWCQn5PJe7lEcBAQGSjY2NZGJiImlqakpWVlaSQqGQDA0NJWdn57yurkB48OCBBEgPHjxQdyiC8MnIyMiQnJycJEACpPHjx0sZGRlqi+fy/ReS47e7JMdvd0nLj95RWxzCW2RkSNKP7pI02UT5unVA3REJn7DM+/frr8mTJ2fJGx4eLgHSqVOnVNL9/PykqlWrZrv+Ll26SO7u7tKtW7ek9PR0af/+/ZK+vr6ko6Mj56levbrk7e0thYeHS2lpadKGDRskDQ0NqWTJkpIkSdLjx48lQDIwMJDmz58vXbp0SZo9e7akUCiko0ePvnUf69SpI5mYmEgKhULS1NSUXF1dJS0tLWn8+PHS4cOHpdjY2DwcsZzluYZw5MiRtGjRghcvXqCvr8+ZM2e4d+8eXl5e/PDDD/lXUhUEoUBTKBScP3+esmXLAjBr1iwuXbqktnjcihphoqdsFv393mDm+gdnmVFJKABePoHSTZV/t10Nbj5vzi8IuXD9+nV5soyYmBjGjRuXL+tdtGgRbm5ulC5dGh0dHYYOHUrv3r3lPhMAGzZsQJIk7O3t0dXV5aeffqJLly5ynoyMDABatWrFyJEj8fT0ZOzYsTRv3pwVK1a8NYZ///2XmJgYbt68ycaNG2nTpg3e3t4sX76cBg0aYG5uTpkyZd57X/NcILx8+TKjR49GQ0MDTU1NkpOTcXBwYO7cuYwfP/69AxIE4dNhaWkptyUE5I5m6mCgo8X5CT5YGCrHoVt2NIQWS04Ql5T6lk8KH8ydQ/BHV1hQDhZ4KKczMy4K5TuBa0PlrBeCkA+MjY0xMTGRX7q6WafJs7S0RFNTk8jISJX0yMjIHNviWVlZsWPHDuLj47l37x7BwcEYGRlRosR/zRhcXFz4999/efnyJQ8ePODcuXOkpqbKeSwtLdHS0soyz3CZMmXyNIGHm5sbnTt3Zu7cuRw8eJDnz58TEhLCpk2baNOmTa7Xk5M8Fwi1tbXlUq+1tbW8M6ampipznwqCUDhYWVnRrVs3ALp06cLGjRvVVjOnq6WJ/4jaVHW2ACAoPJbjt8Ug+R9dSgJs7g4b20LwLoi5DzEPlD2EAYqWhc6/qTdGodDR0dHBy8uLQ4cOyWkZGRkcOnSI6tWrv/Gzenp62Nvbk5aWxrZt22jVqlWWPIaGhtja2vLixQv27dsn59HR0aFKlSrcvHlTJf+tW7dwdHR8r31ydnamQ4cOzJo1673WA+8w7EzFihU5f/48bm5ueHt7M2nSJJ4+fcqGDRvw8PB474AEQfj0+Pr6ytMlde/enaCgIL7//nu1xGJtrMcf/b+g0cJj3HnykoiYJLXEUWjdPQq/vnazbLUUrN1Bz1T5XowbKKjJqFGj6NmzJ5UrV6Zq1aosXLiQ+Ph4evfuDUCPHj2wt7dn9uzZgLLTXHh4OJ6enoSHhzNlyhQyMjJU5nXft28fkiRRqlQp7ty5g5+fH6VLl5bXCeDn50enTp2oU6cO9erVw9/fn3/++YejR49+1P1/kzwXCGfNmkVcnHLi8JkzZ9KjRw8GDx6Mm5tbvo+JIwjCp6Fbt26UK1eOKlWqkJqaysqVK5kxYwZaWnm+xOQLDQ0FtVwtufPkJdcfx6olhs9aRjrcOQgPzkJaMmhoQsNpymUvn/yXr0wLaLUM9LJOdyoI6tCpUyeioqKYNGkSEREReHp64u/vT9GiRQG4f/++SvvApKQkJkyYwN27dzEyMqJp06Zs2LABMzMzOU9mm8WHDx9iYWFBu3btmDlzJtra2nKeNm3asGLFCmbPns3w4cMpVaoU27Zto1atWh9t399GjEOIGIdQEPJLWFgYzs7OgLLW8PUR/T+mJYdv88N+ZZvGFd28aOyRf+N1FXprGykLg5m0DeC7x8q/ox9A+EVlIbBEPRBzSwsfkLh/5588tyGsX78+0dHRWdJjY2OpX79+fsQkCMInytHRUZ5g/cCBA9mOBfax+Jb9rwA4aONFjtx88obcQq5d3fpfYdDQGqoNhhrD/1tu5gBlW4NLfVEYFIRPSJ4LhEePHiUlJSVLelJSEsePH8+XoARB+DQpFAr+/vtvTE1NycjIoHjx4kyYMIGYmJiPHotbUWO2f1VDft973XkCH0Z/9Dg+KzHhsK2v8m+bcuB3G5p8D/XyZ4gPQRCyOnz4MO7u7sTGZm3+EhMTQ9myZfOl/JXrAmFgYCCBgYGAcryfzPeBgYFcunSJtWvXynP25afw8HC6detGkSJF0NfXp1y5cly4cEFeLkkSkyZNwtbWFn19fXx8fLh9+3a+xyEIQu4oFAq+/PJLAJ48ecLMmTNZs2aNWmKpWNycdb3/m8au65qzpKRlqCWWz0JyrHK4GICmYtxZQfgYFi5cSP/+/TExydoW19TUlIEDBzJ//vz33k6u2xBqaGig+H/1f3Yf0dfXZ/HixfTp0+e9g8r04sULKlasSL169Rg8eDBWVlbcvn0bFxcXXFxcAJgzZw6zZ89m/fr1ODs7M3HiRK5evcr169dzNWk0iDYIgvAh3L59m0aNGhEWFgYoJ2jPbF/4se25+pivfgsAYHzT0gyo46KWOD4bGRmg8U4znwpCvioM929HR0f8/f1zHHw6ODiYRo0a5WlMw+zk+owODQ0lJCQESZI4d+4coaGh8is8PJzY2Nh8LQyCsrDn4ODAunXrqFq1Ks7OzjRq1EguDEqSxMKFC5kwYQKtWrWifPny/Prrrzx69ChPcwQKgpD/3NzcVCZyz5zkXR2alrPFp4yyF+GsPcHcefJSbbF8ki7/DrOLQ/r/B/kWhUFB+GgiIyNVeiy/TktLi6ioqPfeTq7PakdHR5ycnMjIyKBy5co4OjrKL1tbWzQ1Nd87mNft3LmTypUr06FDB6ytralYsSKrV6+Wl4eGhhIREaEyUbWpqSnVqlXLcaJqUE5W/eok2JnD6AiCkL9q1qxJly5dAOX5qk6jGpaU/x6z9Qovk9PUGM0nJvBPSI6BG/+oOxJBKHTs7e0JCgrKcXlgYCC2trbvvZ13+pl3+/ZtVq1axYwZM5g2bZrKKz/dvXuX5cuX4+bmxr59+xg8eDDDhw9n/fr1AERERADI4wdlKlq0qLwsO7Nnz8bU1FR+vT6djCAI+UOhUDBo0CAAjhw5wqJFi9QWi7udCUPruQIQcD+ahvP/5XFMotri+WQ8C4G7/5+e0MBCvbEIQiHUtGlTJk6cSFJS1kH2ExMTmTx5Ms2bN3/v7eR5HMLVq1czePBgLC0tsbGxkdsVgvLiHxAQ8N5BZdLR0aFy5cqcOnVKThs+fDjnz5/n9OnTnDp1ipo1a/Lo0SOV0nHHjh1RKBRs3rw52/UmJyerDIcRHh6Ou7v7Z90GQRDUJSYmRh7E9W0/1j60hJQ0Bm64KE9n19rTjoWdK6otngJPkuAnT3gRpnw/KhhM3r8mQhDyS2FoQxgZGUmlSpXQ1NRk6NChlCpVClC2HVy6dCnp6ekEBARkqRzLqzxPIzBjxgxmzpzJt99++14bzg1bW9tsJ4Petm0bgDwZdWRkpEqBMDIyEk9PzxzXq6urqzLxdXZduQVByB+mpqY8fPiQYsWKERkZSUxMDKampmqJxUBHi196V6X/rxc4HPyE4AjRXCRH6Wmwrsl/hUGfKaIwKAhqULRoUU6dOsXgwYMZN26c3LFXoVDg6+vL0qVL37swCO/wyPjFixd06NDhvTecGzVr1nzjZNDOzs7Y2NioTFQdGxvL2bNn3zpRtSAIH4+9vb38A65KlSq8fKm+Th2aGgrGN1X21nv4IpHYpFS1xVKgpSVB3P9nHynZGGqNVG88glBITZs2DSsrK/bs2cPTp085e/YsZ86c4enTp+zZsyffRm/Ic4GwQ4cO7N+/P182/jYjR47kzJkzzJo1izt37vD777+zatUqhgwZAihLxyNGjGDGjBns3LmTq1ev0qNHD+zs7GjduvVHiVEQhNxp1qwZoGyDbGxszPjx49VWMCxmrg/Ay+Q0yk/ZT4MfjxKdkHXA/ULnxT24uVf5t64RmNhDve+g00b1xiUIhdjUqVPla6W5uTlVqlShatWqmJub5+t28vzI2NXVlYkTJ3LmzBnKlSuXpSv08OHDc/hk3lWpUoXt27czbtw4pk2bhrOzMwsXLqRr165ynjFjxhAfH8+AAQOIjo6mVq1a+Pv753oMQkEQPo7Vq1dTsmRJubnJ7NmzuXfvHhs3blRpi/wx6Glr0q+WMzsuh/P0ZQohUfF4TjvAwVF1cLU2/qixFAiSBH8PhcsbwdgWSjVRpvfdp964BEHIduznDyHPnUreVDWpUCi4e/fuewf1sRWGRqmCUFBERUXRsmVLzpw5A4ChoSF2dna4ubmxdOlSrKys0NPT+yBDWWVn3clQpv5zHYCvG7gx8pXhaQqFlATY1g9u7la+NygCfiFiHmLhk1AY7t8aGhpERkZiZWX1QbeT5wLh56gwfKEEoSBJSEigQYMGcqEwOyVLlmTq1Kl07tz5g8cz1z+YZUdDAFjV3YtGZW0++DYLjMu/w47Byr+dakOPv0Hj4xTGBeF9FYb7t4aGBqampm99kvL8+fP32k6eHxlnSklJITQ0FBcXF7S03nk1giAUQgYGBpw+fZqXL19y9+5dDh48yKxZs3j27Jmc59atW3Tp0oWSJUtSqVKlDxpPS087uUA4YMNFNvatRi03yw+6zQIh4Tn4j1X+bVkSOv8mCoOCUABNnTr1g4/OkOcawoSEBIYNGyYPDn3r1i1KlCjBsGHDsLe3Z+zYsR8k0A+pMPzCEIRPQVpaGsnJyVy4cIG6desC0KJFC7Zv3/7BHyE/eJ5A7bnKAZiLmuhydrzPWz7xGfAfB2eWKf9usQi8eqk1HEHIq8Jw/9bQ0CAiIgJra+sPu528fmDcuHFcuXKFo0ePqnTc8PHxyXEgaEEQhNzQ0tLC0NAQb29vvvnmGwD++eefj3JtcbAw4I8BXwAQGZtMUHjMB9+mWsQ+hqT/j73aeDYMvQgNJkH5TuqNSxCEbH2sTnd5LhDu2LGDJUuWUKtWLZUgy5YtS0hISL4GJwhC4TV06FD577Nnz36UbX5Rogg2Jsofus0XnyD+c5vvOCII5peGDW0g5P/T0Vm6Qu3RoK2v3tgEQcjWx+rqkecCYVRUVLbVlvHx8R996AhBED5fjo6OrFy5EoCffvqJrVu3fpTtjm1SWv779hP1DaCd716EwZaeyr/DL0BqglrDEQQhdzIyMj7442J4hwJh5cqV2b17t/w+sxC4Zs0aMTuIIAj5qlGjRvLfa9as+SjbbF3RnsqOygFffz0V9tF+nX9Qh2fAogrw7H/t3Xd8U1UbwPFf0pXu0lK6hFLKnmVP2bI3IgIvU1AQkCEiCIqAgCACgiCobAegIigqyF6W1bJHKZtCW1b3bnLfP2JTYhktHel4vu8nLzcnJ+c+OWDz9N4zruift5gKFTuaNiYhRKb4+/uzbds2o7J169bh4+NDiRIlePPNN0lKSsr2ebKcEM6ePZsPPviAESNGkJqayhdffEGbNm1YvXo1s2bNynZAQgiRpnTp0oYfhDt27GDLli15cl5vF1sANp+8Q/dl/7D11B1uPYwnVavLk/PnuIML0o/rvQl13zBdLEKILJk+fTrnz583PD979ixvvPEGrVu3ZtKkSfz+++/MmTMn2+fJckLYpEkTTp06RWpqKtWqVePvv/+mRIkS+Pv7U7t27WwHJIQQj2vatCnW1vrxbd27d6dRo0YkJ+fuNnMjmvtS0V2/Y8mp25GM2XCKpp/tpc3CAySlanP13DlOpwWvf5ftmXQLOnwGNs6mjUkIkWmnT5+mVatWhucbNmygfv36fPPNN4wfP57FixezadOmbJ/nhRYQ9PX15Ztvvsn2yYUQ4nns7e0JCAigdevW3L17F39/f06cOEGjRo1y7ZxlS9jx5zsv83NgCPsv3+fi3WiuPYjj2oM4KkzdTpcanoxuWZZybgVgmzu1Gfi2gkpdQJO765gJIXJeREQEbm5uhuf79++nffv2hud169bl9u3b2T5Plq8Q/vnnn+zYkXF/yx07dvDXX39lOyAhhPivSpUqERISYligukuXLty7dy9Xz6lWq3itTkmW9q3FngnNmdW9Kup/5839dvouryw8wKJdl3M1hmy5ewrCzuqPW0yGxjm3z7wQIu+4ublx/fp1QL8pSGBgIA0aNDC8HhMTg4WFRbbPk+WEcNKkSWi1GW+ZKIpSIBelFkIUDCqVioED9bNkHz58yC+//JKn5+9X35tz09uyrF/6rimLdgVz40FcnsbxTIoC9y7CD73h62awvAls/8DUUQkhsqFDhw5MmjSJgwcPMnnyZGxsbHj55ZcNr585cwZfX99snyfLCWFwcDCVK1fOUF6xYkWuXLmS7YCEEOJpRo8ebfhBePfu3Tw/v42lOR2qeXD24/TZzz2++gedLh/MRA5cD0tqwbIGcHm7vkxlBhU7mDYuIUS2zJw5E3Nzc5o1a8Y333zDN998g6WlpeH1VatWGa3I8KKyPIbQ0dGRa9euUbp0aaPyK1euYGtrm+2AhBDiaVQqFa1ateLgwYO5fsv4Wew1Fix4rQbjN53mUVwyJ29HUvvfpWpM4uFV+C19IW+K+UDNftB4LJhl/1aSEMJ0ihcvzoEDB4iKisLOzi7DNp4//fQTdnZ22T5Plq8Qdu3albFjxxrtSnLlyhXeffddunTpku2AhBDiWdIWaDVlQgjQo9ZLhpnIPb/6hyW7g003Azn2Hjj/e8todCCMOQVN35NkUIhCxNHR8Yl7ujs7OxtdMXxRWU4I582bh62tLRUrVsTHxwcfHx8qVaqEi4sL8+fPz3ZAQgjxLK6uroB+G81du3aZNJYxrcoZjj/feZkNx7I/0++FeDeEdwLhw4fgkv2xREKIoueFbhn/888/7Ny5k9OnT2NtbU316tVp2rRpbsQnhBBG6tatazheuXIlrVu3Nlks7at5cOC9FrResJ9krY6AmxEMbFQ67wLY9ynEP4Tag8CtCpi90EpiQgiRtYQwJSUFa2trTp06RZs2bXJkEKMQQmSFt7c3X331FSNGjODBgwemDodSLjZ8/loNRv94kt9O38XCTM3UjpUoZpv9WzhPFXUH7gTAvn93J6jQXp8QCiHEC8rSLWMLCwtKlSr1xGVnhBAir5QsWRKAyMhI0wbyrwZlXLC30v9+/UtgCDVn7uSLXcG5sw9yUgwsawib+uufq9TgWinnzyOEKFKyfH9hypQpfPDBB6xfvx5nZ9n+SAiR95ycnAD9Cv75gau9FaentWHKlnP8eOwWAAt3XeaVym5U9nTIuRMlx8HKNpAUpX9e/XWo0h0cPHLuHEKIfGP8+PGZrrtgwYLnV3qGLCeEX375JVeuXMHT0xNvb+8MS80EBgZmKyAhhHietITw6tWrhIaG4uFh+oRIrVYxp0c1RrbwpcncvQBM/vUsm95qgJV5xpmBL+TyDrh3QX9cezB0XpQz7Qoh8qWTJ08aPQ8MDCQ1NZUKFSoAcPnyZczMzKhdu3a2z5XlhLBbt27ZPqkQQmRH2kxjgB49euDv72/CaIy9VMyGN5uW4esD1zh9O5I5f15iWufKqFSqHGi8DnReDLePQptPst+eECJf27t3r+F4wYIF2Nvbs3btWooV0697GhERweDBg412LnlRKiVXBrkULCEhIZQsWZLbt2/z0ksvmTocIUQmfPDBB8yZo59UMW3aND7++GPTBvSYiLhk+n17lAuh0QC0reLGot41sbbMoSuFQgigaH1/e3l58ffff1OlivEEsnPnztGmTZts796U5XUIQT+Q+9tvv2Xy5Mk8evQI0F/GvHPnTraCEUKIzJo9e7Zhg/fp06ebfKHqxxWzteSHYfUpYW8FwI7z4dSauZNVh64Tk5hCcqouaxNOFAUubIXo0FyKWAiR30VHR3P//v0M5ffv3ycmJibb7Wc5ITxz5gzly5dn7ty5zJ8/3zDLb/PmzUyePDnbAQkhRGZt2LDBcHzr1i0TRpKRk40lhye1pHMNTwASUrTM2HaBah//Tfmpf1Fuyl98/Nv5zDW28yPYNAAWVIQru3MxaiFEftW9e3cGDx7M5s2bCQkJISQkhF9++YU33niDHj16ZLv9LCeE48ePZ9CgQQQHB6PRaAzlHTp04MCBA9kOSAghMsvb25uaNWsCpt/K7kkszNQs6VOTox+0ok+9Upir08cRpuoU1vxzg2HrThAenUhsUmrGBrSpsHMa/LNY/9zeE7wb5VH0Qoj8ZPny5bRv356+ffvi7e2Nt7c3ffv2pV27dixbtizb7Wd5Usnx48dZsWJFhnIvLy/CwsKyHZAQQmRF2t7GT7qVkl+4OWiY06Mas7tXJSlVR1Kqjh7LDnP1fhw7L4Sz80I4ACXsrWjk68KYJiXwOTYDzqRfAcXeQ79HsbmVaT6EEMKkbGxsWLZsGZ999hlXr14FwNfXN8NqLy8qy1cIraysiI6OzlB++fJlo5l/QgiRF9J+7hSEJa9UKhUaCzMcrS34452XeadVOYrZWJA2AfleTBJbTt3l668+fywZVEHN/8Hb/pIMCiGwtbWlevXqVK9ePceSQXiBhLBLly7MmDGDlJQUQP8D7tatW7z//vv07NkzxwITQojMSLtCuHjx4ny1/MzzaCzMGP9KeU5+1IZrsztw8sNXWNjRA28XG37UtqJ50uecrzIBpt6DrkvBupipQxZCmNjBgwf53//+R8OGDQ0TedevX8+hQ4ey3XaWE8LPP/+c2NhYSpQoQUJCAs2aNaNs2bLY29sza9asbAckhBBZ0bdvX8Pxrl27TBhJFikKBO+EtZ1RrXiZYssq0313C3aNbUIlDwduKB50DKjFqE3nOBMSaepohRAm9ssvv9C2bVusra05efIkSUlJAERFRTF79uxst//C6xAeOnSIM2fOEBsbS61atWjdunW2gzGVorSOkRCF0axZs5g6dSpqtZqHDx8adjLJ137sA0F/ZiwfsJXzmpp0X/oPyVqdofiNJj5M7VgpZxa4FqKQKErf3zVr1mTcuHEMGDAAe3t7Tp8+TZkyZTh58iTt27fP9jyOLE8qSdOkSROaNGmSrZMLIUROqFSpEgA6nY5PP/2UTz/91MQR/Uf8I0iJB8d/v7COfZOeDLqU0+86onEEF1+wK0EVIOiTdvx2+i5jNpwCYOWh65R2saF/w9Km+ARCCBMLCgqiadOmGcodHR0NSwBmxwstTL179246deqEr68vvr6+dOrUqWDdqhFCFCqdO3c27O0ZHBxs4mgeo02FX0fAPB8IXJdebvnvQPCqPWH0CajQDrwbgl0JQxWVSkVXPy8uzmjHS8WsAfhw63mu3IvNy08ghMgn3N3duXLlSobyQ4cOUaZMmWy3n+WEcNmyZbRr1w57e3vGjBnDmDFjcHBwoEOHDixdujTbAQkhRFZZWFgYtq5L2z3J5OIewoJKcPoH/fNbj014qdId3r8Br656bjPWlmZ83b+O4XmXLw/xKC45h4MVQuR3w4YNY8yYMRw9ehSVSsXdu3f5/vvvmTBhAiNGjMh2+1m+ZTx79mwWLlzIqFGjDGXvvPMOjRs3Zvbs2YwcOTLbQQkhRFY5OzsD+SQhjA6F1e0g7t/Fssu3h57fpL9uYa1/ZFJlTwfefaU8n++8THyyll9P3uGNJj45HLQQIj+bNGkSOp2OVq1aER8fT9OmTbGysmLChAmMHj062+1n+QphZGQk7dq1y1Depk0boqKish2QEEK8iGLF9MuynDlzhq1bt5o2mEMLIeKG/rjJOOjzI1jZZ6vJN5uVoaK7vo3bj+KzGaAQoqBRqVRMmTKFR48ece7cOY4cOcL9+/eZOXNmjrT/QusQ/vrrrxnKt27dSqdOnXIkKCGEyKrHx9AMGjQIrVZrumDMLfV/lm8HLaZCDswMtjI34/W6JQFY888NbjyIy3abQhRFS5cupXTp0mg0GurXr8+xY8eeWjclJYUZM2bg6+uLRqOhRo0abN++3ahOTEwMY8eOxdvbG2traxo1asTx48ef2ubw4cNRqVQsWrQoS3G3bNmS6dOnY2lpSeXKlalXrx52dnZERETQsmXLLLX1JFm+ZVy5cmVmzZrFvn37aNiwIQBHjhzh8OHDvPvuuyxevNhQ95133sl2gEIIkRkuLi74+/vTsGFDIiMjqVq1KmfOnMHCwiJvAnh0DdTm4FQKWk+H81uh3Rwwe+HFHDLwcbUzHM/dfomv/lc7x9oWoijYuHEj48ePZ/ny5dSvX59FixbRtm1bgoKCDIvcP27q1Kl89913fPPNN1SsWJEdO3bQvXt3/vnnH8M+6kOHDuXcuXOsX78eT09PvvvuO1q3bs2FCxfw8vIyau/XX3/lyJEjeHp6Zjn2ffv2cfbsWU6ePMn3339v2KUkOTmZ/fv3v0BvGMvyOoQ+Ppkbt6JSqbh27doLBZXXitI6RkIUdoMGDWLt2rUAlC9fnhMnTmBvn73btc91/zIsrQfNJ0Pz9/8tCwLXCjl6Gp1OYerWc/xw9BYAtb2L0b2mF/9r4J2j5xGioMjq93f9+vWpW7cuX375JaBfqqpkyZKMHj2aSZMmZajv6enJlClTjOZH9OzZE2tra7777jsSEhKwt7dn69atdOzY0VCndu3atG/fnk8++cRQdufOHerXr8+OHTvo2LEjY8eOZezYsZn+rGq1mpMnT/LWW28RFxfH77//TunSpQkPD8fT0zPbd0WyfMv4+vXrmXoUlGRQCFG4rFmzhqlTpwL6PdZ3796d+ycNOQYocPyxiSM5nAwCqNUqPupUGS8n/YSUgJsRTN1yjpAIGVMoxPMkJycTEBBgtJGGWq2mdevWT932MikpCY1GY1RmbW1t2CouNTUVrVb7zDqgTzz79+/Pe++9R5UqVV74M3h4eLB//36qVatG3bp12bdv3wu39V8vtA6hEELkZzNmzKBs2bIAhIeH596JdFrYPRO2/nv1oHLX3DvXvzQWZuyZ0Iw/3knfGOD4jXwws1oIE4qJiSE6OtrwSNvW7XEPHjxAq9Xi5uZmVO7m5vbUXT7atm3LggULCA4ORqfTsXPnTjZv3kxoaCgA9vb2NGzYkJkzZ3L37l20Wi3fffcd/v7+hjoAc+fOxdzcPFtD6dJ2KbKysuKHH35gzJgxtGvXjmXLlr1wm4+ThFAIUeioVCoaN24MQERERO6daNtYODg//XnFjk+tmpOszM2o4ulIy4r6MU/jNp7mi13BpDy21Z0QRUnlypVxdHQ0PObMmZMj7X7xxReUK1eOihUrYmlpyahRoxg8eDBqdXr6tH79ehRFwcvLCysrKxYvXkyfPn0MdQICAvjiiy9Ys2ZNtrae/O8Iv6lTp/L999/z+eefv3Cbj5OEUAhRKKUtQ5OrCeHlHfo/7T3gvavgm/2Zflkx9LG1CBfuuszSvRl3MRCiKLhw4QJRUVGGx+TJkzPUKV68OGZmZhnuGoSHh+Pu7v7Edl1dXdmyZQtxcXHcvHmTS5cuYWdnZ7Sqga+vL/v37yc2Npbbt29z7NgxUlJSDHUOHjzIvXv3KFWqFObm5pibm3Pz5k3effddSpcunenPeP36dYoXL25U1rNnT44cOcKqVc9f5P55JCEUQhRKaQnhvHnzuHz5cs6fIDUZmozXH486DrbFn10/FzQqW5x9E5pjptZfdVi8O5iYxJQ8j0MIU7O3t8fBwcHwsLKyylDH0tKS2rVrG40r1ul07N6927BqytNoNBq8vLxITU3ll19+oWvXjMNDbG1t8fDwICIigh07dhjq9O/fnzNnznDq1CnDw9PTk/fee48dO3Zk+jN6e3sbXZlMU7VqVQYOHJjpdp4m59ZDEEKIfMTPz89wvHLlSubOnZuzJzC3hEqd4dHVbC86nR2li9uydWRjOi05hE6Bah//zWt1XqJffW9qlHQyWVxC5Efjx49n4MCB1KlTh3r16rFo0SLi4uIYPHgwAAMGDMDLy8twy/no0aPcuXMHPz8/7ty5w8cff4xOp2PixImGNnfs2IGiKFSoUIErV67w3nvvUbFiRUObLi4uuLi4GMVhYWGBu7u7YQ/2Z8U7c+ZMbG1tGT9+/DPrLliwIMv98bgXSggPHjzIihUruHr1Kj///DNeXl6sX78eHx8fmjRp8vwGhBAil3Xp0oW3336bZcuWMW/ePMqWLcuwYcNevMEHwfodSK4fgBp9oNlEcPSCDp/lXNAvqLKHA33rlzIsR7PpRAibToTQooIrc3tWp4SD5jktCFE09O7dm/v37/PRRx8RFhaGn58f27dvN0w0uXXrltFVuMTERKZOncq1a9ews7OjQ4cOrF+/HicnJ0OdtFvUISEhODs707NnT2bNmpUja6CePHmSlJQUw/HTZGdsoqGNrK5D+Msvv9C/f3/69evH+vXruXDhAmXKlOHLL7/kzz//5M8//8x2UHlN1iEUonC6cuUK5cqVA/R7HYeGhmJpaZn5BhKj4dYRCD0Ne9PXE0NtDq+uhspdcjji7AmLSmTbmbt8tiOIpFT9BBNLczW/j2pCBXfTXcUUIrfI93fOyfIYwk8++YTly5fzzTffGGW/jRs3JjAwMEeDE0KI7ChbtiwnTpwA4NGjRzRq1CjDTL2nuvkPzCsDP/QyTgY7zIfxl/JdMgjg7qhh6MtlOD2tDZ2qewCQnKrjg1/PmjgyIUR+l+VbxkFBQTRt2jRDuaOjI5GRkTkRkxBC5JhatWrx5ptv8vXXXxMQEPDMGYVGnH2hem849Z1+T2LPmlC6if6Rz2kszFjSpyZlXO1YvDuYgJsRxCWlYmslw8aFKEieN27wcXk+htDd3Z0rV65kmCp96NAho2nYQgiRH6hUKlasWMHevXsJDg5m3bp1RgPCn8reDbotha5fQg6Mz8lrKpWKkS18+XJPMDoFXlmwn8OTWubIWCMhRN541rjBx+XEf9dZTgiHDRvGmDFjWLVqFSqVirt37+Lv78+ECRP48MMPsx2QEELkhmrVqhEcHMz7779Pr169nr4ve8gJ/T7ENfvpnxfgBMrK3IwRzX1Zuvcqd6MS+fNsGB3/vZUshMj/9u7dm2fnyvKkEkVRmD17NnPmzCE+Xr9/ppWVFRMmTGDmzJm5EmRuk0GpQhR+AQEB1KlTB4BNmzbRq1evJ1dcUgceBoOlHYy/ABrHPIwyd3T44iAXQqMB8HTU8M3AOlTxLPifS4ii+P194cIFbt26RXJysqFMpVLRuXPnbLWb5YQwTXJyMleuXCE2NpbKlStjZ2eXrUBMqSj+gxKiKBowYADr16/HxsaGwMDAjGuA3b8MS+vqj7suhZr/y/sgc8Gh4AdM+Ok0YdGJAPRv4M3MblVNHJUQ2VeUvr+vXbtG9+7dOXv2LCqVyjBBLu12sVarzVb7WZ5lPGTIEGJiYrC0tKRy5crUq1cPOzs74uLiGDJkSLaCEUKI3NStWzcA4uPjadGiBffu3Ut/Mf5RejJoXQz8+uV9gLmkSbni+E9uydvNfQFYf+QmE38+zfEbj4iMT37Ou4UQ+cGYMWPw8fHh3r172NjYcP78eQ4cOECdOnXYt29fttvPckK4du1aEhISMpQnJCSwbt26bAckhBC5pUePHsybNw+A0NBQpkyZon/h/mWY99iYwg7zC/TYwSdRqVT0rV/K8HzTiRB6Lfenydy9PIqTpFCI/M7f358ZM2ZQvHhx1Go1arWaJk2aMGfOHN55551st5/phDA6OpqoqCgURSEmJobo6GjDIyIigj///JMSJUpkOyAhhMhNY8eOpX79+gCcO3dOX3hlV3qFHt9CtVdNEFnue6mYDddmd2Biuwo0LqvfSis2KZX3fjpNYkr2bjcJIXKXVqvF3l6/wHzx4sW5e/cuoN/jOCgoKNvtZzohdHJywtnZGZVKRfny5SlWrJjhUbx4cYYMGcLIkSOzHdCzfPrpp6hUKsaOHWsoS0xMZOTIkbi4uGBnZ0fPnj0JDw/P1TiEEAWXhYWFYb2u8PBw0Omgak/o/T0M+gOqP2WySSGhVqt4u3lZvh/agH7/XjHcfekeW0/dMXFkQohnqVq1KqdPnwagfv36zJs3j8OHDzNjxowcWfYv08vO7N27F0VRaNmyJb/88gvOzs6G1ywtLfH29sbT0zPbAT3N8ePHWbFiBdWrVzcqHzduHH/88Qc//fQTjo6OjBo1ih49enD48OFci0UIUbB5eOiXXgm9fR3lTgCqknWhUicTR5X3JrSpwK8n7xCfrGXnhXB61S6JWl24bpULUVhMnTqVuLg4AGbMmEGnTp14+eWXcXFxYePGjdluP8uzjG/evEnJkiWNNn/ObbGxsdSqVYtly5bxySef4Ofnx6JFi4iKisLV1ZUffviBV1/V3+K5dOkSlSpVwt/fnwYNGmSq/aI0S0kIAfGR97Etph/iUqN8KVp27Mlnn32GmZmZiSPLe98ducnULfpb58Ob+TKpfUUTRyRE5hX17+9Hjx5RrFixHFmYOstZnbe3N2q1mvj4eC5dusSZM2eMHrlh5MiRdOzYkdatWxuVBwQEkJKSYlResWJFSpUqhb+/f67EIoQo+GyS7tO6jD75O335FgsXLmTWrFmZ3+e4EGlbxR0vJ2sAlu+/yoBVxzh5K8LEUQkhMiNtKF9OyPJOJffv32fw4MH89ddfT3w9u+vg/NeGDRsIDAzk+PHjGV4LCwvD0tISJycno3I3NzfCwsKe2mZSUhJJSUmG5zExMTkWrxAin0uKhR9fZ8f/bLhpWZF3jrixbds2pk2bhp+fH126dDF1hHnK1d6KvROa02rBPm4/SuDA5fscuHyfL/vWpFP13BsGJITIusTERM6cOcO9e/fQ6XRGr2X3Z1eWrxCOHTuWyMhIjh49irW1Ndu3b2ft2rWUK1eO3377LVvB/Nft27cZM2YM33//PRqNJsfanTNnDo6OjoZH5cqVc6xtIUQ+FvcAvqwDkTdRq1T4+DVl7ty5hpePHDliwuBMx9Jczd9jm/Fe2/SFukf9cJK31p+QJWmEyCe2b99OqVKlaNCgAV26dKFbt26GR/fu3bPdfpYTwj179rBgwQLq1KmDWq3G29ub//3vf8ybN485c+ZkO6DHBQQEcO/ePWrVqoW5uTnm5ubs37+fxYsXY25ujpubG8nJyURGRhq9Lzw8HHd396e2O3nyZKKiogyPCxcu5GjcQoh86souiH+oP67QAV6ZTuXKlfnss88A/S+Lt2/fNmGApmNtacbIFmXZMrIx9hr9zaMd58NpMGc3B4Pvmzg6IcTo0aPp1asXoaGh6HQ6o0dO3J3NckIYFxdnWG+wWLFi3L+v/0FRrVo1AgMDsx3Q41q1asXZs2c5deqU4VGnTh369etnOLawsGD37t2G9wQFBXHr1i0aNmz41HatrKxwcHAwPNLW9RFCFHI1XocJl6Hbcv0yM5a2AFSpUsVQZcKECaaKLl/wK+lEwNRXGNDQG4DkVB2Tfjlr4qiEEOHh4YwfPx43N7dcaT/LCWGFChUMCyDWqFGDFStWcOfOHZYvX25YyiGn2NvbU7VqVaOHra0tLi4uVK1aFUdHR9544w3Gjx/P3r17CQgIYPDgwTRs2DDTM4yFEEWMdTHw6wOPrZTQtm1bxowZA8CmTZtYtGiRiYLLHyzN1czoWpV1Q+oBcCcyga5fHkKnK3qTboTIL1599dUc2aLuabK87Mx3331HamoqgwYNIiAggHbt2vHo0SMsLS1Zs2YNvXv3zq1YAWjevLlh2RnQD7B89913+fHHH0lKSqJt27YsW7bsmbeM/6uoT1sXolDTaeHnwRB7H5qMhbKvGCWDabRaLa6urkRERODs7MzDhw/zPtZ8aNi6E+y8oF/sf8+7zSjjamfiiIRIV5S+v+Pj4+nVqxeurq5Uq1YNCwsLo9ezu31dlhPCJwV46dIlSpUqRfHixbMVjKkUpX9QQhQpqUnwQ2+4tlf/3Ks2DN391H2KQ0NDDQvsR0dHy3ASQFEUGs7ZQ1h0osw8FvlOUfr+XrlyJcOHD0ej0eDi4mK03IxKpeLatWvZaj/Lt4xnzJhBfHy84bmNjQ21atXC1taWGTNmZCsYIYTIMYoCf7ybngx61oIhfz81GQT9DiYODg4ADBs2LC+izPdUKhUV3PWJ8agfTnL1fqyJIxKiaJoyZQrTp08nKiqKGzducP36dcMju8kgvEBCOH36dGJjM/5AiI+PZ/r06dkOSAghcsTZn+Hkev2xgxf0+wnMnr/0qp+fHwAbN24kJCQkFwMsOF6vW9Jw/M+VByaMRIiiKzk5md69e+faTnFZblVRlCeuin369Gmj/Y2FEMKk7l9MPx6yA2wzN6Tl8fVUb926ldNRFUjtq3nwZtMyAHy49TxHrsn4SiHy2sCBA3Nkz+KnyfROJWl75alUKsqXL2+UFGq1WmJjYxk+fHiuBCmEEFnmWBJ8mkKVHuBU8vn1097m6EjDhg3x9/d/5o5HRU0jXxe+PqC/LTVg1TH8J7XExc7KxFEJUXRotVrmzZvHjh07qF69eoZJJQsWLMhW+5lOCBctWoSiKAwZMoTp06fj6OhoeM3S0pLSpUs/c+0/IYTIU3UG6x8vIG2Vgp49e3Lnzh3DRJOirHmFEqweVJfBa46TnKrj3Z9Os2ZwPVOHJUSRcfbsWWrWrAnAuXPnjF7Lif2MM50QDhw4EAAfHx8aN26MuXmWt0EWQojct+tjuH4Ahu154SaaNm3Kr7/+CoCXlxcffvghHTt2pEaNGjm6jWZB07yCK11qePLb6bvsC7rPH2dCaVbBFUszNRZmqhz5UhJCPNnevXtztf0sjyG0t7fn4sX0sTlbt26lW7dufPDBByQny56XQggTURTY/gEcWgh3AkCb+sJNjR07lpUrVxqez5w5kwYNGlClSpUi/XNOpVKx4LUa2FqaATDyh0CqTttB+al/4fvBn7y76TQJydnfQksIYSwlJYVWrVoRHByca+fIckL41ltvcfnyZQCuXbtG7969sbGx4aeffmLixIk5HqAQQmTKg2A4slR/7FQqUzOKn2XIkCEEBQXxwQcf0LhxY0D/M69p06bs2LEjR/YOLYjMzdR89b/alC1hZ7SCj06BXwJDqPTRdoauPc5HW8/xMDbJdIEKUYhYWFhw5syZXD1HlhemdnR0JDAwEF9fX+bOncuePXvYsWMHhw8f5vXXXy+QG8MXpYUthSi0ru6F9d30x+/f0G9Rl4PGjBnD4sWLDc/bt2/P2rVrcXV1zdHzFCRanUJyqo7kVB2fbr/Ij8cy/vz/bVRjqr/klPfBiSKhKH1/jxs3DisrKz799NNcaT/Lv0IrioJOpwNg165ddOrUCYCSJUvy4IGsTyWEMAFFgfP6MX+UaZ7jySDAvHnzaNiwIR9++CFXrlzhr7/+YujQoWzdujXHz1VQmKlVWFuaYW1pxpwe1Xm7eVkOX3nAkWsP2XLqLgBdvjxMj1peNCuvT5ytzNW8XM4VWysZhy5EVqSmprJq1Sp27dpF7dq1sbW1NXo9u7OMs3yFsGXLlpQsWZLWrVvzxhtvcOHCBcqWLcv+/fsZOHAgN27cyFZAplCUfsMQolC6tg/WddUf1xoAXZbk2qm0Wi29evUyTDoJDw+nRIkSuXa+guqnE7d57+cn3+Kq6uXAT281wvrfsYhCvKii9P3dokWLp76mUqnYs+fFJ9LBCySEZ86coV+/fty6dYvx48czbdo0AEaPHs3Dhw/54YcfshWQKRSlf1BCFEoPrsD5zXD8Wxj4O7hWyNXTRUVF4ezsjE6nw9fXl+DgYJlh+wS3H8Xz7cFrXHsQh05RuPEgnjuRCQA0Le/KuiGybI3IHvn+zjlZTgifJjExETMzswwLJRYE8g9KCJFV8+fP57333gP0P0O8vLxMHFH+pygKb60P4O8L4QCsf6MeL5crumMwRfYVte/vyMhIVq5caVjtpUqVKgwZMsRobegXlWMb4mk0mgKZDAohCriUBP0Ywjw2YcIEfH19AVi/fn2en78gUqlUrOhfm2I2+u+K/iuPyUxkITLpxIkT+Pr6snDhQh49esSjR49YsGABvr6+BAYGZrv93NkhWQgh8sp3PWHOS3B5R56fumLFigBMnjyZxYsXGybciadTqVQs6O1neF77k128//MZw61kRVE4dv0Re4PukaKV/hQizbhx4+jSpQs3btxg8+bNbN68mevXr9OpUyfGjh2b7fZz7JZxQVbULjkLUWjcOAxrOuiPh/wNpern6emPHz9OvXrp4+C++eYbhg4dmqcxFFRLdgfz+c7LRmUV3OyJT0nl9iN9climuC1L+takimf2b4eJwqkofX9bW1tz8uRJwy+iaS5cuECdOnWIj4/PVvtyhVAIUTDdPZWeDKrUULxcnodQt25dzp49i5mZfrbs0aNH8zyGgmp0q3Lsm9Ccsa3T/96CwmMMySDAtQdxdFx8iG8PXkOrK/LXLkQR5+DgwK1btzKU3759G3t7+2y3n+WEcMaMGU/MQhMSEpgxY0a2AxJCiOdKTYJV7dKf9/sZbJxNEkrVqlVZs2YNAPv27UNuumRe6eK2jG1dnoMTWzCtc2UmtCnPh50qs2ZwXT7vVcNQ75M/LuL7wZ98/Nt5tp8LIzGlaO4SI4q23r1788Ybb7Bx40Zu377N7du32bBhA0OHDqVPnz7Zbj/Lt4zNzMwIDQ3NsO7Ww4cPKVGiRIHczqkoXXIWolB4dB0W++mP+28B36evz5UXjhw5QsOGDQH9D+21a9diZWVl0pgKg7uRCbz9fSCnbkcaldf2Lsb3Q+ujsZB1DIu6ovT9nZyczHvvvcfy5ctJTdXv1W5hYcGIESP49NNPs/0zJ8tXCBVFeeJ6W6dPn8bZ2TS/oQshipiYUP2fxXxMngwC1K5dm2rVqgGwceNGNBoNhw8fNnFUBZ+nkzVbRjZmz7vNGNWiLDVKOgEQcDOCRp/uITQq4dkNCFGIWFpa8sUXXxAREcHJkyc5deoUjx49YuHChTnyC2imrxAWK1YMlUpFVFQUDg4ORkmhVqslNjaW4cOHs3Tp0mwHldeK0m8YQhQKqckQeQuSY8HTz9TRAPqfg//73//YsGGDoUzWJ8xZiqLw/i9n2HQixFCmUoGdpTlVvByY2bUq5dyyP5ZKFBzy/Z1zMp0Qrl27FkVRGDJkCIsWLTJaBNHS0pLSpUsbbpkUNPIPSgiRU37//Xe6dOkCwG+//Ubnzp1NHFHh8+vJED7bHsTdqESj8nIl7Ng5vpmJohKmUBS+v9Vq9XN3QlKpVIbbyC8qy2MI9+/fT+PGjTE3LzwbkxeFf1BCFBrhFyDyJrhWBGcfU0fzRK+//jobN27Ex8eH8+fPY21tbeqQCqXoxBTik7Qs33+VNf/cAOD1uiWZ3b0aarVsJVgUFIXv761btz71NX9/f8MaqImJiU+tlxlZHkMYFxfH7t27M5Tv2LGDv/76K1vBCCHEc51cDz++DkeXmzqSp/Lz8wPg+vXrrF692rTBFGIOGgvcHTV83KUKVTwdANhw/DatF+4nKiHFxNEJkTO6du2a4VGxYkXWrFnD/Pnz6dWrF0FBQdk+T5YTwkmTJj1xJrGiKEyaNCnbAQkhxFNd2wdHlumPHTxNGsqzjB49murVqwMwcuRIDhw4YOKICr8fhjagort+/OC1+3FM//28rF2YhxRFISFZy7X7sfxz9QHn7kRxNiSKPZfCWXXoOjO3XSAhueCtQpLf3L17l2HDhlGtWjVSU1M5deoUa9euxdvbO9ttZ/m+b3BwMJUrV85QXrFiRa5cuZLtgIQQIgNFgau74ach6WXl2pounuewtbXlu+++MySFzZo148iRI9Svn7c7qRQljjYWbBnZmP99e5QTNyPYHHiHzYF3eLu5LxPbVXx+AyLTtDqFnwNuc/T6I0IiErgfk8SNh3HP3VK8X/1SlHG1y5sgC5moqChmz57NkiVL8PPzY/fu3bz88ss5eo4sJ4SOjo5cu3aN0qVLG5VfuXIFW1vbnIpLCCH0dDpY3Q5uP7YLyPDDUCJ/f8lXq1aNv/76i/bt2wPQoEEDxo4dy/z58w07m4icpbEw44s+NZm8+SwHLt8HYNm+q1wMjebbgXVJ0ep4EJtEZHwKEfHJmKlUuDtqJEnJJEVROHU7kgk/nebq/bgn1rGxNMPSXI1Wq2BlYYa1pZoqHo6UcrGRdSNf0Lx585g7dy7u7u78+OOPdO3aNVfOk+VJJW+99Rb+/v78+uuv+Pr6AvpksGfPntStW5dvv/02VwLNTUVhUKoQBVZMGHzXE8LPgWNJaDMTqnQ3dVSZ9ssvv/Dqq68alTk7O2NmZsagQYOYN2+eiSIr3BJTtHRacogr92INZWoVPOkusr2VOcOb+zLs5TJYmsuOrk8ScDOCyZvPcDk8vT9rlHSib72SeDha4+6owc1Bg4PG/LkzYnNSUfj+VqvVWFtb07p162f+Mrl58+ZsnSfLCWFUVBTt2rXjxIkThs4PCQnh5ZdfZvPmzTg5OWUrIFMoCv+ghCjwEiLAupipo3ghjx49Ytq0aXz77bcZZgLu3r2bli1bmiiywk2nU5jw02k2n7xjKFOrwMXOCmcbS2KTUrkTaby49ZI+NelcI/+OT81LOp3CkWsPWfPPDf6+EG4or+NdjNfqlqRX7ZfyNPl7kqLw/T1o0KBM9XN2J7BlOSEE/WXjnTt3cvr0aaytralevTpNmzbNViCmVBT+QQlR4DwIhr8mQvevwc7V1NHkiMTERMLDw4mLi6NKlSqG8vfee4/SpUvj5+dH3bp1sbCwMGGUhUuKVseZkCjM1CocrS0o7WJj9OV6NzKBCT+d5p+rDw1l3i42fPdGfUo625giZJO7dj+WXwJD2HQihPsxSYbyal6OvNe2Ak3L55//HuX7O+e80LVxlUpFmzZtGD16NCNHjizQyaAQIh9KSYSfh8DVPRByzNTR5BiNRoO3tzeVK1fm77//NpR/9tlnjBw5ksaNG6PRaFi2bJkJoyxcLMzU1PYuhl9JJ3yK22a40uLpZM0Pwxrw/dD6mP+7duHNh/G8PG8v7/98hoexSU9qttDaf/k+LT/fz9K9Vw3JYLPyriztW4utIxvnq2TwRS1dupTSpUuj0WioX78+x449/WdMSkoKM2bMwNfXF41GQ40aNdi+fbtRnZiYGMaOHYu3tzfW1tY0atSI48ePG7Xx/vvvU61aNWxtbfH09GTAgAHcvXs31z7ji8hyQqjT6Zg5cyZeXl7Y2dlx/fp1AD788ENWrlyZ4wEKIYqAB1fg6xawsCrMLw+zPSDsjP61uPumjS2XvPLKKwQHBzNu3DiGDBlCgwYNAP3P2JEjRzJ06NAnLvElckfjssU5N70tk9qnT1baeOI2tT/ZxY7zYbzAzbQC59ydKAau0idH9hpzpnasxD+TWrJ2SD06VvcoFIt9b9y4kfHjxzNt2jQCAwOpUaMGbdu25d69e0+sP3XqVFasWMGSJUu4cOECw4cPp3v37pw8edJQZ+jQoezcuZP169dz9uxZ2rRpQ+vWrblzRz9UIT4+nsDAQD788EMCAwPZvHkzQUFBhh2N8oss3zKeMWMGa9euZcaMGQwbNoxz585RpkwZNm7cyKJFi/D398+tWHONXHIWIo+FngHb4ulrCcY/gnn/2XXEygHKtoZuX4GFJu9jNIHLly/Tpk0bbt68CegX/G/Tpo2Joyp6HsYm8f4vZ9h10ThJcHfQkJCixc7KnNrexXi3TXnMzdR4OmpMPpYuu7Q6hcFrjhtmZ/88vCF1SjubOKrny+r3d/369albty5ffvkloP8FrGTJkowePfqJayl7enoyZcoURo4caSjr2bMn1tbWfPfddyQkJGBvb8/WrVvp2LGjoU7t2rVp3749n3zyyRPjOH78OPXq1ePmzZuUKlUqqx87V2R52Zl169bx9ddf06pVK4YPH24or1GjBpcuXcrR4IQQhYhOCwGr4fBi/dZzGif9jOFaA8DSDmoNhOqvgcYRzCyhmA+YW5o66jxVvnx5zpw5Q40aNbhx4wYDBw7k/PnzODvn/y/mwsTFzopvB9blwOX7fHPwGgeDHwAQFq2fEBSVkMKdyAR+O62/5eegMaernxfl3exoV9UDV3srk8WeVQE3H7Fi/zVO3IzgUVwyAJ90q1ogksHHxcTEEB0dbXhuZWWFlZXx30NycjIBAQFMnjzZUKZWq2nduvVTL2YlJSWh0Rj/Qmptbc2hQ4cASE1NRavVPrPOk0RFRaFSqfLVRNwsJ4R37tyhbNmyGcp1Oh0pKbJVkBDiKTb2h6A/0p/bu4NzGf2xuSV0WWyauPIZBwcHPvjgA958803CwsJwcXHhk08+YcqUKaYOrchpWt6VpuVdiYpPYW/QPazM1fi42rL60A02nrhtqBedmMr6I/qruh9uPU+90s70a1CK9lU98t0yNjqdwuGrD9h0IoR/rjzg4b9JIICluZpapZzoUM3DhBG+mP9umDFt2jQ+/vhjo7IHDx6g1Wpxc3MzKndzc3vqBa22bduyYMECmjZtiq+vL7t372bz5s2G4Rz29vY0bNiQmTNnUqlSJdzc3Pjxxx/x9/d/Yq4E+sll77//Pn369MHBweEFP3HOy3JCWLlyZQ4ePJhhm5Sff/6ZmjVr5lhgQohCJP5RejLo5A39fwUXX9PGlI/179+fy5cv8/nnn6MoClOnTuXcuXP88MMPBf7WZEHkaGNBt5pehudzX63OR50rY21hRsCtCK7fj8P/2kO2nrqDToFjNx5x7MYjplidY0yrcvyvgTfWlqZdlPnq/Vh+Dghh4/HbhiuBoF+Gp5FvcVpWLEHP2i/haF0wZ7hfuHABL6/0v6P/Xh18UV988QXDhg2jYsWKqFQqfH19GTx4MKtWrTLUWb9+PUOGDMHLywszMzNq1apFnz59CAgIyNBeSkoKr732Goqi8NVXX+VIjDkly2MIt27dysCBA5k8eTIzZsxg+vTpBAUFsW7dOrZt28Yrr7ySW7HmGhlDKEQuSoyGud6g6PTrCL5/w9QRFRhJSUnUr1+f06dPA3D27FmqVq1q4qjE0+h0Cr+evMPSvVe49sB4J49KHg74FLfBwkzNy+Vc6V7TC7NcnKShKArHrj9i0a5gLoVFExFvfAeviqcD41qXp25pZxxtCmYSCFn7/k5OTsbGxoaff/6Zbt26GcoHDhxIZGQkW7dufep7ExMTefjwIZ6enkyaNIlt27Zx/vx5ozpxcXFER0fj4eFB7969iY2N5Y8/0u+KpCWD165dY8+ePbi4uLzYh84lL7QO4cGDB5kxYwanT58mNjaWWrVq8dFHHxXYwc+SEAqRi3RaeHgFdk6DUg2gyVhTR1SgKIpCxYoVuXz5MgAPHz6UMYUFwNmQKL4/epMNx28/tc74V8ozorkvFmaZu6184W40R6495EJoNJHxKTyKS8LCTI2VhRnONhao1SqSU3UkJGsJvhfLrUfxRu8vV8KOdlXd6VzDk/Ju9tn6fPnFi0wqqVevHkuWLAH0w91KlSrFqFGjnjip5L9SUlKoVKkSr732GrNnz35inYiICHx8fJg3bx5vvvmm4X2vvfYawcHB7N27F1fX/Ld8T6YSwsWLF/Pmm2+i0Wi4desWJUuWLFS3LSQhFELkZ+vXr2fAgAEA/P7773Tq1MnEEYnMSkzR4n/1IRHxyUQnpLDh+G0uhcUY1RnezJe3mpahmK0lUQkppGh1uNhaolKpSEzRcicygTl/XmLXxfCnnOXpSrvY0KtOSXrVeYkS9oVvtn5Wv783btzIwIEDWbFiBfXq1WPRokVs2rSJS5cu4ebmxoABA/Dy8mLOnDkAHD16lDt37uDn58edO3f4+OOPuX79OoGBgYYJITt27EBRFCpUqMCVK1d477330Gg0HDx4EAsLC1JSUnj11VcJDAxk27ZtRmMYnZ2dsbTMH5PnMpUQmpubc/fuXUqUKIGZmRmhoaGUKFEiL+LLE5IQCpHDwi+A/5dw8x/o8BmUaQ5mBfe2VH7w+uuvs3HjRkC/PE25cuVMHJF4URdDo5n++3mOXHtkVO5ia0lEfDI6BczUKuw15kT+51ZvvdLO1C/jjLujBhtLMyzM1MQkpvIgJgkLczWWZmo0FmZ4OGrwK+lEMdv8kWzklhf5/v7yyy/57LPPCAsLw8/Pj8WLF1O/fn0AmjdvTunSpVmzZg0A+/fvZ8SIEVy7dg07Ozs6dOjAp59+iqdn+vaGmzZtYvLkyYSEhODs7EzPnj2ZNWsWjo6OANy4cQMfH58McQDs3buX5s2bv3gH5KBMJYSlSpVi8uTJdOjQAR8fH06cOEHx4sWfWregkYRQiByk08KnpSA5Nr3stfVQOX8twlrQrF27lkGDBgEwd+5cJk6caNqARLaFRMTz9YFrrPO/+dy6Jeyt+KxXDZoVgp1CcpJ8f+ecTM0ynjp1KqNHj2bUqFGoVCrq1q2boY6iKKhUKllZX4iiTJsKX9ZOTwa7LtOPG5QZxdk2cOBA9uzZw7p16wgNDTV1OCIHvFTMhhldqzKhbQVO347kcngs5d3sqFvamZCIeEKjEqngZo+zrSXmmRxnKMSLylRC+Oabb9KnTx9u3rxJ9erV2bVrV76bHSOEMLGkGP3+wxE39M/rD4ea/UwaUmFTrVo1AO7fL5zb+RVVDhoLXi7nysvl0q/+lS1hT9kShWPihygYMr0Oob29PVWrVmX16tU0btw4x9b4EUIUEgkRkPTvlcGqPaH9XNPGUwilzUz8/vvv6d69Oz179jRxREKIwiLL16AHDhxIQkIC3377LZMnT+bRI/2g2MDAQMNGzkKIIkSnhQfB4FQKBv4Gnb+ADvNNHVWhVK9ePcPx8uXLTRiJEKKwyXJCeObMGcqXL8/cuXOZP38+kZGRAGzevNlof0AhRBFw6wh81Rj2fap/bmYBtQeBjayTlxsqVarE9u3bAdi1axdvvvnmE3dDEEKIrMpyQjhu3DgGDRpEcHCw0WbOHTp04MCBAzkanBAiH4u8Davawv2LEHZGP4ZQ5LomTZpQrFgxAL755hsaNWpkuFMjhBAvKssJ4YkTJ3jrrbcylHt5eREWFpYjQQkh8rHoUPhpMCx6bAu1DvPBSgbA5wVbW1vOnz/Pd999B+i34woODjZxVEKIgi7LCaGVlRXR0dEZyi9fvpwvt2IRQuSwwHVwfrP+2MIWeq6EMs1MG1MR4+HhQb9+/QyL6e7evdvEEQkhCrosJ4RdunRhxowZpKToV09XqVTcunWL999/X2a8CVEUWFjr/yzXBibfhmqvmjaeIixtI4ApU6awatUqE0cjhCjIspwQfv7558TGxlKiRAkSEhJo1qwZZcuWxd7enlmzZuVGjEIIU9PpIDVJf9xwJJhZQqtpoDYzbVxF3OO7lbzxxhssW7bMhNEIIQqyTK9DmMbR0ZGdO3dy6NAhzpw5Q2xsLLVq1aJ169a5EZ8QwhSiQuDKbrhxEC7/DUnR0GUJ+PXVJ4GjTkAxb1NHWeTVqVOHgIAAateuDcD27dt5++23TRyVEKIgytRexoWd7IUoxGN0WlhYFWLuZnyt6XvQcmrexySe6e+//6Zt27YAHDp0iMaNG5s4IiHyhnx/55wsXSHU6XSsWbOGzZs3c+PGDVQqFT4+Prz66qv0798flUqVW3EKIfJCSiIsqZ2eDPr1g9IvQ5nmoHEESxuThieerEqVKqhUKhRFoUmTJtSrV49PPvmEV155xdShCSEKiEyPIVQUhS5dujB06FDu3LlDtWrVqFKlCjdv3mTQoEF07949N+MUQuSFlHioPRBsikOtgdBtGfj1AQcPSQbzMS8vL/744w/s7fVL/xw7dow2bdrQv39/YmJkfUghxPNl+grhmjVrOHDgALt376ZFixZGr+3Zs4du3bqxbt06BgwYkONBCiFykaLA8W/1exE3m6h/1H9Lf0VQFBjt27cnMjKSAwcO0LFjR+Lj4w1rFa5bt07u4AghninTVwh//PFHPvjggwzJIEDLli2ZNGkS33//fY4GN2fOHOrWrYu9vT0lSpSgW7duBAUFGdVJTExk5MiRuLi4YGdnR8+ePQkPD8/ROIQo1P6coH9E3wFtqr5MksECSa1W07x5c0JCQvD19QXgu+++Y/To0SaOTAiR32U6ITxz5gzt2rV76uvt27fn9OnTORJUmv379zNy5EiOHDnCzp07SUlJoU2bNsTFxRnqjBs3jt9//52ffvqJ/fv3c/fuXXr06JGjcQhRqJ37d5Hpm/6g6Ewbi8gRxYoVIzAw0LBZwF9//WXiiIQQ+V2mbxk/evQINze3p77u5uZGREREjgSVJm0T9zRr1qyhRIkSBAQE0LRpU6Kioli5ciU//PADLVu2BGD16tVUqlSJI0eO0KBBgxyNR4hCJ+wsJPy7D+4bf4O5pWnjETnGwcGB48ePU7p0aa5du0Z8fDw2NjIOVAjxZJm+QqjVajE3f3r+aGZmRmpqao4E9TRRUVEAODs7AxAQEEBKSorRGogVK1akVKlS+Pv752osQhR49y7C8ib6Y2tnsHYyaTgi53l6eqJW63/MV6lSBVllTAjxNJm+QqgoCoMGDcLKyuqJryclJeVYUE+i0+kYO3YsjRs3pmrVqgCEhYVhaWmJk5OTUV03NzfCwsKe2lZSUpJRvDILTxRJuz5OP+68yFRRiFxkYWHBhAkTmDdvHjdu3CAsLAwPDw9ThyWEyIcyfYVw4MCBlChRAkdHxyc+SpQokaszjEeOHMm5c+fYsGFDttuaM2eOUeyVK1fOgQiFyOeiQiD0TPrzUg3BrRq8uhoqdzVdXCJXzZ07F29v/a4yVatWZc+ePXKlUAiRQaavEK5evTo343imUaNGsW3bNg4cOGC0Erm7uzvJyclERkYaXSUMDw/H3d39qe1NnjyZ8ePHG57fuXNHkkJRuKUkwvKXwVwDr64E70bQZKz+IQq9Fi1asGbNGh49ekSrVq1Yt24d/fv3N3VYQoh8JNNXCE1BURRGjRrFr7/+yp49e/Dx8TF6vXbt2lhYWLB7925DWVBQELdu3aJhw4ZPbdfKygoHBwfDI20xVyEKpfhHsKiqfvJIzF3jq4SiSPjmm29YsmSJ4fmRI0dMGI0QIj/K0tZ1eW3kyJH88MMPbN26FXt7e8O4QEdHR6ytrXF0dOSNN95g/PjxODs74+DgwOjRo2nYsKHMMBbi1A9wdS+c3wy6fyd81R8ODYabNi6R58zNzRk1ahQajYZhw4axbNkyypUrxzvvvGOYdCKEKNry9U+Cr776iqioKJo3b46Hh4fhsXHjRkOdhQsX0qlTJ3r27EnTpk1xd3dn8+bNJoxaiHziTiCc3ZSeDNYeDO0+NW1MwqSaNm1qOB43bhzOzs507tyZlJQUE0YlhMgPVIqMLiYkJISSJUty+/ZtozGKQhQoKYn6LegqdgRnH7i2D8LOgYVGv6xMxY5g/uRVAkTRERERQZcuXfD390er1QLg7+8vd1VEgSTf3zknX98yFkJkkqLAgc/g4Hz9biON34EyzfUPIR5TrFgxDh48SGJiIt7e3ty7d4+2bdty4cIFvLy8TB2eEMJE8vUtYyHEc4SegcOLYXFNfTIIcHmHaWMSBYJGo+GDDz4AIDo6mk2bNpk4IiGEKckVQiHyu4RI/VU/MwswtwYzc4i9B5sGwK3/7Mhj6wrdlpokTFHwvPPOO+zYsYO//vqLW7dumTocIYQJyRVCIfKrq3vgy3ow1xvm+cCcl+DwQv1rtq5Q9t8tG82tocHbMHAbvHcFipU2WciiYFGpVLRv3x6ARYsW8fPPP5s4IiGEqcgVQiFMKSURkmL0Ez+sHlsPMyYcfngdtP/ZEtLMUv+nSgVNxusnirhW1D8X4gV06NCBd955B4BevXrJ4HwhiihJCIXIDdoUfaKncYL/rvOmKHB6A5xYCaGnQZsMvi2h/6/pdT4vn348ZAe8VFdfT/VYW2o1lKiUqx9DFH6+vr6cP3+eKlWqAFCyZElmz57NpEmTUMkvGkIUGXLLWIicpNPCiVWwqLr+Nu/JdemvBW2HH/voy7cMh5Dj+iQPQG1h3I65Rn/rt+WHUKoBqM3AwlqWjRG5onLlynzyySeG5x988AErVqwwYURCiLwmVwiFyEmXt8O2cenPLe3Sj2NCIehP/bHKDDxrwiszoFRD/aSRx409B3auuR+vEP+aMmUKffv2pVKlSiQlJTFixAguX77M3LlzsbCweH4DQogCTRJCIXJKQgT8OTH9+agAcCqV/ty9GrT5BFzKgVft/yR8/7lYL8mgMAEfHx8OHTpEw4YNSU1NZeHChQQGBvLbb7/h4OBg6vCEELlIbhkLkVPWdYXoEP1xm1lQvCyYW6a//lIdaDQaKrSThE/kW3Xq1OHBgwfUqlULgP379/Pqq6+aOCohRG6ThFCIFxH/CHZO008KSeNYUv9nyQZQq79p4hIiBzg6OnLixAm6d+8OwM6dO7l//76JoxJC5Ca5ZSxEVmlT4bfRcGmbfs9gjxr68nKv6GcDNx4jy8CIAk+lUvHzzz9jb29PfHw8pUqVIjIyEisrmdgkRGEkVwiFyKrt7+uTQYDIx3Z3qD0ImoyVZFAUGmq1mo8//hiAxMRErl69atqAhBC5RhJCIZ5EUfSP/wo7C8e/1R/buMDL7+ZtXELksffee4/KlSsDEBoaauJohBC5RRJCIf4rOQ6WNYDpxeBBcHr5oYWwvIn+WKWGUSfA0tY0MQqRh9zd3QHo1KkTcXFxJo5GCJEbJCEU4nFRITDbE+5fAhSwsEl/LSEi/bjbV2DjnOfhCWEKNWrox8kmJiby559/mjgaIURukIRQiDR3AmFZw/TnLaaCvXv685r9YcBvMOkW1Hg97+MTwkTmzp1LmTJlADh9+vRzagshCiJJCIVIc/xbSIrWH/daA83e028Zl6Z4OSjTDDSOJglPCFOxsLDgf//7HwCzZs3i559/NnFEQoicJgmhEGmi/l1Uusk4qNLdtLEIkc907NjRcDxnzhx0Ot0zagshChpJCEXRlZIAe+ekP28/V/9npS6miUeIfKxevXps2bIFgMDAQIYNG2bagIQQOUoSQlE0JcfDD73h8BeQEKkvcykHdYaAh58pIxMi32rVqhVubm4A/P333yaORgiRk2SnElE0HVkK1/frjwPW6BeUNjOHTgtNGZUQ+ZqdnR0XL17E2dmZkJAQ6tSpw8iRI7GxscHGxoamTZvi6ChjbIUoiCQhFEWPTgenN+qPncvorwoKITKlWLFiNGzYEH9/fwICAhgyJP2/Hy8vL/755x9KlSplwgiFEC9CbhmLokWbAltHwsN/F5zuMB80DqaNSYgC5sCBA6xZs4aePXvSrl076tSpA8CdO3fw9vamUaNG/O9//+Pu3bsmjlSIjJYuXUrp0qXRaDTUr1+fY8eOPbVuSkoKM2bMwNfXF41GQ40aNdi+fbtRnZiYGMaOHYu3tzfW1tY0atSI48ePG9VRFIWPPvoIDw8PrK2tad26NcHBweQrilBu376tAMrt27dNHYrIbd+0VpRpDvrH0oaKkpxg6oiEKBRWrFihAEYPKysr5fr166YOTRRiWf3+3rBhg2JpaamsWrVKOX/+vDJs2DDFyclJCQ8Pf2L9iRMnKp6ensoff/yhXL16VVm2bJmi0WiUwMBAQ53XXntNqVy5srJ//34lODhYmTZtmuLg4KCEhIQY6nz66aeKo6OjsmXLFuX06dNKly5dFB8fHyUhIf98B0lCqEhCWGRotYpy5idFWVJHUTb2VxSdztQRCVGo3LlzR/nxxx+VwYMHG5JCW1tb5fTp06YOTRRSWf3+rlevnjJy5EjDc61Wq3h6eipz5sx5Yn0PDw/lyy+/NCrr0aOH0q9fP0VRFCU+Pl4xMzNTtm3bZlSnVq1aypQpUxRFURSdTqe4u7srn332meH1yMhIxcrKSvnxxx8zFXdekFvGovALOwd/TAC1Gqq9CqOOw2vrQKUydWRCFCqenp68/vrrrFy5kjfffBOAuLg4atSoQbFixXB2dsbDw4MNGzaYOFJRFCUnJxMQEEDr1q0NZWq1mtatW+Pv7//E9yQlJaHRaIzKrK2tOXToEACpqalotdpn1rl+/TphYWFG53V0dKR+/fpPPa8pSEIoCrfQM7C8MYSehshbpo5GiCJBpVKxYsUKVqxYgerfX7wiIyOJiIggLCyMPn36sH//fhNHKQqTmJgYoqOjDY+kpKQMdR48eIBWqzUsnZTGzc2NsLCwJ7bbtm1bFixYQHBwMDqdjp07d7J582ZCQ0MBsLe3p2HDhsycOZO7d++i1Wr57rvv8Pf3N9RJazsr5zUFSQhF4bbv34WnQ45BdKhpYxGiiHnzzTdJSkoiKCiIS5cuGV0Neeutt0wYmShsKleujKOjo+ExZ86c578pE7744gvKlStHxYoVsbS0ZNSoUQwePBi1Oj19Wr9+PYqi4OXlhZWVFYsXL6ZPnz5GdQqCghWtEFmxdRQE/ak/fmUmlKpv2niEKIIsLCwoX748FSpUoEGDBobbxUFBQfz2228mjk4UFhcuXCAqKsrwmDx5coY6xYsXx8zMjPDwcKPy8PBw3N3dn9iuq6srW7ZsIS4ujps3b3Lp0iXs7OwoU6aMoY6vry/79+8nNjaW27dvc+zYMVJSUgx10trOynlNQRJCUfhE3oYv68LJ9frnnjWhwdumjUkIAcBrr71m+BLs2rUrDx8+NHFEojCwt7fHwcHB8LCysspQx9LSktq1a7N7925DmU6nY/fu3TRs2PCZ7Ws0Gry8vEhNTeWXX36ha9euGerY2tri4eFBREQEO3bsMNTx8fHB3d3d6LzR0dEcPXr0uefNS5IQisIhKVb/ADCzhAeX9cc+zWDYXv0uJEIIk1OpVHz//feG540bN2bbtm1otVpDWXJyMvHx8aYITxRy48eP55tvvmHt2rVcvHiRESNGEBcXx+DBgwEYMGCA0dXFo0ePsnnzZq5du8bBgwdp164dOp2OiRMnGurs2LGD7du3c/36dXbu3EmLFi2oWLGioU2VSsXYsWP55JNP+O233zh79iwDBgzA09OTbt265ennfxZJCEXBpiiw8yOY4wU3D+vLbFyg1xoYuhv6/SSziYXIZ1q2bMnQoUMB/a3jzp074+fnx4EDB/jiiy9wdXXFzs6OGTNmoNPpTBytKEx69+7N/Pnz+eijj/Dz8+PUqVNs377dMOHj1q1bhskgAImJiUydOpXKlSvTvXt3vLy8OHToEE5OToY6UVFRjBw5kooVKzJgwACaNGnCjh07sLCwMNSZOHEio0eP5s0336Ru3brExsayffv2DLOTTUmlKIpi6iBMLSQkhJIlS3L79m1eeuklU4cjsuLhVVhSS39cdxi0n6dfXkYIka9FRUWxatUqli9fzuXLl59ar3Xr1vzxxx9YWlrmYXSioJDv75wj35yiYAvemX7cZKwkg0IUEI6OjowbN47z588zbtw4zMzMMDMzw9nZmdq1a1OlShUAdu3ahZWVFX/++aeJIxaicJNvT1EwHZgPyxrC9vf1zyt0BEf57VCIgsbc3JwFCxaQmppKSkoKDx8+5MSJE5w7d85onFbHjh2xsLBg9OjR3L5924QRC1E4SUIoCobUZLj02BWC0NNw74L+WG0O9WVNMyEKOtV/xvvOnTuXX375xfA8NTWVL7/8khYtWsikEyFymCSEIn+Lva+/GvhpKTi0ELSp+vJ6w6DrMv3kkSlhUKaZScMUQuSOHj16kJKSwoEDB+jfvz8AV69epUqVKk/cjUII8WJkLQ6Rv2hT4PZR/Z83D8OBz9Jfi7oNF7dC1Z7g09R0MQoh8pS5uTkvv/wyTZo0wd7enmXLlnHjxg00Gg1VqlTBycmJVq1aMXHiRGxtbU0drhAFkiSEIn+5cRA2DYSkaOPyOm9AlW7g3cQkYQkhTE+lUrF06VJKly5tGF94/vx5AA4fPoy/vz9///23KUMUj1EUhfv373P48GGioqLo3r07jo6Opg5LPIUsO4NMWzcZbQocXQ7HvgaPGtD7O335/SD45Q1QmUGphlBnMLhWMG2sQoh8RafTcfToUe7fv8/EiRMJCgoC4MaNG3h7e5s4uqIhKiqKY8eOcfPmTW7dukVgYCCXLl0iJCQEc3NzdDodCQkJhvpBQUGUL18+R2OQ7++cI1cIRd5LSYCre2Hr25AQoS+zdk5/3bUCDD9kmtiEEAWCWq02bPvVpUsXvL29uXXrFqVLl+bOnTt4enqaOMLCKywsjL59+7J3796n1nl8fKe1tTWNGjWS2/n5nCSEIm+Fn4fVHSAxMr3MrSq0mWmykIQQBd+MGTMYNGgQAF5eXly6dIkKFeTOQk47efIkdevWNWw1aGdnR5MmTfDw8KB8+fKULVsWZ2dnzMzMcHJyomzZspIIFhCSEIq8kxABK9tA8r97Dns3hhp9wK8vqM1MG5sQokAbOHAgiYmJDB8+HIBRo0bJDic5SKvV0q9fPzZu3GgoGzNmDJ9//jlmZvLzuzCQZWdE7lEUeHwfUutiMPYs1HsTXv8BBv8JtfpLMiiEyBFvvfUWs2fPBtJ3OJk0aZKJoyr4goODcXd3NySDxYsXZ9OmTSxatEiSwUJErhCKnBN7H8LPwYPLcMsfbvpDbJj+tW7LoUp3sHGGDp89ux0hhHhBQ4YM4fTp04bkZe7cuTx8+JCvv/7asPB1QkICZ8+eJTk5mWrVqsnM16e4e/cu06dP5+uvvzaU9e3bl6+++goHBwcTRiZyg8wyRmYpZdmto7DrY7h7EqaGpZd/9ypc2fnk95hrYMQ/4OKbJyEKIYq2pKQkvL29CQ8PB+CVV14hJiaGq1evcv/+faO6fn5+LFq0iGbNZIF70F8RnD17NmvWrDGUOTk58dtvv/Hyyy+bLrAnkO/vnCNXCEXWKAps6APxD/XPU5PA3Ep/bGEN9p7gVBLcq0HpJuBZC1QqsHIAayeThS2EKFqsrKy4fPkyPj4+PHr0iJ07jX9ZtbS0JDk5GYBTp07RvHlz3njjDZYsWYK1tbUpQs4XQkJC8PPzM2wNaGlpydKlSxk8eLDcHi7kJCEUmZMcD0kxEHI8PRl8/cf0ZBCg11pQy7BUIUT+4ODgwP79+1mzZg0ajQZXV1fq169PuXLlcHbWL3W1ceNG+vTpA8DKlStZuXIlq1evZsCAAahz+edZijaF+NR4HK3Sb1kfDztOXEocydpkkrRJaBUtiqKgU3SGY62iRUFBq9P/WatELaq5VgPgXvw9fr78M7YWtgysMtDQ7oZLG7gbdxcUUNC3p6CgKIrhz/joeD5p8wkA5pbmvDf/PSYNnISDgwORiZHM95+PmdqM6Y2mG9pdeXYl5x+eN8SoQ6f/89+HVtGi1WlZ2HwhThqnXO1PkT2SEIrnS4iEL+tC3L30smKloWIH43qSDAoh8pmqVasyf/78p77++uuv06VLF95++23Wrl0LwODBg9myZQvr16/H3t7+ie87/+A8cSlxJGoTSdYmk6JLIT4lnpjkGGJSYohNjiUqOYr4lHhiU2JJSElgaLWhtPJuBcChO4d4e9fbVHGpwo+dfjS0++HhD7kTeydLn3FMrTGGhPBBwgO+Ov0VbjZuRgnh71d/58yDM89s59G+R4Zj19dccWrgZBgrmKhNZOvVrVioLYwSwlP3TrEvZN9zY0zUJmblIwkTkIRQPJlOp7/Vq1Lpb/V2Wwbf9wIre3DyhqbvmjpCIYR4Jp2i4378fULjQknRpaBVtOh0OlKVVLQ6LVpFS6qSSmxyLM0mNMO1rSuf/+9zFJ3C1q1bcXBwYOHGhfyq/Epll8p81forQ9sjdo0gIikiS/HcT0gfu6gx06CgEJsSa1SnQrEKuGhcMFebY2VmhZnaDLVKrX+gxkxthgoVZiozVCr9n76O6WOznTXO9K7QGwdL40kf7X3a41fCDxUq1Co1qDAcq1ChKApLvlgCQM3WNRk6fih+JfwM77e3tGdsrbGYq43Thl4VetHYq7G+HZUKNWrDsZnKDDOVGWq12ugqqMifZFIJMijViKLAtb2wvrt+wejX1ukngui0oFLrE0QhhMhDiqKQqE0kLiWO2ORYHiY+JC4ljnJO5fCw8wAgOCKYVedWUcqhFCNqjAAgKimKJhuytv+5NlGL7UZbTuw9YShzbOhIkzea8P2r33PmzBlcXV35OOhjEpVELM0ssTKzwsLMAltzW2wtbbG3sMfO0g4nKyesza2xs7DD1sKWcsXK4Wmn30ElSZtEbHIsxTTF9AmaiSQkJPDhhx+ycuVKIiMjAVi9erVhke/8Tr6/c45cIRR6l/6AS3/CpW3pu4iEn4OgP6HRaFkrUAiRJYqikKRNIj41HhtzGzTmGgAeJT7i3INzaHVaWpRqYag/Zs8YYlNiSdGlkKJNIUWXQpI2iYeJD0lISSBVSc1wjk8af0LXsl0BuBN7h23XtlHDtYYhIXS0cqSScyUeJT7CxsIGM5UZ5mpz1Co15ipzzNT6K1h2FnY4WDlgb2mPg6UDLV5twa0Tt+jSpQvJyclE+Ufxh/8fOA11Mpxbo9GwdOlSrK2t6dKlS5Z347Ays8LK2ur5FXNJUFAQK1euZMGCBYZdRwBatmxJly5dTBaXMB1JCIui5Hj9eMBipfXP7wfBhr7GdTSO0GcDeDfK8/CEEPlTqi6VFWdWEBYXRlhcmD5506WQqkvVJ3CpSYYxdfEp8STr9LN45zebT9vSbQEIDA9k3L5x1HOvZ5QQHg8/TkxyzHNjsDG3obh1cewt7SmmKWYo97LzYkSNEVR0rmhUf2OnjYb1B7OiUttKXL16la+++oolS5YQE6OPzcXFhYcPH5KYmMgbb7xhqF+rVi0aNmzI8OHDqVq1apbPl1du3LhB9+7dOXXqlKFMrVYzbNgwpk+fjpubm+mCEyYlCWFRoShwN1C/fdx3PaH0yzBom/41x5Lp9dp9Cl61oWQ908QphMg1iqIQnRxNTHIMKboUkrXJ3Iu/R0RSBBGJETTybES5YuUA+OfuPywJXEJJ+5LMazYPADOVGWvPryUhNSFL541LiTMcO1o5UtapLFWKVzGq81GDj1BQsFRbYmFmgbnaHAu1Bc4aZ+ws7LCztMPa3Pqpt1fLFStniP1xL5IMpnnppZeYNWsWM2fO5PTp02g0GipWrMimTZsYO3YsKSkpPHyoX3UhMDCQwMBAli5dSunSpZk2bVqezFTOrPPnzzN16lS2bNliKCtbtixDhw7l7bfffurkGVF0yBhCisAYhLsnYcvbcO+Ccfm0yPQxgfs/g+q90q8aCiHypbSlPCzUFgDcj7/PifATeNh6GE0C+PLklzxIeMCDhAfcT7jPg/gHPEp89MRbr2k+bPAhr1V4DdAvfzJkxxDKOpXl166/GuosObkEc5U5nnaeaMw1WKj1yZu52hxrc2v9rdB/H8U0xdCYaTArxENOHj58yI0bN9i9ezczZ84kNjZ9kkiZMmWYNm0a/fr1M8kafvfu3eP999/nr7/+MizQDWBtbc20adOYOHFithLm/KDQf3/noUKTEC5dupTPPvuMsLAwatSowZIlS6hXL3NXuQrkPyhFgRuH4OY/+jF/SdEQ91Cf4PVJX8KAH/voxwGmMddA8fLQYAT49c3QrBDixaXqUolP1S89Epsci7utu2F25b34ewSEB+Bg6UBjr8aG92y5soWY5Bi0Ov2M1yRtEnEpcSSkJpCiTSFZl0yyNpmIxAhiUmIIiwtjXO1x9CrfC9AvXzJi1whquNbguw7fGdpt+3Nb/bpzT2Btbm24Audg6YCHrQdOGie6+nY1xBabHEtAeABOGidquNbIrS4rVFJSUvjll1/45JNPOH/+vKHcycmJnj17Urx4cTQaDe3bt6d+/fovfB5FUYiKiuL27dvExMTg5eWFq6srcXFxJCYm8vDhQ44cOcKoUaOMxgeWKlWKsWPHMmjQIIoVK/aMMxQcBfL7O58qFLeMN27cyPjx41m+fDn169dn0aJFtG3blqCgIEqUKGHq8HKGNhXMzPWJ4LFv4MgyiLiesd5/lgQwTAYpUQWaT4JKnWWmsCjS0hYDTkhNID5F/+dL9i8ZErerkVc5EHIAWwtbw9UygCmHpuiTtH/HzMWnxBOfGk9cShxxKXHEp8RnWGttVpNZdPHVD9C/+PAiEw9MpKpLVaOE8KtTXz01cXuaxNT087hoXKjnXg87CztSdamGZUH6VepHfGo8LtYuuFq74mrtiou1C05WToYJHs9iZ2lHs5KylVtWWFhY8Prrr/P6669z/vx5evXqxcWLF4mMjGTlypWGetOnT6d27dp888031KxZM9Pt//bbb3z77bfs3r3bsJNIZtSoUYMFCxbQokWLAn9FUOSeQpEQLliwgGHDhjF48GAAli9fzh9//MGqVauYNGmSiaPLJEXRT+4I/htuHYHkGP22cKmJEPcAGo+F+m+mrw0YeUv/Pq86+okfVvZgWxwsbPRtpf1H3/4z6PaV/nUh8om03RF0ik6/wwE6w64LaTscpJXrFJ3hSpmrtSs2FjYA3Ii6wZkHZyhhU4IGHg0AfbI36eAkw+zWmOQYYpJjSEhNIDE10bDzw38tarGIVqX0CwZffHSRBQELaOjR0Cgh3HNrT4Y1457GyswKOws7VKR/+TprnKnrXhcfBx+jus1LNicyKVK/ZpvaDCszK2wtbA1X8dJuvzpYOeBg6YCTlRPlnNLHylVyqcTKtiv5rwFVBmQqVpE7qlSpwoULF3j48CE//fQTd+/eJSYmhsWLF6PT6QgICDBMRFm/fj2+vk/f5z0iIoJXX32VPXv2GJWbmZlhb29PdHQ0Op0OAHNzcxwdHfHx8aFFixb07t2b2rVr5+pnFYVDgU8Ik5OTCQgIYPLkyYYytVpN69at8ff3N2FkevfCzpCSEqtfx0+XiqJL0V/tS4zSr+tXujEKij75+1o/o9dep+D073/cKUCouTmqiMsYpn7UG0a4xpYEuxLgUhbQb0VkEH3DuCwxARLvY29hj6uNKwBanZYb/9bzcfQxDNQOiwszzPQzahP9l/iT2FrY8pJ9+qX6oEdBKCj4OvpiYWZhaPdR4qMntvv408dfsza3xtcp/YfkpUeXSNGm4Ovka0gKwuPCCYsPe2Z8/23X0sySKi7pA9rTdhwoX6y8YWulsLgwrkZeNdraCTAkKmn/S9sGSkHBTGVGy1ItDe0eCz3GvYR7+Ln6GfonNDaUo2FHDbEato9KO89jbT9+3t4Vehuu/BwIOcC1yGvUda9rGJgfFhfGb1d/e+L7H//TsLWUTj8ObYTfCMMCtn9e+5PDdw/T9KWmhhmh9+LvMevILMP7Ht866/HtqXSKznDLM1WXyoLmC/Bx1Cc+a8+v5eszX9PZtzOT6ul/QYtJiaHxj+lXyTLr61e+pqFnQwCOhB5h1tFZvOL9iiEhNFebs+vWLnSK7rltWagtsDa3xsbCBjXpA/99HHzoXKZzhtuk79Z5F61Oa1iqxMbCBlsLW2wtbLExTz+2tbDF0swyw/mquVZjVdtVGcon15+coUwUHi4uLgwfPtzwfObMmcyZM4fZs2cD4O/vT9myZalfvz4dO3bk999/58aNG9SqVQsrKyuCg4O5ePGi4f29evViwoQJVK9eHY1Gf6VXp9MRGxuLra2t7DcsXliBTwgfPHiAVqvNMFXezc2NS5cuPfE9SUlJJCUlGZ6nLSeQG0b91Z+L6md8OQU+dlzSC4AhduUZV74PmFsRlhJDx5NzsH14kCOPVZ0WfoDDJw9nKZbuZbszo/EMABJSE+i2tRsAJ/53Aisz/XpYXwR+wbZr27LUbvOXmrOk1RLD875/9CVZl8zOV3fibusOwLoL61h/YX2W2q1evDrfd/ze8HzU7lGEx4ezsdNGKrtUBuD3a7/zReAXWWr3JbuX+KvnX4bn0/2nc/HRRb5q/RVNvPSL2B6+c5iP/T/OUru2FrYc6Zv+t7Ty3Er+ufsPs5vMNiSEFx9d5MPDH2apXYBXy7+K+b//uf51/S+2XdvGhDoTjBLCJSeXPKuJJxpYZaAhITz38By/Xf2N4tbFDQlhYmoie27veVYTTxSfmn47K0WXQnRytNFM08cTsOdRocJCbYGlmaVRYl/SviSNPBtRvlj59LoqFVPqTzFMcLCzsMPe0h4bCxs0ZhqszKzQmGuwMbcx/LLyX1WKV2H2y7MzlL9a/tVMxyzE09jZ2TFr1iwmT57M3Llz+eQT/d7BR48e5ejRo4Z6O3bsMHqfWq1m1apV9O/fP8PMZbVabdhiTogXVeATwhcxZ84cpk+f/vyKOcBSZY5Gl0TanSPVY/+PSqWf5EHaa/pyizItoJr+y0cVE4LtucXYmNsYtWttbo29xWO3gVWPHxqPEUkbM2Jtbm1UVswq46BiWwtbnDXOxu8n45iTx8eh2Fsa3452tXElRZti9D57S3vcbIyT9ieNZXn8PS7WLkavudu6GwbCp3GwdOAlu/Srk/9t8/H20l5zt3E3qvOS/Uska5ON+sdJ40RF54qG96dtyaRSqfRl/277pEJlKHv8/QAVnSuiVbRGn6O4dXGaeDUxep/R8VP+fDyBqlmiJipUlHEsY9RXPcr1MMSbth1V2mdOe562BVbaVa7HY272UjNcrV2pVryaocxZ48xHDT8ybEf1+OPxrbPSnqfNNvW29za00aNcD1qWbImDVfoXlo2FDQd6H8iw3VXaI63dxz/DfzX2amw0Fi/N47d5hciv7OzsmDlzJlOnTuXrr79m/fr1hISE0Lp1a5o2bcqDBw948OABjRo1onTp0nh5eckagSJXFfhZxsnJydjY2PDzzz/TrVs3Q/nAgQOJjIxk69atGd7z3yuEd+7coXLlyjJLSQghhChAZJZxzskfK2Zmg6WlJbVr12b37t2GMp1Ox+7du2nYsOET32NlZYWDg4PhIQtyCiGEEKIoKxS3jMePH8/AgQOpU6cO9erVY9GiRcTFxRlmHQshhBBCiKcrFAlh7969uX//Ph999BFhYWH4+fmxfft2GW8hhBBCCJEJhSIhBBg1ahSjRo0ydRhCCCGEEAVOgR9DKIQQQgghskcSQiGEEEKIIk4SQiGEEEKIIk4SQiGEEEKIIk4SQiGEEEKIIk4SQiGEEEKIIk4SQiGEEEKIIk4SQiGEEEKIIk4SQiGEEEKIIk4SQiGEEEKIIq7QbF2XHTqdDoDQ0FATRyKEEEKIzEr73k77HhcvThJCIDw8HIB69eqZOBIhhBBCZFV4eDilSpUydRgFmkpRFMXUQZhaamoqJ0+exM3NDbU65+6ix8TEULlyZS5cuIC9vX2OtVuYSB9lnvRV1kh/ZY30V9ZIf2VebvaVTqcjPDycmjVrYm4u17iyQxLCXBQdHY2joyNRUVE4ODiYOpx8Sfoo86Svskb6K2ukv7JG+ivzpK8KBplUIoQQQghRxElCKIQQQghRxElCmIusrKyYNm0aVlZWpg4l35I+yjzpq6yR/soa6a+skf7KPOmrgkHGEAohhBBCFHFyhVAIIYQQooiThFAIIYQQooiThFAIIYQQoogrkgnhgQMH6Ny5M56enqhUKrZs2WL0enh4OIMGDcLT0xMbGxvatWtHcHCwUZ2rV6/SvXt3XF1dcXBw4LXXXjPseJImMDCQV155BScnJ1xcXHjzzTeJjY19bnxnzpzh5ZdfRqPRULJkSebNm2f0+vnz5+nZsyelS5dGpVKxaNGiF+qHZynofbR582bq1KmDk5MTtra2+Pn5sX79+hfrjOco6H21Zs0aVCqV0UOj0bxYZzxHQe+r5s2bZ+grlUpFx44dX6xDMqGg91lKSgozZszA19cXjUZDjRo12L59+4t1xnPk575KTExk0KBBVKtWDXNzc7p165ahTmhoKH379qV8+fKo1WrGjh37It2QKXPmzKFu3brY29tTokQJunXrRlBQUIaYR44ciYuLC3Z2dvTs2TNDX9y6dYuOHTtiY2NDiRIleO+990hNTTWqs2/fPmrVqoWVlRVly5ZlzZo1z41PURQ++ugjPDw8sLa2pnXr1hn+rmbNmkWjRo2wsbHBycnphfpBpCuSCWFcXBw1atRg6dKlGV5TFIVu3bpx7do1tm7dysmTJ/H29qZ169bExcUZ3t+mTRtUKhV79uzh8OHDJCcn07lzZ8N+infv3qV169aULVuWo0ePsn37ds6fP8+gQYOeGVt0dDRt2rTB29ubgIAAPvvsMz7++GO+/vprQ534+HjKlCnDp59+iru7e851zGMKeh85OzszZcoU/P39OXPmDIMHD2bw4MHs2LEj5zrpXwW9rwAcHBwIDQ01PG7evJkznfMfBb2vNm/ebNRP586dw8zMjF69euVcJ/1HQe+zqVOnsmLFCpYsWcKFCxcYPnw43bt35+TJkznXSf/Kz32l1WqxtrbmnXfeoXXr1k+sk5SUhKurK1OnTqVGjRrZ64zn2L9/PyNHjuTIkSPs3LmTlJQU2rRpY+gLgHHjxvH777/z008/sX//fu7evUuPHj2MPlPHjh1JTk7mn3/+Ye3ataxZs4aPPvrIUOf69et07NiRFi1acOrUKcaOHcvQoUOf+7N43rx5LF68mOXLl3P06FFsbW1p27YtiYmJhjrJycn06tWLESNG5GDPFGFKEQcov/76q+F5UFCQAijnzp0zlGm1WsXV1VX55ptvFEVRlB07dihqtVqJiooy1ImMjFRUKpWyc+dORVEUZcWKFUqJEiUUrVZrqHPmzBkFUIKDg58az7Jly5RixYopSUlJhrL3339fqVChwhPre3t7KwsXLszSZ86qgt5HaWrWrKlMnTo1cx/6BRXEvlq9erXi6Oj4wp/5RRXEvvqvhQsXKvb29kpsbGzmP3g2FMQ+8/DwUL788kuj9/Xo0UPp169fFj991uS3vnrcwIEDla5duz6zTrNmzZQxY8Zkqr2ccO/ePQVQ9u/fryiK/nNbWFgoP/30k6HOxYsXFUDx9/dXFEVR/vzzT0WtVithYWGGOl999ZXi4OBg+DcxceJEpUqVKkbn6t27t9K2bdunxqLT6RR3d3fls88+M5RFRkYqVlZWyo8//pihvql+hhU2RfIK4bMkJSUBGN0yU6vVWFlZcejQIUMdlUpltKaSRqNBrVYb1bG0tDTaG9na2hrAUOdJ/P39adq0KZaWloaytm3bEhQURERERA58wuwraH2kKAq7d+8mKCiIpk2bvshHfmEFpa9iY2Px9vamZMmSdO3alfPnz2fnY7+QgtJXj1u5ciWvv/46tra2Wf24OaIg9FlSUlKGIQjW1tbPbDc3mLqv8ruoqChAf3cFICAggJSUFKOrmRUrVqRUqVL4+/sD+r//atWq4ebmZqjTtm1boqOjDT9D/P39M1wRbdu2raGNJ7l+/TphYWFG73N0dKR+/frPfJ/IHkkI/yPtH/zkyZOJiIggOTmZuXPnEhISQmhoKAANGjTA1taW999/n/j4eOLi4pgwYQJardZQp2XLloSFhfHZZ5+RnJxMREQEkyZNAjDUeZKwsDCj/7gAw/OwsLDc+MhZVlD6KCoqCjs7OywtLenYsSNLlizhlVdeydG+eJ6C0FcVKlRg1apVbN26le+++w6dTkejRo0ICQnJ8f54loLQV487duwY586dY+jQoTny+V9EQeiztm3bsmDBAoKDg9HpdOzcudNw6z0vmbqv8jOdTsfYsWNp3LgxVatWBfR/f5aWlhnG5rm5uRn+bjPz9/+0OtHR0SQkJDwxnrT3Pul9+eV7sDCShPA/LCws2Lx5M5cvX8bZ2RkbGxv27t1L+/btDb8Rurq68tNPP/H7779jZ2eHo6MjkZGR1KpVy1CnSpUqrF27ls8//xwbGxvc3d3x8fHBzc3NqI6dnR12dna0b9/eZJ85qwpKH9nb23Pq1CmOHz/OrFmzGD9+PPv27cvRvniegtBXDRs2ZMCAAfj5+dGsWTM2b96Mq6srK1asyPkOeYaC0FePW7lyJdWqVaNevXo50wEvoCD02RdffEG5cuWoWLEilpaWjBo1isGDBxtdYcsLBaGvTGXkyJGcO3eODRs25Pm5v//+e0Nf2dnZcfDgwTyPQeiZmzqA/Kh27dqcOnWKqKgokpOTcXV1pX79+tSpU8dQp02bNly9epUHDx5gbm6Ok5MT7u7ulClTxlCnb9++9O3bl/DwcGxtbVGpVCxYsMBQ588//yQlJQVIv+Xg7u6eYRZX2vPcmkDyIgpCH6nVasqWLQuAn58fFy9eZM6cOTRv3jznO+QZCkJfPc7CwoKaNWty5cqVnOuETCoofRUXF8eGDRuYMWNGzndCFuX3PnN1dWXLli0kJiby8OFDPD09mTRpktG584op+yq/GjVqFNu2bePAgQO89NJLhnJ3d3eSk5OJjIw0ukoYHh5u+Lt1d3fn2LFjRu399+//af9GHBwcsLa2pkuXLtSvX9/wmpeXl+FKa3h4OB4eHkbv8/Pzy/6HFk9m6kGMpsZ/Bh4/yeXLlxW1Wq3s2LHjqXV2796tqFQq5dKlS0+ts3LlSsXGxkaJiIh4ap20QdrJycmGssmTJ+erSSVPkp/7KM3gwYOVZs2aPbNOdhWGvkpNTVUqVKigjBs37pmfI7sKcl+tXr1asbKyUh48ePDM+HNaQe6zNMnJyYqvr68yefLkZ36O7MpvffW4/DCpRKfTKSNHjlQ8PT2Vy5cvZ3g9bVLJzz//bCi7dOnSEyeVhIeHG+qsWLFCcXBwUBITExVF0U8qqVq1qlHbffr0ydSkkvnz5xvKoqKiZFJJLiuSCWFMTIxy8uRJ5eTJkwqgLFiwQDl58qRy8+ZNRVEUZdOmTcrevXuVq1evKlu2bFG8vb2VHj16GLWxatUqxd/fX7ly5Yqyfv16xdnZWRk/frxRnSVLligBAQFKUFCQ8uWXXyrW1tbKF1988czYIiMjFTc3N6V///7KuXPnlA0bNig2NjbKihUrDHWSkpIM8Xt4eCgTJkxQTp48mekZbplR0Pto9uzZyt9//61cvXpVuXDhgjJ//nzF3NzcMJswJxX0vpo+fbqyY8cO5erVq0pAQIDy+uuvKxqNRjl//nwO9VC6gt5XaZo0aaL07t07m72ROQW9z44cOaL88ssvytWrV5UDBw4oLVu2VHx8fDKdPGVFfu4rRVGU8+fPKydPnlQ6d+6sNG/e3BDr49LKateurfTt21c5efJkrvy3OGLECMXR0VHZt2+fEhoaanjEx8cb6gwfPlwpVaqUsmfPHuXEiRNKw4YNlYYNGxpeT01NVapWraq0adNGOXXqlLJ9+3bF1dXVKNm/du2aYmNjo7z33nvKxYsXlaVLlypmZmbK9u3bnxnfp59+qjg5OSlbt25Vzpw5o3Tt2lXx8fFREhISDHVu3rypnDx5Upk+fbpiZ2dn6LuYmJgc7Kmio0gmhHv37lWADI+BAwcqiqIoX3zxhfLSSy8pFhYWSqlSpZSpU6caLaugKPqlFdzc3BQLCwulXLlyyueff67odDqjOv3791ecnZ0VS0tLpXr16sq6desyFd/p06eVJk2aKFZWVoqXl5fy6aefGr1+/fr1J8afk1e/CnofTZkyRSlbtqyi0WiUYsWKKQ0bNlQ2bNjw4h3yDAW9r8aOHauUKlVKsbS0VNzc3JQOHToogYGBL94hz1DQ+0pR0q+S/P333y/WCVlU0Pts3759SqVKlRQrKyvFxcVF6d+/v3Lnzp0X75BnyO995e3t/cT4Hvek1729vV+4T57mSecBlNWrVxvqJCQkKG+//bZSrFgxxcbGRunevbsSGhpq1M6NGzeU9u3bK9bW1krx4sWVd999V0lJSTGqs3fvXsXPz0+xtLRUypQpY3SOp9HpdMqHH36ouLm5KVZWVkqrVq2UoKAgozoDBw584mfYu3fvi3ZLkaZSFEXJzi1nIYQQQghRsMksYyGEEEKIIk4SQiGEEEKIIk4SQiGEEEKIIk4SQiGEEEKIIk4SQiGEEEKIIk4SQiGEEEKIIk4SQiGEEEKIIk4SQiGEEEKIIk4SQiFEkVa6dGkWLVpk6jCEEMKkJCEUQuSJQYMGoVKpUKlUWFhY4ObmxiuvvMKqVavQ6XSZbmfNmjU4OTll+fxPe9/x48d58803s9yeEEIUJpIQCiHyTLt27QgNDeXGjRv89ddftGjRgjFjxtCpUydSU1NNEpOrqys2NjYmObcQQuQXkhAKIfKMlZUV7u7ueHl5UatWLT744AO2bt3KX3/9xZo1awBYsGAB1apVw9bWlpIlS/L2228TGxsLwL59+xg8eDBRUVGGq40ff/wxAElJSUyYMAEvLy9sbW2pX78++/bte+77/nvLWKVSsWLFCjp16oSNjQ2VKlXC39+fK1eu0Lx5c2xtbWnUqBFXr141+mxbt26lVq1aaDQaypQpw/Tp002W5AohRFZJQiiEMKmWLVtSo0YNNm/eDIBarWbx4sWcP3+etWvXsmfPHiZOnAhAo0aNWLRoEQ4ODoSGhhIaGsqECRMAGDVqFP7+/mzYsIEzZ87Qq1cv2rVrR3Bw8DPf9yQzZ85kwIABnDp1iooVK9K3b1/eeustJk+ezIkTJ1AUhVGjRhnqHzx4kAEDBjBmzBguXLjAihUrWLNmDbNmzcrFnhNCiBykCCFEHhg4cKDStWvXJ77Wu3dvpVKlSk987aefflJcXFwMz1evXq04Ojoa1bl586ZiZmam3Llzx6i8VatWyuTJk5/6PkVRFG9vb2XhwoWG54AydepUw3N/f38FUFauXGko+/HHHxWNRmN0ntmzZxu1u379esXDw+OJn0kIIfIbc1MnpEIIoSgKKpUKgF27djFnzhwuXbpEdHQ0qampJCYmEh8f/9SxfmfPnkWr1VK+fHmj8qSkJFxcXLIcT/Xq1Q3Hbm5uAFSrVs2oLDExkejoaBwcHDh9+jSHDx82uiKo1WqfG7cQQuQXkhAKIUzu4sWL+Pj4cOPGDTp16sSIESOYNWsWzs7OHDp0iDfeeIPk5OSnJlaxsbGYmZkREBCAmZmZ0Wt2dnZZjsfCwsJwnJaoPqksbXZ0bGws06dPp0ePHhna0mg0WT6/EELkNUkIhRAmtWfPHs6ePcu4ceMICAhAp9Px+eefo1brhzhv2rTJqL6lpSVardaorGbNmmi1Wu7du8fLL7/8xPM86X05pVatWgQFBVG2bNlcaV8IIXKbJIRCiDyTlJREWFgYWq2W8PBwtm/fzpw5c+jUqRMDBgzg3LlzpKSksGTJEjp37szhw4dZvny5URulS5cmNjaW3bt3U6NGDWxsbChfvjz9+vVjwIABfP7559SsWZP79++ze/duqlevTseOHZ/4vpy6lfvRRx/RqVMnSpUqxauvvopareb06dOcO3eOTz75JEfOIYQQuUlmGQsh8sz27dvx8PCgdOnStGvXjr1797J48WK2bt2KmZkZNWrUYMGCBcydO5eqVavy/fffM2fOHKM2GjVqxPDhw+nduzeurq7MmzcPgNWrVzNgwADeffddKlSoQLdu3Th+/DilSpV65vtyQtu2bdm2bRt///03devWpUGDBixcuBBvb+8cO4cQQuQmlaIoiqmDEEIIIYQQpiNXCIUQQgghijhJCIUQQgghijhJCIUQQgghijhJCIUQQgghijhJCIUQQgghijhJCIUQQgghijhJCIUQQgghijhJCIUQQgghijhJCIUQQgghijhJCIUQQgghijhJCIUQQgghijhJCIUQQgghirj/A3IoDmyGvZF9AAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from cycler import cycler\n",
     "\n",
@@ -2521,20 +684,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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XL1/G5cuXxX319PRgYGAAAwMDmJqaokqVKrCxscEXX3yB2rVrq+uSiIiINI5GJVLXr1/HF198IX7+MKB70KBB2LFjB16+fInnz5+L26tUqYLff/8dkyZNwpo1a1CxYkX8+OOPcHd3F+v4+Pjg1atXmD17NmJjY+Hi4oKAgACFAeirVq2Cjo4OvL29kZGRAXd3d/zwww/FcMXqIZFI0KZNG7x8+RJz587FlStX8P79e0RGRiI5ORnZ2dnIzs7G+/fvkZiYiBcvXgAAfvnlF0yePBmdOnVS8xUQERFpBo2dR0qbqGIeCk0gCAKSk5Px/PlzZGRkIC0tDVFRUbh27RpWrVol1qtWrRqqVKmCQYMGwd7eXo0RExFRafBhsLmHh4dKn9pTxd9vJlIqUFISqU+5fv16rkHp5cuXx4wZM+Dg4IAyZcqoKTIiIirpNDmR0qhbe6S5GjdujPj4eBw4cADHjx/HqVOnEB8fj2+++QYA0LBhQ9jY2GDo0KEoV66cmqMlIiIqHuyRUoHS0CP1TxkZGejbty9u376t8HAAkPMU5Pfffw8DAwM1RUdERCWNJvdIafX0B6QehoaGOHLkCB4/fozExETs2rULjRs3BpCz5I67uzvGjx+fa/4uIiKikoaJFBWKhYUFBgwYgMDAQIXpIu7cuYM+ffrg5cuXaoyOiIioaDGRIpWwsrJCSEgIXr16hWHDhonl/fr1w+PHj9UYGRERUdFhIkUqVa5cOWzduhX79+8Xy8aNG4eIiAg1RkVERFQ0mEhRkfDx8cGGDRsAAJmZmRgzZgwSExPVGxQREZGKcfoDKjKjR49Gamoqpk6divT0dHTv3h1mZmYwMDCAh4cH+vbtCxMTE3WHSUREpDT2SFGRkUgkmDJlCpYvXy4+VpqcnIw3b95g79692LZtm5ojJCIiKhwmUlTkvv32W7x58wanT59GQEAAHBwcAADR0dFqjoyIiKhwmEhRsdDT08OXX34Jd3d3LF26FAAQHh4OzgdLRETajGOkqNh96JH6MFXCwIEDYWBgAH19fRgaGsLZ2VmlM9cSEREVFSZSVOwaN26Mxo0b4/r16wgPD8fcuXNz1bGxsUHTpk3RtWtXODk5FX+QRERE+cC19lSgtK21pyr+/v74+eefkZGRgaysLGRmZiIqKgpZWVm56lpYWKB+/fqYN28eJBKJGqIlIiJ10eS19phIqQATKdXJzs7G1atXsX//fhw4cADx8fEK2xs1aoTBgwejTp06aoqQiIiKmyYnUhxsThpFT08PzZs3x9q1axEbG4tr167h2LFjqFixIoCcRZHHjRuHS5cuqTlSIiIiJlKkwSQSCRo3boyuXbsiNDQU3333nbht27ZtyM7OVmN0RERETKRIS1hbW2PRokXYvXs3AODp06dYs2aNmqMiIqLSjokUaZVu3bqhfv36AIDffvsNI0eORGRkpJqjIiKi0oqJFGkVU1NThIaGisnUo0eP8PXXX3OWdCIiUgsmUqR1dHV1cePGDSxYsEAs69+/P7Zv345z584hMTFRfcEREVGpwgk5SSvp6elh5syZsLOzw9ChQwEAu3btAgBUq1YNW7duVWd4RERUSrBHirTakCFD8OLFC3h7e6NRo0bQ0dHBkydP4OHhgaCgIAQHByMzM1PdYRIRUQnFCTlVgBNyao4+ffrgwIEDCmU6OjpYunQpGjdurKaoiIioMDghJ1Ex2b9/P9avX4+GDRuiatWqAAC5XI4pU6bg6tWrnHuKiIhUiokUlThjx45FaGgonj59isOHD4vl06ZNQ+/evXMtO0NERKQsJlJUovXo0QMnT55Ew4YNAQBv376Fj48PIiIi1BwZERGVBEykqMRr3749QkNDsXjxYrFs2rRpCAsLw9WrV5lUERGR0jj9AZUa06ZNw9u3b7Fs2TK8evUKvr6+4rYxY8bA29sbOjr8bkFERPnHRIpKDYlEgrlz5yI6OhqXL19GYmIi3rx5AwD44YcfYGNjg9atW6s5SiIi0ib8+k2lipGREfbs2YMnT54gNjZWYZ2+u3fvqjEyIiLSRkykqNTS09NDpUqVMHPmTADg0jJERFRgTKSo1CtfvjwA4OTJk3j+/LmaoyEiIm3CRIpKvcqVK4vvBw0ahIMHD6ovGCIi0ipMpKjU8/T0xNy5c8XPGzduxNGjR8HVk4iI6L8wkaJST09PD3PmzEFcXJw4/cHatWvh6+uLCxcucFkZIiL6KCZSRP+vfPnyCA0NRaVKlQAAN2/exKxZs7BlyxY1R0ZERJqKiRTRPzRo0AB3797FnDlzxEHohw4dwvnz59UcGRERaSImUkT/Ympqirlz5+LJkydi2aZNmzhmioiIcmEiRfQRZmZmOH36NAAgJiYGGRkZao6IiIg0DRMpok9o06YN9PRyVlIaMGAAZDKZmiMiIiJNwkSK6BN0dXUxePBgAMDr169x4MABNUdERESahIkU0X/YsmUL2rRpAwDYunUrZs+ejZSUFDVHRUREmoCJFFE+LF26VJwW4fz589i/f7+aIyIiIk3ARIooH1xdXfHkyRNxOZl/PtFHRESlFxMponzS19fHmjVrAABXrlxhrxQRETGRIiqIZs2aicvIbN68GZGRkWqOiIiI1ImJFFEBWFtbIzExUfw8btw4yOVy9QVERERqpXGJ1IYNG1C5cmVIpVK4urri6tWrH62blZWF+fPnw8nJCVKpFA0aNEBAQIBCneTkZEycOBGOjo4wMjJC8+bNce3aNYU6KSkpGDduHCpWrAgjIyPUrl0bmzZtKpLrI+1nZmaGnTt3Asj52Xnz5o2aIyIiInXRqETqwIED8PX1xZw5cxAaGooGDRrA3d0d8fHxedafOXMmNm/ejHXr1uHevXsYNWoUunfvjrCwMLHOsGHDEBQUhN27d+P27dvo0KED3NzcEB0dLdbx9fVFQEAA9uzZg/v372PixIkYN24cfv311yK/ZtJOAwcOFNfie/HihZqjISIidZEIGrSAmKurK5o0aYL169cDAORyORwcHDB+/HhMnz49V307OzvMmDEDY8eOFcu8vb1hZGSEPXv2IC0tDWZmZjh+/Di8vLzEOo0aNYKnpycWLlwIAKhbty58fHwwa9asj9b5lHfv3sHCwgJJSUkwNzdX+vpJuzRs2BA3b94EABw9ehSWlpZqjYeIqKR6//49ZDIZPDw8oK+vr7LjquLvt8b0SGVmZuLGjRtwc3MTy3R0dODm5oaQkJA898nIyIBUKlUoMzIywoULFwAA2dnZkMlkn6wDAM2bN8evv/6K6OhoCIKAM2fO4NGjR+jQocNHz/vu3TuFF5U+/fv3F98fOnSIY6WIiEohjUmkXr9+DZlMBhsbG4VyGxsbxMbG5rmPu7s7/P398fjxY8jlcgQFBeHIkSN4+fIlgJyxLM2aNcOCBQsQExMDmUyGPXv2ICQkRKwDAOvWrUPt2rVRsWJFGBgYwMPDAxs2bEDr1q3zPO/ixYthYWEhvhwcHFTUCqRNJk+ejJ49ewIA9u3bh06dOmHTpk2c9ZyIqBTRmERKGWvWrEH16tXh7OwMAwMDjBs3DoMHDxYfTweA3bt3QxAE2Nvbw9DQEGvXrkXfvn0V6qxbtw6XL1/Gr7/+ihs3bmDlypUYO3YsTp06led5/fz8kJSUJL6ioqKK/FpJM02YMEFM/tPS0nDgwAF88803ao6KiIiKi8YkUuXKlYOuri7i4uIUyuPi4mBra5vnPtbW1jh27BhSU1MRGRmJBw8ewNTUFFWrVhXrODk5ITg4GCkpKYiKisLVq1eRlZUl1klLS8N3330Hf39/dO7cGfXr18e4cePg4+ODFStW5HleQ0NDmJubK7yodGrVqhViY2Nx/fp11K5dGwAQHh6OtLQ0NUdGRETFQWMSKQMDAzRq1AinT58Wy+RyOU6fPo1mzZp9cl+pVAp7e3tkZ2fj8OHD6Nq1a646JiYmqFChAt6+fYvAwECxTlZWFrKyshR6qABAV1eXY14o3xo1aoTbt29DV1cXADglAhFRKaExiRSQMw3B1q1bsXPnTty/fx+jR49GamoqBg8eDCDnkXM/Pz+x/pUrV3DkyBGEh4fj/Pnz8PDwgFwux9SpU8U6gYGBCAgIQEREBIKCgvDFF1/A2dlZPKa5uTnatGmDKVOm4OzZs4iIiMCOHTuwa9cudO/evXgbgLSajo4OrK2tAeRM5UFERCWfnroD+CcfHx+8evUKs2fPRmxsLFxcXBAQECCOQXn+/LlCz1F6ejpmzpyJ8PBwmJqaomPHjti9e7fCY+hJSUnw8/PDixcvYGVlBW9vbyxatEjh8cn9+/fDz88PX331FRISEuDo6IhFixZh1KhRxXbtVDJUqVIFsbGx+O2339ClSxdUr15d3SEREVER0qh5pLQV55GiDx48eIBatWoByBlLd+DAAVhYWKg5KiIi7abJ80hpVI8UkbZzdnbGrFmzsGDBAmRkZGDWrFnw9vbGo0ePEB8fD0NDQ1haWqJTp04ffYiCiIi0B3ukVIA9UvRPaWlp8PT0RHBw8CfrrV+/HnXq1CmmqIiItBd7pIhKESMjI/zyyy/w9vZGTEwMdHR0UK1aNVSvXh1RUVE4cuQIAGDlypXYtm2bmqMlIqLCYCJFVATKlSv30R6p1atXY9KkSYiOjoZMJhOnTCAiIu2jUdMfEJUGY8eOhY6ODjIzMzFp0iTw7joRkfZiIkVUzPT19cUFsW/fvo2YmBg1R0RERMpiIkWkBn/88Yf41F7//v2xd+9eREdHIysrS82RERFRQTCRIlIDiUSCoUOHip9//PFH9O/fH126dMHJkyfVGBkRERUEEykiNVm4cCGuXLkCNzc3ccb+9PR0LF68GPHx8WqOjoiI8oOJFJEaNW3aFEFBQcjIyMD58+fFch8fHy58TESkBZhIEWkAPT09tGzZEmvWrBHLbt++rcaIiIgoP5hIEWmQCRMmwMvLCwBw7949sVwmk+HOnTsIDw9XV2hERJQHTshJpGHs7OwAAIcOHUJ0dDSMjIxw6dIlpKWlAchZDLlq1arw8vKCh4cHJ/QkIlIjrrWnAlxrj1TpwoULaNWqVb7rr127FvXq1SvCiIiI1Itr7RFRvrVs2RLx8fHo168fXr9+jZo1a6Jdu3bo2LEjpFIpLl++jPXr1yMgIABAzu1AHR0dSKVSpKenQ1dXFxKJBDo6OqhduzbGjh2LqlWrqvmqiIhKJvZIqQB7pEgdDh48iG+++QaxsbGfrFe7dm1s2LChmKIiIlI99kgRkcr17t0bvXr1wsWLF5GcnAxLS0uYmpoiOzsbOjo6WLJkCfbv34979+4hOTkZZmZmAABBEJCVlQV9fX1IJBI1XwURkXZjj5QKsEeKNFFiYiLKlCkjfjYzM0NmZiYyMzMhCAKkUimWLVvG8VVEpPE0uUeK0x8QlVCWlpaYMWOG2BOVnJyMjIwMfPjulJ6ejgkTJmDIkCGIiIhQZ6hERFqLPVIqwB4p0mRyuRwhISGQy+UwMTGBsbExgoODMXXqVLx7906sZ29vj0mTJqFRo0ZqjJaIKDdN7pFiIqUCTKRIG2VlZWHbtm2YMGECMjMzxfJmzZphwYIFnJ+KiDSGJidSvLVHVErp6+tj5MiRSElJwQ8//ACpVAoACAkJwf3799UcHRGRdmAiRVTK6evrY/To0Xj79i2srKwAAOPHj0dKSoqaIyMi0nxMpIgIACCVSjFu3Djx86FDh9QYDRGRdmAiRUSiOXPmwN3dHQCwa9cuHD16VM0RERFpNg42VwEONqeSJDw8HE5OTuLnOnXqwMjICJUrV8aIESNUOtCTiCg/NHmwOWc2JyIFVatWRUREBKpXr47s7GzcvXsXAHD9+nXUqFED7du3V3OERESag7f2iCiXypUr4/bt29i+fTvWrVsHY2NjAMD333+P8PBwNUdHRKQ52CNFRHlydnaGs7MzAMDOzg7e3t4AgMOHD2PKlCnqDI2ISGOwR4qI/lOPHj3E5OmPP/5AQEAAOLySiIiJFBHl05AhQ8T3S5cuha+vL5MpIir1mEgRUb44Ozvjzp07qFKlCgDg5s2buHTpksLyMkREpQ0TKSLKtzp16iA8PBzVqlUDAMycORNeXl64ceOGmiMjIlIPJlJEVGBLly5F1apVAQDZ2dlYsGAB0tLS1BwVEVHxK1QiderUKbRo0UJcLT4rK0tVcRGRBuvRoweePn2KvXv3AgCSkpKwceNGNUdFRFT8CpVIjR07FlOnTsW6devw7NkzzJs3T1VxEZEW8Pb2RoUKFQAADx48UHM0RETFr1CJlLGxMbp27YrGjRtjy5YtOHXqlKriIiItYGhoiIMHDwLIWWqBiKi0KVQi9erVKxw6dAi3bt1CdnY2n94hKoVsbGwAAHFxcUhISFBzNERExatQiZSvry8CAgIwfPhwlC9fHvfu3UPv3r2xYMECHDt2TEUhEpEms7W1Fd+PGDECKSkpaoyGiKh4FSqRGjp0KH766SdcuXIFSUlJuH//PgYMGAA9PT0cOnRIVTESkQYzMzMTZz1/8+YNOnfujGXLliE1NVXNkRERFb1CrbVXpkwZODg4oE6dOqhbty7q1auHunXrokOHDjA0NFRVjESk4ZYtWwZra2tMnToVAPDnn3/CyclJXJ+PiKikKlSP1OjRo1GuXDm0aNECNWvWxI0bNzBlyhRUqVJFXOyUiEqHKVOm4O3bt7CzswMA3L17V80REREVvUIlUhs2bMCxY8cQGRmJ/fv3o0ePHjh16hRiYmJw/vx5VcVIRFrC0tJS7JU6c+YMx0oSUYlX6JnNHRwcsGXLFmzZsgU7d+6Ep6cn7t27B2tra1XER0RapmvXruL7X375BTKZTI3REBEVrUKNkbp//z4ePnyIhw8f4v79+3j69ClSU1Nx9+5d1K5dW1UxEpEWqVy5MoKCgtC+fXtER0ejY8eO+Oyzz9C9e3eUK1cOVapUgUQiUXeYREQqIREEQVB2Zx0dHdSvXx+9e/dGp06dUKtWLejr66syPq3w7t07WFhYICkpCebm5uoOh0jt0tPT0bFjR5w5cybXtjFjxqBXr15qiIqItNX79+8hk8ng4eGh0jxDFX+/C3Vrb8WKFWjUqBGOHz+O9u3bo3nz5hg8eDBWrlyJwMDAwhyaiLSYVCrFX3/9hXfv3mH+/PmoW7euuI3jJ4moJClUj9S/RURE4M6dO7hz5w7u3r2LPXv2qOrQGo09UkT/LTQ0FI0aNQIAtG7dGm5ubmjVqpWaoyIibaDJPVIFTqQiIyNx69Yt2NjYoGnTpkqdtKRhIkX03zIzM1G5cmW8fPkSAKCrq4vDhw/DwsJCzZERkabT5ESqQLf2fv75Z9SoUQNdu3ZFs2bN0LhxY7x69UqpExNR6WJgYIA7d+5g06ZNAACZTIZnz56pNygiokIqUCI1b9489OvXDw8ePMDJkycBANOnT1dpQBs2bEDlypUhlUrh6uqKq1evfrRuVlYW5s+fDycnJ0ilUjRo0AABAQEKdZKTkzFx4kQ4OjrCyMgIzZs3x7Vr13Id6/79++jSpQssLCxgYmKCJk2a4Pnz5yq9NqLSzsrKCiNHjkSzZs0AAJMnT0ZiYqJ6gyIiKoQCJVLh4eGYM2cOatSogXbt2mHPnj3Yv3+/yoI5cOAAfH19MWfOHISGhqJBgwZwd3dHfHx8nvVnzpyJzZs3Y926dbh37x5GjRqF7t27IywsTKwzbNgwBAUFYffu3bh9+zY6dOgANzc3REdHi3WePn2Kli1bwtnZGWfPnsWtW7cwa9YsSKVSlV0bEf2Pq6srgJxeqe7du+PixYtqjoiISDkFGiOlo6OD2NhYlC9fXiwzMjJCRESEwgrwynJ1dUWTJk2wfv16AIBcLoeDgwPGjx+fZ8+XnZ0dZsyYgbFjx4pl3t7eMDIywp49e5CWlgYzMzMcP34cXl5eYp1GjRrB09MTCxcuBAD06dMH+vr62L17t1Jxc4wUUcHIZDKsWrVKXOzYzs4OO3fuhJ5eoaa2I6ISqsSMkQKAnTt34tKlS0hJSQEA6Onp4f3790qd/J8yMzNx48YNuLm5/S84HR24ubkhJCQkz30yMjJy9RoZGRnhwoULAIDs7GzIZLJP1pHL5fj9999Ro0YNuLu7o3z58nB1df3k0hYZGRl49+6dwouI8k9XVxfffvuteCs+JiYGc+fOVW9QRERKKFAi1apVKyxcuBAtW7aEpaUlqlevjvT0dPz00084c+YMkpOTlQ7k9evXkMlksLGxUSi3sbFBbGxsnvu4u7vD398fjx8/hlwuR1BQEI4cOSI+FWRmZoZmzZphwYIFiImJgUwmw549exASEiLWiY+PR0pKCpYsWQIPDw+cPHkS3bt3R48ePRAcHJzneRcvXgwLCwvx5eDgoPR1E5Vmbdu2Rbt27QAAFy9exLRp09QcERFRwRQokQoODkZSUhIePnyIPXv2oHv37mjTpg02btyIdu3aoUyZMqhVq1ZRxZrLmjVrUL16dTg7O8PAwADjxo3D4MGDoaPzv8vavXs3BEGAvb09DA0NsXbtWvTt21esI5fLAeSsDzZp0iS4uLhg+vTp6NSpk/h00b/5+fkhKSlJfEVFRRX9xRKVQIaGhjh16hT69u0LALh69Sp7eIlIqyg1s3n16tXRp08fLFu2DKdOnUJCQgKePn2Kn3/+Gd27d1cqkHLlykFXVxdxcXEK5XFxcR8df2VtbY1jx44hNTUVkZGRePDgAUxNTVG1alWxjpOTE4KDg5GSkoKoqChcvXoVWVlZYp1y5cpBT08v19qAtWrV+uhTe4aGhjA3N1d4EZHy9u3bB0NDQwDA6dOn1RwNEVH+FWqJmH+qUqUKevXqhe+//16p/Q0MDNCoUSOFX6JyuRynT58WH5X+GKlUCnt7e2RnZ+Pw4cMKq89/YGJiggoVKuDt27cIDAwU6xgYGKBJkyZ4+PChQv1Hjx7B0dFRqWshooL7sIzM2rVrMWvWLCQlJak5IiKi/6ZRj8j4+vpi0KBBaNy4MZo2bYrVq1cjNTUVgwcPBgAMHDgQ9vb2WLx4MQDgypUriI6OhouLC6KjozF37lzI5XJMnTpVPGZgYCAEQUDNmjXx5MkTTJkyBc7OzuIxAWDKlCnw8fFB69at8cUXXyAgIAAnTpzA2bNni/X6iUozf39/dO7cGe/evcOFCxdQpUoVDBkyRN1hERF9kkYlUj4+Pnj16hVmz56N2NhYuLi4ICAgQByA/vz5c4XxT+np6Zg5cybCw8NhamqKjh07Yvfu3bC0tBTrJCUlwc/PDy9evICVlRW8vb2xaNEihccnu3fvjk2bNmHx4sWYMGECatasicOHD6Nly5bFdu1EpV3r1q0RExODli1b4ubNm9i9ezfat2/PhzmISKOpdNHi0orzSBGpzoULF8TFjF1dXbFkyRI1R0RE6lai5pEiIipKLVq0wIgRIwDk3L6PiIgAv+8RkaYqUCL1119/oXbt2nk+npyUlIQ6derg/PnzKguOiEofiUSCBQsWQCKRAACGDBmCPn36cPA5EWmkAiVSq1evxvDhw/Ps/rKwsMDIkSPh7++vsuCIqHQqX748fvzxR3Feuvj4eH5JIyKNVKBE6u+//4aHh8dHt3fo0AE3btwodFBEREOGDMG9e/fEyTpXrlyZa5oSIiJ1K1AiFRcX98lBXnp6enj16lWhgyIi+mDUqFHi+4sXL6oxEiKi3AqUSNnb2+POnTsf3X7r1i1UqFCh0EEREX3QunVrTJkyBQBw6tQpZGZmqjkiIqL/KVAi1bFjR8yaNQvp6em5tqWlpWHOnDno1KmTyoIjIgKAatWqAQBevnyJbdu2qTkaIqL/KdA8UnFxcfjss8+gq6uLcePGoWbNmgCABw8eYMOGDZDJZAgNDRUn0CwtOI8UUdFKSEhArVq1EB8fj1q1auGHH35Qd0hEVIw0eR6pAs1sbmNjg0uXLmH06NHw8/MT53aRSCRwd3fHhg0bSl0SRURFz8rKCr/99huaNm2K+/fv4/Xr1yhXrpy6wyIiKtitvfnz58Pa2hp//PEHXr9+jStXruDy5ct4/fo1/vjjD1SpUqWo4iSiUq569eri3FK9evVSWOCciEhdCnRrT1dXFy9fvkT58uWLMiatw1t7RMVj3bp1mDBhgvjZ0dERK1asYO8UUQmnybf2CtQjxWUaiEidxo8fj/Pnz6NixYoAgMjISAwfPhyBgYG4evUqEhIS1BwhEZU2BRojBUDsWiciUoeWLVsiKioKvr6+WLVqFRITE8WFjfX09NCjRw+YmZlBKpWiTZs2sLa2VnPERFSSFejWno6ODiwsLP4zmSpt3wp5a4+o+GVmZmLFihU4f/48Xr58ib///jvPeu3bt8d3331XzNERkSpp8q29AvdIzZs3DxYWFkqdjIhIVQwMDBQSpKioKHz//fdITExEUlISTp06haysLAQFBWHkyJEoW7asGqMlopKqwD1SsbGxHGz+L+yRItI8mZmZsLS0RFpaGipUqIBdu3ZBT6/A3x2JSANoco9UgQabc3wUEWkLAwMD+Pj4AMiZEZ0LqhNRUeBTe0RUYm3evFmcGuH333+HTCZTc0REVNIUKJGSy+W8rUdEWsPAwABDhgwBAJw/fx5BQUFqjoiISpoCJVIhISH47bffFMp27dqFKlWqoHz58hgxYgQyMjJUGiARUWFMnDhRfH/+/Hn1BUJEJVKBEql58+bh7t274ufbt29j6NChcHNzw/Tp03HixAksXrxY5UESESmrQoUK2LRpEwDg0qVLGDNmDF68eKHmqIiopChQIvX333+jXbt24uf9+/fD1dUVW7duha+vL9auXYuDBw+qPEgiosLw9vZGhQoVAAD379/H1KlTOeaTiFSiQInU27dvYWNjI34ODg6Gp6en+LlJkyaIiopSXXRERCpQrlw5REdHY+rUqQBynuK7d++emqMiopKgQImUjY0NIiIiAOTM0RIaGorPP/9c3J6cnKzS+R2IiFRFIpFg6dKlqFy5MgBg3LhxSEpKUm9QRKT1CpRIdezYEdOnT8f58+fh5+cHY2NjtGrVStx+69YtODk5qTxIIiJVWblypfj+wYMHaoyEiEqCAiVSCxYsgJ6eHtq0aYOtW7di69atMDAwELdv27YNHTp0UHmQRESq0qNHDzRv3hxA6VsXlIhUr0DrJZQrVw7nzp1DUlISTE1Noaurq7D90KFDMDU1VWmARESq9mE+PN7aI6LCKlCP1AcWFha5kigAsLKyUuihIiLSRNbW1gCAK1euqDkSItJ2SiVSRETazM7ODgBw8+ZNnDt3Ts3REJE2YyJFRKXOyJEjxfePHj1SYyREpO2YSBFRqVOhQgVMmjQJQM60LUREymIiRUSlkqWlJQDg/fv36g2EiLRavp/a8/X1zfdB/f39lQqGiKi4fEikUlNT1RsIEWm1fCdSYWFhCp9DQ0ORnZ2NmjVrAsgZZ6Crq4tGjRqpNkIioiLwIZEKCQnB7du3Ua9ePfUGRERaKd+J1JkzZ8T3/v7+MDMzw86dO1GmTBkAOevwDR48WGGmcyIiTVW3bl3x/erVq/Hjjz9CIpGoMSIi0kZKjZFauXIlFi9eLCZRAFCmTBksXLhQYfkFIiJN9dlnn+HQoUMAgPDwcCxdulTNERGRNlIqkXr37h1evXqVq/zVq1d8AoaItEaPHj1Qq1YtAMDFixchCIKaIyIibaNUItW9e3cMHjwYR44cwYsXL/DixQscPnwYQ4cORY8ePVQdIxFRkdDR0REn5ExJSUF6erqaIyIibVOgtfY+2LRpE7799lv069cPWVlZOQfS08PQoUOxfPlylQZIRFSUrKysoKOjA7lcjsTERBgZGak7JCLSIkolUsbGxvjhhx+wfPlyPH36FADg5OQEExMTlQZHRFTUdHR0UK5cOcTHxyMxMREVKlRQd0hEpEUKNSGniYkJ6tevj/r16zOJIiKtVb58eQDAjh071BsIEWkdpROp8+fPo3///mjWrBmio6MBALt378aFCxdUFhwRUXFwcnICAFy9elXsZSciyg+lEqnDhw/D3d0dRkZGCAsLQ0ZGBgAgKSkJ33//vUoDJCIqaps2bRLfP3nyRI2REJG2USqRWrhwITZt2oStW7dCX19fLG/RogVCQ0NVFhwRUXGwtbVFv379AABLlixBfHy8miMq3SZOnIj169erOwyifFFqsPnDhw/RunXrXOUWFhZITEwsbExERMXO09MT+/btAwCcO3cOPXv2VHNE+bNkyRIEBgbmKm/SpAmWLVumhojo6NGjOHDgABISEuDk5IQJEyaI85XlJSAgINeEsPr6+jh58qT4OSEhAVu2bMH169eRkpKC+vXrY8KECahYsaJYZ8OGDQgMDIRUKsXw4cPRvn17cdvZs2dx8uRJ3jUqAkolUra2tnjy5AkqV66sUH7hwgVUrVpVFXERERWr/v374+DBgzhx4gTevHmj7nAKpGnTppg2bZpC2T/vFhSFrKysIj+HNvrrr7+wceNGTJo0CbVq1cIvv/yCqVOnYteuXQqrgfybiYkJdu3alec2QRAwa9Ys6OnpYeHChTA2NsahQ4fw7bffYvv27TAyMsKlS5dw+vRpLF++HC9evMCyZcvQtGlTWFhYICUlBT/99BNWrFhRVJddqil1a2/48OH45ptvcOXKFUgkEsTExGDv3r349ttvMXr0aFXHSERULJydnQHkTM6pTfT19WFlZaXwMjMzE7d/8cUX+P333zFr1ix4eHigf//+uHjxosIxIiIiMG3aNHh6eqJHjx74/vvvkZSUJG6fOHEi1qxZg/Xr16Nr166YOnUqgJwZ4fv3748OHTpg0qRJCAgIwBdffIGUlBSkpaXBy8sLwcHBCue6cOECPD098f79+3xdX3JyMr7//nt07twZHh4emDZtGl68eCFuj42NxXfffYfOnTvD09MTX3/9NS5fvizuu3DhQnTr1g3u7u7o378//vzzz4I1cAEcOnQIXl5e8PT0ROXKleHr6wupVJqvc/773/CDFy9e4N69e5g4cSKcnZ1RqVIlTJo0CRkZGfjrr78AAJGRkXBxcUHNmjXRrl07mJiY4OXLlwCAzZs3o0uXLrCxsSmaiy7llOqRmj59OuRyOdq1a4f379+jdevWMDQ0xLfffovx48erOkYiomLx4Y/Xo0ePIAhCiVrEeOfOnRg5ciRGjRqFI0eOYNGiRdi/fz/Mzc2RkpICX19fdOzYEWPHjkVGRga2bNmCefPmwd/fXzxGYGAgunTpgnXr1gEAXr58iblz58Lb2xsdO3bEkydPsHHjRrG+kZERvvjiC/z5559o06aNWP7nn3+idevWMDY2zlfsS5YsQXR0NBYtWgRjY2Ns2bIF06dPx44dO6Cnp4c1a9YgOzsba9asgVQqRWRkpDix6rZt2xAZGYmlS5fCwsIC0dHR4gNSedmzZw/27t37yXh27NiRZ1KSlZWFR48e4auvvhLLdHR08Nlnn+Hu3bufPGZaWhr69OkDuVyO6tWrY9iwYahSpYp4XAAwMDBQOK6+vj5u374NLy8vODk54bfffkNycjJiYmKQkZEBe3t73L59G48fP8bEiRM/eX5SnlKJlEQiwYwZMzBlyhQ8efIEKSkpqF27NkxNTVUdHxFRsflnInX06FGtWfIqJCQEnp6eCmVfffUV+vfvL3728PBAu3btAADDhg3DkSNH8ODBAzRt2hRHjx5FtWrVMHz4cLH+1KlT0bt3b0RFRcHBwQEAULFiRYwaNUqss2XLFjg4OIhllSpVQkREBPbs2SPW8fLywrhx4/DmzRuULVsWb9++xZUrV/J9m+nFixe4dOkS1q1bh7p16wIAZsyYAR8fH1y4cAFt27ZFfHw8WrduLQ4tsbOzE/ePj49HtWrVULNmTQA5Q1M+pUuXLvjiiy8+WadcuXJ5liclJUEul+e6hVemTBk8f/78o8dzcHDA1KlT4eTkhJSUFBw8eBDjx4/H9u3bYW1tjUqVKsHGxgZbt27F5MmTIZVK8csvv+DVq1fibeimTZuiffv2GDVqFAwNDTF9+nRIpVKsWrUK06ZNw6+//oqjR4/C3NwckydPFpM0Kjylbu19+eWXmDdvHgwMDFC7dm00bdoUpqamePv2Lb788stCBbRhwwZUrlwZUqkUrq6uuHr16kfrZmVlYf78+XBycoJUKkWDBg0QEBCgUCc5ORkTJ06Eo6MjjIyM0Lx5c1y7du2jxxw1ahQkEglWr15dqOsgIu3j5eUlvl+3bp3WTIXQsGFD/PjjjwqvLl26KNT55/hVIyMjmJiY4O3btwCAp0+f4ubNm/D09BRfAwcOBADExMSI+9WoUUPhmFFRUWKC8sGH26Mf1KpVC5UrVxYHxAcFBcHGxgYNGjTI17VFRkZCV1dXYbC2hYUFHBwcEBkZCSBn8endu3dj3Lhx2L59u8JcYF26dMGZM2cwbNgwbNq0CXfu3Pnk+czNzWFvb//Jl66ubr5iz686derA3d0d1apVg4uLC+bPnw8LCwucOHECQM4SbPPmzcOLFy/QpUsXeHh4ICwsDK6urtDR+d+f8a+//hp79+7Ftm3b0KpVK+zbtw+NGjWCnp4edu/ejbVr18LLywuLFy9WafylnVI9UmfPnsXt27cRFhaGvXv3irOaZ2Zm5roXXhAHDhyAr68vNm3aBFdXV6xevRru7u54+PChOPPwP82cORN79uzB1q1b4ezsjMDAQHTv3h2XLl1Cw4YNAeR887pz5w52794NOzs77NmzB25ubrh37x7s7e0Vjnf06FFcvnxZ4dsMEZUe9vb2CAsLE39/DB8+HIcOHfpoD4SmkEqluX6f/ZueXu5f94IgAMi5rdSsWTOMHDkyV51/jtWRSqVKxefl5YVjx46hX79+CAgIgKenp0pvm3p5eaFJkya4fPkyrl+/jn379mH06NHo0aMHXF1dsX//fly+fBk3btzA5MmT0a1bt4+O5y3MrT0LCwvo6OiICeoHb9++VWjH/6Knp4fq1auLk10DQM2aNfHjjz8iJSUF2dnZsLS0xOjRo3Mlsh88f/4cQUFB2Lp1K/744w/Ur18flpaWaNu2LZYtW4b379/n+9YqfZrSM5ufOnUKsbGx+Pzzz/Hs2TOVBOPv74/hw4dj8ODBqF27NjZt2gRjY2Ns27Ytz/q7d+/Gd999h44dO6Jq1aoYPXo0OnbsiJUrVwLI+eVw+PBhLFu2DK1bt0a1atUwd+5cVKtWTeE+PgBER0dj/Pjx2Lt3L59EISrFXFxc8MMPP4ifBw4ciMzMTDVGVPSqV6+OZ8+ewdbWNlfvy6cWcXZwcMCjR48Uyh4+fJirXvv27REXF4fDhw8jMjIS7u7u+Y7N0dERMpkM9+/fF8uSkpIQFRWl8OR4+fLl0aVLF8yfPx+9e/fG77//Lm6ztLSEh4cHZsyYgbFjx+K333776Pm6dOmSq3fv36+PJdb6+vqoUaOGwnyKcrkcoaGhqFOnTr6vWSaTITw8PM/ky9TUFJaWlnjx4gUePXqEFi1a5KojCAJWrlyJMWPGwMjICHK5HDKZTDz2h7hINZTqkQKAChUqIDg4GIMHD0aTJk1w6NChT86T8V8yMzNx48YN+Pn5iWU6Ojpwc3NDSEhInvtkZGTk+oZkZGQkLlOTnZ0NmUz2yTpAzg/UgAEDMGXKlHz9sGdkZCgMVnz37t1/XyARaY3Ro0dDKpViyJAhSEtLQ3R0tEaPKcnKykJCQoJCma6uLiwsLPK1f7du3fD7779jwYIF6NOnD8zMzBAdHY0zZ87g22+//eitrM6dO+PQoUPYvHmzONj838MrAMDMzAytWrXC5s2b0bhxY1hbW+f72ipWrIgWLVpgxYoVmDx5MoyMjLB161aUK1dOTCLWr1+Ppk2bwsHBAcnJyQgLC0OlSpUA5Aw2r1GjBqpUqYLMzExcvnxZ3JYXc3NzmJub5zu+f+vVqxeWLFmCGjVqiNMfpKenw8PDQ6zz/fffw9raWhyTtnPnTtSuXRv29vZISUnBgQMHEBcXp3Cr+ezZs7C0tET58uURHh6O9evXo0WLFmjSpEmuGH7//XdYWlqiefPmAIC6deti586duHfvHq5cuQJHR0eOaVYhpQebA4ChoSH27duHhQsXio+kKuv169eQyWS5ukttbGzw4MGDPPdxd3eHv78/WrduDScnJ5w+fRpHjhwRM24zMzM0a9YMCxYsQK1atWBjY4Off/4ZISEhqFatmnicpUuXQk9PDxMmTMhXrIsXL8a8efOUvFIi0gaDBw/GnDlzEBUVpfETDV+9ehXe3t4KZQ4ODh+dl+jfypUrh3Xr1mHLli2YMmUKsrKyYGNjg6ZNmyqMwfm3ChUqYO7cudi4cSMOHz6MOnXqoH///li1apXCE2YA0LFjR5w+fTrXoPj8mDZtGtatWwc/Pz9kZ2ejfv36WLJkiXi7Ui6XY82aNXj16hVMTEzQpEkTjB07FkBOL9GPP/6I2NhYGBoaol69epg9e3aBY8ivL7/8EklJSdixY4c4IefSpUsVepfi4+MV2jUlJQUrV65EQkICTE1NUaNGDaxfv16hx+3Nmzf44Ycf8PbtW5QtWxYdOnTAgAEDcp0/ISEBe/bsUZgZvlatWujVqxf8/PxgaWmJ6dOnF83Fl1IS4cNN8gLQ0dFBbGyswrilw4cPY9CgQUhLSxMTmYKIiYmBvb09Ll26hGbNmonlU6dORXBwMK5cuZJrn1evXmH48OE4ceIEJBIJnJyc4Obmhm3btiEtLQ1AziDKIUOG4Ny5c9DV1cVnn32GGjVq4MaNG7h//z5u3LgBLy8vhIaGimOjKleujIkTJ370cdG8eqQcHByQlJRUqG8yRKRZ6tevj9u3b2P+/Plo1aqVusPRCnv27MGvv/6KgwcPKpSfPHkSGzZswC+//MLhE1Rg79+/h0wmg4eHh0p/ft69ewcLC4tC/f1WaoxURERErnvE3t7euHz58kfHM/2XcuXKQVdXF3FxcQrlcXFxH31c1draGseOHUNqaioiIyPx4MEDmJqaKjyd4uTkhODgYKSkpCAqKgpXr15FVlaWWOf8+fOIj49HpUqVoKenBz09PURGRmLy5Mm5Zm7/wNDQUOz+LWw3MBFprg+PsT9+/FjNkWiuY8eO4cGDB4iJicHJkyexf/9+hTFQ6enpiI6Oxr59+9C5c2cmUVTiKHVrz9HRMc/yunXrivN8FJSBgQEaNWqE06dPo1u3bgByumtPnz6NcePGfXLfD0+sZGVl4fDhw+jdu3euOiYmJuLjvoGBgeIaVAMGDICbm5tCXXd3dwwYMACDBw9W6lqIqGQoW7YsgJwHW9q1a/fR332lWXR0NPbs2YN3797BxsYGvXv3VpiQcv/+/dizZw/q16+vUE5UUuT71p6vry8WLFgAExMT+Pr6frLuP2fCLYgDBw5g0KBB2Lx5M5o2bYrVq1fj4MGDePDgAWxsbDBw4EDY29uLc2BcuXIF0dHRcHFxQXR0NObOnYuIiAiEhobC0tISQM5MvIIgoGbNmnjy5AmmTJkCqVSK8+fPf/Sb0X/d2vs3VXQNEpHmOXv2rDg5o4uLC5YtW8YeFSI10ORbe/nukQoLCxOnqQ8LC/tovcLMDeLj44NXr15h9uzZiI2NhYuLCwICAsQB6M+fP1cYoJeeno6ZM2ciPDwcpqam6NixI3bv3i0mUUDOY7J+fn548eIFrKys4O3tjUWLFvGXIRH9p7Zt28LX1xf+/v64efMmjh07hl69eqk7LCLSIEoNNidF7JEiKrlevHiBSpUqiZNXbt26VeGpXyIqeprcI6X0hJxERKVBxYoVFeZG+jDhLxERUIBbe/81LuqflB0jRUSkidq3b49Vq1Zh0qRJiIyMhFwu/+T8SkRUehRojFR+qHL9JCIiTSCRSDBmzBhMmTIFaWlpWLduHb755ht1h0VEGiDfidSZM2eKMg4iIo1mYGCAhg0b4tq1azh+/DhGjBjxyXXoiKh0UHqtPQC4d+8enj9/rrCgp0QiQefOnQsdGBGRpgkKCoKlpSUEQcClS5fQrl07dYdERGqmVCIVHh6O7t274/bt25BIJOLTLB9u6ymzRAwRkaazsLCAq6srrly5goULF6Jq1aoavZgxERU9pUZLfvPNN6hSpQri4+NhbGyMu3fv4ty5c2jcuDHOnj2r4hCJiDTHPx+mGTJkCCIjI9UYDRGpm1KJVEhICObPn49y5cpBR0cHOjo6aNmyJRYvXowJEyaoOkYiIo3RvHlz7Ny5U/z8z6kRiKj0USqRkslkMDMzA5Cz2HBMTAyAnDX4Hj58qLroiIg00MCBAzF06FAAwOvXr9UcDRGpk1KJVN26dfH3338DAFxdXbFs2TJcvHgR8+fPR9WqVVUaIBGRJmrcuDEAIDExUb2BEJFaKZVIzZw5E3K5HAAwf/58REREoFWrVvjjjz+wdu1alQZIRKSJbG1tAQAxMTG4d++ewtPLRFR6qGytvYSEBJQpU6ZUTsjJtfaISp/Q0FA0atRI/Fy5cmX89NNPnPGcqAiUirX2rKysSmUSRUSlU8OGDTFv3jx8/vnn0NXVxbNnzxR664modFC6Ryo9PR23bt1CfHx8rl8cXbp0UUlw2oI9UkSlm5eXF/744w8AwJo1a1C/fn01R0RUsmhyj5RSE3IGBARg4MCBeT6tIpFIOCEnEZUqBw4cgKWlJWQyGZ49e8ZEiqgUUerW3vjx49GrVy+8fPkScrlc4cUkiohKG1NTUwwYMAAAsGrVKjx+/FjNERFRcVEqkYqLi4Ovry9sbGxUHQ8RkVbq3r27+H758uX8UklUSiiVSPXs2ZNLwRAR/UOXLl2wf/9+AMDjx4+xceNGNUdERMVBqTFS69evR69evXD+/HnUq1cv18AvLhNDRKVR9+7dYWdnh5iYGNy9e1fd4RBRMVAqkfr5559x8uRJSKVSnD17VmHaA4lEwkSKiEolAwMDHDp0CC1atMCDBw+QlZWl0ieMiEjzKHVrb8aMGZg3bx6SkpLw7NkzREREiK/w8HBVx0hEpDUqVqwovp81a5YaIyGi4qBUIpWZmQkfHx/O4EtE9C8ODg5o27YtAODWrVucoJOohFMqExo0aBAOHDig6liIiLSeRCLBr7/+CgBIS0tDSkqKmiMioqKk1BgpmUyGZcuWITAwEPXr1881BsDf318lwRERaSNTU1MYGxvj/fv3uH37Nlq0aKHukIioiCiVSN2+fRsNGzYEANy5c0dhG9fbI6LSTiKRwM7ODk+ePMHMmTOxYMECtGzZUt1hEVERUHqtPfofrrVHRP927NgxhUk6bWxs0LBhQ3Tq1Al16tRRY2RE2keT19or8BiprKwstGvXjksgEBF9Qrdu3XDt2jXUqlULQM6KEAEBAZg8eTLS09PVHB0RqUqBEyl9fX3cunWrKGIhIipRGjdujHv37uHx48dYvHgxACAjIwOenp5YsmQJ0tLS1BwhERWWUk/t9e/fHz/99JOqYyEiKpGqVauG6dOnY9GiRZBKpQCAwMBA9OrVC48ePVJzdERUGEoNNs/Ozsa2bdtw6tQpNGrUCCYmJgrb+dQeEVFu3333HSZPnoxRo0Zhx44dSE1NxZEjRzB9+nR1h0ZESlIqkbpz5w4+++wzAMj1bYpP7RERfZyhoSG2b98OR0dHzJs3D4GBgejQoYP4O5WItAuf2lMBPrVHRAX19OlTVKtWDQBQvXp1bNmyRc0REWkuTX5qT6keKQBITEzETz/9hPv37wMA6tSpgyFDhsDCwkLZQxIRlRpOTk7YuXMnBg0ahBcvXkAul3PZLSItpNT/tdevX4eTkxNWrVqFhIQEJCQkwN/fH05OTggNDVV1jEREJVLv3r0hkUiQlpaGNWvWqDscIlKCUonUpEmT0KVLFzx79gxHjhzBkSNHEBERgU6dOmHixIkqDpGIqGSSSqVwcXEBAPz666+YPXs2MjMz1RsUERWI0j1S06ZNg57e/+4M6unpYerUqbh+/brKgiMiKumCg4NhbW0NADh//jwuXbqk5oiIqCCUSqTMzc3x/PnzXOVRUVEwMzMrdFBERKWFmZkZnj17BgcHBwDI83crEWkupRIpHx8fDB06FAcOHEBUVBSioqKwf/9+DBs2DH379lV1jEREJZqxsTH69+8PANi+fTuSk5PVHBER5ZdST+2tWLECEokEAwcORHZ2NoCcpWNGjx6NJUuWqDRAIqLSoH79+uL7fv36YenSpahdu7YaIyKi/CjUPFLv37/HkydPIJFI4OTkBGNjY1XGpjU4jxQRFVZ2djYGDBiA/fv3i2UjR45Enz591BgVkWbQ5HmkCjVpibGxMerXr4969eqV2iSKiEgV9PT0sG/fPmzevFks27ZtG7KystQYFRH9lwIlUjo6OtDV1f3k659P8hERUf5JJBKMGDECL1++BABkZWUhKipKzVER0acUKOs5evToR7eFhIRg7dq1kMvlhQ6KiKg0s7W1Rf369XHr1i0MHz4cJ06cYK8/kYYqUCLVtWvXXGUPHz7E9OnTceLECXz11VeYP3++yoIjIiqtvvrqK9y6dQtyuRwHDx7E119/re6QiCgPSo+RiomJwfDhw1GvXj1kZ2fj5s2b2LlzJxwdHVUZHxFRqTR16lQMHDgQALBz506cO3dOzRERUV4KnEglJSVh2rRpqFatGu7evYvTp0/jxIkTqFu3blHER0RUak2fPl18P2fOHOzcuZNLyBBpmALd2lu2bBmWLl0KW1tb/Pzzz3ne6iMiItWoVasW/v77bzRo0AAAsGPHDhw7dgyDBw+GoaEh9PX14erqChMTEzVHSlR6FWgeKR0dHRgZGcHNzQ26urofrXfkyBGVBKctOI8UERWl5ORk9OzZEydPnsy1rX379vjuu+/UEBVR8dHkeaQK1CM1cOBASCQSpU5ERETKMTMzQ2BgIK5fv47FixcjPj4e9+/fx5s3bxAUFITKlSujX79+6g6TqFQq1MzmlIM9UkRU3N69ewdbW1ukpaUBACwsLGBlZYUaNWrgyy+/RJMmTfjFl0oMTe6RKtTM5kVhw4YNqFy5MqRSKVxdXXH16tWP1s3KysL8+fPh5OQEqVSKBg0aICAgQKFOcnIyJk6cCEdHRxgZGaF58+a4du2awjGmTZuGevXqwcTEBHZ2dhg4cCBiYmKK7BqJiArL3Nwc8fHxqFOnDoCcB4EiIiIQGBiIadOmYfLkyZwVnagYaFQideDAAfj6+mLOnDkIDQ1FgwYN4O7ujvj4+Dzrz5w5E5s3b8a6detw7949jBo1Ct27d0dYWJhYZ9iwYQgKCsLu3btx+/ZtdOjQAW5uboiOjgaQk+WGhoZi1qxZCA0NxZEjR/Dw4UN06dKlWK6ZiEhZpqam+Pvvv3Hz5k2cPn0a8+fPh729PQAgLCwMI0eOBG86EBUtjbq15+rqiiZNmmD9+vUAALlcDgcHB4wfP17hMeAP7OzsMGPGDIwdO1Ys8/b2hpGREfbs2YO0tDSYmZnh+PHj8PLyEus0atQInp6eWLhwYZ5xXLt2DU2bNkVkZCQqVar0n3Hz1h4RaZIxY8Zg48aNAIAff/wRTk5Oao6IqHB4ay8fMjMzcePGDbi5uYllOjo6cHNzQ0hISJ77ZGRkQCqVKpQZGRnhwoULAHJWU5fJZJ+sk5ekpCRIJBJYWlp+9Lzv3r1TeBERaYoffvgBzZo1A5DTK3/w4EFkZGSoOSqikkljEqnXr19DJpPBxsZGodzGxgaxsbF57uPu7g5/f388fvwYcrkcQUFBOHLkiLjgp5mZGZo1a4YFCxYgJiYGMpkMe/bsQUhIiFjn39LT0zFt2jT07dv3o9np4sWLYWFhIb4cHBwKceVERKrXv39/8f3GjRsxYcIEJCYmqi8gohJKYxIpZaxZswbVq1eHs7MzDAwMMG7cOAwePBg6Ov+7rN27d0MQBNjb28PQ0BBr165F3759Fep8kJWVhd69e0MQBLFbPC9+fn5ISkoSX1ydnYg0zZgxY3D79m00btwYAPDo0SPMnDlTzVERlTwak0iVK1cOurq6iIuLUyiPi4uDra1tnvtYW1vj2LFjSE1NRWRkJB48eABTU1NUrVpVrOPk5ITg4GCkpKQgKioKV69eRVZWlkId4H9JVGRkJIKCgj55r9TQ0BDm5uYKLyIiTVO3bl2EhIRg+PDhAIC7d+9+clgDERWcxiRSBgYGaNSoEU6fPi2WyeVynD59WrzX/zFSqRT29vbIzs7G4cOH81y6xsTEBBUqVMDbt28RGBioUOdDEvX48WOcOnUKZcuWVd2FERGpkZ6eHjZv3oxy5coBAObPn4/s7Gw1R0VUcmhMIgUAvr6+2Lp1K3bu3In79+9j9OjRSE1NxeDBgwHkzKzu5+cn1r9y5QqOHDmC8PBwnD9/Hh4eHpDL5Zg6dapYJzAwEAEBAYiIiEBQUBC++OILODs7i8fMyspCz549cf36dezduxcymQyxsbGIjY3l4qBEVCJIJBKcOHECQM7vvDNnzqg5IqKSo0BLxBQ1Hx8fvHr1CrNnz0ZsbCxcXFwQEBAgDkB//vy5wtim9PR0zJw5E+Hh4TA1NUXHjh2xe/duhaftkpKS4OfnhxcvXsDKygre3t5YtGiR+PhkdHQ0fv31VwCAi4uLQjxnzpxB27Zti/SaiYiKw+effw4XFxfcvHkT33//PerWrYsKFSqoOywiradR80hpK84jRUTa4NSpU2jfvj0AYM6cOfyiSFqD80gREZHaubm5oUOHDgDAqRCIVISJFBFRKWJtbQ0AOH36NGQymZqjIdJ+TKSIiEqRD+Oi7ty5g1OnTqk5GiLtx0SKiKgUGT16tPh+//79fDqZqJCYSBERlSJVq1bFrl27AADPnj1Djx49cOjQITVHRaS9mEgREZUyXbp0Qbt27QAAqamp2LlzJ/gAN5FymEgREZUyFhYWOHXqFGJiYgDkJFMpKSlqjopIOzGRIiIqpWxtbWFkZAQAWLduHXuliJTARIqIqJSSSCRo0qQJACAoKAgzZszg4HOiAmIiRURUih09ehRlypQBAISEhODkyZNqjohIuzCRIiIqxaysrHD37l1IpVIAwIMHD9QcEZF2YSJFRFTKVahQAYsXLwYA/P7775g2bRpevXql5qiItAMTKSIigoeHh/j+6tWr6N27N9avX88B6ET/gYkUERHB2dkZmZmZGD9+PExNTQEAhw8fho+PD96+favm6Ig0FxMpIiICAOjr62Pt2rV49eoVWrVqBQB49eoVRo8ejYSEBDVHR6SZmEgREZECqVSK4OBgDB06FAAQFxeHPXv2qDkqIs3ERIqIiHKRSCTYsGGDOM9UVFSUmiMi0kxMpIiIKE+GhoaYM2cOAOD69et4+PChmiMi0jxMpIiI6KNq1aolvvf391djJESaiYkUERF9VNWqVbFq1SoAwKNHjzjonOhfmEgREdEnjRgxAhKJBADg7e0Nf39/ZGdnqzkqIs3ARIqIiD7J2NgYixYtEj+fOHECV65cUWNERJqDiRQREf0nPz8/xMXFwdDQEACwf/9+9koRgYkUERHlU/ny5TFu3DgAwJ07d/Ddd9/h1KlTePPmjZojI1IfJlJERJRv48ePh52dHQDg2rVrWLRoEcaPH881+ajUYiJFRET55ujoiBcvXmDDhg3o0KEDAODly5d48uSJmiMjUg8mUkREVCASiQRjxoxBYGAgHBwcAOQ82ff69Ws1R0ZU/JhIERGR0v45See9e/fUGAmRejCRIiIipfXs2RMeHh4AgL/++gsymUzNEREVLyZSRERUKDVq1AAABAcHIyAgQM3REBUvJlJERFQo06dPF+eXWrFiBQICAiCXy9UcFVHxYCJFRESFUqFCBYWZzpcuXQo/Pz81RkRUfJhIERFRoTVo0ACXLl0Sn+K7evUqFixYwDFTVOIxkSIiIpVo1qwZnj9/jtatWwPIGXw+fPhwJCUlqTkyoqLDRIqIiFTql19+weeffw4AiIiIQK9evThhJ5VYTKSIiEilrK2tce7cOYwdOxYAkJWVhR07dqg3KKIiwkSKiIhUTl9fH+vXr8ekSZMAABcvXsTVq1fVHBWR6jGRIiKiIjNt2jTxvb+/Pxc3phKHiRQRERUZGxsbHDhwAADw+vVrZGdnqzkiItViIkVEREWqW7duAACZTIaYmBj1BkOkYkykiIioSBkYGMDKygoAMGTIEGRkZKg5IiLVYSJFRERF7rvvvgMAyOVyREREqDkaItVhIkVEREVu8uTJ+OyzzwAAW7du5Vp8VGIwkSIiomLh7OwMAAgNDcW5c+fUHA2RajCRIiKiYjF//nzx/cKFC/Hq1Ss1RkOkGkykiIioWDg5OWHdunUAcp7g2717t5ojIio8JlJERFRsRowYgVatWgEATpw4gYSEBDVHRFQ4euoOgIiISg8DAwOsWLECrq6uAABvb29Uq1YN+vr6MDU1xahRo1C1alU1R0mUf+yRIiKiYtWkSROMHz9e/PzkyRPcv38f165dw9ChQ7F79248evQIqampaoySKH80LpHasGEDKleuDKlUCldX108ucpmVlYX58+fDyckJUqkUDRo0QEBAgEKd5ORkTJw4EY6OjjAyMkLz5s1x7do1hTqCIGD27NmoUKECjIyM4ObmhsePHxfJ9RERlXYSiQRr165FUlISjhw5gj179mDOnDni9m3btmHkyJHo1asXXr58qcZIif6bRiVSBw4cgK+vL+bMmYPQ0FA0aNAA7u7uiI+Pz7P+zJkzsXnzZqxbtw737t3DqFGj0L17d4SFhYl1hg0bhqCgIOzevRu3b99Ghw4d4ObmhujoaLHOsmXLsHbtWmzatAlXrlyBiYkJ3N3dkZ6eXuTXTERUWpmbm6N79+746quvMHfuXNy6dQt16tRBmTJlAABpaWno168f7t+/r+ZIiT5OImjQUtyurq5o0qQJ1q9fDyBnBlwHBweMHz8e06dPz1Xfzs4OM2bMwNixY8Uyb29vGBkZYc+ePUhLS4OZmRmOHz8OLy8vsU6jRo3g6emJhQsXQhAE2NnZYfLkyfj2228BAElJSbCxscGOHTvQp0+f/4z73bt3sLCwQFJSEszNzQvbDEREpZpMJsOxY8fQs2dPsaxDhw5o2LAhAMDU1BTNmjWDrq6uukKkYvb+/XvIZDJ4eHhAX19fZcdVxd9vjRlsnpmZiRs3bsDPz08s09HRgZubG0JCQvLcJyMjA1KpVKHMyMgIFy5cAABkZ2dDJpN9sk5ERARiY2Ph5uYmbrewsICrqytCQkLyTKQyMjIU1op69+5dAa+WiIg+RldXF97e3vjzzz/h6ekJADh58iROnjwp1hk9ejR69+6trhCJRBpza+/169eQyWSwsbFRKLexsUFsbGye+7i7u8Pf3x+PHz+GXC5HUFAQjhw5It5TNzMzQ7NmzbBgwQLExMRAJpNhz549CAkJEet8OHZBzrt48WJYWFiILwcHh0JdOxER5ebh4YGoqCj4+PigWbNm+Pzzz+Hk5AQA2LhxI3777Tc1R0ikQYmUMtasWYPq1avD2dkZBgYGGDduHAYPHgwdnf9d1u7duyEIAuzt7WFoaIi1a9eib9++CnUKys/PD0lJSeIrKipKFZdDRET/UrFiRezfvx+XLl1CSEgITp06JW7766+/1BgZUQ6NSaTKlSsHXV1dxMXFKZTHxcXB1tY2z32sra1x7NgxpKamIjIyEg8ePICpqanCHCROTk4IDg5GSkoKoqKicPXqVWRlZYl1Phy7IOc1NDSEubm5wouIiIpe5cqVERwcDAAICwvDjh071BsQlXoak0gZGBigUaNGOH36tFgml8tx+vRpNGvW7JP7SqVS2NvbIzs7G4cPH0bXrl1z1TExMUGFChXw9u1bBAYGinWqVKkCW1tbhfO+e/cOV65c+c/zEhFR8fvss8+gp5czxHfnzp0KY1aJipvGDDYHAF9fXwwaNAiNGzdG06ZNsXr1aqSmpmLw4MEAgIEDB8Le3h6LFy8GAFy5cgXR0dFwcXFBdHQ05s6dC7lcjqlTp4rHDAwMhCAIqFmzJp48eYIpU6bA2dlZPKZEIsHEiROxcOFCVK9eHVWqVMGsWbNgZ2eHbt26FXsbEBHRp5mamuL169ewtLQEkDOWys3NDd988w1MTU2RkJCAwMBAGBkZoXPnzny6j4qURiVSPj4+ePXqFWbPno3Y2Fi4uLggICBAHAj+/PlzhbFN6enpmDlzJsLDw2FqaoqOHTti9+7d4v9cQM5UBn5+fnjx4gWsrKzg7e2NRYsWKTw+OXXqVKSmpmLEiBFITExEy5YtERAQkOtpPyIi0gwWFhbw9fWFv78/AODUqVM4deoU6tSpg7t374r1fvvtNyxevBjW1tbqCpVKOI2aR0pbcR4pIiL1SE9Px9ixY7Ft27aP1jExMcHKlStRs2bNYoyMVEmT55FiIqUCTKSIiNRHEARcvHgRO3bsgCAIMDc3R6tWrRAcHIy1a9eK9erUqYMxY8agdu3aaoyWlMFEqoRjIkVEpJkOHjwIHx8fhTILCwt8//33TKi0iCYnUhrz1B4REZGq9e7dGxEREejWrRvKli0LIGfs7NixY/HgwQM1R0clARMpIiIq0SpXroyjR4/i9evXOHbsmFg+evRo/Prrr7h58ybCw8PBGzSkDI16ao+IiKgode3aFbt27cLAgQMBAKtWrRK3lStXDsuWLUOVKlXUFR5pIY6RUgGOkSIi0i6BgYFYu3YtHj58iKdPn4rln3/+uThXIRUvmUyG169f4/bt2+Lf1fj4eNy7dw9xcXHQ19fHzZs3NW6MFHukiIio1HF3d4e7uzuAnFU0xo8fjx9++AGXL1/GhQsX0LJlSzVHWLIJgoD79+/j3LlzuH//PhISEhATEwO5XP7RfUxNTYsxwvxjj5QKsEeKiEi7paeno0yZMkhPTwcAzJo1C19++aWaoyqZBEHA6tWr8euvv+bapqOjA1tbW+jq6sLY2Bi6urqwtrZG/fr10bZtW/To0UOlsbBHioiISAWkUilCQkLQsGFDAMCCBQtQu3btjy5eT8q5d+8e5s2bh/j4eAA57T5x4kTUq1cPDg4OcHZ2hqmpqbiyiEQiUWe4+cIeKRVgjxQRUckQHByMtm3bAsiZEX3o0KFo3769xt5W0hYymQxbtmzBwYMHxbJGjRohKCgIZcqUUVtcnEeKiIhIhdq0aQNfX18AQGpqKtauXYsxY8YgOTlZzZFpr7i4OIwaNUohiVq6dCkuXryo1iRKVdgjpQLskSIiKjmysrJw7NgxfPfdd3jy5AkAoGHDhli0aBEePHiA2NhYvHz5Eo8ePYK5uTmqVauGtm3bonz58mqOXLO8fPkSK1euxI0bN8Sy9u3bY/Xq1RozqzyXiNEQTKSIiEqerKwsdOrUCSdPnsxX/ZYtW2LWrFkwMDAo4sg0W0pKChYvXoxLly6JZRUqVMDw4cMxdepUmJiYqDE6RUykNAQTKSKikqtp06a4du0aAMDIyAjOzs6oWLEizM3NcffuXdy8eVOh/tKlS9G0aVM1RKp+MpkMkyZNwu3btwEAjo6O6NOnD6ZMmQIrKyuNGzzOp/aIiIiKWEhICB49egQTExNYWFjA3NxcISFITk5G37598fvvvwMApk2bhtatW2PGjBmlrnfq6NGjYhL11VdfYcuWLTA2NlZzVEWLg82JiIg+QVdXF7Vq1UKlSpVgYWGRq1fFzMwMv/32Gw4dOgQ9vZz+iXPnzqFbt2549OiROkJWmw89d8bGxpg/f36JT6IAJlJEREQq0bNnT8TFxaF169YAgLS0NIwcORKrV69GWlqamqMrWgkJCTh48CCuXr0KANi5cyeqVq2q5qiKB8dIqQDHSBER0QcymQybNm3CuHHjxDITExM0b94cVlZWiI6OhrW1Ndzc3DTm6TVlpaWlYdOmTQqzlOvr6+PevXuoVq2aGiPLHw421xBMpIiI6N8yMjLg6emJM2fOfLROpUqVoKenB3d3d9StWxc1a9aErq5uMUapnNu3b2Pfvn24fPmyWCaVSuHh4YGvv/4anTp10orrYCKlIZhIERHRx0RFRcHX1xcPHz5EmTJlUKdOHWzcuDHPupaWlvj888/Rv39/2NvbF3Ok/y0iIgKrVq0SB5QDgLm5Obp3747p06fD2dlZjdEVHBMpDcFEioiICuLGjRvYt28fZDIZbt26hZcvX+LBgwcKdZo0aYLx48fDwcFBTVHmSE5Oxvnz5/HHH3/g7t27Yrm1tTXmzZuHnj17omzZstDR0b5h10ykNAQTKSIiKqxXr15hzJgx+OWXXxTKbWxsMGXKFLi4uBTr7bKsrCysW7cOJ06cUCi3trZG//794evri4oVKxZbPEWBiZSGYCJFRESq8v79eyxfvhwbN25EXFycwjZ9fX1IpVI0b94cI0aMgJWVVYGOLZfL8fDhQ9y9excJCQl4/fo1MjIyUKFCBRgbGyMtLQ2ZmZl48+YNwsLC8O7dO3HfOnXq4Ouvv8aIESNKzN86JlIagokUERGpmiAIWLNmDfbu3YvQ0FDI5fJcdfr27YuhQ4fmq6fq9OnT2Lx5M169epXvGCQSCVq0aIHt27fDyclJ42YmLywmUhqCiRQRERWl5ORkvH79GklJSZgzZ47CdAMA0K1bNwwePBhSqRRhYWFITU1FvXr1IJPJEB4ejiNHjoiLB+vq6uKzzz6Do6MjLC0toa+vj8TERKSlpcHExAT6+vooV64cqlatis6dO8PGxgb6+vrquOwix0RKQzCRIiKi4nT//n306dMHt27dKtB+derUwY8//ojGjRuLs7CXZqr4+619Q+yJiIhKuVq1auHvv/9GZGQkPD09c22XSqXie0dHR7i4uGDAgAG4evUqPv/8cyZRKsSWJCIi0lKVKlXCH3/8gZiYGPz8889ISEiAh4cHmjdvjrdv3yIxMRGVKlUqdYsnFyfe2lMB3tojIiLSPry1R0RERKRGTKSIiIiIlMREioiIiEhJTKSIiIiIlMREioiIiEhJTKSIiIiIlMREioiIiEhJTKSIiIiIlMREioiIiEhJTKSIiIiIlMREioiIiEhJTKSIiIiIlMREioiIiEhJeuoOoCQQBAFAzirSREREpB0+/N3+8HdcGUykVCA5ORkA4ODgoOZIiIiIqKCSk5NhYWGh1L4SoTBpGAEA5HI5YmJiYGZmBolEotJjv3v3Dg4ODoiKioK5ublKj11SsI3yj21VMGyvgmF7FQzbK/+Kqq0EQUBycjLs7Oygo6PcaCf2SKmAjo4OKlasWKTnMDc35/9o/4FtlH9sq4JhexUM26tg2F75VxRtpWxP1AccbE5ERESkJCZSREREREpiIqXhDA0NMWfOHBgaGqo7FI3FNso/tlXBsL0Khu1VMGyv/NPktuJgcyIiIiIlsUeKiIiISElMpIiIiIiUxESKiIiISElMpIiIiIiUxESKiIiISElMpJR07tw5dO7cGXZ2dpBIJDh27JjC9ri4OHz99dews7ODsbExPDw88PjxY4U6T58+Rffu3WFtbQ1zc3P07t0bcXFxCnVCQ0PRvn17WFpaomzZshgxYgRSUlL+M75bt26hVatWkEqlcHBwwLJlyxS23717F97e3qhcuTIkEglWr16tVDt8jLa3z5EjR9C4cWNYWlrCxMQELi4u2L17t3KNkQ/a3l47duyARCJReEmlUuUa4z9oe1u1bds2V1tJJBJ4eXkp1yD5oO1tlpWVhfnz58PJyQlSqRQNGjRAQECAco3xHzS5rdLT0/H111+jXr160NPTQ7du3XLVefnyJfr164caNWpAR0cHEydOVKYZ8mXx4sVo0qQJzMzMUL58eXTr1g0PHz7MFfPYsWNRtmxZmJqawtvbO1dbPH/+HF5eXjA2Nkb58uUxZcoUZGdnK9Q5e/YsPvvsMxgaGqJatWrYsWPHf8YnCAJmz56NChUqwMjICG5ubrn+rRYtWoTmzZvD2NgYlpaWSrUDEyklpaamokGDBtiwYUOubYIgoFu3bggPD8fx48cRFhYGR0dHuLm5ITU1Vdy/Q4cOkEgk+Ouvv3Dx4kVkZmaic+fOkMvlAICYmBi4ubmhWrVquHLlCgICAnD37l18/fXXn4zt3bt36NChAxwdHXHjxg0sX74cc+fOxZYtW8Q679+/R9WqVbFkyRLY2tqqrmH+n7a3j5WVFWbMmIGQkBDcunULgwcPxuDBgxEYGKi6RvoHbW8vIGfphpcvX4qvyMhI1TTOv2h7Wx05ckShne7cuQNdXV306tVLdY30L9reZjNnzsTmzZuxbt063Lt3D6NGjUL37t0RFhamukb6f5rcVjKZDEZGRpgwYQLc3NzyrJORkQFra2vMnDkTDRo0KFxj/Ifg4GCMHTsWly9fRlBQELKystChQwexLQBg0qRJOHHiBA4dOoTg4GDExMSgR48eCtfk5eWFzMxMXLp0CTt37sSOHTswe/ZssU5ERAS8vLzwxRdf4ObNm5g4cSKGDRv2n7+Ply1bhrVr12LTpk24cuUKTExM4O7ujvT0dLFOZmYmevXqhdGjRyvfEAIVGgDh6NGj4ueHDx8KAIQ7d+6IZTKZTLC2tha2bt0qCIIgBAYGCjo6OkJSUpJYJzExUZBIJEJQUJAgCIKwefNmoXz58oJMJhPr3Lp1SwAgPH78+KPx/PDDD0KZMmWEjIwMsWzatGlCzZo186zv6OgorFq1qkDXXBDa3j4fNGzYUJg5c2b+LroQtLG9tm/fLlhYWCh9zcrSxrb6t1WrVglmZmZCSkpK/i+8ELSxzSpUqCCsX79eYb8ePXoIX331VQGvvmA0ra3+adCgQULXrl0/WadNmzbCN998k6/jqUJ8fLwAQAgODhYEIee69fX1hUOHDol17t+/LwAQQkJCBEEQhD/++EPQ0dERYmNjxTobN24UzM3NxZ+JqVOnCnXq1FE4l4+Pj+Du7v7RWORyuWBrayssX75cLEtMTBQMDQ2Fn3/+OVf9wvwOY49UEcjIyAAAhVsbOjo6MDQ0xIULF8Q6EolEYZZWqVQKHR0dhToGBgYKK1IbGRkBgFgnLyEhIWjdujUMDAzEMnd3dzx8+BBv375VwRUWjra1jyAIOH36NB4+fIjWrVsrc8mFoi3tlZKSAkdHRzg4OKBr1664e/duYS5bKdrSVv/0008/oU+fPjAxMSno5aqENrRZRkZGrlvFRkZGnzxuUVB3W2m6pKQkADk9+gBw48YNZGVlKfSeOTs7o1KlSggJCQGQ8+9fr1492NjYiHXc3d3x7t078XdISEhIrh44d3d38Rh5iYiIQGxsrMJ+FhYWcHV1/eR+ymAiVQQ+/KD4+fnh7du3yMzMxNKlS/HixQu8fPkSAPD555/DxMQE06ZNw/v375Gamopvv/0WMplMrPPll18iNjYWy5cvR2ZmJt6+fYvp06cDgFgnL7GxsQo/lADEz7GxsUVxyQWiLe2TlJQEU1NTGBgYwMvLC+vWrUP79u1V2hb5oQ3tVbNmTWzbtg3Hjx/Hnj17IJfL0bx5c7x48ULl7fEp2tBW/3T16lXcuXMHw4YNU8n1K0Mb2szd3R3+/v54/Pgx5HI5goKCxFukxUndbaXJ5HI5Jk6ciBYtWqBu3boAcv79DAwMco09srGxEf9t8/Pv/7E67969Q1paWp7xfNg3r/1U/XeQiVQR0NfXx5EjR/Do0SNYWVnB2NgYZ86cgaenp/gNxNraGocOHcKJEydgamoKCwsLJCYm4rPPPhPr1KlTBzt37sTKlSthbGwMW1tbVKlSBTY2Ngp1TE1NYWpqCk9PT7Vdc0FoS/uYmZnh5s2buHbtGhYtWgRfX1+cPXtWpW2RH9rQXs2aNcPAgQPh4uKCNm3a4MiRI7C2tsbmzZtV3yCfoA1t9U8//fQT6tWrh6ZNm6qmAZSgDW22Zs0aVK9eHc7OzjAwMMC4ceMwePBghR6d4qANbaUuY8eOxZ07d7B///5iP/fevXvFtjI1NcX58+eL9fx6xXq2UqRRo0a4efMmkpKSkJmZCWtra7i6uqJx48ZinQ4dOuDp06d4/fo19PT0YGlpCVtbW1StWlWs069fP/Tr1w9xcXEwMTGBRCKBv7+/WOePP/5AVlYWgP91Ddva2uZ6KuLD56IYWK4MbWgfHR0dVKtWDQDg4uKC+/fvY/HixWjbtq3qG+Q/aEN7/ZO+vj4aNmyIJ0+eqK4R8klb2io1NRX79+/H/PnzVd8IBaTpbWZtbY1jx44hPT0db968gZ2dHaZPn65w7uKizrbSVOPGjcNvv/2Gc+fOoWLFimK5ra0tMjMzkZiYqNArFRcXJ/7b2tra4urVqwrH+/e//8d+RszNzWFkZIQuXbrA1dVV3GZvby/27MXFxaFChQoK+7m4uBT+ov9JqZFVpAD/GpCYl0ePHgk6OjpCYGDgR+ucPn1akEgkwoMHDz5a56effhKMjY2Ft2/ffrTOh8GbmZmZYpmfn5/GDDbPiya3zweDBw8W2rRp88k6qlAS2is7O1uoWbOmMGnSpE9eR2Fpc1tt375dMDQ0FF6/fv3J+FVNm9vsg8zMTMHJyUnw8/P75HUUlqa11T9pwmBzuVwujB07VrCzsxMePXqUa/uHwea//PKLWPbgwYM8B5vHxcWJdTZv3iyYm5sL6enpgiDkDDavW7euwrH79u2br8HmK1asEMuSkpKKZLA5EyklJScnC2FhYUJYWJgAQPD39xfCwsKEyMhIQRAE4eDBg8KZM2eEp0+fCseOHRMcHR2FHj16KBxj27ZtQkhIiPDkyRNh9+7dgpWVleDr66tQZ926dcKNGzeEhw8fCuvXrxeMjIyENWvWfDK2xMREwcbGRhgwYIBw584dYf/+/YKxsbGwefNmsU5GRoYYf4UKFYRvv/1WCAsLy/cTIyW9fb7//nvh5MmTwtOnT4V79+4JK1asEPT09MQnc1RN29tr3rx5QmBgoPD06VPhxo0bQp8+fQSpVCrcvXtXRS30P9reVh+0bNlS8PHxKWRr5I+2t9nly5eFw4cPC0+fPhXOnTsnfPnll0KVKlXynXQUhCa3lSAIwt27d4WwsDChc+fOQtu2bcVY/+lDWaNGjYR+/foJYWFhRfL/4ujRowULCwvh7NmzwsuXL8XX+/fvxTqjRo0SKlWqJPz111/C9evXhWbNmgnNmjUTt2dnZwt169YVOnToINy8eVMICAgQrK2tFZLk8PBwwdjYWJgyZYpw//59YcOGDYKurq4QEBDwyfiWLFkiWFpaCsePHxdu3boldO3aVahSpYqQlpYm1omMjBTCwsKEefPmCaampmLbJScn57sdmEgp6cyZMwKAXK9BgwYJgiAIa9asESpWrCjo6+sLlSpVEmbOnKnweK8g5Dzia2NjI+jr6wvVq1cXVq5cKcjlcoU6AwYMEKysrAQDAwOhfv36wq5du/IV399//y20bNlSMDQ0FOzt7YUlS5YobI+IiMgzflX1uGh7+8yYMUOoVq2aIJVKhTJlygjNmjUT9u/fr3yD/Adtb6+JEycKlSpVEgwMDAQbGxuhY8eOQmhoqPIN8gna3laC8L9v5SdPnlSuEQpI29vs7NmzQq1atQRDQ0OhbNmywoABA4To6GjlG+QTNL2tHB0d84zvn/La7ujoqHSbfExe5wEgbN++XayTlpYmjBkzRihTpoxgbGwsdO/eXXj58qXCcZ49eyZ4enoKRkZGQrly5YTJkycLWVlZCnXOnDkjuLi4CAYGBkLVqlUVzvExcrlcmDVrlmBjYyMYGhoK7dq1Ex4+fKhQZ9CgQXlew5kzZ/LdDpL/bwwiIiIiKiA+tUdERESkJCZSREREREpiIkVERESkJCZSREREREpiIkVERESkJCZSREREREpiIkVERESkJCZSREREREpiIkVERESkJCZSREREREpiIkVERESkpP8DpjPXkJIWZ/MAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import matplotlib.dates as mdates\n",
     "\n",
@@ -2564,46 +716,16 @@
     "\n",
     "loss = letid.calc_energy_loss(timesteps)\n",
     "\n",
-    "ax.text(mdates.datestr2num(\"1999-08-02\"), 0.994, s=f\"Energy loss = {loss*100:.2f}%\")\n",
+    "ax.text(mdates.datestr2num(\"1999-08-02\"), 0.994, s=f\"Energy loss = {loss * 100:.2f}%\")\n",
     "\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_15280\\3402272254.py:45: DeprecationWarning: You are passing x=0          0.0\n",
-      "1          1.0\n",
-      "2          2.0\n",
-      "3          3.0\n",
-      "4          4.0\n",
-      "         ...  \n",
-      "8755    8755.0\n",
-      "8756    8756.0\n",
-      "8757    8757.0\n",
-      "8758    8758.0\n",
-      "8759    8759.0\n",
-      "Name: Timedelta, Length: 8760, dtype: float64 as a positional argument. Please change your invocation to use keyword arguments. From SciPy 1.14, passing these as positional arguments will result in an error.\n",
-      "  energy = simpson(timesteps[\"p_mp\"]/1000, timesteps['Timedelta'])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import matplotlib.dates as mdates\n",
     "from scipy.integrate import cumulative_trapezoid, simpson\n",
@@ -2715,7 +837,7 @@
     "ax3.text(\n",
     "    mdates.datestr2num(\"1999-03-02\"),\n",
     "    2,\n",
-    "    s=f\"Energy loss = {loss*energy:.1f} kWh ({loss*100:.2f}%)\",\n",
+    "    s=f\"Energy loss = {loss * energy:.1f} kWh ({loss * 100:.2f}%)\",\n",
     ")\n",
     "\n",
     "ax1.set_ylabel(\"Module $P_{MP}$ [W]\")\n",
@@ -2736,281 +858,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th>Temperature</th>\n",
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-       "<p>8760 rows × 12 columns</p>\n",
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-      ],
-      "text/plain": [
-       "                           Temperature  Injection          NA            NB  \\\n",
-       "time                                                                          \n",
-       "1999-01-01 00:30:00-07:00          0.0        NaN  100.000000  0.000000e+00   \n",
-       "1999-01-01 01:30:00-07:00          0.0        NaN  100.000000  1.683090e-15   \n",
-       "1999-01-01 02:30:00-07:00          0.0        NaN  100.000000  3.366181e-15   \n",
-       "1999-01-01 03:30:00-07:00          0.0        NaN  100.000000  5.049271e-15   \n",
-       "1999-01-01 04:30:00-07:00          0.0        NaN  100.000000  6.732362e-15   \n",
-       "...                                ...        ...         ...           ...   \n",
-       "1999-12-31 19:30:00-07:00          0.0        NaN   29.749662  6.666236e+01   \n",
-       "1999-12-31 20:30:00-07:00          0.0        NaN   29.749662  6.666236e+01   \n",
-       "1999-12-31 21:30:00-07:00          0.0        NaN   29.749662  6.666236e+01   \n",
-       "1999-12-31 22:30:00-07:00          0.0        NaN   29.749662  6.666236e+01   \n",
-       "1999-12-31 23:30:00-07:00          0.0        NaN   29.749662  6.666236e+01   \n",
-       "\n",
-       "                                     NC         tau        Jsc       Voc  \\\n",
-       "time                                                                       \n",
-       "1999-01-01 00:30:00-07:00  0.000000e+00  120.000000  41.451328  0.661315   \n",
-       "1999-01-01 01:30:00-07:00  0.000000e+00  120.000000  41.451328  0.661315   \n",
-       "1999-01-01 02:30:00-07:00  5.269266e-36  120.000000  41.451328  0.661315   \n",
-       "1999-01-01 03:30:00-07:00  1.580780e-35  120.000000  41.451328  0.661315   \n",
-       "1999-01-01 04:30:00-07:00  3.161560e-35  120.000000  41.451328  0.661315   \n",
-       "...                                 ...         ...        ...       ...   \n",
-       "1999-12-31 19:30:00-07:00  3.587983e+00   90.001455  41.342623  0.656723   \n",
-       "1999-12-31 20:30:00-07:00  3.587983e+00   90.001455  41.342623  0.656723   \n",
-       "1999-12-31 21:30:00-07:00  3.587983e+00   90.001455  41.342623  0.656723   \n",
-       "1999-12-31 22:30:00-07:00  3.587983e+00   90.001455  41.342623  0.656723   \n",
-       "1999-12-31 23:30:00-07:00  3.587983e+00   90.001455  41.342623  0.656723   \n",
-       "\n",
-       "                                 Isc        FF       Pmp  Pmp_norm  \n",
-       "time                                                                \n",
-       "1999-01-01 00:30:00-07:00  10.072673  0.840101  5.596088  1.000000  \n",
-       "1999-01-01 01:30:00-07:00  10.072673  0.840101  5.596088  1.000000  \n",
-       "1999-01-01 02:30:00-07:00  10.072673  0.840101  5.596088  1.000000  \n",
-       "1999-01-01 03:30:00-07:00  10.072673  0.840101  5.596088  1.000000  \n",
-       "1999-01-01 04:30:00-07:00  10.072673  0.840101  5.596088  1.000000  \n",
-       "...                              ...       ...       ...       ...  \n",
-       "1999-12-31 19:30:00-07:00  10.046257  0.839281  5.537249  0.989486  \n",
-       "1999-12-31 20:30:00-07:00  10.046257  0.839281  5.537249  0.989486  \n",
-       "1999-12-31 21:30:00-07:00  10.046257  0.839281  5.537249  0.989486  \n",
-       "1999-12-31 22:30:00-07:00  10.046257  0.839281  5.537249  0.989486  \n",
-       "1999-12-31 23:30:00-07:00  10.046257  0.839281  5.537249  0.989486  \n",
-       "\n",
-       "[8760 rows x 12 columns]"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "mechanism_params = \"repins\"\n",
     "\n",
@@ -3032,384 +882,9 @@
   },
   {
    "cell_type": "code",
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+   "execution_count": null,
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-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.963991</td>\n",
-       "      <td>4.0</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",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\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>8755</th>\n",
-       "      <td>1999-12-31 19:30:00-07:00</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>30.470917</td>\n",
-       "      <td>6.606591e+01</td>\n",
-       "      <td>3.463173e+00</td>\n",
-       "      <td>90.203213</td>\n",
-       "      <td>41.343585</td>\n",
-       "      <td>0.656761</td>\n",
-       "      <td>9.955535</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.9</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.939501</td>\n",
-       "      <td>8755.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8756</th>\n",
-       "      <td>1999-12-31 20:30:00-07:00</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>30.470917</td>\n",
-       "      <td>6.606591e+01</td>\n",
-       "      <td>3.463173e+00</td>\n",
-       "      <td>90.203213</td>\n",
-       "      <td>41.343585</td>\n",
-       "      <td>0.656761</td>\n",
-       "      <td>9.955535</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.975338</td>\n",
-       "      <td>8756.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8757</th>\n",
-       "      <td>1999-12-31 21:30:00-07:00</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>30.470917</td>\n",
-       "      <td>6.606591e+01</td>\n",
-       "      <td>3.463173e+00</td>\n",
-       "      <td>90.203213</td>\n",
-       "      <td>41.343585</td>\n",
-       "      <td>0.656761</td>\n",
-       "      <td>9.955535</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.941186</td>\n",
-       "      <td>8757.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8758</th>\n",
-       "      <td>1999-12-31 22:30:00-07:00</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>30.470917</td>\n",
-       "      <td>6.606591e+01</td>\n",
-       "      <td>3.463173e+00</td>\n",
-       "      <td>90.203213</td>\n",
-       "      <td>41.343585</td>\n",
-       "      <td>0.656761</td>\n",
-       "      <td>9.955535</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.912427</td>\n",
-       "      <td>8758.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8759</th>\n",
-       "      <td>1999-12-31 23:30:00-07:00</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>30.470917</td>\n",
-       "      <td>6.606591e+01</td>\n",
-       "      <td>3.463173e+00</td>\n",
-       "      <td>90.203213</td>\n",
-       "      <td>41.343585</td>\n",
-       "      <td>0.656761</td>\n",
-       "      <td>9.955535</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.892318</td>\n",
-       "      <td>8759.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>8760 rows × 28 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                      Datetime  Temperature  Injection          NA  \\\n",
-       "0    1999-01-01 00:30:00-07:00          0.0        NaN  100.000000   \n",
-       "1    1999-01-01 01:30:00-07:00          0.0        NaN  100.000000   \n",
-       "2    1999-01-01 02:30:00-07:00          0.0        NaN  100.000000   \n",
-       "3    1999-01-01 03:30:00-07:00          0.0        NaN  100.000000   \n",
-       "4    1999-01-01 04:30:00-07:00          0.0        NaN  100.000000   \n",
-       "...                        ...          ...        ...         ...   \n",
-       "8755 1999-12-31 19:30:00-07:00          0.0        NaN   30.470917   \n",
-       "8756 1999-12-31 20:30:00-07:00          0.0        NaN   30.470917   \n",
-       "8757 1999-12-31 21:30:00-07:00          0.0        NaN   30.470917   \n",
-       "8758 1999-12-31 22:30:00-07:00          0.0        NaN   30.470917   \n",
-       "8759 1999-12-31 23:30:00-07:00          0.0        NaN   30.470917   \n",
-       "\n",
-       "                NB            NC         tau        Jsc       Voc       Isc  \\\n",
-       "0     0.000000e+00  0.000000e+00  120.000000  41.451328  0.661315  9.981480   \n",
-       "1     1.683090e-15  0.000000e+00  120.000000  41.451328  0.661315  9.981480   \n",
-       "2     3.366181e-15  5.269266e-36  120.000000  41.451328  0.661315  9.981480   \n",
-       "3     5.049271e-15  1.580780e-35  120.000000  41.451328  0.661315  9.981480   \n",
-       "4     6.732362e-15  3.161560e-35  120.000000  41.451328  0.661315  9.981480   \n",
-       "...            ...           ...         ...        ...       ...       ...   \n",
-       "8755  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
-       "8756  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
-       "8757  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
-       "8758  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
-       "8759  6.606591e+01  3.463173e+00   90.203213  41.343585  0.656761  9.955535   \n",
-       "\n",
-       "      ...  i_x  i_xx  Effective irradiance  ghi  dhi  dni  wind_speed  \\\n",
-       "0     ...  0.0   0.0                   NaN  0.0  0.0  0.0         1.8   \n",
-       "1     ...  0.0   0.0                   NaN  0.0  0.0  0.0         1.7   \n",
-       "2     ...  0.0   0.0                   NaN  0.0  0.0  0.0         1.5   \n",
-       "3     ...  0.0   0.0                   NaN  0.0  0.0  0.0         1.3   \n",
-       "4     ...  0.0   0.0                   NaN  0.0  0.0  0.0         1.3   \n",
-       "...   ...  ...   ...                   ...  ...  ...  ...         ...   \n",
-       "8755  ...  0.0   0.0                   NaN  0.0  0.0  0.0         0.9   \n",
-       "8756  ...  0.0   0.0                   NaN  0.0  0.0  0.0         1.2   \n",
-       "8757  ...  0.0   0.0                   NaN  0.0  0.0  0.0         1.6   \n",
-       "8758  ...  0.0   0.0                   NaN  0.0  0.0  0.0         1.7   \n",
-       "8759  ...  0.0   0.0                   NaN  0.0  0.0  0.0         1.8   \n",
-       "\n",
-       "      temp_air  precipitable_water  Timedelta  \n",
-       "0          0.0            0.891869        0.0  \n",
-       "1          0.0            0.908158        1.0  \n",
-       "2          0.0            0.932199        2.0  \n",
-       "3          0.0            0.955004        3.0  \n",
-       "4          0.0            0.963991        4.0  \n",
-       "...        ...                 ...        ...  \n",
-       "8755       0.0            0.939501     8755.0  \n",
-       "8756       0.0            0.975338     8756.0  \n",
-       "8757       0.0            0.941186     8757.0  \n",
-       "8758       0.0            0.912427     8758.0  \n",
-       "8759       0.0            0.892318     8759.0  \n",
-       "\n",
-       "[8760 rows x 28 columns]"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "timesteps"
    ]
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Passivated Wafer.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Passivated Wafer.ipynb
index d4707c925..f4805990e 100644
--- a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Passivated Wafer.ipynb	
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Passivated Wafer.ipynb	
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,23 +48,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Working on a  Windows 10\n",
-      "Python version  3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n",
-      "Pandas version  2.2.0\n",
-      "pvdeg version  0.2.4.dev83+ge2ceab9.d20240422\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# This information helps with debugging and getting support :)\n",
-    "import sys, platform\n",
+    "import sys\n",
+    "import platform\n",
     "\n",
     "print(\"Working on a \", platform.system(), platform.release())\n",
     "print(\"Python version \", sys.version)\n",
@@ -82,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -114,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -132,20 +122,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.57051594134322e+21"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from scipy.constants import elementary_charge as q  # Elementary charge, C\n",
     "\n",
@@ -165,20 +144,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "7.064647199192719e+20"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from scipy.constants import convert_temperature\n",
     "\n",
@@ -213,20 +181,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.2"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "exponent = mechanism_params[f\"x_{transition}\"]\n",
     "exponent"
@@ -241,40 +198,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4.711029107876425"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "(dn / dn_lit) ** exponent"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4.711029107876425"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# the \"carrier_factor_wafer\" function yields the same result\n",
     "\n",
@@ -293,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -303,17 +238,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'mechanism': 'LETID', 'v_ab': 46700000.0, 'v_ba': 4.7e-25, 'v_bc': 19900000.0, 'v_cb': 0.0, 'ea_ab': 0.827, 'ea_ba': -1.15, 'ea_bc': 0.871, 'ea_cb': 0.0, 'suns_ab': 1.0, 'suns_bc': 1.0, 'temperature_ab': 410, 'temperature_bc': 410, 'tau_ab': 75, 'tau_bc': 75, 'x_ab': 1, 'x_ba': 1.7, 'x_bc': 1.2, 'structure_ab': 'cell', 'structure_bc': 'cell', 'thickness_ab': 200, 'thickness_bc': 200, 'srv_ab': 90, 'srv_bc': 90, 'doi': 'doi:10.1557/s43577-022-00438-8', 'comments': ''}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# starting defect state percentages\n",
     "nA_0 = 100\n",
@@ -334,18 +261,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_6508\\530898593.py:4: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
-      "  timesteps = pd.date_range(start = '2022-01-01 00:00:00', end = '2022-01-02 00:00:00', freq = 'S') # three weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "temperature = 125  # degrees celsius\n",
     "suns = 0.5  # \"suns\" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.1*Isc.\n",
@@ -377,177 +295,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Datetime</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Injection</th>\n",
-       "      <th>NA</th>\n",
-       "      <th>NB</th>\n",
-       "      <th>NC</th>\n",
-       "      <th>tau</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2022-01-01 00:00:00</td>\n",
-       "      <td>125</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>350.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2022-01-01 00:00:01</td>\n",
-       "      <td>125</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2022-01-01 00:00:02</td>\n",
-       "      <td>125</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2022-01-01 00:00:03</td>\n",
-       "      <td>125</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2022-01-01 00:00:04</td>\n",
-       "      <td>125</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</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>86396</th>\n",
-       "      <td>2022-01-01 23:59:56</td>\n",
-       "      <td>125</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>86397</th>\n",
-       "      <td>2022-01-01 23:59:57</td>\n",
-       "      <td>125</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>86398</th>\n",
-       "      <td>2022-01-01 23:59:58</td>\n",
-       "      <td>125</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>86399</th>\n",
-       "      <td>2022-01-01 23:59:59</td>\n",
-       "      <td>125</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>86400</th>\n",
-       "      <td>2022-01-02 00:00:00</td>\n",
-       "      <td>125</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>86401 rows × 7 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Datetime  Temperature  Injection     NA   NB   NC    tau\n",
-       "0     2022-01-01 00:00:00          125        0.5  100.0  0.0  0.0  350.0\n",
-       "1     2022-01-01 00:00:01          125        0.5    NaN  NaN  NaN    NaN\n",
-       "2     2022-01-01 00:00:02          125        0.5    NaN  NaN  NaN    NaN\n",
-       "3     2022-01-01 00:00:03          125        0.5    NaN  NaN  NaN    NaN\n",
-       "4     2022-01-01 00:00:04          125        0.5    NaN  NaN  NaN    NaN\n",
-       "...                   ...          ...        ...    ...  ...  ...    ...\n",
-       "86396 2022-01-01 23:59:56          125        0.5    NaN  NaN  NaN    NaN\n",
-       "86397 2022-01-01 23:59:57          125        0.5    NaN  NaN  NaN    NaN\n",
-       "86398 2022-01-01 23:59:58          125        0.5    NaN  NaN  NaN    NaN\n",
-       "86399 2022-01-01 23:59:59          125        0.5    NaN  NaN  NaN    NaN\n",
-       "86400 2022-01-02 00:00:00          125        0.5    NaN  NaN  NaN    NaN\n",
-       "\n",
-       "[86401 rows x 7 columns]"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "timesteps"
    ]
@@ -562,12 +312,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "for index, timestep in timesteps.iterrows():\n",
-    "\n",
     "    # first row tau has already been assigned\n",
     "    if index == 0:\n",
     "        pass\n",
@@ -640,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -655,214 +404,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
    "metadata": {},
-   "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>Datetime</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Injection</th>\n",
-       "      <th>NA</th>\n",
-       "      <th>NB</th>\n",
-       "      <th>NC</th>\n",
-       "      <th>tau</th>\n",
-       "      <th>NDD</th>\n",
-       "      <th>time (s)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2022-01-01 00:00:00</td>\n",
-       "      <td>125</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>1.000000e+02</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>350.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2022-01-01 00:00:01</td>\n",
-       "      <td>125</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>9.942178e+01</td>\n",
-       "      <td>5.782160e-01</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>335.384693</td>\n",
-       "      <td>0.000125</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2022-01-01 00:00:02</td>\n",
-       "      <td>125</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>9.887092e+01</td>\n",
-       "      <td>1.128597e+00</td>\n",
-       "      <td>0.000486</td>\n",
-       "      <td>322.563499</td>\n",
-       "      <td>0.000243</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2022-01-01 00:00:03</td>\n",
-       "      <td>125</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>9.834404e+01</td>\n",
-       "      <td>1.654564e+00</td>\n",
-       "      <td>0.001392</td>\n",
-       "      <td>311.194745</td>\n",
-       "      <td>0.000356</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2022-01-01 00:00:04</td>\n",
-       "      <td>125</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>9.783845e+01</td>\n",
-       "      <td>2.158887e+00</td>\n",
-       "      <td>0.002663</td>\n",
-       "      <td>301.021824</td>\n",
-       "      <td>0.000465</td>\n",
-       "      <td>4.0</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",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>86396</th>\n",
-       "      <td>2022-01-01 23:59:56</td>\n",
-       "      <td>125</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>5.800845e-34</td>\n",
-       "      <td>3.370010e-27</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>350.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>86396.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>86397</th>\n",
-       "      <td>2022-01-01 23:59:57</td>\n",
-       "      <td>125</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>5.795712e-34</td>\n",
-       "      <td>3.367028e-27</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>350.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>86397.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>86398</th>\n",
-       "      <td>2022-01-01 23:59:58</td>\n",
-       "      <td>125</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>5.790584e-34</td>\n",
-       "      <td>3.364048e-27</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>350.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>86398.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>86399</th>\n",
-       "      <td>2022-01-01 23:59:59</td>\n",
-       "      <td>125</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>5.785460e-34</td>\n",
-       "      <td>3.361072e-27</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>350.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>86399.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>86400</th>\n",
-       "      <td>2022-01-02 00:00:00</td>\n",
-       "      <td>125</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>5.780341e-34</td>\n",
-       "      <td>3.358098e-27</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>350.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>86400.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>86401 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Datetime  Temperature  Injection            NA            NB  \\\n",
-       "0     2022-01-01 00:00:00          125        0.5  1.000000e+02  0.000000e+00   \n",
-       "1     2022-01-01 00:00:01          125        0.5  9.942178e+01  5.782160e-01   \n",
-       "2     2022-01-01 00:00:02          125        0.5  9.887092e+01  1.128597e+00   \n",
-       "3     2022-01-01 00:00:03          125        0.5  9.834404e+01  1.654564e+00   \n",
-       "4     2022-01-01 00:00:04          125        0.5  9.783845e+01  2.158887e+00   \n",
-       "...                   ...          ...        ...           ...           ...   \n",
-       "86396 2022-01-01 23:59:56          125        0.5  5.800845e-34  3.370010e-27   \n",
-       "86397 2022-01-01 23:59:57          125        0.5  5.795712e-34  3.367028e-27   \n",
-       "86398 2022-01-01 23:59:58          125        0.5  5.790584e-34  3.364048e-27   \n",
-       "86399 2022-01-01 23:59:59          125        0.5  5.785460e-34  3.361072e-27   \n",
-       "86400 2022-01-02 00:00:00          125        0.5  5.780341e-34  3.358098e-27   \n",
-       "\n",
-       "               NC         tau       NDD  time (s)  \n",
-       "0        0.000000  350.000000  0.000000       0.0  \n",
-       "1        0.000000  335.384693  0.000125       1.0  \n",
-       "2        0.000486  322.563499  0.000243       2.0  \n",
-       "3        0.001392  311.194745  0.000356       3.0  \n",
-       "4        0.002663  301.021824  0.000465       4.0  \n",
-       "...           ...         ...       ...       ...  \n",
-       "86396  100.000000  350.000000  0.000000   86396.0  \n",
-       "86397  100.000000  350.000000  0.000000   86397.0  \n",
-       "86398  100.000000  350.000000  0.000000   86398.0  \n",
-       "86399  100.000000  350.000000  0.000000   86399.0  \n",
-       "86400  100.000000  350.000000  0.000000   86400.0  \n",
-       "\n",
-       "[86401 rows x 9 columns]"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "timesteps"
    ]
@@ -876,20 +420,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from cycler import cycler\n",
     "\n",
@@ -912,7 +445,7 @@
     "ax2.plot(\n",
     "    timesteps[\"time (s)\"], timesteps[\"NDD\"] * c_norm, c=\"black\", label=\"NDD modeled\"\n",
     ")\n",
-    "ax.set_ylabel(\"NDD [$\\mu s^{-1}$]\")\n",
+    "ax.set_ylabel(r\"NDD [$\\mu s^{-1}$]\")\n",
     "\n",
     "literature_data_file = os.path.join(DATA_DIR, \"wyller data.csv\")\n",
     "data = pd.read_csv(literature_data_file, header=0)\n",
@@ -944,7 +477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -953,7 +486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -962,18 +495,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_6508\\241191725.py:4: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
-      "  timesteps = pd.date_range(start = '2022-01-01 00:00:00', end = '2022-01-02 00:00:00', freq = 'S') # three weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "temperature = temperatures[1]  # degrees celsius\n",
     "suns = 0.5  # \"suns\" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.1*Isc.\n",
@@ -1005,12 +529,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "for index, timestep in timesteps.iterrows():\n",
-    "\n",
     "    # first row tau has already been assigned\n",
     "    if index == 0:\n",
     "        pass\n",
@@ -1071,7 +594,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1084,7 +607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1093,18 +616,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_6508\\950821369.py:4: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n",
-      "  timesteps = pd.date_range(start = '2022-01-01 00:00:00', end = '2022-01-02 00:00:00', freq = 'S') # three weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "temperature = temperatures[2]  # degrees celsius\n",
     "suns = 0.5  # \"suns\" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.1*Isc.\n",
@@ -1136,12 +650,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "for index, timestep in timesteps.iterrows():\n",
-    "\n",
     "    # first row tau has already been assigned\n",
     "    if index == 0:\n",
     "        pass\n",
@@ -1202,7 +715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1215,7 +728,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1224,7 +737,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1233,7 +746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1251,7 +764,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1260,7 +773,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1275,7 +788,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1284,20 +797,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import itertools\n",
     "\n",
@@ -1307,7 +809,7 @@
     "    ax.plot(\n",
     "        group[\"time (s)\"] + 0.1,\n",
     "        group[\"NDD-normalized\"],\n",
-    "        label=f\"Modeled NDD {name}$\\degree$C\",\n",
+    "        label=rf\"Modeled NDD {name}$\\degree$C\",\n",
     "    )\n",
     "    ax.legend()\n",
     "\n",
@@ -1318,7 +820,7 @@
     "    ax.scatter(\n",
     "        group[\"X\"],\n",
     "        group[\"Y\"],\n",
-    "        label=f\"Wyller NDD {name}$\\degree$C\",\n",
+    "        label=rf\"Wyller NDD {name}$\\degree$C\",\n",
     "        marker=next(marker),\n",
     "    )\n",
     "    ax.legend()\n",
@@ -1326,27 +828,16 @@
     "ax.set_xscale(\"log\")\n",
     "\n",
     "ax.set_xlabel(\"Time [s]\")\n",
-    "ax.set_ylabel(\"NDD [$\\mu s^{-1}$]\")\n",
+    "ax.set_ylabel(r\"NDD [$\\mu s^{-1}$]\")\n",
     "\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import itertools\n",
     "\n",
@@ -1359,7 +850,7 @@
     "        group[\"time (s)\"] + 0.1,\n",
     "        group[\"NDD-normalized\"],\n",
     "        color=next(colors),\n",
-    "        label=f\"Modeled {name}$\\degree$C\",\n",
+    "        label=rf\"Modeled {name}$\\degree$C\",\n",
     "        linewidth=3,\n",
     "    )\n",
     "    ax.legend(prop={\"size\": 12})\n",
@@ -1371,7 +862,7 @@
     "    ax.scatter(\n",
     "        group[\"X\"],\n",
     "        group[\"Y\"],\n",
-    "        label=f\"Literature data  {name}$\\degree$C\",\n",
+    "        label=rf\"Literature data  {name}$\\degree$C\",\n",
     "        color=next(colors),\n",
     "        marker=next(marker),\n",
     "        s=70,\n",
@@ -1384,7 +875,7 @@
     "\n",
     "\n",
     "ax.set_xlabel(\"Time [s]\", fontsize=16)\n",
-    "ax.set_ylabel(\"Normalized defect \\n density [$\\mu s^{-1}$]\", fontsize=16)\n",
+    "ax.set_ylabel(\"Normalized defect \\n density [$\\\\mu s^{-1}$]\", fontsize=16)\n",
     "\n",
     "ax.set_title(\"Modeled vs. Literature LETID data\", fontsize=16)\n",
     "\n",
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Monte Carlo - Arrhenius.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Monte Carlo - Arrhenius.ipynb
new file mode 100644
index 000000000..1942a7172
--- /dev/null
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Monte Carlo - Arrhenius.ipynb	
@@ -0,0 +1,354 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Monte Carlo - Arrhenius Degradation \n",
+    "\n",
+    "\n",
+    "A monte carlo simulation can be used to predict results of an event with a certain amount of uncertainty. This will be introduced to our use case via mean and standard deviation for each modeling constant. Correlated multivariate monte carlo simulations expand on this by linking the behavior of multiple input variables together with correlation data, in our case we will use correlation coefficients but \n",
+    "\n",
+    "**Objectives**\n",
+    "1. Define necessary monte carlo simulation parameters : correlation coefficients, mean and standard standard deviation, number of trials, function to apply, requried function input\n",
+    "2. Define process for creating and utilizing modeling constant correlation data\n",
+    "3. Preform simple monte carlo simulation using arrhenius equation to calculate degredation and plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n",
+    "# !pip install pvdeg==0.3.3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pvlib\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pvdeg\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This information helps with debugging and getting support :)\n",
+    "import sys\n",
+    "import platform\n",
+    "\n",
+    "print(\"Working on a \", platform.system(), platform.release())\n",
+    "print(\"Python version \", sys.version)\n",
+    "print(\"Pandas version \", pd.__version__)\n",
+    "print(\"Pvlib version \", pvlib.__version__)\n",
+    "print(\"pvdeg version \", pvdeg.__version__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Correlated Monte Carlo Simulation (parameters)\n",
+    "\n",
+    "For this simulation we will be using an arrhenius equation to calculate degredation rate given by $R_D = R_0 * I ^ X * e ^ {\\frac{-Ea}{kT}}$, where R0 is prefactor degredation, I is irradiance, X is the irridiance relation, Ea is activation energy and T is degrees K\n",
+    "\n",
+    "We will use R0, X and Ea to preform a 3 variable monte carlo simulation to calculate degredation.\n",
+    "\n",
+    "### Required inputs\n",
+    "To run a monte carlo simulation with pvdeg.montecarlo the following inputs will be required\n",
+    "- function (currently only works with pvdeg.montecarlo.vecArrhenius() but will eventually work with most pvdeg calculation functions)\n",
+    "- function arguments (ex: metadata, weather, cell temperature, solar position, etc.)\n",
+    "- mean and standard deviation (Required for all correlation constants)\n",
+    "- correlation constants (if not entered, default = 0)\n",
+    "- number of trials to run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Defining Correlation Coefficients\n",
+    "pvdeg.montecarlo stores correlation coefficients in a ``Corr`` object. To represent a given correlation coefficient follow the given syntax below, replacing the values in the brackets with your correlation coefficients\n",
+    "\n",
+    "    {my_correlation_object} = Corr('{variable1}', '{variable2}', {correlation coefficient})\n",
+    "\n",
+    "note: ordering of `variable1` and `variable2` does not matter\n",
+    "\n",
+    "After defining the all known correlations add them to a list which we will feed into our simulation later"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corr_Ea_X = pvdeg.montecarlo.Corr(\"Ea\", \"X\", 0.0269)\n",
+    "corr_Ea_LnR0 = pvdeg.montecarlo.Corr(\"Ea\", \"LnR0\", -0.9995)\n",
+    "corr_X_LnR0 = pvdeg.montecarlo.Corr(\"X\", \"LnR0\", -0.0400)\n",
+    "\n",
+    "corr_coeff = [corr_Ea_X, corr_Ea_LnR0, corr_X_LnR0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(corr_Ea_X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Defining Mean and Standard Deviation\n",
+    "We will store the mean and correlation for each variable, expressed when we defined the correlation cefficients. If a variable is left out at this stage, the monte carlo simulation will throw errors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stats_dict = {\n",
+    "    \"Ea\": {\"mean\": 62.08, \"stdev\": 7.3858},\n",
+    "    \"LnR0\": {\"mean\": 13.7223084, \"stdev\": 2.47334772},\n",
+    "    \"X\": {\"mean\": 0.0341, \"stdev\": 0.0992757},\n",
+    "}\n",
+    "\n",
+    "# and number of monte carlo trials to run\n",
+    "n = 20000"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generating Monte Carlo Input Data\n",
+    "Next we will use the information collected above to generate correlated data from our modeling constant correlations, means and standard deviations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mc_inputs = pvdeg.montecarlo.generateCorrelatedSamples(\n",
+    "    corr=corr_coeff, stats=stats_dict, n=n\n",
+    ")\n",
+    "print(mc_inputs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Sanity Check\n",
+    "We can observe the mean and standard deviation of our newly correlated samples before using them for calculations to ensure that we have not incorrectly altered the data. The mean and standard deviation should be the similar (within a range) to your original input (the error comes from the standard distribution of generated random numbers)\n",
+    "\n",
+    "This also applies to the correlation coefficients originally inputted, they should be witin the same range as those orginally supplied."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# mean and standard deviation match inputs\n",
+    "for col in mc_inputs.columns:\n",
+    "    print(f\"{col} : mean {mc_inputs[col].mean():.10f}, stdev {mc_inputs[col].std():.10f}\")\n",
+    "\n",
+    "print()\n",
+    "\n",
+    "# come up with a better way of checking\n",
+    "print(\"Ea_X\", np.corrcoef(mc_inputs[\"Ea\"], mc_inputs[\"X\"])[0][1])\n",
+    "print(\"Ea_lnR0\", np.corrcoef(mc_inputs[\"Ea\"], mc_inputs[\"LnR0\"])[0][1])\n",
+    "print(\"X_lnR0\", np.corrcoef(mc_inputs[\"X\"], mc_inputs[\"LnR0\"])[0][1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Other Function Requirements \n",
+    "Based on the function chosen to run in the monte carlo simulation, various other data will be required. In this case we will need cell temperature and total plane of array irradiance.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "<b>Please use your own API key: The block below makes an NSRDB API to get weather and meta data and then calculate cell temperature and global poa irradiance. This tutorial will work with the DEMO Key provided, but it will take you less than 3 minutes to obtain your own at <a ref=\"https://developer.nrel.gov/signup/\">https://developer.nrel.gov/signup/</a> so register now.) \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather_db = \"PSM3\"\n",
+    "weather_id = (25.783388, -80.189029)\n",
+    "weather_arg = {\n",
+    "    \"api_key\": \"DEMO_KEY\",\n",
+    "    \"email\": \"user@mail.com\",\n",
+    "    \"names\": \"tmy\",\n",
+    "    \"attributes\": [],\n",
+    "    \"map_variables\": True,\n",
+    "}\n",
+    "\n",
+    "weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate the sun position, poa irradiance, and module temperature. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sol_pos = pvdeg.spectral.solar_position(weather_df, meta)\n",
+    "poa_irradiance = pvdeg.spectral.poa_irradiance(weather_df, meta)\n",
+    "temp_mod = pvdeg.temperature.module(\n",
+    "    weather_df=weather_df, meta=meta, poa=poa_irradiance, conf=\"open_rack_glass_polymer\"\n",
+    ")\n",
+    "\n",
+    "# the function being used in the monte carlo simulation takes numpy arrays so we need to convert from pd.DataFrame to np.ndarray with .tonumpy()\n",
+    "poa_global = poa_irradiance[\"poa_global\"].to_numpy()\n",
+    "cell_temperature = temp_mod.to_numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# must already be numpy arrays\n",
+    "function_kwargs = {\"poa_global\": poa_global, \"module_temp\": cell_temperature}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Runs monte carlo simulation for the example `pvdeg.montecarlo.vecArrhenius` function, using the correlated data dataframe created above and the required function arguments. \n",
+    "\n",
+    "We can see the necessary inputs by using the help command:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "help(pvdeg.montecarlo.simulate)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Running the Monte Carlo Simulation\n",
+    "We will pass the target function, `pvdeg.degredation.vecArrhenius()`, its required arguments via the correlated_samples and func_kwargs. Our fixed arguments will be passed in the form of a dictionary while the randomized monte carlo input data will be contained in a DataFrame. \n",
+    "\n",
+    "All required target function arguments should be contained between the column names of the randomized input data and fixed argument dictionary,   \n",
+    "\n",
+    "(You can use any data you want here as long as the DataFrame's column names match the required target function's parameter names NOT included in the kwargs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = pvdeg.montecarlo.simulate(\n",
+    "    func=pvdeg.degradation.vecArrhenius, correlated_samples=mc_inputs, **function_kwargs\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Viewing Our Data\n",
+    "Let's plot the results using a histogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lnDeg = np.log10(results)\n",
+    "percentile_2p5 = np.percentile(lnDeg, 2.5)\n",
+    "percentile_97p5 = np.percentile(lnDeg, 97.5)\n",
+    "bin_edges = np.arange(lnDeg.min(), lnDeg.max() + 0.1, 0.1)\n",
+    "\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.hist(\n",
+    "    lnDeg,\n",
+    "    bins=bin_edges,\n",
+    "    edgecolor=\"blue\",\n",
+    "    histtype=\"step\",\n",
+    "    linewidth=1,\n",
+    "    label=\"Degradation in Miami\",\n",
+    ")\n",
+    "\n",
+    "plt.axvline(percentile_2p5, color=\"green\", label=\"2.5th percentile\", linewidth=2.0)\n",
+    "plt.axvline(percentile_97p5, color=\"purple\", label=\"97.5th percentile\", linewidth=2.0)\n",
+    "plt.axvline(np.mean(lnDeg), color=\"cyan\", label=\"mean\")\n",
+    "plt.axvline(np.median(lnDeg), linestyle=\"--\", label=\"median\")\n",
+    "plt.xlabel(\"log(Degredation) [log(%/h)]\")\n",
+    "plt.ylabel(f\"Counts (out of {n})\")\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pvdeg",
+   "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": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Monte Carlo - Standoff.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Monte Carlo - Standoff.ipynb
new file mode 100644
index 000000000..9be0581ca
--- /dev/null
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Monte Carlo - Standoff.ipynb	
@@ -0,0 +1,272 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Monte Carlo - Standoff Calculation\n",
+    "\n",
+    "See Monte Carlo - Arrhenius Degredation for a more in depth guide. Steps will be shortened for brevity.\n",
+    "This journal applies a Monte Carlo to the Standoff Calculation\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n",
+    "# !pip install pvdeg==0.3.3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pvlib\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pvdeg\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This information helps with debugging and getting support :)\n",
+    "import sys\n",
+    "import platform\n",
+    "\n",
+    "print(\"Working on a \", platform.system(), platform.release())\n",
+    "print(\"Python version \", sys.version)\n",
+    "print(\"Pandas version \", pd.__version__)\n",
+    "print(\"Pvlib version \", pvlib.__version__)\n",
+    "print(\"Pvdeg version \", pvdeg.__version__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simple Standoff Calculation\n",
+    "\n",
+    "This is copied from another tutorial called `4 - Standards.ipynb`, please visit this page for a more in depth explanation of the process for a single standoff calculation.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "<b>Please use your own API key: The block below makes an NSRDB API to get weather and meta data. This tutorial will work with the DEMO Key provided, but it will take you less than 3 minutes to obtain your own at <a ref=\"https://developer.nrel.gov/signup/\">https://developer.nrel.gov/signup/</a> so register now.) \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather_db = \"PSM3\"\n",
+    "weather_id = (40.633365593159226, -73.9945801019899)  # Manhattan, NYC\n",
+    "weather_arg = {\n",
+    "    \"api_key\": \"DEMO_KEY\",\n",
+    "    \"email\": \"user@mail.com\",\n",
+    "    \"names\": \"tmy\",\n",
+    "    \"attributes\": [],\n",
+    "    \"map_variables\": True,\n",
+    "}\n",
+    "\n",
+    "WEATHER, META = pvdeg.weather.get(weather_db, weather_id, **weather_arg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# simple standoff calculation\n",
+    "height1 = pvdeg.standards.standoff(weather_df=WEATHER, meta=META)\n",
+    "\n",
+    "# more arguments standoff calculation\n",
+    "height2 = pvdeg.standards.standoff(\n",
+    "    weather_df=WEATHER,\n",
+    "    meta=META,\n",
+    "    tilt=None,\n",
+    "    azimuth=180,\n",
+    "    sky_model=\"isotropic\",\n",
+    "    temp_model=\"sapm\",\n",
+    "    x_0=6.1,\n",
+    "    wind_factor=0.33,  # default\n",
+    ")\n",
+    "\n",
+    "print(height1)\n",
+    "print(height2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Defining Correlation Coefficients, Mean and Standard Deviation For Monte Carlo Simulation\n",
+    "\n",
+    "We will leave the list of correlations blank because our variables are not correlated. For a correlated use case visit the `Monte Carlo - Arrhenius.ipynb` tutorial.\n",
+    "\n",
+    "Mean and standard deviation must always be populated if being used to create a dataset. However, you can feed your own correlated or uncorrelated data into the simulate function but column names must be consistent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# These numbers may not make sense in the context of the problem but work for demonstraiting the process\n",
+    "stats = {\"X_0\": {\"mean\": 5, \"stdev\": 3}, \"wind_factor\": {\"mean\": 0.33, \"stdev\": 0.5}}\n",
+    "\n",
+    "corr_coeff = []\n",
+    "\n",
+    "samples = pvdeg.montecarlo.generateCorrelatedSamples(corr_coeff, stats, 500)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(samples)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Standoff Monte Carlo Inputs\n",
+    "\n",
+    "When using the pvdeg.montecarlo.simulate() function on a target function all of the target function's required arguments must still be given. Our non-changing arguments will be stored in a dictionary. The randomized monte carlo input data will also be passed to the target function via the simulate function. All required target function arguments should be contained between the column names of the randomized input data and fixed argument dictionary, "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# defining arguments to pass to the target function, standoff() in this case\n",
+    "function_kwargs = {\n",
+    "    \"weather_df\": WEATHER,\n",
+    "    \"meta\": META,\n",
+    "    \"azimuth\": 180,\n",
+    "    \"tilt\": 0,\n",
+    "    \"temp_model\": \"sapm\",\n",
+    "    \"sky_model\": \"isotropic\",\n",
+    "    \"conf_0\": \"insulated_back_glass_polymer\",\n",
+    "    \"conf_inf\": \"open_rack_glass_polymer\",\n",
+    "    \"T98\": 70,\n",
+    "    \"irradiance_kwarg\": {},\n",
+    "    \"conf_0_kwarg\": {},\n",
+    "    \"conf_inf_kwarg\": {},\n",
+    "    \"model_kwarg\": {},\n",
+    "}\n",
+    "\n",
+    "# notice how we left off parts we want to use in the monte carlo simulation because they are already contained in the dataframe\n",
+    "\n",
+    "results = pvdeg.montecarlo.simulate(\n",
+    "    func=pvdeg.standards.standoff,\n",
+    "    correlated_samples=samples,\n",
+    "    **function_kwargs,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Dealing With Series \n",
+    "Notice how our results are contained in a pandas series instead of a dataframe.\n",
+    "\n",
+    "This means we have to do an extra step to view our results. Run the block below to confirm that our results are indeed contained in a series. And convert them into a simpler dataframe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(type(results))\n",
+    "\n",
+    "# Convert from pandas Series to pandas DataFrame\n",
+    "results_df = pd.concat(results.tolist()).reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(results_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Viewing Our Data\n",
+    "Let's plot the results using a histogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bin_edges = np.arange(results_df[\"x\"].min(), results_df[\"x\"].max() + 0.1, 0.05)\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.hist(\n",
+    "    results_df[\"x\"],\n",
+    "    bins=bin_edges,\n",
+    "    edgecolor=\"blue\",\n",
+    "    histtype=\"step\",\n",
+    "    linewidth=1,\n",
+    "    label=\"Standoff Distance\",\n",
+    ")\n",
+    "plt.ylabel(\"Counts (out of n trials)\")\n",
+    "plt.xlabel(\"standoff distance [m]\")\n",
+    "plt.axvline(np.mean(results_df[\"x\"]), color=\"red\", label=\"mean\")\n",
+    "plt.axvline(np.median(results_df[\"x\"]), linestyle=\"--\", label=\"median\")\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Pysam - Single Location.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Pysam - Single Location.ipynb
new file mode 100644
index 000000000..2248d03bb
--- /dev/null
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Pysam - Single Location.ipynb	
@@ -0,0 +1,108 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pvdeg\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather, meta = pvdeg.weather.get(\n",
+    "    database=\"PVGIS\", id=(40.633365593159226, -73.9945801019899)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "out_dict = pvdeg.pysam.pysam(\n",
+    "    weather_df=weather,\n",
+    "    meta=meta,\n",
+    "    pv_model=\"pysamv1\",\n",
+    "    pv_model_default=\"FlatPlatePVCommercial\",\n",
+    ")\n",
+    "out_dict.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for key, item in out_dict.items():\n",
+    "    if isinstance(item, tuple):\n",
+    "        print(key)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "subarray1_poa_ground_front_cs  \n",
+    "subarray1_ground_rear_spatial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = out_dict[\"subarray1_ground_rear_spatial\"][\n",
+    "    0\n",
+    "]  # these are the distances where the calculations are done\n",
+    "\n",
+    "data = out_dict[\"subarray1_ground_rear_spatial\"][1:]\n",
+    "row = data[15]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "row_arr = np.array(row)\n",
+    "plot = sns.heatmap([row_arr], square=True, cmap=\"magma\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "nrel",
+   "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": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Tools - Degradation.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Tools - Degradation.ipynb
new file mode 100644
index 000000000..5b114decf
--- /dev/null
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Tools - Degradation.ipynb	
@@ -0,0 +1,1047 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Degradation and Acceleration Factors\n",
+    "This tool will provide a simple method for estimating degradation and for calculating acceleration factors. It interfaces with the degradation database to simplify acquisition of the degradation parameters.\n",
+    "\n",
+    "**Requirements**:\n",
+    "- compatible weather file (e.g., PSM3, TMY3, EPW...)\n",
+    "- Accelerated testing chamber parameters\n",
+    "    - chamber irradiance [W/m^2]\n",
+    "    - chamber temperature [C]\n",
+    "    - chamber humidity [%]\n",
+    "    - & etc.\n",
+    "- Activation energies for test material [kJ/mol]\n",
+    "- Other degradation parameters\n",
+    "\n",
+    "**Objectives**:\n",
+    "1. Read in the weather data\n",
+    "2. Gather basic degradation modeling data for a material of interest\n",
+    "3. Calculate absolute degradation rate\n",
+    "4. Run Monte Carlo simulation at a single site\n",
+    "5. Generate chamber or field data for environmental comparison\n",
+    "6. Calculate degradation acceleration factor of field location to chamber (or another location)\n",
+    "7. Run Monte Carlo simulation of the acceleration factor for a single site\n",
+    "8. Select a region of interest and specific data points\n",
+    "9. Produce a map of acceleration factors for a region"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n",
+    "# !pip install pvdeg==0.4.2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.13.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import pvdeg\n",
+    "import pandas as pd\n",
+    "from pvdeg import DATA_DIR\n",
+    "import json\n",
+    "from IPython.display import display, Math\n",
+    "\n",
+    "import pvlib\n",
+    "print(pvlib.__version__)\n",
+    "from pvlib import iotools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Working on a  Windows 11\n",
+      "Python version  3.13.5 | packaged by Anaconda, Inc. | (main, Jun 12 2025, 16:37:03) [MSC v.1929 64 bit (AMD64)]\n",
+      "Pandas version  2.3.1\n",
+      "pvdeg version  0.5.1.dev206+g9401807.d20250808\n",
+      "C:\\Users\\mkempe\\Documents\\GitHub\\new\\PVDegradationTools\\pvdeg\\data\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This information helps with debugging and getting support :)\n",
+    "import sys, platform\n",
+    "print(\"Working on a \", platform.system(), platform.release())\n",
+    "print(\"Python version \", sys.version)\n",
+    "print(\"Pandas version \", pd.__version__)\n",
+    "print(\"pvdeg version \", pvdeg.__version__)\n",
+    "print(DATA_DIR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Read In the Weather Data\n",
+    "\n",
+    "The function has these minimum requirements when using a weather data file:\n",
+    "- Weather data containing (at least) DNI, DHI, GHI, Temperature, RH, and Wind-Speed data at module level.\n",
+    "- Site meta-data containing (at least) latitude, longitude, and time zone\n",
+    "\n",
+    "Alternatively one may can get meterological data from the NSRDB or PVGIS with just the longitude and latitude. This function for the NSRDB (via NSRDB 'PSM3') works primarily for most of North America and South America. PVGIS works for most of the rest of the world (via SARAH 'PVGIS'). See the tutorial \"Weather Database Access.ipynb\" tutorial on PVdeg or Jensen et al. https://doi.org/10.1016/j.solener.2023.112092 for satellite coverage information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\mkempe\\Documents\\GitHub\\new\\PVDegradationTools\\pvdeg\\data\\psm3_demo.csv\n",
+      "{'Source': 'NSRDB', 'Location ID': 145809.0, 'City': '-', 'State': '-', 'Country': '-', 'Clearsky DHI Units': 'w/m2', 'Clearsky DNI Units': 'w/m2', 'Clearsky GHI Units': 'w/m2', 'Dew Point Units': 'c', 'DHI Units': 'w/m2', 'DNI Units': 'w/m2', 'GHI Units': 'w/m2', 'Solar Zenith Angle Units': 'Degree', 'Temperature Units': 'c', 'Pressure Units': 'mbar', 'Relative Humidity Units': '%', 'Precipitable Water Units': 'cm', 'Wind Direction Units': 'Degrees', 'Wind Speed Units': 'm/s', 'Cloud Type -15': 'N/A', 'Cloud Type 0': 'Clear', 'Cloud Type 1': 'Probably Clear', 'Cloud Type 2': 'Fog', 'Cloud Type 3': 'Water', 'Cloud Type 4': 'Super-Cooled Water', 'Cloud Type 5': 'Mixed', 'Cloud Type 6': 'Opaque Ice', 'Cloud Type 7': 'Cirrus', 'Cloud Type 8': 'Overlapping', 'Cloud Type 9': 'Overshooting', 'Cloud Type 10': 'Unknown', 'Cloud Type 11': 'Dust', 'Cloud Type 12': 'Smoke', 'Fill Flag 0': 'N/A', 'Fill Flag 1': 'Missing Image', 'Fill Flag 2': 'Low Irradiance', 'Fill Flag 3': 'Exceeds Clearsky', 'Fill Flag 4': 'Missing CLoud Properties', 'Fill Flag 5': 'Rayleigh Violation', 'Surface Albedo Units': 'N/A', 'Version': '3.0.6', 'latitude': 39.73, 'longitude': -105.18, 'tz': -7.0, 'altitude': 1820.0}\n",
+      "{'Source': 'NSRDB', 'Location ID': 145809.0, 'City': 'West Pleasant View', 'State': 'Colorado', 'Country': 'United States', 'Clearsky DHI Units': 'w/m2', 'Clearsky DNI Units': 'w/m2', 'Clearsky GHI Units': 'w/m2', 'Dew Point Units': 'c', 'DHI Units': 'w/m2', 'DNI Units': 'w/m2', 'GHI Units': 'w/m2', 'Solar Zenith Angle Units': 'Degree', 'Temperature Units': 'c', 'Pressure Units': 'mbar', 'Relative Humidity Units': '%', 'Precipitable Water Units': 'cm', 'Wind Direction Units': 'Degrees', 'Wind Speed Units': 'm/s', 'Cloud Type -15': 'N/A', 'Cloud Type 0': 'Clear', 'Cloud Type 1': 'Probably Clear', 'Cloud Type 2': 'Fog', 'Cloud Type 3': 'Water', 'Cloud Type 4': 'Super-Cooled Water', 'Cloud Type 5': 'Mixed', 'Cloud Type 6': 'Opaque Ice', 'Cloud Type 7': 'Cirrus', 'Cloud Type 8': 'Overlapping', 'Cloud Type 9': 'Overshooting', 'Cloud Type 10': 'Unknown', 'Cloud Type 11': 'Dust', 'Cloud Type 12': 'Smoke', 'Fill Flag 0': 'N/A', 'Fill Flag 1': 'Missing Image', 'Fill Flag 2': 'Low Irradiance', 'Fill Flag 3': 'Exceeds Clearsky', 'Fill Flag 4': 'Missing CLoud Properties', 'Fill Flag 5': 'Rayleigh Violation', 'Surface Albedo Units': 'N/A', 'Version': '3.0.6', 'latitude': 39.73, 'longitude': -105.18, 'tz': -7.0, 'altitude': 1820.0, 'ISO3166-2-lvl4': 'US-CO', 'Street': 'West 8th Avenue', 'County': 'Jefferson County', 'Zipcode': '80419', 'Country Code': 'US'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get data from a supplied data file (Do not use the next box of code if using your own file)\n",
+    "weather_file = os.path.join(DATA_DIR, 'psm3_demo.csv')\n",
+    "weather_df, meta = pvdeg.weather.read(weather_file,'csv',find_meta=False)\n",
+    "print(weather_file)\n",
+    "print(meta)\n",
+    "\n",
+    "meta = pvdeg.weather.map_meta(meta)\n",
+    "meta = pvdeg.weather.find_metadata(meta)\n",
+    "print(meta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Source': 'NSRDB', 'Location ID': '77855', 'City': '-', 'State': '-', 'Country': '-', 'Dew Point Units': 'c', 'DHI Units': 'w/m2', 'DNI Units': 'w/m2', 'GHI Units': 'w/m2', 'Temperature Units': 'c', 'Pressure Units': 'mbar', 'Wind Direction Units': 'Degrees', 'Wind Speed Units': 'm/s', 'Surface Albedo Units': 'N/A', 'Version': '3.2.0', 'latitude': 33.41, 'longitude': -111.82, 'altitude': 381, 'tz': -7, 'wind_height': 2}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This routine will get a meteorological dataset from anywhere in the world where it is available \n",
+    "#weather_id = (24.7136, 46.6753) #Riyadh, Saudi Arabia\n",
+    "#weather_id = (35.6754, 139.65) #Tokyo, Japan\n",
+    "#weather_id = (-43.52646, 172.62165) #Christchurch, New Zealand\n",
+    "#weather_id = (64.84031, -147.73836) #Fairbanks, Alaska\n",
+    "#weather_id = (65.14037, -21.91633) #Reykjavik, Iceland\n",
+    "weather_id = (33.4152, -111.8315) #Mesa, Arizona\n",
+    "#weather_id = (0,0) # Somewhere else you are interested in.\n",
+    "weather_df, meta = pvdeg.weather.get_anywhere(id=weather_id)\n",
+    "print(meta)\n",
+    "#display(weather_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### POA Irradiance\n",
+    "Next we need to calculate the stress parameters including temperature and humidity. We start with POA irradiance.\n",
+    "Irradiance_kwarg governs the array orientation for doing the POA calculations. \n",
+    "It is defaulted to a north-south single axis tracking. A fixed tilt set of parameters is included but is blocked out. \n",
+    "Look in spectral.py and/or PVLib here for 1-axis kwargs, \n",
+    "https://pvlib-python.readthedocs.io/en/v0.7.2/generated/pvlib.tracking.singleaxis.html#pvlib.tracking.singleaxis  \n",
+    "and for fixed tilt,  \n",
+    "https://pvlib-python.readthedocs.io/en/v0.7.2/generated/pvlib.irradiance.gti_dirint.html?highlight=poa .  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The array axis_azimuth was not provided, therefore an azimuth of  180.0 was used.\n",
+      "The array axis_tilt was not provided, therefore an axis tilt of 0° was used.\n"
+     ]
+    }
+   ],
+   "source": [
+    "#irradiance_kwarg ={\n",
+    "    #\"tilt\": None,\n",
+    "    #\"azimuth\": None,\n",
+    "    #\"module_mount\": 'fixed'}\n",
+    "irradiance_kwarg ={\n",
+    "    \"axis_tilt\": None,\n",
+    "    \"axis_azimuth\": None,\n",
+    "    \"module_mount\": 'single_axis'}\n",
+    "poa_df = pvdeg.spectral.poa_irradiance(weather_df=weather_df, meta=meta, **irradiance_kwarg)\n",
+    "#display(poa_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Get Spectrally Resolved Irradiance Data\n",
+    "This first set of commands will calculate spectrally resolved irradiance data. This may or may not be needed for a given degradation model and can be skipped here. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "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>Unnamed: 0</th>\n",
+       "      <th>RH</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Spectra: [ 300, 325, 350, 375, 400 ]</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2021-01-01 00:00:00-05:00</td>\n",
+       "      <td>95</td>\n",
+       "      <td>-1.1</td>\n",
+       "      <td>[nan, nan, nan, nan, nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2021-01-01 01:00:00-05:00</td>\n",
+       "      <td>72</td>\n",
+       "      <td>-1.1</td>\n",
+       "      <td>[nan, nan, nan, nan, nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2021-01-01 02:00:00-05:00</td>\n",
+       "      <td>72</td>\n",
+       "      <td>-1.3</td>\n",
+       "      <td>[nan, nan, nan, nan, nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2021-01-01 03:00:00-05:00</td>\n",
+       "      <td>72</td>\n",
+       "      <td>-1.5</td>\n",
+       "      <td>[nan, nan, nan, nan, nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2021-01-01 04:00:00-05:00</td>\n",
+       "      <td>72</td>\n",
+       "      <td>-1.7</td>\n",
+       "      <td>[nan, nan, nan, nan, nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8755</th>\n",
+       "      <td>2021-12-31 19:00:00-05:00</td>\n",
+       "      <td>92</td>\n",
+       "      <td>-1.1</td>\n",
+       "      <td>[nan, nan, nan, nan, nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8756</th>\n",
+       "      <td>2021-12-31 20:00:00-05:00</td>\n",
+       "      <td>92</td>\n",
+       "      <td>-0.7</td>\n",
+       "      <td>[nan, nan, nan, nan, nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8757</th>\n",
+       "      <td>2021-12-31 21:00:00-05:00</td>\n",
+       "      <td>92</td>\n",
+       "      <td>-0.4</td>\n",
+       "      <td>[nan, nan, nan, nan, nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8758</th>\n",
+       "      <td>2021-12-31 22:00:00-05:00</td>\n",
+       "      <td>92</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>[nan, nan, nan, nan, nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8759</th>\n",
+       "      <td>2021-12-31 23:00:00-05:00</td>\n",
+       "      <td>93</td>\n",
+       "      <td>-0.6</td>\n",
+       "      <td>[nan, nan, nan, nan, nan]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8760 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     Unnamed: 0  RH  Temperature  \\\n",
+       "0     2021-01-01 00:00:00-05:00  95         -1.1   \n",
+       "1     2021-01-01 01:00:00-05:00  72         -1.1   \n",
+       "2     2021-01-01 02:00:00-05:00  72         -1.3   \n",
+       "3     2021-01-01 03:00:00-05:00  72         -1.5   \n",
+       "4     2021-01-01 04:00:00-05:00  72         -1.7   \n",
+       "...                         ...  ..          ...   \n",
+       "8755  2021-12-31 19:00:00-05:00  92         -1.1   \n",
+       "8756  2021-12-31 20:00:00-05:00  92         -0.7   \n",
+       "8757  2021-12-31 21:00:00-05:00  92         -0.4   \n",
+       "8758  2021-12-31 22:00:00-05:00  92          0.0   \n",
+       "8759  2021-12-31 23:00:00-05:00  93         -0.6   \n",
+       "\n",
+       "     Spectra: [ 300, 325, 350, 375, 400 ]  \n",
+       "0               [nan, nan, nan, nan, nan]  \n",
+       "1               [nan, nan, nan, nan, nan]  \n",
+       "2               [nan, nan, nan, nan, nan]  \n",
+       "3               [nan, nan, nan, nan, nan]  \n",
+       "4               [nan, nan, nan, nan, nan]  \n",
+       "...                                   ...  \n",
+       "8755            [nan, nan, nan, nan, nan]  \n",
+       "8756            [nan, nan, nan, nan, nan]  \n",
+       "8757            [nan, nan, nan, nan, nan]  \n",
+       "8758            [nan, nan, nan, nan, nan]  \n",
+       "8759            [nan, nan, nan, nan, nan]  \n",
+       "\n",
+       "[8760 rows x 4 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\mkempe\\Documents\\GitHub\\new\\PVDegradationTools\\tests\\data\\spectra_pytest.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "# this whole block needs to be replaced with call to calculate spectrally resolved irradiance.\n",
+    "\n",
+    "from pvdeg import TEST_DATA_DIR\n",
+    "INPUT_SPECTRA = os.path.join(TEST_DATA_DIR, r\"spectra_pytest.csv\")\n",
+    "data = pd.read_csv(INPUT_SPECTRA)\n",
+    "display(data)\n",
+    "print(INPUT_SPECTRA)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Cell or Module Surface Temperature\n",
+    "The following will calculate the cell and module surface temperature using the King model as a default. Other models can be used as described at,  \n",
+    "https://pvlib-python.readthedocs.io/en/stable/reference/pv_modeling/temperature.html. The difference is less than one °C for ground mounted systems\n",
+    "but can be as high as 3 °C for a high temperature building integrated system.\n",
+    "\n",
+    "Here the temperatures are added to the dataframe and the module temperature is selected as the default 'temperatue' for the degradation calculations. If it is a cell degradation that is being investigated, temp_cell should be used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp_cell = pvdeg.temperature.cell(weather_df=weather_df, meta=meta, poa=poa_df)\n",
+    "temp_module = pvdeg.temperature.module(weather_df=weather_df, meta=meta, poa=poa_df)\n",
+    "\n",
+    "weather_df['temp_cell'] = temp_cell\n",
+    "weather_df['temp_module'] = temp_module\n",
+    "\n",
+    "weather_df['temperature'] = weather_df['temp_module']\n",
+    "#weather_df['temperature'] = weather_df['temp_cell']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Humidity\n",
+    "Depending on the component for which the calculation is being run on, the desired humidity may be the atmospheric humidity, the module surface humidity, the humidity in front of a cell with a permeable backsheet, the humidity in the backsheet, the humidity in the back encapsulant or another custom humidity location such as a diffusion limited location. The folowing are options for doing all of these calculations. Here all the different humidities are put in the weather_df dataframe, but to select one to be specifically used it should be named 'RH' for most degradation functions (check the documentation of a specific degradation calculation if in doubt). Here the surface humidity is selected as a default."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculate relative humidity at different locations in the module\n",
+    "RH_surface = pvdeg.humidity.surface_relative(weather_df['relative_humidity'], weather_df['temp_air'],temp_module)\n",
+    "RH_front_encapsulant = pvdeg.humidity.front_encapsulant(weather_df['relative_humidity'], weather_df['temp_air'],temp_cell,encapsulant='W001')\n",
+    "RH_back_encapsulant = pvdeg.humidity.back_encapsulant_water_concentration(\n",
+    "    temp_module = temp_module, \n",
+    "    rh_surface = RH_surface,\n",
+    "    backsheet='W017',\n",
+    "    encapsulant='W001')\n",
+    "Ce_back_encapsulant = pvdeg.humidity.back_encapsulant_water_concentration(\n",
+    "    temp_module = temp_module, \n",
+    "    rh_surface = RH_surface,\n",
+    "    backsheet='W017',\n",
+    "    encapsulant='W001', \n",
+    "    output='ce')\n",
+    "RH_backsheet = (RH_surface + RH_back_encapsulant) / 2\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Append the calculated values into the weather DataFrame.\n",
+    "Note: putting the values into the weather_df DataFrame is not strictly necessary, but may be convenient for later use in the degradation calculations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather_df['RH_surface'] = RH_surface\n",
+    "weather_df['RH_front_encapsulant'] = RH_front_encapsulant\n",
+    "weather_df['Ce_back_encapsulant'] = Ce_back_encapsulant   \n",
+    "weather_df['RH_back_encapsulant'] = RH_back_encapsulant\n",
+    "weather_df['RH_backsheet'] = RH_backsheet\n",
+    "\n",
+    "weather_df['poa_global'] = poa_df['poa_global']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each of the necessary arrays of data can be individually sent to a function for calculation in the function call, or they can be combined into a single dataframe. The degradation functions are set up to first check for a specific data set in the function call but if not found it looks for specific data or a suitable substitute in the weather dataframe.\n",
+    "\n",
+    "You can select one of the RH values to be used as the relative humidity in the degradation model calculations by assigning it to to column \"RH\" in the dataframe.\n",
+    "Alternatively, the \"RH\" data can be sent to the degradation function explicitly in the function call."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather_df['RH'] = RH_surface\n",
+    "#weather_df['RH'] =RH_front_encapsulant\n",
+    "#weather_df['RH'] = Ce_back_encapsulant      \n",
+    "#weather_df['RH'] = RH_back_encapsulant\n",
+    "#weather_df['RH'] = RH_backsheet\n",
+    "\n",
+    "#display(weather_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Gather Basic Degradation Modeling Data for a Material of Interest\n",
+    "\n",
+    "First we need to gather in the parameters for the degradation process of interest. This includes things such as the activiation energy and parameters defining the sensitivity to moisture, UV light, voltage, and other stressors.\n",
+    "For this tutorial we will need solar position, POA, PV cell and module temperature. Let's gernate those individually with their respective functions.\n",
+    "The blocked out text will produce a list of key fields from the database for each entry."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h2 style=\"color: white;\">JSON Output at fp: C:\\Users\\mkempe\\Documents\\GitHub\\new\\PVDegradationTools\\pvdeg\\data\\DegradationDatabase.json</h2><div><div><strong style=\"color: white;\">D001:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Michael Kempe\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"arrhenius\",<br>      <span style=\"color: plum;\">\"Equation\":</span> \"R_D=R_0\\\\cdot e^{ \\\\left( \\\\frac{-E_a}{R\\\\cdot T_K } \\\\right) }\",<br>      <span style=\"color: plum;\">\"R_D\":</span> {<br>        \"units\": \"%/h\"<br>      },<br>      <span style=\"color: plum;\">\"R_0\":</span> {<br>        \"units\": \"%/h\"<br>      },<br>      <span style=\"color: plum;\">\"E_a\":</span> {<br>        \"units\": \"kJ/mol\"<br>      }<br>    }</pre></div></div><div><strong style=\"color: white;\">D002:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Weston Wall\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"Error Analyses and Associated Risk for Accelerated Weathering Results/2009 Presentation\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"Richard M. Fischer, Warren D. Ketola\",<br>      <span style=\"color: plum;\">\"Reference\":</span> \"R. M. Fischer and W. D. Ketola, 'Error Analyses and Associated Risk for Accelerated Weathering Results,' Third International Service Life Symposium, Sedona, AZ February 2004, 2004.\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"Gloss, color shift, cracking, gloss loss, fluorescence loss, retroreflectance loss, adhesive transfer (clean removal), shrinkage, yellowing\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"Paints and Coatings\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"Vant_Hoff_TOW_Schwarzchild\",<br>      <span style=\"color: plum;\">\"Equation\":</span> \"R_D=R_0\\\\cdot G^P \\\\left( \\\\frac{1}{b+m\\\\cdot TOW} \\\\right) \\\\cdot  T_f^{ \\\\frac{T}{10}}\",<br>      <span style=\"color: plum;\">\"R_D\":</span> {},<br>      <span style=\"color: plum;\">\"R_0\":</span> {<br>        \"units\": \"%/h\"<br>      },<br>      <span style=\"color: plum;\">\"x\":</span> {<br>        \"value\": 0.64,<br>        \"stdev\": 0.2<br>      },<br>      <span style=\"color: plum;\">\"b\":</span> {<br>        \"value\": 1.07,<br>        \"stdev\": 0.11<br>      },<br>      <span style=\"color: plum;\">\"m\":</span> {<br>        \"value\": -2.8e-05,<br>        \"stdev\": 4.88e-05<br>      },<br>      <span style=\"color: plum;\">\"T_f\":</span> {<br>        \"value\": 1.41,<br>        \"stdev\": 0.23<br>      }<br>    }</pre></div></div><div><strong style=\"color: white;\">D003:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Weston Wall\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"10.1002/.pip1172\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"Life Prediction for CIGS Solar Modules\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"D.J. Coyle, H.A. Blaydes, R.S. Northey, J.E. Pickett, K.R. Nagarkar, R.A. Zhao, and J.O. Gardner\",<br>      <span style=\"color: plum;\">\"Reference\":</span> \"Coyle, D. J., et al. (2011). 'Life prediction for CIGS solar modules part 2: degradation kinetics, accelerated testing, and encapsulant effects.' Progress in Photovoltaics: Research and Applications.\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"Temperature, humidity, CIGS, Moisture ingress, thin film\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"CIGS\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"CIGS_Efficiency, ITO_ECA0\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"Arrhenius_BET4\",<br>      <span style=\"color: plum;\">\"Equation\":</span> \"R_D=R_0\\\\cdot e^{ \\\\left( \\\\frac{-E_a}{R\\\\cdot T_K } \\\\right) } \\\\cdot \\\\left( \\\\frac{RH}{1-RH+E} \\\\right)\",<br>      <span style=\"color: plum;\">\"R_D\":</span> {<br>        \"units\": \"%/h\"<br>      },<br>      <span style=\"color: plum;\">\"R_0\":</span> {<br>        \"value\": 845000000,<br>        \"units\": \"%/h\"<br>      },<br>      <span style=\"color: plum;\">\"E_a\":</span> {<br>        \"value\": 33.472,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"E\":</span> {<br>        \"value\": 0.04<br>      }<br>    }</pre></div></div><div><strong style=\"color: white;\">D004:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Weston Wall\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"10.1002/.pip1173\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"Life Prediction for CIGS Solar Modules\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"D.J. Coyle, H.A. Blaydes, R.S. Northey, J.E. Pickett, K.R. Nagarkar, R.A. Zhao, and J.O. Gardner\",<br>      <span style=\"color: plum;\">\"Reference\":</span> \"Coyle, D. J., et al. (2011). 'Life prediction for CIGS solar modules part 2: degradation kinetics, accelerated testing, and encapsulant effects.' Progress in Photovoltaics: Research and Applications.\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"Temperature, humidity, CIGS, Moisture ingress, thin film\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"CIGS\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"CIGS_Efficiency, ITO_ECA1\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"Arrhenius_BET5\",<br>      <span style=\"color: plum;\">\"Equation\":</span> \"R_D=R_0\\\\cdot e^{ \\\\left( \\\\frac{-E_a}{R\\\\cdot T_K } \\\\right) } \\\\cdot \\\\left( \\\\frac{RH}{1-RH+E} \\\\right)\",<br>      <span style=\"color: plum;\">\"R_D\":</span> {<br>        \"units\": \"%/h\"<br>      },<br>      <span style=\"color: plum;\">\"R_0\":</span> {<br>        \"value\": 73000000000000,<br>        \"units\": \"%/h\"<br>      },<br>      <span style=\"color: plum;\">\"E_a\":</span> {<br>        \"value\": 67.3624,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"E\":</span> {<br>        \"value\": 0.04<br>      }<br>    }</pre></div></div><div><strong style=\"color: white;\">D005:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Weston Wall\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"10.1002/.pip1174\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"Life Prediction for CIGS Solar Modules\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"D.J. Coyle, H.A. Blaydes, R.S. Northey, J.E. Pickett, K.R. Nagarkar, R.A. Zhao, and J.O. Gardner\",<br>      <span style=\"color: plum;\">\"Reference\":</span> \"Coyle, D. J., et al. (2011). 'Life prediction for CIGS solar modules part 2: degradation kinetics, accelerated testing, and encapsulant effects.' Progress in Photovoltaics: Research and Applications.\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"Temperature, humidity, CIGS, Moisture ingress, thin film\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"CIGS\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"CIGS_Efficiency, AZO\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"Arrhenius_BET6\",<br>      <span style=\"color: plum;\">\"Equation\":</span> \"R_D=R_0\\\\cdot e^{ \\\\left( \\\\frac{-E_a}{R\\\\cdot T_K } \\\\right) } \\\\cdot \\\\left( \\\\frac{RH}{1-RH+E} \\\\right)\",<br>      <span style=\"color: plum;\">\"R_D\":</span> {<br>        \"units\": \"%/h\"<br>      },<br>      <span style=\"color: plum;\">\"R_0\":</span> {<br>        \"value\": 1.1e+17,<br>        \"units\": \"%/h\"<br>      },<br>      <span style=\"color: plum;\">\"E_a\":</span> {<br>        \"value\": 78.6592,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"E\":</span> {<br>        \"value\": 0.04<br>      }<br>    }</pre></div></div><div><strong style=\"color: white;\">D006:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Weston Wall\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"10.1109/PVSC45281.2020.9300357\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"Highly Accelerated UV Stress Testing for Transparent Flexible Frontsheets\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"Michael D Kempe, Peter Hacke, Joshua Morse, Michael Owen-Bellini, Derek Holsapple, Trevor Lockman, Samantha Hoang, David Okawa, Tamir Lance, Hoi Hong Ng\",<br>      <span style=\"color: plum;\">\"Reference\":</span> \"Kempe, M. D., et al. (2020). Highly Accelerated UV Stress Testing for Transparent Flexible Frontsheets. 2020 47th IEEE Photovoltaic Specialists Conference (PVSC).\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"humidity, irradiance, frontsheet, UV cut-on\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"Flexible Frontsheet, Frontsheet Coatings\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"UV Cut On\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"arrhenius\",<br>      <span style=\"color: plum;\">\"Equation\":</span> \"R_D=R_0\\\\cdot RH^n\\\\cdot G_{340}^P\\\\cdot e^{ \\\\left( \\\\frac{-E_a}{R\\\\cdot T_K } \\\\right) }\",<br>      <span style=\"color: plum;\">\"R_D\":</span> {<br>        \"units\": \"%/h\"<br>      },<br>      <span style=\"color: plum;\">\"R_0\":</span> {<br>        \"units\": \"%/h\"<br>      },<br>      <span style=\"color: plum;\">\"E_a\":</span> {<br>        \"value\": 35.3,<br>        \"stdev\": 8.9,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"n\":</span> {},<br>      <span style=\"color: plum;\">\"p\":</span> {<br>        \"value\": 0.37,<br>        \"stdev\": 0.26<br>      }<br>    }</pre></div></div><div><strong style=\"color: white;\">D007:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Weston Wall\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"10.1109/PVSC45281.2020.9300357\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"Highly Accelerated UV Stress Testing for Transparent Flexible Frontsheets\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"Michael D Kempe, Peter Hacke, Joshua Morse, Michael Owen-Bellini, Derek Holsapple, Trevor Lockman, Samantha Hoang, David Okawa, Tamir Lance, Hoi Hong Ng\",<br>      <span style=\"color: plum;\">\"Reference\":</span> \"Kempe, M. D., et al. (2020). Highly Accelerated UV Stress Testing for Transparent Flexible Frontsheets. 2020 47th IEEE Photovoltaic Specialists Conference (PVSC).\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"humidity, irradiance, frontsheet, ultraviolet, transmittance\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"Flexible Frontsheet, Frontsheet Coatings\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"UV Transmittance 310nm-350nm\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"arrhenius\",<br>      <span style=\"color: plum;\">\"Equation\":</span> \"R_D=R_0\\\\cdot RH^n\\\\cdot G_{340}^P\\\\cdot e^{ \\\\left( \\\\frac{-E_a}{R\\\\cdot T_K } \\\\right) }\",<br>      <span style=\"color: plum;\">\"R_D\":</span> {<br>        \"units\": \"%/h\"<br>      },<br>      <span style=\"color: plum;\">\"R_0\":</span> {<br>        \"units\": \"%/h\"<br>      },<br>      <span style=\"color: plum;\">\"E_a\":</span> {<br>        \"value\": 53.2,<br>        \"stdev\": 16.6,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"n\":</span> {},<br>      <span style=\"color: plum;\">\"p\":</span> {<br>        \"value\": 0.45,<br>        \"stdev\": 0.24<br>      }<br>    }</pre></div></div><div><strong style=\"color: white;\">D008:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Weston Wall\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"10.1109/PVSC45281.2020.9300357\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"Highly Accelerated UV Stress Testing for Transparent Flexible Frontsheets\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"Michael D Kempe, Peter Hacke, Joshua Morse, Michael Owen-Bellini, Derek Holsapple, Trevor Lockman, Samantha Hoang, David Okawa, Tamir Lance, Hoi Hong Ng\",<br>      <span style=\"color: plum;\">\"Reference\":</span> \"Kempe, M. D., et al. (2020). Highly Accelerated UV Stress Testing for Transparent Flexible Frontsheets. 2020 47th IEEE Photovoltaic Specialists Conference (PVSC).\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"humidity, irradiance, yellowness, yellowness index\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"Flexible Frontsheet, Frontsheet Coatings\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"yellowness_index\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"arrhenius\",<br>      <span style=\"color: plum;\">\"Equation\":</span> \"R_D=R_0\\\\cdot RH^n\\\\cdot G_{340}^P\\\\cdot e^{ \\\\left( \\\\frac{-E_a}{R\\\\cdot T_K } \\\\right) }\",<br>      <span style=\"color: plum;\">\"R_D\":</span> {<br>        \"units\": \"%/h\"<br>      },<br>      <span style=\"color: plum;\">\"R_0\":</span> {<br>        \"units\": \"%/h\"<br>      },<br>      <span style=\"color: plum;\">\"E_a\":</span> {<br>        \"value\": 40.4,<br>        \"stdev\": 22.5,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"n\":</span> {},<br>      <span style=\"color: plum;\">\"p\":</span> {<br>        \"value\": 0.5,<br>        \"stdev\": 0.25<br>      }<br>    }</pre></div></div><div><strong style=\"color: white;\">D009:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Weston Wall\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"10.1109/PVSC45281.2020.9300357\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"Highly Accelerated UV Stress Testing for Transparent Flexible Frontsheets\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"Michael D Kempe, Peter Hacke, Joshua Morse, Michael Owen-Bellini, Derek Holsapple, Trevor Lockman, Samantha Hoang, David Okawa, Tamir Lance, Hoi Hong Ng\",<br>      <span style=\"color: plum;\">\"Reference\":</span> \"Kempe, M. D., et al. (2020). Highly Accelerated UV Stress Testing for Transparent Flexible Frontsheets. 2020 47th IEEE Photovoltaic Specialists Conference (PVSC).\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"humidity, irradiance, frontsheet, transmittance\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"Flexible Frontsheet, Frontsheet Coatings\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"SPQEWT\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"arrhenius\",<br>      <span style=\"color: plum;\">\"Equation\":</span> \"R_D=R_0\\\\cdot RH^n\\\\cdot G_{340}^P\\\\cdot e^{ \\\\left( \\\\frac{-E_a}{R\\\\cdot T_K } \\\\right) }\",<br>      <span style=\"color: plum;\">\"R_D\":</span> {<br>        \"units\": \"%/h\"<br>      },<br>      <span style=\"color: plum;\">\"R_0\":</span> {<br>        \"units\": \"%/h\"<br>      },<br>      <span style=\"color: plum;\">\"E_a\":</span> {<br>        \"value\": 30.7,<br>        \"stdev\": 23.5,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"n\":</span> {},<br>      <span style=\"color: plum;\">\"p\":</span> {<br>        \"value\": 0.53,<br>        \"stdev\": 0.22<br>      }<br>    }</pre></div></div><div><strong style=\"color: white;\">D010:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Jada Swailes\",<br>      <span style=\"color: plum;\">\"DateEntered\":</span> \"3/21/2023\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"10.1016/j.polymdegradstab.2013.04.001\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"Pickett  Coyle Hydrolysis Kinetics of condensation polymers under humidity aging conditions.pdf\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"James E. Pickett, Dennis J. Coyle\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"PC, humidity, hydrothermal, polycarbonate, polyarylate, aryl ester, aromatic polyester, hydrolysis\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"Bisphenol-A polycarbonate (PC)\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"Hydrolysis\",<br>      <span style=\"color: plum;\">\"Comments\":</span> \"RH is a fraction between 0 and 1. Data from Table 5. The paper provided variability for a 95% confidence interval which was divided by 2 to get the standard deviation.\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"arrhenius\",<br>      <span style=\"color: plum;\">\"Equation\":</span> \"t_{fail}=R_0\\\\cdot \\\\frac {e^{ \\\\left( \\\\frac{E_a}{R\\\\cdot T_K } \\\\right) }}{RH^2}\",<br>      <span style=\"color: plum;\">\"t_fail\":</span> {<br>        \"units\": \"days\"<br>      },<br>      <span style=\"color: plum;\">\"R_0\":</span> {<br>        \"value\": 65200000000.0,<br>        \"units\": \"days\"<br>      },<br>      <span style=\"color: plum;\">\"ln(R_0)\":</span> {<br>        \"value\": 24.9,<br>        \"stdev\": 1.6,<br>        \"units\": \"ln(days)\"<br>      },<br>      <span style=\"color: plum;\">\"E_a\":</span> {<br>        \"value\": 92,<br>        \"stdev\": 4.8,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"n\":</span> {<br>        \"value\": 2,<br>        \"stdev\": 0,<br>        \"units\": \"NA\"<br>      }<br>    }</pre></div></div><div><strong style=\"color: white;\">D011:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Jada Swailes\",<br>      <span style=\"color: plum;\">\"DateEntered\":</span> \"3/21/2023\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"10.1016/j.polymdegradstab.2013.04.001\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"Pickett  Coyle Hydrolysis Kinetics of condensation polymers under humidity aging conditions.pdf\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"James E. Pickett, Dennis J. Coyle\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"PET, humidity, hydrothermal, polyester, PET, polyarylate, aryl ester, aromatic polyester, hydrolysis, poly(ethylene terephthalate)\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"poly(ethylene terephthalate), Average of three materials, PET-A,B,C\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"Hydrolysis\",<br>      <span style=\"color: plum;\">\"Comments\":</span> \"RH is a fraction between 0 and 1. Data from Table 5. The paper provided variability for a 95% confidence interval which was divided by 2 to get the standard deviation.\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"arrhenius\",<br>      <span style=\"color: plum;\">\"Equation\":</span> \"t_{fail}=R_0\\\\cdot \\\\frac {e^{ \\\\left( \\\\frac{E_a}{R\\\\cdot T_K } \\\\right) }}{RH^2}\",<br>      <span style=\"color: plum;\">\"t_fail\":</span> {<br>        \"units\": \"days\"<br>      },<br>      <span style=\"color: plum;\">\"R_0\":</span> {<br>        \"value\": 1.689e+17,<br>        \"units\": \"days\"<br>      },<br>      <span style=\"color: plum;\">\"ln(R_0)\":</span> {<br>        \"value\": 39.3,<br>        \"stdev\": 1.1,<br>        \"units\": \"ln(days)\"<br>      },<br>      <span style=\"color: plum;\">\"E_a\":</span> {<br>        \"value\": 129,<br>        \"stdev\": 3.35,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"n\":</span> {<br>        \"value\": 2,<br>        \"stdev\": 0,<br>        \"units\": \"NA\"<br>      }<br>    }</pre></div></div><div><strong style=\"color: white;\">D012:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Jada Swailes\",<br>      <span style=\"color: plum;\">\"DateEntered\":</span> \"3/21/2023\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"10.1016/j.polymdegradstab.2013.04.001\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"Pickett  Coyle Hydrolysis Kinetics of condensation polymers under humidity aging conditions.pdf\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"James E. Pickett, Dennis J. Coyle\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"PET, humidity, hydrothermal, polyester, pET, aryl ester, aromatic polyester, hydrolysis, poly(ethylene terephthalate)\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"poly(ethylene terephthalate), PET-D\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"Hydrolysis\",<br>      <span style=\"color: plum;\">\"Comments\":</span> \"RH is a fraction between 0 and 1. Data from Table 5. The paper provided variability for a 95% confidence interval which was divided by 2 to get the standard deviation. This is the time to failure for the PET-D material.\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"arrhenius\",<br>      <span style=\"color: plum;\">\"Equation\":</span> \"t_{fail}=R_0\\\\cdot \\\\frac {e^{ \\\\left( \\\\frac{E_a}{R\\\\cdot T_K } \\\\right) }}{RH^2}\",<br>      <span style=\"color: plum;\">\"t_fail\":</span> {<br>        \"units\": \"days\"<br>      },<br>      <span style=\"color: plum;\">\"R_0\":</span> {<br>        \"value\": 7.835e+16,<br>        \"units\": \"days\"<br>      },<br>      <span style=\"color: plum;\">\"ln(R_0)\":</span> {<br>        \"value\": 38.9,<br>        \"stdev\": 1,<br>        \"units\": \"ln(days)\"<br>      },<br>      <span style=\"color: plum;\">\"E_a\":</span> {<br>        \"value\": 128,<br>        \"stdev\": 2.95,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"n\":</span> {<br>        \"value\": 2,<br>        \"stdev\": 0,<br>        \"units\": \"NA\"<br>      }<br>    }</pre></div></div><div><strong style=\"color: white;\">D013:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Jada Swailes\",<br>      <span style=\"color: plum;\">\"DateEntered\":</span> \"3/21/2023\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"10.1016/j.polymdegradstab.2013.04.001\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"Pickett  Coyle Hydrolysis Kinetics of condensation polymers under humidity aging conditions.pdf\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"James E. Pickett, Dennis J. Coyle\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"RPA, resorcinol polyarylate, humidity, hydrothermal, polyester, polyarylate, aryl ester, aromatic polyester, hydrolysis\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"resorcinol polyarylate (RPA-A)\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"Hydrolysis\",<br>      <span style=\"color: plum;\">\"Comments\":</span> \"RH is a fraction between 0 and 1. Data from Table 5. The paper provided variability for a 95% confidence interval which was divided by 2 to get the standard deviation.\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"arrhenius\",<br>      <span style=\"color: plum;\">\"Equation\":</span> \"t_{fail}=R_0\\\\cdot \\\\frac {e^{ \\\\left( \\\\frac{E_a}{R\\\\cdot T_K } \\\\right) }}{RH^2}\",<br>      <span style=\"color: plum;\">\"t_fail\":</span> {<br>        \"units\": \"days\"<br>      },<br>      <span style=\"color: plum;\">\"R_0\":</span> {<br>        \"value\": 64649000000000.0,<br>        \"units\": \"days\"<br>      },<br>      <span style=\"color: plum;\">\"ln(R_0)\":</span> {<br>        \"value\": 31.8,<br>        \"stdev\": 1.85,<br>        \"units\": \"ln(days)\"<br>      },<br>      <span style=\"color: plum;\">\"E_a\":</span> {<br>        \"value\": 105,<br>        \"stdev\": 5.45,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"n\":</span> {<br>        \"value\": 2,<br>        \"stdev\": 0,<br>        \"units\": \"NA\"<br>      }<br>    }</pre></div></div><div><strong style=\"color: white;\">D014:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Jada Swailes\",<br>      <span style=\"color: plum;\">\"DateEntered\":</span> \"3/21/2023\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"10.1016/j.polymdegradstab.2013.04.001\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"Pickett  Coyle Hydrolysis Kinetics of condensation polymers under humidity aging conditions.pdf\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"James E. Pickett, Dennis J. Coyle\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"Humidity, hydrothermal, polyester, polyarylate, aryl ester, aromatic polyester, resorcinol polyarylate, hydrolysis\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"RPA-B,C\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"Hydrolysis\",<br>      <span style=\"color: plum;\">\"Comments\":</span> \"RH is a fraction between 0 and 1. Data from Table 5. The paper provided variability for a 95% confidence interval which was divided by 2 to get the standard deviation.\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"arrhenius\",<br>      <span style=\"color: plum;\">\"Equation\":</span> \"t_{fail}=R_0\\\\cdot \\\\frac {e^{ \\\\left( \\\\frac{E_a}{R\\\\cdot T_K } \\\\right) }}{RH^2}\",<br>      <span style=\"color: plum;\">\"t_fail\":</span> {<br>        \"units\": \"days\"<br>      },<br>      <span style=\"color: plum;\">\"ln(R_0)\":</span> {<br>        \"value\": 30,<br>        \"stdev\": 2,<br>        \"units\": \"ln(days)\"<br>      },<br>      <span style=\"color: plum;\">\"R_0\":</span> {<br>        \"value\": 1.98626e+49,<br>        \"units\": \"days\"<br>      },<br>      <span style=\"color: plum;\">\"E_a\":</span> {<br>        \"value\": 102,<br>        \"stdev\": 6.05,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"n\":</span> {<br>        \"value\": 2,<br>        \"stdev\": 0,<br>        \"units\": \"NA\"<br>      }<br>    }</pre></div></div><div><strong style=\"color: white;\">D015:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Weston Wall\",<br>      <span style=\"color: plum;\">\"DateEntered\":</span> \"3/27/2023\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"10.1021/jacs.2c00391\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"Water-Accelerated Photo-oxidation of CH3NH3PbI3 Perovskite: Mechanism, rate orders, and rate constants\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"Timothy D. Siegler, Wiley A. Dunlap-Shohl, Yuhuan Meng, Wylie F. Kau, Preetham P. Sunkari, Chang En Tsai, Zachary J. Armstrong, Yu-chia Chen, David A. C. Beck, Marina Meila, Hugh W. Hillhouse\",<br>      <span style=\"color: plum;\">\"Reference\":</span> \"Journal of the American Chemical Society 2022 144 (12), 5552-5561\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"Dry Photooxidation, Water Photooxidiation\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"Halide Perovskite (MAPbI3)\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"Optical Transmittance In-Situ measurement\",<br>      <span style=\"color: plum;\">\"Comments\":</span> \"LED Light at 550nm\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"Perovskite_1\",<br>      <span style=\"color: plum;\">\"Equation\":</span> \"R_D=R_{0,WPO}\\\\cdot e^{ \\\\left( \\\\frac{-E_{a,WPO}}{R\\\\cdot T_K } \\\\right) } \\\\cdot \\\\left< \\\\frac{P_{O_2}\\\\cdot P_{H_2 O}\\\\cdot G_{550}^{0.7} }{ \\\\left[ 1+K_{2W}\\\\cdot P_{O_2} \\\\left(1+K_{3W}\\\\cdot G_{550}^{0.7} \\\\right) \\\\right]^2}  \\\\right> +R_{0,DPO}\\\\cdot e^ {\\\\left( \\\\frac{-E_{a,DPO}}{R\\\\cdot T_K} \\\\right) } \\\\cdot \\\\frac{P_{O_2} \\\\cdot G_{550}^{0.7} }{1+K_{2D}\\\\cdot P_{O_2} \\\\cdot \\\\left( 1+K_3D\\\\cdot G_{550}^{0.7}  \\\\right)} +R_{0,hum}\\\\cdot e^ { \\\\left( \\\\frac {-E_a^{hum}}{R\\\\cdot T_K } \\\\right) } \\\\cdot P_{H_2 O}\\\\cdot G_{550}^{0.7}+R_{0,therm}\\\\cdot e^{ \\\\left( \\\\frac{-E_a^{therm}}{R\\\\cdot T_K } \\\\right) }\",<br>      <span style=\"color: plum;\">\"R_D\":</span> {<br>        \"units\": \"mol/(m^2\\u00b7s)\"<br>      },<br>      <span style=\"color: plum;\">\"R_0,WPO\":</span> {<br>        \"value\": 3.16e-25,<br>        \"units\": \"mol/(m^2\\u00b7s\\u00b7kpa^2 )\\u00b7(photons/(m^2\\u00b7s))^(-0.7)\"<br>      },<br>      <span style=\"color: plum;\">\"E_a,WPO\":</span> {<br>        \"value\": -8.6827,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"K_2W\":</span> {<br>        \"value\": 0.0044,<br>        \"units\": \"1/kPa\"<br>      },<br>      <span style=\"color: plum;\">\"K_3W\":</span> {<br>        \"value\": 4.32e-15,<br>        \"units\": \"(photons/(m^2\\u00b7s))^(-0.7)\"<br>      },<br>      <span style=\"color: plum;\">\"R_0,DPO\":</span> {<br>        \"value\": 5.45e-15,<br>        \"units\": \"mol/(m^2\\u00b7s\\u00b7kpa )\\u00b7(photons/(m^2\\u00b7s))^(-0.7)\"<br>      },<br>      <span style=\"color: plum;\">\"E_a,DPO\":</span> {<br>        \"value\": 59.82,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"K_2D\":</span> {<br>        \"value\": 0.00328,<br>        \"units\": \"1/kPa\"<br>      },<br>      <span style=\"color: plum;\">\"K_3D\":</span> {<br>        \"value\": 6.97e-15,<br>        \"units\": \"(photons/(m^2\\u00b7s))^(-0.7)\"<br>      },<br>      <span style=\"color: plum;\">\"R_0,hum\":</span> {<br>        \"value\": 9.2e-22,<br>        \"units\": \"mol/(m^2\\u00b7s\\u00b7kpa)\\u00b7(photons/(m^2\\u00b7s))^(-0.7)\"<br>      },<br>      <span style=\"color: plum;\">\"E_a^hum\":</span> {<br>        \"value\": 19.3,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"E_a^therm\":</span> {<br>        \"value\": 43.42,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"R_0,therm\":</span> {<br>        \"value\": 0.00041,<br>        \"units\": \"mol/(m^2\\u00b7s)\"<br>      }<br>    }</pre></div></div><div><strong style=\"color: white;\">D016:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Weston Wall\",<br>      <span style=\"color: plum;\">\"DateEntered\":</span> \"4/7/2023\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"10.1002/pip.2908\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"Evaluation and modeling of the potential effects of a module manufacturing anomaly\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"Michael D Kempe, Dirk C Jordan\",<br>      <span style=\"color: plum;\">\"Reference\":</span> \"Kempe MD, Jordan DC. Evaluation and modeling of the potential effects of a module manufacturing anomaly. Prog Photovolt Res Appl. 2017. https://doi.org/10.1002/pip.2908\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"Damp Heat, Durability, Kinetics, Manufacturing Quality, Reliability, Solder Flux, Metallization contact resistance\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"multi-crystalline Silicon\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"Fill Factor\",<br>      <span style=\"color: plum;\">\"Comments\":</span> \"Degradation of metallization contact to the cell due to flux\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"FF_Arrhenius\",<br>      <span style=\"color: plum;\">\"Equation\":</span> \"FF_t=FF_0\\\\cdot \\\\left( 1- \\\\frac{A_i}{A_o} \\\\cdot \\\\left< 1-e^{\\\\left[ -e^{\\\\left( B_1- \\\\frac{B_2}{R\\\\cdot T_K }-B_3\\\\cdot RH \\\\right) \\\\cdot t } \\\\right] } \\\\right> \\\\right)\",<br>      <span style=\"color: plum;\">\"FF\":</span> {<br>        \"units\": \"%\"<br>      },<br>      <span style=\"color: plum;\">\"B_1\":</span> {<br>        \"value\": 14.7,<br>        \"stdev\": 4.4<br>      },<br>      <span style=\"color: plum;\">\"B_2\":</span> {<br>        \"value\": 76.22,<br>        \"stdev\": 0.135,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"B_3\":</span> {<br>        \"value\": -0.005,<br>        \"stdev\": 0.012,<br>        \"units\": \"1/%\"<br>      },<br>      <span style=\"color: plum;\">\"A\":</span> {<br>        \"value\": 0.073,<br>        \"stdev\": 0.02<br>      },<br>      <span style=\"color: plum;\">\"Corr-B_1-B_2\":</span> 0.985,<br>      <span style=\"color: plum;\">\"Corr-B_1-B_3\":</span> 0.216,<br>      <span style=\"color: plum;\">\"Corr-B_2-B_3\":</span> 0.0452<br>    }</pre></div></div><div><strong style=\"color: white;\">D017:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Weston Wall\",<br>      <span style=\"color: plum;\">\"DateEntered\":</span> \"4/17/2023\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"PV CONNECTORS, BOTTLENECK OF 40 YEARS LIFETIME MODULES?\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"Xuanji Yu , Ben Huang, Ruirui Lv, Jean-Nicolas Jaubert , Tao Xu, Guangchun Zhang\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"Connectors, Polymer sealing failure, metal pin corrosion, 40 year reliability\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"Connectors\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"Wet insulation resistance < 400Mohm\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"Arrhenius_Connector\",<br>      <span style=\"color: plum;\">\"Equation\":</span> \"R_D=R_{0,T} \\\\cdot e^ {\\\\left( \\\\frac {-E_{a,T}}{R\\\\cdot T_K } \\\\right)}+R_{0,H}\\\\cdot RH^n\\\\cdot e^{ \\\\left( \\\\frac{-E_{a,H}}{R\\\\cdot T_K } \\\\right) }+R_{0,P}\\\\cdot G^P\\\\cdot e^{ \\\\left( \\\\frac {-E_{a,P}}{R\\\\cdot T_K } \\\\right) }\",<br>      <span style=\"color: plum;\">\"R_D\":</span> {},<br>      <span style=\"color: plum;\">\"R_0,T\":</span> {},<br>      <span style=\"color: plum;\">\"E_a,T\":</span> {},<br>      <span style=\"color: plum;\">\"R_0,H\":</span> {},<br>      <span style=\"color: plum;\">\"n\":</span> {},<br>      <span style=\"color: plum;\">\"E_a,H\":</span> {},<br>      <span style=\"color: plum;\">\"R_0,P\":</span> {},<br>      <span style=\"color: plum;\">\"p\":</span> {},<br>      <span style=\"color: plum;\">\"E_a,P\":</span> {}<br>    }</pre></div></div><div><strong style=\"color: white;\">D018:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Jada Swailes\",<br>      <span style=\"color: plum;\">\"DateEntered\":</span> \"4/14/2023\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"10.1016/j.solmat.2011.02.013\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"An Arrhenius approach to estimating organic photovoltaic module weathering acceleration factors\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"Olivier Haillant, David Dumbleton, Allen Zielnik\"<br>    }</pre></div></div><div><strong style=\"color: white;\">D019:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Weston Wall\",<br>      <span style=\"color: plum;\">\"DateEntered\":</span> \"4/27/2023\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"10.1109/JPHOTOV.2018.2850527\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"Quantification of Environmental Effects on PV Module Degradation: A Physics-Based Data Driven Modeling Method\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"Arun Bala Subramaniyan, Rong Pan, Joseph Kuitche, GovindaSamy TamizhMani\",<br>      <span style=\"color: plum;\">\"Reference\":</span> \"A. Bala Subramaniyan, R. Pan, J. Kuitche and G. TamizhMani, 'Quantification of Environmental Effects on PV Module Degradation: A Physics-Based Data-Driven Modeling Method,' in IEEE Journal of Photovoltaics, vol. 8, no. 5, pp. 1289-1296, Sept. 2018, doi: 10.1109/JPHOTOV.2018.2850527.\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"Outdoor, mono-crystalline Silicon, effeciency,  Cumulative effects model\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"mono-Crystalline Silicon\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"Overall power generated\",<br>      <span style=\"color: plum;\">\"Comments\":</span> \"Uncertainties given using 95% confidence interval\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"Arrhenius_Coffin_Manson_Peck\",<br>      <span style=\"color: plum;\">\"Equation\":</span> \"R_D=B_0\\\\cdot e^{\\\\left( \\\\frac{-B_1}{R\\\\cdot T_K}\\\\right)}\\\\cdot \\u0394T^{B_2} \\\\cdot UV_{Daily}^{B_3}\\\\cdot {RH_{Daily}}^{B_4}\",<br>      <span style=\"color: plum;\">\"R_D\":</span> {<br>        \"units\": \"%/year\"<br>      },<br>      <span style=\"color: plum;\">\"R_0\":</span> {<br>        \"value\": 0.35,<br>        \"stdev\": 0.037,<br>        \"units\": \"1/sec\"<br>      },<br>      <span style=\"color: plum;\">\"E_a\":</span> {<br>        \"value\": 67.5,<br>        \"stdev\": 7.25,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"m\":</span> {<br>        \"value\": 2.41,<br>        \"stdev\": 0.03<br>      },<br>      <span style=\"color: plum;\">\"p\":</span> {<br>        \"value\": 0.75,<br>        \"stdev\": 0.04375<br>      },<br>      <span style=\"color: plum;\">\"n\":</span> {<br>        \"value\": 1.52,<br>        \"stdev\": 0.063<br>      }<br>    }</pre></div></div><div><strong style=\"color: white;\">D020:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Weston Wall\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"10.1109/PVSC45281.2020.9300638\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"Failure modes of polyolefin encapsulated double glass modules and corresponding degradation modeling: Part 1 optical durability\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"Ben X. J. Yu, Ruirui Lv, Jean-Nicolas Jaubert, Guoqiang Xing, Julien Dupuis, Eric Sandre, Christophe Dugue, Gilles Goaer\",<br>      <span style=\"color: plum;\">\"Reference\":</span> \"B. X. J. Yu et al., 'Failure modes of polyolefin encapsulated double glass modules and corresponding degradation modeling: Part 1 optical durability,' 2020 47th IEEE Photovoltaic Specialists Conference (PVSC), Calgary, AB, Canada, 2020, pp. 1002-1007, doi: 10.1109/PVSC45281.2020.9300638.\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"Bifacial, Encapsulants, Delamination, Yellowing, Sevice Life predication\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"Encapsulents, (POE, TPO, EVA)\"<br>    }</pre></div></div><div><strong style=\"color: white;\">D021:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Weston Wall\",<br>      <span style=\"color: plum;\">\"DateEntered\":</span> \"5/15/2023\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"10.1109/PVSC.2018.8547944\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"Acceleration Factor Modeling for Degradation Rate Prediction of Photovoltaic Encapsulant Discoloration\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"Archana Sinha, Shantanu Pore, Arun Balasubramaniyan, GovindaSamy TamizhMani\",<br>      <span style=\"color: plum;\">\"Reference\":</span> \"A. Sinha, S. Pore, A. Balasubramaniyan and G. TamizhMani, 'Acceleration Factor Modeling for Degradation Rate Prediction of Photovoltaic Encapsulant Discoloration,' 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC), Waikoloa, HI, USA, 2018, pp. 1342-1346, doi: 10.1109/PVSC.2018.8547944.\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"Acceleration Factor, Arrhenius, degradation rate, encapsulant discoloration, Lifetime prediction\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"EVA Encapsulent, Si-cells\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"Encapsulent Discoloration, Isc loss\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"arrhenius\",<br>      <span style=\"color: plum;\">\"Equation\":</span> \"R_D=R_0\\\\cdot G^p\\\\cdot e^{\\\\left( \\\\frac{E_a}{R\\\\cdot T_K} \\\\right)}\",<br>      <span style=\"color: plum;\">\"R_D\":</span> {<br>        \"value\": 0.1,<br>        \"units\": \"%/year\"<br>      },<br>      <span style=\"color: plum;\">\"E_a\":</span> {<br>        \"value\": 29.91,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"p\":</span> {},<br>      <span style=\"color: plum;\">\"R_0\":</span> {}<br>    }</pre></div></div><div><strong style=\"color: white;\">D022:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Weston Wall\",<br>      <span style=\"color: plum;\">\"DateEntered\":</span> \"5/15/2023\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"10.1002/apmc.1994.052160108\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"The kinetics and mechanism of polyethylene photo-oxidation\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"J. J. C. Cruz-Pinto, M. E. S. Carvalho, J. F. A. Ferreira\",<br>      <span style=\"color: plum;\">\"Reference\":</span> \"Cruz Pinto, Jose Joaquim & Carvalho, M. & Ferreira, J.. (2003). The kinetics and mechanism of polyethylene photo-oxidation. Die Angewandte Makromolekulare Chemie. 216. 113 - 133. 10.1002/apmc.1994.052160108. \",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"Polyethylene, Photoxidation, Weathering, Mechanism, Kinetics\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"Polyethylene\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"Photoxidation\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"Arrhenius_G_DeltaT\",<br>      <span style=\"color: plum;\">\"Equation\":</span> \"R_D=R_0\\\\cdot G\\\\cdot e^{\\\\left( \\\\frac{E_a}{R\\\\cdot T_K} \\\\right)}\\\\cdot \\u0394T\",<br>      <span style=\"color: plum;\">\"E_a\":</span> {<br>        \"value\": 58.24,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"R_D\":</span> {<br>        \"units\": \"%/hour\"<br>      },<br>      <span style=\"color: plum;\">\"R_0\":</span> {}<br>    }</pre></div></div><div><strong style=\"color: white;\">D024:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Weston Wall\",<br>      <span style=\"color: plum;\">\"DateEntered\":</span> \"5/26/2023\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"doi:10.1109/JPHOTOV.2020.3025240\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"Temporary Recovery of the Defect Responsible for Light- and Elevated Temperature-Induced Degradation: Insights Into the Physical Mechanisms Behind LeTID\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"Wolfram Kwapil, Jonas Schon, Tim Niewelt, and Martin C. Schubert\",<br>      <span style=\"color: plum;\">\"Reference\":</span> \"W. Kwapil et al., IEEE J. Photovoltaics, vol. 10, no. 6, pp. 1591-1603, 2020, doi: 10.1109/JPHOTOV.2020.3025240.\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"Degradation, Defect reactions, light- and elevated temperature-induced degradation, LeTID, model, silicon defects.\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"mc-Si cells\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"LeTID\",<br>      <span style=\"color: plum;\">\"Comments\":</span> \"Cell_200Micron\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"LeTID\",<br>      <span style=\"color: plum;\">\"v_ab\":</span> {<br>        \"value\": 33000000,<br>        \"units\": \"(cm^3x)/s\"<br>      },<br>      <span style=\"color: plum;\">\"v_ba\":</span> {<br>        \"value\": 4.7e-25,<br>        \"units\": \"(cm^3x)/s\"<br>      },<br>      <span style=\"color: plum;\">\"v_bc\":</span> {<br>        \"value\": 13100000,<br>        \"units\": \"(cm^3x)/s\"<br>      },<br>      <span style=\"color: plum;\">\"v_cb\":</span> {<br>        \"value\": 0,<br>        \"units\": \"(cm^3x)/s\"<br>      },<br>      <span style=\"color: plum;\">\"E_(a, ab)\":</span> {<br>        \"value\": 78.7895,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"E_(a, ba)\":</span> {<br>        \"value\": -110.9653,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"E_(a, bc)\":</span> {<br>        \"value\": 84.0442,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"E_(a, cb)\":</span> {<br>        \"value\": 0,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"x_ab\":</span> {<br>        \"value\": 1<br>      },<br>      <span style=\"color: plum;\">\"x_ba\":</span> {<br>        \"value\": 1.7<br>      },<br>      <span style=\"color: plum;\">\"x_bc\":</span> {<br>        \"value\": 1.2<br>      }<br>    }</pre></div></div><div><strong style=\"color: white;\">D025:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Weston Wall\",<br>      <span style=\"color: plum;\">\"DateEntered\":</span> \"6/8/2023\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"doi:10.1557/s43577-022-00438-8\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"Long-term impact of light- and elevated temperature-induced degradation on photovoltaic arrays\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"I.L. Repins, D.C. Jordan, M.Woodhouse, M. Theristis, J.S. Stein, H.P. Seigneur, D.J. Colvin, J.F. Karas, A.N. McPherson, C. Deline\",<br>      <span style=\"color: plum;\">\"Reference\":</span> \"I. Repins et al., MRS Bulletin, 2023. doi: 10.1557/s43577-022-00438-8.\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"LeTID, Indoor, Outdoor\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"p-Si Cells\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"LeTID\",<br>      <span style=\"color: plum;\">\"Comments\":</span> \"Cell_200Micron\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"LeTID\",<br>      <span style=\"color: plum;\">\"v_ab\":</span> {<br>        \"value\": 46700000,<br>        \"units\": \"(cm^3x)/s\"<br>      },<br>      <span style=\"color: plum;\">\"v_ba\":</span> {<br>        \"value\": 4.7e-25,<br>        \"units\": \"(cm^3x)/s\"<br>      },<br>      <span style=\"color: plum;\">\"v_bc\":</span> {<br>        \"value\": 19900000,<br>        \"units\": \"(cm^3x)/s\"<br>      },<br>      <span style=\"color: plum;\">\"v_cb\":</span> {<br>        \"value\": 0,<br>        \"units\": \"(cm^3x)/s\"<br>      },<br>      <span style=\"color: plum;\">\"E_(a, ab)\":</span> {<br>        \"value\": 78.7895,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"E_(a, ba)\":</span> {<br>        \"value\": -110.9653,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"E_(a, bc)\":</span> {<br>        \"value\": 84.0442,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"E_(a, cb)\":</span> {<br>        \"value\": 0,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"x_ab\":</span> {<br>        \"value\": 1<br>      },<br>      <span style=\"color: plum;\">\"x_ba\":</span> {<br>        \"value\": 1.7<br>      },<br>      <span style=\"color: plum;\">\"x_bc\":</span> {<br>        \"value\": 1.2<br>      }<br>    }</pre></div></div><div><strong style=\"color: white;\">D026:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Weston Wall\",<br>      <span style=\"color: plum;\">\"DateEntered\":</span> \"6/8/2023\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"doi:10.1016/j.solmat.2017.08.007\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"Light-induced lifetime degradation in high-performance multicrystalline silicon: Detailed kinetics of the defect activation\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"Dennis Bredemeier, Dominic Walter, Jan Schmidt\",<br>      <span style=\"color: plum;\">\"Reference\":</span> \"D. Bredemeier et al., Sol. Energy Mater. Sol. Cells, vol. 173, pp. 2-5, 2017, doi: 10.1016/j.solmat.2017.08.007.\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"multi-Si, LeTID, \",<br>      <span style=\"color: plum;\">\"Material\":</span> \"multi-Si\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"LeTID\",<br>      <span style=\"color: plum;\">\"Comments\":</span> \"ab158micronwafer_bc200microncell\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"LeTID\",<br>      <span style=\"color: plum;\">\"v_ab\":</span> {<br>        \"value\": 937000000,<br>        \"units\": \"(cm^3x)/s\"<br>      },<br>      <span style=\"color: plum;\">\"v_ba\":</span> {<br>        \"value\": 0,<br>        \"units\": \"(cm^3x)/s\"<br>      },<br>      <span style=\"color: plum;\">\"v_bc\":</span> {<br>        \"value\": 21500000,<br>        \"units\": \"(cm^3x)/s\"<br>      },<br>      <span style=\"color: plum;\">\"v_cb\":</span> {<br>        \"value\": 0,<br>        \"units\": \"(cm^3x)/s\"<br>      },<br>      <span style=\"color: plum;\">\"E_(a, ab)\":</span> {<br>        \"value\": 85.8775,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"E_(a, ba)\":</span> {<br>        \"value\": 0,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"E_(a, bc)\":</span> {<br>        \"value\": 90.7021,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"E_(a, cb)\":</span> {<br>        \"value\": 0,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"x_ab\":</span> {<br>        \"value\": 1<br>      },<br>      <span style=\"color: plum;\">\"x_ba\":</span> {<br>        \"value\": 1.7<br>      },<br>      <span style=\"color: plum;\">\"x_bc\":</span> {<br>        \"value\": 1.2<br>      }<br>    }</pre></div></div><div><strong style=\"color: white;\">D027:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Weston Wall\",<br>      <span style=\"color: plum;\">\"DateEntered\":</span> \"6/9/2023\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"doi:10.1109/JPHOTOV.2021.3078367\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"The Influence of Minority Carrier Density on Degradation and Regeneration Kinetics in Multicrystalline Silicon Wafers\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"Guro Marie Wyller, Marie Syre Wiig, Ida Due-Sorensen, Rune Sondena\",<br>      <span style=\"color: plum;\">\"Reference\":</span> \"G. Wyller et al., IEEE J. Photovoltaics, vol. 11, no. 4, pp. 878-889, 2021, doi: 10.1109/JPHOTOV.2021.3078367.\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"p-type mc-Si Perc, Degradation, LeTID, Hydrogen Involvement\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"p-type mc-Si Perc\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"LeTID\",<br>      <span style=\"color: plum;\">\"Comments\":</span> \"wafer_180micron\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"LeTID\",<br>      <span style=\"color: plum;\">\"v_ab\":</span> {<br>        \"value\": 1900000000000,<br>        \"units\": \"(cm^3x)/s\"<br>      },<br>      <span style=\"color: plum;\">\"v_ba\":</span> {<br>        \"value\": 0,<br>        \"units\": \"(cm^3x)/s\"<br>      },<br>      <span style=\"color: plum;\">\"v_bc\":</span> {<br>        \"value\": 0.284,<br>        \"units\": \"(cm^3x)/s\"<br>      },<br>      <span style=\"color: plum;\">\"v_cb\":</span> {<br>        \"value\": 0,<br>        \"units\": \"(cm^3x)/s\"<br>      },<br>      <span style=\"color: plum;\">\"E_(a, ab)\":</span> {<br>        \"value\": 133.1584,<br>        \"stdev\": 13.5088,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"E_(a, ba)\":</span> {<br>        \"value\": 0,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"E_(a, bc)\":</span> {<br>        \"value\": 54.0353,<br>        \"stdev\": 11.579,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"E_(a, cb)\":</span> {<br>        \"value\": 0,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"x_ab\":</span> {<br>        \"value\": 1<br>      },<br>      <span style=\"color: plum;\">\"x_ba\":</span> {<br>        \"value\": 1.7<br>      },<br>      <span style=\"color: plum;\">\"x_bc\":</span> {<br>        \"value\": 1.2<br>      }<br>    }</pre></div></div><div><strong style=\"color: white;\">D028:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Weston Wall\",<br>      <span style=\"color: plum;\">\"DateEntered\":</span> \"6/9/2023\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"doi:10.1109/JPHOTOV.2021.3078368\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"The Influence of Minority Carrier Density on Degradation and Regeneration Kinetics in Multicrystalline Silicon Wafers\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"Guro Marie Wyller, Marie Syre Wiig, Ida Due-Sorensen, Rune Sondena\",<br>      <span style=\"color: plum;\">\"Reference\":</span> \"G. Wyller et al., IEEE J. Photovoltaics, vol. 11, no. 4, pp. 878-889, 2021, doi: 10.1109/JPHOTOV.2021.3078367.\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"p-type mc-Si Perc, Degradation, LeTID, Hydrogen Involvement\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"p-type mc-Si Perc\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"LeTID\",<br>      <span style=\"color: plum;\">\"Comments\":</span> \"wafer_200micron\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"LeTID\",<br>      <span style=\"color: plum;\">\"v_ab\":</span> {<br>        \"value\": 483000,<br>        \"units\": \"(cm^3x)/s\"<br>      },<br>      <span style=\"color: plum;\">\"v_ba\":</span> {<br>        \"value\": 0,<br>        \"units\": \"(cm^3x)/s\"<br>      },<br>      <span style=\"color: plum;\">\"v_bc\":</span> {<br>        \"value\": 0,<br>        \"units\": \"(cm^3x)/s\"<br>      },<br>      <span style=\"color: plum;\">\"v_cb\":</span> {<br>        \"value\": 0,<br>        \"units\": \"(cm^3x)/s\"<br>      },<br>      <span style=\"color: plum;\">\"E_(a, ab)\":</span> {<br>        \"value\": 64.0704,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"E_(a, ba)\":</span> {<br>        \"value\": 0,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"E_(a, bc)\":</span> {<br>        \"value\": 90.7986,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"E_(a, cb)\":</span> {<br>        \"value\": 0,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"x_ab\":</span> {<br>        \"value\": 1<br>      },<br>      <span style=\"color: plum;\">\"x_ba\":</span> {<br>        \"value\": 1.7<br>      },<br>      <span style=\"color: plum;\">\"x_bc\":</span> {<br>        \"value\": 1.2<br>      }<br>    }</pre></div></div><div><strong style=\"color: white;\">D029:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Weston Wall\",<br>      <span style=\"color: plum;\">\"DateEntered\":</span> \"6/12/2023\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"doi:10.1063/1.5123890\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"Determination of BO-LID and LeTID related activation energies in Cz-Si and FZ-Si using constant injection conditions\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"Alexander Graf, Axel Herguth, Giso Hahn\",<br>      <span style=\"color: plum;\">\"Reference\":</span> \"A. Graf, et al., AIP Conference Proceedings, vol. 2147, no. 1, p. 140003, 2019, doi: 10.1063/1.5123890.\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"Si, Boron Doped, LeTID, Degradation\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"Si, CZ FZ grown\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"LeTID\",<br>      <span style=\"color: plum;\">\"Comments\":</span> \"wafer_152micron\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"LeTID\",<br>      <span style=\"color: plum;\">\"v_ab\":</span> {<br>        \"value\": 483000,<br>        \"units\": \"(cm^3x)/s\"<br>      },<br>      <span style=\"color: plum;\">\"v_ba\":</span> {<br>        \"value\": 0,<br>        \"units\": \"(cm^3x)/s\"<br>      },<br>      <span style=\"color: plum;\">\"v_bc\":</span> {<br>        \"value\": 0,<br>        \"units\": \"(cm^3x)/s\"<br>      },<br>      <span style=\"color: plum;\">\"v_cb\":</span> {<br>        \"value\": 0,<br>        \"units\": \"(cm^3x)/s\"<br>      },<br>      <span style=\"color: plum;\">\"E_(a, ab)\":</span> {<br>        \"value\": 75.2364,<br>        \"stdev\": 8.7,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"E_(a, ba)\":</span> {<br>        \"value\": 0,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"E_(a, bc)\":</span> {<br>        \"value\": 59.8248,<br>        \"stdev\": 8.7,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"E_(a, cb)\":</span> {<br>        \"value\": 0,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"x_ab\":</span> {<br>        \"value\": 1<br>      },<br>      <span style=\"color: plum;\">\"x_ba\":</span> {<br>        \"value\": 1.7<br>      },<br>      <span style=\"color: plum;\">\"x_bc\":</span> {<br>        \"value\": 1.2<br>      }<br>    }</pre></div></div><div><strong style=\"color: white;\">D030:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Weston Wall\",<br>      <span style=\"color: plum;\">\"DateEntered\":</span> \"6/15/2023\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"10.1007/3-540-61218-1_6\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"Wavelength sensitivity in polymer photodegradation\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"Anthony L Andrady\",<br>      <span style=\"color: plum;\">\"Reference\":</span> \"Andrady, A.L. (1997). Wavelength sensitivity in polymer photodegradation. In: Polymer Analysis Polymer Physics. Advances in Polymer Science, vol 128. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61218-1_6\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"Photodegradation, Vinyl Chloride, Activation Spectrum, Action Spectrum\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"Naturally Occuring Polymers, Synthetic Polymers\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"Photodegradation\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"Activation Spectrum\",<br>      <span style=\"color: plum;\">\"Equation\":</span> \"Activation Spectrum={E_L}\\\\cdot R_0\\\\cdot e^{\\\\left( -B_g\\\\cdot \\u03bb \\\\right)}\",<br>      <span style=\"color: plum;\">\"E_L\":</span> {},<br>      <span style=\"color: plum;\">\"R_0\":</span> {},<br>      <span style=\"color: plum;\">\"B_g\":</span> {},<br>      <span style=\"color: plum;\">\"L\":</span> {}<br>    }</pre></div></div><div><strong style=\"color: white;\">D031:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Weston Wall\",<br>      <span style=\"color: plum;\">\"DateEntered\":</span> \"6/16/2023\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"A Characterization of the Relationship Between Light Intensity and Degradation Rate for Weathering Durability\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"Henry K. Hardcastle\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"Durability, EMMA, Degradation Rate\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"Polycarbonate\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"Yellowness index\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"Arrhenius\",<br>      <span style=\"color: plum;\">\"Equation\":</span> \"R_D=R_0\\\\cdot G_{340}^P\\\\cdot e^{ \\\\left( \\\\frac{-E_a}{R\\\\cdot T_K } \\\\right) }\",<br>      <span style=\"color: plum;\">\"R_D\":</span> {<br>        \"units\": \"YI/s\"<br>      },<br>      <span style=\"color: plum;\">\"p\":</span> {<br>        \"value\": 0.89<br>      },<br>      <span style=\"color: plum;\">\"E_a\":</span> {<br>        \"units\": \"kJ/mol\"<br>      }<br>    }</pre></div></div><div><strong style=\"color: white;\">D032:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Weston Wall\",<br>      <span style=\"color: plum;\">\"DateEntered\":</span> \"6/16/2023\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"10.4229/EUPVSEC20142014-5CO.5.4\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"PV AGING MODEL APPLIED TO SEVERAL METEOROLOGICAL CONDITIONS\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"Beno\\u00eet Braisaz1 , Chlo\\u00e9 Duchayne, Mike Van Iseghem, Khalid Radouane\",<br>      <span style=\"color: plum;\">\"Reference\":</span> \"Braisaz, B., et al. (2014). PV aging model applied to several meteorological conditions.\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"Aging, Damp heat, Outdoor, PV modeling, Models, PV module, degradation rate, Activation Energy, PID, cracks, UV\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"Double Diode module\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"Damp Heat Corrosion Model\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"arrhenius\",<br>      <span style=\"color: plum;\">\"Equation\":</span> \"R_D=R_0\\\\cdot RH^n\\\\cdot e^{ \\\\left( \\\\frac{-E_a}{R\\\\cdot T_K } \\\\right) }\",<br>      <span style=\"color: plum;\">\"R_D\":</span> {},<br>      <span style=\"color: plum;\">\"R_0\":</span> {},<br>      <span style=\"color: plum;\">\"n\":</span> {<br>        \"value\": 2<br>      },<br>      <span style=\"color: plum;\">\"E_a\":</span> {<br>        \"value\": 127.3689,<br>        \"units\": \"kJ/mol\"<br>      }<br>    }</pre></div></div><div><strong style=\"color: white;\">D033:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Weston Wall\",<br>      <span style=\"color: plum;\">\"DateEntered\":</span> \"6/8/2023\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"doi:10.1016/j.solmat.2017.08.007\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"Light-induced lifetime degradation in high-performance multicrystalline silicon: Detailed kinetics of the defect activation\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"Dennis Bredemeier, Dominic Walter, Jan Schmidt\",<br>      <span style=\"color: plum;\">\"Reference\":</span> \"D. Bredemeier et al., Sol. Energy Mater. Sol. Cells, vol. 173, pp. 2-5, 2017, doi: 10.1016/j.solmat.2017.08.007.\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"multi-Si, LeTID, \",<br>      <span style=\"color: plum;\">\"Material\":</span> \"multi-Si\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"LeTID\",<br>      <span style=\"color: plum;\">\"Comments\":</span> \"ab158micronwafer_bc200microncell\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"LeTID\",<br>      <span style=\"color: plum;\">\"x_ab\":</span> {<br>        \"value\": 1<br>      }<br>    }</pre></div></div><div><strong style=\"color: white;\">D034:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Weston Wall\",<br>      <span style=\"color: plum;\">\"DateEntered\":</span> \"6/9/2023\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"doi:10.1109/JPHOTOV.2021.3078368\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"The Influence of Minority Carrier Density on Degradation and Regeneration Kinetics in Multicrystalline Silicon Wafers\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"Guro Marie Wyller, Marie Syre Wiig, Ida Due-S\\u00f8rensen, Rune S\\u00f8nden\\u00e5\",<br>      <span style=\"color: plum;\">\"Reference\":</span> \"G. Wyller et al., IEEE J. Photovoltaics, vol. 11, no. 4, pp. 878-889, 2021, doi: 10.1109/JPHOTOV.2021.3078367.\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"p-type mc-Si Perc, Degradation, LeTID, Hydrogen Involvement\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"p-type mc-Si Perc\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"LeTID\",<br>      <span style=\"color: plum;\">\"Comments\":</span> \"wafer_200micron\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"LeTID\",<br>      <span style=\"color: plum;\">\"x_bc\":</span> {<br>        \"value\": 1.2<br>      }<br>    }</pre></div></div><div><strong style=\"color: white;\">D035:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Weston Wall\",<br>      <span style=\"color: plum;\">\"DateEntered\":</span> \"5/26/2023\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"doi:10.1109/JPHOTOV.2020.3025240\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"Temporary Recovery of the Defect Responsible for Light- and Elevated Temperature-Induced Degradation: Insights Into the Physical Mechanisms Behind LeTID\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"Wolfram Kwapil, Jonas Sch\\u00f6n, Tim Niewelt, and Martin C. Schubert\",<br>      <span style=\"color: plum;\">\"Reference\":</span> \"W. Kwapil et al., IEEE J. Photovoltaics, vol. 10, no. 6, pp. 1591-1603, 2020, doi: 10.1109/JPHOTOV.2020.3025240.\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"Degradation, Defect reactions, light- and elevated temperature-induced degradation, LeTID, model, silicon defects.\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"mc-Si cells\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"LeTID\",<br>      <span style=\"color: plum;\">\"Comments\":</span> \"Cell_200Micron\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"LeTID\",<br>      <span style=\"color: plum;\">\"x_ba\":</span> {<br>        \"value\": 1.7<br>      }<br>    }</pre></div></div><div><strong style=\"color: white;\">D036:</strong> <span onclick=\"this.nextElementSibling.style.display = this.nextElementSibling.style.display === 'none' ? 'block' : 'none'\" style=\"cursor: pointer; color: white;\">&#9660;</span><div style=\"display: none;\"><pre style=\"color: white; background-color: black; padding: 10px; border-radius: 5px;\">    {<br>      <span style=\"color: plum;\">\"DataEntryPerson\":</span> \"Michael Kempe\",<br>      <span style=\"color: plum;\">\"DateEntered\":</span> \"2/14/2025\",<br>      <span style=\"color: plum;\">\"DOI\":</span> \"10.1109/PVSC45281.2020.9300357\",<br>      <span style=\"color: plum;\">\"SourceTitle\":</span> \"Highly Accelerated UV Stress Testing for Transparent Flexible Frontsheets\",<br>      <span style=\"color: plum;\">\"Authors\":</span> \"Michael D Kempe, Peter Hacke, Joshua Morse, Michael Owen-Bellini, Derek Holsapple, Trevor Lockman, Samantha Hoang, David Okawa, Tamir Lance, Hoi Hong Ng\",<br>      <span style=\"color: plum;\">\"Reference\":</span> \"Kempe, M. D., et al. (2020). Highly Accelerated UV Stress Testing for Transparent Flexible Frontsheets. 2020 47th IEEE Photovoltaic Specialists Conference (PVSC).\",<br>      <span style=\"color: plum;\">\"KeyWords\":</span> \"Humidity, Irradiance, reciprocity\",<br>      <span style=\"color: plum;\">\"Material\":</span> \"Flexible Frontsheet, Frontsheet Coatings\",<br>      <span style=\"color: plum;\">\"Degradation\":</span> \"UV Cut On, UV Transmittance 310nm-350nm, Yellowness index, SPQEWT\",<br>      <span style=\"color: plum;\">\"EquationType\":</span> \"arrhenius\",<br>      <span style=\"color: plum;\">\"Equation\":</span> \"R_D=R_0\\\\cdot RH^n\\\\cdot G_{340}^P\\\\cdot e^{ \\\\left( \\\\frac{-E_a}{R\\\\cdot T_K } \\\\right) }\",<br>      <span style=\"color: plum;\">\"R_D\":</span> {<br>        \"units\": \"%/h\"<br>      },<br>      <span style=\"color: plum;\">\"R_0\":</span> {<br>        \"units\": \"%/h\"<br>      },<br>      <span style=\"color: plum;\">\"E_a\":</span> {<br>        \"value\": 38.7,<br>        \"stdev\": 21.7,<br>        \"units\": \"kJ/mol\"<br>      },<br>      <span style=\"color: plum;\">\"n\":</span> {},<br>      <span style=\"color: plum;\">\"p\":</span> {<br>        \"value\": 0.49,<br>        \"stdev\": 0.22<br>      },<br>      <span style=\"color: plum;\">\"Corr-E_a-P\":</span> {<br>        \"value\": -0.606<br>      }<br>    }</pre></div></div></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#kwarg_variables = pvdeg.utilities._read_material(name=None, fname=\"DegradationDatabase\", item=(\"Material\", \"Equation\", \"KeyWords\", \"EquationType\"))\n",
+    "#print(json.dumps(kwarg_variables, skipkeys = True, indent = 0 ).replace(\"{\" + \"\\n\", \"{\").replace('\\\"' + \"\\n\", \"\\\"\").replace(': {' , ':' + \"\\n\" + \"{\").replace('},' + \"\\n\", '},' +'\\n' +'\\n'))\n",
+    "pvdeg.utilities.display_json(pvdeg_file=\"DegradationDatabase\", fp=DATA_DIR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This next set of codes will take the data from the extracted portion of the Json library and create a list of variables from it. If more variables need to be modified or added, this is where it should be done."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"DataEntryPerson\": \"Michael Kempe\",\n",
+      "\"DateEntered\": \"2/14/2025\",\n",
+      "\"DOI\": \"10.1109/PVSC45281.2020.9300357\",\n",
+      "\"SourceTitle\": \"Highly Accelerated UV Stress Testing for Transparent Flexible Frontsheets\",\n",
+      "\"Authors\": \"Michael D Kempe, Peter Hacke, Joshua Morse, Michael Owen-Bellini, Derek Holsapple, Trevor Lockman, Samantha Hoang, David Okawa, Tamir Lance, Hoi Hong Ng\",\n",
+      "\"Reference\": \"Kempe, M. D., et al. (2020). Highly Accelerated UV Stress Testing for Transparent Flexible Frontsheets. 2020 47th IEEE Photovoltaic Specialists Conference (PVSC).\",\n",
+      "\"KeyWords\": \"Humidity, Irradiance, reciprocity\",\n",
+      "\"Material\": \"Flexible Frontsheet, Frontsheet Coatings\",\n",
+      "\"Degradation\": \"UV Cut On, UV Transmittance 310nm-350nm, Yellowness index, SPQEWT\",\n",
+      "\"EquationType\": \"arrhenius\",\n",
+      "\"Equation\": \"R_D=R_0\\\\cdot RH^n\\\\cdot G_{340}^P\\\\cdot e^{ \\\\left( \\\\frac{-E_a}{R\\\\cdot T_K } \\\\right) }\",\n",
+      "\"R_D\":\n",
+      "{\"units\": \"%/h\"},\n",
+      "\"R_0\":\n",
+      "{\"units\": \"%/h\"},\n",
+      "\"E_a\":\n",
+      "{\"value\": 38.7,\n",
+      "\"stdev\": 21.7,\n",
+      "\"units\": \"kJ/mol\"},\n",
+      "\"n\":\n",
+      "{},\n",
+      "\"p\":\n",
+      "{\"value\": 0.49,\n",
+      "\"stdev\": 0.22},\n",
+      "\"Corr-E_a-P\":\n",
+      "{\"value\": -0.606}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "deg_data = pvdeg.utilities.read_material(fp=DATA_DIR, key=\"D036\", pvdeg_file=\"DegradationDatabase\")\n",
+    "print(json.dumps(deg_data, skipkeys = True, indent = 0 ).replace(\"{\" + \"\\n\", \"{\").replace('\\\"' + \"\\n\", \"\\\"\").replace(': {' , ':' + \"\\n\" + \"{\").replace('\\n'+'}', '}'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we pull out the relevant equation code identifier needed for running the calculations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pvdeg.degradation.arrhenius\n"
+     ]
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\Large R_D=R_0\\cdot RH^n\\cdot G_{340}^P\\cdot e^{ \\left( \\frac{-E_a}{R\\cdot T_K } \\right) }$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "func = \"pvdeg.degradation.\" + deg_data[\"EquationType\"]\n",
+    "print(func)\n",
+    "display(Math(\"\\\\Large \" + deg_data[\"Equation\"]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Calculate Absolute Degradation Rate\n",
+    "\n",
+    "To do this calculation, we must have degradation parameter data for a process that is complete with all the necessary variables. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "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>Unnamed: 0</th>\n",
+       "      <th>RH</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Spectra: [ 300, 325, 350, 375, 400 ]</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2021-01-01 00:00:00-05:00</td>\n",
+       "      <td>95</td>\n",
+       "      <td>-1.1</td>\n",
+       "      <td>[nan, nan, nan, nan, nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2021-01-01 01:00:00-05:00</td>\n",
+       "      <td>72</td>\n",
+       "      <td>-1.1</td>\n",
+       "      <td>[nan, nan, nan, nan, nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2021-01-01 02:00:00-05:00</td>\n",
+       "      <td>72</td>\n",
+       "      <td>-1.3</td>\n",
+       "      <td>[nan, nan, nan, nan, nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2021-01-01 03:00:00-05:00</td>\n",
+       "      <td>72</td>\n",
+       "      <td>-1.5</td>\n",
+       "      <td>[nan, nan, nan, nan, nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2021-01-01 04:00:00-05:00</td>\n",
+       "      <td>72</td>\n",
+       "      <td>-1.7</td>\n",
+       "      <td>[nan, nan, nan, nan, nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8755</th>\n",
+       "      <td>2021-12-31 19:00:00-05:00</td>\n",
+       "      <td>92</td>\n",
+       "      <td>-1.1</td>\n",
+       "      <td>[nan, nan, nan, nan, nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8756</th>\n",
+       "      <td>2021-12-31 20:00:00-05:00</td>\n",
+       "      <td>92</td>\n",
+       "      <td>-0.7</td>\n",
+       "      <td>[nan, nan, nan, nan, nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8757</th>\n",
+       "      <td>2021-12-31 21:00:00-05:00</td>\n",
+       "      <td>92</td>\n",
+       "      <td>-0.4</td>\n",
+       "      <td>[nan, nan, nan, nan, nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8758</th>\n",
+       "      <td>2021-12-31 22:00:00-05:00</td>\n",
+       "      <td>92</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>[nan, nan, nan, nan, nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8759</th>\n",
+       "      <td>2021-12-31 23:00:00-05:00</td>\n",
+       "      <td>93</td>\n",
+       "      <td>-0.6</td>\n",
+       "      <td>[nan, nan, nan, nan, nan]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8760 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     Unnamed: 0  RH  Temperature  \\\n",
+       "0     2021-01-01 00:00:00-05:00  95         -1.1   \n",
+       "1     2021-01-01 01:00:00-05:00  72         -1.1   \n",
+       "2     2021-01-01 02:00:00-05:00  72         -1.3   \n",
+       "3     2021-01-01 03:00:00-05:00  72         -1.5   \n",
+       "4     2021-01-01 04:00:00-05:00  72         -1.7   \n",
+       "...                         ...  ..          ...   \n",
+       "8755  2021-12-31 19:00:00-05:00  92         -1.1   \n",
+       "8756  2021-12-31 20:00:00-05:00  92         -0.7   \n",
+       "8757  2021-12-31 21:00:00-05:00  92         -0.4   \n",
+       "8758  2021-12-31 22:00:00-05:00  92          0.0   \n",
+       "8759  2021-12-31 23:00:00-05:00  93         -0.6   \n",
+       "\n",
+       "     Spectra: [ 300, 325, 350, 375, 400 ]  \n",
+       "0               [nan, nan, nan, nan, nan]  \n",
+       "1               [nan, nan, nan, nan, nan]  \n",
+       "2               [nan, nan, nan, nan, nan]  \n",
+       "3               [nan, nan, nan, nan, nan]  \n",
+       "4               [nan, nan, nan, nan, nan]  \n",
+       "...                                   ...  \n",
+       "8755            [nan, nan, nan, nan, nan]  \n",
+       "8756            [nan, nan, nan, nan, nan]  \n",
+       "8757            [nan, nan, nan, nan, nan]  \n",
+       "8758            [nan, nan, nan, nan, nan]  \n",
+       "8759            [nan, nan, nan, nan, nan]  \n",
+       "\n",
+       "[8760 rows x 4 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\mkempe\\Documents\\GitHub\\new\\PVDegradationTools\\tests\\data\\spectra_pytest.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pvdeg import TEST_DATA_DIR\n",
+    "INPUT_SPECTRA = os.path.join(TEST_DATA_DIR, r\"spectra_pytest.csv\")\n",
+    "data = pd.read_csv(INPUT_SPECTRA)\n",
+    "display(data)\n",
+    "print(INPUT_SPECTRA)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. VantHoff Degradation\n",
+    "\n",
+    "Van 't Hoff Irradiance Degradation\n",
+    "\n",
+    "For one year of degredation the controlled environmnet lamp settings will need to be set to IWa.\n",
+    "\n",
+    "As with most `pvdeg` functions, the following functions will always require two arguments (weather_df and meta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Arrhenius\n",
+    "Calculate the Acceleration Factor between the rate of degredation of a modeled environmnet versus a modeled controlled environmnet\n",
+    "\n",
+    "Example: \"If the AF=25 then 1 year of Controlled Environment exposure is equal to 25 years in the field\"\n",
+    "\n",
+    "Equation:\n",
+    "$$ AF = N * \\frac{ I_{chamber}^x * RH_{chamber}^n * e^{\\frac{- E_a}{k T_{chamber}}} }{ \\Sigma (I_{POA}^x * RH_{outdoor}^n * e^{\\frac{-E_a}{k T_outdoor}}) }$$\n",
+    "\n",
+    "Function to calculate IWa, the Environment Characterization (W/m²). For one year of degredation the controlled environmnet lamp settings will need to be set at IWa.\n",
+    "\n",
+    "Equation:\n",
+    "$$ I_{WA} = [ \\frac{ \\Sigma (I_{outdoor}^x * RH_{outdoor}^n e^{\\frac{-E_a}{k T_{outdood}}}) }{ N * RH_{WA}^n * e^{- \\frac{E_a}{k T_eq}} } ]^{\\frac{1}{x}} $$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# relative humidity within chamber (%)\n",
+    "rh_chamber = 15\n",
+    "# arrhenius activation energy (kj/mol)\n",
+    "Ea = 40\n",
+    "I_chamber = 1000  # irradiance within chamber (W/m^2)\n",
+    "temp_chamber = 25  # temperature within chamber (C)\n",
+    "\n",
+    "rh_surface = pvdeg.humidity.surface_relative(rh_ambient=weather_df['relative_humidity'],\n",
+    "                                               temp_ambient=weather_df['temp_air'],\n",
+    "                                               temp_module=temp_module)\n",
+    "\n",
+    "arrhenius_deg = pvdeg.degradation.arrhenius_deg(weather_df=weather_df, meta=meta,\n",
+    "                                                rh_outdoor=rh_surface,\n",
+    "                                                I_chamber=I_chamber,\n",
+    "                                                rh_chamber=rh_chamber,\n",
+    "                                                temp_chamber=temp_chamber,\n",
+    "                                                poa=poa_df,\n",
+    "                                                temp=temp_cell,\n",
+    "                                                Ea=Ea)\n",
+    "\n",
+    "irr_weighted_avg_a = pvdeg.degradation.IwaArrhenius(weather_df=weather_df, meta=meta,\n",
+    "                                                    poa=poa_df,\n",
+    "                                                    rh_outdoor=weather_df['relative_humidity'],\n",
+    "                                                    temp=temp_cell,\n",
+    "                                                    Ea=Ea)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Quick Method (Degradation)\n",
+    "\n",
+    "For quick calculations, you can omit POA and both module and cell temperature. The function will calculate these figures as needed using the available weather data with the default options for PV module configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The array surface_tilt angle was not provided, therefore the latitude of  33.4 was used.\n",
+      "The array azimuth was not provided, therefore an azimuth of  180.0 was used.\n",
+      "The array surface_tilt angle was not provided, therefore the latitude of  33.4 was used.\n",
+      "The array azimuth was not provided, therefore an azimuth of  180.0 was used.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# chamber settings\n",
+    "I_chamber= 1000\n",
+    "temp_chamber=60\n",
+    "rh_chamber=15\n",
+    "\n",
+    "# activation energy\n",
+    "Ea = 40\n",
+    "\n",
+    "vantHoff_deg = pvdeg.degradation.vantHoff_deg(weather_df=weather_df, meta=meta,\n",
+    "                                              I_chamber=I_chamber,\n",
+    "                                              temp_chamber=temp_chamber)\n",
+    "\n",
+    "irr_weighted_avg_v = pvdeg.degradation.IwaVantHoff(weather_df=weather_df, meta=meta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The array surface_tilt angle was not provided, therefore the latitude of  33.4 was used.\n",
+      "The array azimuth was not provided, therefore an azimuth of  180.0 was used.\n",
+      "The array surface_tilt angle was not provided, therefore the latitude of  33.4 was used.\n",
+      "The array azimuth was not provided, therefore an azimuth of  180.0 was used.\n"
+     ]
+    }
+   ],
+   "source": [
+    "rh_surface = pvdeg.humidity.surface_relative(rh_ambient=weather_df['relative_humidity'],\n",
+    "                                               temp_ambient=weather_df['temp_air'],\n",
+    "                                               temp_module=temp_module)\n",
+    "\n",
+    "arrhenius_deg = pvdeg.degradation.arrhenius_deg(weather_df=weather_df, meta=meta,\n",
+    "                                                rh_outdoor=rh_surface,\n",
+    "                                                I_chamber=I_chamber,\n",
+    "                                                rh_chamber=rh_chamber,\n",
+    "                                                temp_chamber=temp_chamber,\n",
+    "                                                Ea=Ea)\n",
+    "\n",
+    "irr_weighted_avg_a = pvdeg.degradation.IwaArrhenius(weather_df=weather_df, meta=meta,\n",
+    "                                                    rh_outdoor=weather_df['relative_humidity'],\n",
+    "                                                    Ea=Ea)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Solder Fatigue\n",
+    "\n",
+    "Estimate the thermomechanical fatigue of flat plate photovoltaic module solder joints over the time range given using estimated cell temperature. Like other `pvdeg` funcitons, the minimal parameters are (weather_df, meta). Running the function with only these two inputs will use default PV module configurations ( open_rack_glass_polymer ) and the 'sapm' temperature model over the entire length of the weather data. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The array surface_tilt angle was not provided, therefore the latitude of  33.4 was used.\n",
+      "The array azimuth was not provided, therefore an azimuth of  180.0 was used.\n"
+     ]
+    }
+   ],
+   "source": [
+    "fatigue = pvdeg.fatigue.solder_fatigue(weather_df=weather_df, meta=meta)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you wish to reduce the span of time or use a non-default temperature model, you may specify the parameters manually. Let's try an explicit example.\n",
+    "We want the solder fatigue estimated over the month of June for a roof mounted glass-front polymer-back module.\n",
+    "\n",
+    "1. Lets create a datetime-index for the month of June.\n",
+    "2. Next, generate the cell temperature. Make sure to explicity restrict the weather data to our dt-index for June. Next, declare the PV module configuration.\n",
+    "3. Calculate the fatigue. Explicity specify the time_range (our dt-index for June from step 1) and the cell temperature as we caculated in step 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The array surface_tilt angle was not provided, therefore the latitude of  33.4 was used.\n",
+      "The array azimuth was not provided, therefore an azimuth of  180.0 was used.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# select the month of June\n",
+    "time_range = weather_df.index[weather_df.index.month == 6]\n",
+    "\n",
+    "# calculate cell temperature over our selected date-time range.\n",
+    "# specify the module configuration\n",
+    "temp_cell = pvdeg.temperature.cell(weather_df=weather_df.loc[time_range], meta=meta,\n",
+    "                                   temp_model='sapm',\n",
+    "                                   conf='insulated_back_glass_polymer')\n",
+    "\n",
+    "\n",
+    "fatigue = pvdeg.fatigue.solder_fatigue(weather_df=weather_df, meta=meta,\n",
+    "                                       time_range = time_range,\n",
+    "                                       temp_cell = temp_cell)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "august2025",
+   "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": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Tools - Edge Seal Oxygen Ingress.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Tools - Edge Seal Oxygen Ingress.ipynb
new file mode 100644
index 000000000..f6e593a8d
--- /dev/null
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Tools - Edge Seal Oxygen Ingress.ipynb	
@@ -0,0 +1,309 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tools - Edge Seal Oxygen Ingress Calculator tool\n",
+    "\n",
+    "### Calculation of oxygen ingress profile through an edge seal and into the encapsulant.\n",
+    "\n",
+    "**Requirements:**\n",
+    "- Local weather data file or site longitude and latittude.\n",
+    "- Properties and dimensions of the edge seal.\n",
+    "\n",
+    "**Objectives:**\n",
+    "1. Import weather data.\n",
+    "2. Set up the calculations.\n",
+    "3. Calculate oxygen ingress into an edge seal.\n",
+    "3. Incorporate an oxygen consumption model.\n",
+    "4. Plot the data.\n",
+    "\n",
+    "**Background:**\n",
+    "\n",
+    "This performs a 1-D finite difference model for oxygen ingress through an edge seal and into an encapsulant. This is effectively an infinitely long module with a prescribed width.The output is then displayed graphically."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n",
+    "# !pip install pvdeg==0.4.2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pvdeg\n",
+    "import pandas as pd\n",
+    "from pvdeg import DATA_DIR\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This information helps with debugging and getting support :)\n",
+    "import sys, platform\n",
+    "print(\"Working on a \", platform.system(), platform.release())\n",
+    "print(\"Python version \", sys.version)\n",
+    "print(\"Pandas version \", pd.__version__)\n",
+    "print(\"pvdeg version \", pvdeg.__version__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Import Weather Data\n",
+    "\n",
+    "The function has these minimum requirements when using a weather data file:\n",
+    "- Weather data containing (at least) DNI, DHI, GHI, Temperature, RH, and Wind-Speed data at module level.\n",
+    "- Site meta-data containing (at least) latitude, longitude, and time zone\n",
+    "\n",
+    "Alternatively one may can get meterological data from the NSRDB or PVGIS with just the longitude and latitude. This function for the NSRDB (via NSRDB 'PSM3') works primarily for most of North America and South America. PVGIS works for most of the rest of the world (via SARAH 'PVGIS'). See the tutorial \"Weather Database Access.ipynb\" tutorial on PVdeg or Jensen et al. https://doi.org/10.1016/j.solener.2023.112092 for satellite coverage information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get data from a supplied data file (Do not use the next box of code if using your own file)\n",
+    "weather_file = os.path.join(DATA_DIR, \"psm3_demo.csv\")\n",
+    "weather_df, meta = pvdeg.weather.read(weather_file, \"csv\")\n",
+    "print(meta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This routine will get a meteorological dataset from anywhere in the world where it is available\n",
+    "# weather_id = (24.7136, 46.6753) #Riyadh, Saudi Arabia\n",
+    "# weather_id = (35.6754, 139.65) #Tokyo, Japan\n",
+    "# weather_id = (-43.52646, 172.62165) #Christchurch, New Zealand\n",
+    "# weather_id = (64.84031, -147.73836) #Fairbanks, Alaska\n",
+    "# weather_id = (65.14037, -21.91633) #Reykjavik, Iceland\n",
+    "weather_id = (33.4152, -111.8315)  # Mesa, Arizona\n",
+    "weather_df, meta = pvdeg.weather.get_anywhere(id=weather_id)\n",
+    "print(meta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This computes a module temperature. Here the default is an open rack system, but other options include:\n",
+    "#       'open_rack_glass_glass',\n",
+    "#       'close_mount_glass_glass',\n",
+    "#       'insulated_back_glass_polymer'\n",
+    "\n",
+    "temperature = pvdeg.temperature.temperature(\n",
+    "    weather_df=weather_df,\n",
+    "    meta=meta,\n",
+    "    cell_or_mod=\"module\",\n",
+    "    temp_model=\"sapm\",\n",
+    "    conf=\"open_rack_glass_polymer\",\n",
+    ")\n",
+    "\n",
+    "temperature = pd.DataFrame(temperature, columns=[\"module_temperature\"])\n",
+    "temperature[\"time\"] = list(range(8760))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Set up the Calculations\n",
+    "\n",
+    "There is a library of some materials and the relevant oxygen ingress parameters that can be used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "es = \"OX005\"  # This is the number for the edge seal in the json file\n",
+    "enc = \"OX003\"  # This is the number for the encapsulant in the json file\n",
+    "esw = 1.5  # This is the edge seal width in [cm]\n",
+    "encw = 10  # This is the encapsulant width in [cm]\n",
+    "sn = 20  # This is the number of edge seal nodes to use\n",
+    "en = 50  # This is the number of encapsulant nodes to use\n",
+    "pressure = 0.2109 * (1 - 0.0065 * meta.get(\"altitude\") / 288.15) ** 5.25588\n",
+    "print(pvdeg.utilities._read_material(name=\"OX003\", fname=\"O2permeation\"))\n",
+    "print(pvdeg.utilities._read_material(name=\"W003\", fname=\"H2Opermeation\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Run the Calculations\n",
+    "\n",
+    "This runs the calculations for diffusion using a simple 1-D finite difference calculation. The first set of calculations is just for diffusion, then the next two (when written) will include some consumption of oxygen. In typical PV applications, it is common for oxygen ingress distance to be limited by its consumption rate in the encapsulant."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "oxygen_profile = pvdeg.diffusion.esdiffusion(\n",
+    "    temperature=temperature,\n",
+    "    edge_seal=es,\n",
+    "    encapsulant=enc,\n",
+    "    edge_seal_width=esw,\n",
+    "    encapsulant_width=encw,\n",
+    "    seal_nodes=sn,\n",
+    "    encapsulant_nodes=en,\n",
+    "    press=pressure,\n",
+    "    repeat=2,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This sets up an a variable with the output folder information.\n",
+    "output_folder = os.path.join(\n",
+    "    os.path.dirname(os.path.dirname(os.getcwd())), \"TEMP\", \"results\"\n",
+    ")\n",
+    "try:\n",
+    "    os.makedirs(output_folder)\n",
+    "except OSError as error:\n",
+    "    print(error)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_lines = 10\n",
+    "times = oxygen_profile.index.tolist()\n",
+    "for index in range(n_lines):\n",
+    "    plt.plot(\n",
+    "        oxygen_profile.iloc[\n",
+    "            int(np.trunc((index + 1) * (len(oxygen_profile) - 1) / n_lines))\n",
+    "        ],\n",
+    "        label=np.round(\n",
+    "            times[int(np.trunc((index + 1) * ((len(oxygen_profile) - 1) / n_lines)))]\n",
+    "            / 365.25\n",
+    "            / 24,\n",
+    "            2,\n",
+    "        ),\n",
+    "    )\n",
+    "plt.legend(title=\"Time [year]\")\n",
+    "plt.ylabel(\"Oxygen Concentration [g/cm³]\")\n",
+    "plt.xlabel(\"Distance From Edge [cm]\")\n",
+    "plt.ticklabel_format(axis=\"y\", style=\"plain\")\n",
+    "\n",
+    "plt.savefig(\n",
+    "    os.path.join(output_folder, \"Edge_Seal_O2_ingress.png\"), bbox_inches=\"tight\"\n",
+    ")  # Creates an image file of the standoff plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Save data outputs.\n",
+    "\n",
+    "This cell contains a number of pre-scripted commands for exporting and saving data. The code to save plots is located after the plot creation. First check that the output folder exists."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fpath = os.path.join(DATA_DIR, \"O2permeation.json\")\n",
+    "with open(fpath) as f:\n",
+    "    data = json.load(f)\n",
+    "f.close()\n",
+    "\n",
+    "material_list = \"\"\n",
+    "for key in data:\n",
+    "    if \"name\" in data[key].keys():\n",
+    "        material_list = material_list + key + \"=\" + data[key][\"name\"] + \"\\n\"\n",
+    "material_list = material_list[0 : len(material_list) - 1]\n",
+    "print(material_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Your results will be stored in %s\" % output_folder)\n",
+    "print(\"The folder must already exist or the file will not be created\")\n",
+    "\n",
+    "# Writes the meterological data to an *.csv file.\n",
+    "pvdeg.weather.write(\n",
+    "    data_df=weather_df,\n",
+    "    metadata=meta,\n",
+    "    savefile=os.path.join(output_folder, \"WeatherFile.csv\"),\n",
+    ")\n",
+    "\n",
+    "# Writes a file with the edge seal oxygen profile calculations.\n",
+    "pd.DataFrame(oxygen_profile).to_csv(\n",
+    "    os.path.join(output_folder, \"ES_Oxygen_profile.csv\")\n",
+    ")\n",
+    "\n",
+    "# Writes a file with temperature data used in the model calculations.\n",
+    "pd.DataFrame(temperature).to_csv(\n",
+    "    os.path.join(output_folder, \"ES_Temperature_profile.csv\")\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pvdeg",
+   "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": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb
deleted file mode 100644
index f1a6a3a22..000000000
--- a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb	
+++ /dev/null
@@ -1,777 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tools - Module Standoff for IEC TS 63126\n",
-    "\n",
-    "### Calculation of module standoff distance according to IEC TS 63126\n",
-    "\n",
-    "**Requirements:**\n",
-    "- Local weather data file or site longitude and latittude\n",
-    "\n",
-    "**Objectives:**\n",
-    "1. Import weather data.\n",
-    "2. Calculate installation standoff - Level 1 and Level 2.\n",
-    "3. Calculate $X_{eff}$ from provided module temperature data.\n",
-    "4. Calculate $T_{98}$ for a given azimuth, tilt, and $X_{eff}$.\n",
-    "5. Plot $X_{min}$ for all azimuth and tilt for a given $T_{98}$.\n",
-    "6. Plot $X_{min}$ for Level 1, Level 2, or a $T_{98}$ for a given region.\n",
-    "\n",
-    "**Background:**\n",
-    "\n",
-    "This notebook calculates the a minimum effective standoff distance ($X_{eff}$) necessary for roof-mounted PV modules to ensure that the $98^{th}$ percentile operating temperature, $T_{98}$, remains under 70°C for compliance to IEC 61730 and IEC 61215. For higher $T_{98}$ values above 70°C or 80°C testing must be done to the specifications for Level 1 and Level 2 of IEC TS 63126. This method is outlined in the appendix of IEC TS 63126 and is based on the model from *[King 2004] and data from **[Fuentes, 1987] to model the approximate exponential decay in temperature, $T(X)$, with increasing standoff distance, $X$, as,\n",
-    "\n",
-    "$$ X = -X_0 \\ln\\left(1-\\frac{T_0-T}{\\Delta T}\\right), Equation 1 $$\n",
-    "\n",
-    "where $T_0$ is the temperature for $X=0$ (insulated-back) and $\\Delta T$ is the temperature difference between an insulated-back ($X=0$) and open-rack mounting configuration ($X=\\infty)$.\n",
-    "\n",
-    " We used pvlib and data from the National Solar Radiation Database (NSRDB) to calculate the module temperatures for the insulated-back and open-rack mounting configurations and apply our model to obtain the minimum standoff distance for roof-mounted PV systems to achieve a temperature lower than a specified $T_{98}$. The following figure showcases this calulation for the entire world for an $X_{eff}$ that results in $T_{98}$=70°C. Values of $X_{eff}$ higher than this will require Level 1 or Level 2 certification. \n",
-    "\n",
-    "$*$ D. L. King, W. E. Boyson, and J. A. Kratochvil, \"Photovoltaic array performance model,\" SAND2004-3535, Sandia National Laboratories, Albuquerque, NM, 2004. '\\\n",
-    "$**$ M. K. Fuentes, \"A simplified thermal model for Flat-Plate photovoltaic arrays,\" United States, 1987-05-01 1987. https://www.osti.gov/biblio/6802914\n"
-   ]
-  },
-  {
-   "attachments": {
-    "62279573-41e3-45dd-bf62-4fa60c1e7e69.png": {
-     "image/png": 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-    }
-   },
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![T98 70C standoff Map.png](attachment:62279573-41e3-45dd-bf62-4fa60c1e7e69.png)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n",
-    "# !pip install pvdeg==0.3.3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from pathlib import Path\n",
-    "import pvdeg\n",
-    "import pandas as pd\n",
-    "from pvdeg import DATA_DIR\n",
-    "import dask\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import seaborn as sns\n",
-    "import math"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Working on a  Windows 10\n",
-      "Python version  3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n",
-      "Pandas version  2.2.0\n",
-      "pvdeg version  0.2.4.dev83+ge2ceab9.d20240422\n",
-      "dask version 2024.1.1\n",
-      "C:\\Users\\mspringe\\OneDrive - NREL\\msp\\projects\\2023_DegradationTools\\Github\\PVDegradationTools\\pvdeg\\data\n"
-     ]
-    }
-   ],
-   "source": [
-    "# This information helps with debugging and getting support :)\n",
-    "import sys, platform\n",
-    "\n",
-    "print(\"Working on a \", platform.system(), platform.release())\n",
-    "print(\"Python version \", sys.version)\n",
-    "print(\"Pandas version \", pd.__version__)\n",
-    "print(\"pvdeg version \", pvdeg.__version__)\n",
-    "print(\"dask version\", dask.__version__)\n",
-    "print(DATA_DIR)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. Import Weather Data\n",
-    "\n",
-    "The function has these minimum requirements when using a weather data file:\n",
-    "- Weather data containing (at least) DNI, DHI, GHI, Temperature, RH, and Wind-Speed data at module level.\n",
-    "- Site meta-data containing (at least) latitude, longitude, and time zone\n",
-    "\n",
-    "Alternatively one may can get meterological data from the NSRDB or PVGIS with just the longitude and latitude. This function for the NSRDB (via NSRDB 'PSM3') works primarily for most of North America and South America. PVGIS works for most of the rest of the world (via SARAH 'PVGIS'). See the tutorial \"Weather Database Access.ipynb\" tutorial on PVdeg or Jensen et al. https://doi.org/10.1016/j.solener.2023.112092 for satellite coverage information.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'Source': 'NSRDB', 'Location ID': 145809.0, 'City': '-', 'State': '-', 'Country': '-', 'Clearsky DHI Units': 'w/m2', 'Clearsky DNI Units': 'w/m2', 'Clearsky GHI Units': 'w/m2', 'Dew Point Units': 'c', 'DHI Units': 'w/m2', 'DNI Units': 'w/m2', 'GHI Units': 'w/m2', 'Solar Zenith Angle Units': 'Degree', 'Temperature Units': 'c', 'Pressure Units': 'mbar', 'Relative Humidity Units': '%', 'Precipitable Water Units': 'cm', 'Wind Direction Units': 'Degrees', 'Wind Speed Units': 'm/s', 'Cloud Type -15': 'N/A', 'Cloud Type 0': 'Clear', 'Cloud Type 1': 'Probably Clear', 'Cloud Type 2': 'Fog', 'Cloud Type 3': 'Water', 'Cloud Type 4': 'Super-Cooled Water', 'Cloud Type 5': 'Mixed', 'Cloud Type 6': 'Opaque Ice', 'Cloud Type 7': 'Cirrus', 'Cloud Type 8': 'Overlapping', 'Cloud Type 9': 'Overshooting', 'Cloud Type 10': 'Unknown', 'Cloud Type 11': 'Dust', 'Cloud Type 12': 'Smoke', 'Fill Flag 0': 'N/A', 'Fill Flag 1': 'Missing Image', 'Fill Flag 2': 'Low Irradiance', 'Fill Flag 3': 'Exceeds Clearsky', 'Fill Flag 4': 'Missing CLoud Properties', 'Fill Flag 5': 'Rayleigh Violation', 'Surface Albedo Units': 'N/A', 'Version': '3.0.6', 'latitude': 39.73, 'longitude': -105.18, 'tz': -7.0, 'altitude': 1820.0}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Get data from a supplied data file (Do not use the next box of code if using your own file)\n",
-    "weather_file = os.path.join(DATA_DIR, \"psm3_demo.csv\")\n",
-    "WEATHER_df, META = pvdeg.weather.read(weather_file, \"csv\")\n",
-    "print(META)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Column \"relative_humidity\" not found in DataFrame. Calculating...\n",
-      "{'Source': 'NSRDB', 'Location ID': '77855', 'City': '-', 'State': '-', 'Country': '-', 'Dew Point Units': 'c', 'DHI Units': 'w/m2', 'DNI Units': 'w/m2', 'GHI Units': 'w/m2', 'Temperature Units': 'c', 'Pressure Units': 'mbar', 'Wind Direction Units': 'Degrees', 'Wind Speed Units': 'm/s', 'Surface Albedo Units': 'N/A', 'Version': '3.2.0', 'latitude': 33.41, 'longitude': -111.82, 'altitude': 381, 'tz': -7, 'wind_height': 2}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# From Tutorial 5 EXAMPLE, this works.\n",
-    "# API_KEY = 'your_api_key_here'\n",
-    "API_KEY = \"DEMO_KEY\"  # you can activate this line to use the demonstration API key but it has limited usage.\n",
-    "# The example API key here is for demonstation and is rate-limited per IP.\n",
-    "# To get your own API key, visit https://developer.nrel.gov/signup/\n",
-    "\n",
-    "weather_db = \"PSM3\"  # This pulls data from the NSRDB just for locations in North and South America.\n",
-    "weather_id = (33.4152, -111.8315)\n",
-    "weather_arg = {\n",
-    "    \"api_key\": API_KEY,\n",
-    "    \"email\": \"user@mail.com\",\n",
-    "    \"names\": \"tmy\",\n",
-    "    \"attributes\": [],\n",
-    "    \"map_variables\": True,\n",
-    "}\n",
-    "\n",
-    "WEATHER_df, META = pvdeg.weather.get(weather_db, weather_id, **weather_arg)\n",
-    "print(META)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'latitude': 24.7136, 'longitude': 46.6753, 'altitude': 646.0, 'wind_height': 10, 'Source': 'PVGIS'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "weather_db = \"PVGIS\"  # This pulls data for most of the world.\n",
-    "weather_id = (24.7136, 46.6753)  # Riyadh, Saudi Arabia\n",
-    "# weather_id = (35.6754, 139.65) #Tokyo, Japan\n",
-    "weather_arg = {\"map_variables\": True}\n",
-    "WEATHER_df, META = pvdeg.weather.get(weather_db, weather_id)\n",
-    "print(META)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. Calculate Installation Standoff Minimum - Level 1 and Level 2\n",
-    "\n",
-    "According to IEC TS 63126, Level 0, Level 1 and Level 2 certification is limited to T₉₈<70°C, <80°C and <90°C, respectively. Level 0 certification is essentially compliance to IEC 61730 and IEC 61215. The default value of T₉₈<70°C represents the minimium gap to avoid higher temperature certification according to IEC TS 63126. This minimum standoff ($x_{min}$) is the distance between the bottom of the module frame and the roof and can be extimated for a given environment as, \n",
-    "\n",
-    "$$ X_{min} = -X_0 \\ln\\left(1-\\frac{T_{98,0}-T}{ T_{98,0}- T_{98,inf}}\\right), Equation 2 $$\n",
-    "\n",
-    "where $T_{98,0}$ is the $98^{th}$ percentile temperature for an insulated back module and $T_{98,inf}$ is the $98^{th}$ percentile temperature for an open rack mounted module.\n",
-    "\n",
-    "Once the meterological data has been obtained, the input parameter possibilities are:\n",
-    "\n",
-    "- T₉₈ : Does not necessarily need to be set at 70°C or 80°C for IEC TS 63216, you might want to use a different number to compensate for a thermal aspect of the particular system you are considering. The default is 70°C.\n",
-    "- tilt : tilt from horizontal of PV module. The default is 0°.\n",
-    "- azimuth : azimuth in degrees from North. The default is 180° for south facing.\n",
-    "- sky_model : pvlib compatible model for generating sky characteristics (Options: 'isotropic', 'klucher', 'haydavies', 'reindl', 'king', 'perez'). The default is 'isotropic'.\n",
-    "- temp_model : pvlib compatible module temperature model. (Options: 'sapm', 'pvsyst', 'faiman', 'sandia'). The default is 'sapm'.\n",
-    "- conf_0 : Temperature model for hotest mounting configuration. Default is \"insulated_back_glass_polymer\".\n",
-    "- conf_inf : Temperature model for open rack mounting. Default is \"open_rack_glass_polymer\".\n",
-    "- x_0 : thermal decay constant [cm] (see documentation). The default is 6.5 cm.\n",
-    "- wind_factor : Wind speed power law correction factor to account for different wind speed measurement heights between weather database (e.g. NSRDB) and the tempeature model (e.g. SAPM). The default is 0.33."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following is the minimum function call. It defaults to horizontal tilt and T₉₈=70°C."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The array tilt angle was not provided, therefore the latitude tilt of 24.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The estimated T₉₈ of an insulated-back module is 89.6°C. \n",
-      "The estimated T₉₈ of an open-rack module is 63.8°C. \n",
-      "Level 0 certification is valid for a standoff greather than 9.3 cm. \n",
-      "Level 1 certification is required for a standoff between than 9.3 cm, and 3.0 cm. \n",
-      "Level 2 certification is required for a standoff less than 3.0 cm.\n"
-     ]
-    }
-   ],
-   "source": [
-    "standoff = pvdeg.standards.standoff(weather_df=WEATHER_df, meta=META)\n",
-    "print(pvdeg.standards.interpret_standoff(standoff))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following is a full function call for both T₉₈=70°C and 80°C separately even though the second standoff distance can be calculated using only T98_0 and T98_inf. With this function, one may also want to change the tilt, azimuth, or T98. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "First calculation standoff =  9.3  cm.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "Second calculation standoff =  3.0  cm.\n",
-      "The estimated T₉₈ of an insulated-back module is 89.6°C. \n",
-      "The estimated T₉₈ of an open-rack module is 63.8°C. \n",
-      "Level 0 certification is valid for a standoff greather than 9.3 cm. \n",
-      "Level 1 certification is required for a standoff between than 9.3 cm, and 3.0 cm. \n",
-      "Level 2 certification is required for a standoff less than 3.0 cm.\n"
-     ]
-    }
-   ],
-   "source": [
-    "standoff_1 = pvdeg.standards.standoff(\n",
-    "    weather_df=WEATHER_df,\n",
-    "    meta=META,\n",
-    "    T98=70,\n",
-    "    tilt=META[\"latitude\"],\n",
-    "    azimuth=None,\n",
-    "    sky_model=\"isotropic\",\n",
-    "    temp_model=\"sapm\",\n",
-    "    conf_0=\"insulated_back_glass_polymer\",\n",
-    "    conf_inf=\"open_rack_glass_polymer\",\n",
-    "    x_0=6.5,\n",
-    "    wind_factor=0.33,\n",
-    ")\n",
-    "print(\"First calculation standoff = \", \"%.1f\" % standoff_1[\"x\"].iloc[0], \" cm.\")\n",
-    "standoff_2 = pvdeg.standards.standoff(\n",
-    "    weather_df=WEATHER_df,\n",
-    "    meta=META,\n",
-    "    T98=80,\n",
-    "    tilt=META[\"latitude\"],\n",
-    "    azimuth=None,\n",
-    "    sky_model=\"isotropic\",\n",
-    "    temp_model=\"sapm\",\n",
-    "    conf_0=\"insulated_back_glass_polymer\",\n",
-    "    conf_inf=\"open_rack_glass_polymer\",\n",
-    "    x_0=6.5,\n",
-    "    wind_factor=0.33,\n",
-    ")\n",
-    "print(\"Second calculation standoff = \", \"%.1f\" % standoff_2[\"x\"].iloc[0], \" cm.\")\n",
-    "print(pvdeg.standards.interpret_standoff(standoff_1=standoff_1, standoff_2=standoff_2))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. Calculate $X_{eff}$ from provided module temperature data.\n",
-    "\n",
-    "To do this calculation, one must use a set of data with: \n",
-    "   - meterological irradiance data sufficient to calculate the POA irradiance (DHI, GHI, and DNI),\n",
-    "   - ambient temperature data,\n",
-    "   - wind speed at module height, (wind_factor=0.33 will be used unless otherwise specified)\n",
-    "   - temperature measurements of the module in the test system. Ideally this would be measured under a worst case scenario that maximizes the module temperature for a given site,\n",
-    "   - geographic meta data including longitude and latitude,\n",
-    "\n",
-    "To create a weather file of your own, copy the format of the example file 'xeff_demo.csv'. This is formatted with the first row containing meta data variable names, the second row containing the corresponding values, the third row containing meteorological data headers, and all the remaining rows containing the meteorological data.\n",
-    "\n",
-    "To do this calculation, one should also filter the data to remove times when the sun is not shining or when snow is likely to be on the module. The recommendations and programmed defaults are to use poa_min=100 W/m² and data when the minimum ambient temperature t_amb_min=0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The effective standoff for this system is 2.0 cm.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Read the weather file\n",
-    "weather_file = os.path.join(DATA_DIR, \"xeff_demo.csv\")\n",
-    "xeff_weather, xeff_meta = pvdeg.weather.read(weather_file, \"csv\")\n",
-    "# Pull measured temperature and calculate theoretical insulated back module temperature and open rack module temperature\n",
-    "T_0, T_inf, xeff_poa = pvdeg.standards.eff_gap_parameters(\n",
-    "    weather_df=xeff_weather,\n",
-    "    meta=xeff_meta,\n",
-    "    sky_model=\"isotropic\",\n",
-    "    temp_model=\"sapm\",\n",
-    "    conf_0=\"insulated_back_glass_polymer\",\n",
-    "    conf_inf=\"open_rack_glass_polymer\",\n",
-    "    wind_factor=0.33,\n",
-    ")\n",
-    "\n",
-    "# Now calculate X_eff.\n",
-    "x_eff = pvdeg.standards.eff_gap(\n",
-    "    T_0,\n",
-    "    T_inf,\n",
-    "    xeff_weather[\"module_temperature\"],\n",
-    "    xeff_weather[\"temp_air\"],\n",
-    "    xeff_poa,\n",
-    "    x_0=6.5,\n",
-    "    poa_min=100,\n",
-    "    t_amb_min=0,\n",
-    ")\n",
-    "print(\"The effective standoff for this system is\", \"%.1f\" % x_eff, \"cm.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4. Calculate $T_{98}$ for a given azimuth, tilt, and $X_{eff}$.\n",
-    "\n",
-    "Equation 2 can be reorganized as,\n",
-    "\n",
-    "$$ T_{98} = T_{98,0}  -( T_{98,0}- T_{98,inf}) \\left(1-e^{-\\frac{x_{eff}}{x_{0}}}\\right), Equation 3 $$\n",
-    "\n",
-    "and used to calculate the $98^{th}$ percential temperature, $T_{98}$, for a PV system having a given effective standoff height, $X_{eff}$,  for an arbitrarily oriented module. Here, $T_{98,0}$ is the $98^{th}$ percentile for an insulated-back module and $T_{98,inf}$ is the $98^{th}$ percentile for a rack-mounted module. The input parameter possibilities are the same as shown in Objective #2 above, but the example below uses the default parameters. The actual tilt [degrees], azimuth [degrees] and $X_{eff}$ [cm] can be modifed as desired."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The 98ᵗʰ percential temperature is estimated to be 89.6 °C.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# This is the minimal function call using the common default settings to estimate T₉₈.\n",
-    "T_98 = pvdeg.standards.T98_estimate(\n",
-    "    weather_df=WEATHER_df,\n",
-    "    meta=META,\n",
-    "    tilt=META[\"latitude\"],\n",
-    "    azimuth=None,\n",
-    "    x_eff=0,\n",
-    ")\n",
-    "print(\"The 98ᵗʰ percential temperature is estimated to be\", \"%.1f\" % T_98, \"°C.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 5. Plot $X_{min}$ for all azimuth and tilt for a given $T_{98}$.\n",
-    "\n",
-    "The temperature of a system is affected by the orientation. This section will scan all possible tilts and azimuths calculating the minimum standoff distance for a given $T_{98}$. Similar additional factors as above can also be modified but are not included here for simplicity. The tilt_step and azimuth_step are the number of degrees for each step for the 90° and 180° tilt and azimuth spans, respectively. The default for this calculation is for $T_{98}$=70°C, the boundary between Level 0 and Level 1 requirements. The temperature model information given below is unnecessary as these are default values that would get populated automatically. However, they were included here for clarity into a standard practice as per IEC TS 63126.\n",
-    "\n",
-    "$$ X_{min} = -X_0 \\ln\\left(1-\\frac{T_{98,0}-T}{ T_{98,0}- T_{98,inf}}\\right), Equation 2 $$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                     \r"
-     ]
-    }
-   ],
-   "source": [
-    "# Scans through all the azimuth and tilt running the minimum standoff calculation\n",
-    "# Set up keyword parameters for the calculation\n",
-    "\n",
-    "kwarg_x = dict(\n",
-    "    sky_model=\"isotropic\",\n",
-    "    temp_model=\"sapm\",\n",
-    "    conf_0=\"insulated_back_glass_polymer\",\n",
-    "    conf_inf=\"open_rack_glass_polymer\",\n",
-    "    T98=70,\n",
-    "    x_0=6.5,\n",
-    "    wind_factor=0.33,\n",
-    ")\n",
-    "# Run the calculation\n",
-    "x_azimuth_step = 10\n",
-    "x_tilt_step = 10\n",
-    "standoff_series = pvdeg.utilities.tilt_azimuth_scan(\n",
-    "    weather_df=WEATHER_df,\n",
-    "    meta=META,\n",
-    "    tilt_step=x_tilt_step,\n",
-    "    azimuth_step=x_azimuth_step,\n",
-    "    func=pvdeg.standards.standoff_x,\n",
-    "    **kwarg_x,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The next cell creates a plot of the calculated data. Some of the things you may want to change are:\n",
-    "- cmap=\"Spectral_r\": Change to have different colors\n",
-    "- plt.title : This will change the plot title.\n",
-    "- figsize=(16,4) : Change the plot dimensions and/or aspect ratio.\n",
-    "- vmax=None : This can be set to a numeric value to control the depth scale maximum\n",
-    "- vmin=0 : This controls the minimum of the depth scale.\n",
-    "- v_ticks=37 : This changes the number of vertical tick marks\n",
-    "- h_ticks=10 : This changes the number of horizontal tick marks\n",
-    "- Unblock the last two lines to ouput the plot as an *.png image file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[WinError 183] Cannot create a file when that file already exists: 'c:\\\\Users\\\\mspringe\\\\OneDrive - NREL\\\\msp\\\\projects\\\\2023_DegradationTools\\\\Github\\\\PVDegradationTools\\\\TEMP\\\\results'\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1600x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "standoff_series_df = pd.DataFrame(\n",
-    "    {\n",
-    "        \"Tilt\": standoff_series[:, 0],\n",
-    "        \"Azimuth\": standoff_series[:, 1],\n",
-    "        \"Xₘᵢₙ\": standoff_series[:, 2],\n",
-    "    }\n",
-    ")\n",
-    "x_fig = plt.figure(figsize=(16, 4))\n",
-    "plt.title(\n",
-    "    \"Plot of $\\it{Xₘᵢₙ}$ for all orientations for $\\it{T₉₈}$=\"\n",
-    "    + \"%.0f\" % kwarg_x[\"T98\"]\n",
-    "    + \"°C.\",\n",
-    "    fontsize=15,\n",
-    "    y=1.08,\n",
-    ")\n",
-    "x_fig = sns.heatmap(\n",
-    "    standoff_series_df.pivot(index=\"Tilt\", columns=\"Azimuth\", values=\"Xₘᵢₙ\"),\n",
-    "    cbar_kws={\"label\": \"Xₘᵢₙ\", \"format\": \"%.0f\", \"pad\": 0.02},\n",
-    "    cmap=\"Spectral_r\",\n",
-    "    vmin=0,\n",
-    "    vmax=None,\n",
-    ")\n",
-    "\n",
-    "h_ticks = 37\n",
-    "x_number = math.ceil(360 / x_azimuth_step) + 1\n",
-    "x_ticks = [\n",
-    "    (x * (360 / (h_ticks - 1)) / x_azimuth_step + 0.5) for x in range(h_ticks - 1)\n",
-    "]\n",
-    "x_labels = [(\"%.0f\" % (360 / (h_ticks - 1) * x)) for x in range(h_ticks)]\n",
-    "x_ticks.append(x_number - 0.5)\n",
-    "x_fig.set_xticks(x_ticks)\n",
-    "x_fig.set_xticklabels(x_labels, rotation=90)\n",
-    "\n",
-    "v_ticks = 10\n",
-    "y_number = math.ceil(90 / x_tilt_step) + 1\n",
-    "y_ticks = [(x * (90 / (v_ticks - 1)) / x_tilt_step + 0.5) for x in range(v_ticks - 1)]\n",
-    "y_labels = [(\"%.0f\" % (90 / (v_ticks - 1) * x)) for x in range(v_ticks)]\n",
-    "y_ticks.append(y_number - 0.5)\n",
-    "x_fig.set_yticks(y_ticks)\n",
-    "x_fig.set_yticklabels(y_labels, rotation=0)\n",
-    "\n",
-    "x_fig.set_xlabel(\"Azimuth [°]\", fontsize=15, labelpad=10)\n",
-    "x_fig.set_ylabel(\"Tilt [°]\", fontsize=15)\n",
-    "x_fig.figure.axes[-1].set_ylabel(\"$\\it{Xₘᵢₙ}$ [cm]\", size=15)\n",
-    "x_fig.invert_yaxis()\n",
-    "\n",
-    "output_folder = os.path.join(\n",
-    "    os.path.dirname(os.path.dirname(os.getcwd())), \"TEMP\", \"results\"\n",
-    ")\n",
-    "try:\n",
-    "    os.mkdir(output_folder)\n",
-    "except OSError as error:\n",
-    "    print(error)\n",
-    "\n",
-    "plt.savefig(\n",
-    "    output_folder + \"\\Standoff_Scan.png\", bbox_inches=\"tight\"\n",
-    ")  # Creates an image file of the standoff plot\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 6. Plot $T_{98}$  for all azimuth and tilt for a given $X_{eff}$.\n",
-    "\n",
-    "The temperature of a system is affected by the orientation and the effective standoff, $X_{eff}$, of the system. This section will scan all possible tilts and azimuths calculating the $T_{98}$ for a given $X_{eff}$. As above, additional factors can be modified but are not included here for simplicity. The tilt_step and azimuth_step are the number of degrees for each step for the 90° and 180° tilt and azimuth spans, respectively. The default for this calculation is for $X_{eff}$=10 cm, a common effective standoff distance on a rooftop system. A value of $X_{eff}$=None will run the calculations for an open rack system and $X_{eff}$=0 for an insulated-back system."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                     \r"
-     ]
-    }
-   ],
-   "source": [
-    "# Scans through all the azimuth and tilt running the 98ᵗʰ percentile temperature calculation.\n",
-    "# Set up keyword parameters for the calculation\n",
-    "kwarg_T = dict(\n",
-    "    sky_model=\"isotropic\",\n",
-    "    temp_model=\"sapm\",\n",
-    "    conf_0=\"insulated_back_glass_polymer\",\n",
-    "    conf_inf=\"open_rack_glass_polymer\",\n",
-    "    x_eff=5,\n",
-    "    x_0=6.5,\n",
-    "    wind_factor=0.33,\n",
-    ")\n",
-    "# Run the calculation\n",
-    "T_azimuth_step = 10\n",
-    "T_tilt_step = 10\n",
-    "T98_series = pvdeg.utilities.tilt_azimuth_scan(\n",
-    "    weather_df=WEATHER_df,\n",
-    "    meta=META,\n",
-    "    tilt_step=T_tilt_step,\n",
-    "    azimuth_step=T_azimuth_step,\n",
-    "    func=pvdeg.standards.T98_estimate,\n",
-    "    **kwarg_T,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The next cell creates a plot of the calculated data. Some of the things you may want to change are:\n",
-    "- cmap=\"Spectral_r\": Change to have different colors\n",
-    "- plt.title : This will change the plot title.\n",
-    "- figsize=(16,4) : Change the plot dimensions and/or aspect ratio.\n",
-    "- vmax=None : This can be set to a numeric value to control the depth scale maximum\n",
-    "- vmin=None : This controls the minimum of the depth scale.\n",
-    "- v_ticks=37 : This changes the number of vertical tick marks\n",
-    "- h_ticks=10 : This changes the number of horizontal tick marks\n",
-    "- Unblock the last two lines to ouput the plot as an *.png image file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1600x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# This produces the plot of the data\n",
-    "T98_series_df = pd.DataFrame(\n",
-    "    {\"Tilt\": T98_series[:, 0], \"Azimuth\": T98_series[:, 1], \"T₉₈\": T98_series[:, 2]}\n",
-    ")\n",
-    "T98_fig = plt.figure(figsize=(16, 4))\n",
-    "if kwarg_T[\"x_eff\"] == None:\n",
-    "    plt.title(\n",
-    "        \"Plot of $\\it{T₉₈}$ for all orientations for an open-rack mounting.\",\n",
-    "        fontsize=15,\n",
-    "        y=1.08,\n",
-    "    )\n",
-    "else:\n",
-    "    plt.title(\n",
-    "        \"Plot of $\\it{T₉₈}$ for all orientations for $X_{eff}$=\"\n",
-    "        + \"%.0f\" % kwarg_T[\"x_eff\"]\n",
-    "        + \" cm.\",\n",
-    "        fontsize=15,\n",
-    "        y=1.08,\n",
-    "    )\n",
-    "T98_fig = sns.heatmap(\n",
-    "    T98_series_df.pivot(index=\"Tilt\", columns=\"Azimuth\", values=\"T₉₈\"),\n",
-    "    cbar_kws={\"label\": \"Xₘᵢₙ\", \"format\": \"%.0f\", \"pad\": 0.02},\n",
-    "    cmap=\"Spectral_r\",\n",
-    "    vmin=None,\n",
-    "    vmax=None,\n",
-    ")\n",
-    "\n",
-    "h_ticks = 37\n",
-    "x_number = math.ceil(360 / T_azimuth_step) + 1\n",
-    "x_ticks = [\n",
-    "    (x * (360 / (h_ticks - 1)) / T_azimuth_step + 0.5) for x in range(h_ticks - 1)\n",
-    "]\n",
-    "x_labels = [(\"%.0f\" % (360 / (h_ticks - 1) * x)) for x in range(h_ticks)]\n",
-    "x_ticks.append(x_number - 0.5)\n",
-    "T98_fig.set_xticks(x_ticks)\n",
-    "T98_fig.set_xticklabels(x_labels, rotation=90)\n",
-    "\n",
-    "v_ticks = 10\n",
-    "y_number = math.ceil(90 / T_tilt_step) + 1\n",
-    "y_ticks = [(x * (90 / (v_ticks - 1)) / T_tilt_step + 0.5) for x in range(v_ticks - 1)]\n",
-    "y_labels = [(\"%.0f\" % (90 / (v_ticks - 1) * x)) for x in range(v_ticks)]\n",
-    "y_ticks.append(y_number - 0.5)\n",
-    "T98_fig.set_yticks(y_ticks)\n",
-    "T98_fig.set_yticklabels(y_labels, rotation=0)\n",
-    "\n",
-    "T98_fig.set_xlabel(\"Azimuth [°]\", fontsize=15, labelpad=10)\n",
-    "T98_fig.set_ylabel(\"Tilt [°]\", fontsize=15)\n",
-    "T98_fig.figure.axes[-1].set_ylabel(\"$\\it{T₉₈}$ [°C]\", size=15)\n",
-    "T98_fig.invert_yaxis()\n",
-    "\n",
-    "plt.savefig(\n",
-    "    os.path.join(output_folder, \"T98_Scan.png\"), bbox_inches=\"tight\"\n",
-    ")  # Creates an image file of the standoff plot\n",
-    "plt.show(T98_fig)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 7. Plot $X_{min}$ for Level 1, Level 2, and $T_{98}$ for a given region.\n",
-    "\n",
-    "This last Objective is much more complicated and is set up to utilize acess to a lot of computational power to run many sites simultaneously to create a regional map of standoff distance. This is presented as doing the computations on Amazon Web Services (AWS) for which you will need a paid account, and will be covered on a subsequent journal."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 8. Save data outputs.\n",
-    "\n",
-    "This cell contains a number of pre-scripted commands for exporting and saving data. The code to save plots is located after the plot creation and is blocked by default. First check that the output folder exists, then unblock the code for data you would like to save."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Your results will be stored in c:\\Users\\mspringe\\OneDrive - NREL\\msp\\projects\\2023_DegradationTools\\Github\\PVDegradationTools\\TEMP\\results\n",
-      "The folder must already exist or the file will not be created\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Your results will be stored in %s\" % output_folder)\n",
-    "print(\"The folder must already exist or the file will not be created\")\n",
-    "\n",
-    "pvdeg.weather.write(\n",
-    "    data_df=WEATHER_df,\n",
-    "    metadata=META,\n",
-    "    savefile=os.path.join(output_folder, \"WeatherFile.csv\"),\n",
-    ")  # Writes the meterological data to an *.csv file.\n",
-    "\n",
-    "pd.DataFrame(standoff_series_df).to_csv(\n",
-    "    os.path.join(output_folder, \"Standoff_Scan.csv\")\n",
-    ")  # Writes a file with the Tilt and Azimuth scan calculations of standoff.\n",
-    "\n",
-    "pd.DataFrame(T98_series_df).to_csv(\n",
-    "    os.path.join(output_folder, \"T98_Scan.csv\")\n",
-    ")  # Writes a file with the Tilt Azimuth scan calculations of T98."
-   ]
-  }
- ],
- "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": "3.11.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Tools-Edge Seal Oxygen Ingress.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Tools-Edge Seal Oxygen Ingress.ipynb
new file mode 100644
index 000000000..26fed9d00
--- /dev/null
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Tools-Edge Seal Oxygen Ingress.ipynb	
@@ -0,0 +1,16 @@
+{
+ "cells": [],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Van't Hoff Degradation Model.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Van't Hoff Degradation Model.ipynb
new file mode 100644
index 000000000..66b3c0e92
--- /dev/null
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Van't Hoff Degradation Model.ipynb	
@@ -0,0 +1,361 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# A - Van't Hoff Degradation\n",
+    "### Calculate site specific degradation according to the Van't Hoff equation\n",
+    "***\n",
+    "Michael Kempe\n",
+    "\n",
+    "2023.08.31\n",
+    "***\n",
+    "\n",
+    "**Requirements**:\n",
+    "- compatible weather file (PSM3, TMY3, EPW) or lattitude and longitude of desired site\n",
+    "- Accelerated testing chamber parameters\n",
+    "    - chamber irradiance [W/m^2]\n",
+    "    - chamber temperature [°C]\n",
+    "- 10°C acceleration factor\n",
+    "\n",
+    "**Steps**:\n",
+    "1. Read/find the weather data\n",
+    "2. Generate basic modeling data\n",
+    "3. Calculate VantHoff degradation acceleration factor\n",
+    "4. Expand calculations to a region"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n",
+    "# pip install pvdeg==0.1.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "import pvdeg\n",
+    "from pvdeg import DATA_DIR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Read In the Weather File\n",
+    "\n",
+    "This is usually the first step. Use a PSM3, TMY3, or EPW file. For this demo, use the provided PSM3 weather file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PSM_FILE = os.path.join(DATA_DIR, \"psm3_demo.csv\")\n",
+    "WEATHER, META = pvdeg.weather.read(PSM_FILE, \"psm\")\n",
+    "print(\n",
+    "    \"Latitude =\",\n",
+    "    META[\"latitude\"],\n",
+    "    \"Longitude =\",\n",
+    "    META[\"longitude\"],\n",
+    "    META[\"Country\"],\n",
+    "    META[\"City\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Generate Basic Modeling Data\n",
+    "\n",
+    "For this tutorial we will need solar position, POA, PV cell and module temperature. Let's gernate those individually with their respective functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sol_pos = pvdeg.spectral.solar_position(weather_df=WEATHER, meta=META)\n",
+    "\n",
+    "poa_df = pvdeg.spectral.poa_irradiance(\n",
+    "    weather_df=WEATHER, meta=META, sol_position=sol_pos\n",
+    ")\n",
+    "\n",
+    "temp_cell = pvdeg.temperature.cell(weather_df=WEATHER, meta=META, poa=poa_df)\n",
+    "\n",
+    "temp_module = pvdeg.temperature.module(weather_df=WEATHER, meta=META, poa=poa_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. VantHoff Degradation\n",
+    "\n",
+    "Van't Hoff Irradiance Degradation Equation:\n",
+    "$$ R_o = R_D ·  G^p  · T_f^{\\frac{T}{10} }$$\n",
+    "\n",
+    "For the yearly average degredation outdoors to be the same as the controlled environmnet, the lamp settings will need to be set to *G$_{WA}$* and the temperature set to *T$_{oeq}$*.\n",
+    "\n",
+    "As with most `pvdeg` functions, the following functions will always require two arguments (weather_df and meta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# chamber irradiance (W/m²)\n",
+    "I_chamber = 1600\n",
+    "# chamber temperature (°C)\n",
+    "temp_chamber = 85\n",
+    "# Schwartzchild Coefficient\n",
+    "p = 0.64\n",
+    "# Acceleration factor for every 10°C\n",
+    "Tf = 1.41\n",
+    "\n",
+    "# calculate the Van't Hoff Acceleration factor\n",
+    "vantHoff_deg = pvdeg.degradation.vantHoff_deg(\n",
+    "    weather_df=WEATHER,\n",
+    "    meta=META,\n",
+    "    I_chamber=I_chamber,\n",
+    "    temp_chamber=temp_chamber,\n",
+    "    poa=poa_df,\n",
+    "    temp=temp_cell,\n",
+    "    p=p,\n",
+    "    Tf=Tf,\n",
+    ")\n",
+    "\n",
+    "# calculate the Van't Hoff weighted irradiance\n",
+    "irr_weighted_avg_v = pvdeg.degradation.IwaVantHoff(\n",
+    "    weather_df=WEATHER, meta=META, poa=poa_df, temp=temp_cell, p=p, Tf=Tf\n",
+    ")\n",
+    "\n",
+    "print(\n",
+    "    \"AF =\",\n",
+    "    round(vantHoff_deg, 1),\n",
+    "    \"(°C) , and G_WA =\",\n",
+    "    round(irr_weighted_avg_v),\n",
+    "    \"(W/m²)\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Arrhenius\n",
+    "Calculate the Acceleration Factor between the rate of degredation of a modeled environmnet versus a modeled controlled environmnet\n",
+    "\n",
+    "Example: \"If the *AF*=25 then 1 year of Controlled Environment exposure is equal to 25 years in the field\"\n",
+    "\n",
+    "Equation:\n",
+    "$$ AF = N · \\frac{ G_{chamber}^x · RH_{chamber}^n · e^{\\frac{- E_a}{k T_{chamber}}} }{ \\Sigma (G_{POA}^x · RH_{outdoor}^n · e^{\\frac{-E_a}{k T_outdoor}}) }$$\n",
+    "\n",
+    "Function to calculate *G$_{WA}$*, the Environment Characterization (W/m²). If the controlled environmnet lamp settings are set at *G$_{WA}$*, and the temperature set to *T$_{eq}$*, then the degradation will be the same as the yearly average outdoors.\n",
+    "\n",
+    "Equation:\n",
+    "$$ G_{WA} = [ \\frac{ \\Sigma (G_{outdoor}^x · RH_{outdoor}^n e^{\\frac{-E_a}{k T_{outdood}}}) }{ N · RH_{WA}^n · e^{- \\frac{E_a}{k T_eq}} } ]^{\\frac{1}{x}} $$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# relative humidity within chamber (%)\n",
+    "rh_chamber = 15\n",
+    "# arrhenius activation energy (kj/mol)\n",
+    "Ea = 40\n",
+    "\n",
+    "rh_surface = pvdeg.humidity.surface_relative(\n",
+    "    rh_ambient=WEATHER[\"relative_humidity\"],\n",
+    "    temp_ambient=WEATHER[\"temp_air\"],\n",
+    "    temp_module=temp_module,\n",
+    ")\n",
+    "\n",
+    "arrhenius_deg = pvdeg.degradation.arrhenius_deg(\n",
+    "    weather_df=WEATHER,\n",
+    "    meta=META,\n",
+    "    rh_outdoor=rh_surface,\n",
+    "    I_chamber=I_chamber,\n",
+    "    rh_chamber=rh_chamber,\n",
+    "    temp_chamber=temp_chamber,\n",
+    "    poa=poa_df,\n",
+    "    temp=temp_cell,\n",
+    "    Ea=Ea,\n",
+    ")\n",
+    "\n",
+    "irr_weighted_avg_a = pvdeg.degradation.IwaArrhenius(\n",
+    "    weather_df=WEATHER,\n",
+    "    meta=META,\n",
+    "    poa=poa_df,\n",
+    "    rh_outdoor=WEATHER[\"relative_humidity\"],\n",
+    "    temp=temp_cell,\n",
+    "    Ea=Ea,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Quick Method (Degradation)\n",
+    "\n",
+    "For quick calculations, you can omit POA and both module and cell temperature. The function will calculate these figures as needed using the available weather data with the default options for PV module configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# chamber settings\n",
+    "I_chamber = 1000\n",
+    "temp_chamber = 60\n",
+    "rh_chamber = 15\n",
+    "\n",
+    "# activation energy\n",
+    "Ea = 40\n",
+    "\n",
+    "vantHoff_deg = pvdeg.degradation.vantHoff_deg(\n",
+    "    weather_df=WEATHER, meta=META, I_chamber=I_chamber, temp_chamber=temp_chamber\n",
+    ")\n",
+    "\n",
+    "irr_weighted_avg_v = pvdeg.degradation.IwaVantHoff(weather_df=WEATHER, meta=META)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "rh_surface = pvdeg.humidity.surface_relative(\n",
+    "    rh_ambient=WEATHER[\"relative_humidity\"],\n",
+    "    temp_ambient=WEATHER[\"temp_air\"],\n",
+    "    temp_module=temp_module,\n",
+    ")\n",
+    "\n",
+    "arrhenius_deg = pvdeg.degradation.arrhenius_deg(\n",
+    "    weather_df=WEATHER,\n",
+    "    meta=META,\n",
+    "    rh_outdoor=rh_surface,\n",
+    "    I_chamber=I_chamber,\n",
+    "    rh_chamber=rh_chamber,\n",
+    "    temp_chamber=temp_chamber,\n",
+    "    Ea=Ea,\n",
+    ")\n",
+    "\n",
+    "irr_weighted_avg_a = pvdeg.degradation.IwaArrhenius(\n",
+    "    weather_df=WEATHER, meta=META, rh_outdoor=WEATHER[\"relative_humidity\"], Ea=Ea\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Solder Fatigue\n",
+    "\n",
+    "Estimate the thermomechanical fatigue of flat plate photovoltaic module solder joints over the time range given using estimated cell temperature. Like other `pvdeg` funcitons, the minimal parameters are (weather_df, meta). Running the function with only these two inputs will use default PV module configurations ( open_rack_glass_polymer ) and the 'sapm' temperature model over the entire length of the weather data. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "fatigue = pvdeg.fatigue.solder_fatigue(weather_df=WEATHER, meta=META)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you wish to reduce the span of time or use a non-default temperature model, you may specify the parameters manually. Let's try an explicit example.\n",
+    "We want the solder fatigue estimated over the month of June for a roof mounted glass-front polymer-back module.\n",
+    "\n",
+    "1. Lets create a datetime-index for the month of June.\n",
+    "2. Next, generate the cell temperature. Make sure to explicity restrict the weather data to our dt-index for June. Next, declare the PV module configuration.\n",
+    "3. Calculate the fatigue. Explicity specify the time_range (our dt-index for June from step 1) and the cell temperature as we caculated in step 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# select the month of June\n",
+    "time_range = WEATHER.index[WEATHER.index.month == 6]\n",
+    "\n",
+    "# calculate cell temperature over our selected date-time range.\n",
+    "# specify the module configuration\n",
+    "temp_cell = pvdeg.temperature.cell(\n",
+    "    weather_df=WEATHER.loc[time_range],\n",
+    "    meta=META,\n",
+    "    temp_model=\"sapm\",\n",
+    "    conf=\"insulated_back_glass_polymer\",\n",
+    ")\n",
+    "\n",
+    "\n",
+    "fatigue = pvdeg.fatigue.solder_fatigue(\n",
+    "    weather_df=WEATHER, meta=META, time_range=time_range, temp_cell=temp_cell\n",
+    ")"
+   ]
+  }
+ ],
+ "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/load_pvgis_distributed.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/load_pvgis_distributed.ipynb
new file mode 100644
index 000000000..63e81a39e
--- /dev/null
+++ b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/load_pvgis_distributed.ipynb
@@ -0,0 +1,472 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pvdeg\n",
+    "from global_land_mask import globe\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from dask.distributed import LocalCluster, Client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating Coordinates List\n",
+    "\n",
+    "Lets Generate a Grid of Latitude and Longitude Coordinates over the UK."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# decrease the arange step size for more fine resolution\n",
+    "# increase the arange step size for increased granularity\n",
+    "lon_UK = np.arange(-10.5, 1.76, 1)\n",
+    "lat_UK = np.arange(49.95, 60, 2)\n",
+    "lon_grid_UK, lat_grid_UK = np.meshgrid(lon_UK, lat_UK)\n",
+    "land_UK = globe.is_land(lat_grid_UK, lon_grid_UK)\n",
+    "\n",
+    "lon_land_UK = lon_grid_UK[land_UK]\n",
+    "lat_land_UK = lat_grid_UK[land_UK]\n",
+    "\n",
+    "lon_Scan = np.arange(-10.5, 31.6, 0.3)\n",
+    "lat_Scan = np.arange(60, 71.2, 0.3)\n",
+    "lon_grid_Scan, lat_grid_Scan = np.meshgrid(lon_Scan, lat_Scan)\n",
+    "land_Scan = globe.is_land(lat_grid_Scan, lon_grid_Scan)\n",
+    "\n",
+    "lon_land_Scan = lon_grid_Scan[land_Scan]\n",
+    "lat_land_Scan = lat_grid_Scan[land_Scan]\n",
+    "\n",
+    "coords = list(\n",
+    "    zip(lat_land_UK, lon_land_UK)\n",
+    ")  # easiest way to make a list of the right shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dask\n",
+    "\n",
+    "We are using dask to parallelize the weather API calls. We need to start a dask client as shown below. This can also be done with `pvdeg.geospatial.start_dask`.  \n",
+    "\n",
+    "Click on the link to open a localhost dashboard for the dask client. This will allow us to see what happens when we run functions using dask."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "workers = 4\n",
+    "\n",
+    "cluster = LocalCluster(\n",
+    "    n_workers=workers,\n",
+    "    processes=True,\n",
+    ")\n",
+    "\n",
+    "client = Client(cluster)\n",
+    "\n",
+    "print(client.dashboard_link)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Requesting Weather\n",
+    "\n",
+    "Now that we have our coordinates, and dask client initialized we can make our parallel api calls using `pvdeg.weather.weather_distributed`.  \n",
+    "\n",
+    "We want data from PVGIS so we will use that as the database. For more information about the databases look at the `pvdeg.weather.get` docstring. We will pass in the coordinates list which is a list of tuple pairs for the latitude and longiutudes to request weather at.\n",
+    "\n",
+    "`weather_ds` is the collected weather dataset from the API calls to PVGIS.  \n",
+    "`meta_df` is the collected meta dataframe from the API calls to PVGIS.  \n",
+    "`failed_gids` will be a list of the failed indexes from the input coordinates list that we could not get weather from using PVGIS. These may have failed randomly so it is worth trying again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather_ds, meta_df, failed_gids = pvdeg.weather.weather_distributed(\n",
+    "    database=\"PVGIS\", coords=coords\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Viewing Result\n",
+    "\n",
+    "The result is stored in an xarray dataset with a dask array backend. This allows us to parallelize the computation/api requests but makes it a little harder to view the data. We can inspect the dataset using the following but we will not be able to inspect any values.\n",
+    "\n",
+    "```\n",
+    "weather_ds\n",
+    "```\n",
+    "\n",
+    "To load the values from the dask arrays we need to use `.compute()` as follows.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather_ds.compute()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Saving Geospatial Data Locally\n",
+    "\n",
+    "The goal of `pvdeg.store` is to create a living local database of meteoroligical data that grows overtime as your geospatial data needs grow. To do this `PVDeg` will save to a folder called `PVDeg-Meteorological` your user home directory. For me this is located at `C:\\Users\\tford\\PVDeg-Meteorological`. This directory will contain a `zarr` store, this is a popular format for storing multi-dimensional array data, not dissimilar to `h5` files. It was chosen over `h5` because `zarr` stores arrays in chunked compressed files that make access very easy without opening an entire file like `h5`. This is an oversimplification of the design process but we felt `zarr` was a better fit.\n",
+    "\n",
+    "## Store\n",
+    "\n",
+    "We can use `pvdeg.store.store` to save geospatial data to our living dataset in the common form provided by `pvdeg`. The data is stored in various groups and subfolders but they will be arranged based on the *source* and *periodicity*.\n",
+    "\n",
+    "For example:  \n",
+    "    - Hourly PVGIS data will be saved to a group called \"PVGIS-1hr\"  \n",
+    "    - 30 minute PVGIS to a group called \"PVGIS-30min\"  \n",
+    "    - 15 minute PVGIS will be saved to a group called \"PVGIS-15min\"  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pvdeg.store.store(weather_ds, meta_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load\n",
+    "\n",
+    "`PVDeg` makes use of `dask` to handle larger than memory datasets. Trandionally, this was only useful in our HPC environment but as your local database grows overtime, it will eventually surpass the limits of your computer's volatile memory. Additionally, `dask` allows us to parallelize geospatial calculations via `pvdeg.geospatial.analysis`. This ability can be utilized on local machines or HPC clusters alike.  \n",
+    "\n",
+    "`PVDeg` implements the ability to access your local living database via `pvdeg.store.get`. This method takes a string called `group`. Groups are created automatically in your store when you save data using `pvdeg.store.store`. As described in the `pvdeg.store.store` docstring and the *Store* section above, NSRDB will follow a similar scheme but it not implemented yet.  \n",
+    "    - Hourly PVGIS data will be saved to a group called \"PVGIS-1hr\"  \n",
+    "    - 30 minute PVGIS to a group called \"PVGIS-30min\"  \n",
+    "    - 15 minute PVGIS will be saved to a group called \"PVGIS-15min\"  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load PVGIS-1hr Data\n",
+    "\n",
+    "The example below shows us loading the hourly tmy data from PVGIS that we gathered and saved to our zarr store in the above cells. This gets us the form of a weather xarray.Dataset (`geo_weather` in this example) and a metadata dataframe (`geo_meta` in this example).\n",
+    "\n",
+    "These can be treated like any other geospatial data shown in the `pvdeg` tutorials and tools or documentation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geo_weather, geo_meta = pvdeg.store.get(group=\"PVGIS-1hr\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Inspecting the Results\n",
+    "\n",
+    "explain *.compute() and dask here*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(geo_weather.sel(gid=0).dni)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geo_meta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Geospatial Calculations from Locally Stored Data\n",
+    "\n",
+    "As shown above we can load from our `zarr` store and treat it like any other geospatial data in `pvdeg`.\n",
+    "\n",
+    "For demonstration we can run the analysis below to estimate effective standoff height and operating temperatures for the provided data. It may look like the `geo_res` contains empty results but that is because we did not have input data for all of the points in the input grid (think of this as a 2D plane formed between the latitude and longitude axes). Clicking on the stack of three circles in the bottom cell will expand the datavariable (like an attribute of the multidimensional array structure) and show the results.\n",
+    "\n",
+    "Additionally, we can interpolate and plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "func = pvdeg.standards.standoff\n",
+    "\n",
+    "template = pvdeg.geospatial.auto_template(func=func, ds_gids=geo_weather)\n",
+    "\n",
+    "geo_res = pvdeg.geospatial.analysis(\n",
+    "    weather_ds=geo_weather, meta_df=geo_meta, func=func, template=template\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geo_res"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This plot lacks information on the area and does not include some political boundary lines. For more information on plotting look at the `Scenario - Non-uniform Mountain Downselect.ipynb` tutorial in the tutorials and tools folder."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pvdeg.geospatial.plot_sparse_analysis_land(\n",
+    "    geo_res, data_var=\"T98_0\", method=\"nearest\", resolution=10j\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Growing Our Living Store\n",
+    "\n",
+    "What if we want to download more points from Europe? We can keep our old download in the store and shelve it to look at northern Europe.\n",
+    "\n",
+    "We will start by generating a range of points that cover Europe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lon_EU = np.arange(-25.0, 51.0, 1)  # Adjusted for EU longitudes\n",
+    "lat_EU = np.arange(34.0, 73.0, 2)  # Adjusted for EU latitudes\n",
+    "\n",
+    "# Create meshgrid for EU\n",
+    "lon_grid_EU, lat_grid_EU = np.meshgrid(lon_EU, lat_EU)\n",
+    "\n",
+    "# Check land coverage in the EU\n",
+    "land_EU = globe.is_land(lat_grid_EU, lon_grid_EU)\n",
+    "\n",
+    "# Extract land coordinates in the EU\n",
+    "lon_land_EU = lon_grid_EU[land_EU]\n",
+    "lat_land_EU = lat_grid_EU[land_EU]\n",
+    "\n",
+    "# Define the Scan grid ranges\n",
+    "lon_Scan = np.arange(-10.5, 31.6, 0.3)\n",
+    "lat_Scan = np.arange(60, 71.2, 0.3)\n",
+    "lon_grid_Scan, lat_grid_Scan = np.meshgrid(lon_Scan, lat_Scan)\n",
+    "land_Scan = globe.is_land(lat_grid_Scan, lon_grid_Scan)\n",
+    "\n",
+    "lon_land_Scan = lon_grid_Scan[land_Scan]\n",
+    "lat_land_Scan = lat_grid_Scan[land_Scan]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(lon_land_EU, lat_land_EU, c=\"r\", s=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w, m, failed_gids = pvdeg.weather.weather_distributed(\n",
+    "    database=\"PVGIS\", coords=[(lat_land_EU[0], lon_land_EU[0])]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pvdeg.store.store(weather_ds=w, meta_df=m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loaded_weather, loaded_meta = pvdeg.store.get(group=\"PVGIS-1hr\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loaded_meta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loaded_weather"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loaded_weather.sel(gid=22).compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.plot(loaded_weather.sel(gid=0).dhi)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wet, met = pvdeg.store.get(\"PVGIS-1hr\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loaded_meta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "func = pvdeg.standards.standoff\n",
+    "\n",
+    "template = pvdeg.geospatial.auto_template(func=func, ds_gids=loaded_weather)\n",
+    "\n",
+    "loaded_geo_res = pvdeg.geospatial.analysis(\n",
+    "    weather_ds=loaded_weather, meta_df=loaded_meta, func=func, template=template\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pvdeg.geospatial.plot_sparse_analysis_land(loaded_geo_res, data_var=\"T98_inf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "deg",
+   "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": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}