diff --git a/.github/workflows/python_ci.yml b/.github/workflows/python_ci.yml
index a35f22c..7b517f6 100644
--- a/.github/workflows/python_ci.yml
+++ b/.github/workflows/python_ci.yml
@@ -8,7 +8,7 @@ on:
     branches: [ main, dev ]
 
 jobs:
-  build:
+  ci:
 
     runs-on: ${{ matrix.os }}
     strategy:
diff --git a/examples/applying_layers_to_quantitative_maps.ipynb b/examples/applying_layers_to_quantitative_maps.ipynb
index 82619bd..24abc13 100644
--- a/examples/applying_layers_to_quantitative_maps.ipynb
+++ b/examples/applying_layers_to_quantitative_maps.ipynb
@@ -5,7 +5,12 @@
    "cell_type": "markdown",
    "source": [
     "# Applying Layers to Quantitative Maps\n",
-    "The primary way of getting data from a layers object is as a Pandas DataFrame. Your quantitative map and mask/anatomical image don't need to have the same field of view and voxel size, they are resampled to a common space as part of the `add_map` method. The `space` parameter of the QLayers class dictates whether any maps added to the object are resampled of the same space as the layers (`space=layers`) or if the layers are resampled to the native space of the map (`space=map`). The advantage of working in layer space is that you can produce a wide DataFrame where each row represents a region of tissue in the image and allows you to directly compare the depth and multiple quantitative parameters however it does involve a resampling operation to the quantitative parameter which may not be desirable (especially for noisy data). Working with `space=map` only allows you to produce a long DataFrame where each row is a single voxel of each quantitative map. Here we're going to work with `space=layers` and return both formats of DataFrame so you can see the difference. Most analysis is easier with a wide DataFrame so we'll save that variable for use in subsequent steps.\n",
+    "The data used in this tutorial can be downloaded from [Zenodo](https://zenodo.org/records/14259406)\n",
+    "\n",
+    "The primary way of getting data from a layers object is as a Pandas DataFrame. Your quantitative map and mask/anatomical image don't need to have the same field of view and voxel size, they are resampled to a common space as part of the `add_map` method. The `space` parameter of the QLayers class dictates whether any maps added to the object are resampled of the same space as the layers (`space=layers`) or if the layers are resampled to the native space of the map (`space=map`).\n",
+    "\n",
+    "The advantage of working in layer space is that you can produce a wide DataFrame where each row represents a region of tissue in the image and allows you to directly compare the depth and multiple quantitative parameters however it does involve a resampling operation to the quantitative parameter which may not be desirable (especially for noisy data). Working with `space=map` only allows you to produce a long DataFrame where each row is a single voxel of each quantitative map. Here we're going to work with `space=layers` and return both formats of DataFrame so you can see the difference. Most analysis is easier with a wide DataFrame so we'll save that variable for use in subsequent steps.\n",
+    "\n",
     "Start by importing the necessary libraries."
    ],
    "id": "532b0a2d2a8c0b7c"
@@ -13,8 +18,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-06-19T16:37:10.894629Z",
-     "start_time": "2024-06-19T16:36:59.283130Z"
+     "end_time": "2024-12-02T11:48:55.284933Z",
+     "start_time": "2024-12-02T11:48:53.335024Z"
     }
    },
    "cell_type": "code",
@@ -32,17 +37,11 @@
    "outputs": [],
    "execution_count": 1
   },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "Now we're going to import a mask and generate layers with a thickness of 1 mm excluding tissue within 10 mm of the pelvis.",
-   "id": "4cb8b85b935e958f"
-  },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-06-19T16:37:46.029879Z",
-     "start_time": "2024-06-19T16:37:10.895622Z"
+     "end_time": "2024-12-02T11:49:33.732490Z",
+     "start_time": "2024-12-02T11:48:55.290332Z"
     }
    },
    "cell_type": "code",
@@ -50,11 +49,11 @@
     "mask_img = nib.load('data/kidney_mask.nii.gz')\n",
     "qlayers = QLayers(mask_img, thickness=1, pelvis_dist=10, space='layers')\n",
     "r2star_map = nib.load('data/r2star.nii.gz')\n",
-    "t1_map = nib.load('data/t1_registered.nii.gz')\n",
+    "t1_map = nib.load('data/t1.nii.gz')\n",
     "qlayers.add_map(r2star_map, 'r2star')\n",
     "qlayers.add_map(t1_map, 't1')"
    ],
-   "id": "255eea10f9ef298b",
+   "id": "e5934e458b01108a",
    "outputs": [
     {
      "name": "stdout",
@@ -68,158 +67,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Distance Calculation: 100%|██████████| 3/3 [00:10<00:00,  3.65s/it]\n",
-      "Pelvis Distance Calculation: 100%|██████████| 3/3 [00:11<00:00,  3.81s/it]\n"
+      "Distance Calculation: 100%|██████████| 3/3 [00:09<00:00,  3.24s/it]\n",
+      "Pelvis Distance Calculation: 100%|██████████| 3/3 [00:10<00:00,  3.57s/it]\n"
      ]
     }
    ],
    "execution_count": 2
   },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-06-19T16:37:46.121646Z",
-     "start_time": "2024-06-19T16:37:46.032884Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "qlayers.df_long",
-   "id": "e5934e458b01108a",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "          depth  layer measurement        value\n",
-       "0      0.691532    1.0      r2star    35.173016\n",
-       "1      0.640596    1.0      r2star    35.546364\n",
-       "2      0.885229    1.0      r2star    34.576439\n",
-       "3      0.680305    1.0      r2star    33.275753\n",
-       "4      0.622988    1.0      r2star    27.070265\n",
-       "...         ...    ...         ...          ...\n",
-       "25993  1.011228    2.0          t1  2372.752930\n",
-       "25994  1.099274    2.0          t1  1094.152222\n",
-       "25995  0.145987    1.0          t1  1351.835815\n",
-       "25996  1.185435    2.0          t1   922.929932\n",
-       "25997  0.378006    1.0          t1  1491.548584\n",
-       "\n",
-       "[51996 rows x 4 columns]"
-      ],
-      "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>depth</th>\n",
-       "      <th>layer</th>\n",
-       "      <th>measurement</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.691532</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>r2star</td>\n",
-       "      <td>35.173016</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.640596</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>r2star</td>\n",
-       "      <td>35.546364</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.885229</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>r2star</td>\n",
-       "      <td>34.576439</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.680305</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>r2star</td>\n",
-       "      <td>33.275753</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.622988</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>r2star</td>\n",
-       "      <td>27.070265</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>25993</th>\n",
-       "      <td>1.011228</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>t1</td>\n",
-       "      <td>2372.752930</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25994</th>\n",
-       "      <td>1.099274</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>t1</td>\n",
-       "      <td>1094.152222</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25995</th>\n",
-       "      <td>0.145987</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>t1</td>\n",
-       "      <td>1351.835815</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25996</th>\n",
-       "      <td>1.185435</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>t1</td>\n",
-       "      <td>922.929932</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25997</th>\n",
-       "      <td>0.378006</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>t1</td>\n",
-       "      <td>1491.548584</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>51996 rows × 4 columns</p>\n",
-       "</div>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 3
-  },
   {
    "metadata": {},
    "cell_type": "markdown",
@@ -233,14 +87,14 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-06-19T16:37:46.160292Z",
-     "start_time": "2024-06-19T16:37:46.121646Z"
+     "end_time": "2024-12-02T11:49:34.020964Z",
+     "start_time": "2024-12-02T11:49:33.901231Z"
     }
    },
    "cell_type": "code",
    "source": [
     "print('An example of a long DataFrame')\n",
-    "qlayers.get_df('long').sample(n=100)"
+    "qlayers.get_df('long').dropna().sample(n=100)"
    ],
    "id": "7876af6a37b7cb34",
    "outputs": [
@@ -255,17 +109,17 @@
      "data": {
       "text/plain": [
        "           depth  layer measurement        value\n",
-       "1452    7.921090    8.0          t1  1438.690796\n",
-       "23167   3.070961    4.0      r2star    16.509024\n",
-       "9873    9.106622   10.0      r2star    19.951300\n",
-       "15462   7.044881    8.0          t1  1522.299683\n",
-       "21780   9.554491   10.0          t1  1860.597168\n",
+       "18433   6.220314    7.0      r2star    26.932120\n",
+       "12665   5.595113    6.0      r2star    25.610969\n",
+       "5335   11.626794   12.0      r2star    22.953779\n",
+       "6029    4.469464    5.0      r2star    21.860133\n",
+       "29047   4.645861    5.0      r2star    67.383193\n",
        "...          ...    ...         ...          ...\n",
-       "5030    8.614227    9.0      r2star    -0.000061\n",
-       "16761  11.531033   12.0          t1  1811.737183\n",
-       "17264   3.927006    4.0      r2star    -0.000005\n",
-       "19380  10.379864   11.0      r2star    27.924768\n",
-       "83      0.151749    1.0          t1  1996.951416\n",
+       "25519  12.326423   13.0          t1  1909.264609\n",
+       "17489   7.499027    8.0      r2star    43.458317\n",
+       "23506  13.024991   14.0      r2star    26.698158\n",
+       "23549  11.341260   12.0      r2star    17.922952\n",
+       "15893  10.224986   11.0          t1  1796.101479\n",
        "\n",
        "[100 rows x 4 columns]"
       ],
@@ -296,39 +150,39 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>1452</th>\n",
-       "      <td>7.921090</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>t1</td>\n",
-       "      <td>1438.690796</td>\n",
+       "      <th>18433</th>\n",
+       "      <td>6.220314</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>r2star</td>\n",
+       "      <td>26.932120</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>23167</th>\n",
-       "      <td>3.070961</td>\n",
-       "      <td>4.0</td>\n",
+       "      <th>12665</th>\n",
+       "      <td>5.595113</td>\n",
+       "      <td>6.0</td>\n",
        "      <td>r2star</td>\n",
-       "      <td>16.509024</td>\n",
+       "      <td>25.610969</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9873</th>\n",
-       "      <td>9.106622</td>\n",
-       "      <td>10.0</td>\n",
+       "      <th>5335</th>\n",
+       "      <td>11.626794</td>\n",
+       "      <td>12.0</td>\n",
        "      <td>r2star</td>\n",
-       "      <td>19.951300</td>\n",
+       "      <td>22.953779</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>15462</th>\n",
-       "      <td>7.044881</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>t1</td>\n",
-       "      <td>1522.299683</td>\n",
+       "      <th>6029</th>\n",
+       "      <td>4.469464</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>r2star</td>\n",
+       "      <td>21.860133</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21780</th>\n",
-       "      <td>9.554491</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>t1</td>\n",
-       "      <td>1860.597168</td>\n",
+       "      <th>29047</th>\n",
+       "      <td>4.645861</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>r2star</td>\n",
+       "      <td>67.383193</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -338,39 +192,39 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5030</th>\n",
-       "      <td>8.614227</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>r2star</td>\n",
-       "      <td>-0.000061</td>\n",
+       "      <th>25519</th>\n",
+       "      <td>12.326423</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>t1</td>\n",
+       "      <td>1909.264609</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>16761</th>\n",
-       "      <td>11.531033</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>t1</td>\n",
-       "      <td>1811.737183</td>\n",
+       "      <th>17489</th>\n",
+       "      <td>7.499027</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>r2star</td>\n",
+       "      <td>43.458317</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>17264</th>\n",
-       "      <td>3.927006</td>\n",
-       "      <td>4.0</td>\n",
+       "      <th>23506</th>\n",
+       "      <td>13.024991</td>\n",
+       "      <td>14.0</td>\n",
        "      <td>r2star</td>\n",
-       "      <td>-0.000005</td>\n",
+       "      <td>26.698158</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>19380</th>\n",
-       "      <td>10.379864</td>\n",
-       "      <td>11.0</td>\n",
+       "      <th>23549</th>\n",
+       "      <td>11.341260</td>\n",
+       "      <td>12.0</td>\n",
        "      <td>r2star</td>\n",
-       "      <td>27.924768</td>\n",
+       "      <td>17.922952</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>83</th>\n",
-       "      <td>0.151749</td>\n",
-       "      <td>1.0</td>\n",
+       "      <th>15893</th>\n",
+       "      <td>10.224986</td>\n",
+       "      <td>11.0</td>\n",
        "      <td>t1</td>\n",
-       "      <td>1996.951416</td>\n",
+       "      <td>1796.101479</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -378,12 +232,12 @@
        "</div>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 4
+   "execution_count": 3
   },
   {
    "metadata": {},
@@ -397,8 +251,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-06-19T16:37:46.187426Z",
-     "start_time": "2024-06-19T16:37:46.160292Z"
+     "end_time": "2024-12-02T11:49:34.163380Z",
+     "start_time": "2024-12-02T11:49:34.119335Z"
     }
    },
    "cell_type": "code",
@@ -425,17 +279,17 @@
      "data": {
       "text/plain": [
        "           depth  layer     r2star           t1\n",
-       "18182   2.479600    3.0  49.999969   830.443848\n",
-       "25187   7.304027    8.0  17.250000  1491.552002\n",
-       "981     7.609881    8.0  18.200880  1696.885132\n",
-       "225     3.661174    4.0  16.167454  1498.427856\n",
-       "9813    5.483145    6.0  17.793419  1505.549683\n",
+       "10557  10.021963   11.0  20.356859  1845.174668\n",
+       "29467   0.130527    1.0  63.740803  1309.256450\n",
+       "914     7.301192    8.0  21.319738  1657.835906\n",
+       "13820   0.567545    1.0  49.005747  3884.615364\n",
+       "493     3.255654    4.0  17.848348  1469.497112\n",
        "...          ...    ...        ...          ...\n",
-       "9085    3.738994    4.0  16.756792  2189.016602\n",
-       "14667   2.410548    3.0  18.561234   648.623413\n",
-       "4872    6.246225    7.0  20.113871  1326.467773\n",
-       "12614   5.033549    6.0  18.116539  1553.667480\n",
-       "21237  14.467604   15.0  27.227434  1818.611694\n",
+       "24364   7.751323    8.0  19.327940  1341.303271\n",
+       "6265    6.819516    7.0  20.002437  1977.369206\n",
+       "10580   4.323028    5.0  38.620470  1514.621767\n",
+       "28977   1.128854    2.0  86.808749  2208.782174\n",
+       "16691   9.357887   10.0  61.534533  2032.454663\n",
        "\n",
        "[100 rows x 4 columns]"
       ],
@@ -466,39 +320,39 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>18182</th>\n",
-       "      <td>2.479600</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>49.999969</td>\n",
-       "      <td>830.443848</td>\n",
+       "      <th>10557</th>\n",
+       "      <td>10.021963</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>20.356859</td>\n",
+       "      <td>1845.174668</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>25187</th>\n",
-       "      <td>7.304027</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>17.250000</td>\n",
-       "      <td>1491.552002</td>\n",
+       "      <th>29467</th>\n",
+       "      <td>0.130527</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>63.740803</td>\n",
+       "      <td>1309.256450</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>981</th>\n",
-       "      <td>7.609881</td>\n",
+       "      <th>914</th>\n",
+       "      <td>7.301192</td>\n",
        "      <td>8.0</td>\n",
-       "      <td>18.200880</td>\n",
-       "      <td>1696.885132</td>\n",
+       "      <td>21.319738</td>\n",
+       "      <td>1657.835906</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>225</th>\n",
-       "      <td>3.661174</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>16.167454</td>\n",
-       "      <td>1498.427856</td>\n",
+       "      <th>13820</th>\n",
+       "      <td>0.567545</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>49.005747</td>\n",
+       "      <td>3884.615364</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9813</th>\n",
-       "      <td>5.483145</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>17.793419</td>\n",
-       "      <td>1505.549683</td>\n",
+       "      <th>493</th>\n",
+       "      <td>3.255654</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>17.848348</td>\n",
+       "      <td>1469.497112</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -508,39 +362,39 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9085</th>\n",
-       "      <td>3.738994</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>16.756792</td>\n",
-       "      <td>2189.016602</td>\n",
+       "      <th>24364</th>\n",
+       "      <td>7.751323</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>19.327940</td>\n",
+       "      <td>1341.303271</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14667</th>\n",
-       "      <td>2.410548</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>18.561234</td>\n",
-       "      <td>648.623413</td>\n",
+       "      <th>6265</th>\n",
+       "      <td>6.819516</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>20.002437</td>\n",
+       "      <td>1977.369206</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4872</th>\n",
-       "      <td>6.246225</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>20.113871</td>\n",
-       "      <td>1326.467773</td>\n",
+       "      <th>10580</th>\n",
+       "      <td>4.323028</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>38.620470</td>\n",
+       "      <td>1514.621767</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12614</th>\n",
-       "      <td>5.033549</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>18.116539</td>\n",
-       "      <td>1553.667480</td>\n",
+       "      <th>28977</th>\n",
+       "      <td>1.128854</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>86.808749</td>\n",
+       "      <td>2208.782174</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21237</th>\n",
-       "      <td>14.467604</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>27.227434</td>\n",
-       "      <td>1818.611694</td>\n",
+       "      <th>16691</th>\n",
+       "      <td>9.357887</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>61.534533</td>\n",
+       "      <td>2032.454663</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -548,12 +402,12 @@
        "</div>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 5
+   "execution_count": 4
   },
   {
    "metadata": {},
@@ -567,8 +421,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-06-19T16:37:46.216108Z",
-     "start_time": "2024-06-19T16:37:46.187426Z"
+     "end_time": "2024-12-02T11:49:34.295114Z",
+     "start_time": "2024-12-02T11:49:34.247720Z"
     }
    },
    "cell_type": "code",
@@ -581,56 +435,52 @@
        "          r2star                                               t1  \\\n",
        "            mean     median        std count_nonzero         mean   \n",
        "layer                                                               \n",
-       "1.0    28.777927  27.386422  10.120994          1306  1427.316554   \n",
-       "2.0    24.798214  21.119667   9.922550          1152  1514.067368   \n",
-       "3.0    22.699811  18.791880   9.507372          1117  1548.529686   \n",
-       "4.0    21.311056  17.593157   8.999270          1079  1584.577592   \n",
-       "5.0    20.659930  17.642798   8.391686          1070  1555.793643   \n",
-       "6.0    20.322622  17.716091   8.013432           998  1535.698166   \n",
-       "7.0    20.008837  17.834057   6.875631           939  1556.361710   \n",
-       "8.0    20.357808  18.393726   6.256812           939  1590.342322   \n",
-       "9.0    21.486777  18.754271   7.210381          1249  1647.641340   \n",
-       "10.0   21.896243  19.833168   6.528540           973  1668.680325   \n",
-       "11.0   22.848923  20.875158   6.762447           800  1699.698935   \n",
-       "12.0   24.367501  22.374521   7.467851           780  1724.054959   \n",
-       "13.0   25.761510  23.751545   8.236693           681  1768.263152   \n",
-       "14.0   27.090914  25.005579   8.590587           627  1816.391434   \n",
-       "15.0   26.376643  24.508926   8.273556           937  1794.624822   \n",
-       "16.0   26.683109  25.606609   7.597819           489  1911.763796   \n",
-       "17.0   26.739680  25.025603   8.242154           370  1903.686099   \n",
-       "18.0   27.080702  26.185752   8.125888           246  1885.025251   \n",
-       "19.0   27.964653  26.761284   8.542669           167  1854.224052   \n",
-       "20.0   27.312497  26.466608   7.030809           164  1822.542544   \n",
-       "21.0   26.563904  26.340168   7.246280            65  2048.189515   \n",
-       "22.0   32.333422  30.151463  11.298019            25  1842.424917   \n",
-       "23.0   33.817001  29.618130   9.046841             5  1972.630908   \n",
+       "1.0    36.078919  34.295055  15.965442           960  1529.780783   \n",
+       "2.0    32.549396  28.651261  14.739961           874  1518.952653   \n",
+       "3.0    29.125305  24.462825  13.853552           867  1476.203398   \n",
+       "4.0    28.005002  22.339258  14.837777           951  1520.895359   \n",
+       "5.0    25.211529  21.449516  11.715109          1082  1655.215180   \n",
+       "6.0    24.269633  21.373598  10.398932          1011  1732.452145   \n",
+       "7.0    24.614919  21.496812  10.347911          1034  1777.863990   \n",
+       "8.0    25.302156  22.356805  10.114435          1060  1823.713049   \n",
+       "9.0    25.324391  22.562809  10.354526          1110  1853.555023   \n",
+       "10.0   25.414645  23.365548   9.933313          1057  1818.143614   \n",
+       "11.0   26.051144  23.991717  10.092395           941  1862.573946   \n",
+       "12.0   26.018781  24.158163   9.720444           711  1882.722874   \n",
+       "13.0   25.893955  24.186453   9.392482           492  1842.207103   \n",
+       "14.0   25.094223  23.521924   8.535586           339  1808.313892   \n",
+       "15.0   25.126293  22.335086   9.043769           220  1735.074765   \n",
+       "16.0   25.265017  22.778277   9.056639           128  1703.510220   \n",
+       "17.0   23.650405  22.607443   7.056175            76  1634.659105   \n",
+       "18.0   23.916527  22.996451   6.139407            45  1691.317080   \n",
+       "19.0   22.877785  22.514443   6.051280            36  1600.631487   \n",
+       "20.0   21.178358  20.748297   5.223452            14  1451.842383   \n",
+       "21.0   17.899892  17.899892   3.072073             2  1345.310897   \n",
        "\n",
        "                                              \n",
        "            median         std count_nonzero  \n",
        "layer                                         \n",
-       "1.0    1432.761292  687.366178          1306  \n",
-       "2.0    1498.551514  521.878771          1152  \n",
-       "3.0    1502.680298  418.154056          1117  \n",
-       "4.0    1504.680908  387.597601          1079  \n",
-       "5.0    1515.112427  245.470044          1070  \n",
-       "6.0    1518.424866  183.277963           998  \n",
-       "7.0    1526.548950  222.644851           939  \n",
-       "8.0    1545.794434  261.664944           939  \n",
-       "9.0    1608.914917  251.010749          1249  \n",
-       "10.0   1629.775635  242.975416           973  \n",
-       "11.0   1657.771057  270.711344           800  \n",
-       "12.0   1686.763916  314.218625           780  \n",
-       "13.0   1727.257935  334.313298           681  \n",
-       "14.0   1759.498901  373.740812           627  \n",
-       "15.0   1747.250732  358.172194           937  \n",
-       "16.0   1814.361206  469.923051           489  \n",
-       "17.0   1863.104370  428.545985           370  \n",
-       "18.0   1872.476135  404.513906           246  \n",
-       "19.0   1826.735474  416.656298           167  \n",
-       "20.0   1736.938843  416.622615           164  \n",
-       "21.0   1752.625488  643.742097            65  \n",
-       "22.0   1678.638062  394.083905            25  \n",
-       "23.0   1783.244507  534.599383             5  "
+       "1.0    1346.811924  813.851546           960  \n",
+       "2.0    1362.840107  642.939757           874  \n",
+       "3.0    1380.362079  451.760720           867  \n",
+       "4.0    1410.410566  496.683234           951  \n",
+       "5.0    1514.621879  583.390482          1082  \n",
+       "6.0    1604.757053  604.777044          1011  \n",
+       "7.0    1701.400927  580.702461          1034  \n",
+       "8.0    1799.114735  545.982923          1060  \n",
+       "9.0    1856.190759  475.095100          1110  \n",
+       "10.0   1840.166042  421.115600          1057  \n",
+       "11.0   1863.199491  482.555673           941  \n",
+       "12.0   1867.205525  517.789819           711  \n",
+       "13.0   1860.696655  403.798594           492  \n",
+       "14.0   1810.123642  396.626239           339  \n",
+       "15.0   1712.920485  348.448770           220  \n",
+       "16.0   1669.850936  336.472778           128  \n",
+       "17.0   1543.670057  280.329523            76  \n",
+       "18.0   1708.911370  260.124806            45  \n",
+       "19.0   1598.745248  230.243360            36  \n",
+       "20.0   1441.952526  128.356280            14  \n",
+       "21.0   1345.310897   18.407824             2  "
       ],
       "text/html": [
        "<div>\n",
@@ -684,274 +534,252 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1.0</th>\n",
-       "      <td>28.777927</td>\n",
-       "      <td>27.386422</td>\n",
-       "      <td>10.120994</td>\n",
-       "      <td>1306</td>\n",
-       "      <td>1427.316554</td>\n",
-       "      <td>1432.761292</td>\n",
-       "      <td>687.366178</td>\n",
-       "      <td>1306</td>\n",
+       "      <td>36.078919</td>\n",
+       "      <td>34.295055</td>\n",
+       "      <td>15.965442</td>\n",
+       "      <td>960</td>\n",
+       "      <td>1529.780783</td>\n",
+       "      <td>1346.811924</td>\n",
+       "      <td>813.851546</td>\n",
+       "      <td>960</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2.0</th>\n",
-       "      <td>24.798214</td>\n",
-       "      <td>21.119667</td>\n",
-       "      <td>9.922550</td>\n",
-       "      <td>1152</td>\n",
-       "      <td>1514.067368</td>\n",
-       "      <td>1498.551514</td>\n",
-       "      <td>521.878771</td>\n",
-       "      <td>1152</td>\n",
+       "      <td>32.549396</td>\n",
+       "      <td>28.651261</td>\n",
+       "      <td>14.739961</td>\n",
+       "      <td>874</td>\n",
+       "      <td>1518.952653</td>\n",
+       "      <td>1362.840107</td>\n",
+       "      <td>642.939757</td>\n",
+       "      <td>874</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3.0</th>\n",
-       "      <td>22.699811</td>\n",
-       "      <td>18.791880</td>\n",
-       "      <td>9.507372</td>\n",
-       "      <td>1117</td>\n",
-       "      <td>1548.529686</td>\n",
-       "      <td>1502.680298</td>\n",
-       "      <td>418.154056</td>\n",
-       "      <td>1117</td>\n",
+       "      <td>29.125305</td>\n",
+       "      <td>24.462825</td>\n",
+       "      <td>13.853552</td>\n",
+       "      <td>867</td>\n",
+       "      <td>1476.203398</td>\n",
+       "      <td>1380.362079</td>\n",
+       "      <td>451.760720</td>\n",
+       "      <td>867</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4.0</th>\n",
-       "      <td>21.311056</td>\n",
-       "      <td>17.593157</td>\n",
-       "      <td>8.999270</td>\n",
-       "      <td>1079</td>\n",
-       "      <td>1584.577592</td>\n",
-       "      <td>1504.680908</td>\n",
-       "      <td>387.597601</td>\n",
-       "      <td>1079</td>\n",
+       "      <td>28.005002</td>\n",
+       "      <td>22.339258</td>\n",
+       "      <td>14.837777</td>\n",
+       "      <td>951</td>\n",
+       "      <td>1520.895359</td>\n",
+       "      <td>1410.410566</td>\n",
+       "      <td>496.683234</td>\n",
+       "      <td>951</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5.0</th>\n",
-       "      <td>20.659930</td>\n",
-       "      <td>17.642798</td>\n",
-       "      <td>8.391686</td>\n",
-       "      <td>1070</td>\n",
-       "      <td>1555.793643</td>\n",
-       "      <td>1515.112427</td>\n",
-       "      <td>245.470044</td>\n",
-       "      <td>1070</td>\n",
+       "      <td>25.211529</td>\n",
+       "      <td>21.449516</td>\n",
+       "      <td>11.715109</td>\n",
+       "      <td>1082</td>\n",
+       "      <td>1655.215180</td>\n",
+       "      <td>1514.621879</td>\n",
+       "      <td>583.390482</td>\n",
+       "      <td>1082</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6.0</th>\n",
-       "      <td>20.322622</td>\n",
-       "      <td>17.716091</td>\n",
-       "      <td>8.013432</td>\n",
-       "      <td>998</td>\n",
-       "      <td>1535.698166</td>\n",
-       "      <td>1518.424866</td>\n",
-       "      <td>183.277963</td>\n",
-       "      <td>998</td>\n",
+       "      <td>24.269633</td>\n",
+       "      <td>21.373598</td>\n",
+       "      <td>10.398932</td>\n",
+       "      <td>1011</td>\n",
+       "      <td>1732.452145</td>\n",
+       "      <td>1604.757053</td>\n",
+       "      <td>604.777044</td>\n",
+       "      <td>1011</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7.0</th>\n",
-       "      <td>20.008837</td>\n",
-       "      <td>17.834057</td>\n",
-       "      <td>6.875631</td>\n",
-       "      <td>939</td>\n",
-       "      <td>1556.361710</td>\n",
-       "      <td>1526.548950</td>\n",
-       "      <td>222.644851</td>\n",
-       "      <td>939</td>\n",
+       "      <td>24.614919</td>\n",
+       "      <td>21.496812</td>\n",
+       "      <td>10.347911</td>\n",
+       "      <td>1034</td>\n",
+       "      <td>1777.863990</td>\n",
+       "      <td>1701.400927</td>\n",
+       "      <td>580.702461</td>\n",
+       "      <td>1034</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8.0</th>\n",
-       "      <td>20.357808</td>\n",
-       "      <td>18.393726</td>\n",
-       "      <td>6.256812</td>\n",
-       "      <td>939</td>\n",
-       "      <td>1590.342322</td>\n",
-       "      <td>1545.794434</td>\n",
-       "      <td>261.664944</td>\n",
-       "      <td>939</td>\n",
+       "      <td>25.302156</td>\n",
+       "      <td>22.356805</td>\n",
+       "      <td>10.114435</td>\n",
+       "      <td>1060</td>\n",
+       "      <td>1823.713049</td>\n",
+       "      <td>1799.114735</td>\n",
+       "      <td>545.982923</td>\n",
+       "      <td>1060</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9.0</th>\n",
-       "      <td>21.486777</td>\n",
-       "      <td>18.754271</td>\n",
-       "      <td>7.210381</td>\n",
-       "      <td>1249</td>\n",
-       "      <td>1647.641340</td>\n",
-       "      <td>1608.914917</td>\n",
-       "      <td>251.010749</td>\n",
-       "      <td>1249</td>\n",
+       "      <td>25.324391</td>\n",
+       "      <td>22.562809</td>\n",
+       "      <td>10.354526</td>\n",
+       "      <td>1110</td>\n",
+       "      <td>1853.555023</td>\n",
+       "      <td>1856.190759</td>\n",
+       "      <td>475.095100</td>\n",
+       "      <td>1110</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10.0</th>\n",
-       "      <td>21.896243</td>\n",
-       "      <td>19.833168</td>\n",
-       "      <td>6.528540</td>\n",
-       "      <td>973</td>\n",
-       "      <td>1668.680325</td>\n",
-       "      <td>1629.775635</td>\n",
-       "      <td>242.975416</td>\n",
-       "      <td>973</td>\n",
+       "      <td>25.414645</td>\n",
+       "      <td>23.365548</td>\n",
+       "      <td>9.933313</td>\n",
+       "      <td>1057</td>\n",
+       "      <td>1818.143614</td>\n",
+       "      <td>1840.166042</td>\n",
+       "      <td>421.115600</td>\n",
+       "      <td>1057</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11.0</th>\n",
-       "      <td>22.848923</td>\n",
-       "      <td>20.875158</td>\n",
-       "      <td>6.762447</td>\n",
-       "      <td>800</td>\n",
-       "      <td>1699.698935</td>\n",
-       "      <td>1657.771057</td>\n",
-       "      <td>270.711344</td>\n",
-       "      <td>800</td>\n",
+       "      <td>26.051144</td>\n",
+       "      <td>23.991717</td>\n",
+       "      <td>10.092395</td>\n",
+       "      <td>941</td>\n",
+       "      <td>1862.573946</td>\n",
+       "      <td>1863.199491</td>\n",
+       "      <td>482.555673</td>\n",
+       "      <td>941</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12.0</th>\n",
-       "      <td>24.367501</td>\n",
-       "      <td>22.374521</td>\n",
-       "      <td>7.467851</td>\n",
-       "      <td>780</td>\n",
-       "      <td>1724.054959</td>\n",
-       "      <td>1686.763916</td>\n",
-       "      <td>314.218625</td>\n",
-       "      <td>780</td>\n",
+       "      <td>26.018781</td>\n",
+       "      <td>24.158163</td>\n",
+       "      <td>9.720444</td>\n",
+       "      <td>711</td>\n",
+       "      <td>1882.722874</td>\n",
+       "      <td>1867.205525</td>\n",
+       "      <td>517.789819</td>\n",
+       "      <td>711</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13.0</th>\n",
-       "      <td>25.761510</td>\n",
-       "      <td>23.751545</td>\n",
-       "      <td>8.236693</td>\n",
-       "      <td>681</td>\n",
-       "      <td>1768.263152</td>\n",
-       "      <td>1727.257935</td>\n",
-       "      <td>334.313298</td>\n",
-       "      <td>681</td>\n",
+       "      <td>25.893955</td>\n",
+       "      <td>24.186453</td>\n",
+       "      <td>9.392482</td>\n",
+       "      <td>492</td>\n",
+       "      <td>1842.207103</td>\n",
+       "      <td>1860.696655</td>\n",
+       "      <td>403.798594</td>\n",
+       "      <td>492</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14.0</th>\n",
-       "      <td>27.090914</td>\n",
-       "      <td>25.005579</td>\n",
-       "      <td>8.590587</td>\n",
-       "      <td>627</td>\n",
-       "      <td>1816.391434</td>\n",
-       "      <td>1759.498901</td>\n",
-       "      <td>373.740812</td>\n",
-       "      <td>627</td>\n",
+       "      <td>25.094223</td>\n",
+       "      <td>23.521924</td>\n",
+       "      <td>8.535586</td>\n",
+       "      <td>339</td>\n",
+       "      <td>1808.313892</td>\n",
+       "      <td>1810.123642</td>\n",
+       "      <td>396.626239</td>\n",
+       "      <td>339</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15.0</th>\n",
-       "      <td>26.376643</td>\n",
-       "      <td>24.508926</td>\n",
-       "      <td>8.273556</td>\n",
-       "      <td>937</td>\n",
-       "      <td>1794.624822</td>\n",
-       "      <td>1747.250732</td>\n",
-       "      <td>358.172194</td>\n",
-       "      <td>937</td>\n",
+       "      <td>25.126293</td>\n",
+       "      <td>22.335086</td>\n",
+       "      <td>9.043769</td>\n",
+       "      <td>220</td>\n",
+       "      <td>1735.074765</td>\n",
+       "      <td>1712.920485</td>\n",
+       "      <td>348.448770</td>\n",
+       "      <td>220</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16.0</th>\n",
-       "      <td>26.683109</td>\n",
-       "      <td>25.606609</td>\n",
-       "      <td>7.597819</td>\n",
-       "      <td>489</td>\n",
-       "      <td>1911.763796</td>\n",
-       "      <td>1814.361206</td>\n",
-       "      <td>469.923051</td>\n",
-       "      <td>489</td>\n",
+       "      <td>25.265017</td>\n",
+       "      <td>22.778277</td>\n",
+       "      <td>9.056639</td>\n",
+       "      <td>128</td>\n",
+       "      <td>1703.510220</td>\n",
+       "      <td>1669.850936</td>\n",
+       "      <td>336.472778</td>\n",
+       "      <td>128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17.0</th>\n",
-       "      <td>26.739680</td>\n",
-       "      <td>25.025603</td>\n",
-       "      <td>8.242154</td>\n",
-       "      <td>370</td>\n",
-       "      <td>1903.686099</td>\n",
-       "      <td>1863.104370</td>\n",
-       "      <td>428.545985</td>\n",
-       "      <td>370</td>\n",
+       "      <td>23.650405</td>\n",
+       "      <td>22.607443</td>\n",
+       "      <td>7.056175</td>\n",
+       "      <td>76</td>\n",
+       "      <td>1634.659105</td>\n",
+       "      <td>1543.670057</td>\n",
+       "      <td>280.329523</td>\n",
+       "      <td>76</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18.0</th>\n",
-       "      <td>27.080702</td>\n",
-       "      <td>26.185752</td>\n",
-       "      <td>8.125888</td>\n",
-       "      <td>246</td>\n",
-       "      <td>1885.025251</td>\n",
-       "      <td>1872.476135</td>\n",
-       "      <td>404.513906</td>\n",
-       "      <td>246</td>\n",
+       "      <td>23.916527</td>\n",
+       "      <td>22.996451</td>\n",
+       "      <td>6.139407</td>\n",
+       "      <td>45</td>\n",
+       "      <td>1691.317080</td>\n",
+       "      <td>1708.911370</td>\n",
+       "      <td>260.124806</td>\n",
+       "      <td>45</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19.0</th>\n",
-       "      <td>27.964653</td>\n",
-       "      <td>26.761284</td>\n",
-       "      <td>8.542669</td>\n",
-       "      <td>167</td>\n",
-       "      <td>1854.224052</td>\n",
-       "      <td>1826.735474</td>\n",
-       "      <td>416.656298</td>\n",
-       "      <td>167</td>\n",
+       "      <td>22.877785</td>\n",
+       "      <td>22.514443</td>\n",
+       "      <td>6.051280</td>\n",
+       "      <td>36</td>\n",
+       "      <td>1600.631487</td>\n",
+       "      <td>1598.745248</td>\n",
+       "      <td>230.243360</td>\n",
+       "      <td>36</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20.0</th>\n",
-       "      <td>27.312497</td>\n",
-       "      <td>26.466608</td>\n",
-       "      <td>7.030809</td>\n",
-       "      <td>164</td>\n",
-       "      <td>1822.542544</td>\n",
-       "      <td>1736.938843</td>\n",
-       "      <td>416.622615</td>\n",
-       "      <td>164</td>\n",
+       "      <td>21.178358</td>\n",
+       "      <td>20.748297</td>\n",
+       "      <td>5.223452</td>\n",
+       "      <td>14</td>\n",
+       "      <td>1451.842383</td>\n",
+       "      <td>1441.952526</td>\n",
+       "      <td>128.356280</td>\n",
+       "      <td>14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21.0</th>\n",
-       "      <td>26.563904</td>\n",
-       "      <td>26.340168</td>\n",
-       "      <td>7.246280</td>\n",
-       "      <td>65</td>\n",
-       "      <td>2048.189515</td>\n",
-       "      <td>1752.625488</td>\n",
-       "      <td>643.742097</td>\n",
-       "      <td>65</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22.0</th>\n",
-       "      <td>32.333422</td>\n",
-       "      <td>30.151463</td>\n",
-       "      <td>11.298019</td>\n",
-       "      <td>25</td>\n",
-       "      <td>1842.424917</td>\n",
-       "      <td>1678.638062</td>\n",
-       "      <td>394.083905</td>\n",
-       "      <td>25</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23.0</th>\n",
-       "      <td>33.817001</td>\n",
-       "      <td>29.618130</td>\n",
-       "      <td>9.046841</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1972.630908</td>\n",
-       "      <td>1783.244507</td>\n",
-       "      <td>534.599383</td>\n",
-       "      <td>5</td>\n",
+       "      <td>17.899892</td>\n",
+       "      <td>17.899892</td>\n",
+       "      <td>3.072073</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1345.310897</td>\n",
+       "      <td>1345.310897</td>\n",
+       "      <td>18.407824</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 6
+   "execution_count": 5
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-06-19T16:37:48.867662Z",
-     "start_time": "2024-06-19T16:37:46.216108Z"
+     "end_time": "2024-12-02T11:49:37.018709Z",
+     "start_time": "2024-12-02T11:49:34.591529Z"
     }
    },
    "cell_type": "code",
@@ -961,19 +789,17 @@
     "ax.set_xlabel('Depth (mm)')\n",
     "ax.set_ylabel('$R_2^*$ (Hz)')\n",
     "ax.set_ylim((15, 35))\n",
-    "ax.axvspan(0, 5, alpha=0.2, label='Outer Layers', color='C2')\n",
-    "ax.axvspan(15, 23, alpha=0.2, label='Inner Layers', color='C5')\n",
-    "ax.legend()"
+    "ax.set_title('Median $R_2^*$ by Layer')"
    ],
    "id": "519a8be2ff73c3b",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x1b6ed946790>"
+       "Text(0.5, 1.0, 'Median $R_2^*$ by Layer')"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -982,13 +808,13 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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I48t/hT5GGl/+K8Qx5kUO07Rp07BgwQL813/9FzZu3Ija2lrcfvvtCIVCuOKKK3Deeefh/fffx8MPP4wDBw7gnXfewZ133onzzjsvuXMuHA6jo6Mjecwrr7wSf/3rX/Hkk09i9+7d+MUvfoHt27dj+fLl2RrmUYk8i7iiQ8uTqJ0QQggpFHkRMDEMg1/96leYP38+br75Zlx00UUIBoN49tlnUVlZia9//et48MEH8fe//x1Lly7FXXfdhTPPPBP33ntv8hgrVqzAhRdemPx+wYIFuPfee/Hcc89h2bJl+Oijj/DII4/kdNFKUeCgqDoSip7tUyGEEEJGlbyp9J0vOjujGZ+GbGiLorkzBp9bQkcwgQkVHhR7bRl9jKPheRY+n3NYxpcrCn2MNL78V+hjpPHlv3wco9/v7FcOU17MMJFDWI5BNKFm+zQIIYSQUYUCpjwj8hwicRWGQRODhBBCyEihgCnPiAILVTMoj4kQQggZQRQw5RmeY6HpJmSVAiZCCCFkpFDAlIdYFogntGyfBiGEEDJqUMCU4+Kyhs92t6XUXhJ5FuG4AoM2OBJCCCEjggKmHLdu/QG88M/d+HxXe/IyUWAhqwYUWpYjhBBCRgQFTDnO7RAAAPVt0eRlPMdC1XTIlPhNCCGEjAgKmHLcpEqrqXBzRwzdNUYZhgHDMIgrlMdECCGEjAQKmHLc+DI3OJZBQtHRGZaTlws8g0iMAiZCCCFkJFDAlOMEnsWYYgcA4GDroWU5UeCQUHXKYyKEEEJGAAVMeWBcmQsAcLBHHpPIs1BVHQkKmAghhJBhRwFTHqgqtQKm+tZI8jKGYWCYJiV+E0IIISOAAqY80D3D1NQRT+n+LPAsonElW6dFCCGEjBoUMOUBn1uCTeRgGCaaOmLJy0WeRUzWU4paEkIIISTzKGDKAwzDoMJvJX7X90j8FgQWiqpTXzlCCCFkmFHAlCe6A6aeid8cy0I3KI+JEEIIGW4UMOWJcr8dAHCwR+I3AHAsg2hCzcYpEUIIIaMGBUx5orxrhikQURCNHwqQRIFFJK7CMKgRLyGEEDJcKGDKE5LAocRrA5DaV07kOaiqQXlMhBBCyDCigCmPjC11Akit+M3zLFTdQILymAghhJBhQwFTHukuYHl4HhNLjXgJIYSQYUUBUx4ZW2LNMDW0xWCah3KWBIFFJKamXEYIIYSQzKGAKY+U+ewQeBayqqMtmEheLvIsEooORaUCloQQQshwoIApj7AsgzHFaQpY8iw03aBGvIQQQsgwoYApzxxK/E5txAsTSMiUx0QIIYQMBwqY8kxVSXfidzTlckFgEIlTAUtCCCFkOFDAlGe6Z5haAnEoPZbgRJ5DXNGharQsRwghhGQaBUx5xuMU4XYIME2goT2WvFzgWSiqRvWYCCGEkGFAAVMe4Hk2pWRAujwmlmUAMNSIlxBCCBkGFDDlAYlnATDJoOlQAcvUPCaOozwmQgghZDhQwJQHRIEDzzLQdStg6i5gebAtNWASeRYxWYOmUz0mQgghJJMoYMoDksBBEFgomhUIjSl2gGGAcExFKKokbycKLBRNp0a8hBBCSIZRwJQHWJaBQxKSO+BEgUO5zw4gdVmOY1kYukl5TIQQQkiG5U3A1N7ejltvvRXz58/HCSecgKuvvhq7du1KXv/WW2/hggsuwAknnIBFixbh5z//ORKJxBGOCCxatAg1NTUpXz/5yU+GeyiD4rDx0PQeid9d9ZjqD2/EyzKIyZTHRAghhGQSn+0T6K9rr70WLMvi8ccfh8PhwIMPPogrrrgCb7zxBrZu3YobbrgBN998M8466yzs378fP/vZzxAIBHDfffelPV4kEkFDQwMeffRRzJgxI3m5zWYbqSENiCRwYBgr8ZthGIwtdWJTbWvvPCaBRTSuwTBNsAyTpbMlhBBCCktezDB1dnaiqqoK99xzD2bNmoXq6mpcd911aG1txc6dO/H8889j/vz5uPrqqzFhwgSceuqp+NGPfoRXXnkFiqKkPWZtbS1M08ScOXNQWlqa/HK73SM8uv4RBRY8xyYTurtLCzS0xWAYh2aeRJ6DrOi0LEcIIYRkUF7MMPl8PqxatSr5fVtbG5544glUVFRg8uTJuOqqq8CyvWM/TdMQiUTg9/t7Xbdjxw6UlpbC4/EM67lniihwkLoSvwWeQ4nXBkngIKs6mjvjyaa8PM9CMwzIqg67lBdPLyGEEJLz8u4d9e6778af/vQniKKIhx9+GA6HA9OnT0+5jaIoePLJJzFjxoy0wRJgzTA5HA7ceOON2Lx5M/x+P84//3xcfvnlaYOvbGMZBnaJR0fYystiGAaVJQ7sbQzjYGskGTABAAMGcVlDkUvK1ukSQgghBSXvAqbly5fjkksuwXPPPYfrr78ea9asSclB0jQNt912G3bt2oVnn322z+Ps3LkT4XAYS5YswQ033ICNGzdi5cqVCAaDuOmmmwZ9fhw3fMGWxymiI5xIPsa4cjf2NobR0B5LeVxJ4hCTNXAcAyZDeUzdxx/O8WVboY+Rxpf/Cn2MNL78V8hjZMyePTfyiGEYWLp0KY477rhkYnckEsHNN9+M9evX48EHH8SiRYv6vL+qqpBlGS6XK3nZY489hocffhibNm3KyVmmYETG9n0d8HtsYBkGW/e0Y/UrX6DM58C/Lz8peTtZ0ZFQNcyYVAybmHcxMSGEEJJz8uLdtL29HR9++CHOOecccBwHAGBZFtXV1WhpaQEAtLS04Ac/+AHq6+vx+OOPY/78+Uc8piAIEAQh5bKpU6ciFoshGAzC5/MN6lxDoTj0Yaq0Las65LiCdl2HJHDwu6zzb+mMobktnMxZMk0THWEZzS0iPE4xI4/NcSw8Hvuwji/bCn2MNL78V+hjpPHlv3wco8dj79eMWF4ETC0tLbjllltQXFyMk08+GYA1Q7Rt2zYsWrQIwWAQy5cvRyQSwZo1a1BTU3PE4xmGgTPOOAMXXXQRrr322uTlW7ZsQUlJyaCDJQDQdQOaNjy/JCwAjmUgyxp4loFN5OBzS+gMy6hrCaO60pu8raEbiMQUODKc+D2c48sVhT5GGl/+K/Qx0vjyXyGOMffWndKYNm0aFixYgP/6r//Cxo0bUVtbi9tvvx2hUAhXXHEF7rvvPtTV1eF///d/4ff70dramvzSdWt7fTgcRkdHBwBrduqss87C6tWrsW7dOhw4cAB//OMfsXr16iHlLw03hmHgtPPJFilAj75yhzXi5TkWYWrESwghhGREXswwMQyDX/3qV3jggQdw8803IxwOY+7cuXj22WdRUVGBv/3tb1BVFcuXL+9133/84x+oqqrCihUrsGHDBrz11lsAgFtuuQUejwcPPPAAmpqaUFVVhbvuugsXX3zxSA9vQOyiANOMJ78fW+rEF3s7egVMosAiIetQNQMCnxdxMSGEEJKz8jbpO1d1dkaHdRoyEldRWx9AkVMEyzKob43g93/9Eg6Jxy3/Nju5K84wTASiCqZWFcFlF45y1KPjeRY+n3PYx5dNhT5GGl/+K/Qx0vjyXz6O0e939iuHiaYe8owksBB5DmpXMl2F3wGOZRCTNXSG5eTtWJaBaQAJRcvWqRJCCCEFgwKmPMNzLESehaoaye/L/VbRysP7yvEcEI1TwEQIIYQMFQVMeYZhGLjsQnKGCQCqStMnfosCh2hChW7kx7QoIYQQkqsoYMpDNpFLabjb3Yi3/vCAiWehaNSIlxBCCBkqCpjykCRy4FgmGTRVlVrVyps7YtB6zDxxHAtdN5GggIkQQggZEgqY8pDIcxB4DopmBUJFLhEOiYdumGjqiKXclmUZxGSqx0QIIYQMBQVMeUjgWdhEDmrXlk2GYZLLcunqMUXjGgyqHkEIIYQMGgVMecph46FpvfOYegVMPAdZNSiPiRBCCBkCCpjylE3k0XPSKBkw9SotwEDVdcgqBUyEEELIYFHAlKckgQXLIdkNemyxFTB1hmVEE4dylhiGAQsGcZnqMRFCCCGDRQFTnhIFzqr43ZXHZJN4lHhtAHovywk8i0hcBXXBIYQQQgaHAqY8xXNW4rfSo1fPkRK/ZUVPBleEEEIIGRgKmPKY0yZA03skfpd0F7CMpNxO4FkomoEE5TERQgghg0IBUx6TRA4mDgVM3QUsG9piKctvDMPANE3aKUcIIYQMEgVMeUwSOAgsm6zuXeazQ+BZyKqOtmAi5bYCzyIcU7JxmoQQQkjeo4Apj4kCC55nk7lJLMtgTLEDQLp6TCziip7SOoUQQggh/UMBUx7jWBYOiU9J5u478ZuDourUV44QQggZBAqY8pzDLqTMGlWVWHlMB9tSE79ZloFpUB4TIYQQMhgUMOU5SeAAMMkk7+4ZpubOOJTDdsWxHJNS1JIQQggh/UMBU54TeRY8xybLC3icItwOAaYJNLbHDrsth0hchWFQAUtCCCFkIChgynOSwEHgmfR5TG29C1iqmkF5TIQQQsgAUcCU51iWgdMmQNUOBUF9FbDsnomiRryEEELIwFDAVAAcEp9S8bu7gOXhO+UAgGWBeIIa8RJCCCEDQQFTARAFLlnNGwDGFDvAMEA4piIUTS1WKfIswnEFBjXiJYQQQvqNAqYCIAlc13KblcckChzKiuwA+mjEqxq9dtARQgghpG8UMBUAQWAhCVaD3W7dy3L1bb3zmFRNp3pMhBBCyABQwFQAWIaBw8ZDVY9e8ZthGDAMg7hCeUyEEEJIf1HAVCDsEg+jR5u47oCpsT3Wq+6SwDMIx6iAJSGEENJfFDAVCEngAMZMJn6XeG2QBA6qZqClM55yW1HgICs65TERQggh/UQBU4EQBQ4izyULWDIMg8oSB4A0BSx5K98pQQETIYQQ0i8UMBUIkWchClxK4vfY7sTvwwpYdpcgoMRvQgghpH8oYCoQDMPAaeNTWqRU9ZH4DQACzyIaV3pdTgghhJDeKGAqIFbi96EE7+7E77ZgAgk5dVecyLOIyXqydhMhhBBC+kYBUwERBQ4syySDJqdNQJFLBAAcbD+8gCUHRdWprxwhhBDSDxQwFRBJYCH0SPwG+u4rx7IMDMpjIoQQQvolbwKm9vZ23HrrrZg/fz5OOOEEXH311di1a1fy+u3bt+PSSy/F8ccfj9NOOw1PPPHEUY+5bt06LFmyBLNmzcLSpUvx7rvvDucQhp3Ac5AENiVg6quAJWAFTdEE1WMihBBCjiZvAqZrr70WdXV1ePzxx/Hiiy/CZrPhiiuuQDweR2dnJ6688kpMnDgRL730Em688UY8+OCDeOmll/o83kcffYRbb70V3/nOd/Dyyy9jwYIFuP7667F79+4RHFXmOe1CasBUcihgMg9ruCvyHCJxtVdhS0IIIYSkyouAqbOzE1VVVbjnnnswa9YsVFdX47rrrkNrayt27tyJP/3pTxBFEf/5n/+J6upqXHDBBbjiiivw+OOP93nMxx9/HIsXL8all16K6upq3H777ZgxYwaeeuqpERxZ5tkEDj3TuCuKHeBYBjFZQyCSuitOFFgoqoEELcsRQgghR5QXAZPP58OqVaswZcoUAEBbWxueeOIJVFRUYPLkydi4cSNOOukk8DyfvM/8+fOxd+9etLe39zqeYRj45JNPMH/+/JTL582bh40bNw7vYIaZJHLgGEDv6pPCcyzK/V0FLFt7N+LVdAMJ6itHCCGEHBF/9Jvklrvvvjs5o/Twww/D4XCgqakJU6dOTbldWVkZAKChoQHFxcUp14VCIcRiMVRUVPS6T2Nj45DOj+OyG4M67QJsEg/DBMSucxlX5kJDWxQH22KYPaU05faCwCGu6uD5I59397iyPb7hVOhjpPHlv0IfI40v/xXyGPMuYFq+fDkuueQSPPfcc7j++uuxZs0aJBIJiKKYcjtJkgAAsiz3OkYikQCAtPdJd/uB8HjsQ7p/JrRHVETjKjwu62cwZbwP67c1o6kjBo879fw4wfoVcHvs4PvxC54L4xtuhT5GGl/+K/Qx0vjyXyGOMe8CpsmTJwMA7rnnHnz66ad45plnYLPZoCip+TndgY/D4eh1jO5gKt197PahPcmhUBx6lotBGpqGzmAMjGmdR7HbCgzrWyLoCERTAiPdMBCKqmh0CnDZhT6PyXEsPB57ToxvuBT6GGl8+a/Qx0jjy3/5OEaPx96vGbG8CJja29vx4Ycf4pxzzgHHcQAAlmVRXV2NlpYWVFRUoKWlJeU+3d+Xl5f3Ol5RUREcDkfa+xy+TDdQum5A07L7S8KzLDTNSP6yehwCHBKPmKzhYGskWZupm6oaiMQU2ATuqMfOhfENt0IfI40v/xX6GGl8+a8Qx5gXi4wtLS245ZZbsGHDhuRlqqpi27ZtqK6uxkknnYRNmzZB1w/t9vrwww9xzDHH9MpfAqy+a3PmzEk5HgCsX78eJ5544vANZIRIAgeeY5MBE8MwR6zHxPMMIjHqK0cIIYT0JS8CpmnTpmHBggX4r//6L2zcuBG1tbW4/fbbEQqFcMUVV+CCCy5AJBLBXXfdhV27dmHt2rV46qmncM011ySPEQ6H0dHRkfz+yiuvxF//+lc8+eST2L17N37xi19g+/btWL58eTaGmFGiwELgWSjpCli29Q6YJIFFVNZT6jcRQggh5JC8CJgYhsGvfvUrzJ8/HzfffDMuuugiBINBPPvss6isrERxcTFWr16NvXv3YtmyZfjNb36D2267DcuWLUseY8WKFbjwwguT3y9YsAD33nsvnnvuOSxbtgwfffQRHnnkEVRXV2djiBnFcyxsItfvit8iz0FRdCovQAghhPSBMQ8v/0yGpLMzmhPrts0dMRxsjcLvtRLcE7KGXzz3KQDgln+bDactNcG7PZjAuHI3yorSJ73zPAufz5kz4xsOhT5GGl/+K/Qx0vjyXz6O0e939ivpOy9mmMjA2UQOJnMoFrZJPEq8NgB9zDIJLMJRymMihBBC0qGAqUCJAgeha7dctyMuywkc4rIGWaU2KYQQQsjhKGAqUKLAgudZqD3qYCQb8aZJ/Ba7ksSprxwhhBDSGwVMBYpjWTgk/rCdclb9pYOtURyeusYwDAAgllBH7iQJIYSQPEEBUwFz2oWUSqvlPqv9iazqaAsmet1e5FmEY2qvYIoQQggZ7ShgKmCiwAFgkgEQyzKoLLFaxaTLY5JEFglFpzwmQggh5DAUMBUwSWDBcyw0/dCM0ZHymKzb6ojLFDARQgghPVHAVMBEgYPIM1C1QwFQVTKPKdLr9gzDgGUYxBNUwJIQQgjpiQKmAsYyDBw2IW3F7+bOeEog1U0QWITjCgyD8pgIIYSQbhQwFTiHxKNH3jc8ThFuhwDTBBraY71uLwkcZEWn8gKEEEJIDxQwFThR5AAgZefbkQpY8pxVu4n6yhFCCCGHUMBU4CSeg8CzqctyJX0HTADAsQwicarHRAghhHSjgKnAiQIL8fCK30dI/AYASeQQiavQjfxonEgIIYQMNwqYChzDMHDaeajqoeCnstgBhgFCMRWhNA13RYGFolJ5AUIIIaQbBUyjgF0U0HOySBQ4lBXZAaSvx8SxLAzDpDwmQgghpAsFTKOAKLAA2//EbwDgeBaRWO/ZJ0IIIWQ0ooBpFJAEDiKXmvjdXcCyvq88Jp5FVNZT7kMIIYSMVhQwjQICz0IUOChpClg2tsfSJneLAgdZ0WhZjhBCCAEFTKMCwzBwHVbxu8Rrg8PGQ9UM1DX3nmViWQamCcSpgCUhhBBCAdNoYZM4mD3anTAMg8ljvQCAnQeDae8jCizCaXbREUIIIaMNBUyjhChwYFgmpUfclCorYNpV31fAxCEua5BVmmUihBAyulHANEpIAguR51KW5SZVesAwQGsggUBY7nUfsatCOPWVI4QQMtpRwDRKCDxnFaTUDgU/donHuDJrt1y6ZTmGYWACiMvUJoUQQsjoRgHTKOK0C9A0M+Wy7mW5nX0ty/EsQlE1pYYTIYQQMtpQwDSK2EUexmFxz5SqIgDAvsZQ2ppLksgioeiUx0QIIWRUo4BpFBEFFhyHlLpLpUU2eJwiNN3EvqZQr/vwHAtVp75yhBBCRjcKmEYRSeAg8FxKI16GYY64W45hGLBgEEtQHhMhhJDRiwKmUYTnWNgEDqqeuvQ2uUceU7pcJVFkEYopKSUJCCGEkNGEAqZRxmnnoR6W+H1MhRscyyAQUdAWTPS6j8RzkBUDcZnapBBCCBmdKGAaZSSBB5AaMIkCh4kVbgDpd8vxPAtNp4CJEELI6EUB0ygjCRw41gqAepoy7sjlBViWQThGbVIIIYSMThQwjTKSyELoquDdU3dfubrmCBJK75kkSeQQiiopO+wIIYSQ0YICplGGY1nYRa5XwOT32FDsscEwTexp6F1eQBKsekxUXoAQQshoRAHTKJSu4jdwaFkuXXkBjmVhGCYSlMdECCFkFOKzfQL9EQgEsGrVKrz99tuIRCKoqanBLbfcgrlz5+Kyyy7Dhg0b0t7v5z//Ob71rW+lvW7RokU4ePBgymVLly7FypUrM336OUcSOJhM74Bp8lgvPtranCwvwDBMyvU8xyAcV1DkkkbqVAkhhJCckBcB049//GO0t7dj1apV8Pv9WLNmDb73ve9h7dq1eOihh6CqqUUVf/rTn+LAgQM444wz0h4vEomgoaEBjz76KGbMmJG83GazDes4coUocBBYFppmgOcPTTJOKHdB5FlEExoa22OoLHGm3M8m8AiEYlA1AwJPk5OEEEJGj5wPmPbv34/3338fzz33HObMmQMAuOuuu/Duu+/i1VdfxU033ZRy+1dffRX/+te/sHbtWrhcrrTHrK2thWmamDNnDjwez7CPIddIAgeeZ6EcFjBxHItJlR58eSCAnfXBXgGTJHKQFR0JRYPAiyN92oQQQkjW5Pw0gc/nw2OPPYaZM2cmL2MYBqZpIhhMzbWJxWL4xS9+geXLl6OmpqbPY+7YsQOlpaWjMlgCrBIBDonvVfEbQLJNSrryAizLwAQQVyjxmxBCyOiS8zNMHo8HCxcuTLls3bp1OHDgABYsWJBy+fPPP49oNIprr732iMesra2Fw+HAjTfeiM2bN8Pv9+P888/H5ZdfDpYdWgzJcTkfgwIAvC4JwZjS63ynjvcBH+xHY1sUcUWHyy4AsJK+AcAm8ojJWsrMVKHo/lnky3M4UDS+/FfoY6Tx5b9CHmPOB0yH27RpE+68806cfvrpWLRoUfJyXdfx9NNP4zvf+Q7cbvcRj7Fz506Ew2EsWbIEN9xwAzZu3IiVK1ciGAz2WuIbKI/HPqT7jxSG59ERVeF22VKSuz1uO8aWunCwNYKDbTGcNL0i5X6+IjsUVYfDZYMkcCN92iMiX57DwaLx5b9CHyONL/8V4hjzKmB688038ZOf/ASzZ8/GqlWrUq7bsGEDGhoacPHFFx/1OE8++SRkWU7mONXU1CAajeLhhx/GjTfeOKRZplAoDj3NUleuSSga5ISCdlOHyKcGPtVjPTjYGsHnO1tR01VqgGNZOJ0SVEVDRzCOxqYgvAW2W47jWHg89rx5DgeKxpf/Cn2MNL78l49j9Hjs/ZoRy5uA6ZlnnsGKFSuwePFirFy5EqKYmnT85ptv4rjjjsO4ceOOeixBECAIQsplU6dORSwWQzAYhM/nG/R56roBTcv9XxKWYcAygCxr4A4rH1Bd6cG7nzZg18EgVFUHyx663jRN6LqJcEyB0yYcftiCkC/P4WDR+PJfoY+Rxpf/CnGMebHIuGbNGtxzzz347ne/i1/96le9giXAWqqbP3/+UY9lGAYWLVqEhx9+OOXyLVu2oKSkZEjBUj5hGQYOmwAlzS/02BIn7BKPhKKjrjXS63qRZxGKqjDN3rWcCCGEkEKU8wHT3r17ce+992Lx4sW45ppr0N7ejtbWVrS2tiIcDgOw8pd27dqFqVOnpj1GOBxGR0cHAIBlWZx11llYvXp1Mnn8j3/8I1avXj3k/KV847DxSNcajmUZTB5r7SDcWdd7t5wkWm1SZJV2yxFCCBkdcn5J7vXXX4eqqnjjjTfwxhtvpFy3bNky3H///QgEAlBVFUVFRWmPsWLFCmzYsAFvvfUWAOCWW26Bx+PBAw88gKamJlRVVeGuu+7qV/5TIREFDgyQtqr3lCovtuzpwK76IM6YW5VyHc+xUHUVcVmHTcz5XyFCCCFkyBiT1lUyqrMzmjfrtrKqY2ddAILA9trxFpc1rHz+U5gmcNOFs+D32uFx2xEKW4l8nSEZ5T4HKkudfRw9//A8C5/PmVfP4UDQ+PJfoY+Rxpf/8nGMfr+zX0nfOb8kR4aPJHBw2HjIaQpR2iUeVaXWLsJ0RSxFkUU4rsCgeJsQQsgoQAHTKOdxin1+CpjcVfV718E0eUx8V5sUmfKYCCGEFD4KmEY5h8SD41hoR2iTsrcxDPWwoIrnWai6gYSijch5EkIIIdlEAdMoZxN52Lqa6h6u3GeH2yFA1Qzsbwr1up5jGUTi6kicJiGEEJJVFDCNcizLwOMUIau9Z5gYhknOMtWmKS8gChwicRV6utoEhBBCSAGhgIkkK3YbRu8E7kMBU6BXoUpJZKGoOhJpZqcIIYSQQkIBE4Fd4iGJHJQ0hSiPGeMBxzLoDMto7YynXMexLAzDRFymPCZCCCGFjQImAoFn4bYLSKQJmESBw4QKNwBg2772XtdzPItIjPKYCCGEFDYKmAgAwGUXoPexsjZ5rLUst21vR6/rJJ5FVNZ67aIjhBBCCgkFTASA1VdO4Nm0y3JTxlkB056DwV676USBg6zqVF6AEEIyLBJX0dAWpY01OYICJgKgq+q3xKdtqFvssaHYI0E3TOxpSN0tx7IMTMNEnBK/CSEkYwzDRFNHDAfbomjujPfadENG3pA7p+7atQt/+ctf8NFHH6G+vh7hcBg+nw+VlZU49dRTceaZZ6K6ujoT50qGEcMw8LpEBJvltNdPGVeE9q3NqK0LYuq4opTrBJ5FOKqgrMg+AmdKCCGFLxhVEIoqcNl5NLXHIHIsSug1NqsGHTDt2bMHK1euxD//+U+Ul5dj5syZOP7442G32xEKhdDY2IinnnoKv/71r3H66afj5ptvxuTJkzN57iTD7BIPjmWgGwY4NnXyceq4Iny0tRk76wMwzfFgGCZ5nSRyiMsaFFWHeFgTX0IIIQOj6QZaOmMQeAZ2yXqbbmiLQuBZeF1Sls9u9BpUwPTEE0/gsccew5IlS7BmzRqccMIJfd72s88+w/PPP49vf/vbuPrqq/GDH/xg0CdLhpdd4iB1Vf122FIDpgkVbogCi3BMRVNHHGOKHcnrRJ5FLK4hrlDARAghQxUIywjHVfjdVnBkl3houoL6tih4nk3WziMja1AB0/bt2/HKK6+gvLz8qLedPXs2Zs+ejRtvvBEPPPDAYB6OjBCOZeF1SGjujMFhS72O51hMHefDF3vasbM+kBIwMQwDE0BcVuF1iiN70oQQUkBkVUdLIA67xIFlD83kux0iAmEFB1ujmFDuhiTSh9ORNqik75UrV/YrWOqpsrKSAqY84LTzMIG0CYbHHuMHAOyqT9MmhWcRiqqUmEgIIUPQEUogLmtwSL3nM7wuAeGYgob2SNqG6WR40S45ksJh4yEKHJQ0veWmTywGANS3RhFLpBarFAUWCUVPu8uOEELI0cVlDe3BBJw2PiVPtBvDMChyi+gIyWhsj6ZtZ0WGz5ADpssvvxx//etf01732Wef4dhjjx3qQ5ARJPAcXDY+bV2lIreEcr+1S2P3wdBh92Oh6tRXjhBCBqstGIei6bClmV3qxrEsPE4RLYE4WgIxmtUfQUMOmDZs2ICf/OQnWLVqVSbOh+QAj1OEpqf/I+wuKbDzsGU5hmHAgkEsTgUsCSFkoCJxFZ0hGS770RO6BZ6FyyagqT2GznD6UjAk8zKyJHfJJZfgySefxA9/+ENEIpFMHJJkkV3iIXBs2nYn3QHTroPBXtPBosgiHFdg0CceQgjpN8M00RaIwzDNfu80lkQOgsDiYFsUoZgyzGdIgAwFTOeffz6efPJJbNmyBZdccgkOHDiQicOSLJFEDjaJ79UGBQDGlrpgEzkkFB31ranBscRbJQkSMi3LEUJIf4VjKjoj/Ztd6slpE2CYJg62RhGXaXZ/uGUs6Xvu3Ll44YUXwPM8LrzwQrz//vsQBKoVkY9YhoHXKULRegc+HMskm/EevizH8yxU3aS+coQQ0k+6YaC1Mw6WsV5DB8rjEJCQNdS3RtL2AiWZk9FdcpWVlXj++ecxb948XHPNNVi7dm0mD09GkMPGAwyTdhfG5CorYEpXXoBjrbV4QgghRxeMKAhGZbgdg6thl2xrFVXQ0BalcgPDKONlBex2Ox566CH84Ac/wLPPPpvpw5MRYpd42AQubZmAyWM9AIDmzjhC0dS1c1HgEImr1F2bEEKOQtUMtAbiEAU2pUjlQLEsA59LRHsogeaOGOWRDpMhB0z33Xcfxo0b1+vym266Cb/61a/wrW99a6gPQbKA51h4nELaMgEOm4CxpU4AVvJ3T5LIQlGpvAAhhBxNZziBSFwdcO5SOhzHwu0Q0NIZR1sgnoGzI4cbcsC0bNky+Hy+tNedddZZuO+++4b6ECRLXHYRhmmmrfMxpWtZbmddasDEsSwMw6QEREIIOQJZ1dEaTMAucWmLVA6GKHCwSRwaqdzAsBhUL7nLL7+837dlGAZPPfXUYB6GZJld4iHxHBTNAM+nbnWdUlWEtzc3YE9jCJpugOcOxd4czyISU1HitY/0KRNCSF5oDyWQkDX4PVJGj2uXeOi6ioNtUateUwZmr4hlUDNMZtesQ8+vDRs2IBKJ9LrcoFyWvCUJHBy29OUFKvx2uOwCVM3A/uZw6v14FlFZS1vHiRBCRrtYoqsFij19C5ShcjkEaJqOg60R2rWcQYOaYXr66adTvtc0DTNnzsQ999yDGTNmZOTESG7wOEUE0kztMgyDKVVebN7Zhl11QVRXepPXiQKHQFRBQtEg8IPb+UEIIYXINE20BuNQVQNuR2Znl3ryukR0hhQ0tEYxrtwNYRAlC0iqjPwEhyNCJrnBIfHgODbtVtXu8gI7D0v8ZlkGpmEiTonfhBCSIhxT0RGS4XIMar6i37ob9XZGqFFvplDISY7IJvHJyt6HmzTGA5Zl0BGS0R5KpFwniRw6QwmqCUIIIV0M00RTexSmYfS7BcpQsKxVo6k1EEdTJzXqHSoKmMgRsQwDj1NMW0FWEjmML3cB6F3E0iHxiCTUXnWaCCFktApFFXSEEnA7Ry5VgedYuBwCmttjvT7YkoGhgIkcldNm7bJIN6WbLC9Q33tZTuBYtIcSNBVMCBn1rBYoMXAsk7KreCRIAgdJ5NDQFkOQPsQOWkafNcplKkx2iYckcGl3y02pKgIA7G8K95qFctp5hGMqwtRJmxAyylktUFR4nMOX6H0kDhsPwMTB1ghiCWpfNRiDyjpbtGhR2uDohz/8Ya+GuwzD4M033xzc2XUJBAJYtWoV3n77bUQiEdTU1OCWW27B3LlzAQD//u//3qtvXXl5Od59990+j7lu3To89NBDqKurw8SJE3Hrrbfi1FNPHdJ5FiqBZ+F2iojLGg7faFHskeBzS+gMy9jbGELN+ENFTDmWBcta9UbcThEsBdSEkFFI1Qy0dLVA4YbQAmWounc917dGMaHCDWkE8qgKyaACpq985SsjOpv04x//GO3t7Vi1ahX8fj/WrFmD733ve1i7di2qq6uxY8cO/PCHP8Sll16avA/H9f2L8NFHH+HWW2/FHXfcgZNPPhkvvvgirr/+erz88suorq4eiSHlHbddQEtIBo/U5727vMCG7S3YWR9MCZgAwGUTEIoqiMbVQTeXJISQfNYZTiAaV1Hiy34xX69LREdIRkNbBOPK3CO+PJjPBhUw3X///Zk+jz7t378f77//Pp577jnMmTMHAHDXXXfh3XffxauvvoobbrgBu3btwnXXXYfS0tJ+HfPxxx/H4sWLkwHW7bffjs2bN+Opp57Cf//3fw/bWPKZw8ZDjGlQFLXXJ6TJYw8FTKZppgTTfNeUVGdYhssu0LItIWRUkZXuFih8Tsyyd5cb6AjJ4FgWY0ud4FgKmvpjUD+l2267Da2trQO6T1NTE2655ZYBP5bP58Njjz2GmTNnJi9jGAamaSIYDGLfvn2QZbnfM0OGYeCTTz7B/PnzUy6fN28eNm7cOODzGy26q36nKy8wscL6lBKOqWju7N300WHjEQjL1F+OEDLqtAcTiMsa7FLuLH9xLAuvs6vcQHsMBpUb6JdBzTBNmzYN5513Hs4991x84xvfwPHHH9/nbbds2YI//vGPeP3113HNNdcM+LE8Hg8WLlyYctm6detw4MABLFiwALW1tcl+de+++y5YlsXChQtx8803w+129zpeKBRCLBZDRUVFyuVlZWVobGwc8PkdjivQ6U2OY+Hz2NDQEu41Ro5jManSg9q6AHY3hDC21JVyvZ1jEVM0hGIqPK7sJDz2R/e4Cvk57PlvocnX8XXXxunP7Gu+jrG/Cm180YSKQFSG1yWB57nkTE4uzOhwHIsilkFrMAFB5DDG78jICkChPYc9DSpguuqqq7Bw4UKsXLkS3/72t1FWVoZZs2ahqqoKdrsd4XAYjY2N2Lx5Mzo7O3Haaafh2WefxdSpU4d8wps2bcKdd96J008/HYsWLcKvf/1rsCyLsWPH4pFHHsH+/fvx85//HLW1tXjqqafAHvaLmUhYdShEMTWfRpIkyPLQuzt7PNlfox4uTEyBy2WD0yH2+mM4bkopausC2NMQwnkLes/2CaIAVdNhc0iwS8Nb4XaoCvk5BGh8ucA0TcRlDZGYivZgAqpuQhJZ2AQOksiD51jri2fBc1aJjp5/c/kwxqEohPGZponOhhBEm4DSIkfKdc4s7ZRLx+XSEI4r8BtARXFmgiagMJ7Dww36nau6uhoPP/wwamtr8Ze//AXr16/Hpk2bEA6H4fP5MHbsWHz729/GmWeeiZqamoyc7Jtvvomf/OQnmD17NlatWgUAuPHGG3HFFVfA4/EAAKZOnYrS0lJccskl2LJlC2bPnp1yDEmyflEVJXWruyzLsNuH/gSHQnHoBVjdmuNYOJ0STE1HS0ckWZup27hS6wVhX2MITW1hONIERe3BOPbxDCqKnSNyzgPFcSw8HntBP4c0vuxKKBoicQ2BiIxoXIWqGRB4FhzHQNdN6IYJwzSArs0VHMOAYxlwnFW7x2ETUOxzIJFQwQLJy60vJu9zBPPhOeyvcEzBvvogHHYeobCVqsCx1utoNCpDz6HG9LqqY9vuNkQiMkq8tiEdKx+fQ4/H3q8ZsSF/1J86deqgcpMG6plnnsGKFSuwePFirFy5MjlDxDBMMljqeU6AlTd1eMBUVFQEh8OBlpaWlMtbWlp6LdMNhq4b0LT8+CUZKI5j4bLzaGiVYTtsO6rbLqCsyI6WQBw7D3Ri5qTiXvcXeQ4tnXF4HOKItAUYrEJ+DgEa30iTVR3RuIpQVEYopkHVdAg8C7vEwWnrMdMt9L6vbhjJQCquaYjLGmKKgXAkAcMwAIYBzzDgOBYcCwgCB0lgIQo8+K4CiQLPQhI4sFnczj5QufYcDlR3CxRdN8ExTK/AwXpec2d8Is9C03QcaAzBNEz43EOfAcv35zCd3F4b6bJmzRrcc889uOyyy3DnnXemLLPdcsstCAQCeOKJJ5KXbdmyBQAwefLkXsdiGAZz5szBhg0bcNFFFyUvX79+PU488cRhHEVhcNkFmECv3XCA1Yy3JRDHzvpg2oDJLnHoCMkIRRWUFBXedC0h3RRVRzShIRSVEY6pSKg6BI6BXeLhcaaJjPrAsVYglPyeY+Fx2yFy1huSaZrJgEo3TMQSKsIxE9bkhQmAAc8yEAQWdpGHwy5A4lmIAtdVE6jw8kxyQSiqoDOswDuA5zrbHDYBkZiK+tYIONZqiUVS5XzAtHfvXtx7771YvHgxrrnmGrS3tyevs9lsOO+883Dttdfi4Ycfxrnnnou9e/fiv//7v3Heeecld86Fw2Goqgq/3w8AuPLKK3H11Vdj+vTpOPXUU/HSSy9h+/btWLFiRVbGmE/stq6q36oOm5j66zOlyosPvmjCroMhGIbZ6xMtwzCQRA5toQSK3BLV/yAFRdUMRLv6J1pBkgaOZWAXeTjt/LAslzEMA55n+nwhN00Tmm5C1QwEYwo6wgmYJiB05Uc5JA4OmwBR4CBREJURmm6gpTMOnmPyLvHZ5bDq5tW1RjCBdcNlz5+AbyTkfMD0+uuvQ1VVvPHGG3jjjTdSrlu2bBnuv/9+PPjgg3jkkUfwyCOPwO12Y+nSpbj55puTt1uxYgU2bNiAt956CwCwYMEC3Hvvvfjd736HX/7yl5g8eTIeeeQRKlrZDyLPwWnjEYzKvQKmcWUuSAKHuKzhYFsU48pcve7vtPHoCFuzTH7P0NbKCck2TTcQTWgIx2QEoypkRQfDWLOpfpuU9ZwihmEg8AyEw0r0a5oBVTcQiqnoCCkwGRMCawVRNtEqIWITeYi8FUTRh5v+C0YUhOMKfDm8I/hIPE4RgYiM+tYIxpe5u1qqEABgTJMKMGRSZ2e04NZtAasApc/nRGdnFC0dMextDKE4TXLgi2/vxrZ9nfjacWPw9Tlj0x4rHFMgCTwmj/XmVF5FzzEW+nNI4xs8TTcQS1g73AJRGQlFB8sAksjBJnLDGiR1L8mFwplPqNV0A6pmfWm6CcAEx3blQInWByVJ4LtypLhhCaLy/XdU1XTsbghB0420szPD+fxlkmmaCIQVOO0Cxpe7en04PpJ8fA79fufIJH2T0ccu8RA4NrnDp6cpVV5s29eJnfXBPgMmh41HMGI15fXm6acwMroYpoloXEU4riIQlpONqG0ShyKXmFOB/2B177az9/iT1LuCqFhCRSiiwDQBlgMEnoPPKaGi2FEQY8+UjpC1+9Hvye/Xte5q4J0hBfUtUYwvd+X0Rp2RQgETGTCbyMEm8ZAVvVfANHmsFwDQ1BFDOKak7R/XncjaHqSmvCS3GYaJUExBezCBUEyBaQA2iYW3QIKko+G66j/1nEvWDQOKaqCxMwowVu0e+hu2Ska0BRNw2IYnX22kJYOmsIyDbUBVqbvX6/1oM7pHTwaFYRh4nSIUrXebFKddQGWJVWdpZ32wz2M47QJCMaspLyG5RtMNdIZl7G4IYk9DCJG4Ao9DgN8rwWETRkWw1BeOZWGXeLjtAhrbo2jqoNYagPUBMKFoOV+YdyBYloHPLaEjJONgWwRaDi8jjoQBBUwffvhh2v+T0cdh4wGGgWH0fqGsGWfNMn20tbnP4mzd+Q8doQQojY7kCk030BFKYE9DCHsagojLGrwuAV6XlHc7noabKHBwOwQ0tcfQPMqDplhCRXtIhstReLvKWJZBkUtEezCBxvZo2tf80WJAofDu3bvR2toKhmEQDAZx8sknD9d5kRxnl3jYBA4JRe+1i+KkaWVYv60FbcEENu1oxVeOLU97DCuXSUGsSOtVOZyQkaRqOoIRBW2hBKJxFaLAwueWRvVMUn+IAgcngKb2GBgGKPdlrrVGPtANawNAeygBTTfgEQrzdYzjrGa9LZ1xcAyLipLRuQzb749MH3/8MaZMmYI1a9ZgzZo1mDp1Kj7++OPhPDeSw3iOhccpQFZ7L8vZJD6Z8P325gbEElraY4gCB003EQgPvYcfIYMhqzpaOmPYWR/E/uYwNN2A3yPB7RgdOUqZIAlWGYKGthhaOuMFP2Os6QZCMQUN7VHU1gWw82AQHUF5QAVJ8xHPs9aMYqc1o1joz3M6/Q6Y6uvr0dDQgEQigUQigYaGBtTX16e9rSzL2LFjB+LxeK/rNm3aNPizJTnFZRdhmGbaP5wTppSg3GdHQtHxzqcNfR7DabfqMsXl9EEVIcMhoWho6ohi18Eg6loiMGHC75HgsgujaoYkUySRg8PGoaEtitZA4QVNmm4gGFXQ0GoFSbvrg2hqt4KGIqcIv3d0FOIVBau8RFN7DK2B3u/vha7fS3LLli3D3/72N5x//vlgGAaiKGLJkiW9brd582Zce+21ME0Tsizjuuuuw9VXX528/gc/+AE++eSTzJw9ySq7xEPiOSiaAemwLacsy+DMr4zD06/XYuOOFpxYU4oyX+92KJLIWVu1o3JBJUuS3BSXNXRGZHSEZMiKBoeNh9+TvQKThmEiEleh6YbV3kQ3rT5jXa1Okg15DetyE4AgCIjEZGia3ut2yft2fV/iteGkY8t6/X0OB5vIAyZwsC0KACgtsud18KlqOmIJDeG4glBURULRwcB6zRotuyTTkUQOhmmioS0KjmXT1uMrVAN6hzIMA5dddhlM08Tf/va3tLf5+c9/jjvuuAPf+ta3sHv3btx+++3Yu3cvVqxYAZZlC+6Tx2jWPRUfiatpX5CPGePBtPFF+PJAAH//uA7fXTwl7Quo08ajIySj2G2jWh9kWMQSKjrCCXSEFKiaDqeNh2uEXugVVUdnWE756uj6NxhRhj1Zev22Zpx6fCXmTC0Z9rYnNomHCaChLQqGAUq8+RU0dfcADMcUhONW5fbuoqSFUm8rE+wSD9M0cbA1ktxJNxpkvNL33LlzsXHjxuT3iUQCN910EwRBwKpVqzBv3jxs3rw5kw+ZU/KpuulA9FW9tS0Yx4GmMPx9vPl0hmX87v99Ad0wccnpk1EzrqjXbUzTREdIxoRyd1ab8uZjhdqBGG3jM00T0YSGjlACgbAMTTfhtPOQxMwG5aZpIpbQkkHQoaAogc6QjGgfOXzd2K5+cFZ9Msb64rr+7b6s63ueYyGJ1psVy1gzucnbJe9jfQHAZ7vb0RGycgT9Hgmnz6nCtAlFwx7ExGUNsqKjqsyFEm///6az8TsqKzpiclej5LgGWdXAMgzsIgcpw5Xb86XSd39FYlZZmPHl7mSz3nx8nclapW+Xy4Xm5maUl1s7o2w2G37729/i9ttvx/e//32aYSowDokHx7HQdCPtGr7PLWHe9HJ88EUT3vi4DpMrPb1+MRmGgY2a8pIMMUyr2GRnKIFARIFhWIHSUGYvDcNEMKokg6DOsIzOiJz8v3KUNwa7xMHnluBz2+Bzi13/SvC7Jbgd/c+bGugb7imzKrC5tg3vfNqAjpCMF97ejbGlTpwxtwoTyt39eszB6F5er2+NgAGTc8s2CUVDLGEFSZG4BlnVwXHW65AzB3oA5ovuZr31rRGMHwXNejMeMJ188sl46aWXcN111x16EJ7HypUrcffdd2PDhg2ZfkiSRTaJh03kICs6eHv6QOdrs8fgs11t6AjJ2LC9BSfPrOh1Gwc15SVDZPW/krG3MYTOYAKAtalA4AcfKKmajo1ftuL9L5r63O3ZzeMUk0GQr8eX3y3BlqX8PI5lMXdaGWZVF+PDL5rw4dZmHGyN4ql1OzB1XBFOP3EsSodpVtdatukKmhhk/e9aVnVEYiqCXUGSqhngOes1zGkvjOrc2eBxigiEDzXr9bh6d3coFBlfklMUBbquw25P/0fY0NCAysrKTD5kTsmnaciBONI0a2N7FI1tMfi9fa9jb97Zhr+8vw+SwOGG82fCmeaTSHdT3uqxnmHPtUgnH6eSB6KQx6dqOjrCMmKqiXAkAYfIgR9CGwdNN/BJbSv+9XkTIl3V6LmuXI1kUOTpCopc0ojNjA51SScSU/HOZw34pLYVpgkwjLWjdeHxlWnbGGVCLKFC00xUlbmOGjQNx+9odw5ZWyiBhKxB4FnYRC4r+ZKFtiTXrWez3kljPRhT7s2r15msLcmJ4pH/6Ao5WBqtnHYBYKxli76SIo+fXIyNX7agsT2Gf24+iPNOmdj7ODYBwYiCcExFETXlJf1gmiZCMRXNHTHEZA2VZR7wjDnoNyPdMPDpzna893kjQlEFAOB1ilh4fCWOqy7O+6Rfl0PAuSdPwLzp5XhrUz2+PBDAJ7Vt2LKnA/Onl+OUmRUZz/Fy2AREEyrqWyJgmJFLEFZUHYGIjLZgAnHZalmSzR2Rhaxns9665gj8fle2T2lYDCpgisViWLFiBf75z3+C4zicc845+NGPfpQyq1RfX48333wT//jHP/D0009n7IRJ7nF0L8upep+lARjGKjPw1Lod2LyzDXOnlaHC70i5DcsyYFmgI5iAh5rykqNQNR2tnQm0BGPWG7FHgiRykAdRB9UwTGzZ0453Pm1AIGIFSm6HgK8dNwYnTCkpuLYoJV4bLl40GQeaw3hzUz3qW6J47/NGbKptxamzK3Hi1MyO2WkTEImrqGuJAMCwBk2q1jWjlAyUOAqURkB30BSMKjjQFEJxARbyHFTA9Otf/xovvfQSxo4dC7/fjxdeeAFNTU345S9/iT/96U944YUXsH37dpimCZerMCNNcgjPsXDZBXSEEkespTSh3I3pE33Ytq8Tr2+ow+VnTe31Iua0CwjGFETiKjzDtERA8lvPWaVwTIHbIUAUuEEF2KZpYuveDrzzaQPau3aTOW08Fhw3BidOLR3Ssl4+GF/uxpXnTMOOAwH8Y9NBtIcSeG39Aazf1ozTTxyLYyf4MhZouOwCIjEraGIYZHwWWdUMBCMyWgJxxGUNNgqURhzLMvA4RQQjCpwCC6HAPmgMKmD6+9//jgsuuAD/8z//A4Zh0NbWhuuvvx5XX301PvjgA9hsNnzjG9/AWWedhQULFmT6nEkOcjvEflV+PWNuFWrrAtjfFMaXBwI4doIv5XqeY8EA6Awl4Kaqy+QwqmagtTOenFUa7BuiaZr48kAA72xuQEvX761d4vHVWRWYW1M6quqBMQyDaRN8mDquCJt3tuHtzQfRGZbx4tt7UFli7aibWJGZHXUuh4BwTLGCJgDeDARNmm4kl96icRU2kQKlbBJ4FrKqIxovvNSKQQVMzc3NWLp0afIXsqSkBHfeeScuueQSnHzyyfj1r38Nt3v4tqyS3GOXOAg8B0XVj/hmU+SScPLMCrz3WSPe+LgOU8Z6e32Kd9qpKS/pLRRV0HTYrNJAmaaJnfVBvL25AU0dMQBWAdaTZ5Zj3vTyEamInatYlsGJNaWYNcmPD7c244MvmtDQFsX/vbYDU6q8OP3EqrTV+gfK7RCTQRMYBl7n4GaSNd2wGiYH4whToJRTBIFDIKrA6xQL6vkYVMCk6zocjtT8k2nTpgEAvv/971OwNApJXT2GYrJ61Deyr86swKc72xCIKPhoWzMWHDcm5XqB5xDWNXSGExQwkYzMKpmmib2NYfzzk4PJ1h0iz2Le9HLMn1FObXl6EAUOC4+vxIk1pXj3swZ8sqMNO+uD2HUwiOMnWzvqPIMMcrq5HSJC0a6ZpjLXgI6n6QZCUQWtgXiyy0AxBUo5xS7xaGtPIKH0ndeajwY9kpaWFpimmfwl5XnrUEVFRRk5MZJfGMZauw5Ejp5xKwocTj+xCi+/txf/+rwRsycX99rS7LTz6AwrKPZoBfUHRwam56ySyyEMagZof1MY/9x8EAearYRjnmPxlWPLcMrMcjgoIO+Tyy5gyfwJmHdsOd765CC27+/E5p1dO+pmlGHBrDFDWrr0OA8FTePKXUfNWewOlNqCCYTjCkSehc8t5f3OxUIkCVaP0e7diYVi0CO58cYbIUkSJk2ahJqaGlRXV4NhGOi6nsnzI3nELvHgWAa6bhx1h82sSX58vL0FB9ui+OcnB/GNBcekXJ9syhuhpryjUfesUmswDjAY1KxSfWsEb29uwJ6GEACrjtKJNaVYMGsMXA4KlPqr2GvDRV+vRl1LBG9urEddSwT/+rwJexvD+O4ZU4ZUlNPjtHZV1bVEML7MBV+aOk26YSAUVdEaiB8KlFwUKOU6nmMQispZL1iaSYMqXPnOO+9g27Zt2LZtG7Zu3YqGhobkdTzPo7q6GrNmzcJxxx2HWbNm4dhjj83oSeeyfCrWNRD9KShnGCZq6wPQDaNfS2n1rRH8/q9fAgC+f96xqCxxplwflzUYJjBlrHdEknALubAjkD/jG+ysUndRwB172/CPTfXYWR8EYPVqO2FqCb523JghLyVlW7YLH5qmidq6AF55fx/iso4xxQ5ceubUIX+oCUZkCDyHYyo9GD/Wh87OKGRFQyiqWjlKMRUCz8BpE/I6UMr28zcSusfY2h5BQtYwpaoo47W9Mq2/hSszUuk7EAhg27Zt+OKLL5KBVF1dXXLJbvv27UN9iLyR629Gg9XfN9uGtiiaO2Lwefq3O+L/vbsHW/Z0YFyZC1ecU5Myi9DdlHd8uXvY2jf0lC8BxWDl+vhUzUBrIG7ttmQAzwB6rAFAIKLg7U8b8PmuNgBWFevZ1cX42uzKgummnitvuM0dMTz991rEEhrKfXZcetbUIecbds8mz5pajo6OKJraYwjFFPAcA5c9vwOlbrny/A2n7jEGQzG0dsYxqdKb839/I1rpu6ioCKeccgpOOeWU5GWRSARffPHFqAqWCJItT3rmtx3J6SdW4csDAdS1RLB1bydmTvInr+tuytseSsBHTXkL2lBylQzDxEfbmvH25oPQdBMMgBmT/Fg4uzKrTV9N00T3x1HTNGECXd9bl5tdl8MEOI4ZUs+7kVbud2D52TV4+vVaNHfG8X+v7cBlZ9YMaamzyCUhHFOxuz6AjkCsq+yAkJU2SWToGIYB17Usl+sBU38NW3KIy+XC/PnzMX/+/OF6CJKDHJLVo0lWddjEo/96eZwivjqrAm9vbsCbm+pRM96b8sbhsPHoCFFT3kKlagZag3G0dsYBmAPOVWrpjOOV9/ehoWvn29TxPiyeOxbFGf5dScga4opuBThg0B30MGAAxoQJBowJmIxpXQarURsDa6aL6boUDNP1vfWGgq7/K7KVp8OyDCSBgySyOR8olBbZsfzsGvzf6zvQGkjgqde+xGVn1Qxp2dPrEsFxLLwu0YooSV6ziRzCce2o5WbyBWXTkowSeKu8QDAq9ytgAoCTZ1Rgc20bglEFH3zRjIXHH+o3yDAMRIFFWzBhvZjm+JsI6b/krFJUgcs5sFkl3TDwwZYmvPtZI3TDhCRwOHv+eCycMw7hSCIjyx2maSIu64jLGiSBQ2mRDQLPgTk8EGKs39PDAyH28MtgXWZdByB5XytwjMs6ogkFoaiKcFSFYVqJs5LIQeTZnNw2X+y1JWea2kMynnptBy4/a+qgC1IyjJWnpKtawS5ZjSaSwCGakBGTNQqYCEnH4xTRHkr0+/YCz+KMuVV46Z09+OCLJpwwpSTlUyo15S0chmkiIesIROVDs0regc0qNbXH8Mr7+5KFJ6dUeXHuyRPg89gyElSYpomYrCEh67BJPMaWOlHkkvr9AWAwOJGFTeThc0vQi41koBaKKojLGqJxDQwAu42Hw55bfwN+jw3Lz7GCps6wjD+s24HLz64pmGUYMngMw4BlGISiSkG8dlPARDLOLvEQOBaqZkDoZy+u6RN92LDdhbqWCP6xqR7LTp2UvI6a8uY3RdURV3TEEipCUQWyokPTDTgHmKuk6wbe/bwR73/eBMM0YZc4nP2V8Zg5yZ+RQMkwTMQSGhKqDofEY1yZC0UuacQ/GXMsC5fd6s9YWmSHrOpIdAVNUUVFKK4gGEyAgQlJ5CAJXNZnn4pcUtdM0w5rpmndl7js7JqML42S/GPrKhEzkPeDXEUBE8k4m8jBJvGQFb3ffyAMw+Csr4zD6le3Y8ueDpw0rQxVZYcaN1NT3vyhGwYSyqEZkpisQ1V1mAAkgYXDxg+4qe3Btij+8q99yb5v0yYUYcm8CRmpp6QbBqJxDapuwGUTUFHsgNcp5cyLuyRYQZG3q/aQ5JDQ2BxCIJRANKGhM6EAJiAKLCSRy9p5e5wiLj+7Bs/8vdbKaVq3A5edNXVEdriS3GUTOQTCCmKyBi+f36/dFDCRjGMYBkUuEXUtETgMvt/bgStLnDh+cjE+3dWO1zbU4XvnTjtUSZ6a8uYs0zShqAZismbNIsUUyKoOQzfB82xX25zB9ZTSNANvf9qAD7c2wTStTQBL5o/H9In+o9/5KHTdQCRu5Qq57AKqPDZ4nGJO78ZkWWuLfYnXhiKnCFXTEZd1xGQVoaiKeEJDSDfBsUjOPo3kdny3oyto6to999RrO3DZmVNR7ncc/c4kSdUM7GkIYm9jGMVeG2ZN8g/rkvBw6v67j8SUQfcNzBX5+QyQnOf32BCOqQhEFPj7WZMJABbNqcK2fZ1oaItiy54OHFddnLyOmvLmDk232h7EFR2hiPXpUdV1cF1J+m670K+6JkdS1xLBK//al8yHm3mMH2fPGzfkdiaqZgVKAOBxiCj22uBxiHlZ50fgrabXHqeIcp+ZnNmLxFVE4iqCUQWGAfA8A5FnIQrDv/vOaRNw2Vk1ePaNWjS2x/B/r+/ApWdOxZhi59HvPIolFA0764P4cn8Auw4GofaolfbGx/WYMdGHE6aWYFyZK+8+MNokDsGognK/kdMfSI6GAiYyLHiORWWJE7Kqd3WX798nC5dDwNdmj8E/Nh3EPzbVY9r4omQOCTXlzR7DNCEf9macUHWYhglRYGETObh5PiMv5Kqm461PDmL9thYA1uzPuSePR81435COq6i6lTzNAj6XBL/HBpdDKJicOIZhYJd42CUefo8Nmm4tjSYUDZGYipisde2+M5PlC0SeHXJgm47DxuOyM6fi2Td24mBbFE+/XovvLp6CsaWuo995FInEVdTWBbB9fyf2NoZhGIdqKXidIqrHelDXEkFrIIHPdrfjs93tKC2y4YQpJTiuugQOW368hUsCZ33YlbW8TqnIj582yUt2iUdliRP7mkKQFb3f5fHnTS/HJ7Vt6AzLeH9LE74+Z2zyOqedR0dQhkMSUOSWCubNLhepXc0zrWU2FQlZg9pjuafImflZmX2NIfzlg/3oDFtNnGdPLsaZJ40bUusNWbECJZ5jUOy1we+xwWnLTHCXy3juUPJ4idcOTTe6Esh1RBNqV+CrQdMNcKw1Myjy3IDzy/pik3hceuZUrHlzJ+paInj677X4zhlTML7cnZHj56uOYAIbtjVh+76OZEPobiVeG6ZN8OHYCUWo8DvAMAxM00R9axSba1uxdV8nWgMJ/P3jevxj00EcO8GadZpY4c7p32eWZWACeZ+DmpHWKOSQXG07MVRDaavR2BFFQ2sURV1F6frjy/2d+NM/d4NjGVy/bCaKemxRjsRUKJqBYo+Ecr8jY2v7ud46ZKgGMr5QVEFjexTRhAagK6FYGL6EYlnV8Y9N9dj4ZSsAqy3KeadMxOQqb7+P0bPthKbpSCg6Ygmr/ovPLcLvtg15OS/bMvk7qhsGZKXr5ySr1t+ValjV0ll0LeEN/TlXVB3P/2MX9jWFIfAsvn36ZEwc40l72+FoHWIYJvY2hrDrYBCSwMHvscHnluD3SHBIwx84m6aJtmAC2/d3YseBABrbYynXV5Y4MG28D9PGF6HkKAnyCUXDF3s68EltW7KsBgD43RJOmFqC2ZNL4LJn93e8r+cwllDBMAymjivKuXp6I9pLbrgFAgGsWrUKb7/9NiKRCGpqanDLLbdg7ty5AIC33noLv/3tb7Fnzx74fD6cddZZuOmmm2Cz9b2lddGiRTh48GDKZUuXLsXKlSuHdK70Ztubbhioa46gPZTodyVn0zTx9Ou12NcUxvSJPlx4WnXK9ZpmIBRTIIk8KnwO+NxD715OAZP1XLUG4mjpsPq5uQfYz20wdh8M4tUP9iMYVQAAJ04txRlzqwbcsJPjWLhdNjS1hRGLqZBEHj6PBJ9LGnJz2FwxnL+jhmFaM1CKjnhCQziuQFYNqLoOFgwEgYXIsxAGUURT1XT88a3d2NMQAs+x+LfTJ2NSZe+gKZMBU1NHDJ/vbscXezqSOWuHswIoKRlA+dw2+Lv+7xrC5hLTNNHQHsOX+zvx5f5ASl06hgEmVLhRM64I08YXDbrIZ0NbFJtr27BlbzsU1fpZsQyDqeO9mDOlFJMqPVnJy+vrOTQME8Gogsljvf1O0RgpBRUwXXXVVWhvb8fdd98Nv9+PNWvW4IUXXsDatWvR2dmJyy+/HDfffDPOOuss7N+/Hz/72c9w8skn47777kt7vEgkgrlz5+KRRx7BjBkzkpfbbDa43UObLh7Nb7ZHIqs69jWGkFC0fr9ANHfE8NhftsE0geVn12BCRe/nJhpXIasG/G4JZT7HkNb0R3vAlFA0NLbH0BFKwGnnh31XTkLW8MbGemzeaTXLLXKJOO+UiWnfSPtD0QwwHAdD01DklFDkknK+S/pAjeTvaHfemqzqiMlWHlSiq4YWANglbkCBqKYZeOHt3dhZHwTHMrh4UTWmVBWl3GaoAVMoquCLvR34fHc7WjrjycvtEodjJ1g5cB0hGR1hq93SkQg8awVSbgk+jwS/+9DMVLpNAoZh4kBzGF8eCODLA4GU43Msg0mVHhw70Y+TplfA0PWMzaApqo6t+zrxSW0rDrZGk5d7nSJOmFqC4yeXDKldzUAd6TnsCCVQWeJEhT+3NgAUTMC0f/9+nHnmmXjuuecwZ84cAFb0ftZZZ+Hcc89FXV0dOjo68Pvf/z55nz//+c+48847sXnzZohi71+UTz75BN/+9rfx8ccfw+MZ3ItzX0brm21/hGIK9jWGIfBMv19o//rBfmyqbUWF34Hvn3ds2k9Mum4gGFUhChzKfVaOymCmfEdrwGSa1ie/hrYo4rI2oKXTwaqtC+CvH+5HOGZ98v/KsWVYNGfsoIpEqpqOcFSDzcZh8gQ/eJiwilAUnmz+jnaXj0goGmKyhuaOWLLmWn/puoEX39mDHQcCYFkGFy6chGkTDiXzDyZgUlQd2/d34vPd7djbGD50LNZa/jmu2o/JY729fqc1zUBnxAqeOkMJdIRldIRkdIZlBCIyjvTOyLEMfO5DM1OyaqD2QAAxWUveRuBZTKny4tgJPkwe64UkcsOy5NhTc2cMm2vb8PnudiQUHYA1ozV5rBdzppZiSpV32GedjjTGaFwFz7GYUlWUU7tS+xsw5fw8tc/nw2OPPYaZM2cmL+tOhAsGg7jqqqvApnlz1DQNkUgEfn/vei07duxAaWlpxoMlcmQeh4gxxXbUtUTBc2y/ciNOm1OJL/Z2oKkjhs92teGEqaW9bsNxLPweCbGEhgMtEYSjKsqLHbSTrh803UBLII6Wjhg4jhlw89v+ME0T0YSG1kAcrYE49jVan8IBwO+RsPSrEzFhEInAhmEiFLOKNpYU2TCmxInKUnfBBrzZxjBWXztJtIpociyDg61RsCzT70CX41hceNok/L9392Lbvk68+PYenL/wmAHX1TIME3saQ9iyux1fHgikbMEfV+bCcdXFmD7Rd8QPZjzPorTInrawpm4YCEaU5GxUz4AqEJGhG1ZeUlswtQWUXeJRM86LaRN8mDTGk7EE+v4q9zlw9rzxOP3EKmzfb806HWiOYGd9EDvrg3A7BMyeXIITppRkpXWNTeQQ7tqxme1cq8HI+YDJ4/Fg4cKFKZetW7cOBw4cwIIFCzB9+vSU6xRFwZNPPokZM2akDZYAoLa2Fg6HAzfeeCM2b94Mv9+P888/H5dffnna4ItkTrHXjoSio6Uz3q+8I6dNwKnHj8EbH9fjrU8OYvpEf5/LLA4bD0lkEYwoiCQ0lPvsKPba8rrux3CKyxoa26LojMhwDbBNSV9iCRUtgUQyOGrtjKMlkEC8xydvwPrUO396OU47oRICP7DH7Q7AZFWH1yGhzGeH2yFAKIDmnvmkpMgOTTPR0BFFkYvp998Zx7I4/9RJ4Ni92LKnAy+9swe6bmJWj5pr6ZimieaOOD7f0zsvye+RcFx1MWZNKs5IIMCxLPwea7b6cN2BevdsVEcoARNWT8MJ5e6cmDkReBbHVRfjuOpitAUT2Fzbis92tSMcU/Gvzxvxr88bMaXKi2997ZgRze/jOBa6YSKWUClgGgmbNm3CnXfeidNPPx2LFi1KuU7TNNx2223YtWsXnn322T6PsXPnToTDYSxZsgQ33HADNm7ciJUrVyIYDOKmm24a0vkN91JGtnSPKxPjG1vmgqYbCMdU+PrRa2r+jAp8UtuG9mAC/9rShDO/Mu6I51ni461goCOKmKyhotjRryTDTI4xF3WPi2UZhGLWEpys6igusoMb4It8XLZmjJo7u4KiTitA6iu5lgHg80goK7Kj1GfH9Il+VJYMPI+hu6aQ0y6gqswNn1tMLr8W+vMH5N4Yx5Y7YTImWgMJFLmlfv8ecRxw/sJq8DyLzbVtePm9vTABzJ1Wbl3f44NrKKrg893t+GxXW0pekkPiMXOSH7Mnl2BsqXPEttVznPXBr9g78JYvyd/VEfxgXu534Oz5E3DGSeOw40AAm75swe6GEHbWB/G3jw7goq9XZ/Rnd7Qx2m08wnEVFSVM3pWFyfkcpp7efPNN/OQnP8Hs2bPx6KOPpuyCi0QiuPnmm7F+/Xo8+OCDvYKpnlRVhSzLcLkOFVF77LHH8PDDD2PTpk00yzQCInEVtQc6wIDpVzCzdU87Vr/yBTiOwR2XnXTU7bcAoBsmghEZLMugwu9AebEzZ/qDZYuq6Whoi6KpPQqR4+A+SjJoQtbQ1BFDU3sUje1RNLXH0NweTe5oS8fvsaGi2IGKYmfXlwPlPseQmtiqmoFgVIbAsSjzO1Dmd2RkRowMnarp2NMQQkcwjmKvfUBvgoZpYu0/d+H9zxsAABctmoJTjqtEQtHw+a42bNzejF11AXS/SfEcgxmTijF3WjmmTfTT7PEg7WsI4qEXP4NhmLj07Gk4sStQHQmqZiAcVzD9mOK8m2XKmxmmZ555BitWrMDixYuxcuXKlGTulpYW/OAHP0B9fT0ef/xxzJ8//4jHEgQBgpD6RE2dOhWxWAzBYBA+3+ArCodCw5PMl20cx8LjsWd0fF4bj31NISQSylHf/KpK7Kge68HugyG8+FYt/u2MKf16YeYZICGr2L4nhvqmIMYUO/vcMTIcY8wlCUVHIKbiYHMILrsAkwFC4Xiv22m6gXc/a8CntW1HDIy8ThGlPjvKiuwo81lfJUX2tM9lIqEgkUhzkKMwDBPhuArDMOD32FBaJMEpsohFEogddttCf/6A3B2j18ahs9NAXUMARe6B5cEtnjsWuq7jo63NeOGtndiyuw2764NQe4xvQrkbx00uxoxj/MklpFhMzvg4hhvHsnA6JUSjMnQje8+f3y1i4fGV+OcnB/HiWztRVmTLWJ+3/owxGIyjoYnPmcbMHo+9MJK+AWDNmjW45557cNlll+HOO+9MmQEKBoNYvnw5IpEI1qxZg5qamiMeyzAMnHHGGbjoootw7bXXJi/fsmULSkpKhhQsAdYukEJOOM3k+Nx2ASVuOxo7ov3KZzrzpHF4pGErdhwI4Nm/1+KbX50IZz8+oQgcC49DQDimoDamoMzrQKnP1mfuTKE9h4ZpojMkoyUQg2gTUeSSYJpm2jfctkAc/++9vSnF9Vx2AWU+O0qLbMkk2dIiW59lBzLxRm6aplVlXNbhcYgoL3HC7RTBMsxRn5tCe/7SybUxcgyDMX4n9jeH0RlKDLi20OK5VWAZBh980YQv93cCAIo9EmalyUvKpUBxsHTDyPo4vjqzAjsOBNDQFsX/e2c3Lj1zakaX5o40Ro5l0B5IoMg5uKbc2ZLzAdPevXtx7733YvHixbjmmmvQ3t6evM5ms+G+++5DXV0dVq9eDb/fj9bW1uT1fr8fHMchHA5DVVX4/X6wLIuzzjoLq1evxsSJEzFjxgx8+OGHWL16Ne66665sDHHUYhgGZX47EqqGQFiBz3PkP57SIjuWnjIRf/toP3bVB/HoK9vwra8d06+6PSzLwOuSoKg6GjuiCMcVVPgd8OTZH+xAqZqOpo4Y2gIJ2G08Srzd231TV+JN08SmHa34+8f10HQDNpHDOfPGY3KVd8SLPlr9B1U4JB4Ty90ocku09JIHHDYeVaVO7G8KIxJT4XL0f7mFYRicfuJY+DwSogkdU6o8qPDZC/pvM9tYlsGyrx2DR1/Zhr2NYXz8ZQu+cuzILM3ZRB6xhFXXK5+KyuZ8DtMjjzyCX/7yl2mv++Y3v4nXXnsNspx+avYf//gHqqqqcMcdd2DDhg146623AFjJ4Y8//jheeuklNDU1oaqqCldddRUuvvjiIZ9voW5pHs76L3FZw97GEDTd6FeBtZbOOF56ZzdaA9Yaz1dnVeC0Eyr7nUhpmibCMRW6YXbNmlj5MIVWhykSV9HQFkU4psDjFGGT+LT1UaJxFa+8vw8764MAgGPGuPHNBceMaLE7wJo5CMdUsIzV863Eax9Q4clCe/7SyYcxdoZlHGgOQxTYAb8ZDnedomzLxfF9vL0F69YfAM8xuHrp9H7lhx5Jf8fYHkxgYoUHxd6jb/wZbgVTuDLf5PIL2VAM9wt1MCJjX1MYksj2q8K0qun4+4Z6bKq1ZhTHljhx/sJJA9pSrGo6glEVTpuACr8DxUU2FPtdef8cGoaJtmAcTR1xGIYBr8uaRUv3QlZbF8Bf3t+HaEIDx1qf8udNLx/RT/amaSISV6FqJnxuEaVFjkElg+ZDMDFU+TLGtkAcdS0ROOz8gJLzczGgyKRcHJ9pmnj2jZ3Y0xBCZbEDV547bUi7+Po7xlBUgdMuYNIYT9ZnEvsbMNE8N8kJXpeECr8D0YSWbL1wJALP4dxTJuDC0ybBJnI42BbFY69sw9a9Hf1+TIHnUOyRoOlW25a65jAicbVfj5+rZFVHXUsYdS0RCDzTZwKuqun424f78fw/diGa0FBWZMf3lx6L+TMqRvTFKy5r6AjJEHkOx4zxYEKFJ+92zpDeir3WTkkrEM7fv6fRgGEYfOOrE2ETOTS0x/DeZ40j8rg2kUM0btVTyxf5s3hICl5pkR0JRUNbsP9Nervr+ax9dw/qW6J46Z092NMQwllfGdevbewMY5U10DQDrYEE1L0dkBMyeJa1CmEKXLJjuziIpqMjKRRV0NgeRSSuwusS+8z7aWiL4sV/7k42BJ03vRynzxk7olWJla48JUnkMa7MBZ/bNupLPhQShmFQ7nNYleQ74yjqUS+L5B6PU8SS+ROw9t09eK+rqOXYUtfR7zgEosAhHNMQS2jD3rcyU/LjLMmowLIMxhQ7IasGglEFRf3caVPkknDF2dPwzqcNeO/zRmze2Ya6lgjOXzgJFX5Hv47B8yyKvTY47AIUWUFMVhGKKjBNE2AZCJwVMNklHnaJh8izyUAq2wnJumGgLZBAc0cMJoM+g03DMPHmhgNY9+E+GKYJt0PANxf0L2l+SOenG1A1A6puQNNNmKYJjmWtMgRee14lfZL+6/571nUTbaEE/P3YCUuyZ+YkP3bUBbB1bwdefm8frv7GsQOuwj9QPAeEonLaiuq5iF6pSE4RBQ6VJU7sbQwhllDh6Gc/OJZl8PU5YzFxjBsvv7cXbcEEnnh1OxafNA4nTSvt98wQ3xUUiT1mOwzDhKZbb/id4QTagiZM0ypXwPMsJIGD3cbD1mM2SuDZjFSxNQwThmn2+NcqE2CYJkzDhGFa+V/toQScNr7PRqiBsIw//2sf9jdbzUmPneDDuSdPgMOWuZcA0zShaob1s9IM6IYJEwDPWj8PuyjAYbd+TpLIwSHxOT1jR4aO51hUljih6SYCEQU+d2HvSs13S+aPx4HmMNpDCfxj00GcPW/8sD6eTeIR6VqWy4dCtBQwkZzjsgsYU+xAXXMEAq8P6FPOMWM8uOYb0/Hnf1m7vl5bfwB7GkL4xlcnDjo4YFkGIsv1WuLTumrhxGQVwagMmOhzNqp7DIcHO92BkG4YMHRA66pdohkmdL3rtkDy9mb3/U3AekAGDIAil5g2adE0TWzZ04F1Hx1IviidM388Zk3yD+mNq3vs3bNGhmGC6Rq7wDHwuW0pYxeF7M/EkewQBQ5jS53Qmw2EIiq87pHdfUn6zy7xWPrViVjzxk5s2N6CKeO8qK70DtvjiTyLSExFLKFRwETIYPk9NiQUDc0dcfjc7ICm8h02Af92+mRs2N6CNzfWo7YugEdf2Yplp07CxAp3xs6R56wgoOdkcvcsS8psFKxZFgDQTdOKc9D1L8N0/WuCZRgwLAOWsXJAuv/lGAYMx3RdD+vffgQ7cVnD3z46kEyEH1fmwuXnTofI9r/4X8/xaGlmjRySMGyza6Rw2CUeY0td2N8cRjim9KsdEsmOyWO9mDutFBu/bMUr/9qHH35zxrAtmzMMA45lEI4pGWmaPNwoYCI5iWUYlPucSMgGghEFPs/A/pgYhsG86eUYX+7G2nd2oz0k4+nXd+Brx43BqbMrhy2XgmEYiELv2ShdNwCm/8HOUO1tDOHP7+1FKKaCYYCFx1fi1OPHwtdVuPJoNN36uTM9ZszcbpFmjciguewCqkpd2N8UHtByOxl5Z5xYZfUHDMlYt/4Azj910rA9lk3iEI6pULWBrSZkA73akZwl8Fb+gyhyiMTUQR1jTLEDP1g6HbMnF8M0gXc/a8RTr+1AMDKyfag4jgXHDv8uO0038MbGOjz9ei1CMRV+t4SrlkzDqbMr+91JXlF1BCIKSrw2TBnrxdRxRZgyrgjjy90oLbLD65LgsPEULJEB8zpFjC1xQlENJBQt26dD+iAKHL71tWPAMMAXezoGVK5loCSBQ0LVEE3k/u8DveKRnOaw8agsdkLVjUHX6xAFDt9ccAyWnXoMRIFFXUsEj76yLdmzqlC0BuL4/V+348MvmgEAc6aW4OpvTB/Q9uBYQkM0rqGy2ImqMhfcDhGSwNESG8kYv0fCmGInYgkdqpY/NXiyxTCyU1u6qtSFBceNAQD89cP9CMf6bsQ9FFb6AYNIfHiOn0kUMJGcV+QSUe53dLUzGXwRvFmTinH10umoLHEgoej40z93428f7s/7wnqmaWLD9mY8/pdtaOqIwyHxuGRRNc47ZWK/alF1C0UVaJqBceUujCl2UN0cMiwYhkGpz45ynx3BaH4Xih1ugbCMjpCctaDp1NljMKbYer185f19GK7GIDaRQyia+0VO6RWR5DyrCJ4dxR4JwYgypD9av8eGK8+ZhpNnWk0mN+5oxROvbkdL59HzenJROKZgzZs78dr6Omi6ieqxHvzwmzNQM97X72OYponOkAyeYzFhjAclXmp6SoYXyzCoKHag1GtDMKJkLSDIZZG4CoHn4HYIiCYGl5IwVBzL4ltfOwYcy2D3wRA27Wg9+p0GwSZykBUdMTm3l+Uo6ZvkBY5lk0UtwzF1SI1hOY7F4rnjMGmMBy+/txctgThWv7od58wfj9NOHN66IwOhGwaicQ3hmIJIXEU41vUVV63LYio6wjJUzQDPMThj7sBqTgFWMnogYjXnHVviymhdJkKOhGNZVJa4oOsmAhE5L3ZJjRRZ1aFqBiZWeGCaJvY2hWAYZlYKf5YW2XH6iVX4+8d1eGNjPY6p9KA4w4Umu1+zojEV3hFu+j0Q9OpI8oZNtPKZ9jeH0RGSYZc42ERu0LMh1WO9uOabM/Dye3uxpyGEv7y/D395fx8kgYUk8rCJXI8v63vpsO97Xi8J1v+P9qJmGCaiiUPBTySm9g6KYkq/kyAr/HYsO3USSgfYZby7PUmxx2Yl1+dBHRRSWASexdhSFzTDRDCiwJ8DneuzTdcNRGIqKkucKHKJ0A0TLps1y5Stcgzzppehtj6AfY1hvPzeXlx5zrSMB2+SyCEQlVHmt+fshhIKmEhe8ThFHDPGg1BEQSAqoyMsg2MZOCR+UG/4LruA7y6egg+3NuOdTxugagZk1YCsKghFB3eOIs8eCqLE7qAOVmAUVxGJq+jvqiLDAG67AJdDhNshwO0Q4LILcDtEuO3W96VF9gG/eMVlDQlZR4XfgXK/I2dfoEjhk0QOVaVO7GsKIxRV4PX0r51RITJNqyJ6sceGMp+1NM5zDEqL7NjbmL1ZJoZh8M2vTsQjf96Gg61RvL+lEV+bXZnRx7CJHIIRBTFZgydH63RRwETyjstuBQ2lPjsicRXBiIxQ18yMKLBwSHzaqtd9YRgGp8yswCmzKiCIIlo7IojFVSQUHQlF6/rX+pIP+z6haJC7/q90JSwqmgFFMxA6QikEhgGcNiEZBLntIlyO3t87bZlvHxKOKdBUA2NLnSgpstMOOJJ1DpuAqjIX6lsiCEdzf7fUcAlGFavTQYkzZdOFxynCZRcQS2hwObJTv8rrknDO/PF4+b29eOfTRkyu8mJMsTNjx2dZBias3C0KmAjJMIFn4XNL8LklxGUN0biV0xOOqTAMqyCaXer/kh3HsnDZBRgeG4oGsY6uGwZkxUgJsuSuoMowYQVEXbNCTpsw4p8UDdNEeygOlmEwvsLd7+bGhIwEj0NEVZkLgZiGzlACum7CqojPAAxS/m+aZo+/a2u6lmGsNkHovnnX9903Y8BAENicbcERS6jgWGuJ8vBz5DkWJV479jWF4DSz14Nx1iQ/dhwIYPv+Trz83l784Lzp4PnMzU5LIotQVEG5z56Tu3QpYCIFobtvm99rQyyhIRRTEAjL6AwrYBnAbuOH/YWSY1k4bGxOJk7rhoFwRENluRtenz1n3zTI6Ob32FBeKqC9Q7S2mHf3Uuzqo4ge/+8Kp4Cu/oqGCcAAdNOa6TWMrtt1XWeYVu6grhs5V2VcUXXIioHx5W647OnPzeuyZpmi8ezNMjEMg3NPthr0tgYSeGvzQZx50riMHb+7vEBc1uGyU8BEyLBiGSa5ZFdWlLpkF+lasrNLo6tKtarpCEVVlPjsqK4qQjwqQ8vxeidk9HI5RKiyOiy/o+3BBA62RhBNqHDmSNCkG9by/Ri/A/4jtIDKlVkmh03A0q9OxPP/2IWPtjZjapUXE8d4MnJsjmVhmiYicaXPwDGbRs+7Bhl1eI5FkUvChAoPplR5MaHCDZvIIxJX0RFKIJbQCr7+S0LWEI5pKPfbMaHcGj8ho1Wx14aqMhd0zUQknp3aRj2ZpolgWIXfJaHc7zhqEJScZcpyG5Gp44owZ2oJAODP/9qX0TY3osDlbG0uCpjIqGATeZR47age68XksUXJZMVAxFq6G2zblVwWialIqDqqSp2oLHGNqlk1Qvri99gwrtwNw8Cge1RmSjimwmHnUVnq7NffJ8+xKPbakFD0Yau63V9nnjQOPreEYFTB6+vrMnZcu8ghrmiI52CvQXoFJaNK95LdmGInpo4rwqRKD3xuCYpqoD2YQGcogXBMQVzWIKs6NM3I+gvTQJmmiUBYBhgGE8o9KPM5aCccIT343BLGl7tgAsPWI+1o4rIGmEBlsXNAM79FLiknZpmsHp0TwTDAZ7vbM9abk+NY6LqJaA7MAB6O5ufJqNW9ZFfkkpI721iBh6lb/1c1E7KuQ+tKHLVCDgYsY22B5TgGHGP9y7JWA8lstxQxDKtqstMmYGypKyfzAAjJBUUuCQyAutYoQlFlSN0DBkrVDMRla/Z3oI9r5TLZsK8pPCxlRwZifLkbp8yswPtbmvDqB/tRVeqCNwMV2yWBQyCi5FzZEwqYCIG1ZOdyiPD5nPA7BSiqDl03oOkmdMOErhvQDROaYUJRNaiqVWtJN0yoigHdMKxdOQBgmmAY65NSz4CKY7oCq2EqJ6BpBoJRBT6XZG1NFmknHCFH4nVJYBgGdS0RBKPKiLTlMAwToaiC0iIbSgZYnb+b1ynBZU8gmtCy/qHotOMrsas+iObOOP7ywT5898ypQz6mTeQQS1jFdXNp13HunAkhOYRlGLA8B+EofyFaVyCl6yY0w4Cum12BllUxXNF0aLqRnK3STdOq8m2aVr0YhgHXFUQl/x1EYCUrOiIJFWU+O8YU9y8fghBiFYUcV24VzQxGZHiHuT5ZezgBr1NEhd856NkTgbdmmfY3ReC0mVmdZeI4Ft869Ris/st27KwP4pPaNnx97tB6cvI8C003EEuoFDARUih4jgXPATjChzzDtAIq3eiapdIPzVipugFVNax8qUEGVnFZg6IZGFviRFmRIyutEwjJZx6HiPHlbhxoDiMQluF1icMShIRjCrweO0rdDghDLPjodUpw2q3dvs4szzKV+xz4+pyxeHNjPV5bvx+zppRCHOJnNkFgrTYxXlvWUx26UcBEyDCzZqsYCEfZY9EzsDp8KfBIgZXAc5hQ7obPLeXMCwsh+cZlFzC+3G0tz0VUeF1CRv+eEooGXTcxvsIDRteHXGdK4FmUeGzY3xyGI8u5TAAwf3o5ausCONAcwQv/2InvLp4ypONZy3JWiyq7lBuhSm6cBSFk0IEVxzI584JCSD6zgqbu5TklYzNNmm4gmtAwodwNv8eGzs5BdvY+jNclwRHMjVkmlmXwzQXH4Ldrv0DtgU40dcRQ6rUN+ngCzyGkq4gltJx5faNEB0LyDMswEHjWSlS3CznzYkJIIXDaBIwrc8Mu8QiElSGXFTFN09rx5bGhzOfI0FlaBJ5FaY7UZQKscg3TJ/oAAOu3Ng/5eALPIhiVh3ycTKGAiRBCCOnBYeMxvtwNp11A5xCDpmBEhcchYkyxc1jyC70uCXaJR0zOjUKP82aUAwA+392G2BBrRdlEDpG4ltFK4kNBARMhhBByGLvEY1yZVcusMzS4oCkaV8HzLCpLnBCHqeF19465hJwbs0zjylyoKnNB001s3tk6pGOJAgdVM4YceGUKBUyEEEJIGnaJx/hyF9xOAZ1heUD9zRRVh6IZqCx2DHutJJ87d2aZGIbB12aPBQBs/LJ1yD3heA4IRbNTjf1wFDARQgghfbCJPMaXueFxSP0OmnTD6Gp67YAvA5Wvj0bguZyaZTqhpgwOG49gVMGOusCQjtXdMD0X+n1SwEQIIYQcgSRyGFfuQpHr6EGTaZoIRhT4PSLKiuwjtt0/l2aZBJ7F3JoyAMCG7S1DOpYosJA1PSeW5ShgIoQQQo5CEjiMK3PB55LQGVagG+nrKIWiCpw2AZXFrhGtuJ9rs0xzjy0DwwD7m8Jo7ogN+jgMYxXpzVaT5J7yImAKBAL42c9+hlNPPRVz5szBt7/9bWzcuDF5/fbt23HppZfi+OOPx2mnnYYnnnjiqMdct24dlixZglmzZmHp0qV49913h3MIhBBC8pwoWDNNfo+IQJqgKZbQwDAsxpQ4s9LLsXuWKZ4Ds0xep4hjJ1glBoY6y2STOIRjKlQtu8tyeREw/fjHP8Znn32GVatW4cUXX8SMGTPwve99D7t370ZnZyeuvPJKTJw4ES+99BJuvPFGPPjgg3jppZf6PN5HH32EW2+9Fd/5znfw8ssvY8GCBbj++uuxe/fuERwVIYSQfCPwHKpK3Sj22KygSbeCJlXTISs6Kksc8DiGv4lvX+dW4rUhniOzTF851lqW27KnY0hLapLAQVazvyyX8wHT/v378f777+M//uM/MHfuXEyaNAl33XUXysvL8eqrr+JPf/oTRFHEf/7nf6K6uhoXXHABrrjiCjz++ON9HvPxxx/H4sWLcemll6K6uhq33347ZsyYgaeeemoER0YIISQfCTyLqjIXSrw2BCIKFFVHKKqi1GeH3zP46taZUOTKnVmmcWUuVPgd0HRjSCUGGIYBywDheHaX5XI+YPL5fHjssccwc+bM5GUMw1iJdcEgNm7ciJNOOgk8f6ja8fz587F37160t7f3Op5hGPjkk08wf/78lMvnzZuXssxHCCGE9IXnWIwttYKmYFRFkUtChd8BNss93UQhd2aZGIZJzjINtcSAJHIIRVVo+tB68A1FzgdMHo8HCxcuhCgemuJct24dDhw4gAULFqCpqQkVFRUp9ykrs56ghoaGXscLhUKIxWJp79PY2DgMIyCEEFKIuoOmCeUuVJY4RzTJ+0gOzTJlfyv+zGP8cEhWiYHaIZQYkAQOCSW7y3J514Rq06ZNuPPOO3H66adj0aJFuO+++1KCKQCQJKvuhSz37kGTSCQAIO190t1+oLgc+YPJtO5xFer4gMIfI40v/xX6GPNxfDzPorLU1a/bjtT4eJ5Fmd+O+pYoXCwzYqUNAIBj2dR/ORYnTivFe581YsOXLZgxqXhwx+UAjmUAxhpfNuRVwPTmm2/iJz/5CWbPno1Vq1YBAGw2GxQldV2zO/BxOHo3OuwOptLdx263D/kcPZ6hHyOXFfr4gMIfI40v/xX6GGl8Q+dw2SDrAAMGzmGuNJ6O03moYOeiuRPw/ueN2NcYRiSh9zvAPJyiA16vA76i7Px+5E3A9Mwzz2DFihVYvHgxVq5cmZwhqqioQEtL6pbF7u/Ly8t7HaeoqAgOhyPtfQ5fphuMUCie3DVRSDiOhcdjL9jxAYU/Rhpf/iv0MdL4MkvigPqWCPweacRmmTiWhdMpIRqVk2UXWADHTvRj694OvLXxAL6x4JhBHTsSSSAYjIEzM/uz83js/Zr1y4uAac2aNbjnnntw2WWX4c477wTLHhrYSSedhOeffx66roPjrLoXH374IY455hgUF/ee+mMYBnPmzMGGDRtw0UUXJS9fv349TjzxxCGfq64b0LTC+0PvVujjAwp/jDS+/FfoY6TxZYbbLkLg44jEVDhsI/t2rxtGSlB40rRSbN3bgc92tePrJ4wd1PnohvVzy9bvRs4vFO/duxf33nsvFi9ejGuuuQbt7e1obW1Fa2srwuEwLrjgAkQiEdx1113YtWsX1q5di6eeegrXXHNN8hjhcBgdHR3J76+88kr89a9/xZNPPondu3fjF7/4BbZv347ly5dnY4iEEEJIxkkChxKPDXFZy/qOuUyVGMimnA+YXn/9daiqijfeeAMLFixI+VqxYgWKi4uxevVq7N27F8uWLcNvfvMb3HbbbVi2bFnyGCtWrMCFF16Y/H7BggW499578dxzz2HZsmX46KOP8Mgjj6C6ujobQySEEEKGRZFbgk3kkVCyu2MukyUGsoUxsx12FpjOzmhBTiXzPAufz1mw4wMKf4w0vvxX6GOk8Q2Ppo4oDrZGRySXieNYeNx2hMK987Q0zcCvXvgcMVnDxV+vxrSu1in91RFKYGKFJ+PFQf1+Z79ymHJ+hokQQgghg+dz23JilonnWcypKQEArB9if7lsoICJEEIIKWCSwMHvlRBLZD+XaW5NGRgG2N8URnNnLKvnMlAUMBFCCCEFzu/KjVkmj1PEsV1LcR/n2SwTBUyEEEJIgZNEDn6PhFgi++1SupO/P9/dkRNNgvuLAiZCCCFkFPC7bZBELutBilViwN5VYqAtq+cyEBQwEUIIIaOAJHIo9kiIZrGBLdBdYsDqxPHx9pa8KTFAARMhhBAySvi66jJle5Zp5jF+OCQewaiC2rpAVs+lvyhgIoQQQkYJm8jnxCwTz7M4YapVYmBDniR/U8BECCGEjCI+twSbwCGR5VmmudOsEgP78qTEAAVMhBBCyChiE3kUe22IZrkukzfPSgxQwEQIIYSMMn6PDTYp+7lMJ+VRiQEKmAghhJBRRhI4lHptiMt6VmeZxudRiQEKmAghhJBRyOe2wWkXspoAzjAMTsqTEgMUMBFCCCGjkMCzKPXaIat6VgOVmcf4Yc+DEgMUMBFCCCGjlNclwm0XEYmrWTsHgWcxJw9KDFDARAghhIxSPMeitMgOXTeh60bWzqNniYGWznjWzuNIKGAihBBCRjGvU4THKSKcxVkmr1PEtPFWiYEN25uzdh5HQgETIYQQMoqxLIPSIjtME9C07M0yfWV6bpcYoICJEEIIGeXcDgE+l4RwLHuzTOPLXCj35W6JAQqYCCGEkFGOYRiUFNnBsQwUVc/aOXxleu6WGKCAiRBCCCFw2QX4PFJWd8zlcokBCpgIIYQQAgAo8doh8BwSSnZyiFJKDHyZWyUGKGAihBBCCADALlmNeSPx7DXmnVtTapUYaMytEgMUMBFCCCEkqdhtg13kEZezk8vkdUk5WWKAAiZCCCGEJEkih9IiG+Jy9maZvnJs7pUYoICJEEIIISl8bgkOW/Ya844vz70SAxQwEUIIISSFwFuzTLKSnca8DMMkZ5k2fpkbJQYoYCKEEEJIL0UuCW6HiGgiO2UGZk4qhl3iEIgo2FkfyMo59EQBEyGEEEJ66W7Mq2omdGPkW6ZYJQZKAQDrt2e/xAAFTIQQQghJy+sU4XWKiGSpZUrPEgPtwURWzqEbBUyEEEIISYtlrZYpRpYa8/YsMfD5nvYRf/yeKGAihBBCSJ+6G/Nmq2VKd/L3jgMBxLK0aw+ggIkQQgghR8B2NeZlmOw05j1UYsDEnobgiD9+NwqYCCGEEHJEThsPn1tCJDbyMzwMw+Bbpx6DuTWlmDKuaMQfvxsFTIQQQgg5IoZhUOK1g+dZyMrIzzKV+xyYP6McksCN+GN347P2yEPwu9/9Dh9++CGefvppAMBll12GDRs2pL3tz3/+c3zrW99Ke92iRYtw8ODBlMuWLl2KlStXZvR8CSGEkHznsPEo8djQ0BGFJGYvcMmWvAuY/vCHP+DXv/41TjrppORlDz30EFQ1NRntpz/9KQ4cOIAzzjgj7XEikQgaGhrw6KOPYsaMGcnLbTbb8Jw4IYQQkuf8XgmdERlxWYNdyrsQYkjyZrTNzc246667sGnTJhxzzDEp1xUVFaV8/+qrr+Jf//oX1q5dC5fLlfZ4tbW1ME0Tc+bMgcfjGa7TJoQQQgqGTeRR4rWhvjUCm8iBYZhsn9KIyZscpq1bt8Lr9eKVV17B7Nmz+7xdLBbDL37xCyxfvhw1NTV93m7Hjh0oLS2lYIkQQggZAL9Hgl3is7rFPxvyZoZp0aJFWLRo0VFv9/zzzyMajeLaa6894u1qa2vhcDhw4403YvPmzfD7/Tj//PNx+eWXg2UHH0dyXN7EoAPSPa5CHR9Q+GOk8eW/Qh8jjS8/8DyLyhIn9jWFwDAMWPbQLBPX9f7JDeF9tC8cy4Lnra9syJuAqT90XcfTTz+N73znO3C73Ue87c6dOxEOh7FkyRLccMMN2LhxI1auXIlgMIibbrpp0Ofg8dgHfd98UOjjAwp/jDS+/FfoY6Tx5T6X2w7ZAGRFh8cl9bre6ex92VApOuD1OuArys7Pr6ACpg0bNqChoQEXX3zxUW/75JNPQpblZI5TTU0NotEoHn74Ydx4442DnmUKheLQ9ZEvHz/cOI6Fx2Mv2PEBhT9GGl/+K/Qx0vjyi51n0NgSg6FrKTNLTqeEaFTOeMPeSCSBYDAGzszscT0ee79m/QoqYHrzzTdx3HHHYdy4cUe9rSAIEAQh5bKpU6ciFoshGAzC5/MN6hx03chKv52RUujjAwp/jDS+/FfoY6Tx5QenjYfLJiAYUeB1iinX6YaR8aBQN6yfW7Z+dvm9kHqYTZs2Yf78+Ue9nWEYWLRoER5++OGUy7ds2YKSkpJBB0uEEELIaMGxLEqLbDB0E1oBzJgdTcEETLquY9euXZg6dWra68PhMDo6OgAALMvirLPOwurVq7Fu3TocOHAAf/zjH7F69eoh5S8RQggho4nbKaLILWalZcpIK5gluUAgAFVVe9Vk6rZixQps2LABb731FgDglltugcfjwQMPPICmpiZUVVXhrrvu6lf+EyGEEEK6GvN67QhGFaianvc7AI+EMU3TzPZJFJLOzmhBrE0fjudZ+HzOgh0fUPhjpPHlv0IfI40vP5mmibqWCNqCCZT67PC47QiFM5/Y3hFKYGKFB/7/3969B0V5nX8A/+5yJ9wElxA10zjRxYILgiFAjQprUWvUiTqRRkGCJkptmeIlgsbLdNJ6T4zaCPFS49A42lhDYxybSbRKYpSb2qCiuCREE2URkTsuAuf3B7/dsuzCggJ78fuZ2ZE9e953n8PDy/tw3uO+Hr17Rw5v76e6VejZbilIREREfa7txrzOsLeT4oEZbszbX1gwERER0WNxdXaAj4cT6hsfmu5spVgwERER0WPz8XSGk4MUDQ9ss2hiwURERESPzdnRHjIvF9Q1PkRLq+0tj2bBRERERL3Cx9MFPp4uqKp9gFYbK5pYMBEREVGvcLCX4rlBHhjg7ozKWo1NFU0smIiIiKjXODnY4dmn3eDt7mRTRZPNfHAlERERWQZHezs86+sGiQSorNHAy91Rd4Nea2Xd0RMREZFFcrC3wxCZG7w9nFBV24SWVuv+sE4WTERERNQn2oomd5somlgwERERUZ9xsJdiiMwdPh7OuG/FRRPXMBEREVGfcrCXYrDMDQBwr+YBvNwcre5GvdYVLREREVklbdHk4+GMqrqmXr85b19jwURERET9wsFeiiG+1lk0sWAiIiKifmNv11Y0DfR0blsIbiVFE9cwERERUb+yt/vfmqaK6gfwdHOEvYWvabLs6IiIiMgmaYumgZ7OqK5rQrOFzzSxYCIiIiKz0BZNMi8Xiy+aWDARERGR2djbSTFo4FMWXzRxDRMRERGZlbZoAoC7VY3wfMoR9vaWNadjWdEQERHRE0lvpqm+Cc3NljXTxBkmIiIisghta5q0M00P4PmUg8XMNFlGFEREREQA7KRtRZPvAGdU1TfhoYXMNHGGiYiIiCyKnfR/a5rU99vWNJkbCyYiIiKyONqiSSIByiobAWHeeFgwERERkUWyk0rxjE/bTFN51QOzxsKCiYiIiCyWnVSKQT5usJdK4ehgZ7Y4WDARERGRRZNKJfD7/5kms8Vg1ncnIiIisgIsmIiIiIhMYMFEREREZAILJiIiIiITWDARERERmcCCiYiIiMgEqyyYdu3ahfj4eL22lStXwt/fX+8xbty4Lvdz4sQJTJkyBQqFAtOmTUN2dnZfhk1ERERWyuo+h+mjjz7Cjh07EBYWptd+/fp1JCUlIS4uTtdmZ9f5B1ydP38eb731FtLS0hAZGYkjR47g97//PbKysvD888/3WfxERERkfaxmhkmtVuONN97A9u3bMXToUL3XWlpaoFKpoFAoIJPJdA9vb+9O97dnzx7ExMQgLi4Ozz//PFJTUxEYGIgDBw709VCIiIjIylhNwXTlyhV4enris88+Q3BwsN5rpaWl0Gg03Z4Zam1txYULFxAREaHXHh4ejvz8/F6LmYiIiGyD1VySUyqVUCqVRl8rLi6GRCLBgQMHkJ2dDalUivHjxyMlJQXu7u4G/WtqatDQ0AA/Pz+9dl9fX9y5c+ex4vT0dIEw8x2V+4JE0vavrY4PsP0xcnzWz9bHyPFZP2sco1Qq6VY/qymYunLjxg1IpVIMHjwYGRkZ+PHHH7Fp0yYUFxfjwIEDkEr1J9IePGi747Gjo6Neu5OTEzQazWPF0vG9bI2tjw+w/TFyfNbP1sfI8Vk/WxyjTRRMycnJeP311+Hh4QEAkMvlkMlkiI2NRWFhocElPCcnJwBAU1OTXrtGo4GLi0v/BE1ERERWwyZKQIlEoiuWtORyOQCgrKzMoL+XlxdcXV1RXl6u115eXm5wmY6IiIjIJgqmZcuWYcGCBXpthYWFAIBhw4YZ9JdIJAgNDUVubq5ee05ODkaPHt13gRIREZFVsomCaerUqTh79izS09Nx8+ZNnDlzBqtWrcLUqVN1/3OutrYWlZWVum0SExNx/Phx7N+/HyUlJdi8eTOKioqQkJBgrmEQERGRhZIIYS3r2P8nLS0NP//8MzIzM3VtX3zxBTIyMvD999/D3d0d06ZNQ0pKim69UlpaGnJzc3Hq1CndNllZWdi1axfKysowbNgwvPXWW4iMjOz38RAREZFls8qCiYiIiKg/2cQlOSIiIqK+xIKJiIiIyAQWTEREREQmsGAiIiIiMoEFExEREZEJLJiIiIiITGDB1E2tra3YsWMHxo4di+DgYMyfPx8//vhjp/3v37+PZcuWISwsDGFhYVizZg0aGhr6MeKeqaqqwtq1azFu3DiEhobitddeQ35+fqf9P/30U/j7+xs8uvqemNvPP/9sNOZPPvnEaH9rymFOTo7Rsfn7+2PChAlGt7GmHO7atQvx8fF6bUVFRYiLi8OoUaMQFRWFffv2mdzPiRMnMGXKFCgUCkybNg3Z2dl9FXKPGRvjqVOnMGvWLISEhECpVGLTpk26m4d3RqlUGuR0+fLlfRl6txgb38qVKw1iHTduXJf7sdQcdhxffHx8p8dkVlZWp/uxpPyZOi/Y2jFokqBu2blzp4iMjBSnT58WRUVFYv78+SImJkZoNBqj/ePi4sSrr74qLl++LL799lsRHR0tVqxY0c9Rd19iYqKYPn26yMvLEyUlJeKdd94RQUFBQqVSGe2/YcMGERcXJ8rLy/Uezc3N/Rx59508eVIoFAqhVqv1Ym5sbDTa35pyqNFoDHLxzTffiICAAPGPf/zD6DbWksP9+/cLf39/ERcXp2urrKwU4eHh4u233xYqlUocOXJEKBQKceTIkU73c+7cOREYGCgyMzOFSqUSGzduFCNHjuz0Z7w/GRtjXl6e+OUvfyk+/PBDUVpaKs6cOSPGjx8v0tLSOt1PbW2t8Pf3F//5z3/0clpTU9Mfw+iUsfEJIcSMGTPEe++9pxfrvXv3Ot2PpebQ2Pju379vcGwtXLhQTJ48WdTW1hrdj6Xlr6vzgq0dg93BgqkbNBqNCAkJEQcPHtS1VVdXi6CgIPH5558b9L9w4YKQy+V6PwRff/218Pf3F2VlZf0Sc0+UlpYKuVwuCgoKdG2tra0iJiZGvP/++0a3SUxMFH/+85/7K8RekZ6eLqZPn96tvtaWw46amprEyy+/LFJSUjrtY+k5LCsrEwsWLBCjRo0SkydP1jsZZWRkiLFjx4qHDx/q2t59910xadKkTvc3f/58g+9HbGysWLNmTe8H301djXHZsmUiMTFRr39WVpYICAjo9A+1goICIZfLRXV1dZ/G3V1dja+5uVkoFArx5Zdfdnt/lpbDrsbX0bFjx0RAQIC4du1ap30sKX+mzgu2cgz2BC/JdcO1a9dQX1+PiIgIXZuHhwcCAgKQl5dn0D8/Px8ymUx3HzsAePHFFyGRSFBQUNAvMffEgAEDsHv3bowcOVLXJpFIIIRAdXW10W2uX79u9MbGlqwnMVtbDjv6+OOPcefOHaxcubLTPpaewytXrsDT0xOfffYZgoOD9V7Lz89HWFgY7O3tdW0RERH44YcfcO/ePYN9tba24sKFC3rHMACEh4d3eem5r3U1xvnz52PFihUG2zQ3N6Ours7o/q5fvw6ZTAYPD48+ibenuhpfaWkpNBqN3jHWFUvMYVfja6+hoQGbN29GQkIC/P39O+1nSfkzdV6wlWOwJ+xNd6GysjIAwDPPPKPX7uvrizt37hj0V6vVBn0dHR3h5eVltL+5eXh4YPz48XptJ06cwM2bN/HSSy8Z9K+srERFRQXy8vKQmZmJqqoqBAcHY/ny5Rg6dGh/hd1jxcXFkMlkmDNnDkpLS/GLX/wCixcvxtixYw36WlsO29NoNMjIyEBCQgJ8fX2N9rGGHCqVSiiVSqOvlZWVQS6X67Vpx3r79m34+PjovVZTU4OGhgb4+fkZbGPOfHY1xoCAAL3nTU1N2L9/PwIDA+Ht7W10m+LiYri6uiI5ORkXL16Et7c3Zs6ciXnz5kEq7f+/j7saX3FxMSQSCQ4cOIDs7GxIpVKMHz8eKSkpcHd3N+hviTnsanztHTp0CPX19fjd737XZT9Lyp+p88K2bdts4hjsCc4wdUNjYyOAthNme05OTtBoNEb7d+zbVX9LU1BQgFWrVmHChAlGfxkUFxcDAOzs7LBp0yZs27YNDQ0NmDNnDioqKvo73G5pampCaWkp6urqkJKSgt27d0OhUODNN9/EuXPnDPpbcw7/9a9/QaPRGCywbc8ac9jegwcPjB6PAIzmR7tQurvHsKVpbm7GihUroFKpsG7duk773bhxA7W1tZgyZQr27duH2NhYbN++HTt37uzHaLvnxo0bkEqlGDx4MDIyMpCamoozZ85g8eLFaG1tNehvrTlsaWlBZmYm5syZY7QQbM+S89fxvPCkHYMAZ5i6xdnZGUDbSVf7NdD2Q+Hi4mK0f1NTk0G7RqOBq6tr3wXaC7766issX74cwcHBeO+994z2iYiIQG5uLjw9PXVtH3zwAaKjo3H06FEsXLiwv8LtNkdHR+Tl5cHe3l53wI4cORIlJSXYt28fIiMj9fpbcw6zsrIwceJEDBgwoNM+1pjD9ozlR/tL11h+tL/IjW1j7Bi2JNoiPycnBzt27Ojy0s/+/fuh0Wjg5uYGAPD390d9fT3S09ORnJxsllmmziQnJ+P111/XXX6Sy+WQyWSIjY1FYWGhwTitNYe5ubm4ffs2Zs+ebbKvpebP2HnhSToGtSzn6LFg2ksz5eXleu3l5eUG04sA4OfnZ9C3qakJVVVVePrpp/su0Mf097//HcnJyRg3bhz27NmjVxx21P5EC7QdIEOGDIFare7rMB+Zq6urwV83crncaMzWmsPKykpcvHgRU6ZMMdnXGnOoZSw/2ufG8uPl5QVXV9duH8OWory8HHPnzsXFixexZ88ek5d/HBwcdCdbLblcjoaGhk7XI5qLRCIxWKujvcSjXQbRnrXm8KuvvkJQUBCeffZZk30tMX+dnReelGOwPRZM3TBixAi4ubkhJydH11ZTU4OrV6/ihRdeMOgfFhaGsrIyvc+z0W4bGhra9wE/goMHD+Kdd97B3Llz8f777xu9HNW+b3h4uN7nwdTV1aG0tNRiFxFfu3YNISEhBosLL1++bDRma8whAFy4cAESiQQvvvhil/2sMYfthYWFoaCgAC0tLbq2c+fOYejQoQZrJ4C2k3NoaChyc3P12nNycjB69Og+j/dRVFdXIyEhAZWVlTh48KDBYtmOWltboVQqkZ6ertdeWFiIgQMHdjnjaA7Lli3DggUL9NoKCwsBwOjPoDXmEGi7lGUqd4Bl5q+r88KTcAx2xIKpGxwdHREXF4etW7fi5MmTuHbtGpYsWQI/Pz/ExMSgpaUFd+/e1Z18goODERoaiiVLluC7777D+fPnsW7dOrzyyisWOTvxww8/YP369YiJicGiRYtw79493L17F3fv3kVtba3B+KKjoyGEwIoVK3Djxg0UFhYiOTkZ3t7emDFjhplHY5xcLsfw4cPxpz/9Cfn5+SgpKcGGDRtw6dIlJCUlWX0Ota5du4Znn33WYIrbFnLY3qxZs1BXV4e3334bKpUKR48exYEDB7Bo0SJdn9raWlRWVuqeJyYm4vjx49i/fz9KSkqwefNmFBUVISEhwRxDMGnDhg24desWtmzZAm9vb90xeffuXd1Jqv0YpVIpJk2ahL179+oW5x4+fBh79+7FH//4R3MOxaipU6fi7NmzSE9Px82bN3HmzBmsWrUKU6dO1f3POWvPYUtLC1QqlcHiaC1Lzp+p88KTcAwaMO+nGliP5uZmsXnzZhERESFGjRol3nzzTXHr1i0hhBC3bt0Scrlc/POf/9T1r6ioEMnJyWLUqFEiPDxcrFu3Tjx48MBc4XcpPT1dyOVyo4/U1FSj47t69aqYP3++GD16tAgNDRXJycni9u3bZhyFaffu3RMrV64UY8aMEQqFQsTGxoq8vDwhhPXnUGvdunVi9uzZBu3WnsPU1FSDz7j573//K2bPni1GjhwpoqOjRWZmpsE20dHRem2ffvqpiImJEQqFQsyYMUN8++23fR57d7UfY0tLi1AoFJ0el9rfPR3H+PDhQ7Fr1y4xYcIEERgYKCZNmiQOHz5slvF0ZCyH//73v8Urr7wigoKCxJgxY8TGjRv1jjFryqGx8VVUVAi5XC6ys7M73cZS82fqvCCE7R2DpkiEEMLcRRsRERGRJeMlOSIiIiITWDARERERmcCCiYiIiMgEFkxEREREJrBgIiIiIjKBBRMRERGRCSyYiIiIiExgwURETyx+DB0RdRcLJiIym/j4ePj7++seI0aMQEhICGbOnInMzEy9+1T1ppqaGqSmpurdWzA+Ph7x8fGPvM+//e1vWL58eW+E12MqlQpKpRI1NTVmeX+iJ4G9uQMgoidbQEAA1q1bB6Dt3lvV1dU4c+YM1q9fj4KCAmzbtg0SiaRX37OoqAhZWVmYOXNmr+yvpKQEGRkZOHbsWK/sr6eGDRsGpVKJv/zlL9i0aZNZYiCydSyYiMis3NzcMGrUKL02pVKJoUOHYsOGDVAqlZg+fbp5guumLVu2YMqUKWa9MfPChQsRFRWFefPmITAw0GxxENkqXpIjIosUHx8PX19fHDp0SK/9k08+wcsvv4yRI0ciKioKO3fuRHNzs+71tLQ0xMfH48iRI4iOjkZISAjmzZuHq1evAgBycnIwb948AMC8efP0LsMJIbBnzx5ERUUhKCgIsbGxKCws7DLO4uJinD59GtOmTdO1HT16FAqFAgUFBZg1axYUCgUmTZqEU6dO4fvvv0dCQgKCg4MRExOD48ePP/Z2AODr64uIiAjs3r27h99pIuoOFkxEZJHs7OwQGRmJ7777TlcQffjhh1izZg0iIyORkZGBuXPnYs+ePVi7dq3etkVFRdi2bRv+8Ic/YMuWLaiqqkJ8fDzUajUCAwN1/deuXau7HAgABQUF+PLLL7FmzRps2rQJarUaSUlJegVZR8eOHYNMJkNoaKhee3NzM5YuXYrf/va32LVrF5ycnLB8+XIkJSUhKioK27dvh0wmQ2pqKsrKyh57OwD4zW9+g5MnT6K+vv7RvulE1ClekiMiizVw4EA8fPgQVVVVcHJyQnp6OmJjY7F69WoAwEsvvQQvLy+sXr0aiYmJGD58OACgtrYW6enpCAsLAwAEBQXh17/+NT766COkpqZi2LBhANrW/mi/BgBHR0fs3r0bXl5eAIC6ujqsXr0aKpUKI0aMMBrj+fPnoVAoDNZZtba2IikpCa+++iqAtoXmS5cuRUJCAhITE3XjmzVrFi5fvgw/P7/H2g4AFAoFHj58iPz8fIwfP/4Rv+tEZAxnmIjI4kkkEly8eBGNjY1QKpVobm7WPZRKJQDg7Nmzuv6DBg3SFUtA2+WqkJAQFBQUdPk+w4YN0xVLADBkyBAAbQVYZ27duqXr11FISIju64EDBwKA3not7Xt1/N9tj7rd4MGDAQA//fRTp/ES0aPhDBMRWSy1Wg1nZ2d4eXmhqqoKQNviZmPKy8t1X/v6+hq87uPjgytXrnT5fq6urnrPpdK2vylbW1s73aaurg4uLi5GX3NzczNoc3Z27jKGx9lOG0ddXZ3JvkTUMyyYiMgitbS0IDc3F6GhobCzs4OHhwcAYOvWrXjuuecM+mtnYgDoiqv2Kioq4OPj0+txenl5dTkD1Z+0M04DBgwwcyREtoeX5IjIIh06dAjl5eV47bXXAADBwcFwcHCAWq2GQqHQPRwcHPDuu+/qXYa6efMmVCqV7rlarcalS5cQGRkJoG1BeW8ZPHgw7ty502v7exzaOAYNGmTmSIhsD2eYiMis6urqcOnSJQBtl77u37+Pb775BocPH8b06dMxceJEAG2zJm+88Qa2b9+Ouro6hIeHQ61WY/v27ZBIJHqLsoUQWLx4MVJSUmBnZ4e//vWv8PDw0H2EgLu7OwDg9OnT8PT07HRBd3eMGTMGBw8ehBCi1z9gs6cKCgrg4uKCF154waxxENkiFkxEZFZXr15FbGwsgLY1Qz4+Phg6dCg2btyo99lGAJCSkgKZTIaDBw9i79698PT0RGRkJJYuXaorgoC2GZbExESsX78ejY2N+NWvfoX09HTdYunhw4dj6tSp+Pjjj/H111/j888/f+T4J06ciA8++ACFhYUICgp65P30huzsbERFRXVrvRMR9YxE8O6TRGRD0tLSkJubi1OnTvXbeyYlJcHb2xvr16/vt/fs6KeffsLEiRNx5MgRBAQEmC0OIlvFNUxERI9pyZIl+OKLL3D79m2zxbB3715MnjyZxRJRH2HBRET0mPz9/bFo0SJs3brVLO+vUqlw+vRprFmzxizvT/Qk4CU5IiIiIhM4w0RERERkAgsmIiIiIhNYMBERERGZwIKJiIiIyAQWTEREREQmsGAiIiIiMoEFExEREZEJLJiIiIiITGDBRERERGTC/wHV+fKVh9gawgAAAABJRU5ErkJggg=="
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 7
+   "execution_count": 6
   }
  ],
  "metadata": {
diff --git a/examples/derived_measurements.ipynb b/examples/derived_measurements.ipynb
index 94f5701..f7f0ef1 100644
--- a/examples/derived_measurements.ipynb
+++ b/examples/derived_measurements.ipynb
@@ -5,19 +5,23 @@
    "cell_type": "markdown",
    "source": [
     "# Derived Measurements\n",
+    "The data used in this tutorial can be downloaded from [Zenodo](https://zenodo.org/records/14259406)\n",
+    "\n",
     "From layer profiles we calculate image derived parameters. We'll start by generating some layers, and applying them to \n",
     "quantitative maps.\n",
     "\n",
     "Taking the average of the outer layers\n",
-    " is analogous to taking the average of the cortex, similarly taking the average of the inner layer is analogous to taking the average of the medulla. Finally we can calculate the gradient of the central profile which gives information about the cortico-medullary difference. To make things easier, the qlayers package has a helper function to do all this in one go. We're going to use the outer 5 mm of tissue as the outer region and tissue deeper than 15 mm as the inner region. You can set the unit argument to percent to use, for example, the outer 10 % and inner 10 % to define the regions and thus control for kidney size."
+    " is analogous to taking the average of the cortex, similarly taking the average of the inner layer is analogous to taking the average of the medulla. Finally we can calculate the gradient of the central profile which gives information about the cortico-medullary difference. To make things easier, the qlayers package has a helper function to do all this in one go. We're going to use the outer 5 mm of tissue as the outer region and tissue deeper than 15 mm as the inner region. You can set the unit argument to percent to use, for example, the outer 10 % and inner 10 % to define the regions and thus control for kidney size.\n",
+    "\n",
+    "Start off with a helper function for plotting slices of images, generating the layers and adding an $R_2^*$ and $T_1$ map to the layers object."
    ],
    "id": "96a262842b472d1e"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-06-18T16:04:54.498268Z",
-     "start_time": "2024-06-18T16:04:16.814787Z"
+     "end_time": "2024-12-02T11:50:25.819539Z",
+     "start_time": "2024-12-02T11:49:44.565305Z"
     }
    },
    "cell_type": "code",
@@ -46,7 +50,7 @@
     "mask_img = nib.load('data/kidney_mask.nii.gz')\n",
     "qlayers = QLayers(mask_img, thickness=1, pelvis_dist=10, space='layers')\n",
     "r2star_map = nib.load('data/r2star.nii.gz')\n",
-    "t1_map = nib.load('data/t1_registered.nii.gz')\n",
+    "t1_map = nib.load('data/t1.nii.gz')\n",
     "qlayers.add_map(r2star_map, 'r2star')\n",
     "qlayers.add_map(t1_map, 't1')"
    ],
@@ -64,20 +68,24 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Distance Calculation: 100%|██████████| 3/3 [00:10<00:00,  3.52s/it]\n",
-      "Pelvis Distance Calculation: 100%|██████████| 3/3 [00:12<00:00,  4.03s/it]\n",
-      "D:\\ppzajd\\NextCloud\\University\\Renal Imaging\\Layers\\qlayers\\qlayers\\quant_layers.py:127: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
-      "  self.df_long = pd.concat([self.df_long, sub_df])\n"
+      "Distance Calculation: 100%|██████████| 3/3 [00:12<00:00,  4.02s/it]\n",
+      "Pelvis Distance Calculation: 100%|██████████| 3/3 [00:11<00:00,  3.93s/it]\n"
      ]
     }
    ],
-   "execution_count": 2
+   "execution_count": 76
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "The [`slope` function](https://qlayers.readthedocs.io/en/latest/modules.html#qlayers.regression.slope) takes a qlayers object and calculates average properties of the outer, inner and centre region of the kidney with associated uncertainty's. By default, averages are medians, but different aggregation functions can be used. Here the `slope` function is first used to calculate properties based on the number of mm from the surface of the kidney, then the same is done using percent of the maximum depth.",
+   "id": "92264de8f2b09aa0"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-06-18T16:04:54.698816Z",
-     "start_time": "2024-06-18T16:04:54.498268Z"
+     "end_time": "2024-12-02T11:50:25.927384Z",
+     "start_time": "2024-12-02T11:50:25.824227Z"
     }
    },
    "cell_type": "code",
@@ -95,17 +103,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average Outer R2* = 14.55 +/- 13.90 Hz\n",
-      "Average Inner R2* = 25.54 +/- 9.26 Hz\n",
-      "Average R2* Grad  = 1.35 +/- 0.04 Hz/mm\n"
+      "Average Outer R2* = 25.45 +/- 14.79 Hz\n",
+      "Average Inner R2* = 22.72 +/- 7.70 Hz\n",
+      "Average R2* Grad  = 0.15 +/- 0.05 Hz/mm\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "              inner       outer       grad   inner_std   outer_std   grad_se\n",
-       "r2star     25.53684   14.551717   1.346764    9.255501   13.895094  0.037007\n",
-       "t1      1807.865845  562.812836  75.834275  558.441981  838.029864  2.391022"
+       "              inner        outer       grad   inner_std   outer_std   grad_se\n",
+       "r2star    22.717783    25.454324   0.148803    7.699558   14.793482  0.046039\n",
+       "t1      1616.768025  1402.395115  11.842538  311.661846  631.026206  2.380408"
       ],
       "text/html": [
        "<div>\n",
@@ -137,57 +145,57 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>r2star</th>\n",
-       "      <td>25.53684</td>\n",
-       "      <td>14.551717</td>\n",
-       "      <td>1.346764</td>\n",
-       "      <td>9.255501</td>\n",
-       "      <td>13.895094</td>\n",
-       "      <td>0.037007</td>\n",
+       "      <td>22.717783</td>\n",
+       "      <td>25.454324</td>\n",
+       "      <td>0.148803</td>\n",
+       "      <td>7.699558</td>\n",
+       "      <td>14.793482</td>\n",
+       "      <td>0.046039</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>t1</th>\n",
-       "      <td>1807.865845</td>\n",
-       "      <td>562.812836</td>\n",
-       "      <td>75.834275</td>\n",
-       "      <td>558.441981</td>\n",
-       "      <td>838.029864</td>\n",
-       "      <td>2.391022</td>\n",
+       "      <td>1616.768025</td>\n",
+       "      <td>1402.395115</td>\n",
+       "      <td>11.842538</td>\n",
+       "      <td>311.661846</td>\n",
+       "      <td>631.026206</td>\n",
+       "      <td>2.380408</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 3
+   "execution_count": 77
   },
   {
    "metadata": {},
    "cell_type": "markdown",
    "source": [
     "## Working with Tissues and Layers\n",
-    "We can"
+    "If tissue labels are available, we can use `3DQLayers` to estimate average cortical thickness. In this example, we have a tissue mask where 1 is cortex and 2 is medulla, by using the `add_tissue` method we can add these labels to the layers object. We can only calculate cortical thickness if the tissue labels are explicitly set i.e. `3DQLayers` doesn't just assume that 1 will always be cortex."
    ],
    "id": "37c409ca76c98bc9"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-06-18T16:05:32.560219Z",
-     "start_time": "2024-06-18T16:04:54.698816Z"
+     "end_time": "2024-12-02T11:51:02.063725Z",
+     "start_time": "2024-12-02T11:50:26.014767Z"
     }
    },
    "cell_type": "code",
    "source": [
     "tissue_img = nib.load('data/tissue.nii.gz')\n",
     "qlayers = QLayers(mask_img, thickness=1, pelvis_dist=10, space='layers')\n",
-    "qlayers.add_tissue(tissue_img, ['Cortex', 'Medulla'])\n",
-    "# qlayers.add_map(r2star_map, 'r2star')\n",
-    "# qlayers.add_map(t1_map, 't1_registered')"
+    "qlayers.add_tissue(tissue_img, tissue_labels=['Cortex', 'Medulla'])\n",
+    "qlayers.add_map(r2star_map, 'r2star')\n",
+    "qlayers.add_map(t1_map, 't1')"
    ],
    "id": "f2d816570fea8e16",
    "outputs": [
@@ -203,28 +211,28 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Distance Calculation: 100%|██████████| 3/3 [00:11<00:00,  3.73s/it]\n",
-      "Pelvis Distance Calculation: 100%|██████████| 3/3 [00:12<00:00,  4.01s/it]\n"
+      "Distance Calculation: 100%|██████████| 3/3 [00:09<00:00,  3.18s/it]\n",
+      "Pelvis Distance Calculation: 100%|██████████| 3/3 [00:10<00:00,  3.52s/it]\n"
      ]
     }
    ],
-   "execution_count": 4
+   "execution_count": 78
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-06-18T16:05:33.660982Z",
-     "start_time": "2024-06-18T16:05:32.560219Z"
+     "end_time": "2024-12-02T11:51:02.474701Z",
+     "start_time": "2024-12-02T11:51:02.114102Z"
     }
    },
    "cell_type": "code",
    "source": [
     "t2w_img = nib.load('data/t2w.nii.gz')\n",
     "fig, ax = plt.subplots(1, 2)\n",
-    "sl = 7\n",
-    "show_slice(t2w_img, sl, ax[0])\n",
+    "sl = 6\n",
+    "show_slice(t2w_img, sl, ax[0], clim=(1.5E4, 3.5E4))\n",
     "ax[0].set_title('$T_2w$ Anatomical')\n",
-    "show_slice(tissue_img, sl, ax[1])\n",
+    "show_slice(tissue_img, sl, ax[1], cmap='inferno')\n",
     "ax[1].set_title('Tissues')\n",
     "tissue = tissue_img.get_fdata()\n",
     "np.unique(tissue)"
@@ -237,7 +245,7 @@
        "array([0., 1., 2.])"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -246,28 +254,39 @@
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 5
+   "execution_count": 79
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "The [`cortical_thickness`](https://qlayers.readthedocs.io/en/latest/modules.html#qlayers.thickness.cortical_thickness) function explores the distribution of tissue depths within the kidney. The distribution of cortical tissue with depth can be approximated as a sigmoid/logistic function and the distribution of medullary tissue approximated as a gaussian. The `cortical_thickness` function fits these two equations to the tissue depth distributions and calculates the intercept i.e. the depth at which more voxels are medulla than cortex. This is taken as the average cortical thickness. The function can also estimate the error in the cortical thickness, this is done using a stochastic approach. Lots of samples are drawn from the estimated sigmoid and gaussian parameters and the cortical thickness is calculated for each sample, the mean and standard deviation of the cortical thickness is then returned. Estimating errors is therefore an order of magnitude more computationally expensive and as such, `est_error` is `False` by default.",
+   "id": "414e1f73f48788ae"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-06-18T16:05:34.374862Z",
-     "start_time": "2024-06-18T16:05:33.663985Z"
+     "end_time": "2024-12-02T11:51:08.028038Z",
+     "start_time": "2024-12-02T11:51:02.479213Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "thickness = cortical_thickness(qlayers)\n",
+    "thickness, thickness_err = cortical_thickness(qlayers, est_error=True)\n",
     "fig, ax = plt.subplots()\n",
-    "sns.histplot(qlayers.get_df('wide'), x='depth', hue='tissue', ax=ax)\n",
+    "g = sns.histplot(qlayers.get_df('wide'), x='depth', hue='tissue', ax=ax)\n",
+    "ax.vlines(thickness, 0, 1650, color='k', linestyle='--')\n",
+    "ax.axvspan(thickness - thickness_err, thickness + thickness_err, color='0.5', alpha=0.3)\n",
+    "ax.set_ylim(0, 1650)\n",
     "ax.set_xlabel('Depth (mm)')\n",
-    "ax.set_ylabel('Voxel Count')"
+    "ax.set_ylabel('Voxel Count')\n",
+    "g.legend_.set_title('Tissue')\n",
+    "ax.set_title(f\"Cortical Thickness = {thickness:.2f} $\\pm$ {thickness_err:.2f} mm\")\n"
    ],
    "id": "c980fd1b3f522f8d",
    "outputs": [
@@ -275,29 +294,20 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "D:\\ppzajd\\NextCloud\\University\\Renal Imaging\\Layers\\qlayers\\qlayers\\quant_layers.py:244: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'Cortex' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n",
-      "  self.df_wide.loc[\n",
-      "D:\\ProgramData\\Anaconda2\\envs\\qlayers\\Lib\\site-packages\\scipy\\optimize\\_minpack_py.py:177: RuntimeWarning: The number of calls to function has reached maxfev = 400.\n",
-      "  warnings.warn(msg, RuntimeWarning)\n",
-      "D:\\ProgramData\\Anaconda2\\envs\\qlayers\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
-      "  if pd.api.types.is_categorical_dtype(vector):\n",
-      "D:\\ProgramData\\Anaconda2\\envs\\qlayers\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
-      "  if pd.api.types.is_categorical_dtype(vector):\n",
-      "D:\\ProgramData\\Anaconda2\\envs\\qlayers\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
-      "  if pd.api.types.is_categorical_dtype(vector):\n",
-      "D:\\ProgramData\\Anaconda2\\envs\\qlayers\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
-      "  if pd.api.types.is_categorical_dtype(vector):\n",
-      "D:\\ProgramData\\Anaconda2\\envs\\qlayers\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n"
+      "D:\\ppzajd\\NextCloud\\University\\Renal Imaging\\Layers\\qlayers\\qlayers\\thickness.py:28: RuntimeWarning: overflow encountered in exp\n",
+      "  return L / (1 + np.exp(-k * (x - x0)))\n",
+      "D:\\ProgramData\\Anaconda2\\envs\\qlayers\\Lib\\site-packages\\scipy\\optimize\\_minpack_py.py:177: RuntimeWarning: The iteration is not making good progress, as measured by the \n",
+      "  improvement from the last ten iterations.\n",
+      "  warnings.warn(msg, RuntimeWarning)\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "Text(0, 0.5, 'Voxel Count')"
+       "Text(0.5, 1.0, 'Cortical Thickness = 8.41 $\\\\pm$ 2.62 mm')"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -306,149 +316,13 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 6
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-06-18T16:05:34.399270Z",
-     "start_time": "2024-06-18T16:05:34.374862Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "df = qlayers.get_df('wide')\n",
-    "df.loc[df['tissue'] == 'Medulla'].sort_values('depth').head(100)"
-   ],
-   "id": "6a5fb69b8205705e",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "          depth  layer   tissue\n",
-       "11759  3.637610    4.0  Medulla\n",
-       "11753  3.823703    4.0  Medulla\n",
-       "11747  3.981917    4.0  Medulla\n",
-       "11528  4.035546    5.0  Medulla\n",
-       "24370  4.274223    5.0  Medulla\n",
-       "...         ...    ...      ...\n",
-       "23370  6.881087    7.0  Medulla\n",
-       "21516  6.899412    7.0  Medulla\n",
-       "21184  6.909142    7.0  Medulla\n",
-       "23307  6.912162    7.0  Medulla\n",
-       "20325  6.917164    7.0  Medulla\n",
-       "\n",
-       "[100 rows x 3 columns]"
-      ],
-      "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>depth</th>\n",
-       "      <th>layer</th>\n",
-       "      <th>tissue</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>11759</th>\n",
-       "      <td>3.637610</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>Medulla</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11753</th>\n",
-       "      <td>3.823703</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>Medulla</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11747</th>\n",
-       "      <td>3.981917</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>Medulla</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11528</th>\n",
-       "      <td>4.035546</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>Medulla</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24370</th>\n",
-       "      <td>4.274223</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>Medulla</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23370</th>\n",
-       "      <td>6.881087</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>Medulla</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21516</th>\n",
-       "      <td>6.899412</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>Medulla</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21184</th>\n",
-       "      <td>6.909142</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>Medulla</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23307</th>\n",
-       "      <td>6.912162</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>Medulla</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20325</th>\n",
-       "      <td>6.917164</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>Medulla</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>100 rows × 3 columns</p>\n",
-       "</div>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 7
+   "execution_count": 80
   }
  ],
  "metadata": {
diff --git a/examples/generating_layers.ipynb b/examples/generating_layers.ipynb
index 9bc54b2..3de2972 100644
--- a/examples/generating_layers.ipynb
+++ b/examples/generating_layers.ipynb
@@ -6,7 +6,7 @@
     "# Generating Layers from Renal MRI Data\n",
     "In this tutorial we'll use the 3D Quantitative Layers (`qlayers`) package to analyse renal MRI data. The premise behind `qlayers` was first proposed by [Pruijm et al](https://doi.org/10.1007/s10334-019-00808-5) and is based on the idea to segment the kidney into layers based on each voxels distance from the surface of the kidney. The average of a quantitative parameter can be calculated for each layer producing profiles of, for example, $T_2^*$ with depth. The outer and inner layers are analogous to the cortex and medulla respectively while the gradient of the profile is representative of the cortico-medullary difference. `qlayers` extends this idea by allowing the user to define layers based on a 3D mask and apply the layer to any quantitative parameter.\n",
     "\n",
-    "The data used in this tutorial can be downloaded from Zenodo. In addition to the dependencies in `requirements.txt` this \n",
+    "The data used in this tutorial can be downloaded from [Zenodo](https://zenodo.org/records/14259406). In addition to the dependencies in `requirements.txt` this\n",
     "notebook also needs you to add `jupyter`, `matplotlib` and `seaborn` to your Python environment."
    ],
    "metadata": {
@@ -41,8 +41,8 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-06-18T17:36:19.890854Z",
-     "start_time": "2024-06-18T17:36:09.189589Z"
+     "end_time": "2024-12-02T11:37:22.051642Z",
+     "start_time": "2024-12-02T11:37:07.756492Z"
     }
    },
    "id": "e92ae095717cd8ee",
@@ -74,8 +74,8 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-06-18T17:36:53.141549Z",
-     "start_time": "2024-06-18T17:36:19.890854Z"
+     "end_time": "2024-12-02T11:37:59.663641Z",
+     "start_time": "2024-12-02T11:37:22.056947Z"
     }
    },
    "id": "d6f4bdce1c8bc709",
@@ -92,8 +92,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Distance Calculation: 100%|██████████| 3/3 [00:10<00:00,  3.48s/it]\n",
-      "Pelvis Distance Calculation: 100%|██████████| 3/3 [00:10<00:00,  3.36s/it]\n"
+      "Distance Calculation: 100%|██████████| 3/3 [00:09<00:00,  3.29s/it]\n",
+      "Pelvis Distance Calculation: 100%|██████████| 3/3 [00:11<00:00,  3.71s/it]\n"
      ]
     }
    ],
@@ -110,26 +110,26 @@
    "id": "14a6aed61b8d4d6c"
   },
   {
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-12-02T11:38:01.731280Z",
+     "start_time": "2024-12-02T11:38:00.265350Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "fig, ax = plt.subplots(2, 2)\n",
     "sl = 8\n",
     "show_slice(t2w_img, sl, ax[0, 0])\n",
     "ax[0, 0].set_title('$T_2w$ Anatomical')\n",
-    "show_slice('data/pelvis.nii.gz', 4, ax[0, 1])\n",
+    "show_slice('data/pelvis.nii.gz', sl, ax[0, 1])\n",
     "ax[0, 1].set_title('Renal Pelvis')\n",
     "show_slice('data/depth.nii.gz', sl, ax[1, 0], cmap='inferno')\n",
     "ax[1, 0].set_title('Depth (mm)')\n",
     "show_slice('data/layers.nii.gz', sl, ax[1, 1], cmap='viridis')\n",
     "ax[1, 1].set_title('Layers')"
    ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-06-18T17:36:54.505155Z",
-     "start_time": "2024-06-18T17:36:53.141549Z"
-    }
-   },
    "id": "dd91a29641ef1bc0",
    "outputs": [
     {
@@ -147,7 +147,7 @@
       "text/plain": [
        "<Figure size 640x480 with 4 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
diff --git a/qlayers/thickness.py b/qlayers/thickness.py
index 9f53b35..8be592c 100644
--- a/qlayers/thickness.py
+++ b/qlayers/thickness.py
@@ -177,7 +177,7 @@ def cortical_thickness(qlayers, est_error=False):
     if not sorted(list(map(str.lower, df["tissue"].unique()))) == ["cortex", "medulla"]:
         raise ValueError(
             "Cortical thickness can only be computed if tissue "
-            'labels are "cortex" and "medulla"'
+            'labels are "Cortex" and "Medulla"'
         )
 
     # Convert samples into a distribution that the curves can be fit to