diff --git a/docs/_config.yml b/docs/_config.yml
index 60b485868..6d8ef26aa 100644
--- a/docs/_config.yml
+++ b/docs/_config.yml
@@ -9,7 +9,9 @@ theme:
 # Force re-execution of notebooks on each build.
 # See https://jupyterbook.org/content/execute.html
 execute:
-  execute_notebooks: force
+  execute_notebooks: 'off'
+  exclude_tags:
+    - skip-execution
 
 # Define the name of the latex output file for PDF builds
 latex:
diff --git a/docs/tutorials/02_speed.ipynb b/docs/tutorials/02_speed.ipynb
index c88c75240..e47012e0c 100644
--- a/docs/tutorials/02_speed.ipynb
+++ b/docs/tutorials/02_speed.ipynb
@@ -27,16 +27,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mstoffel/turing/projects/autoemulate/autoemulate/compare.py:8: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
-      "  from tqdm.autonotebook import tqdm\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.datasets import make_regression\n",
     "from autoemulate.compare import AutoEmulate"
@@ -126,11 +117,23 @@
        "      <td>StandardScaler</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>Scale output data (scale_output)</th>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Scaler output (scaler_output)</th>\n",
+       "      <td>StandardScaler</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>Do hyperparameter search (param_search)</th>\n",
        "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Reduce dimensionality (reduce_dim)</th>\n",
+       "      <th>Reduce input dimensionality (reduce_dim)</th>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Reduce output dimensionality (reduce_dim_output)</th>\n",
        "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -154,12 +157,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "acc01531f97f4210ad51022bc2181a4b",
+       "model_id": "fc9b674151e04cd48707e84c2df3ed66",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Initializing:   0%|          | 0/8 [00:00<?, ?it/s]"
+       "Cross-validating:   0%|          | 0/8 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -169,7 +172,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time taken: 33.06326484680176 seconds\n"
+      "Time taken: 35.2008638381958 seconds\n"
      ]
     }
    ],
@@ -206,12 +209,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "930891bede6648d59f8376def60235ca",
+       "model_id": "d410b8c97411406085ddbb17c9419dd1",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Initializing:   0%|          | 0/8 [00:00<?, ?it/s]"
+       "Cross-validating:   0%|          | 0/8 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -221,7 +224,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time taken: 19.856790781021118 seconds\n"
+      "Time taken: 51.6426100730896 seconds\n"
      ]
     }
    ],
@@ -253,12 +256,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8db0616a71fd46fb8b544b6992466cfe",
+       "model_id": "108b48d47a8b42b2965eb88b2b08e047",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Initializing:   0%|          | 0/3 [00:00<?, ?it/s]"
+       "Cross-validating:   0%|          | 0/3 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -268,7 +271,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time taken: 2.7409257888793945 seconds\n"
+      "Time taken: 2.5493457317352295 seconds\n"
      ]
     }
    ],
@@ -311,12 +314,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0b1bdebde6bd44b099be93cde2ee0e38",
+       "model_id": "62bfafa094c64248897285ae05ed5a4b",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Initializing:   0%|          | 0/8 [00:00<?, ?it/s]"
+       "Cross-validating:   0%|          | 0/8 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -326,7 +329,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time taken: 15.135623931884766 seconds\n"
+      "Time taken: 18.824736833572388 seconds\n"
      ]
     }
    ],
@@ -362,12 +365,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ee1e27685cb74000b4fb19f311e0a0e0",
+       "model_id": "be7dcacafb8f479faeaf4fe7ddd1cc47",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Initializing:   0%|          | 0/8 [00:00<?, ?it/s]"
+       "Cross-validating:   0%|          | 0/8 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -377,7 +380,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time taken: 26.507158994674683 seconds\n"
+      "Time taken: 29.96882200241089 seconds\n"
      ]
     }
    ],
@@ -407,6 +410,413 @@
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "preprocessing",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "model",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "short",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "fold",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "rmse",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "r2",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "9a78fcae-ccb9-4897-a0d9-305ed3295fcf",
+       "rows": [
+        [
+         "0",
+         "None",
+         "SecondOrderPolynomial",
+         "sop",
+         "2",
+         "8.970131809870282e-06",
+         "0.9999999999999973"
+        ],
+        [
+         "1",
+         "None",
+         "SecondOrderPolynomial",
+         "sop",
+         "4",
+         "8.58428736437979e-06",
+         "0.9999999999999971"
+        ],
+        [
+         "2",
+         "None",
+         "RadialBasisFunctions",
+         "rbf",
+         "2",
+         "9.635911763488489e-06",
+         "0.999999999999997"
+        ],
+        [
+         "3",
+         "None",
+         "RadialBasisFunctions",
+         "rbf",
+         "4",
+         "8.87139008643115e-06",
+         "0.999999999999997"
+        ],
+        [
+         "4",
+         "None",
+         "SecondOrderPolynomial",
+         "sop",
+         "1",
+         "1.1148983870086822e-05",
+         "0.9999999999999958"
+        ],
+        [
+         "5",
+         "None",
+         "RadialBasisFunctions",
+         "rbf",
+         "0",
+         "1.0259918069348035e-05",
+         "0.9999999999999958"
+        ],
+        [
+         "6",
+         "None",
+         "SecondOrderPolynomial",
+         "sop",
+         "0",
+         "1.0404231666040227e-05",
+         "0.9999999999999956"
+        ],
+        [
+         "7",
+         "None",
+         "RadialBasisFunctions",
+         "rbf",
+         "1",
+         "1.148091059383803e-05",
+         "0.9999999999999956"
+        ],
+        [
+         "8",
+         "None",
+         "SecondOrderPolynomial",
+         "sop",
+         "3",
+         "1.2018433501816322e-05",
+         "0.999999999999995"
+        ],
+        [
+         "9",
+         "None",
+         "RadialBasisFunctions",
+         "rbf",
+         "3",
+         "1.2311829471655215e-05",
+         "0.9999999999999949"
+        ],
+        [
+         "10",
+         "None",
+         "GaussianProcess",
+         "gp",
+         "4",
+         "3.2618690614574404",
+         "0.9995886412116087"
+        ],
+        [
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+         "gp",
+         "2",
+         "3.5385211251478808",
+         "0.9995872078237552"
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+        [
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+         "None",
+         "GaussianProcess",
+         "gp",
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+         "4.305551732387653",
+         "0.9993739887974034"
+        ],
+        [
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+         "GaussianProcess",
+         "gp",
+         "0",
+         "3.9833158861974076",
+         "0.9993525825428675"
+        ],
+        [
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+         "GaussianProcess",
+         "gp",
+         "3",
+         "5.783583737546941",
+         "0.9988534665690331"
+        ],
+        [
+         "15",
+         "None",
+         "ConditionalNeuralProcess",
+         "cnp",
+         "4",
+         "5.949516903197393",
+         "0.9986367790781616"
+        ],
+        [
+         "16",
+         "None",
+         "ConditionalNeuralProcess",
+         "cnp",
+         "1",
+         "7.86096588414361",
+         "0.9979296884442821"
+        ],
+        [
+         "17",
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+         "cnp",
+         "0",
+         "8.033505143190851",
+         "0.9974715706446841"
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+        [
+         "18",
+         "None",
+         "ConditionalNeuralProcess",
+         "cnp",
+         "3",
+         "8.964478886047313",
+         "0.9972691090196779"
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+        [
+         "19",
+         "None",
+         "ConditionalNeuralProcess",
+         "cnp",
+         "2",
+         "9.422825964193303",
+         "0.9969419656610843"
+        ],
+        [
+         "20",
+         "None",
+         "SupportVectorMachines",
+         "svm",
+         "4",
+         "38.99234615562758",
+         "0.9416072127539727"
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+        [
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+         "SupportVectorMachines",
+         "svm",
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+         "48.60015304148949",
+         "0.9222114168153333"
+        ],
+        [
+         "22",
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+         "svm",
+         "0",
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+         "0.9104167028184452"
+        ],
+        [
+         "23",
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+         "LightGBM",
+         "lgbm",
+         "4",
+         "49.754522021553456",
+         "0.9035913285731713"
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+         "rf",
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+         "0.6608734805697242"
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+         "3",
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       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -426,8 +836,10 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>preprocessing</th>\n",
        "      <th>model</th>\n",
        "      <th>short</th>\n",
+       "      <th>fold</th>\n",
        "      <th>rmse</th>\n",
        "      <th>r2</th>\n",
        "    </tr>\n",
@@ -435,74 +847,410 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>SecondOrderPolynomial</td>\n",
+       "      <td>sop</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.000009</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>SecondOrderPolynomial</td>\n",
+       "      <td>sop</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.000009</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
        "      <td>RadialBasisFunctions</td>\n",
        "      <td>rbf</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.000010</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>None</td>\n",
+       "      <td>RadialBasisFunctions</td>\n",
+       "      <td>rbf</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.000009</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>None</td>\n",
+       "      <td>SecondOrderPolynomial</td>\n",
+       "      <td>sop</td>\n",
+       "      <td>1</td>\n",
        "      <td>0.000011</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
+       "      <th>5</th>\n",
+       "      <td>None</td>\n",
+       "      <td>RadialBasisFunctions</td>\n",
+       "      <td>rbf</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000010</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>None</td>\n",
        "      <td>SecondOrderPolynomial</td>\n",
        "      <td>sop</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000010</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>None</td>\n",
+       "      <td>RadialBasisFunctions</td>\n",
+       "      <td>rbf</td>\n",
+       "      <td>1</td>\n",
        "      <td>0.000011</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
+       "      <th>8</th>\n",
+       "      <td>None</td>\n",
+       "      <td>SecondOrderPolynomial</td>\n",
+       "      <td>sop</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.000012</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>None</td>\n",
+       "      <td>RadialBasisFunctions</td>\n",
+       "      <td>rbf</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.000012</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>None</td>\n",
        "      <td>GaussianProcess</td>\n",
        "      <td>gp</td>\n",
-       "      <td>5.686234</td>\n",
-       "      <td>0.999072</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.261869</td>\n",
+       "      <td>0.999589</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
+       "      <th>11</th>\n",
+       "      <td>None</td>\n",
+       "      <td>GaussianProcess</td>\n",
+       "      <td>gp</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3.538521</td>\n",
+       "      <td>0.999587</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>None</td>\n",
+       "      <td>GaussianProcess</td>\n",
+       "      <td>gp</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4.305552</td>\n",
+       "      <td>0.999374</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>None</td>\n",
+       "      <td>GaussianProcess</td>\n",
+       "      <td>gp</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.983316</td>\n",
+       "      <td>0.999353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>None</td>\n",
+       "      <td>GaussianProcess</td>\n",
+       "      <td>gp</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5.783584</td>\n",
+       "      <td>0.998853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>None</td>\n",
        "      <td>ConditionalNeuralProcess</td>\n",
        "      <td>cnp</td>\n",
-       "      <td>12.448831</td>\n",
-       "      <td>0.995273</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5.949517</td>\n",
+       "      <td>0.998637</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
+       "      <th>16</th>\n",
+       "      <td>None</td>\n",
+       "      <td>ConditionalNeuralProcess</td>\n",
+       "      <td>cnp</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.860966</td>\n",
+       "      <td>0.997930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>None</td>\n",
+       "      <td>ConditionalNeuralProcess</td>\n",
+       "      <td>cnp</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.033505</td>\n",
+       "      <td>0.997472</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>None</td>\n",
+       "      <td>ConditionalNeuralProcess</td>\n",
+       "      <td>cnp</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8.964479</td>\n",
+       "      <td>0.997269</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>None</td>\n",
+       "      <td>ConditionalNeuralProcess</td>\n",
+       "      <td>cnp</td>\n",
+       "      <td>2</td>\n",
+       "      <td>9.422826</td>\n",
+       "      <td>0.996942</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>None</td>\n",
        "      <td>SupportVectorMachines</td>\n",
        "      <td>svm</td>\n",
-       "      <td>66.633470</td>\n",
-       "      <td>0.880780</td>\n",
+       "      <td>4</td>\n",
+       "      <td>38.992346</td>\n",
+       "      <td>0.941607</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
+       "      <th>21</th>\n",
+       "      <td>None</td>\n",
+       "      <td>SupportVectorMachines</td>\n",
+       "      <td>svm</td>\n",
+       "      <td>2</td>\n",
+       "      <td>48.600153</td>\n",
+       "      <td>0.922211</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>None</td>\n",
+       "      <td>SupportVectorMachines</td>\n",
+       "      <td>svm</td>\n",
+       "      <td>0</td>\n",
+       "      <td>48.007590</td>\n",
+       "      <td>0.910417</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>None</td>\n",
        "      <td>LightGBM</td>\n",
        "      <td>lgbm</td>\n",
-       "      <td>71.021354</td>\n",
-       "      <td>0.869580</td>\n",
+       "      <td>4</td>\n",
+       "      <td>49.754522</td>\n",
+       "      <td>0.903591</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
+       "      <th>24</th>\n",
+       "      <td>None</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>lgbm</td>\n",
+       "      <td>2</td>\n",
+       "      <td>55.304834</td>\n",
+       "      <td>0.898515</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>None</td>\n",
+       "      <td>SupportVectorMachines</td>\n",
+       "      <td>svm</td>\n",
+       "      <td>1</td>\n",
+       "      <td>56.470521</td>\n",
+       "      <td>0.893394</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>None</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>lgbm</td>\n",
+       "      <td>1</td>\n",
+       "      <td>56.882800</td>\n",
+       "      <td>0.892733</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>None</td>\n",
        "      <td>GradientBoosting</td>\n",
        "      <td>gb</td>\n",
-       "      <td>78.972674</td>\n",
-       "      <td>0.836586</td>\n",
+       "      <td>2</td>\n",
+       "      <td>57.594502</td>\n",
+       "      <td>0.891436</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
+       "      <th>28</th>\n",
+       "      <td>None</td>\n",
+       "      <td>GradientBoosting</td>\n",
+       "      <td>gb</td>\n",
+       "      <td>4</td>\n",
+       "      <td>55.219221</td>\n",
+       "      <td>0.881246</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>None</td>\n",
+       "      <td>GradientBoosting</td>\n",
+       "      <td>gb</td>\n",
+       "      <td>1</td>\n",
+       "      <td>59.862824</td>\n",
+       "      <td>0.880366</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>None</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>lgbm</td>\n",
+       "      <td>0</td>\n",
+       "      <td>56.810989</td>\n",
+       "      <td>0.873537</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>None</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>lgbm</td>\n",
+       "      <td>3</td>\n",
+       "      <td>62.546230</td>\n",
+       "      <td>0.867527</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>None</td>\n",
+       "      <td>GradientBoosting</td>\n",
+       "      <td>gb</td>\n",
+       "      <td>3</td>\n",
+       "      <td>62.738683</td>\n",
+       "      <td>0.866212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>None</td>\n",
+       "      <td>GradientBoosting</td>\n",
+       "      <td>gb</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60.886451</td>\n",
+       "      <td>0.853991</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>None</td>\n",
+       "      <td>SupportVectorMachines</td>\n",
+       "      <td>svm</td>\n",
+       "      <td>3</td>\n",
+       "      <td>67.218280</td>\n",
+       "      <td>0.848524</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>None</td>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>rf</td>\n",
+       "      <td>2</td>\n",
+       "      <td>93.064467</td>\n",
+       "      <td>0.711618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>None</td>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>rf</td>\n",
+       "      <td>1</td>\n",
+       "      <td>93.979885</td>\n",
+       "      <td>0.702829</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>None</td>\n",
        "      <td>RandomForest</td>\n",
        "      <td>rf</td>\n",
-       "      <td>112.618563</td>\n",
-       "      <td>0.666424</td>\n",
+       "      <td>4</td>\n",
+       "      <td>92.133046</td>\n",
+       "      <td>0.670829</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>None</td>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>rf</td>\n",
+       "      <td>0</td>\n",
+       "      <td>92.470110</td>\n",
+       "      <td>0.660873</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>None</td>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>rf</td>\n",
+       "      <td>3</td>\n",
+       "      <td>101.063447</td>\n",
+       "      <td>0.651731</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                      model short        rmse        r2\n",
-       "0      RadialBasisFunctions   rbf    0.000011  1.000000\n",
-       "1     SecondOrderPolynomial   sop    0.000011  1.000000\n",
-       "2           GaussianProcess    gp    5.686234  0.999072\n",
-       "3  ConditionalNeuralProcess   cnp   12.448831  0.995273\n",
-       "4     SupportVectorMachines   svm   66.633470  0.880780\n",
-       "5                  LightGBM  lgbm   71.021354  0.869580\n",
-       "6          GradientBoosting    gb   78.972674  0.836586\n",
-       "7              RandomForest    rf  112.618563  0.666424"
+       "   preprocessing                     model short  fold        rmse        r2\n",
+       "0           None     SecondOrderPolynomial   sop     2    0.000009  1.000000\n",
+       "1           None     SecondOrderPolynomial   sop     4    0.000009  1.000000\n",
+       "2           None      RadialBasisFunctions   rbf     2    0.000010  1.000000\n",
+       "3           None      RadialBasisFunctions   rbf     4    0.000009  1.000000\n",
+       "4           None     SecondOrderPolynomial   sop     1    0.000011  1.000000\n",
+       "5           None      RadialBasisFunctions   rbf     0    0.000010  1.000000\n",
+       "6           None     SecondOrderPolynomial   sop     0    0.000010  1.000000\n",
+       "7           None      RadialBasisFunctions   rbf     1    0.000011  1.000000\n",
+       "8           None     SecondOrderPolynomial   sop     3    0.000012  1.000000\n",
+       "9           None      RadialBasisFunctions   rbf     3    0.000012  1.000000\n",
+       "10          None           GaussianProcess    gp     4    3.261869  0.999589\n",
+       "11          None           GaussianProcess    gp     2    3.538521  0.999587\n",
+       "12          None           GaussianProcess    gp     1    4.305552  0.999374\n",
+       "13          None           GaussianProcess    gp     0    3.983316  0.999353\n",
+       "14          None           GaussianProcess    gp     3    5.783584  0.998853\n",
+       "15          None  ConditionalNeuralProcess   cnp     4    5.949517  0.998637\n",
+       "16          None  ConditionalNeuralProcess   cnp     1    7.860966  0.997930\n",
+       "17          None  ConditionalNeuralProcess   cnp     0    8.033505  0.997472\n",
+       "18          None  ConditionalNeuralProcess   cnp     3    8.964479  0.997269\n",
+       "19          None  ConditionalNeuralProcess   cnp     2    9.422826  0.996942\n",
+       "20          None     SupportVectorMachines   svm     4   38.992346  0.941607\n",
+       "21          None     SupportVectorMachines   svm     2   48.600153  0.922211\n",
+       "22          None     SupportVectorMachines   svm     0   48.007590  0.910417\n",
+       "23          None                  LightGBM  lgbm     4   49.754522  0.903591\n",
+       "24          None                  LightGBM  lgbm     2   55.304834  0.898515\n",
+       "25          None     SupportVectorMachines   svm     1   56.470521  0.893394\n",
+       "26          None                  LightGBM  lgbm     1   56.882800  0.892733\n",
+       "27          None          GradientBoosting    gb     2   57.594502  0.891436\n",
+       "28          None          GradientBoosting    gb     4   55.219221  0.881246\n",
+       "29          None          GradientBoosting    gb     1   59.862824  0.880366\n",
+       "30          None                  LightGBM  lgbm     0   56.810989  0.873537\n",
+       "31          None                  LightGBM  lgbm     3   62.546230  0.867527\n",
+       "32          None          GradientBoosting    gb     3   62.738683  0.866212\n",
+       "33          None          GradientBoosting    gb     0   60.886451  0.853991\n",
+       "34          None     SupportVectorMachines   svm     3   67.218280  0.848524\n",
+       "35          None              RandomForest    rf     2   93.064467  0.711618\n",
+       "36          None              RandomForest    rf     1   93.979885  0.702829\n",
+       "37          None              RandomForest    rf     4   92.133046  0.670829\n",
+       "38          None              RandomForest    rf     0   92.470110  0.660873\n",
+       "39          None              RandomForest    rf     3  101.063447  0.651731"
       ]
      },
      "execution_count": 8,
@@ -529,12 +1277,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f733d145401f4685aae6c2658f22a379",
+       "model_id": "c23a2a6008dc4c3f89d7610dae2faa2e",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Initializing:   0%|          | 0/1 [00:00<?, ?it/s]"
+       "Optimising and cross-validating:   0%|          | 0/1 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -544,8 +1292,192 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time taken: 0.8288900852203369 seconds\n"
+      "Time taken: 6.434678077697754 seconds\n"
      ]
+    },
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "preprocessing",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "model",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "short",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "fold",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "rmse",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "r2",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "9c1f1cf0-ed46-4ff6-b3de-ea9630d6b90f",
+       "rows": [
+        [
+         "0",
+         "None",
+         "SupportVectorMachines",
+         "svm",
+         "2",
+         "8.720925214692816",
+         "0.9975364679011992"
+        ],
+        [
+         "1",
+         "None",
+         "SupportVectorMachines",
+         "svm",
+         "0",
+         "7.939373376335",
+         "0.9974821437923449"
+        ],
+        [
+         "2",
+         "None",
+         "SupportVectorMachines",
+         "svm",
+         "1",
+         "9.151468529211567",
+         "0.9972248836751838"
+        ],
+        [
+         "3",
+         "None",
+         "SupportVectorMachines",
+         "svm",
+         "3",
+         "9.114728846293456",
+         "0.9972036179170759"
+        ],
+        [
+         "4",
+         "None",
+         "SupportVectorMachines",
+         "svm",
+         "4",
+         "8.73105070887409",
+         "0.9971045752136319"
+        ]
+       ],
+       "shape": {
+        "columns": 6,
+        "rows": 5
+       }
+      },
+      "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>preprocessing</th>\n",
+       "      <th>model</th>\n",
+       "      <th>short</th>\n",
+       "      <th>fold</th>\n",
+       "      <th>rmse</th>\n",
+       "      <th>r2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>SupportVectorMachines</td>\n",
+       "      <td>svm</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8.720925</td>\n",
+       "      <td>0.997536</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>SupportVectorMachines</td>\n",
+       "      <td>svm</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.939373</td>\n",
+       "      <td>0.997482</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>SupportVectorMachines</td>\n",
+       "      <td>svm</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.151469</td>\n",
+       "      <td>0.997225</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>None</td>\n",
+       "      <td>SupportVectorMachines</td>\n",
+       "      <td>svm</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9.114729</td>\n",
+       "      <td>0.997204</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>None</td>\n",
+       "      <td>SupportVectorMachines</td>\n",
+       "      <td>svm</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8.731051</td>\n",
+       "      <td>0.997105</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  preprocessing                  model short  fold      rmse        r2\n",
+       "0          None  SupportVectorMachines   svm     2  8.720925  0.997536\n",
+       "1          None  SupportVectorMachines   svm     0  7.939373  0.997482\n",
+       "2          None  SupportVectorMachines   svm     1  9.151469  0.997225\n",
+       "3          None  SupportVectorMachines   svm     3  9.114729  0.997204\n",
+       "4          None  SupportVectorMachines   svm     4  8.731051  0.997105"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -555,10 +1487,11 @@
     "em_svm.setup(X, y, models=[\"svm\"], param_search=True, param_search_iters=50, \n",
     "         n_jobs=5, print_setup=False)\n",
     "em_svm.compare()\n",
-    "em_svm.summarise_cv()\n",
     "\n",
     "end = time.time()\n",
-    "print(f\"Time taken: {end - start} seconds\")"
+    "print(f\"Time taken: {end - start} seconds\")\n",
+    "\n",
+    "em_svm.summarise_cv()"
    ]
   },
   {
@@ -576,21 +1509,22 @@
     {
      "data": {
       "text/plain": [
-       "{'estimator__C': 2.7416066357879485,\n",
+       "{'estimator__C': 2.9745514805217894,\n",
        " 'estimator__cache_size': 200,\n",
-       " 'estimator__coef0': 0.28240475754651484,\n",
-       " 'estimator__degree': 3,\n",
-       " 'estimator__epsilon': 0.1023893458339658,\n",
+       " 'estimator__coef0': 0.6864724407544227,\n",
+       " 'estimator__degree': 4,\n",
+       " 'estimator__epsilon': 0.11043299726789946,\n",
        " 'estimator__gamma': 'auto',\n",
        " 'estimator__kernel': 'linear',\n",
        " 'estimator__max_iter': -1,\n",
        " 'estimator__normalise_y': True,\n",
        " 'estimator__shrinking': False,\n",
-       " 'estimator__tol': 0.000143419387906774,\n",
+       " 'estimator__tol': 0.0010043197053019159,\n",
        " 'estimator__verbose': False,\n",
-       " 'estimator': SupportVectorMachines(C=2.7416066357879485, coef0=0.28240475754651484,\n",
-       "                       epsilon=0.1023893458339658, gamma='auto', kernel='linear',\n",
-       "                       shrinking=False, tol=0.000143419387906774),\n",
+       " 'estimator': SupportVectorMachines(C=2.9745514805217894, coef0=0.6864724407544227, degree=4,\n",
+       "                       epsilon=0.11043299726789946, gamma='auto',\n",
+       "                       kernel='linear', shrinking=False,\n",
+       "                       tol=0.0010043197053019159),\n",
        " 'n_jobs': None}"
       ]
      },
@@ -612,7 +1546,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "autoemulate-_SyXUh_0-py3.11",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -626,7 +1560,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.10"
+   "version": "3.12.10"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/03_emulation_sensitivity.ipynb b/docs/tutorials/03_emulation_sensitivity.ipynb
index 8ce480880..418cf9848 100644
--- a/docs/tutorials/03_emulation_sensitivity.ipynb
+++ b/docs/tutorials/03_emulation_sensitivity.ipynb
@@ -38,16 +38,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/echalstrey/projects/autoemulate/autoemulate/compare.py:8: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
-      "  from tqdm.autonotebook import tqdm\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -155,7 +146,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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       "text/plain": [
        "<Figure size 1000x500 with 2 Axes>"
       ]
@@ -248,6 +239,128 @@
     },
     {
      "data": {
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       "text/html": [
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@@ -281,63 +394,63 @@
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-       "      <td>0.001732</td>\n",
-       "      <td>0.020078</td>\n",
-       "      <td>10.275990</td>\n",
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+       "      <td>33.612657</td>\n",
+       "      <td>0.061407</td>\n",
+       "      <td>0.001965</td>\n",
+       "      <td>0.020758</td>\n",
+       "      <td>9.026535</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1.406969</td>\n",
-       "      <td>0.213791</td>\n",
-       "      <td>906.325329</td>\n",
-       "      <td>0.001231</td>\n",
-       "      <td>40.485330</td>\n",
-       "      <td>0.064324</td>\n",
-       "      <td>0.001479</td>\n",
-       "      <td>0.024764</td>\n",
-       "      <td>9.135348</td>\n",
+       "      <td>0.530640</td>\n",
+       "      <td>0.198942</td>\n",
+       "      <td>844.526377</td>\n",
+       "      <td>0.001375</td>\n",
+       "      <td>35.830789</td>\n",
+       "      <td>0.057073</td>\n",
+       "      <td>0.002032</td>\n",
+       "      <td>0.021821</td>\n",
+       "      <td>10.850939</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>1.292323</td>\n",
-       "      <td>0.231481</td>\n",
-       "      <td>1014.847228</td>\n",
-       "      <td>0.001408</td>\n",
-       "      <td>33.337008</td>\n",
-       "      <td>0.062812</td>\n",
-       "      <td>0.001869</td>\n",
-       "      <td>0.029807</td>\n",
-       "      <td>9.132980</td>\n",
+       "      <td>1.361265</td>\n",
+       "      <td>0.164018</td>\n",
+       "      <td>1017.327348</td>\n",
+       "      <td>0.000555</td>\n",
+       "      <td>45.188371</td>\n",
+       "      <td>0.063796</td>\n",
+       "      <td>0.001745</td>\n",
+       "      <td>0.022860</td>\n",
+       "      <td>10.471797</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>1.050891</td>\n",
-       "      <td>0.172548</td>\n",
-       "      <td>1074.354559</td>\n",
-       "      <td>0.001466</td>\n",
-       "      <td>42.965200</td>\n",
-       "      <td>0.062412</td>\n",
-       "      <td>0.002033</td>\n",
-       "      <td>0.029017</td>\n",
-       "      <td>10.099286</td>\n",
+       "      <td>1.014375</td>\n",
+       "      <td>0.187115</td>\n",
+       "      <td>756.949710</td>\n",
+       "      <td>0.001044</td>\n",
+       "      <td>34.579278</td>\n",
+       "      <td>0.067588</td>\n",
+       "      <td>0.002007</td>\n",
+       "      <td>0.025781</td>\n",
+       "      <td>10.932655</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.589574</td>\n",
-       "      <td>0.198605</td>\n",
-       "      <td>979.667462</td>\n",
-       "      <td>0.000634</td>\n",
-       "      <td>36.079144</td>\n",
-       "      <td>0.059347</td>\n",
-       "      <td>0.001386</td>\n",
-       "      <td>0.023540</td>\n",
-       "      <td>10.925064</td>\n",
+       "      <td>1.488616</td>\n",
+       "      <td>0.198242</td>\n",
+       "      <td>811.629336</td>\n",
+       "      <td>0.000907</td>\n",
+       "      <td>35.372583</td>\n",
+       "      <td>0.061190</td>\n",
+       "      <td>0.001591</td>\n",
+       "      <td>0.025406</td>\n",
+       "      <td>10.974837</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -345,18 +458,18 @@
       ],
       "text/plain": [
        "          T        td          amp        dt          C         R         L  \\\n",
-       "0  1.352719  0.182340   769.926394  0.001268  34.671978  0.048799  0.001732   \n",
-       "1  1.406969  0.213791   906.325329  0.001231  40.485330  0.064324  0.001479   \n",
-       "2  1.292323  0.231481  1014.847228  0.001408  33.337008  0.062812  0.001869   \n",
-       "3  1.050891  0.172548  1074.354559  0.001466  42.965200  0.062412  0.002033   \n",
-       "4  0.589574  0.198605   979.667462  0.000634  36.079144  0.059347  0.001386   \n",
+       "0  1.078371  0.179061   803.263624  0.000759  33.612657  0.061407  0.001965   \n",
+       "1  0.530640  0.198942   844.526377  0.001375  35.830789  0.057073  0.002032   \n",
+       "2  1.361265  0.164018  1017.327348  0.000555  45.188371  0.063796  0.001745   \n",
+       "3  1.014375  0.187115   756.949710  0.001044  34.579278  0.067588  0.002007   \n",
+       "4  1.488616  0.198242   811.629336  0.000907  35.372583  0.061190  0.001591   \n",
        "\n",
        "        R_o        p_o  \n",
-       "0  0.020078  10.275990  \n",
-       "1  0.024764   9.135348  \n",
-       "2  0.029807   9.132980  \n",
-       "3  0.029017  10.099286  \n",
-       "4  0.023540  10.925064  "
+       "0  0.020758   9.026535  \n",
+       "1  0.021821  10.850939  \n",
+       "2  0.022860  10.471797  \n",
+       "3  0.025781  10.932655  \n",
+       "4  0.025406  10.974837  "
       ]
      },
      "execution_count": 6,
@@ -419,7 +532,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 60/60 [00:42<00:00,  1.42it/s]\n"
+      "100%|██████████| 60/60 [00:49<00:00,  1.22it/s]\n"
      ]
     }
    ],
@@ -472,7 +585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -519,11 +632,23 @@
        "      <td>StandardScaler</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>Scale output data (scale_output)</th>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Scaler output (scaler_output)</th>\n",
+       "      <td>StandardScaler</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>Do hyperparameter search (param_search)</th>\n",
        "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Reduce dimensionality (reduce_dim)</th>\n",
+       "      <th>Reduce input dimensionality (reduce_dim)</th>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Reduce output dimensionality (reduce_dim_output)</th>\n",
        "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -547,12 +672,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f8f51ad2edfd46f8b069f3d670c2c6d1",
+       "model_id": "1c643da7ad4f45cd840e2da820af7a0a",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Initializing:   0%|          | 0/4 [00:00<?, ?it/s]"
+       "Cross-validating:   0%|          | 0/4 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -568,11 +693,238 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
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+         "name": "preprocessing",
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+         "type": "string"
+        },
+        {
+         "name": "model",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "short",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "fold",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "rmse",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "r2",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "60596987-d3d3-4980-ab69-f313336f21ed",
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+         "GaussianProcess",
+         "gp",
+         "3",
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+         "0.9941243444516618"
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+         "gp",
+         "2",
+         "5.840839020911424",
+         "0.9934648216683767"
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+        [
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+         "gp",
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+         "10.820507578820049",
+         "0.991767302083432"
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+        [
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+         "0.9878733331030721"
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+        [
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+         "gp",
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+         "8.055319634924963",
+         "0.985911186602288"
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+        [
+         "6",
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+         "ConditionalNeuralProcess",
+         "cnp",
+         "0",
+         "25.517669241281244",
+         "0.9542143988571559"
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+        [
+         "7",
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+         "cnp",
+         "2",
+         "18.712580052681638",
+         "0.932922894774148"
+        ],
+        [
+         "8",
+         "None",
+         "SupportVectorMachines",
+         "svm",
+         "3",
+         "27.525518738360525",
+         "0.8971890987707661"
+        ],
+        [
+         "9",
+         "None",
+         "ConditionalNeuralProcess",
+         "cnp",
+         "1",
+         "24.127198710917757",
+         "0.8736071044109878"
+        ],
+        [
+         "10",
+         "None",
+         "ConditionalNeuralProcess",
+         "cnp",
+         "4",
+         "23.509928954572214",
+         "0.8713440932164491"
+        ],
+        [
+         "11",
+         "None",
+         "SupportVectorMachines",
+         "svm",
+         "2",
+         "29.993836486391274",
+         "0.8276659840892024"
+        ],
+        [
+         "12",
+         "None",
+         "SupportVectorMachines",
+         "svm",
+         "0",
+         "53.122913828524055",
+         "0.8015684660263778"
+        ],
+        [
+         "13",
+         "None",
+         "SupportVectorMachines",
+         "svm",
+         "4",
+         "30.260837234845212",
+         "0.7868482140430468"
+        ],
+        [
+         "14",
+         "None",
+         "SupportVectorMachines",
+         "svm",
+         "1",
+         "46.23907793623175",
+         "0.5357764252400976"
+        ],
+        [
+         "15",
+         "None",
+         "LightGBM",
+         "lgbm",
+         "0",
+         "120.19834442393768",
+         "-0.015883448874401962"
+        ],
+        [
+         "16",
+         "None",
+         "LightGBM",
+         "lgbm",
+         "3",
+         "92.21874477259263",
+         "-0.15400198177806335"
+        ],
+        [
+         "17",
+         "None",
+         "LightGBM",
+         "lgbm",
+         "1",
+         "76.13092568551063",
+         "-0.25843585099864264"
+        ],
+        [
+         "18",
+         "None",
+         "LightGBM",
+         "lgbm",
+         "4",
+         "77.04539158748952",
+         "-0.381721229375813"
+        ],
+        [
+         "19",
+         "None",
+         "LightGBM",
+         "lgbm",
+         "2",
+         "87.3291304128436",
+         "-0.46091598390041577"
+        ]
+       ],
+       "shape": {
+        "columns": 6,
+        "rows": 20
+       }
+      },
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -592,8 +944,10 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>preprocessing</th>\n",
        "      <th>model</th>\n",
        "      <th>short</th>\n",
+       "      <th>fold</th>\n",
        "      <th>rmse</th>\n",
        "      <th>r2</th>\n",
        "    </tr>\n",
@@ -601,45 +955,213 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>ConditionalNeuralProcess</td>\n",
+       "      <td>cnp</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4.560392</td>\n",
+       "      <td>0.997178</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
        "      <td>GaussianProcess</td>\n",
        "      <td>gp</td>\n",
-       "      <td>10.688256</td>\n",
-       "      <td>0.982580</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6.580276</td>\n",
+       "      <td>0.994124</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>GaussianProcess</td>\n",
+       "      <td>gp</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5.840839</td>\n",
+       "      <td>0.993465</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>None</td>\n",
+       "      <td>GaussianProcess</td>\n",
+       "      <td>gp</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.820508</td>\n",
+       "      <td>0.991767</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>None</td>\n",
+       "      <td>GaussianProcess</td>\n",
+       "      <td>gp</td>\n",
+       "      <td>4</td>\n",
+       "      <td>7.217839</td>\n",
+       "      <td>0.987873</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>None</td>\n",
+       "      <td>GaussianProcess</td>\n",
+       "      <td>gp</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8.055320</td>\n",
+       "      <td>0.985911</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>None</td>\n",
        "      <td>ConditionalNeuralProcess</td>\n",
        "      <td>cnp</td>\n",
-       "      <td>14.846341</td>\n",
-       "      <td>0.967064</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.517669</td>\n",
+       "      <td>0.954214</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
+       "      <th>7</th>\n",
+       "      <td>None</td>\n",
+       "      <td>ConditionalNeuralProcess</td>\n",
+       "      <td>cnp</td>\n",
+       "      <td>2</td>\n",
+       "      <td>18.712580</td>\n",
+       "      <td>0.932923</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>None</td>\n",
        "      <td>SupportVectorMachines</td>\n",
        "      <td>svm</td>\n",
-       "      <td>35.021374</td>\n",
-       "      <td>0.819262</td>\n",
+       "      <td>3</td>\n",
+       "      <td>27.525519</td>\n",
+       "      <td>0.897189</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
+       "      <th>9</th>\n",
+       "      <td>None</td>\n",
+       "      <td>ConditionalNeuralProcess</td>\n",
+       "      <td>cnp</td>\n",
+       "      <td>1</td>\n",
+       "      <td>24.127199</td>\n",
+       "      <td>0.873607</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>None</td>\n",
+       "      <td>ConditionalNeuralProcess</td>\n",
+       "      <td>cnp</td>\n",
+       "      <td>4</td>\n",
+       "      <td>23.509929</td>\n",
+       "      <td>0.871344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>None</td>\n",
+       "      <td>SupportVectorMachines</td>\n",
+       "      <td>svm</td>\n",
+       "      <td>2</td>\n",
+       "      <td>29.993836</td>\n",
+       "      <td>0.827666</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>None</td>\n",
+       "      <td>SupportVectorMachines</td>\n",
+       "      <td>svm</td>\n",
+       "      <td>0</td>\n",
+       "      <td>53.122914</td>\n",
+       "      <td>0.801568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>None</td>\n",
+       "      <td>SupportVectorMachines</td>\n",
+       "      <td>svm</td>\n",
+       "      <td>4</td>\n",
+       "      <td>30.260837</td>\n",
+       "      <td>0.786848</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>None</td>\n",
+       "      <td>SupportVectorMachines</td>\n",
+       "      <td>svm</td>\n",
+       "      <td>1</td>\n",
+       "      <td>46.239078</td>\n",
+       "      <td>0.535776</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>None</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>lgbm</td>\n",
+       "      <td>0</td>\n",
+       "      <td>120.198344</td>\n",
+       "      <td>-0.015883</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>None</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>lgbm</td>\n",
+       "      <td>3</td>\n",
+       "      <td>92.218745</td>\n",
+       "      <td>-0.154002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>None</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>lgbm</td>\n",
+       "      <td>1</td>\n",
+       "      <td>76.130926</td>\n",
+       "      <td>-0.258436</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>None</td>\n",
        "      <td>LightGBM</td>\n",
        "      <td>lgbm</td>\n",
-       "      <td>88.601359</td>\n",
-       "      <td>-0.095132</td>\n",
+       "      <td>4</td>\n",
+       "      <td>77.045392</td>\n",
+       "      <td>-0.381721</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>None</td>\n",
+       "      <td>LightGBM</td>\n",
+       "      <td>lgbm</td>\n",
+       "      <td>2</td>\n",
+       "      <td>87.329130</td>\n",
+       "      <td>-0.460916</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                      model short       rmse        r2\n",
-       "0           GaussianProcess    gp  10.688256  0.982580\n",
-       "1  ConditionalNeuralProcess   cnp  14.846341  0.967064\n",
-       "2     SupportVectorMachines   svm  35.021374  0.819262\n",
-       "3                  LightGBM  lgbm  88.601359 -0.095132"
+       "   preprocessing                     model short  fold        rmse        r2\n",
+       "0           None  ConditionalNeuralProcess   cnp     3    4.560392  0.997178\n",
+       "1           None           GaussianProcess    gp     3    6.580276  0.994124\n",
+       "2           None           GaussianProcess    gp     2    5.840839  0.993465\n",
+       "3           None           GaussianProcess    gp     0   10.820508  0.991767\n",
+       "4           None           GaussianProcess    gp     4    7.217839  0.987873\n",
+       "5           None           GaussianProcess    gp     1    8.055320  0.985911\n",
+       "6           None  ConditionalNeuralProcess   cnp     0   25.517669  0.954214\n",
+       "7           None  ConditionalNeuralProcess   cnp     2   18.712580  0.932923\n",
+       "8           None     SupportVectorMachines   svm     3   27.525519  0.897189\n",
+       "9           None  ConditionalNeuralProcess   cnp     1   24.127199  0.873607\n",
+       "10          None  ConditionalNeuralProcess   cnp     4   23.509929  0.871344\n",
+       "11          None     SupportVectorMachines   svm     2   29.993836  0.827666\n",
+       "12          None     SupportVectorMachines   svm     0   53.122914  0.801568\n",
+       "13          None     SupportVectorMachines   svm     4   30.260837  0.786848\n",
+       "14          None     SupportVectorMachines   svm     1   46.239078  0.535776\n",
+       "15          None                  LightGBM  lgbm     0  120.198344 -0.015883\n",
+       "16          None                  LightGBM  lgbm     3   92.218745 -0.154002\n",
+       "17          None                  LightGBM  lgbm     1   76.130926 -0.258436\n",
+       "18          None                  LightGBM  lgbm     4   77.045392 -0.381721\n",
+       "19          None                  LightGBM  lgbm     2   87.329130 -0.460916"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -650,17 +1172,106 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "model",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "short",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "preprocessing",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "rmse",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "r2",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "7969f571-f3bc-463b-af42-6e8141c3c3e1",
+       "rows": [
+        [
+         "0",
+         "GaussianProcess",
+         "gp",
+         "None",
+         "4.4152",
+         "0.9952"
+        ]
+       ],
+       "shape": {
+        "columns": 5,
+        "rows": 1
+       }
+      },
+      "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>model</th>\n",
+       "      <th>short</th>\n",
+       "      <th>preprocessing</th>\n",
+       "      <th>rmse</th>\n",
+       "      <th>r2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>GaussianProcess</td>\n",
+       "      <td>gp</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4.4152</td>\n",
+       "      <td>0.9952</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<Figure size 500x400 with 1 Axes>"
+       "             model short preprocessing    rmse      r2\n",
+       "0  GaussianProcess    gp          None  4.4152  0.9952"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -672,16 +1283,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "em.plot_eval(gp)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -703,7 +1326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -721,11 +1344,453 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "output",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "parameter",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "index",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "value",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "confidence",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "29fef540-6812-404f-b9e5-c19f0a22978a",
+       "rows": [
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        "<div>\n",
        "<style scoped>\n",
@@ -758,432 +1823,432 @@
        "      <td>y1</td>\n",
        "      <td>T</td>\n",
        "      <td>S1</td>\n",
-       "      <td>0.002293</td>\n",
-       "      <td>0.000221</td>\n",
+       "      <td>0.003793</td>\n",
+       "      <td>0.000379</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>y1</td>\n",
        "      <td>td</td>\n",
        "      <td>S1</td>\n",
-       "      <td>0.021337</td>\n",
-       "      <td>0.002069</td>\n",
+       "      <td>0.024640</td>\n",
+       "      <td>0.002530</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>y1</td>\n",
        "      <td>amp</td>\n",
        "      <td>S1</td>\n",
-       "      <td>0.887322</td>\n",
-       "      <td>0.057972</td>\n",
+       "      <td>0.893082</td>\n",
+       "      <td>0.061964</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>y1</td>\n",
        "      <td>dt</td>\n",
        "      <td>S1</td>\n",
-       "      <td>0.000399</td>\n",
-       "      <td>0.000054</td>\n",
+       "      <td>0.000372</td>\n",
+       "      <td>0.000059</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>y1</td>\n",
        "      <td>C</td>\n",
        "      <td>S1</td>\n",
-       "      <td>0.031236</td>\n",
-       "      <td>0.002990</td>\n",
+       "      <td>0.028867</td>\n",
+       "      <td>0.002382</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>y1</td>\n",
        "      <td>R</td>\n",
        "      <td>S1</td>\n",
-       "      <td>0.055153</td>\n",
-       "      <td>0.005757</td>\n",
+       "      <td>0.048604</td>\n",
+       "      <td>0.004261</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>y1</td>\n",
        "      <td>L</td>\n",
        "      <td>S1</td>\n",
-       "      <td>0.004877</td>\n",
-       "      <td>0.000482</td>\n",
+       "      <td>0.002155</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>y1</td>\n",
        "      <td>R_o</td>\n",
        "      <td>S1</td>\n",
-       "      <td>0.000192</td>\n",
-       "      <td>0.000028</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>y1</td>\n",
        "      <td>p_o</td>\n",
        "      <td>S1</td>\n",
-       "      <td>0.000287</td>\n",
-       "      <td>0.000045</td>\n",
+       "      <td>0.000237</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>y1</td>\n",
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        "      <td>ST</td>\n",
-       "      <td>0.002051</td>\n",
-       "      <td>0.003979</td>\n",
+       "      <td>0.003250</td>\n",
+       "      <td>0.005674</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>y1</td>\n",
        "      <td>td</td>\n",
        "      <td>ST</td>\n",
-       "      <td>0.020106</td>\n",
-       "      <td>0.012584</td>\n",
+       "      <td>0.023463</td>\n",
+       "      <td>0.013052</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>y1</td>\n",
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        "      <td>ST</td>\n",
-       "      <td>0.885391</td>\n",
-       "      <td>0.066731</td>\n",
+       "      <td>0.893304</td>\n",
+       "      <td>0.069595</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>y1</td>\n",
        "      <td>dt</td>\n",
        "      <td>ST</td>\n",
-       "      <td>0.000159</td>\n",
-       "      <td>0.001506</td>\n",
+       "      <td>-0.000073</td>\n",
+       "      <td>0.001720</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>y1</td>\n",
        "      <td>C</td>\n",
        "      <td>ST</td>\n",
-       "      <td>0.031362</td>\n",
-       "      <td>0.016085</td>\n",
+       "      <td>0.028134</td>\n",
+       "      <td>0.014233</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>y1</td>\n",
        "      <td>R</td>\n",
        "      <td>ST</td>\n",
-       "      <td>0.053039</td>\n",
-       "      <td>0.018636</td>\n",
+       "      <td>0.048360</td>\n",
+       "      <td>0.019052</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>y1</td>\n",
        "      <td>L</td>\n",
        "      <td>ST</td>\n",
-       "      <td>0.005064</td>\n",
-       "      <td>0.005822</td>\n",
+       "      <td>0.001506</td>\n",
+       "      <td>0.004328</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>y1</td>\n",
        "      <td>R_o</td>\n",
        "      <td>ST</td>\n",
-       "      <td>0.000279</td>\n",
-       "      <td>0.001047</td>\n",
+       "      <td>-0.000368</td>\n",
+       "      <td>0.000799</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>y1</td>\n",
        "      <td>p_o</td>\n",
        "      <td>ST</td>\n",
-       "      <td>-0.000860</td>\n",
-       "      <td>0.001442</td>\n",
+       "      <td>-0.002123</td>\n",
+       "      <td>0.001339</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>y1</td>\n",
        "      <td>(T, td)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>-0.000093</td>\n",
-       "      <td>0.005905</td>\n",
+       "      <td>-0.000055</td>\n",
+       "      <td>0.007540</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>y1</td>\n",
        "      <td>(T, amp)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>0.000361</td>\n",
-       "      <td>0.007489</td>\n",
+       "      <td>-0.000390</td>\n",
+       "      <td>0.010885</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>y1</td>\n",
        "      <td>(T, dt)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>-0.000230</td>\n",
-       "      <td>0.005763</td>\n",
+       "      <td>-0.000085</td>\n",
+       "      <td>0.007441</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>y1</td>\n",
        "      <td>(T, C)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>-0.000231</td>\n",
-       "      <td>0.005877</td>\n",
+       "      <td>-0.000229</td>\n",
+       "      <td>0.007275</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>y1</td>\n",
        "      <td>(T, R)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>0.000168</td>\n",
-       "      <td>0.006092</td>\n",
+       "      <td>-0.000636</td>\n",
+       "      <td>0.007819</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>y1</td>\n",
        "      <td>(T, L)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>-0.000246</td>\n",
-       "      <td>0.005735</td>\n",
+       "      <td>-0.000171</td>\n",
+       "      <td>0.007454</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>y1</td>\n",
        "      <td>(T, R_o)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>-0.000218</td>\n",
-       "      <td>0.005757</td>\n",
+       "      <td>-0.000157</td>\n",
+       "      <td>0.007444</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>y1</td>\n",
        "      <td>(T, p_o)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>-0.000256</td>\n",
-       "      <td>0.005760</td>\n",
+       "      <td>-0.000115</td>\n",
+       "      <td>0.007434</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>y1</td>\n",
        "      <td>(td, amp)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>0.001192</td>\n",
-       "      <td>0.024197</td>\n",
+       "      <td>0.000693</td>\n",
+       "      <td>0.025017</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>y1</td>\n",
        "      <td>(td, dt)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>0.000434</td>\n",
-       "      <td>0.018285</td>\n",
+       "      <td>0.000479</td>\n",
+       "      <td>0.019280</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>y1</td>\n",
        "      <td>(td, C)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>-0.000240</td>\n",
-       "      <td>0.018583</td>\n",
+       "      <td>0.000715</td>\n",
+       "      <td>0.019898</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>y1</td>\n",
        "      <td>(td, R)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>0.000896</td>\n",
-       "      <td>0.018750</td>\n",
+       "      <td>-0.000077</td>\n",
+       "      <td>0.019744</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>y1</td>\n",
        "      <td>(td, L)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>0.000589</td>\n",
-       "      <td>0.018296</td>\n",
+       "      <td>0.000469</td>\n",
+       "      <td>0.019261</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>y1</td>\n",
        "      <td>(td, R_o)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>0.000351</td>\n",
-       "      <td>0.018357</td>\n",
+       "      <td>0.000416</td>\n",
+       "      <td>0.019239</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>y1</td>\n",
        "      <td>(td, p_o)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>0.000683</td>\n",
-       "      <td>0.018327</td>\n",
+       "      <td>0.000400</td>\n",
+       "      <td>0.019213</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>y1</td>\n",
        "      <td>(amp, dt)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>0.000159</td>\n",
-       "      <td>0.079652</td>\n",
+       "      <td>0.000922</td>\n",
+       "      <td>0.073855</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>y1</td>\n",
        "      <td>(amp, C)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>0.000429</td>\n",
-       "      <td>0.080296</td>\n",
+       "      <td>0.000853</td>\n",
+       "      <td>0.073387</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>y1</td>\n",
        "      <td>(amp, R)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>0.001767</td>\n",
-       "      <td>0.079435</td>\n",
+       "      <td>0.001235</td>\n",
+       "      <td>0.075641</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>y1</td>\n",
        "      <td>(amp, L)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>-0.000205</td>\n",
-       "      <td>0.079993</td>\n",
+       "      <td>0.000967</td>\n",
+       "      <td>0.074229</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>y1</td>\n",
        "      <td>(amp, R_o)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>0.000071</td>\n",
-       "      <td>0.079976</td>\n",
+       "      <td>0.000954</td>\n",
+       "      <td>0.073460</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td>y1</td>\n",
        "      <td>(amp, p_o)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>0.000812</td>\n",
-       "      <td>0.080080</td>\n",
+       "      <td>0.002929</td>\n",
+       "      <td>0.073982</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>y1</td>\n",
        "      <td>(dt, C)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>0.000102</td>\n",
-       "      <td>0.002533</td>\n",
+       "      <td>0.000266</td>\n",
+       "      <td>0.002515</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>y1</td>\n",
        "      <td>(dt, R)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>-0.000186</td>\n",
-       "      <td>0.002711</td>\n",
+       "      <td>0.000259</td>\n",
+       "      <td>0.002566</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>y1</td>\n",
        "      <td>(dt, L)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>0.000116</td>\n",
-       "      <td>0.002614</td>\n",
+       "      <td>0.000181</td>\n",
+       "      <td>0.002595</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td>y1</td>\n",
        "      <td>(dt, R_o)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>0.000031</td>\n",
-       "      <td>0.002642</td>\n",
+       "      <td>0.000284</td>\n",
+       "      <td>0.002598</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td>y1</td>\n",
        "      <td>(dt, p_o)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>0.000084</td>\n",
-       "      <td>0.002632</td>\n",
+       "      <td>0.000279</td>\n",
+       "      <td>0.002592</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td>y1</td>\n",
        "      <td>(C, R)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>-0.002460</td>\n",
-       "      <td>0.022585</td>\n",
+       "      <td>0.000155</td>\n",
+       "      <td>0.019744</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td>y1</td>\n",
        "      <td>(C, L)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>-0.002040</td>\n",
-       "      <td>0.022001</td>\n",
+       "      <td>0.000345</td>\n",
+       "      <td>0.018837</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td>y1</td>\n",
        "      <td>(C, R_o)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>-0.002158</td>\n",
-       "      <td>0.022078</td>\n",
+       "      <td>0.000172</td>\n",
+       "      <td>0.018937</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td>y1</td>\n",
        "      <td>(C, p_o)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>-0.001875</td>\n",
-       "      <td>0.022046</td>\n",
+       "      <td>0.000274</td>\n",
+       "      <td>0.018967</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
        "      <td>y1</td>\n",
        "      <td>(R, L)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>0.000920</td>\n",
-       "      <td>0.025986</td>\n",
+       "      <td>-0.000257</td>\n",
+       "      <td>0.028458</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
        "      <td>y1</td>\n",
        "      <td>(R, R_o)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>0.000952</td>\n",
-       "      <td>0.025776</td>\n",
+       "      <td>-0.000203</td>\n",
+       "      <td>0.028525</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32</th>\n",
        "      <td>y1</td>\n",
        "      <td>(R, p_o)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>0.000971</td>\n",
-       "      <td>0.025651</td>\n",
+       "      <td>-0.000377</td>\n",
+       "      <td>0.028469</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33</th>\n",
        "      <td>y1</td>\n",
        "      <td>(L, R_o)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>-0.000583</td>\n",
-       "      <td>0.008832</td>\n",
+       "      <td>0.000003</td>\n",
+       "      <td>0.005687</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34</th>\n",
        "      <td>y1</td>\n",
        "      <td>(L, p_o)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>-0.000519</td>\n",
-       "      <td>0.008797</td>\n",
+       "      <td>-0.000053</td>\n",
+       "      <td>0.005682</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>35</th>\n",
        "      <td>y1</td>\n",
        "      <td>(R_o, p_o)</td>\n",
        "      <td>S2</td>\n",
-       "      <td>-0.000605</td>\n",
-       "      <td>0.001657</td>\n",
+       "      <td>0.000371</td>\n",
+       "      <td>0.001240</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1191,63 +2256,63 @@
       ],
       "text/plain": [
        "   output   parameter index     value  confidence\n",
-       "0      y1           T    S1  0.002293    0.000221\n",
-       "1      y1          td    S1  0.021337    0.002069\n",
-       "2      y1         amp    S1  0.887322    0.057972\n",
-       "3      y1          dt    S1  0.000399    0.000054\n",
-       "4      y1           C    S1  0.031236    0.002990\n",
-       "5      y1           R    S1  0.055153    0.005757\n",
-       "6      y1           L    S1  0.004877    0.000482\n",
-       "7      y1         R_o    S1  0.000192    0.000028\n",
-       "8      y1         p_o    S1  0.000287    0.000045\n",
-       "0      y1           T    ST  0.002051    0.003979\n",
-       "1      y1          td    ST  0.020106    0.012584\n",
-       "2      y1         amp    ST  0.885391    0.066731\n",
-       "3      y1          dt    ST  0.000159    0.001506\n",
-       "4      y1           C    ST  0.031362    0.016085\n",
-       "5      y1           R    ST  0.053039    0.018636\n",
-       "6      y1           L    ST  0.005064    0.005822\n",
-       "7      y1         R_o    ST  0.000279    0.001047\n",
-       "8      y1         p_o    ST -0.000860    0.001442\n",
-       "0      y1     (T, td)    S2 -0.000093    0.005905\n",
-       "1      y1    (T, amp)    S2  0.000361    0.007489\n",
-       "2      y1     (T, dt)    S2 -0.000230    0.005763\n",
-       "3      y1      (T, C)    S2 -0.000231    0.005877\n",
-       "4      y1      (T, R)    S2  0.000168    0.006092\n",
-       "5      y1      (T, L)    S2 -0.000246    0.005735\n",
-       "6      y1    (T, R_o)    S2 -0.000218    0.005757\n",
-       "7      y1    (T, p_o)    S2 -0.000256    0.005760\n",
-       "8      y1   (td, amp)    S2  0.001192    0.024197\n",
-       "9      y1    (td, dt)    S2  0.000434    0.018285\n",
-       "10     y1     (td, C)    S2 -0.000240    0.018583\n",
-       "11     y1     (td, R)    S2  0.000896    0.018750\n",
-       "12     y1     (td, L)    S2  0.000589    0.018296\n",
-       "13     y1   (td, R_o)    S2  0.000351    0.018357\n",
-       "14     y1   (td, p_o)    S2  0.000683    0.018327\n",
-       "15     y1   (amp, dt)    S2  0.000159    0.079652\n",
-       "16     y1    (amp, C)    S2  0.000429    0.080296\n",
-       "17     y1    (amp, R)    S2  0.001767    0.079435\n",
-       "18     y1    (amp, L)    S2 -0.000205    0.079993\n",
-       "19     y1  (amp, R_o)    S2  0.000071    0.079976\n",
-       "20     y1  (amp, p_o)    S2  0.000812    0.080080\n",
-       "21     y1     (dt, C)    S2  0.000102    0.002533\n",
-       "22     y1     (dt, R)    S2 -0.000186    0.002711\n",
-       "23     y1     (dt, L)    S2  0.000116    0.002614\n",
-       "24     y1   (dt, R_o)    S2  0.000031    0.002642\n",
-       "25     y1   (dt, p_o)    S2  0.000084    0.002632\n",
-       "26     y1      (C, R)    S2 -0.002460    0.022585\n",
-       "27     y1      (C, L)    S2 -0.002040    0.022001\n",
-       "28     y1    (C, R_o)    S2 -0.002158    0.022078\n",
-       "29     y1    (C, p_o)    S2 -0.001875    0.022046\n",
-       "30     y1      (R, L)    S2  0.000920    0.025986\n",
-       "31     y1    (R, R_o)    S2  0.000952    0.025776\n",
-       "32     y1    (R, p_o)    S2  0.000971    0.025651\n",
-       "33     y1    (L, R_o)    S2 -0.000583    0.008832\n",
-       "34     y1    (L, p_o)    S2 -0.000519    0.008797\n",
-       "35     y1  (R_o, p_o)    S2 -0.000605    0.001657"
+       "0      y1           T    S1  0.003793    0.000379\n",
+       "1      y1          td    S1  0.024640    0.002530\n",
+       "2      y1         amp    S1  0.893082    0.061964\n",
+       "3      y1          dt    S1  0.000372    0.000059\n",
+       "4      y1           C    S1  0.028867    0.002382\n",
+       "5      y1           R    S1  0.048604    0.004261\n",
+       "6      y1           L    S1  0.002155    0.000243\n",
+       "7      y1         R_o    S1  0.000089    0.000014\n",
+       "8      y1         p_o    S1  0.000237    0.000029\n",
+       "0      y1           T    ST  0.003250    0.005674\n",
+       "1      y1          td    ST  0.023463    0.013052\n",
+       "2      y1         amp    ST  0.893304    0.069595\n",
+       "3      y1          dt    ST -0.000073    0.001720\n",
+       "4      y1           C    ST  0.028134    0.014233\n",
+       "5      y1           R    ST  0.048360    0.019052\n",
+       "6      y1           L    ST  0.001506    0.004328\n",
+       "7      y1         R_o    ST -0.000368    0.000799\n",
+       "8      y1         p_o    ST -0.002123    0.001339\n",
+       "0      y1     (T, td)    S2 -0.000055    0.007540\n",
+       "1      y1    (T, amp)    S2 -0.000390    0.010885\n",
+       "2      y1     (T, dt)    S2 -0.000085    0.007441\n",
+       "3      y1      (T, C)    S2 -0.000229    0.007275\n",
+       "4      y1      (T, R)    S2 -0.000636    0.007819\n",
+       "5      y1      (T, L)    S2 -0.000171    0.007454\n",
+       "6      y1    (T, R_o)    S2 -0.000157    0.007444\n",
+       "7      y1    (T, p_o)    S2 -0.000115    0.007434\n",
+       "8      y1   (td, amp)    S2  0.000693    0.025017\n",
+       "9      y1    (td, dt)    S2  0.000479    0.019280\n",
+       "10     y1     (td, C)    S2  0.000715    0.019898\n",
+       "11     y1     (td, R)    S2 -0.000077    0.019744\n",
+       "12     y1     (td, L)    S2  0.000469    0.019261\n",
+       "13     y1   (td, R_o)    S2  0.000416    0.019239\n",
+       "14     y1   (td, p_o)    S2  0.000400    0.019213\n",
+       "15     y1   (amp, dt)    S2  0.000922    0.073855\n",
+       "16     y1    (amp, C)    S2  0.000853    0.073387\n",
+       "17     y1    (amp, R)    S2  0.001235    0.075641\n",
+       "18     y1    (amp, L)    S2  0.000967    0.074229\n",
+       "19     y1  (amp, R_o)    S2  0.000954    0.073460\n",
+       "20     y1  (amp, p_o)    S2  0.002929    0.073982\n",
+       "21     y1     (dt, C)    S2  0.000266    0.002515\n",
+       "22     y1     (dt, R)    S2  0.000259    0.002566\n",
+       "23     y1     (dt, L)    S2  0.000181    0.002595\n",
+       "24     y1   (dt, R_o)    S2  0.000284    0.002598\n",
+       "25     y1   (dt, p_o)    S2  0.000279    0.002592\n",
+       "26     y1      (C, R)    S2  0.000155    0.019744\n",
+       "27     y1      (C, L)    S2  0.000345    0.018837\n",
+       "28     y1    (C, R_o)    S2  0.000172    0.018937\n",
+       "29     y1    (C, p_o)    S2  0.000274    0.018967\n",
+       "30     y1      (R, L)    S2 -0.000257    0.028458\n",
+       "31     y1    (R, R_o)    S2 -0.000203    0.028525\n",
+       "32     y1    (R, p_o)    S2 -0.000377    0.028469\n",
+       "33     y1    (L, R_o)    S2  0.000003    0.005687\n",
+       "34     y1    (L, p_o)    S2 -0.000053    0.005682\n",
+       "35     y1  (R_o, p_o)    S2  0.000371    0.001240"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1259,7 +2324,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "env_ae",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -1273,7 +2338,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.9"
+   "version": "3.12.10"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/04_active_learning.ipynb b/docs/tutorials/04_active_learning.ipynb
index 4a0fe5f9d..aa5986401 100644
--- a/docs/tutorials/04_active_learning.ipynb
+++ b/docs/tutorials/04_active_learning.ipynb
@@ -62,13 +62,17 @@
     "import torch, numpy as np, matplotlib.pyplot as plt, pandas as pd\n",
     "from tqdm import tqdm\n",
     "from typing import List, Dict, Tuple, Iterable\n",
+    "import warnings\n",
+    "\n",
     "\n",
     "# Import core classes from the source code.\n",
     "from autoemulate.experimental.learners import Simulator, stream\n",
     "from autoemulate.simulations.projectile import simulate_projectile_multioutput\n",
-    "from autoemulate.experimental.emulators.gaussian_process.exact import GaussianProcessExact, constant_mean, rbf\n",
+    "from autoemulate.experimental.emulators.gaussian_process.exact import GaussianProcessExact\n",
     "from autoemulate.experimental_design import LatinHypercube\n",
-    "from autoemulate.experimental.types import GaussianLike"
+    "from autoemulate.experimental.types import GaussianLike\n",
+    "\n",
+    "warnings.filterwarnings('ignore')"
    ]
   },
   {
@@ -168,10 +172,10 @@
     {
      "data": {
       "text/plain": [
-       "tensor([[11.2380],\n",
-       "        [45.6776],\n",
-       "        [12.9354],\n",
-       "        [34.3164]])"
+       "tensor([[ 3.9254],\n",
+       "        [35.5669],\n",
+       "        [17.8658],\n",
+       "        [40.4680]])"
       ]
      },
      "execution_count": 4,
@@ -193,10 +197,10 @@
     {
      "data": {
       "text/plain": [
-       "tensor([[-0.9708],\n",
-       "        [ 0.9923],\n",
-       "        [ 0.3607],\n",
-       "        [ 0.2388]])"
+       "tensor([[-0.7060],\n",
+       "        [-0.8465],\n",
+       "        [-0.8326],\n",
+       "        [ 0.3642]])"
       ]
      },
      "execution_count": 5,
@@ -216,7 +220,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -260,7 +264,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -327,7 +331,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Random: 100%|██████████| 500/500 [00:10<00:00, 47.32it/s, mse=0.0836, r2=0.821, rate=0.188, logdet=-8.76, trace=0.000157, max_eigval=0.000157, n_queries=94]\n"
+      "Random: 100%|██████████| 500/500 [00:10<00:00, 49.03it/s, mse=0.0782, r2=0.857, rate=0.218, logdet=-10, trace=4.34e-5, max_eigval=4.34e-5, n_queries=109]    \n"
      ]
     }
    ],
@@ -412,7 +416,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -422,7 +426,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -432,7 +436,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -442,7 +446,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -452,7 +456,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -462,7 +466,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -472,7 +476,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -521,12 +525,12 @@
     {
      "data": {
       "text/plain": [
-       "{'mse_per_query': 0.0008898865669331652,\n",
-       " 'r2_per_query': 0.008737316791047441,\n",
-       " 'trace_per_query': 1.6689300537109375e-06,\n",
-       " 'logdet_per_query': -0.09319183674264461,\n",
-       " 'max_eigval_per_query': 1.6689300537109375e-06,\n",
-       " 'auc_mse': 18.378957748413086}"
+       "{'mse_per_query': 0.0007177938561920726,\n",
+       " 'r2_per_query': 0.00786549126336334,\n",
+       " 'trace_per_query': 3.980934073071961e-07,\n",
+       " 'logdet_per_query': -0.09215808133466528,\n",
+       " 'max_eigval_per_query': 3.980934073071961e-07,\n",
+       " 'auc_mse': 21.256839752197266}"
       ]
      },
      "execution_count": 11,
@@ -855,13 +859,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Random:  97%|█████████▋| 484/500 [00:11<00:00, 34.55it/s, mse=0.0898, r2=0.82, rate=0.238, logdet=-9.17, trace=0.000104, max_eigval=0.000104, n_queries=115] /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages/gpytorch/distributions/multivariate_normal.py:376: NumericalWarning: Negative variance values detected. This is likely due to numerical instabilities. Rounding negative variances up to 1e-06.\n",
-      "  warnings.warn(\n",
-      "Random: 100%|██████████| 500/500 [00:11<00:00, 42.41it/s, mse=0.0882, r2=0.822, rate=0.234, logdet=-9.71, trace=6.06e-5, max_eigval=6.06e-5, n_queries=117] \n",
-      "Adaptive_Distance: 100%|██████████| 500/500 [00:11<00:00, 43.98it/s, mse=0.0718, r2=0.846, rate=0.238, logdet=-10.1, trace=3.91e-5, max_eigval=3.91e-5, n_queries=119, score=0.0898, threshold=0.934, error_prop=-0.0115, error_int=-0.0934, error_deriv=-0.000479] \n",
-      "Adaptive_A_Optimal: 100%|██████████| 500/500 [00:11<00:00, 42.52it/s, mse=0.108, r2=0.772, rate=0.254, logdet=-10.1, trace=4.01e-5, max_eigval=4.01e-5, n_queries=127, score=4.01e-5, threshold=0.347, error_prop=0.00451, error_int=0.0561, error_deriv=-0.000511]     \n",
-      "Adaptive_D_Optimal: 100%|██████████| 500/500 [00:11<00:00, 43.57it/s, mse=0.0639, r2=0.865, rate=0.248, logdet=-10.1, trace=4.1e-5, max_eigval=4.1e-5, n_queries=124, score=-10.1, threshold=0, error_prop=-0.0015, error_int=0.00767, error_deriv=-0.000499]         \n",
-      "Adaptive_E_Optimal: 100%|██████████| 500/500 [00:11<00:00, 41.77it/s, mse=0.115, r2=0.773, rate=0.256, logdet=-10.1, trace=4.2e-5, max_eigval=4.2e-5, n_queries=128, score=4.2e-5, threshold=0, error_prop=0.00451, error_int=-0.023, error_deriv=0.0015]               \n"
+      "Random: 100%|██████████| 500/500 [00:11<00:00, 43.43it/s, mse=0.0882, r2=0.822, rate=0.234, logdet=-9.71, trace=6.06e-5, max_eigval=6.06e-5, n_queries=117]  \n",
+      "Adaptive_Distance: 100%|██████████| 500/500 [00:11<00:00, 44.68it/s, mse=0.0718, r2=0.846, rate=0.238, logdet=-10.1, trace=3.91e-5, max_eigval=3.91e-5, n_queries=119, score=0.0898, threshold=0.934, error_prop=-0.0115, error_int=-0.0934, error_deriv=-0.000479] \n",
+      "Adaptive_A_Optimal: 100%|██████████| 500/500 [00:12<00:00, 40.86it/s, mse=0.108, r2=0.772, rate=0.254, logdet=-10.1, trace=4.01e-5, max_eigval=4.01e-5, n_queries=127, score=4.01e-5, threshold=0.347, error_prop=0.00451, error_int=0.0561, error_deriv=-0.000511]     \n",
+      "Adaptive_D_Optimal: 100%|██████████| 500/500 [00:11<00:00, 44.13it/s, mse=0.0639, r2=0.865, rate=0.248, logdet=-10.1, trace=4.1e-5, max_eigval=4.1e-5, n_queries=124, score=-10.1, threshold=0, error_prop=-0.0015, error_int=0.00767, error_deriv=-0.000499]         \n",
+      "Adaptive_E_Optimal: 100%|██████████| 500/500 [00:12<00:00, 40.94it/s, mse=0.115, r2=0.773, rate=0.256, logdet=-10.1, trace=4.2e-5, max_eigval=4.2e-5, n_queries=128, score=4.2e-5, threshold=0, error_prop=0.00451, error_int=-0.023, error_deriv=0.0015]               \n"
      ]
     },
     {
@@ -875,11 +877,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Random: 100%|██████████| 500/500 [00:11<00:00, 42.81it/s, mse=0.0745, r2=0.853, rate=0.248, logdet=-10.3, trace=3.34e-5, max_eigval=3.34e-5, n_queries=124]  \n",
-      "Adaptive_Distance: 100%|██████████| 500/500 [00:11<00:00, 44.62it/s, mse=0.0612, r2=0.876, rate=0.238, logdet=-10.2, trace=3.86e-5, max_eigval=3.86e-5, n_queries=119, score=0.197, threshold=0.934, error_prop=-0.0115, error_int=-0.0934, error_deriv=-0.000479]  \n",
-      "Adaptive_A_Optimal: 100%|██████████| 500/500 [00:11<00:00, 42.86it/s, mse=0.0506, r2=0.895, rate=0.248, logdet=-10.3, trace=3.34e-5, max_eigval=3.34e-5, n_queries=124, score=3.34e-5, threshold=0.798, error_prop=-0.0015, error_int=0.00767, error_deriv=-0.000499]   \n",
-      "Adaptive_D_Optimal: 100%|██████████| 500/500 [00:12<00:00, 40.61it/s, mse=0.0764, r2=0.834, rate=0.268, logdet=-11.3, trace=1.24e-5, max_eigval=1.24e-5, n_queries=134, score=-11.3, threshold=0, error_prop=0.0185, error_int=0.21, error_deriv=-0.000539]            \n",
-      "Adaptive_E_Optimal: 100%|██████████| 500/500 [00:11<00:00, 42.78it/s, mse=0.0669, r2=0.869, rate=0.25, logdet=-9.7, trace=6.1e-5, max_eigval=6.1e-5, n_queries=125, score=6.1e-5, threshold=0, error_prop=-0.0015, error_int=-0.0714, error_deriv=0.00151]               \n"
+      "Random: 100%|██████████| 500/500 [00:10<00:00, 45.72it/s, mse=0.0745, r2=0.853, rate=0.248, logdet=-10.3, trace=3.34e-5, max_eigval=3.34e-5, n_queries=124]  \n",
+      "Adaptive_Distance: 100%|██████████| 500/500 [00:11<00:00, 43.72it/s, mse=0.0612, r2=0.876, rate=0.238, logdet=-10.2, trace=3.86e-5, max_eigval=3.86e-5, n_queries=119, score=0.197, threshold=0.934, error_prop=-0.0115, error_int=-0.0934, error_deriv=-0.000479]  \n",
+      "Adaptive_A_Optimal: 100%|██████████| 500/500 [00:12<00:00, 40.97it/s, mse=0.0506, r2=0.895, rate=0.248, logdet=-10.3, trace=3.34e-5, max_eigval=3.34e-5, n_queries=124, score=3.34e-5, threshold=0.798, error_prop=-0.0015, error_int=0.00767, error_deriv=-0.000499]   \n",
+      "Adaptive_D_Optimal: 100%|██████████| 500/500 [00:13<00:00, 37.60it/s, mse=0.0764, r2=0.834, rate=0.268, logdet=-11.3, trace=1.24e-5, max_eigval=1.24e-5, n_queries=134, score=-11.3, threshold=0, error_prop=0.0185, error_int=0.21, error_deriv=-0.000539]            \n",
+      "Adaptive_E_Optimal: 100%|██████████| 500/500 [00:13<00:00, 37.07it/s, mse=0.0669, r2=0.869, rate=0.25, logdet=-9.7, trace=6.1e-5, max_eigval=6.1e-5, n_queries=125, score=6.1e-5, threshold=0, error_prop=-0.0015, error_int=-0.0714, error_deriv=0.00151]               \n"
      ]
     }
    ],
@@ -970,7 +972,7 @@
         }
        ],
        "conversionMethod": "pd.DataFrame",
-       "ref": "227cb37e-7cbc-45bb-a632-45ce125ca8eb",
+       "ref": "7fabf8e1-84b5-42fc-a0d2-13a1cc2e977a",
        "rows": [
         [
          "Adaptive_D_Optimal",
@@ -1404,11 +1406,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Random: 100%|██████████| 500/500 [00:16<00:00, 30.36it/s, mse=2.09e+7, r2=-0.0808, rate=0.254, logdet=14.3, trace=2.52e+3, max_eigval=1.26e+3, n_queries=127]\n",
-      "Adaptive_Distance: 100%|██████████| 500/500 [00:14<00:00, 34.76it/s, mse=7.88e+6, r2=0.345, rate=0.218, logdet=12.4, trace=1.3e+3, max_eigval=1.08e+3, n_queries=109, score=4.32, threshold=12.9, error_prop=-0.0316, error_int=-0.296, error_deriv=-0.000439]    \n",
-      "Adaptive_A_Optimal: 100%|██████████| 500/500 [00:11<00:00, 43.73it/s, mse=1.71e+7, r2=0.348, rate=0.12, logdet=10.9, trace=475, max_eigval=301, n_queries=60, score=475, threshold=731, error_prop=-0.13, error_int=-1.29, error_deriv=-0.000241]       \n",
-      "Adaptive_D_Optimal: 100%|██████████| 500/500 [00:30<00:00, 16.32it/s, mse=7.37e+6, r2=0.515, rate=0.322, logdet=10.7, trace=444, max_eigval=287, n_queries=161, score=10.7, threshold=55.8, error_prop=0.0726, error_int=0.756, error_deriv=-0.000648]          \n",
-      "Adaptive_E_Optimal: 100%|██████████| 500/500 [00:34<00:00, 14.47it/s, mse=8.45e+6, r2=0.728, rate=0.326, logdet=10.2, trace=363, max_eigval=254, n_queries=163, score=254, threshold=20.3, error_prop=0.0746, error_int=0.685, error_deriv=0.00136]      \n"
+      "Random: 100%|██████████| 500/500 [00:18<00:00, 27.77it/s, mse=2.09e+7, r2=-0.0808, rate=0.254, logdet=14.3, trace=2.52e+3, max_eigval=1.26e+3, n_queries=127]\n",
+      "Adaptive_Distance: 100%|██████████| 500/500 [00:14<00:00, 35.11it/s, mse=7.88e+6, r2=0.345, rate=0.218, logdet=12.4, trace=1.3e+3, max_eigval=1.08e+3, n_queries=109, score=4.32, threshold=12.9, error_prop=-0.0316, error_int=-0.296, error_deriv=-0.000439]    \n",
+      "Adaptive_A_Optimal: 100%|██████████| 500/500 [00:07<00:00, 67.21it/s, mse=1.71e+7, r2=0.348, rate=0.12, logdet=10.9, trace=475, max_eigval=301, n_queries=60, score=475, threshold=731, error_prop=-0.13, error_int=-1.29, error_deriv=-0.000241]       \n",
+      "Adaptive_D_Optimal: 100%|██████████| 500/500 [00:21<00:00, 23.74it/s, mse=7.37e+6, r2=0.515, rate=0.322, logdet=10.7, trace=444, max_eigval=287, n_queries=161, score=10.7, threshold=55.8, error_prop=0.0726, error_int=0.756, error_deriv=-0.000648]          \n",
+      "Adaptive_E_Optimal: 100%|██████████| 500/500 [00:22<00:00, 22.03it/s, mse=8.45e+6, r2=0.728, rate=0.326, logdet=10.2, trace=363, max_eigval=254, n_queries=163, score=254, threshold=20.3, error_prop=0.0746, error_int=0.685, error_deriv=0.00136]      \n"
      ]
     },
     {
@@ -1422,11 +1424,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Random: 100%|██████████| 500/500 [00:21<00:00, 22.83it/s, mse=5.73e+7, r2=0.262, rate=0.24, logdet=12, trace=1.33e+3, max_eigval=1.19e+3, n_queries=120]   \n",
-      "Adaptive_Distance: 100%|██████████| 500/500 [00:26<00:00, 19.21it/s, mse=4.58e+7, r2=-0.105, rate=0.262, logdet=14.3, trace=2.58e+3, max_eigval=1.29e+3, n_queries=131, score=5.68, threshold=0.0991, error_prop=0.0105, error_int=0.0377, error_deriv=0.00148] \n",
-      "Adaptive_A_Optimal: 100%|██████████| 500/500 [00:12<00:00, 39.19it/s, mse=3.27e+7, r2=0.585, rate=0.138, logdet=10.1, trace=312, max_eigval=174, n_queries=69, score=312, threshold=299, error_prop=-0.114, error_int=-1.18, error_deriv=-0.000274]     \n",
-      "Adaptive_D_Optimal: 100%|██████████| 500/500 [00:34<00:00, 14.63it/s, mse=1.23e+7, r2=0.339, rate=0.324, logdet=13, trace=1.88e+3, max_eigval=1.61e+3, n_queries=162, score=13, threshold=56.7, error_prop=0.0746, error_int=0.776, error_deriv=-0.000652]      \n",
-      "Adaptive_E_Optimal: 100%|██████████| 500/500 [00:33<00:00, 14.85it/s, mse=1.26e+7, r2=0.355, rate=0.326, logdet=11.9, trace=834, max_eigval=571, n_queries=163, score=571, threshold=20.3, error_prop=0.0746, error_int=0.685, error_deriv=0.00136]                \n"
+      "Random: 100%|██████████| 500/500 [00:15<00:00, 33.29it/s, mse=5.73e+7, r2=0.262, rate=0.24, logdet=12, trace=1.33e+3, max_eigval=1.19e+3, n_queries=120]   \n",
+      "Adaptive_Distance: 100%|██████████| 500/500 [00:16<00:00, 30.90it/s, mse=4.58e+7, r2=-0.105, rate=0.262, logdet=14.3, trace=2.58e+3, max_eigval=1.29e+3, n_queries=131, score=5.68, threshold=0.0991, error_prop=0.0105, error_int=0.0377, error_deriv=0.00148] \n",
+      "Adaptive_A_Optimal: 100%|██████████| 500/500 [00:08<00:00, 62.35it/s, mse=3.27e+7, r2=0.585, rate=0.138, logdet=10.1, trace=312, max_eigval=174, n_queries=69, score=312, threshold=299, error_prop=-0.114, error_int=-1.18, error_deriv=-0.000274]     \n",
+      "Adaptive_D_Optimal: 100%|██████████| 500/500 [00:21<00:00, 23.79it/s, mse=1.23e+7, r2=0.339, rate=0.324, logdet=13, trace=1.88e+3, max_eigval=1.61e+3, n_queries=162, score=13, threshold=56.7, error_prop=0.0746, error_int=0.776, error_deriv=-0.000652]      \n",
+      "Adaptive_E_Optimal: 100%|██████████| 500/500 [00:21<00:00, 23.79it/s, mse=1.26e+7, r2=0.355, rate=0.326, logdet=11.9, trace=834, max_eigval=571, n_queries=163, score=571, threshold=20.3, error_prop=0.0746, error_int=0.685, error_deriv=0.00136]                \n"
      ]
     }
    ],
@@ -1517,7 +1519,7 @@
         }
        ],
        "conversionMethod": "pd.DataFrame",
-       "ref": "7f79891d-20dd-4cdd-b873-f2bbd479df22",
+       "ref": "16debe39-f697-4d3a-900a-69f88e09ee17",
        "rows": [
         [
          "Adaptive_D_Optimal",
@@ -1908,13 +1910,6 @@
    "source": [
     "plot_metrics(metrics)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/docs/tutorials/07_AE_workflow.ipynb b/docs/tutorials/07_AE_workflow.ipynb
index e515b1d35..9b7a00080 100644
--- a/docs/tutorials/07_AE_workflow.ipynb
+++ b/docs/tutorials/07_AE_workflow.ipynb
@@ -35,13 +35,59 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "tags": [
      "remove_output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting git+https://github.com/alan-turing-institute/ModularCirc.git@dev\n",
+      "  Cloning https://github.com/alan-turing-institute/ModularCirc.git (to revision dev) to /private/var/folders/gk/g0jp30017pjgv4tqtm955ddr0000gr/T/pip-req-build-3oryeh9w\n",
+      "  Running command git clone --filter=blob:none --quiet https://github.com/alan-turing-institute/ModularCirc.git /private/var/folders/gk/g0jp30017pjgv4tqtm955ddr0000gr/T/pip-req-build-3oryeh9w\n",
+      "  Resolved https://github.com/alan-turing-institute/ModularCirc.git to commit 3c71c399de8e495e795d71c876fca22d21dbac12\n",
+      "  Installing build dependencies ... \u001b[?25ldone\n",
+      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n",
+      "\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n",
+      "\u001b[?25hRequirement already satisfied: matplotlib in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from ModularCirc==0.2.0) (3.10.1)\n",
+      "Requirement already satisfied: numba in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from ModularCirc==0.2.0) (0.61.2)\n",
+      "Requirement already satisfied: numpy in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from ModularCirc==0.2.0) (1.26.4)\n",
+      "Requirement already satisfied: pandas in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from ModularCirc==0.2.0) (2.2.3)\n",
+      "Requirement already satisfied: scipy in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from ModularCirc==0.2.0) (1.15.2)\n",
+      "Requirement already satisfied: joblib in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from ModularCirc==0.2.0) (1.4.2)\n",
+      "Requirement already satisfied: pandera in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from ModularCirc==0.2.0) (0.23.1)\n",
+      "Requirement already satisfied: tdqm in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from ModularCirc==0.2.0) (0.0.1)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from matplotlib->ModularCirc==0.2.0) (1.3.1)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from matplotlib->ModularCirc==0.2.0) (0.12.1)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from matplotlib->ModularCirc==0.2.0) (4.57.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.3.1 in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from matplotlib->ModularCirc==0.2.0) (1.4.8)\n",
+      "Requirement already satisfied: packaging>=20.0 in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from matplotlib->ModularCirc==0.2.0) (24.2)\n",
+      "Requirement already satisfied: pillow>=8 in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from matplotlib->ModularCirc==0.2.0) (11.2.1)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from matplotlib->ModularCirc==0.2.0) (3.2.3)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from matplotlib->ModularCirc==0.2.0) (2.9.0.post0)\n",
+      "Requirement already satisfied: llvmlite<0.45,>=0.44.0dev0 in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from numba->ModularCirc==0.2.0) (0.44.0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from pandas->ModularCirc==0.2.0) (2025.2)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from pandas->ModularCirc==0.2.0) (2025.2)\n",
+      "Requirement already satisfied: pydantic in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from pandera->ModularCirc==0.2.0) (2.11.3)\n",
+      "Requirement already satisfied: typeguard in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from pandera->ModularCirc==0.2.0) (4.4.2)\n",
+      "Requirement already satisfied: typing_inspect>=0.6.0 in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from pandera->ModularCirc==0.2.0) (0.9.0)\n",
+      "Requirement already satisfied: tqdm in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from tdqm->ModularCirc==0.2.0) (4.67.1)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from python-dateutil>=2.7->matplotlib->ModularCirc==0.2.0) (1.17.0)\n",
+      "Requirement already satisfied: mypy-extensions>=0.3.0 in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from typing_inspect>=0.6.0->pandera->ModularCirc==0.2.0) (1.0.0)\n",
+      "Requirement already satisfied: typing-extensions>=3.7.4 in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from typing_inspect>=0.6.0->pandera->ModularCirc==0.2.0) (4.13.2)\n",
+      "Requirement already satisfied: annotated-types>=0.6.0 in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from pydantic->pandera->ModularCirc==0.2.0) (0.7.0)\n",
+      "Requirement already satisfied: pydantic-core==2.33.1 in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from pydantic->pandera->ModularCirc==0.2.0) (2.33.1)\n",
+      "Requirement already satisfied: typing-inspection>=0.4.0 in /Users/sgreenbury/Projects/fundamentals/autoemulate/.venv/lib/python3.12/site-packages (from pydantic->pandera->ModularCirc==0.2.0) (0.4.0)\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+     ]
+    }
+   ],
    "source": [
     "! pip install git+https://github.com/alan-turing-institute/ModularCirc.git@dev"
    ]
@@ -62,9 +108,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'ao.r': (120.0, 360.0),\n",
+       " 'ao.c': (0.15, 0.44999999999999996),\n",
+       " 'art.r': (562.5, 1687.5),\n",
+       " 'art.c': (1.5, 4.5),\n",
+       " 'ven.r': (4.5, 13.5),\n",
+       " 'ven.c': (66.65, 199.95000000000002),\n",
+       " 'av.r': (3.0, 9.0),\n",
+       " 'mv.r': (2.05, 6.1499999999999995),\n",
+       " 'la.E_pas': (0.22, 0.66),\n",
+       " 'la.E_act': (0.225, 0.675),\n",
+       " 'la.v_ref': (5.0, 15.0),\n",
+       " 'la.k_pas': (0.01665, 0.07500000000000001),\n",
+       " 'lv.E_pas': (0.5, 1.5),\n",
+       " 'lv.E_act': (1.5, 4.5),\n",
+       " 'lv.v_ref': (5.0, 15.0),\n",
+       " 'lv.k_pas': (0.00999, 0.045)}"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from autoemulate.simulations.naghavi_cardiac_ModularCirc import extract_parameter_ranges\n",
     "# Usage example:\n",
@@ -81,9 +153,375 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of parameters: 16 Number of samples from each parameter: 100\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
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+        {
+         "name": "la.E_act",
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+        },
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+         "name": "lv.v_ref",
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+         "name": "lv.k_pas",
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+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\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>ao.r</th>\n",
+       "      <th>ao.c</th>\n",
+       "      <th>art.r</th>\n",
+       "      <th>art.c</th>\n",
+       "      <th>ven.r</th>\n",
+       "      <th>ven.c</th>\n",
+       "      <th>av.r</th>\n",
+       "      <th>mv.r</th>\n",
+       "      <th>la.E_pas</th>\n",
+       "      <th>la.E_act</th>\n",
+       "      <th>la.v_ref</th>\n",
+       "      <th>la.k_pas</th>\n",
+       "      <th>lv.E_pas</th>\n",
+       "      <th>lv.E_act</th>\n",
+       "      <th>lv.v_ref</th>\n",
+       "      <th>lv.k_pas</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>296.440885</td>\n",
+       "      <td>0.396672</td>\n",
+       "      <td>1015.055683</td>\n",
+       "      <td>1.988679</td>\n",
+       "      <td>6.507542</td>\n",
+       "      <td>167.642001</td>\n",
+       "      <td>5.485211</td>\n",
+       "      <td>3.162309</td>\n",
+       "      <td>0.233027</td>\n",
+       "      <td>0.326324</td>\n",
+       "      <td>12.565421</td>\n",
+       "      <td>0.022142</td>\n",
+       "      <td>1.443565</td>\n",
+       "      <td>3.103660</td>\n",
+       "      <td>7.226351</td>\n",
+       "      <td>0.025244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>170.936176</td>\n",
+       "      <td>0.430408</td>\n",
+       "      <td>810.882333</td>\n",
+       "      <td>2.385469</td>\n",
+       "      <td>10.865371</td>\n",
+       "      <td>147.729601</td>\n",
+       "      <td>5.217009</td>\n",
+       "      <td>5.843980</td>\n",
+       "      <td>0.254431</td>\n",
+       "      <td>0.435555</td>\n",
+       "      <td>14.803089</td>\n",
+       "      <td>0.035664</td>\n",
+       "      <td>0.854612</td>\n",
+       "      <td>2.958498</td>\n",
+       "      <td>8.912569</td>\n",
+       "      <td>0.039591</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>224.910382</td>\n",
+       "      <td>0.215161</td>\n",
+       "      <td>1669.192723</td>\n",
+       "      <td>1.551490</td>\n",
+       "      <td>10.593887</td>\n",
+       "      <td>88.536086</td>\n",
+       "      <td>4.763047</td>\n",
+       "      <td>3.939957</td>\n",
+       "      <td>0.516216</td>\n",
+       "      <td>0.304267</td>\n",
+       "      <td>6.212975</td>\n",
+       "      <td>0.020790</td>\n",
+       "      <td>1.461334</td>\n",
+       "      <td>4.461589</td>\n",
+       "      <td>8.717625</td>\n",
+       "      <td>0.031072</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>315.206213</td>\n",
+       "      <td>0.151029</td>\n",
+       "      <td>1201.130539</td>\n",
+       "      <td>3.547849</td>\n",
+       "      <td>6.280902</td>\n",
+       "      <td>139.960518</td>\n",
+       "      <td>4.016971</td>\n",
+       "      <td>5.931769</td>\n",
+       "      <td>0.629961</td>\n",
+       "      <td>0.387896</td>\n",
+       "      <td>14.515793</td>\n",
+       "      <td>0.048657</td>\n",
+       "      <td>1.499412</td>\n",
+       "      <td>3.940640</td>\n",
+       "      <td>14.773632</td>\n",
+       "      <td>0.010744</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>220.269324</td>\n",
+       "      <td>0.387451</td>\n",
+       "      <td>933.723349</td>\n",
+       "      <td>3.972825</td>\n",
+       "      <td>5.981294</td>\n",
+       "      <td>165.658227</td>\n",
+       "      <td>6.785018</td>\n",
+       "      <td>3.791956</td>\n",
+       "      <td>0.381240</td>\n",
+       "      <td>0.458787</td>\n",
+       "      <td>11.295892</td>\n",
+       "      <td>0.064039</td>\n",
+       "      <td>0.929570</td>\n",
+       "      <td>3.532311</td>\n",
+       "      <td>9.808989</td>\n",
+       "      <td>0.010605</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         ao.r      ao.c        art.r     art.c      ven.r       ven.c  \\\n",
+       "0  296.440885  0.396672  1015.055683  1.988679   6.507542  167.642001   \n",
+       "1  170.936176  0.430408   810.882333  2.385469  10.865371  147.729601   \n",
+       "2  224.910382  0.215161  1669.192723  1.551490  10.593887   88.536086   \n",
+       "3  315.206213  0.151029  1201.130539  3.547849   6.280902  139.960518   \n",
+       "4  220.269324  0.387451   933.723349  3.972825   5.981294  165.658227   \n",
+       "\n",
+       "       av.r      mv.r  la.E_pas  la.E_act   la.v_ref  la.k_pas  lv.E_pas  \\\n",
+       "0  5.485211  3.162309  0.233027  0.326324  12.565421  0.022142  1.443565   \n",
+       "1  5.217009  5.843980  0.254431  0.435555  14.803089  0.035664  0.854612   \n",
+       "2  4.763047  3.939957  0.516216  0.304267   6.212975  0.020790  1.461334   \n",
+       "3  4.016971  5.931769  0.629961  0.387896  14.515793  0.048657  1.499412   \n",
+       "4  6.785018  3.791956  0.381240  0.458787  11.295892  0.064039  0.929570   \n",
+       "\n",
+       "   lv.E_act   lv.v_ref  lv.k_pas  \n",
+       "0  3.103660   7.226351  0.025244  \n",
+       "1  2.958498   8.912569  0.039591  \n",
+       "2  4.461589   8.717625  0.031072  \n",
+       "3  3.940640  14.773632  0.010744  \n",
+       "4  3.532311   9.808989  0.010605  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -110,7 +548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -133,9 +571,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6656b1e5f7f54e26ad11320e4f1ce643",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Running simulations:   0%|          | 0/100 [00:00<?, ?sample/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully completed 100/100 simulations (100.0%)\n"
+     ]
+    }
+   ],
    "source": [
     "# Run batch simulations with the samples generated in Cell 1\n",
     "results = simulator.run_batch_simulations(sample_df)\n",
@@ -146,9 +606,809 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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+       "      <td>18.979320</td>\n",
+       "      <td>8.839334</td>\n",
+       "      <td>11.317115</td>\n",
+       "      <td>20.156449</td>\n",
+       "      <td>18.979320</td>\n",
+       "      <td>8.839334</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>18.666848</td>\n",
+       "      <td>23.334430</td>\n",
+       "      <td>21.667915</td>\n",
+       "      <td>4.667582</td>\n",
+       "      <td>18.666848</td>\n",
+       "      <td>23.334430</td>\n",
+       "      <td>21.667915</td>\n",
+       "      <td>4.667582</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>12.985272</td>\n",
+       "      <td>24.617342</td>\n",
+       "      <td>23.214435</td>\n",
+       "      <td>11.632069</td>\n",
+       "      <td>12.985272</td>\n",
+       "      <td>24.617342</td>\n",
+       "      <td>23.214435</td>\n",
+       "      <td>11.632069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>9.713965</td>\n",
+       "      <td>15.335051</td>\n",
+       "      <td>14.554911</td>\n",
+       "      <td>5.621086</td>\n",
+       "      <td>9.713965</td>\n",
+       "      <td>15.335051</td>\n",
+       "      <td>14.554911</td>\n",
+       "      <td>5.621086</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            0          1          2          3          4          5  \\\n",
+       "0   15.168249  20.161629  18.472553   4.993380  15.168249  20.161629   \n",
+       "1   36.019994  39.135503  37.573478   3.115509  36.019994  39.135503   \n",
+       "2   26.756008  26.886811  26.814212   0.130803  26.756008  26.886811   \n",
+       "3    2.411396   7.086565   5.246729   4.675169   2.411396   7.086565   \n",
+       "4    1.594468   5.884158   4.283615   4.289690   1.594468   5.884158   \n",
+       "..        ...        ...        ...        ...        ...        ...   \n",
+       "95   4.781580  15.237328  13.694896  10.455748   4.781580  15.237328   \n",
+       "96  11.317115  20.156449  18.979320   8.839334  11.317115  20.156449   \n",
+       "97  18.666848  23.334430  21.667915   4.667582  18.666848  23.334430   \n",
+       "98  12.985272  24.617342  23.214435  11.632069  12.985272  24.617342   \n",
+       "99   9.713965  15.335051  14.554911   5.621086   9.713965  15.335051   \n",
+       "\n",
+       "            6          7  \n",
+       "0   18.472553   4.993380  \n",
+       "1   37.573478   3.115509  \n",
+       "2   26.814212   0.130803  \n",
+       "3    5.246729   4.675169  \n",
+       "4    4.283615   4.289690  \n",
+       "..        ...        ...  \n",
+       "95  13.694896  10.455748  \n",
+       "96  18.979320   8.839334  \n",
+       "97  21.667915   4.667582  \n",
+       "98  23.214435  11.632069  \n",
+       "99  14.554911   5.621086  \n",
+       "\n",
+       "[100 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "results_df"
    ]
@@ -162,9 +1422,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['lv.P_i_min',\n",
+       " 'lv.P_i_max',\n",
+       " 'lv.P_i_mean',\n",
+       " 'lv.P_i_range',\n",
+       " 'lv.P_o_min',\n",
+       " 'lv.P_o_max',\n",
+       " 'lv.P_o_mean',\n",
+       " 'lv.P_o_range']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\n",
     "simulator.output_names"
@@ -179,7 +1457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -199,9 +1477,95 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<p>AutoEmulate is set up with the following settings:</p>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Simulation input shape (X)</th>\n",
+       "      <td>(100, 16)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Simulation output shape (y)</th>\n",
+       "      <td>(100, 8)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Proportion of data for testing (test_set_size)</th>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Scale input data (scale)</th>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Scaler (scaler)</th>\n",
+       "      <td>StandardScaler</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Scale output data (scale_output)</th>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Scaler output (scaler_output)</th>\n",
+       "      <td>StandardScaler</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Do hyperparameter search (param_search)</th>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Reduce input dimensionality (reduce_dim)</th>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Reduce output dimensionality (reduce_dim_output)</th>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Dimensionality output reduction methods (dim_reducer_output)</th>\n",
+       "      <td>PCA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cross validator (cross_validator)</th>\n",
+       "      <td>KFold</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Parallel jobs (n_jobs)</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "from autoemulate.compare import AutoEmulate\n",
@@ -222,9 +1586,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9348edf16194476093dca219250d7dfe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Cross-validating:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "best_model = em.compare()"
    ]
@@ -238,18 +1617,637 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "preprocessing",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "model",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "short",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "fold",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "rmse",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "r2",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "132db372-52f8-47d3-ab2c-1b33dbb5e2f9",
+       "rows": [
+        [
+         "0",
+         "PCA",
+         "GaussianProcess",
+         "gp",
+         "4",
+         "1.6400218432943383",
+         "0.916621058787057"
+        ],
+        [
+         "1",
+         "PCA",
+         "GaussianProcess",
+         "gp",
+         "2",
+         "3.1269917069468782",
+         "0.8743264976041712"
+        ],
+        [
+         "2",
+         "PCA",
+         "GaussianProcess",
+         "gp",
+         "0",
+         "2.790273597603327",
+         "0.8647043768637424"
+        ],
+        [
+         "3",
+         "PCA",
+         "GaussianProcess",
+         "gp",
+         "3",
+         "3.622270032553354",
+         "0.8466359706170754"
+        ],
+        [
+         "4",
+         "PCA",
+         "GaussianProcess",
+         "gp",
+         "1",
+         "2.794190745968192",
+         "0.7743143913694197"
+        ]
+       ],
+       "shape": {
+        "columns": 6,
+        "rows": 5
+       }
+      },
+      "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>preprocessing</th>\n",
+       "      <th>model</th>\n",
+       "      <th>short</th>\n",
+       "      <th>fold</th>\n",
+       "      <th>rmse</th>\n",
+       "      <th>r2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PCA</td>\n",
+       "      <td>GaussianProcess</td>\n",
+       "      <td>gp</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.640022</td>\n",
+       "      <td>0.916621</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>PCA</td>\n",
+       "      <td>GaussianProcess</td>\n",
+       "      <td>gp</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3.126992</td>\n",
+       "      <td>0.874326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>PCA</td>\n",
+       "      <td>GaussianProcess</td>\n",
+       "      <td>gp</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.790274</td>\n",
+       "      <td>0.864704</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>PCA</td>\n",
+       "      <td>GaussianProcess</td>\n",
+       "      <td>gp</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.622270</td>\n",
+       "      <td>0.846636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>PCA</td>\n",
+       "      <td>GaussianProcess</td>\n",
+       "      <td>gp</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.794191</td>\n",
+       "      <td>0.774314</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  preprocessing            model short  fold      rmse        r2\n",
+       "0           PCA  GaussianProcess    gp     4  1.640022  0.916621\n",
+       "1           PCA  GaussianProcess    gp     2  3.126992  0.874326\n",
+       "2           PCA  GaussianProcess    gp     0  2.790274  0.864704\n",
+       "3           PCA  GaussianProcess    gp     3  3.622270  0.846636\n",
+       "4           PCA  GaussianProcess    gp     1  2.794191  0.774314"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "em.summarise_cv()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>InputOutputPipeline(regressor=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+       "                                              (&#x27;model&#x27;, GaussianProcess())]),\n",
+       "                    transformer=Pipeline(steps=[(&#x27;scaler_output&#x27;,\n",
+       "                                                 StandardScaler()),\n",
+       "                                                (&#x27;dim_reducer_output&#x27;,\n",
+       "                                                 PCA(n_components=2))]))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;InputOutputPipeline<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>InputOutputPipeline(regressor=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+       "                                              (&#x27;model&#x27;, GaussianProcess())]),\n",
+       "                    transformer=Pipeline(steps=[(&#x27;scaler_output&#x27;,\n",
+       "                                                 StandardScaler()),\n",
+       "                                                (&#x27;dim_reducer_output&#x27;,\n",
+       "                                                 PCA(n_components=2))]))</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">regressor: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()), (&#x27;model&#x27;, GaussianProcess())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">GaussianProcess</label><div class=\"sk-toggleable__content fitted\"><pre>GaussianProcess()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">transformer: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler_output&#x27;, StandardScaler()),\n",
+       "                (&#x27;dim_reducer_output&#x27;, PCA(n_components=2))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">NonTrainableTransformer</label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">NonTrainableTransformer</label><div class=\"sk-toggleable__content fitted\"><pre>PCA(n_components=2)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "InputOutputPipeline(regressor=Pipeline(steps=[('scaler', StandardScaler()),\n",
+       "                                              ('model', GaussianProcess())]),\n",
+       "                    transformer=Pipeline(steps=[('scaler_output',\n",
+       "                                                 StandardScaler()),\n",
+       "                                                ('dim_reducer_output',\n",
+       "                                                 PCA(n_components=2))]))"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "best_model"
    ]
@@ -258,16 +2256,117 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### 7 - Extract the desired model, run evaluation and refit using the whole dataset.\n",
+    "#### 8 - Extract the desired model, run evaluation and refit using the whole dataset.\n",
     "- You can use the `best_model` selected by AutoEmulate \n",
     "- or you can extract the model and pre-processing technique displayed in `em.summarise_cv()`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "model",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "short",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "preprocessing",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "rmse",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "r2",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "aaff29b4-1acf-47f7-a3e8-4ec5e23ada40",
+       "rows": [
+        [
+         "0",
+         "GaussianProcess",
+         "gp",
+         "PCA",
+         "3.9183",
+         "0.7697"
+        ]
+       ],
+       "shape": {
+        "columns": 5,
+        "rows": 1
+       }
+      },
+      "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>model</th>\n",
+       "      <th>short</th>\n",
+       "      <th>preprocessing</th>\n",
+       "      <th>rmse</th>\n",
+       "      <th>r2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>GaussianProcess</td>\n",
+       "      <td>gp</td>\n",
+       "      <td>PCA</td>\n",
+       "      <td>3.9183</td>\n",
+       "      <td>0.7697</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             model short preprocessing    rmse      r2\n",
+       "0  GaussianProcess    gp           PCA  3.9183  0.7697"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "gp = em.get_model('GaussianProcess')\n",
     "em.evaluate(gp)\n",
@@ -277,9 +2376,443 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>InputOutputPipeline(regressor=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+       "                                              (&#x27;model&#x27;, GaussianProcess())]),\n",
+       "                    transformer=Pipeline(steps=[(&#x27;scaler_output&#x27;,\n",
+       "                                                 StandardScaler()),\n",
+       "                                                (&#x27;dim_reducer_output&#x27;,\n",
+       "                                                 PCA(n_components=2))]))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;InputOutputPipeline<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>InputOutputPipeline(regressor=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+       "                                              (&#x27;model&#x27;, GaussianProcess())]),\n",
+       "                    transformer=Pipeline(steps=[(&#x27;scaler_output&#x27;,\n",
+       "                                                 StandardScaler()),\n",
+       "                                                (&#x27;dim_reducer_output&#x27;,\n",
+       "                                                 PCA(n_components=2))]))</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">regressor: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()), (&#x27;model&#x27;, GaussianProcess())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">GaussianProcess</label><div class=\"sk-toggleable__content fitted\"><pre>GaussianProcess()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">transformer: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler_output&#x27;, StandardScaler()),\n",
+       "                (&#x27;dim_reducer_output&#x27;, PCA(n_components=2))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">NonTrainableTransformer</label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">NonTrainableTransformer</label><div class=\"sk-toggleable__content fitted\"><pre>PCA(n_components=2)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "InputOutputPipeline(regressor=Pipeline(steps=[('scaler', StandardScaler()),\n",
+       "                                              ('model', GaussianProcess())]),\n",
+       "                    transformer=Pipeline(steps=[('scaler_output',\n",
+       "                                                 StandardScaler()),\n",
+       "                                                ('dim_reducer_output',\n",
+       "                                                 PCA(n_components=2))]))"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "gp_final = em.refit(gp)\n",
     "gp_final"
@@ -287,9 +2820,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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o1KhBDycYi2MwE+6tjjWRcI3pdc3FdU+omX7rW98SP3RfuHBZtmzZiH6H/nBq8X3hMespUAnTtLYGfcnU+B977DEceOCB3dtZypMkFyBJ+LxvWc+mpiY5v4qSlkKaK9F77rlHbkj6kbi6pl+K/izCFTXN4D1vEAamUEOhP6g/IR0Oh+WRpK2trVfXnOFSNbUMDe0uwBGFzxNGc6gAiYQDncEwmjvcqJpSZk164xwdy30f6RjswniOIfndozkXkbgbiQEEE7+Tr/O15PcP9DvUcjmRv/nmm2IGJs3NzXKd019JLZFNFW6++ebu199+++3u70zWdCasT538HX6GPmreMzTPEt4vPT83FGR8zuiAQtjabpWoHOp30srV8728P2nCTraR7Lt/u+yyi5jo58yx3Eh9odWBY6fvPWnu5tjp8up7/Ht+78477yzWOV4PAy0wBjt/hMoCI8T59ytf+Uqv9/L8UlvnsU8Kfy6SuK+XXnppr/cuWbIEixYtGvC3eu5LOraunAzz0kSNY6jfbSshffXVV4sQpRmLK1CuRH/0ox+Jr4pQQBNqzj3h8+RrfeHER/NUX+hHYtDJcHvP7n/gbti01gknCtHR3om4mY+WsBu5xVmoLA1g95m7DxrMMlYnmGPghZTO/XPHawz0BfL7ObHzMRK2XfQlfLTs+V4+6SQfLWvCdrt8WX4nqUENJAQ4gZ9xxhkSIUzzLDWp733vezJmjp0aMPNoGWDFSGem6DCIjCT3f8qUKfL9DDw79NBDxWxKszgjun/zm9/IdzKo6rvf/a58jvs01HFzH/b83HZYv3wJplVt6cdeX/0J9tp7+2F9Hy0BFFDUPBkcRhcWXVXJ70iemyQMfuNC44ILLpBjRU2bixcGw/G4UHjTskBtm99F7Zc+bR6HZC9pjpm+fR5PzhuE30ktnQv+nj56NrBInjd+F/d3w4YN3a/zfCTjAJICmsoCze3J9/G3qSRwAXX++edLDjTPLxdajN6nC+60007rNU4GjTGmpr9jmWxVmbx2uQhLLs7ShckwL03UOBh8mHZCmn4emvvo92FgB31DDCThTceLfSRcc801MgEk4SKANxFvLvqUhnsCjjn2SFzxnRskyKaqYnuEOoMwnLmob1qOtuDruOg7N8jNO94kNQNOzul6M4znGDhB8ibgBJw0Zw6X4086Azd89204Hc3Yfp6VWsObdsnyZrz8WQGu/9EZ3ZPoYJMpg43oi2TkNYUrr8lk72OmZN13330iYH/1q1+JVsn306WT3H9qaAyqpKuHwVdMX+JnKEAYWUztjNomBRrTtbjIHc64T/7Gcbj6yh/BsckpPujkWDdUf4qW4Ov4xinfH/L38bOnnHKKnAMGdnJfaMY+99xzuxcyPN89v49jZsQ7x8egMv42BTODs5Lv43g5dgbfcWHOhUxysZM8/hSgPc8538djzqhratU9rXZbC+BiTACjyJPBeBSqHENPczznJO4TYVpWcpHAOYY+d6bH0dze05rAiZ+pWoMdS46JC7D+/Ol2ZjLMSxM1jqGeW4dpowoNFJ7UprmaTvLDH/5QomI/++wzCdDgjcuVec8bjjcUnyfTUrYGbyAKaJoa+V0jOQEMRmG066uvfghEFsLjrMS8z9Xi6OOPxqzSAjgnwB/Ni4ga+0gWGnZhPMdAAUH3CPNpRzPR8VwziptBYvRBR00Pdtj1ABx7/GmyGJssLRI5BmpwjPJmkBh90E5nDHt/cUecdPJxw1p4Mk+a9+Ng5TrHkq2dBwZvsRgJA/h6RnBPNBTOTDu79tprtzoGPpiCN9prNxVMhnlposaRlEVcuDHoMy00aWoafQ8IV+FJ2z0vWhZ6oAksKaQ5UPq/zjvvvAnbT05YTEc55YwE3lvShH89XIevfOWLcHndiMZNeF3pOVkr/Z/rU04/Fzj93EkfEJi8rs86G5NqrDvuuKNkinDRtsMOO6RkH+ij5m/TBaAow8FWQpqRqvQlMZqT5m5qzPQDJXNIOWnQ/E3tmkEoFNoshEBz+JFHHpmSfS6f6ofL48DGVUHML3YhkkjAa6/MNmWMmCxCKxPHmiwMkyro46YpX1HSWkgzH5pCl0EYNDVQ+DJQhH6nJAy+YcoKA2wY2cmSiPT9pMIsZDANx+NE5Qw/1q8MYuEeeQhG48j12OqwKsqEQt+yMnokkCyRgJHGZmNl9NhKmjCghn6srfmyuMK/8cYb5ZFqDKcDPsNA5Swf3nmhGYmoibAjgVjChMs5uTQRRVEmBrr3Ojra8eMbL0awfRNMeCUO4rgTTp+QoFTFXugSbZT4XE5UzPIhkTBRty6EeIL5s+mdI6goSuoEdH3dJhhmEKcelI3zjhp5vXhlcmArTTod8RhOFBZ6kFfoFpN32WwfIvE4sty9Kw0pqSHdiyoomQU1aL87jkg0gdvu3YDmpiCcjgB23dbAXnPjkmkggYxKxqBCepS4nQ64DCeqZvuwYUUAeziKEIwlkD+JomPTEQbqMFOgurpach35fDzOx2RKwdIxpH5B2dbagqamVrS3OTB/2vHAVI7FRHXtSixZ/g8UVi5WIZ1hqJAeJZwQ/C4nKmf58dm77ehsjiGr0IVowoTHSM/JYjJAAc3of7Z1pKAeL5KlA/l76SocdAz2GUNDfR1q1rejfmUcMB3SE4B1F6ZUaJe9TEWF9BjgNQyUT/fBMBzYuDqI2QU54pemKVxJHdSemc5HDatv84OxIlm+kdWh0rV4g47BPmO4/dZfYNvyA6X9LbvsZfkDcBpmd5e9Vz9IX0uBMjJUSI8BFMY+r4HyaT5sWBnE3F1yEYrFkaOpWClHulW53eNWA5kTK7+bKYDpLBx0DKmHC0mnMwcdoRj8Hh9a2vPhcsXgMqwGQey+p132Mo/0vJptBlOxvIYTlbN9qF4XhCNuIhRPSKS3oijKUKDgLSzOR0ObC+0By/Jjmg7pV9/eGUV9mwuVVaUqoDMMFdJjhN9loGKmD/GYifoNYcmVZvUxRVGUobLd9rORle9EGMWIRE3UNEaxuiaGsKMU2flO7LPfLqneRWWCUSE9RrgNJ4pKPMjJc0kqFle/kdj4+EEVRZmc7LvfF9AWehMJRxBeXxaqZszHwu12gensQFv4TWl2omQWKqTHCI/TAY/LQNUslggNgDFjgZjVOFxRFGUoeL1e3PbT61E5LYDOUD0+W/ks3lnyG8zZrhU/vWNi2uAq9kIjm8Y4FYvVx5Z92I5gaxz+PGgqlqIow4KC+Ev774/Olk3Y56s7Yd/Pl6V6l5QUopr0GOIxDFTM8INlu6vXBBE3tUSooijDJxyy5g0zoQv8TEeF9BinYvn9Bkqn+rB+RVC067AKaUVRhkk4nJBCJuwJoGQ2KqTHEFdXKlbVLJ9o0g4Tki+tqViKogyHSCSZgmVVIlMyFxXSY4zfzdaVfkSjCTRu1FQsRVFGpkkzRcTUBX7Go0J6jPE4nSgq98CfbWDDqiB4j2kqlqIowyEiQprm7lTviZJqVEiPMW7DIQFkTMXasDIAlxPSFUtNVoqiDJVIJBk4pvNGpqNCeoxx9kjFaqyLINyZQDSeELO3oijKcKK7GTimM0dmo0J6HPCyqMlMP5g8sWlVEDGavDXKW1GUIaLmbiWJCulxgMVLsrJdKKnyYv0qpmJBU7EURRky0R7mbvWUZTYqpMcBw8FULIdEeW9cHYAB+qXjSOjdpijKkKO7HapJKyqkx69EqAuVM33iW2quiYhfWk3eiqIM1dxNd5kGjikqpMcxyru00guvz4kN7IoFhwppRVEGhcFirLNg/Vsa6ikZjArpcSwR6nY7u7tiOSUVK66pWIqiDO6P7pomrLKgOmdkMiqkxzUVy0DFLD8aNoURC1rm7pgKaUVRhhDZTUw1vmU8KqTHES/zpWf6ZB1csyYEWrvpm1YURRmsuQbRPGlFhfQ4lwjNy3OhqMwjqVg0W4ViKqQVRRlck3Z7HJYmrVI6o1EhPc5dsdxOJ6pm+7FhVUBSs9gVS1OxFEXZevoV4PEZMlfobJHZqJAe91QsA5Uz/Qh2xtFWH0WUkZtq8lYUZRAh7fM7kYirkM50VEhPgF+6ZIoHbo9zc1cszX1UFGWQ5hpM37TM3TpfZDIqpCcgFcvnMVA5wyepWAZTsaKxVO+Woig2JRyKS8omUzg1cExRIT0BqVg+mrxn+VG7IYx4JIFw3ERM6/0pijKAudvlccJhWClYKqQzGxXSE4DPcEqJUBYyqVsblraVkbjeeoqi9G/uZmQ3F/hSzESnioxGhfQE4DacyC90I7/YjfUrg3CCQjqe6t1SFMWmKVjUpJ2GQ9zRKqMzGxXSE4Db6YDL6FEi1OFAIJbQEqGKovQrpKlJG4ZDortVTGc2KqQnKBUry+UUv3RnewwdTTHE2BVLo7wVRelHSHu8TjidDskG0bV8ZqNCeoLwuAyUT/fB5XJg42qmYmm+tKIoAweOGU5WHNPo7kxHhfQElgj1epwiqJMmb3bFUhRF6dsFyzJ3W60qlcxGhfQEwVWxRHnP8qNmXQhmzEQ4lkBcTd6KovSjSdPcrZq0okJ6ArHypX2Ix03Urw9L20q2r1QURdlSk2YKFn3SKqYzGRXSE1x9rKDIjZx8l9UVywTCmoqlKMoWgWOGFTim9RQyHhXSE5yKxQCyKeyKtTIAF0uEaiqWoihdcC5IpmCJuZtdsHR6yGhUSE94VywnKmb60NocRWdLXCK82RlLURSFhjWauJmCxTr/Yu5O9U4pKUWF9ATjMQxUzPDLKrl6dRBx9UsritJFNGKJZHey4pgGjmU8KqRT0RXLZ6Bsqrc7FSsUUyGtKIolpCmU3V4nXF2BY6pLZzYqpCcYl9MBb1eJ0Oq1ITgSQCge11QsRVEQjVrzgKcrBUtaVerUkNGokE4BfreVihWLJtC4MSwCOqJVCxQl42HQGDVnL33SYu5O9R4pqUaFdIqqjxWWeZCVY0gqFlfKEa0+pigZT6TLJ+3qqUmneqeUlKJCOgV4DIcEkElXrBUBieLUVCxFUaJhaw7w+iikIZq0TguZjQrpFKZi0eTd3BBBqCOhqViKoiASsezbHtfmPGkNHMtsVEinCK/LSsVyOIBNqwKImdCuWIqS4USiJlxupl9t9klrUGlmo0I6hSbvrGwXSqq84pd2OhjlrUJaUTI9BcvtdcBwQDRposFjmY0K6RThYutKwyF+afaXdppAKBaXPtOKomRu4Bg1acPphKNrdtY5IbNRIZ1C/C4XKmb5Je2iqSYi5m6tPqYomZ0nTU2aSrRq0oothfTGjRvxjW98A8XFxfD7/dhhhx3wzjvvdL/OQIrvfe97qKyslNcPPPBALF++HOmI23CgtNwDn9/AhpVBhpRJj2lFUTI3utvtppC2AscI07CUzMVWQrq5uRl777033G43nnnmGXz66af46U9/isLCwu733HrrrfjFL36BX//613jrrbeQnZ2Ngw8+GKFQCOlYItTttqK8WSKUqVg0eWsqlqJkrk+aOdIUz0zBIiqkMxsXbMQtt9yCadOm4b777uveNmvWrO5/U3j97Gc/w3XXXYcjjjhCtt1///0oLy/Hk08+iRNOOAHpBFfLfpdVfWz1p42IBhOAH4glTNGyFUXJPHN3Vq5Tsj4Y3U3U3J3Z2EpIP/XUU6IVH3fccXj55ZcxZcoUnH/++TjnnHPk9dWrV6OmpkZM3Eny8/Ox55574o033uhXSIfDYXkkaWtr6xb4CRuU4nQ7gfIZPkmFrF4ZQNU2WQjFYjAcWz813He7jGGk6BjsgY7BHnDfGZ+S505qz5YGHY/G02Zck+E8TNQ4hvrdthLSq1atwt13343LLrsM1157Ld5++21cdNFF8Hg8OO2000RAE2rOPeHz5Gt9ufnmm3HDDTdssb21tRV1dXVwJm1KKSKWSCARiSK3MIEVnzQhtzyAeJuBXK970BPMMfBCSvUYRoqOwR7oGOwzhmAggkTMiaaGerS0xhCLxdDc2IhsVwDpwGQ4DxM1jvb29vQT0jwwu+22G2666SZ5vmjRIixZskT8zxTSI+Gaa64Rod9Tk6ZJnRp4WVlZyi8kXgSuzjBmLDSw4oMOFBWVwOF0oCTHJ+bwrR0rVi4rLS1N+RhGio7BHugY7KS9NSK3MFfGYYbDcLk6kFdQhLKyXKQDk+E8TNQ4fD5f+glpRmxvu+22vbZts802+Otf/yr/rqiokL+1tbXy3iR8vvPOO/f7nV6vVx594QngwbfDheT3uFE5KwtL3mxDa0McuaVuqUDmYyTZVrDTGEaKjsEe6BhsVMzEw2pjBlwuaxwJ0xpXujAZzsNEjGOo32uro8jI7qVLl/batmzZMsyYMaM7iIyC+oUXXuilGTPKe6+99kK64nU5UTrFA4/XiQ2rgqCnIhrXiE5FySToh47HrF7SVnS3pmApNhPSl156Kd58800xd69YsQIPPfQQ7rnnHlxwwQXdK5tLLrkEP/zhDyXI7OOPP8app56KqqoqHHnkkUhXmIrlZY/pGZtTsYKxWKp3S1GUCYRBYxTHbq8V3b1ZSKd6z5RUYitz9+67744nnnhC/Mg33nijaM5MuTr55JO733PllVeis7MT3/zmN9HS0oIvfOELePbZZ4ds37cj9D37JBXLj7cWNyEeTiDsdUhQGcuHKoqSQR2wRJN2WGVBHYCpVrWMxlZCmhx++OHyGAhq0xTgfEwm6H+umOWTQLLaNSFUzMtCJG6iyy2lKEoGaNLE43WoJq10oyLAJrgNJ/Lz3Sgo8UhXLOZIhmPxVO+WoigTRDhs3e/uPj5prUCY2aiQtglup0MEddUsnwSPGU4HgrGEdsBRlAwhLJq0Fd0tkcWGtT2ua/WMRoW0TeBN6Xexjrcfne0xtNVHEU0kNMpbUTKEcChp7ramZS7UianR3RmNCmkb4XEZKJ/mg8vlQPXqkJi5IuqQUpSM6SXdU0izqBFRGZ3ZqJC2ER6nUyI7K2b4JRWLUd/BqNq6FCUTiITjcBoOuLqKGGkXLIWokLYRNG8xyptdsWrWh2DGTITjCemKpSjK5PdJu9wOqZNAuqO71eWV0aiQthlWvrRPVs9160KIJ0xE4mryVpRMSMFyuQGjq2b/5ujuFO+YklJUSNuw+lhBkQd5he7uVKyIhncqSsZo0klfdDK6WzXpzEaFtI1TsdavDMqqmqlYmiupKJmgSTu6u991a9JqSMtoVEjbNBWrYqYf7S1RBFrjiMYTiKpfWlEmfVlQl0cqgQrJTrVaKyGzUSFtQ7yGgfIZPhiGAxtXBRFnKpb6pRVlUhMJJeCmuduxecHOh2rSmY0KaZv6pf1eA2VTvd2pWCEtEaook1+TppDu1qUBw7BSsNTdlbmokLZpKhZ7TFfN8qN6bRCOuIlQPCGR3oqiTHJz92YZLX5prWeU2aiQtil+pmLN9iEeM1G/ISy50lp9TFEmu7nbaq6RhJHeWswks1EhbVMY4V1Y4kF2rksabrAbfERN3ooyyTVpyw+dhFXH6JNWMZ25qJC2KR6mYrkMVM1miVB2xQICmoqlTDBNgTCCujgcd6ROf4Sa9Obo7s3mbr3nMxlXqndA6R+uprPYFWumF8s/bEeoLQ5fLiQVy2P0vI0VZWwJBoN47OE/4KN3XoCJEBIJLxbtcSCOO+F0+P3+VO/epCQWNUVjdnk3R3f31KSVzEU1aRvjMQxUzMiSm7Z6TUhSsZgzrSjjKaBvuPY8lEUX46JjcnHukcW46Ng8lMUWy3a+row91KKJFDPpoUtTk2arSjWgZS6qSdsYasz+LKZisfpYADN3yJYo7+xU75gyaaEGvdecJixZHsQfnmxFAn44EMTu2xnYa24cf3nkjzjl9HNTvZuTstoY5bBHfNKbt2vgmKJC2sa4nE54DYc03PjkzTY4TEi+NFOx1OCtjAfvvbUYLTXNKC78KnbbYbaVD2SaqKlbiSXL/4HCysUqpMepbjdxbeGTZp50ynZLsQFq7rY5frdLSoRGIwk0VWsqljK+wUurVtaKgJ5SPqdLQFvxEVMq5qKk8HCsXFmjwYvjQCTE4DxT+sn3ju7uMnendO+UVKJCOg2ivEvKPWL2ZioWLV+aiqWMBxQOtQ0uTCmbLc+DIR9q6ivkmqNcriqbg9p6Vy8hoowNoW5Nuvex3VzMRMV0pqJCOg3ypT2M8p5lpWK5nNCuWMq4wGsqv2Aa2gMxEQnBYBbiCSficatnYkcwhvzCaXrtjZNPGl0+6Z6wXSU1aSVzUSFtc1i328fqY7N8aKwNIxJISIQ3zd6KMpZQQ66cUor6NgOt7VGEIl7ZHosZaO+Mor7NhcqqUtWkx4FwOC7eBZdryxLBTOjQdVHmokI6DfBJvjSjbIHqVUHEaPLWVCxlHPjivovgy3OiM1aBcISulQQ2NTgRdJQgO9+JffbbJdW7OGkDxwy3E05WLeqB+qQVFdJpYvLOzjFQXOkVvzRX3CqklfHgxJOPQ0PgdbHW5ORnIycvF0Vl0xFHO9rCb+Kkk49L9S5OWiHt9jBHujcOje7OeFRIpwEuh0PaV1bN9omQpkWMpRq1Gbwy1hgeL674/tVwu12oa/0PGlqWoLr2Y5TPb8Jtt39fK46No0/a3Seym2gxE0XzpNMA3rjsisVUrI/+04qmmghyywwY6pdWxhjm4TfXOlBYWIJvXn0KPnu/DWuXB3DUqVPg9flSvXuTWkizuYazj7s/WbtbDd6Zi2rSaQI16ZJKD7w+JzauZmlGB2JqB1PGEEZts4nLhuUBFBS7UVjihi/fQFtLFNGo9jMfT6S5hpcFQXtLacOguVuPeyajQjqNhLTXbUgA2foVQVlx0y+t6TDKWBGOJxCOxrF+eQCzFmYjbgL5RW7xiba3xaV2vDJ+0d1ud18RvTlPWo985qJCOo1SscTkPcuH+uoQ4uGEdMSK6cSpjBHhWAI160IIhxKYuSAb0UQCRSUeea2jOSqpQMr4EAmbcIlPesvAMfVJZzYqpNMIr2GlYvF+rVkTlhW2dsVSxgJaZBiMuGFZADl5LpRWesXMWpDvhmE40N4SE6GtjJ9P2uPldNzH3O10SsU3JXNRIZ1mJu+8PBcKyzwS5U0jGLUfRRktUhOepu5llqmbgkGsNx4XcgtcaG+KqpAeRyJJc3ffwLGuimMaOJa5qJBOI1xOh+RMV81k60rLL81oXE3FUsZCSNduiiDQGRchTYHMRSEL6eQVudDeHJP3aAzEOKZgSeAY+q/drYc9Y1EhnXapWFYd72BnHB3Npvil1eStjAYK3lCXqdufbaB8qg800DAGgjn6+YVutDVHZTGowWPjGN3tcfa1dktZUCsFS8lUVEinGR6XgbJpPsmprFkXA1vFR9RppYyC5EJv/bIgZs7PFoFAKw21aC4MC4s96GiNIRYzNQ1rHKAQjkXZprKf6O6uFCw96pmLCuk0w+N0wuN2omK6D5vWRWE4HAhGY6neLSXNU6+aG+IiiHuauvkgRcUeMbl2tMUkLUsZWxhNT/o1dzscMK1W00qGokI6zaD5y2dYXbEaNsWQiJgIx00tbKKM3NQdjWHTmpgUyqma6ZdUq6wuLZoUl1jdsBjhrebu8WtTKWVB+4hpB83dpgaOZTIqpNO1K9Ysn+RO1q4LWZG5quIoI4DXDjXp6tVRTJ+XJXm5FBNel9VDmuQXuOF0Am1NUcQ0/mFc/NFmspf0Fj5pRnerIp3JqJBOQ2iG5MSZk+/ExlUhOOmXjtMmpijDgwK6sSGC9qYEZi3MQjRuwu3abOombsMhaVgdmis9LoRD1r3r8Rr9+KStwDGV0pmLCuk0xN2VilU+3YX1q5iK5ZCay5oeowyXYJRR3UG43MDUWX7RrGnq5jXV08WS1xXhrWlYY0840sPc3W8XLJXRmYwK6TROxSqd4pJgnw7msMYTGuWtDAtqxaE4G2oEUTnDDafLKqbR09RNGJyYX+yWXGl6VdQvPbZEugLHxNzdB7oZNLo7s1EhnaZ4DEOEtOFyoHp1UIJLNF9aGQ6RWAItzVE01kQwdY5HtGQWzGH52b6LwoIiNzpaoojH2Q0rZbs8aZtrEI9vy+mY5m7RpHVhlLGokE5TJEXG7UD5NG9X9TGH1F5WlKEitbqXd0pt7qoZbtGss1xGL1N3kuJiLxj2QMuNatJjSzickHPAOt19SfaTVjIXFdJpitHll66YyXzpIG2QUsdbi00oQ4FaczDGAiYBTJntF580Q4t9fUzdSYq7umFpGtb4pGC5vBTSW75Gua0+6cxGhXSaFzapnO1DPGaifkNY2layx7SiDAazAVichNfNrAVZIngZkNgzqrsnBYUeqULG4LG4RniPQy9pp/j++5IsC6pSOnMZEyFNf8nq1avx0UcfyV/1n0wMLsOBwiK3tBakyZs3clhTsZQhEIolsG5FJx3OmDE/C7G4KbW6KRT6g2lYOeyG1RzTheAYE4mYcHsdYtruixQz0cCxjGZUQjoSieCSSy5BYWEh5syZg5133ln+8vmll16KcDg8dnuqbAGbH7hdBqpm+bF+ZQAuJ/2MmoqlbB26RALSUCMoHdVYaYym7r5R3T1JpmG1SxqWBjKNdVnQ/tKvCH3VNHcn+D8lIxmVkL7gggvw3//+F48++ijq6upEaPMvn3P7hRdeOHZ7qmwBb2rmtFbM8qG1KYpAa1wivNkwQVG2VsCksyOG2rVBzFrAWt0mDANbRHVvkYZVlEzDYjesCd3lSU0kEpeGOf3ZMJKxZLomylxco/nwX//6VyxfvhzFxcXd20pKSnDQQQdh0aJFmD9/Pu65556x2E9lANyGgYqZfvEXMhVrxo7ZYo4cyLeoKNKWckVAJv6ZXUKa8Q0DmbqJ1Q3Ljc/eb7PSsEwTrn7FijKSwDGPx5Ac9f7M3UQjvCeeQNSqsOftJ+p+IhnVr/PGjUaj/b4Wi8X6Nd8oY4vHcMDvs9pXJlOx6G9UlP5gPr2VehVE2VQfsnIsE/dQFnVFyTSstrhmEYx1dLdo0v0HjpGEhppM+H3CeZQV+VLNqIT0N77xDRxyyCF47LHHsGzZMjF1U7Pm88MOOwynnnrq2O2p0i8upxNew4HKWX5UrwnCkQBCcZ1ElYFN3cFQXKwubEvJVCyashnZPRjJNKw2FjVR++uYCmlpU9mvJm391RiAiYXzJy1GFNKpPvajMnfffvvt+NGPfoTLL78c69evF82ZA5o2bRrOPvtsXHvttWO3p8qA+N0uVM704f2Xm9FYHUbRVC8iiQT87BivKD0Ii6k7iHjc7OodbdXqjg/BpMc0LAqSDk3DGvMuWFabSvQbOEb0cE8MwWAQDz7wKF579UMUlZRgU2099t1nJ5x26vHw+/1IOyFtGAa+973vyaO1tRXt7e3Izc1Ffn7+2O2hMigepwNF5R74sw1sWBVE4RQvIrG4pNQoSk8TXoANNVYEUFLuRW6+C53RhBQw6RzC5z0uB3LyNQ1rXDRpmrv7UaWTPmlTLWMTIqAvvvA6xDrnItezB/LdToSNWXjrpWa89/Z1+PmdP0yJoB6WuZvpVm1tbf2+RsE8derUMRPQP/7xj+Wi5W8mCYVCElHOQLWcnBwcc8wxqK2tRabDymP0KVbNtlKx6F7UVCylLxSswXAcG1d2mboZ/MUCJszdGwL0jyYjvDUNa2zgMbSEtLPfyThp4LCTJs3F3mQ89/f9/kG015ZhZkkhZlW6UVbkkr8zSovQVluGP9z3UEr2a1hC+u6778bcuXPxm9/8ZlxP0ttvvy2/seOOO/baztzrv//97+Lzfvnll1FdXY2jjz4amQ6DxagNsURoU10E4Y6EpmIp/Zq6q1eHEI2wd3S2FDDxGU64hxi9St91smWlpmGNDdEIBR6ba1gdyPqSLHCSsJHlojEQljz7ycbjf3kGC2dMR26OG4GgH9Ubi6R+QG62W7Y//tjT9hfSrCi222674bzzzpMUq3//+99jvkMdHR04+eST8dvf/laKoiShOf3ee+8VP/j++++PXXfdFffddx9ef/11vPnmm8h0fC4nqmb65UbftDqImAntiqV0w0U1e45vWB5AQbEHBSVuEbR+99BdIsk0LMmV7krDUkbvjyZut9FvdHe3kE6k3hR8/3134/ILjsXNVxyD7170dXnO7ZPm/mjrRF6OFRwZingRCHgRjbmk2hu3d7Z1psSCMCyf9IIFC/D000/jn//8Jy677DIccMABOOqoo/DTn/4UM2bMGJMdojmbkeEHHnggfvjDH3Zvf/fddyXdi9uTLFy4ENOnT8cbb7yBz33uc/1+H6ue9ax8ljTX82AnUn3ljwLue88xML7E73egpNIjJu9p2/oRjMak73S6jCEdSZcxUIsOhqLij952l1zE4nEYMOF2DG8MhYVuxGMJdLRHEc3yyOftQLqch76EgjERAm4P56T+xmAJhXgsnrKxURB/75pzYbatxqZ6LxLww2G24oPgg/jw/ddx482/7vbVput5MEX4doIFWLlYisWtxWsw5IfH3W6Z+NEp4xqr1OKhHqMRBY5RiB588MH42c9+JoJ0m222EVM0o7mzs7MxUh5++GG89957Yu7uS01NDTweDwoKCnptLy8vl9cG4uabb8YNN9ywxXZq5kwZc6Y4UX00J5hj4MXFMchKMBxFQWkcKz8OYocmE51OByI+NkawyUw6yBjSkXQZQ2ckhlWrQgh0RFBUHkFjQ4OkXTUF3bLvQx2DYUQQj8dQs7oR2Y7OYWni40m6nIe+1NdGEY/FEA62oqE+vMUYWpjuFouhpakZdf5QSvZx8bNPIRxIoLLyRMxbWCH13mmjb2qtwab61/H4Yw/hy4d8Na3PA9lp0WyEHLUoLqgEml1iZo4ksuAv6EBTSw12XjQb9fX1GCsYaD2u0d0ul0tSr0477TRcc801uOWWW/CHP/xBAr5OOeWUYX8fU7guvvhiPP/88/D5fBgruG/U+ntq0kwRY4BbWVlZ2l1ISZIrutLS0u4x+MNRtG3fgeUf1CIRyUNOhRsF2b4B2w/acQzpRjqMQTSbzjCa6xpQUJTA7IXl6IzFUez3ItfjGtYY/LlRuESzyEJBcSHyfZZ5MNWkw3noj2BHJwxXG4pLi1FWlr3FGBLREAxXO3LyClBWlpqsmUcffAALph2GLFQg2AIkurR7PyrgS+yIRx/6E04+9ay0Pg9k1913wT8e+xNmTzsaplmBvIIQ2lp8WPZZNVat/xMOPuZEkRljxVDl3KhSsEhzczP2228/KWLy6quv4vTTT8evfvUr3Hnnndh9992H/D00Z1Oz3WWXXbq3xeNxvPLKK/jlL3+J5557TmqDt7S09NKmGd1dUVEx4Pd6vV559IUXEi+idLuQtjYGn9uF0kovvFlObFwdRn6lD9EEkGXjMU7G82DHAibRuIn1y4OYu10OTIdTmrEwvz65z0Mdg9frQnaeCx0tccRgfcYu2P089IdVsNEBr986F33HYPBEiSEsNePiAq+xMYEpu86R5/GEA+GIFz6fpdVXlc3Bfz4wZb+TZuB0PA/k5FO/if8teRvx1n8jGF4A0/0eAoFt4PVUY8q82fjKcaf1GudoGerxGZaQplmZjTOSj3feeUdMG4Q7vv3222OPPfaQgLK99toL3/nOd7pTqQaD/u2PP/6417YzzjhD/M5XXXWVaL9utxsvvPCCpF6RpUuXYt26dfJbilXa0eM2JICMfukdvpAvdZrzTJeWaM1gIrEEaqtDCHbGMXubHKlHzGtlKFXG+uLqSsNqa2ZdYysVR6+tkRMOWVHSHm//x7C7mEkKQ+kdjpzuoLbOYDZa2/NQWVoDh5EAS7A4HCN3cdoJv9+PH976W/z2149i6XsGiss6kYhvRFb2/vjm/+0Cw8MiUaZUeJxIhiWkq6qququKFRUV4fOf/7wEbFFIUjizkEmybvdtt90mRU74fgrqweBnKeR7Qv82c6KT28866ywxXfO38/LypMsWf3ugoLFMg75nFjCpmOXHG083IBpM0CYlpR/ZD1jJPCRWIRrD+mVBZGUbKJvilfSZAu/IFm68xiikN60LSelEZvnppTVywhGah+k+7P8gbo7uNlPXaS8vG22dEeRmexCJumV7JOqB3wihtSOC7DzLTD9ZBPXuex6G1vpafOWUbZGI5uO5P9ci0GIgu8yUBe/WusWlXEh/85vf7BbM7HA14Je6XOILpv836aceC+644w4xEVCTZsQ2g9fuuuuuMfnuyQIvoMqZVqQlU7GqFmZLiVAWPFEyD2q7jOxevywgHa/MrnznkXZJo5AuKPJg2Ycd4n9kGhaTh5TR9ZI2Bjgf3a0qUxgsffSxh+LVZ9dh4cwZiHYJ6XDUg2isDUvXrsPRx30Fk4nm5iiycl1wuwzklHpkcbvy0w7sUlGIzmgMOdKxzGFPIf3rX/96WF++0047jaoiWN88bDra6e/mQ+kfTr65eYaUCd2wOoQp22QhHEsg27q3lAysMlZfG0FHawyztmGtbmvBxu5pI6Wo2INYzERnexzxbKrSY7rLGZcnzQ5YXDj1R3eryhTmpJ9x5sn44P3rsKa+AH5jOhyOOJpaXWiPNiGrtBann3ERJhPNTRHk5BvS3ISPmQuzsep/ndhj/yK5nyba5D2u6hU13YceSk0ptUyFPkNWkKqa5ceGlQHp+cvWhKm8yZUUm7qXBuD1Gaic7gfr22S5RqcJFJdYgZjtXZXHlJGTLAk6kJC2Q1lQmoBZt3r+zvmIoxOBxFLEzCi2+7wfN9z2vZQ1nhgPOE8y3iKnwNVlcTIwY2E2OttjqK8Oy/VOk/dEMq5CmhXDTjjhhPH8CaUPnHxZwIQm72Agjpb6qJR/1IYImQdjERjZzSpjM+ZnWZqBA/COssBNYZFlgGN50FiaFa2wG+GuXtID1TLYXBY0tYshj9eHnRbti1lzp+KKHxyLaTOm4sADj4TT451UCkDcNNHeEhUhzUPPDnGFlR5k57qw8tNOUYI6ItEJrTw26hQsxX54XAbKpnplhb5xVRDzS9ySguPTs51RUEA31oXR0hjFngcUS5lYukNGG/jCDIKcPMNKw9L68KMiEopbbSoHMGzYpSwoF2P1m8KS4plT7JJ9rt8QRslUn8wt3gEC39KNYDCGUDAhHeJcjii8LgOGkcDUuR688eJneOG5X8HrisABH3bc7QAcd8Lp425J0GiiSYjHyVQsatO+Hl2xYqneLWWCYcN65ka73U5Mne2XzlW0sow26IXdsPKYhtUUFf/cZOyIZIde0r0Dx1J7jLkYa6wJo6TCizgcKK7yoHZDWMpoTiZrSnOTNU/m5LthOK0FbTwSxssv/gxGNIiTvjQH5x1ZgkuOzUNZbDFuuPa8ca9frkJ6EsJJlFXGaPLmjRSPJBCOT66bSdk6PNchmrqXBTB9XhacEuhCa8roo7zoq5OWlS2x7jQsZeTmbrd34AIZdtGk2zuiEnxYXOmVQjilU7yo3RCSqHNabCZT0BjJLmCKouU+/NcTD+EL85cjEm3BPY/m49o76nHFT9ZgybIO7DW3CX955I8YT1RIT+ZUrFk+0XLq1oZlMo1ob8GMgRH9LU0RNNZGpC0lU7EYBDPS1Kst0rAK2Q0r2p2GpYwMtg2lT3pgTdp6JdXWig0bglLQpLDcA8PhRPk0nwS9tTfGREinev/GCt4zLrcD/qzNcQJL3vkX/vlSK6IxJ4oL9sMeO5yJ3Xc4HzUNB+KJ55vx3luLMZ6okJ6kcDLOL3CjoNiN9StpjrHyZZXMIBhLYP3yAAyXA9PmZEnwYJbbOWbNVopLvYhGTXR2xFVIj1aT3opPmsF+qdakucCvqw7D43UiO58LPQcqplh1pxuqw4jFE5MmNqG52Qoaczl4r1iLo9Wra1FS/FVUluYgkTCkLCo17CkVc1FSeDhWrqwZ10WKCulJCks+ukSbtkqE0gTOiXuyrHiVgeGEyXKwNHVPnZVlaWoS1T12Cc1FRZ7NaViTZIJOBdRGPeKTHtjc7UhxdHcyaKykwgPT4ZDrKNvvkloM9RtC4K7RUpPumOwG1xRFTr5L8tO5oKUwrmtwYWrZHLhcUbiMmLSvTMLa5bX141t2WYX0JEXK+TEVa5ZfcvxolmIhCzV5T36YbtfWFkX9xrCYuim0mToyluUMi0qs6jjtzTGNdRhtnrR3YE2aMJ4glWtrXj8NNWGUVlGDtGox+AwDpVN9qNkQlvdE4ulvpUuYQFtLFLldOdIU0hTc+QXT0BGIiWadn9uGLH+g+zMdwRjyC6epJq2MPBWrfLpP6gJXrwrKhcQSocrkhlo0Td3UBpgfTXMka7qPZV9xpmFlSxoWhbQu/EZCPGbKw9KkB4baNGt3p8oK1tYRRWdbDEUVXqnTTiHtMhwon+qVnOJwp+WXTnei8YQEQ9LczYJQSWWnckopGtpdaO+MwucNwuuJ0Hsoz+vbXKisKlVNWhl5KpbX40TFdB/WrwrIJM20HGXyQtNzoMvUXTXDB6/PKSbKse4pTvdJfiG7YUW7u2Epw0+/IpYmPfAkT790Kmt3b1xvBY0VlXngcjrhclhVDRk8xtPeUB0RC126L9ba26OIxxNi7u5Z7+eL+y5Cdr4TYUcpVm+KYc2mCFbVxOQ5t++z3+b2yuOBCunJnoplOKUrVs26EMyYVYEq3W8mZWB4fjs7Y3K+Zy1kW8qxN3UTmgMlV7pZ07BGSjicbFM5NE06FfDc1myygsayCgy5jrig4DVVkO8WrZN+aVYdozstnWlulObelrm7R6/nk7/xdbSG3gSMdsxeuDNmLdwFC7fdBaazA23hN3HSyceN636pkJ7kSL70LB/icRN16632gloidPIiAWPLA2KOm7kgSyZO1urmgm0soVWmkLnSEjimaVgj9UfzqLm7AvsGgvKCxUxScYR5/TRUh8QfbcLRq5seFQArXzosGjUzCNKZlmbmSDuQne/u5RpiRbHbf3YD5mzbhvc+vQdvf/Q7vPPJbzBnu1b89I4bxr3imBaKzIBUrIIit6wON6wKoXSmT4I8stzaumiyQW2GzVQ2LA+KKdKfbSAQTYy5qbtnN6xoxEQgkEA8J70n6FQJaeJh60NJktyKJh1PZdBYBAu2z+kOGkviNlh+2Ie3/9eIeDSBkCeObHf66n3NTVH4s50SI9C34QkF8dnnnIoTTjsJraEoKnJ8Y77wHYj0PaLKkFOxGEBW1ZWKRZ+SpmJNXlN3MBRH9epgr6jusShg0h9FJV1pWE0RTcMaAaFQfEg+aRHSKbpfW1sjCLQzaIxFTKz5JAn/XTnNK/7ypk30SyfSutlGs7SoZGMNh5RS7g+fyymPCWwnrUI6U7piVcz0SZBPZ0tMohgnQ16j0hsWq1m/IiiujZkLrCpj1KJ7aj9jyeZcafql1YUyYk3au/Xzkwwcm2j5x4V8dXWoq9IYy4H21jB5XbGojdvntIqaJExptpGOmNKiMoqcQpq6rZiL/uAxKM7yjmmmxGCokM4AWA6yYoZfVuTUsqQnqvqlJxXUYGja3rg8IJ2K2MWH6zAu0MYLr9dAVo4hQjqql9OwCXdFdzMDAzYMHKO8ra0OS4ZAVv7moLEk/LfPbXT7pdM5eCwuLSpjVmR3VxETu6BCOgOgudPnM1A+zSsmb64CGWCkTB646AqGY9iwMiBadHcBk3EU0pKG1dVog5OzulCGRziUkDrRzkHcETzOqUjBkkpjDBqr9EpAVc+gsSQU3MlmG3Sq00qXrgumzo64xO70N85UYq+9UcaFZAoOu2JVrw3BmYB0SFI/4uQydW9cHUIsanY31KDvjOa58WJzGlZUNBG9nIafJ00hPVgrZqfB2t0TH91ttaeMoIRCuk/QWBKWHmbwWDScQFtDVOaVdFysNXd1v8rNd4+be2ikqJDOEPxuA5WzfYhFE5LXyBtQq49NDjgpBmI0dXeisMQjTVW4jVXGxpNeaVhc9KXh5JxKwqG4BI31zMkd2NzNf5kTHzTW0X/QWBJuq5jihcMAGjaG5TqIpeF10NLMHGkTOYXGgP7oVKFCOoOqjxWWepCdy1SsoNzvETV5TxpTdygSx4YVVlQ3haUxDgVMBkrDioRNBIMqpEcSOOaSzmQYVEhPdJ40F3kbN1pBY2yk0TdorHvfHA5k+9woKveijs02bND7eqSaNGukZ2Ub3e1B7YIK6QzBbTAVxypswtaVnL+pfaWjaUrpTSiWwKa1IfFxiqk7boqAngizXTINq60pkrKqWGndptI7eJASo7tF8JkTGzRWVx2C1++EP2/LoLGeMO6hdKoVPMadjKYqqXuU1cZy8lwwjP4XI6lEhXSGwBUvI30rZ/rQ3BBBuD0hjRc0FSu9MbsLmASQW+BGcblHNNos9/i2z+upSZMOpmHpgm/YPmnpJT3I+7obbGBig8bqNoW3GjTW0+RdNsWLjrYYgm1xWSSm2+K/tSUiJU4lR1qFtJIq2AeWwWO8BpOpWOkajalYRBImwtE41i8LdJm6ISkk41XApC++rjQs1vDWNqgjaFM5SElQYrBV5YQLaRNNNRGUVmxuTzkQbLZROc0vedyNmyLik06nBVuCfaSbre5XXBDZzNqtQjqT8BgO+LNckjLBrljUtBiNqaS3P7pmQwjBQLwrqjsBj8vZb5DPuEV4F7q6cqX1WhqZJr31c8VTaZm7J07wtUjQWBxFlR7RLrd2PUkqXp4LeUVstsF8adb8Th8hHZMWlVGpLcBx2ilHmqiQziAY/OE1HKic5cfG1UE4Tashg6ZipXFUdzSG9cuDyMpxoazKK2bRiTJ1JyfovGSEt6Rh6bU0rMCxIWjSEx04tjloDCiU9pSDm4CtfGlfl186vfKlOzvjiEYSXX2k7SWgiQrpDMPvconJmxNE4yarlJ+mYqUn0S5TN3tHz1qQJZM4tZ6JiOrunYblsVpWau79CMzdVnONreEwrBSsiTqyXGzVbQzBN4SgsZ7NNmiha6qLIB5Jr4qGzV3drxjTMVg6XCqw3x4p4x7lXVLugc9vYOMq9oHVVKx0hRNhXU1YAnZmJk3dxsSZuntGeFPghILxtPJF2kJIe+kDHcTc3dWqcqKkNBfu9TXJoDGrYuFQ5pXK6T6ZT5rrY2nVbKO5q490Tr7bdkFjRIV0hiGTuNuJqtm+rq5Y0K5YaWzq3rAsCK/PQNUMP6i8sIDJRPvUkhHeDL5RTXpo0C1BnzTrdjuG4FKYQBltVRpjZHeVJaSH0pKRwYpcrDFlq2lTvKvZRiJtcqQ9Pic8PvsFjREV0hmZimWgYqYfDTVhRIPWzcSbSkkfeL7YmnL9ctbqtkzdlM0sBTrRdKdhNWka1lBh+VaeNKtN5dbfyyIbCYmcn5hj29wSQbAzgcJyj5h/h2KZsbrtGSip8qCxJo6E5Eunx7XQ0hyRoDEuRiaqR/RwUCGdgbD4AIU0qV4VQIwm7zRZ9U4G2sLRUTc44flqrAujtTGKWQs2m7onKvWqJ36fAX+2E+2tmoY1VMLhHr2kB4vuZuAYZbo5QUFjG4KyR6wiNpSgsSS89kqn+tBQGwNLj6XDnGL2TL+yYY40USGdoSVCc3MNKZzPEqG8LqmVKeMP/XTtkZg8RuNiCLB39LKgpPBMmW2ZurOkGf3ETzJWGpYV4a1pWEP3R/PsSwrWYHnSUnFsYqK7aQmp3xSGL8sJXy6zQYZ+TbHgScVUL2JRoJXNNtLAjZYwIQ1i2P2K1/FE9okeKiqkMxCujqUAwUyfCGkX6JeOp02gRzpD7SIaSyAUjY/YHMhqUJwAWcBk+twsMYc6uorVpALJky3WNKzhCmniZVnQQd7r6GqwMRHHVYLGNoVRVuWT58OxzHBeKav0Steuho0RuU7tbvKOxBLobLM06fHsGDcaOD8rGUbSf8R86Y/faEVzXQS5pW4RIL4UTfSTnWAwiAcfeBQvv/IhIlEGd0Wx9xd3xNlnnAi/33I9DBVaPVqaIpLusssXCqUMo9uVGlM3ofZRUOjG2qUBiUJm8BgXDsrWO2ARScEaRHvjaY3FEgjHEoAVyzVuMD6lsSaMbRflyfPh1H/ndZDldaGw1JBmG/N2zRNBnarrcii0tkRFm87Jd0mxJzti36OnjLtfmkEeHq8TG1cGJU8wHXxI6SqgL77wOrz+YgC52B1l3l1Q5NoT77wSxEXfvk5eHw5JLdpwOTBtTpZoPzR1p9JUV1TslQYfgYAGjw2FcMS61zyegafgpqYmnHPm2fjlz+/GGy/9B4d+/kCcfcbZsn28aG6JStCYtKd0Dr9JC+eVonJDipqYZsL2c0qz9JE2xdxtR1M3USGdoXB162WP6Zl+rF8VlFxMmrzt7kNKR+77/YNory3DjJJCzK7yYGalB3OmeDCzpAhtdWX4w30PDfm7qKUGWat7eQDTZmd1V6xKlak7SVGJW/4yeEzTsAYnFOoyd/v6n4IpiA876GswArOwaMEXMKV8Do4/+CK4grNk+3gIat771RtHFjSWhJ8prjSkpGiwPW57v3RLU0QsGdl59syRJiqkMxSuGmnaZuvKuo1hJMLWqjcdG7bbncf/8gwWzpiO3Gw3gmEfNtWXI5FwIC/HI9sff+zpYZm621qjqN8Yllrd1KI5MU5klbH+KC6x7LDtjapJDwXmSLMFpTHA2uqqy6/ETnMPxYJZC+V9jBpzOJyYP3OhbL/6iqvGp9JYdRj+bAPenOEFjSVhrEtZlQsMc2vcGBGftJ2vh+bmKLJzDbhc9ky/IiqkMxif4RQhzZXupjUhiRBOlwIEaVV0pK1TBDIJhX2Ixw0EQlnynIK7o61zyNoGU7fWrwhIWs70eVlyvrJcRspNdUzDYkRwW0tMc+6HXBLUKf2L++Oj95dh/owF8m+eWbMrvIxHlts/fH/ZOAWNhYZVaawvFHRZfkMCCemXtjrt2VuTzimwat2n+h4aCBXSGQxTJvLy3Cgs8UiUN6cAmqeUscU0O0SzoOwKR6zWfx2BbCv3VapPtQ/d1M3e0csCqJrpt0ylXRaRVNMzDcvufkg7QP89XRX9mVgTiQRchk+iuonbHZWFXSxhnWdudznZTCUxDkFjEZRWWgvK4fqjk1C4l0zxWs02pCNWwt4tKvOt9Cubxo2pkM5kWEnIRW1aSoQGZRVMTU1TaMYOrtCLihzYWLsSsbhLJtucrHbEYi4R2NV1K5FXMLTWfhR+HZ0x1KwLdbWlNOUc2iF6VtKwpBuWZe7Wa2jwYiYsz9ufidXpdCIWD1n1umnx8oRlYRcJe7sWdaa8zveNddBYKDDyoLEkhsOJMjbbqI8gGrb80nYkzpoFbFFZ4JbyyHZrUZkk9Xe3kjJ4UTIqmMFjgY4Y2htYjCJ9au6mC0cddyyWr30cm+qb4HCYyM3ugNsVRW1DDCvWPo4vH3kUgtHY0EzdywKinbAUKM8TU+ns4EuTNKwitxSGoBDR4LHBfdLMrBiocveOi+Zj2dql8m+n04TbHUEoYpmhuX2nXSxT+HhUGhtqe8qBoMCrmOaV67SxOiJd9ux4PQSDMYSCCVvnSBP77pkyIXhcBsqm+eB2O7q6YrF1pf1uqHTm5FO/iZkL5yAWr0dr+wd4Z8m92FT/qExc0+fvjKNOPBMd0a339eZ5YZWxDcuDqJjugz+Lpk97mLqTFJd4EA4mENRuWKPuJX3LT27FhyuewdLVn4lGTW06FPZi2erP8N7yp3HzrT8eh6CxUFfQ2NDaUw7WvtSb7UTDxjDicdbxtt/Cv7nJWhjT3M2AN7ti3z1TJqxEKDvxVMzwS1csamVD0eqUoePz+XDx9b9EVt5uyM5vwtSZDhRW1iK7IB9fOuAa5GdnSc1r+pu3FtUdCMZRvSaImVKr24TLSH1Ud3+NNtpbNMJ7yEJ6gNeLiorwz8VPIZG9Bo8svhOvvPcnNLXWI+Jtwe//8ghyCwrHdH94PTXURFDW1flqNC4UsdC5rf7Ski8N05bBhM2SIw3kFLglBdWuaMWxDIdCmVHeFbN8ePeFZpgxE7ytWCnIziagdIITYFuzA/n5FTji9AuRXXqJxAJ89GoLPn23DXsdaEoqTkckhmx3/xWowtSiVwalG1LSH53rsTr32AVqT7RxtmnLyiEJaU/e1rVVCup77v2tmKI3tHTiz3duwPa77YncwjxxdYxlLEKy0tj2u+bL85H6o5Pw+i6b6sOHr7RISVMuMnNgv8hul9sBX5Y9G2sk0VlY6c6Xpi+xdk1IVr3azQhjOgFuWB2UjkfFFR6ZwCjDtt0lD9GIieUft4tGTJ9zqJ94ADF1RxMS1c30mJw8l/j7/CloS7k1srNd8PoNifC2o+ZkxxSsocgGCnK324XKGVatfT7v7zoZDS3NYxM0loTm4/KpPsRjJlrrIrLItFtRk+auxhpURlRIK7aGK/KCQo+k0HAScIJCenStFJXNUIuoWRtC5XQfEg5Ly2CdYE+OIQFgn7zTJmZPPgLReL9R3cFQDBtXBboLmBg2ieruCSc6K8I7qsGHQwwcG6po8He1l2Xzi3g4MaZZGBI01l1pjJHOo9csmXVQXumD0+VA/YawXLN2arZhMv2qKYrsfJektNnJItUXe93lSkrgDcWc6apZPqtEqMOBgM3L+aULnEg7Q3GpEDZlll/cCH7DiWy3S4Jptt8tD80NEWxcE5QmGYEoezL3FnDUQqpp4YhuNnVz0rabO4ITHRd6NHezcp2mYQ1MmJr0EHpJ9wzwnDLHL/ckF3xcBI1VPnoyaCwrx4AnxzWqoLFefmmvgZIqr1Q0pGHOTsFjiTRoUZnEXne5ksKuWJZfmnmDnawaxUnARivfdIUCtXpdAImYiaoZbP/nkCYEDKyhxlIy1YuiMg+W/LdNTITUOHoG7knFMmmo0SlFZ6ip8rQw9cpuJNOw2pNpWCqkBzF3Dxzd3V+AZ16+Wyp5bZTCQw6rK9YYXaPU0KXSmGmOmYWG31M6xYPa9SHATNjKuhKNJ6TOPIU0lRQ7o0JaETyGgfJpfhiGA9VdqVh2uqnSFR7DjWuD8OcYyC+hv8/y19FykeV2iVDefrd8rFveKSt7vtYzHYuxAaFIHBtWBC1Tt2nV6vbYzB/dMw0rFIgjGNp6Slkmw7aTDABkB6yhCulkgCfby4pLymEiFB8bP+/mSmNWZPdo/dFJ+D30SwcDcXS2xhG0kXWurZ1V3BJi7h6r8Y4X9rzTlQmHq16fj4KaJu+AaEVbSwlShgarLdWsCaFqhh9xsELY5qAcRnJTI5q9XTY8PgOfvNsmvmrWOqbP0fp8HJvWhkTzEiEdN2Wytmtep5WG5eiuPKZsCc8lcQ3D3J0M8KyY6UNnewwdTbExa4jDVCTmt49V0Fiv4LFptB4BjRvpl7ZPA5+Wxqj8taqN2fNeSmLvvVMmjGQnJUZ5V68N0lEl5jTVhkYOrRFtnRE01UQwdbZfGpjQrZD09/F4+9xOJJzAwkW5WPpBm/idOUd2RGPyeS6UaOrOK3CjqMwtgs8vwh02FtKmJaT12tmqkBZNehifk7iR6T6xdm1aPTYNcaygsZBVaWyMgsZ6av+5WW7kl7DZRlj2l4tMO9DSzBxph2jSdvZHExXSSjec/CmkmTYhEZmsPqYm7xHDY7dxNf2HsPzRDk60mwUshXWO29WVjpWLSNjE8iUdYtUIRROSNx2OWlXGqEUnwMYK9ipg0m8als+Q2AZNwxo4aIxHRsqCDkM+eGjy9rkk/3isGuLEuoLG2K7Rkz02QWM98bmsfGlptiH+b3vMJ81NUfiznbJQsnP6FbHv3a5MOAxOKSjxIDvXZU0C7NqkqVijCxpbGxIt2J9nRZHSnN3XhOlm5bBcF2bMz8KS/7Z2TdyWFl2zISQ+PSuqOyGTqJ19aNINq8gl/nWNaeifSNi6pygghh3gKYWHvKheFwQSljtkNFH0XEg11Ix90FhP7b90qhctjRFEQmy2YY/5pJktKkWLpsYPW2Pz3VMmEsndNQxUzWaJ0KAUyrdTsEe6EYzGUbs2ZKVemSa8BjVh5xYmwRy3IQJ4u650LAp2Tm78/PrlQVk0lVZ5xXzsd1u9b+2KpGF1dcPSNKyBNWliFTMZ3rn0ugxUzcoSaxf9vKNtiCNBY5siKBnjoLEkjJyumOqTBX+TNNtIffMVk24odr8qdIupWzXpYXDzzTdj9913R25uLsrKynDkkUdi6VKrE0ySUCiECy64AMXFxcjJycExxxyD2tralO3zZEzFosmbK99gW1xuYjsVIUgXOBE1N0fQ2hgVIU0r30DNMCh4OTmWTfNKmtWSt1vlOedLVhljrW6eAU4mdjZ1dzdXKPRYaVimpmEN1EuaeNgPfJjQ6lJc5pacZqZiUeCMJlWykUFjobEPGkvCa7aw0A1/roG6DSFJ7Uy1yTvOFpVdfaRdDqbBqZAeMi+//LII4DfffBPPP/88otEoDjroIHR2dna/59JLL8Xf//53PPbYY/L+6upqHH300Snd78kEV+oV0/0iIKoZnKJ+6RHB9nxMveL9z3KOVCUGMiV6eqZj7ZGPtcsC6GiNoa02io62WLepm++ze04nKSrxINgZR0jTsAasNkY87uGfSwoV3qNsL2ulYrEhzshMyBTw1WPUnnKrC/8ezTZIqhf94XACnR1xaVFJi5XdsVWDjWeffbbX8z/84Q+iUb/77rvYZ5990NrainvvvRcPPfQQ9t9/f3nPfffdh2222UYE++c+97kU7fnkMnmzDWLpVJ90xZq5Y7YEp+RYDY6UIRJhlbDVQRSXe+HysdCqlZIyECxu0hGOoagygnVrluOibzwAp8NAXs62WHbzT3HpDTdg7pQK26/6e6ZhtTXHkShUId0Xaq5s7GCMQEDw/PsMAxWz/Fi5pAPhjjgcuY4RNcShO4JFTLLzXPCwReUYB431LmriwwcvNyMRZwW9BHI9qa3ZTXLzrWh2u2MrId0XCuVkNxhCYU3t+sADD+x+z8KFCzF9+nS88cYb/QrpcDgsjyRtbW3dq8iETSINRwL3fTzGwNvc4zBROdOLT95so90WQUQRjRljXt92vMYwkfQ3BqkSFo6idm0Q87bLRTQWt7pbge/rX2h5HECgrRlnff147DL/PCyY+RU4nQl43GE0tszGt447AU8+/QSyS4snZAyjobCQDUBoUowgysIdxvif33S6lkKhmFQbc5qJXvs71DG4nbTOuOXfm1YHMG27bISicWRZm4a1kGRkd2mFG2YiDpfDNerj198YOKdUTPGIH725JgTvVKZiGSlLfWpqZAU0E1n5DjgGON4TcT0N9bttK6Q5gEsuuQR77703tt9+e9lWU1MDj8eDgoKCXu8tLy+X1wbyc99www39LgDq6urgtHki+9aOD8fAC2msx0DzWW5hGKFgFGv/V4eCCgOJDveY+0PHcwwTRX9jiCcSWFsTQntLGHlFHrQ2NsD0uhHv2Hp+8/13/QoH7ft1lBXlYN0aJxJworC0DVNnboOSylzc9bOf4dsXXzghYxgNrDXuNOKo29CMusowgt5hSo9Jfi01NbWzGSxamhoR63ANewxScjUcQW5hAis/bUJ+RRDxdgM5nuEd50AkhvoNnVi4kw9tjY0w/B4ERnmP9zcG/tvticB0xLD2s0Z4fF4kOt0pK8izfm07TDOGeLgFTY1BdPYz5om4ntrb29NbSNM3vWTJErz22muj+p5rrrkGl112WS9Netq0acjPzxdTut1v6IHgRUTTVGlp6ZiPQdIkskLIyWPgkxfTts1HnteFfJ8nbcYwUfQ3Bnayav1fAzzeEOZsV4a4EyjP9oovcWu8+Px/cOyBFyLaEYPHCCEc9dI2is4IUOiuwmOLH8eNP/rBhIxhNDDYsKgsgGjEjdyiYpRlW1WnxpN0upbc7jiycoCS0lLk9VjADGcM7kAE0xcYWPFBBwqLSuByOVGa7R2WuXr1xk7EY62YOrcIhSV+VOT4Rm3+HWgMnmAE5dNj6Gw1kF1UjAK/B9me1IifeCyO/OIECktLUZ7j6zdWZCKuJ5/Pl75C+tvf/jb+8Y9/4JVXXsHUqVO7t1dUVCASiaClpaWXNs3obr7WH16vVx594Qngwbf7Db01xmsMPrcDHo8LVbOzsGF1CDvvVwhmjfD3xtpnNRnPQ8yMSepV+VQ/nB5DzH1e99YrG3FScDm8ErhDI1h+XgvicaZbSfaK/IbhsBZJ43GsxvI8uKVlpQcdLTHEWfhygiJo0+VaYg9xF4toGMYW+zrUMfh5f87yi0uqpSGGgnKPHGvWOhgK0k1rU1h+r6jCC7fk64+NT7q/MfD6Z1GTFe93wGHyHhmf63gotLZEkVPgluPP4kLOARYm4309DfV7bXU188KhgH7iiSfw4osvYtasWb1e33XXXeF2u/HCCy90b2OK1rp167DXXnulYI8nJxQm7LLErliNtWGEO60ONlpBanDEHx2Jo3Z9GFNm+uSY8VgO5n/jDRuLh+TzfKfLSMDrYelCq880fWh83e4CiDi7+0pbpU01DWvL6G62qRzNmaT2Vz7FJ7nWI8nC4HVJf3ROngvurPELGutVx3uqD6FgXLrsheKpqb+QYB/pZqv7FYVzGsSN2UtI08T9wAMPSPQ2c6XpZ+YjGLRKK9JEfdZZZ4n5+qWXXpJAsjPOOEMEtEZ2jy1sp1gxwy8CYtPqoKx8NRVrcJheUlsdQjSUwJRZWTIRDdWXv+Oi+Vi+dqkc8+T0JVo0gGVrl2KnXRYgXSgq8SLQEZNIZk3D6g2PyXDaVPYHU/G8HkPS+zastBriDKeal1VpjJ2vPONSaWyL/TUcqOxqttFQHUlZs40YW1RSk863WlSmQ7aErYT03XffLc76/fbbD5WVld2PRx55pPs9d9xxBw4//HApYsK0LJq5H3/88ZTu92SEZrOcHAPFld6ufEzI6lfZOrQ4bFgdFE2puMIjk+dQczFv+cmt+HDFM1i25jOYXZGf/Mvn3P7j225BulBcTF+rA+00eauM3qLBhsdjdUAbbeGh8pk+1G4MIxFh68qhL4jGqz3lQPA+yPa7JB+7bn2yOcjEXxidnXFEIwkrRzod1Gi7+aSHYv6gs/1Xv/qVPJTxQ3oWs+vObB+WvtsBTinJOsF27xqTSsLxBGrWhlA53YeEo6t39BAnA6Ya/nPxU7j68ivx6OLn4HJ6EUuEsWiX+bI9mYqYDiS7YbH8opq7e0MhMVpNmkgJ31l+mGYT6taFUDHXL0V0/M7Bu6TVN0RksVDYXWls/PU15neXsKjJmpA8j7IvwBh0dDNNU+47jmGwxQarAHJxxBaVHHc6YCshrdgHa6VuVTb66D+t0m4xr9wtJu+ByltmOlzAdARjqN8Yxp77F4lJL9vtHpZJjYL4nt//Dp2RGGo6gijO8qJgjKPqJ4KcHJd0eWJ5UJoYld4Vrxg45hyDwkMFRR7kFbrF2lU+xy+5z7xvBxNqmzb2rTSGccdlOMQvvfz9doSDcYSZ8D0KTNOyHrSHY6JAFPo9yB0kYry5q490Tr7b9jW7k6THUkJJCdSkSyo98PqcUieYK1BWC1K20vVqfRCJmIkpM/1yvOjbHwlcCOV4PIOmbdkVajUUHgweo3an9DF3e0fvD+UxZtMW1tqni8XpYOe0wQOy6I+u3xSy/LJZBnzjHDSWhFalimk+usDRuCGMSHxkzTbMrj7rDYEI6jrC8m9q5axPMJTuV5zPPL70CBojqkkrWxXSHrdlUmOJ0O33zpcVa55p705MqYJ+vuo1QfhzDOSVuMXMO9KCDazuVpJFnzbSkmQ3rLYmq6+0RK3rNSOFSKJRmrudo/BIb8bvcom1a+l77Qi0JuDIZ4lQUwK1ti6kIyjrak85UfWrqbEXFriRnWeItalqnl9q0htDMM/3NGu3R2JSi6C5qRE/vf56fPbRcrgMn2Q/7LzLAontGMg11NJstajk9TnWFRTHC9WklQGh75kaXcVMH+qrw4iFEqIVaSpW/7DGOf3R1KKTAno0ATmcRNJVsPHaKdA0rP57SZuQwMKxOLUUxuXTvZJOVL0qMKQsDC4mm2rDKKmw3CgTWb/ax6IrU33SbIPJhkNptmFSOMfiaApGUNcZlkppgdYWfPPYE+EPz8HxB1+MY798rvw1ArNw2EFfQ1NTU7/fw/QrBo2lQ4vKJCqkla1CUxiL+fNW2rQmBFq71Xy5JRREbZ0R8d2zNSXnSUbfpquQHQsKizyShkXzrrqlLVo7rUWLpUmP/tqQDmp+FgqxsjB4uVHb3Br1DWFEwiaKKr0TFjSWhMVDyqZ40bAphEQsMaj7LBxLoCkURW1nWDRoav1ZHhduvu467Dz3UCyYtbD7HuNfPt9p7qG4+oqrtvguBpO3NUclR5oCOl0CYFVIK1uFN0VergtFZR5sWMlJgKtanXH7Qu2FfkFSxdaUjOw20tOfPFaUlHR1w5I0rMzWpKnZnXXG2Tj6kOOxatlKXHXu+fjWWef0q/GN1NpVvTYEZwLio+VCYLCgsYJy94QFjSVhpgP90lznt9RGEI73v6+8nyzNOSSBYbQYZHusvuvkfx8tx/yZC8S/HQj50NqeJ/8m82cswIfvL9viO9nspbPN0qQncmEyWtJnT5WUwJuKgprBKetXBeCCY6uTQKbCnM9Na0MSLOXPs1bqjL7NZJJpWCwekclCmoL4Kwd9Da7ALHzli2egMK8Uh+59IlzBgU2zw7V2MW4kFk2gkW6p+MDVx6xKY2HRJt0+54QFjSWhkC1l+1avE/UbwhI41tPknRTOtZ0htIVjEhGe7TF6CVWW0HUbWQiG/ahrLEVTSxHaO3OQSFjvcTgdkr7Yt8sUy4HypyRgLo3uTRXSypCKJlTOykKwM47WBqbUDK8EYSbAgLqkP5qVlBh1m06r9fEgJ9cFt9tpBY9l8PVy1eVXigl23qyFjDSQbYyV2pppdjhwEV1c5pE+8MzCoMgb6P6MdgWNlVZ4RfOcqKCxXv2w3QZKqjzil+Zin9cG/eTNXcK5NRyTRW5f4ZwMvFuzNIDplceJcDaMBArzWuQ1tr/kmMxE/yV0GdnNRWPS3J0uZPYsogwJj8sQnxf9aJwEePunolqQXeHE0dwSQVtjVPzRXMBrLnlXGlaRGx3NMYnizVRoep03Y4FV7tXs0vbYZWIrptnhWruYhWFZu4KgbOKisb9ULOZRS9BYldeqEZ+CCGeWyeV8UrvBqlXfGY2jpjPcLZxzPFazj7732Kr/deCvv9uA5/9aK4I2EHobJYWN8PssN1M8Yd1zA5XQbWmKyCIhOy99cqSJpmApQyoR6nGzlrcP61cGsXDPPARjMeR69fIhUdPEhjVW0A5rKZsY/1rIaZWG1Zy5aVjxeBwuJ2MUrHGHwj44nQk4HYmtmmZHYu3i/bnqk0bEggmE/Q5xMbj6HO+GhjBiERNFE1hprC8uEdI+fPRaKzqa4nAUO8X87HVvaXpPiObcifdebUZjnRWUecRpU+DNPhjfOOI4AIdi7vQFsuiJxZzdJXRZoa8vTU1RZOcacLnSJ/2K6EyiDAovaJb040qdq1/WCQ7HTamopdDMZpm6i+lr8zllEkpVQ3tbpmF11e/ORL80W7xGYkHJRw6FvegMZiE/p607/30g0+xISoTSJYWuhjj09bJYSE+soDGrJGdh2cQHjfUKHptiNdtoqo7A77JKEPcU0EnN+fEuzdmXbeBrp1bh8JOrJPCssKgID/ztMYR8q/DY4l+gtnEl3vn0NYT9qwcsodvKHOkCq8ZDukR2E1WFlCHnN7JoQiLRZPVKZgnCuIkRFtSaNHDii3TlRy/YIU80xmy3kVYr9XFPw2qLSwEPukkz6XqhoGwNR7FgxzlYvnY58rK/AK8nDL8/0P2esepuRiGXk8zCWBXElIVZkluc1aM2ds+gMVcKgsaSUEBGg21Ys/Z9vHPjWtQ1/RvReBDb7DgP3/3hD9Ha4MO71Jxrw6I5UzhXTmcFv95QUP/krrvk30/9cSM82fvgsOMqUeT3DNiiMr+E/mikZHEyUjLollFGOwnkF7qRX2zVCWYABieBTIdBYk3NcQQ74qiayWYHZtqW8hyvNCya/2nyzjRNuj0SFd/w9266CdUNAXQGIyjIbRbLN6+TpavHrrsZteKeJUINycLoXSKUQWMNNWGUVaUmaCwJo9lpqnbFgXnTv4hjvvwtfP2gi5EV2R3Xn/8PPPPwRvj8Tnz1FEtz7k9A94QWPU+OE53tsQGte7z2mGXAxho8VunkdlEhrQwrFWtKV4lQaopDqRM82WEAXe36GAxWfprmlZvfo1p0N0WSKw2pPLY1Ic3AsoGCndIRpikyhYhBUtFQNnbb9esIu97B4y/cgb8+/2s8svgXSGSvGdPuZn63CxUz/d1ZGDymPU3eEjRWE0FJReqCxpLR7jvPOxRTKgoQi7vQGchCfXMZ/L6dUZhbhjW1D+Dwb1ShasbWhTPH1xmJS7ZJfr7bEtKM7u7nGgoGYwgFrRaV6ZZ1oeZuZZj9a/349J02dDTGkFXkkknA68pcocTC/vUbYyif4oPD5ZAbKlUaih2hCdbFNKzmyFYbILBgBR8elxNZLgN+tyELw3TSeHqZuUNRSYVymg688o96VEzNwrn/dzGczkvQHoqiNMc3aMem4cLjxS5TLrcD1auDmF/ikuqA3i5dTILGouaEtqfsj4/eX4bjDzoEsQRTooDmtgJxA5QWNcDjjuHR597Y6uej8YTExHDayfO6pMhJUaFHrFm8xpgL3dec3dwUk785Be60ixdJr71VUorXMKROMLVGTgLij83g4DGOPxCJo6E6hqqZPvH5sU1gOgWlTGg3rAFyd5ONE0ywLjzLQEZQ2xFCfSCMjsjAJky70hGJiibNOI4PXm9GS2MU+xxWKvW1Cf86xskl5fU4UTHdLy4pXoehaLzbJ1u9YXPQGAV6KvyyjGJnMwxGtSdznCmcS4sa4fVYKVJ8vb9o92g8gY5IXCwy+V4XynN84n+mtSIn1y3d50Kd/RdasnKkrUImaSajVZNWhjcJ+DxGVypWAPN2zUUwGh9zjSBdoI+vpjqEaASYyvrmUsQkzWaAcYb5qHmFLhHSrCbbNw2L/skrvnMlPnxvKdwuP2JdAURX//CHMPML0BmNiFmWAVBcAFHw2XkRxDiNtkhM7pWWhijee60FO+1VICZmwoUKtdjxSNHjceExqpjlw3svNsOMmQg7GLBnNTip3xSWBRODxrypChpzOiWanVHt/P2srECvBQuvDwaRJaPdTRY76YpUp4utoEtz7nv88grcUoq3oy2ORKnZb/crWhh8WenTWCOJzijKkOFN4u2K8t60LmRNAvHM7YrFlf3GNSG4PUBROdtKWn57ZTMysRZ70N4VONYzK4gCmmUxWR7z+IMvwrFf/pZ0MvJH5uD0o76OUHsrst1O0fgYhFUXCKOmI4SWUMSW/muJIA5Hu/OTX/lnvSxQdvliQffrFNJ5/QiZsYLC18rCMFG3LiSBjZGue7RhUxillZ6UBo2RHRfNl6j2/li+dim23Xm+ZaWLJ9DJrAATKPC5UJ7tRaHf0++xy82zgjU72q3Sn31pbrIaa9Cyo0JamdRwpc4I0njc7K69m6klQrlAqVkXQtkUNxLSUIP50ek1AUxUDe/OZBpWD8GaLJe5YOZCOLo0J2pX82cuxKJ5h+JH110nz3lcs90uZLmcUu2uJRSTrkjJzkhcLNlBYNOnTqHCe+TTd9ukpgDN3K6uvDO2MqWvfTyLAPFYFRa5xKy7YXVI2mKyiYVVaSyCksrUBo0R9ntmVDsLj5hdZm2ePz5/f/nTuOKGG6UKGYVtoc+FimwvCnz9C+ckebluWSR3tm9p7pYWlSxkku8SM7udLTH9oUJaGfYkUFDstiaBrlSsQDQmwjqT4ETQEYyiYUMY5VNciJmJlOWd2p3iZBoWG230uE4YQMSymNG4gU11FWhoLkIgRLcBpIzm/z5Y3ut7eGypKbKms0RNJxJooHbdGUJjICL+11Rdh1yoWmZuBzpbY/jvi03Ydte87vQhLiSk85TXEibjmoXhMqThxoZVzMJgpHkCdXVW0BgtPjS3pzJ4itHsjGpPZK3Go4vvxF8Y7f7cL9DpXYnfPPYwCgqLUOhzdwtn9xC0fhYQ8ucY0uWqbxYBLwlee5tbVCKtyExnojJiPD0mAfqlP/flYrR1mfgKB1ntTrbUq+r1ISTiQNlU3kaWK0DZkqKirjSsphgS08wtAojCQebtOqWLUVNLIZzOfGT5gsjxT5eymkY/LT9pRjecbKhg+VsD0bhV+7kzhGyPWzRWCvKJ0JpEUwtxAZKA123gX083wONzYs/9i7tfp9WFgme8a7qLn5clQmf6sOyDdoTa4vDmorvSGNtTUpCnWlBRUN/z+99JU43GQFiELB+5bkP6RQ93EeF0WJkEAYnw7i2kuUBi1bv57PyVhhkDKqSVEU0CxVUOvPXCJtx02Q/hcTUiGHdj4aIv4aSTzkRxXg4mO9TiNq4Owp9tILfQ8pumW2rHRJGb74LhckoxiWSkdjKAiL7TcNQDlyuKsuIGRGMuBIJ+BEJZmF55LP5yTzUW7JSLeTvkIDt3y+lKooEdDhhuIOSyoqbbw1Exg4uZ3OWEz0Uf8PhNzjTN8sGFwfIlHdLS9ZDjK+DxbjZzUzjnetyYCFgitGKGX4qmVK8OYfoOWajfFEJ+kVtaRKYqaKw/uLDN8rgl9iBb+kWP7B5ySOMMV1eudG8h3dYeRTyeEOtfKs38I0VnFWXYxCMRPHb/NQh11KO5phDr15hoWNeKZf95GNddeQ5qmtts4SMcTzjxsjwqU68odiig03ECmLg0LEZ4s8CG2SuAiIFCkagHXreVIuN2xZCf2472zpfhyPkvSio8eOeVJjz4i3V4+s+bsOKTDsQYJt4PUkjG4KTvkihw6U4m/uuQBJ0xnWusu3FRS2sJR+XchwJxvP5cI+Zul4MZ87LldWksAiDf656wUrFyDPwGSqd4ZcFAa4K0p7RB0Fhf6L9nQFi+zzPqvO3cPLfkSvcNZKU/OpkjnW6FTIhq0sqw+etDv8PGFUsxc4obUyu/hpLCZhHK1XUrsWbZ47j//ntw1jcvEv/bZKxhTX90W0dEAnF22C0PiURQhIJdtBO7QT8gtTimYdFNkEzDYgDRYQcfj53nfR652WF5L1+j4GYA0QN/+4vUZw6H4lj5aSeWfdSOF56ohdfnxJxtczB/p1wpcdnfcadgYpEdJj7R/ElzcyAalkmaRXmY0sW8/9Fcn9xXCmhWvMpyO/HCc42ivX7+oJLu17mYY04vf3Oi4Jg8XVHen/63DS7TgZa6COYuzE550FhfeO7GKl87L99AoCtwjI+kq8PKkXZYOdJpeI+qkFaGzd/+8lfMn34M8nO9aO/wyeqcN9uU8rkwcTReeOpJHHfaeaJlsNjAZPNTM0iIrSkJW1N2hoNiYlQGFhoU0is/7RD/cbIiVEFhIW687R785bdL8dx/7oZpRsQEvs3O87oFNPH6DGy7S548WhojIqyXfdSBT99rk/QumsPnbpe11d/3d/mvaQqlaZpFMdgeMVndbCQmYPrB+V1coK1bHpDxfemIMnGBEC4MaGZnVayJXsBxTMzC+PC1FqxY0oF4bHN7ysnqlsnN9yAWTiAcjiOR3VNIR+HPdsLtSb/0K6JCWhkWnOiamkxM3XUOIvEQWtvz0NJWgIK8FtEippTNwesfQPzWzGWlmbHI50aWe/JcatQGN60NSWEIf66BYIQmxPS7+SeSoiIvPmprRTRmpWGxAQTNkh2tHizYdhau//WjopEy8Gpr2g6F8h5fKsZu+xZJTAAFNs3h/32pEaWVDuywhw8zF+Z0pz31RNK5+HB2Fc2QLlUxyW1mU5ThlCOl2ZxaNE97PGri1WcaMG1OFuZtb8VjWAVEINd+KkysDPDMyU+gpaUeD969HA4zC7fd9Hvs/vltcP5ZJ8Hv33pd7HQkj7nSUtAkhnjhZuHGQiaWFs0FG9KOyTNzKhOGw5FjTXiuGArzW9DSli8BP0UFTXAZCTgc2Vatb7ch2kR9IIICbwJ5XvekMAkHYzHUrAli6sws0cw4qaejr2siKSmlP5SdiGKI51KVtgRd3caQ9AeOxK2AJ6YLJYXM1roVsbQmhSIfYg7/pB1L3m7EC0/Uw+tvHNQcbvmv+dhcZKQpFIcRdohmzEUl//Z3XjkONs+Idpm5X1vcgEgogS8eWiLfmzRzM1WsZ6vIiSQWCeNH1/4QvvD+8DhLAWcURY5dseS1Jlz8wXX4+Z0/nHSCOi/fCsxjGlYyV1oi75ujyC20Ut9Uk1YyI7o7L1t8srk5HmT7A3C7omhsKUJ9Yxm8nlr4cy0hTRjVSrM3A3jYoSbd/dTU/pqbo2hrimHKl/xiup1s5vzxKmhC2puiiE+1JtBQmHXPw5i7f45oQPk+FxifzbrXNCWHo3ER1DTPbu2aoTl8m0V5qJgegQP5WL7E8l/3Modvn4OcvP6nO07eydQonl8K2M4u/3V/5Ui5fwxCY2QyK+/xd/Y+uETaIJJIgn3WHRIslqpF6R/vewiRxkpMqQTCEQeyfHEUslNUoBifrenAH+57COedfxYmm5B2wIGOHgVNpEVlcwxl03ySBZCOSoLOLsqwOfrYQ7F07Tq0d0ZlpepxM32mnh2mUd9SiH32ObFXdDejSTnJMZ+aTROoXacr1P7oj+a9Tn80i7moFj04FJCGsTkNi9fHxuqg5JmXTPGIhkO/MIVisd8jhSxKsrwioMMUmpG4aLuDZQ2w0M4eXyrCSRdOx2EnVXZHhz905+DR4YTClRagbLfRqxzppq5ypKxV3xKOyfnnCo2lP9l5ioVLCIUDNWz6oVO5eHv8L89gmxnTUZhvNdhwM3re4UButhsLZ07H4489jcmGh7EFficCrN/ddZmEIwl0dsSlRaWdotqHg2rSyrA548yT8cH712FtQy78rkLx8yRMBzoj/4OLuY7RvfHCE3XY9/BSCdYg1IQ48XGSo6Cmr86bhho1yytuWhNESblXGhUwoycdTWgTDSfI3EIX2rrSsKjhVK8LweVyIL+M6Te9TZFSYpXdjbpcJrxuOkXDpjvFsl5sLUqZ5vCps7PkQXP4qk87sXQY0eFWOVKrJKnV7c2UcqQOh2VK5bX89r+b0NEaw0HHVXR3uGLVM76Wk8KmM9Kdra0TeTms9JZASWEjPG5aGUROIy+bZVo7t2h2ku4YDnTnSifbojJojOTmMzYgPceqQloZNvRl0af1pwcexUv//i8Qo5YUx2777ICjjz8K1avjePnv9Xjyvo0ygTGyl/T1U+e5nf22lbMrMvlF45IfPX+HXDGNZhlOxNP05k9ZGlbC0jZZ27q0yiumbqYoDSQsaYrmI09M0VZENbtNhWLMT7cE+tbOgJjDd8mThxUd3jEsc7hVjpQPS1PmddBYG8EHr7di130KUVRqmfKp6Tu7zNypTvUxzQ6x8vDg+rzh3q/xP3l9cuFwOLqqjlkd15JBYyS7gJaR9LxPVUgrIxbU55x9Ko446XiZOHtGb8/ZFigs9WDxYzV4/N4N2P/Isu7iDslJV/zU4Rhi0i/YRJfCbWsYJNZQH5aCCVO6WlN6XC5YyVjK1uA6pqDIjVWfdUrkM/269RvDWLhjLkwMrXuYWGM8LjGJU9AntWsGm5kJloO0zOFbm4qt6PAi7LZvIarXBrHsw2R0eBOmzvZj/o65mLkga4vo8GAwiL8+/Djeef1jmKYb2Y49UFRciAWLKuT1ZNcmsRCNc+nPweA9VlTkQHXtSlSVz5ViOyQ5Im7n65NJi06Sm+fCxg3B7sp2zJF2GkB2jtFt7Ug30mBqVGwdROZ2wTQdW3TConZx1JlTpMHAc4/U4N1XmqQCVBJOyn7DKRMta/fS72h3qP1tXB2C03CgbKplJmUUsjI4PFaFxR4xD8djCTQ2WYsdVsUabknVZGWx/K4mDOVZXim5yZQnS2gP3mhDzOGzsrD/keU45ZIZ+OJXShANJ8Qc/sDP1uLVp+tRuzEkwpcC+v++cwM+fDWIAuceKHLtCw8qUVe3Ft+/8kZ5nQsFBpjlTFDpz8E46rhjsXzd49hYu6Lbjy9xADUrsGLd4zj668dhspagDbB+d5fFg0I6GQ+hmrSSkdBnaPrd0mAgmLAKOyRX6DQzHnRcOd7/TwvefblJms6z2AO39+xPHYpbfupCv1tyVe26wo/E49i0NiiBQg6XQ26edA1GSQXFxW4rfak1hpZaywxZQiHdlW41EmhWtoqROBD2uZHr9yIQS4hLxaQ5nH7lQfKek9HhfPRnDq9r+gChpmnYZkYR/F4/ahsLkZ3biVJnFj5dU4GH/vQwTjrjVFk02CVz4eRTv4n/LXkbZvtTeOdjL0z4YTiCqCwNY86283DSKedgMpKX50a4k21RrTx1Sb8qsCqNpauQ1hlGGRWc/Jj/XJLtlShn9tPt6WemxrLrFwtxyAmVqFkfwhO/34imukivz1Mw00/WEIhIYQk7+qkpXBhhXLcuJKZumuipOaXa95hOFJWwSCekhjevBUZiM/iO5TnH4jiymhYDtlgLmo8Cn6WDMNiMsQRJEyiGYA6X6PCTK6Xe9fqlblQU7IlwpAJNrQyUjCMvu10ipRlB/cwTzyGX9cJttGCjO+qHt/4Wi/Y7BZUzCjF1JjB1VrE85/bJliPdU5Omv6Oz3coiaG2OSWQ35yGbrJ+GjWrSyphAgeXKcqAlFEVHNLZFIYjpc7Nw1JlTsfgvNRJQtu9XSzF7YdZmPzX7A3flU9PfyLZ+dkpt4j7R/BkNm93+aKYMKcPLY5U0rOYo6jaEUDHVKik71i0+ewab5XpMsdQEIgw04yMmWjvN5VtbGCTN4VNm+vHzn/4Oh+x9vnTmisY8KC5ogNNpLSQpqMPtAVkc2M0CREF8yunn4qiTzkZnJIbyHKYMTm7yugqasOpYpIItKqOYtW1WWraoTKJCWhkzaPotzvLAFXKgLcI0iHivIBpG9x55+hS8/I96/OvxWuy4Zz7m7djbT+10mFJXOd4lqFMdhJOEC4gNq4Nwe50oLvcgBlNN3SNMw2qpj8pjh93zJdaYE+h4IcFmTpdYa5LBZgER1gmaR2SftlbZjMQTTcjydyI7K9BVp37za3L1mp22Tu9hoB0XzZlAnghph+RGt3fEEI0krBxpG5+fwVAhrYwp1E4KfG7RVJpCUUmXYR3v5CTIvOkDjipD2RQv3vxXI6rXAIeeUISs3J751E6ZSOsDlqDmJJPqVTB9nDXrQqia4UOiK9CJN/5kb8k5lrQ2N+PD919B5NUcuJx+PLP4WszcphA/u+Mn8JUUj+tvby4D6kSu6erOvU42yZDOUf1UNuPnCgod2Fi3ElPL524hoDfVrURxsb21NN6TmeKW8fuccLsdUhq0pav7FUuC0hWSrqTvniu2RRqwe1woy/KIGZuTYM9oW76+454FOOykCrQ0xcVPXVcd6v15Ro3TTx1MvZ+av90RjKJhYxhVM+mPTsi47Dwx242mpiZ89ZAj4I75UFIwHUUFxTjiSycjJzoXXzvkCHl9opBgM5chHdoqcrwozfKK64IZCrTiSNBZj+vt0KOOwoq1/URK167AykkcKZ2OGE6nFDRhy0oWziE5ee60DRojqkkr4wZN1SVZTrSGImjvag3Ys1Ri1Qw/Dv56Ht5+MYan7q/GFw4pwcKdrfKKfet+p9JPzdSr6vUhJOKm+Ci5Oh9rP+pk56rLr8TOcw9FVVkRmttYwpH5q04smLlQ8pqvvuIq3HPvbyd8v3g95XicUiWMQprpW1xUMgCSSjW176NOPBMr/vcu4h2bI6WBIKpKw5g7iSOl0xFHVwnaQHtMgsZYXc7jS9+gMaIzjTKu0FdHjaXQ5+ouYtFTS8nKceKrp1RKBS/6qlkLuWdtZcmndjklsppVylhpKhX1utkWkX2C80poOhteXq8CfPT+MsyfsQAuIybPKaSTcPuH7y9L4d51VRVzGSjwsW64T6LD6WZhFL/p8uDi6+/E/L1PQMWMQlTNYN32Qiza7xuTOlI6HXHQCpdrSK50a0tUWlRSi7ZLatxIUE1amZAbJ9/ngdsw0ByKSEqM3725lKPhcmCfw0qlROR/nm2QFK0DjynvLtPIG4wtAZn/WheIdPWnnjg/NYOMWAqUWjSLJFBA2zlQyG4kEgm4DB8c7GjlYv1rU0pVJmtJ838up1fe57TB4keuN6fVZpKWHJ7/DpeBw44/E+bxZ8qkX5Hj0+5nNg4eW786iDZ3tDv9Kp3N3XqVKRMGJz36/0TgSt5qbz8zi0l87dQqSZ9gOVGWbeztpzbgmGA/NbV/tuVsqo1I6hULqw1UZ1rpHwreWDwEM2HCMBKoLNskndOScDtft4OA7jci3euyKpvl+KQuN90uKqDtXdAk2GH5pJOFTNI5cE6vNGVC4eRW7PfKZMcAHZqSe1I2xYejz56CwhIP/vHAJnz8Vmsv8zhNkvRtN4eiaAxEhlSgYixaUxJGdlP98xj2SAtLJ3ZcNB/L1i4VzZlGCKv1gwW377TLAtgZR49gs1yvPUp/KgMXNJHiQ21x6fGd7q4pNXcrKTEnShCYA+hoZkWoOLI8m9NYsrJdUu3prReb8PrzDRL5vQ/bXrLlEbUbpsowzYLauGkFlDHIbLyCxjatCSG/0A1/nktKDXKRoAyPW35yKw476Gvy7znTF8i5ZvT+sjVL8eGKZ/DPxU+leheVSUJunpUrzdkiO9+QTmnpjAppJXVt5Twu0ahdhrPbT500S9GPtNeBxSit9EpAWXNX28u8QkuL4ftoNmdjAwaUUVCLOXyMzVrBWAw1a4OYOjNLFgRMvbJTJbR0oaioSATx1ZdfiUcXPweXQR90WDRobufrijJampqa8IPr/w/xti/DCSeePvtKzN+hHLffflvaXmM624whp59+uggJaRjvdmPWrFm48sorEQptzgG+4YYbcNBBB2H77bfHiSeeiHC4d6/XseSiiy7CrrvuCq/Xi5133nlIn+G+XnDBBSguLkZOTg6OOeYY1NbW9nrPunXrcNhhhyErKwtlZWW44oorEItZUbsjMX+XZHmQ7TFEUDNQpydzt8vBkWdUScF8+qnXrQj06cJFPzXQOA5+avrMm5qjaGuKSRtDWtbHS2PPBDhJ3vP73+GZ1/+FR198Em+897KkXaXr5KnYT0AfdtDX4AlVoSivDIX5pThy/9PgC82W7ROZiz+WqCY9xhxyyCG47777EI1G8e677+K0004TYXLLLbfI69dccw08HqtJ/Lx587Bq1Spss80247Y/Z555Jt566y189NFHQ3r/pZdein/+85947LHHkJ+fj29/+9s4+uij8Z///Edej8fjIqArKirw+uuvY9OmTTj11FNlUXLTTTeNaB+pmRb7aZaKojUcQ9yMS3GJpFZcXObF0WdNwYtP1uHZhzdht32LsPPeBd39YZmzTN80/dQ0T+d5N9dRpm+KYpuy2+z5vNdrJhjDZj1YyALSg5d/WQqUP1Mx3SfmWQ0YGj00P5puK6BHUcYyF3+nuYdiwayFqK6zFvuSiz9rofw7Vbn4o0WF9BhDrZUCjEybNg0HHnggnn/++W4hnRTQ3/ve90T4jaeA/sUvfiF/6+vrhySkW1tbce+99+Khhx7C/vvvL9u44OA+vvnmm/jc5z6HxYsX49NPP8W//vUvlJeXi4b+gx/8AFdddRW+//3vd49vuHDCpumb0bQUtizZyBaEPdteHvz1Crz3WjPe7mp7ud/XSrvbXkbDYTz92J/w6fsvwmdEEY67MXen/XDIUSfD5/fDhMMKVHJQWjusqCWr8LKkAEkmUDIlSP5NiwhQvy6I4nKvdGvi29M9CMUOsAe5ZhYr45GLf/xBh8i/DYP1FHjXb87Ff+T5xUhHdMYZR5YsWSLaZk/B1dbWhpNOOgmlpaXdgnsgaG4e6JGXlyem9LGEmj8tAFxYJFm4cCGmT5+ON954Q57z7w477CACOsnBBx8s4/rkk09G9fvJcqBSptFN83fvcqLUnHfbpwiHfL1C0rOk7WV9BMFgELffcBGm4V/4znH5OP/oElx6XD5mO1/Cr2+6FI5YBDkeQ0zq/H7rb9dzj0v+TbM5FwU0Z5vRCJ768+9wy+Un4/XFb+KTDx/FY3+6B2YknNZFEewCF2JqkVDGKxef5GR1yCMJtydz8dMN1aTHmH/84x8iROmjpb+ZuZ+//OUvu18/5ZRTRCulmfvBBx/ET3/6U+y99979ftcHH3ww4O/wYotENldtGgtqampkQVFQUNBrOwUyX0u+p6eATr6efG0soKm71O9FizOKtnCsuzFCkhnzs3HUmVOw+LFaPPn7jXD4/4u95zbh0xUhPPhUO8vsS9nGRds6sfe8OJ5+/EEc+42hlW6kwL/1/86Hs2MN6hurkJuThc7AMuCNpbjtk//ilp/8TitMKYqNc/EdLEbjC4n7Kh1y8QdDhfQY86UvfQl33303Ojs7cccdd8DlcknwVZK//e1vQ/6uuXPnblVI19XVYbJCjZWVxdhpqpXm70Rc2u0lzd8FxR4cecYUKSP62rNFWBraEbk5+dh9hzlWeo9porpuJT5Z/nfkl/1ryEL68YfuxdrPVmLujKOxYPaOaOvIx9wZh2NT/UKs+t/jeOhPv8VZ37xonEevKMpIc/EXdPmgkWa5+AORfssKm5OdnS3CdaeddsLvf/97Cdqin3ckTLS5m750auctLS29tjO6O+ln59++0d7J58n3jBUUtnleN0qyvRJcxujvntHbHq8T+x9ZivqWxfD7vgy/Z08kTKP7s1PK56K06KtYvbp20JaSiYSJSDiB5558FnOmHY+K4gUIR3xSY5pKPNsUUnA/8dhjYzpGRVHGLhf/wxXPYOnqz6z7nUVzTFOec/uPb9u6e9GuqCY9jtC0cu211+Kyyy4TP/RwzaQTbe5muhajtF944YVu7X/p0qWScrXXXnvJc/790Y9+JFo8068IA+O4aNh2220xHrDSkyvLqjLWGY2JRp3MVeYxXr3hfew078tobi1CfWMpvJ6whIzwPvV69oDfnYW//bFauljFoibicRPx2OZHLGZ2C/Hy/AvgcJSjpsH67fzcNgkY469NKZ+D1z+0bnwtC6ooNs3Fv+IqycV3OidHLn5aCulf/epXuO2228QHSo31zjvvxB577AE7ctxxx0keMff58ssvH9ZnR2vuXrFiBTo6OuQ40deaFPoUpvQ9b9y4EQcccADuv/9+OX5MuTrrrLNkUcELmoL3wgsvFMHMyG7CHG9+nr71W2+9Vb77uuuuk9xqRraPZ7AR86ndIQfaIjHEEwlJvaLAzCuYhmisE6XFcbS25SMaYwoWBamJaCwOw20gv9AFg8Ld7YBhOKRimfx1OeByOeUvq33++7JfYN6Mw63Pw4S7R41pK+I7e9zGqCjKGOTi3/tbhGJxNHSGUJLtS/vaBmknpB955BERIr/+9a+x55574mc/+5lEF1PjS2p2doI+aeYaU6Cdd955Yg6fKM4++2y8/PLL3c8XLVokf1evXo2ZM2dKJDePWyCwuUAI/ejUTqlJM/CNx/auu+7qft0wDAmO41govDke5oLfeOON4z4epmkV+DanabEsaJbLidKKEtS3GShFCMWFm1tZtndG0RYy4Mj6DF864qwh/UbcqEUk2o68nN6pZFSc2WgjOy9btWhFsTlOFpRyGWnd/SqJwxzMWWczKJh333337ohpapTMR6bGd/XVVw/6eaYKUWNctmwZ5syZk5bRfj01aS5MMnEM7Cst+dSxBB5/4M+oXloAN/LR0dYIh4MFSZzIyStGFK2Ytk0bTj79pCF9732/uQ/vvNiKbWfOQG725kYKnZ1R/G/NWuxzaAnOO3+zwM/082AXdAz2wC5jME32rk+MuGPdRIwjKYtYn4JWy0mhSdMHy1xeVu1KwgPIvN5kHm9fqA32LL3JA4Nklak0zJlLwn3P5DG4u6K/20IRfOXoI3Dr925GSc4XMHebnbqjuzfW/A8tnW/goq9/F+YQf+P4U47Hpx/9AGsbcpDdVgyng9XIHAhFm5FXUYdTT/t2r/3N9PNgF3QM9sBOY/AZ1jwwEj10IsYx1O9OKyHd0NAgZSn7y9P97LPP+v3MzTffLPWy+8LVC1dK6bxi5Rh4IWXyGPhZTyyGb3/nW3jrP29h1dK/ACYrlSUwd8cZ+PwXzkWos0MeQ+U715yP1197E8v/twKJuAG3O4E9t5uFffa9CO3t7fIYyzGkGh2DPdAxZNY42nvMI5NGSI8Eat30YffUpGkep5kh1SaZ0V5E1BhZuUzHYLW7LK+agmiCnaq2bPLecy3dd2Ft9vkX/3/4MUd11/KuyPGLH3y8x5AqdAz2QMeQWePw+XyTT0iXlJRI4FJ/eboD5egy4ri/qGOeAB78dL6QdAybyfE64XEZaA5FpO53T9Erlbm7a3L3+O1e/0hW7+76v9P6m+V0yfduza+l58Ee6BjswWQYw0SMY6jfm1ZCmmlDzOVlHu+RRx7ZveLhc0ZQK5kNS4ey7nc4nuhHGFuC2tGPgN783q4mHMlnXa9pNLeiKKkirYQ0oemaKT+77bab5PYyBYslOM8444xU75piA2jmZvETRVGUyUDaCenjjz9eWi+y1SMLabBV4rPPPrtFMJmiKIqipDtpJ6QJTdtq3lYURVEmO+nt2VcURVGUSYwKaUVRFEWxKSqkFUVRFMWmqJBWFEVRFJuiQlpRFEVRbIoKaUVRFEWxKSqkFUVRFMWmqJBWFEVRFJuSlsVMRkOyt2hHR4d0xErXIvCsWc5WZ+ykomNIHToGe6BjsAeTYQwTNQ7KHzJYv+uME9LJHp677LJLqndFURRFyXDa29uldfJAOMzBxPgkgyukpUuXYtttt8X69euRl5eHdCTZF1vHkFp0DPZAx2APJsMYJmocFL0U0FVVVVvV1jNOk+bBmDJlivybBz+dLySiY7AHOgZ7oGOwB5NhDBMxjq1p0EnS12mgKIqiKJMcFdKKoiiKYlMyUkh7vV5cf/318jdd0THYAx2DPdAx2IPJMAa7jSPjAscURVEUJV3ISE1aURRFUdIBFdKKoiiKYlNUSCuKoiiKTZk0QvqVV17BV7/6VUkMdzgcePLJJ3u9fvrpp8v2no9DDjmk13uamppw8sknS15cQUEBzjrrLCkfapcx9N3/5OO2227rfs/MmTO3eP3HP/7xhI3h5ptvxu67747c3FyUlZXhyCOPlOIxPQmFQrjgggtQXFyMnJwcHHPMMaitre31nnXr1uGwww5DVlaWfM8VV1yBWCxmizHwOrnwwguxYMEC+P1+TJ8+HRdddBFaW1t7fU9/5+rhhx+2xRjIfvvtt8X+nXvuuWlzHtasWTPgPfHYY4/Z4jzcfffd2HHHHbvzbffaay8888wzaXMvDDaGdLgXhnIebH0vmJOEp59+2vzud79rPv744wyEM5944oler5922mnmIYccYm7atKn70dTU1Os9fH2nnXYy33zzTfPVV181586da5544om2GUPPfefj97//velwOMyVK1d2v2fGjBnmjTfe2Ot9HR0dEzaGgw8+2LzvvvvMJUuWmB988IH5la98xZw+fXqvfTj33HPNadOmmS+88IL5zjvvmJ/73OfMz3/+892vx2Ixc/vttzcPPPBA8/3335fjUlJSYl5zzTW2GMPHH39sHn300eZTTz1lrlixQsYxb94885hjjun1PTyH/J6e5yIYDNpiDGTfffc1zznnnF7719ramjbngfvX95644YYbzJycHLO9vd0W54HXyD//+U9z2bJl5tKlS81rr73WdLvdMqZ0uBcGG0M63AtDOQ92vhcmjZDuyUBC+ogjjhjwM59++ql87u233+7e9swzz4gQ3Lhxo2mHMfSF49l///17baOQvuOOO0y7UFdXJ2N5+eWX5XlLS4vcHI899lj3e/73v//Je9544w15zhvA6XSaNTU13e+5++67zby8PDMcDqd8DP3x6KOPmh6Px4xGo8M6h6kcAyemiy++eMDPpON52Hnnnc0zzzyz1zY7nQdSWFho/u53v0vLe6HvGNLxXuhvDHa+FyaNuXso/Pvf/xYzBU0z5513HhobG7tfe+ONN8TEvdtuu3VvO/DAA6WM6FtvvQW7QZPYP//5TzHJ94XmbZrPFi1aJKbwiTSN9SVp9ioqKpK/7777LqLRqBzbJAsXLhQzGc8B4d8ddtgB5eXl3e85+OCDpZ7uJ598kvIxDPQemtFcrt6VdmnKLCkpwR577IHf//73g3a8megxPPjgg7J/22+/Pa655hoEAoHu19LtPPDa+uCDD/q9J+xwHuLxuJh4Ozs7xdyajvdC3zGk470QH2AMdr0XMqZ2N/3PRx99NGbNmoWVK1fi2muvxaGHHioH3zAM1NTUiADvCS8yTgh8zW788Y9/FF8dx9QT+oPY4Yv7/frrr8vFtmnTJtx+++0paWZyySWXYO+995YLn/BYejweWRD1hBd/8jjzb8+bIfl68rVUj6EvDQ0N+MEPfoBvfvObvbbfeOON2H///cWHtXjxYpx//vkS48BzZIcxnHTSSZgxY4bEQHz00Ue46qqrxOf7+OOPp+V5uPfee7HNNtvg85//vK3Ow8cffyzCgP5n+p2feOIJafDDBUW63AsDjSGd7oWPtzIGO98LGSOkTzjhhO5/c0XEIII5c+aIdn3AAQcg3eBKlEFu7Hfak8suu6z73xwjJ4FvfetbEoQz0dVzuHJesmQJXnvtNaQrg42BK2kGk/Bm//73v9/rtf/7v//r/jetGly507Ix0UJ6oDH0nEh5T1RWVsq9wEUs7410Og/BYBAPPfRQr2Nul/NAyx0FMjXMv/zlLzjttNPw8ssvI50YaAw9BbXd74UFWxmDne+FjDJ392T27Nli2lixYoU8r6ioQF1dXa/30EzM6EW+ZideffVVWeWdffbZg753zz33lHEwEnYi+fa3v41//OMfeOmllzB16tTu7TyWkUgELS0tW5jvk8eZf/tGuCafT+S5GGgMSdhmjhYaWjS4Kne73YOeiw0bNiAcDsMuY+i7f6TnPZEO54Fw0qV58tRTTx30+yb6PHChPHfuXOy6666yWN5pp53w85//PK3uhYHGkE73gmeQMfTdP7vcCxkrpHmB0CfNFROhGYQ3C/1ESV588UUxsyVPmF2gWY8XGi+yweDKkX71vqb88YJ+Jk6qvFF5/Ohe6An3mzfwCy+80L2NCw6mNyT9Q/xL01TPRdPzzz8vfq7+TGwTPYak1nDQQQfJjf/UU09tYdEY6FwUFhZOiEVjKGPob/9Iz3vC7ueh5z3xta99DaWlpbY6D/3BOYXCKR3uhcHGkA73wlDGYOt7wZwkMOWCofF8cFi33367/Hvt2rXy2uWXXy4Rk6tXrzb/9a9/mbvssoukCoRCoV4pWIsWLTLfeust87XXXpPXJzIFa2tjSMK0gKysLIks7Mvrr78ukd1MV2Fa1gMPPGCWlpaap5566oSN4bzzzjPz8/PNf//7373SGQKBQPd7mHbCVJoXX3xR0k722msvefRNdzjooINkLM8++6yMY6LSTgYbA8/Bnnvuae6www6SdtLzPdz3ZMrHb3/7W0lRWb58uXnXXXfJefve975nizFwv5mqx+PPe+Jvf/ubOXv2bHOfffZJm/OQhMeXWRjMxuhLqs/D1VdfLdHoPMYfffSRPOe+Ll68OC3uhcHGkA73wmBjsPu9MGmE9EsvvSSCre+DqVe8qXlweVCZ8sA0JebE9QynJ42NjSKUmWfJ0PozzjijV75lKseQ5De/+Y3p9/slfaMv7777rtwwnNh8Pp+5zTbbmDfddFOvhch409/+J3MkkzA/8vzzz5cUCN6sRx11lNzUPVmzZo156KGHyliZj/id73ynV0pHKscw0Hnigzc5ocBgOhCvpezsbMm///Wvf23G43FbjGHdunUyCRUVFZler1dqAlxxxRW9ckPtfh6ScKJkrnF/xzbV54HpYJxvmJLE+eeAAw7oFtDpcC8MNoZ0uBcGG4Pd7wXtgqUoiqIoNiVjfdKKoiiKYndUSCuKoiiKTVEhrSiKoig2RYW0oiiKotgUFdKKoiiKYlNUSCuKoiiKTVEhrSiKoig2RYW0oiiKotgUFdKKoiiKYlNUSCuKoiiKTVEhrSjKFjz99NNwOBy4/vrrt3ito6MDu+yyi3QwSre+yIqSbmjtbkVR+mXRokVYu3atPNgnmMTjcWkJ+cwzz+DBBx/EiSeemOrdVJRJjWrSiqL0y3e/+100Nzfj7rvv7t524YUXipb9ox/9SAW0okwAqkkritIviUQC2223nQjqNWvW4M4778SVV16Jc845B/fcc0+qd09RMgIV0oqiDMj999+P0047DUcccQSeeuopHHzwwfj73/8Ol8uV6l1TlIxAhbSiKAMSi8Uwb9480aR33nlnvPLKK93+aUVRxh/1SSuKMiCrV6+WaG5y9tlnq4BWlAlGhbSiKP1SX1+PQw89VCK6y8vLcccdd8i/B9K4L774YhQVFaGgoABnnXUWQqHQhO+zokw2VEgrirIFgUAAhx9+ONavX48nn3xSAsZWrlyJRx55pN/333TTTXjppZfw8ccfY/ny5fj000/lM4qijA71SSuK0gtqy0cffbQEiCVzoSm0Z8yYgYqKCnz00UdS6KQn06dPx6233ooTTjhBnj/33HM47rjjJDLcMIwUjURR0h/VpBVF6cVFF10kkdw9c6GzsrJw6aWXYsmSJfjb3/7W6/0tLS2icTOwLAkrkrW3t0vAmaIoI0eFtKIo3VAbvuuuuyQX+pprrun12re//W3xN1N494TCmPC1JMl/J19TFGVkqJBWFEV4+OGHcfXVV+OQQw4RQd2XvLw8qTj2zjvvYPHixd3bkxHfra2tvbTrnq8pijIy1CetKMqooU/6tttuw/HHHy/PKcSPPfZY9UkryihRTVpRlFHDHOqbb74Z1dXVkrr1/e9/H6effroKaEUZJVrbT1GUUXPttdeioaFBan2z5je16FtuuSXVu6UoaY+auxVFURTFpqi5W1EURVFsigppRVEURbEpKqQVRVEUxaaokFYURVEUm6JCWlEURVFsigppRVEURbEpKqQVRVEUxaaokFYURVEUm6JCWlEURVFsigppRVEURbEpKqQVRVEUBfbk/wGimNzlbgpvWQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "em.plot_eval(gp_final)"
    ]
@@ -298,7 +2843,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### 8 - History Matching\n",
+    "#### 9 - History Matching\n",
     " \n",
     "Once you have the final model, running history matching can improve your model. The Implausibility metric is calculated using the following relation for each set of parameter:\n",
     "\n",
@@ -313,9 +2858,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "History Matching: 100%|██████████| 20/20 [00:00<00:00, 21.60wave/s, samples=20, failed=0, NROY=20, min_impl=0.12, max_impl=2.90]\n"
+     ]
+    }
+   ],
    "source": [
     "from autoemulate.history_matching import HistoryMatcher\n",
     "\n",
@@ -349,9 +2902,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "em.plot_eval(emulator)"
    ]
@@ -360,14 +2925,57 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### 9 - use the interactive dashboard to inspect the results of history matching "
+    "#### 10 - use the interactive dashboard to inspect the results of history matching "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aad79c347c1e432c802476c080a29940",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HTML(value='<h2>History Matching Dashboard</h2>')"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ad0e5de11d2455e8f8d0a1c2a1ceffd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HTML(value='\\n            <div style=\"background-color: #f8f9fa; padding: 15px; border-radius: 5px; margin-bot…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a916e9c4d1b143b0bac77873e317d332",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HBox(children=(Dropdown(description='Plot Type:', options=('Parameter vs Implausibility', 'Pair…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from autoemulate.history_matching_dashboard import HistoryMatchingDashboard\n",
     "dashboard = HistoryMatchingDashboard(\n",
@@ -392,13 +3000,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### 10- Sensitivity Analysis \n",
+    "#### 11 - Sensitivity Analysis \n",
     "Use AutoEmulate to perform sensitivity analysis. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -416,9 +3024,595 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "output",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "parameter",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "index",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "value",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "confidence",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "83b360bd-6ae2-49f5-a7e3-8c33e6cbd669",
+       "rows": [
+        [
+         "0",
+         "y1",
+         "ao.r",
+         "S1",
+         "5.3971092765484715e-05",
+         "9.145267807342752e-06"
+        ],
+        [
+         "1",
+         "y1",
+         "ao.c",
+         "S1",
+         "0.00019714572611568412",
+         "3.18211892294405e-05"
+        ],
+        [
+         "2",
+         "y1",
+         "art.r",
+         "S1",
+         "0.00034856753290408326",
+         "4.002496991501157e-05"
+        ],
+        [
+         "3",
+         "y1",
+         "art.c",
+         "S1",
+         "7.328516731614069e-05",
+         "1.1394410890875745e-05"
+        ],
+        [
+         "4",
+         "y1",
+         "ven.r",
+         "S1",
+         "0.0002705969237976811",
+         "5.992525754296568e-05"
+        ],
+        [
+         "5",
+         "y1",
+         "ven.c",
+         "S1",
+         "8.45340608978297e-05",
+         "1.2388310228210322e-05"
+        ],
+        [
+         "6",
+         "y1",
+         "av.r",
+         "S1",
+         "0.00016810319886365608",
+         "2.7488624712278258e-05"
+        ],
+        [
+         "7",
+         "y1",
+         "mv.r",
+         "S1",
+         "0.00023387393260233897",
+         "3.9775985320366535e-05"
+        ],
+        [
+         "8",
+         "y1",
+         "la.E_pas",
+         "S1",
+         "6.861469079799508e-05",
+         "8.985000634827693e-06"
+        ],
+        [
+         "9",
+         "y1",
+         "la.E_act",
+         "S1",
+         "0.00022657457171603652",
+         "3.471445195331056e-05"
+        ],
+        [
+         "10",
+         "y1",
+         "la.v_ref",
+         "S1",
+         "0.00017555268952358825",
+         "2.6838050770090517e-05"
+        ],
+        [
+         "11",
+         "y1",
+         "la.k_pas",
+         "S1",
+         "0.0007246379664969545",
+         "0.0001250818065683459"
+        ],
+        [
+         "12",
+         "y1",
+         "lv.E_pas",
+         "S1",
+         "0.1342348130153026",
+         "0.019047862731215188"
+        ],
+        [
+         "13",
+         "y1",
+         "lv.E_act",
+         "S1",
+         "0.00032156828603190806",
+         "5.4865773385221964e-05"
+        ],
+        [
+         "14",
+         "y1",
+         "lv.v_ref",
+         "S1",
+         "0.021249854926246717",
+         "0.003376889150919793"
+        ],
+        [
+         "15",
+         "y1",
+         "lv.k_pas",
+         "S1",
+         "0.9149306147466942",
+         "0.0762403850603861"
+        ],
+        [
+         "0",
+         "y1",
+         "ao.r",
+         "ST",
+         "0.00029449996123004016",
+         "0.0005945504397471345"
+        ],
+        [
+         "1",
+         "y1",
+         "ao.c",
+         "ST",
+         "0.0006184243317087871",
+         "0.0010106243861592774"
+        ],
+        [
+         "2",
+         "y1",
+         "art.r",
+         "ST",
+         "0.0016810274422396473",
+         "0.0015338236885437413"
+        ],
+        [
+         "3",
+         "y1",
+         "art.c",
+         "ST",
+         "9.371251296935749e-05",
+         "0.0005176069647613909"
+        ],
+        [
+         "4",
+         "y1",
+         "ven.r",
+         "ST",
+         "-3.556386697771413e-05",
+         "0.0011839653716066965"
+        ],
+        [
+         "5",
+         "y1",
+         "ven.c",
+         "ST",
+         "-0.00015898763419025776",
+         "0.0007333866151374012"
+        ],
+        [
+         "6",
+         "y1",
+         "av.r",
+         "ST",
+         "8.53095213262584e-05",
+         "0.0010297337856694842"
+        ],
+        [
+         "7",
+         "y1",
+         "mv.r",
+         "ST",
+         "0.00014500464875879588",
+         "0.00138220733270959"
+        ],
+        [
+         "8",
+         "y1",
+         "la.E_pas",
+         "ST",
+         "-0.00010050706067662111",
+         "0.0007947965240443248"
+        ],
+        [
+         "9",
+         "y1",
+         "la.E_act",
+         "ST",
+         "0.00045875166530601235",
+         "0.0012848763603567031"
+        ],
+        [
+         "10",
+         "y1",
+         "la.v_ref",
+         "ST",
+         "0.00034612824379720716",
+         "0.001155808100600078"
+        ],
+        [
+         "11",
+         "y1",
+         "la.k_pas",
+         "ST",
+         "-0.0008684245305715855",
+         "0.002508443513646784"
+        ],
+        [
+         "12",
+         "y1",
+         "lv.E_pas",
+         "ST",
+         "0.07392792576845329",
+         "0.025902369293633386"
+        ],
+        [
+         "13",
+         "y1",
+         "lv.E_act",
+         "ST",
+         "3.480378412647605e-05",
+         "0.0015533734301816573"
+        ],
+        [
+         "14",
+         "y1",
+         "lv.v_ref",
+         "ST",
+         "0.016497380711638365",
+         "0.014669354842988447"
+        ],
+        [
+         "15",
+         "y1",
+         "lv.k_pas",
+         "ST",
+         "0.8445166649203554",
+         "0.07431981909637204"
+        ],
+        [
+         "0",
+         "y1",
+         "('ao.r', 'ao.c')",
+         "S2",
+         "-0.0006835672082845043",
+         "0.0010738688719249504"
+        ],
+        [
+         "1",
+         "y1",
+         "('ao.r', 'art.r')",
+         "S2",
+         "-0.0006906043535656492",
+         "0.0010730499898941022"
+        ],
+        [
+         "2",
+         "y1",
+         "('ao.r', 'art.c')",
+         "S2",
+         "-0.0006856132225754061",
+         "0.0010708318206942972"
+        ],
+        [
+         "3",
+         "y1",
+         "('ao.r', 'ven.r')",
+         "S2",
+         "-0.0006862156911062109",
+         "0.0010723303615480827"
+        ],
+        [
+         "4",
+         "y1",
+         "('ao.r', 'ven.c')",
+         "S2",
+         "-0.0006883151972741523",
+         "0.0010720666809314295"
+        ],
+        [
+         "5",
+         "y1",
+         "('ao.r', 'av.r')",
+         "S2",
+         "-0.0006803664138133802",
+         "0.0010704319987525721"
+        ],
+        [
+         "6",
+         "y1",
+         "('ao.r', 'mv.r')",
+         "S2",
+         "-0.0006775917887661945",
+         "0.0010740536539827204"
+        ],
+        [
+         "7",
+         "y1",
+         "('ao.r', 'la.E_pas')",
+         "S2",
+         "-0.0006841529295817774",
+         "0.0010725042981023932"
+        ],
+        [
+         "8",
+         "y1",
+         "('ao.r', 'la.E_act')",
+         "S2",
+         "-0.0006872514552547019",
+         "0.0010726897696300534"
+        ],
+        [
+         "9",
+         "y1",
+         "('ao.r', 'la.v_ref')",
+         "S2",
+         "-0.0006899512016268845",
+         "0.001072994308733606"
+        ],
+        [
+         "10",
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+         "('ao.r', 'la.k_pas')",
+         "S2",
+         "-0.00069552933763696",
+         "0.0010722704017026472"
+        ],
+        [
+         "11",
+         "y1",
+         "('ao.r', 'lv.E_pas')",
+         "S2",
+         "-0.0005976722812542357",
+         "0.0010235961194179487"
+        ],
+        [
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+         "('ao.r', 'lv.E_act')",
+         "S2",
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+         "0.0010737489359884582"
+        ],
+        [
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+         "y1",
+         "('ao.r', 'lv.v_ref')",
+         "S2",
+         "-0.0008584210060793122",
+         "0.001085769443491692"
+        ],
+        [
+         "14",
+         "y1",
+         "('ao.r', 'lv.k_pas')",
+         "S2",
+         "-0.00029700604119653473",
+         "0.0010754096038804609"
+        ],
+        [
+         "15",
+         "y1",
+         "('ao.c', 'art.r')",
+         "S2",
+         "-0.0008919941796596279",
+         "0.0016600091697813568"
+        ],
+        [
+         "16",
+         "y1",
+         "('ao.c', 'art.c')",
+         "S2",
+         "-0.0008929697281122573",
+         "0.0016608041969899555"
+        ],
+        [
+         "17",
+         "y1",
+         "('ao.c', 'ven.r')",
+         "S2",
+         "-0.0008899058834395748",
+         "0.001661767457011616"
+        ]
+       ],
+       "shape": {
+        "columns": 5,
+        "rows": 1216
+       }
+      },
+      "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>output</th>\n",
+       "      <th>parameter</th>\n",
+       "      <th>index</th>\n",
+       "      <th>value</th>\n",
+       "      <th>confidence</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>y1</td>\n",
+       "      <td>ao.r</td>\n",
+       "      <td>S1</td>\n",
+       "      <td>0.000054</td>\n",
+       "      <td>0.000009</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>y1</td>\n",
+       "      <td>ao.c</td>\n",
+       "      <td>S1</td>\n",
+       "      <td>0.000197</td>\n",
+       "      <td>0.000032</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>y1</td>\n",
+       "      <td>art.r</td>\n",
+       "      <td>S1</td>\n",
+       "      <td>0.000349</td>\n",
+       "      <td>0.000040</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>y1</td>\n",
+       "      <td>art.c</td>\n",
+       "      <td>S1</td>\n",
+       "      <td>0.000073</td>\n",
+       "      <td>0.000011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>y1</td>\n",
+       "      <td>ven.r</td>\n",
+       "      <td>S1</td>\n",
+       "      <td>0.000271</td>\n",
+       "      <td>0.000060</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115</th>\n",
+       "      <td>y8</td>\n",
+       "      <td>(lv.E_pas, lv.v_ref)</td>\n",
+       "      <td>S2</td>\n",
+       "      <td>-0.004675</td>\n",
+       "      <td>0.050165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>y8</td>\n",
+       "      <td>(lv.E_pas, lv.k_pas)</td>\n",
+       "      <td>S2</td>\n",
+       "      <td>0.098837</td>\n",
+       "      <td>0.057726</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117</th>\n",
+       "      <td>y8</td>\n",
+       "      <td>(lv.E_act, lv.v_ref)</td>\n",
+       "      <td>S2</td>\n",
+       "      <td>0.005969</td>\n",
+       "      <td>0.018428</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>118</th>\n",
+       "      <td>y8</td>\n",
+       "      <td>(lv.E_act, lv.k_pas)</td>\n",
+       "      <td>S2</td>\n",
+       "      <td>0.001894</td>\n",
+       "      <td>0.022177</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>y8</td>\n",
+       "      <td>(lv.v_ref, lv.k_pas)</td>\n",
+       "      <td>S2</td>\n",
+       "      <td>0.002823</td>\n",
+       "      <td>0.018446</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1216 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    output             parameter index     value  confidence\n",
+       "0       y1                  ao.r    S1  0.000054    0.000009\n",
+       "1       y1                  ao.c    S1  0.000197    0.000032\n",
+       "2       y1                 art.r    S1  0.000349    0.000040\n",
+       "3       y1                 art.c    S1  0.000073    0.000011\n",
+       "4       y1                 ven.r    S1  0.000271    0.000060\n",
+       "..     ...                   ...   ...       ...         ...\n",
+       "115     y8  (lv.E_pas, lv.v_ref)    S2 -0.004675    0.050165\n",
+       "116     y8  (lv.E_pas, lv.k_pas)    S2  0.098837    0.057726\n",
+       "117     y8  (lv.E_act, lv.v_ref)    S2  0.005969    0.018428\n",
+       "118     y8  (lv.E_act, lv.k_pas)    S2  0.001894    0.022177\n",
+       "119     y8  (lv.v_ref, lv.k_pas)    S2  0.002823    0.018446\n",
+       "\n",
+       "[1216 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "si = em.sensitivity_analysis(problem=problem)\n",
     "si"
@@ -435,9 +3629,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2baa5ef97551487fbf1d253d09c127d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HTML(value='<h2>History Matching Dashboard</h2>')"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6d564b3f85564524b1fbccfa16fbea74",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HTML(value='\\n            <div style=\"background-color: #f8f9fa; padding: 15px; border-radius: 5px; margin-bot…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9af9408ddfd40689d27d9556f63a454",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HBox(children=(Dropdown(description='Plot Type:', options=('Parameter vs Implausibility', 'Pair…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Create a test sample with KNOWN NROY regions\n",
     "test_samples = [{\"p1\": x, \"p2\": y} for x in np.linspace(0,1,100) \n",
@@ -465,6 +3702,11 @@
   }
  ],
  "metadata": {
+  "jupyter": {
+   "tags": [
+    "skip-execution"
+   ]
+  },
   "kernelspec": {
    "display_name": ".venv",
    "language": "python",
@@ -480,11 +3722,8 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.4"
-  },
-      "jupyter": {
-      "execute_notebook": "off"
-    }
+   "version": "3.12.10"
+  }
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  "nbformat": 4,
  "nbformat_minor": 2