diff --git a/.gitignore b/.gitignore
index d6e4a0c..62293e8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -130,8 +130,7 @@ dmypy.json
 
 .scikit-mol.code-workspace.swp
 scikit-mol.code-workspace
-notebooks/25747817
-*.ipynb
+
 
 # test data
 tests/data/
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 11326b6..d03dc45 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -20,4 +20,5 @@ repos:
       args: [ --fix ]
       types_or: [ python, pyi ]
     # Run the formatter.
-    - id: ruff-format
\ No newline at end of file
+    - id: ruff-format
+    
\ No newline at end of file
diff --git a/Makefile b/Makefile
index 0e7119d..5b26e8e 100644
--- a/Makefile
+++ b/Makefile
@@ -1,13 +1,6 @@
 sync-notebooks:
+	uv run jupytext --set-formats docs//notebooks//ipynb,docs//notebooks//scripts//py:percent --sync docs/notebooks/*.ipynb
 	uv run ruff format "docs/notebooks/"
-	uv run jupytext --sync "docs/notebooks/*.ipynb"
 
 run-notebooks:
-	# Execute the notebooks, gives a .nbconvert.ipynb extension
-	jupyter nbconvert --to notebook --execute *ipynb
-	# move the .nbconvert.ipynb to the original .ipynb
-	for file in *.nbconvert.ipynb; do 
-		fname=${file/.nbconvert.ipynb/};
-		rm $fname.ipynb
-		mv $file $fname.ipynb
-	done
\ No newline at end of file
+	uv run jupytext --execute docs/notebooks/*ipynb
\ No newline at end of file
diff --git a/docs/contributing.md b/docs/contributing.md
index 5095a71..45b04a4 100644
--- a/docs/contributing.md
+++ b/docs/contributing.md
@@ -94,11 +94,14 @@ Scikit-Learn has a check_estimator that we should strive to get to work, some cl
 ## Notebooks
 
 Another way of contributing is by providing notebooks with examples on how to use the project to build models together with Scikit-Learn and other tools. There are .ipynb files in the `docs/notebooks` and .py files in the `script` subfolder as the first are useful for online rendering in the documentation, whereas the latter is useful for sub version control.
-If you want to create new notebook you can create .ipynb file, and then you run `make sync-notebooks` to create the corresponding .py file for the commit.
+
+If you want to create new notebook you can first create .ipynb file, and then you run `make sync-notebooks` to create the corresponding .py file for the commit.
+
+If you updated any of the existing py/ipynb files, you can run `make sync-notebooks` to update the outdated file in the pair. The .py files are used for nice diffs, and the .ipynb files are used for rendering in the documentation.
 
 `make sync-notebooks` will sync all the notebooks with the .py files in the `scripts` folder.
 
-`make run-notebooks` will sync, run and save the notebooks, expects an ipython kernel with scikit-mol installed called Python3.
+`make run-notebooks` will sync, run and save the notebooks, expects an ipython kernel with scikit-mol installed.
 
 ## Documentation
 
diff --git a/docs/notebooks/01_basic_usage.ipynb b/docs/notebooks/01_basic_usage.ipynb
index 719369c..3968cf3 100644
--- a/docs/notebooks/01_basic_usage.ipynb
+++ b/docs/notebooks/01_basic_usage.ipynb
@@ -25,10 +25,10 @@
    "id": "2c8cad03",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:16.292725Z",
-     "iopub.status.busy": "2024-11-24T09:27:16.292083Z",
-     "iopub.status.idle": "2024-11-24T09:27:16.306663Z",
-     "shell.execute_reply": "2024-11-24T09:27:16.304935Z"
+     "iopub.execute_input": "2025-05-08T16:22:29.627872Z",
+     "iopub.status.busy": "2025-05-08T16:22:29.627571Z",
+     "iopub.status.idle": "2025-05-08T16:22:29.632065Z",
+     "shell.execute_reply": "2025-05-08T16:22:29.631373Z"
     }
    },
    "outputs": [],
@@ -42,10 +42,10 @@
    "id": "8d5b2333",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:16.313611Z",
-     "iopub.status.busy": "2024-11-24T09:27:16.313028Z",
-     "iopub.status.idle": "2024-11-24T09:27:16.510254Z",
-     "shell.execute_reply": "2024-11-24T09:27:16.509620Z"
+     "iopub.execute_input": "2025-05-08T16:22:29.634712Z",
+     "iopub.status.busy": "2025-05-08T16:22:29.634423Z",
+     "iopub.status.idle": "2025-05-08T16:22:29.845389Z",
+     "shell.execute_reply": "2025-05-08T16:22:29.844169Z"
     }
    },
    "outputs": [],
@@ -78,10 +78,10 @@
    "id": "0a625dda",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:16.513123Z",
-     "iopub.status.busy": "2024-11-24T09:27:16.512856Z",
-     "iopub.status.idle": "2024-11-24T09:27:17.089043Z",
-     "shell.execute_reply": "2024-11-24T09:27:17.088357Z"
+     "iopub.execute_input": "2025-05-08T16:22:29.850211Z",
+     "iopub.status.busy": "2025-05-08T16:22:29.848822Z",
+     "iopub.status.idle": "2025-05-08T16:22:30.986417Z",
+     "shell.execute_reply": "2025-05-08T16:22:30.984810Z"
     }
    },
    "outputs": [
@@ -118,10 +118,10 @@
    "id": "9a801d0f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:17.091942Z",
-     "iopub.status.busy": "2024-11-24T09:27:17.091571Z",
-     "iopub.status.idle": "2024-11-24T09:27:17.098501Z",
-     "shell.execute_reply": "2024-11-24T09:27:17.097922Z"
+     "iopub.execute_input": "2025-05-08T16:22:30.991850Z",
+     "iopub.status.busy": "2025-05-08T16:22:30.990911Z",
+     "iopub.status.idle": "2025-05-08T16:22:31.011512Z",
+     "shell.execute_reply": "2025-05-08T16:22:31.010309Z"
     }
    },
    "outputs": [
@@ -130,7 +130,8 @@
       "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-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -275,12 +276,21 @@
        "/* Toggleable label */\n",
        "#sk-container-id-1 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
        "}\n",
        "\n",
        "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
@@ -433,7 +443,8 @@
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-unfitted-level-1);\n",
@@ -532,7 +543,7 @@
        "  /* 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>MorganFingerprintTransformer(radius=3)</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\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;MorganFingerprintTransformer<span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>MorganFingerprintTransformer(radius=3)</pre></div> </div></div></div></div>"
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MorganFingerprintTransformer(radius=3)</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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>MorganFingerprintTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.fingerprints/#scikit_mol.fingerprints.MorganFingerprintTransformer\">?<span>Documentation for MorganFingerprintTransformer</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>MorganFingerprintTransformer(radius=3)</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "MorganFingerprintTransformer(radius=3)"
@@ -561,10 +572,10 @@
    "id": "500dc6f7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:17.101153Z",
-     "iopub.status.busy": "2024-11-24T09:27:17.100929Z",
-     "iopub.status.idle": "2024-11-24T09:27:17.105319Z",
-     "shell.execute_reply": "2024-11-24T09:27:17.104586Z"
+     "iopub.execute_input": "2025-05-08T16:22:31.015226Z",
+     "iopub.status.busy": "2025-05-08T16:22:31.014511Z",
+     "iopub.status.idle": "2025-05-08T16:22:31.022448Z",
+     "shell.execute_reply": "2025-05-08T16:22:31.021051Z"
     }
    },
    "outputs": [
@@ -572,7 +583,7 @@
      "data": {
       "text/plain": [
        "{'fpSize': 2048,\n",
-       " 'parallel': False,\n",
+       " 'n_jobs': None,\n",
        " 'radius': 3,\n",
        " 'safe_inference_mode': False,\n",
        " 'useBondTypes': True,\n",
@@ -605,10 +616,10 @@
    "id": "3a27b07a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:17.107710Z",
-     "iopub.status.busy": "2024-11-24T09:27:17.107495Z",
-     "iopub.status.idle": "2024-11-24T09:27:17.111268Z",
-     "shell.execute_reply": "2024-11-24T09:27:17.110754Z"
+     "iopub.execute_input": "2025-05-08T16:22:31.026293Z",
+     "iopub.status.busy": "2025-05-08T16:22:31.025546Z",
+     "iopub.status.idle": "2025-05-08T16:22:31.032975Z",
+     "shell.execute_reply": "2025-05-08T16:22:31.031700Z"
     }
    },
    "outputs": [
@@ -641,10 +652,10 @@
    "id": "0f141920",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:17.113572Z",
-     "iopub.status.busy": "2024-11-24T09:27:17.113344Z",
-     "iopub.status.idle": "2024-11-24T09:27:17.117356Z",
-     "shell.execute_reply": "2024-11-24T09:27:17.116845Z"
+     "iopub.execute_input": "2025-05-08T16:22:31.036752Z",
+     "iopub.status.busy": "2025-05-08T16:22:31.036118Z",
+     "iopub.status.idle": "2025-05-08T16:22:31.043904Z",
+     "shell.execute_reply": "2025-05-08T16:22:31.042561Z"
     }
    },
    "outputs": [
@@ -675,10 +686,10 @@
    "id": "481e527f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:17.119855Z",
-     "iopub.status.busy": "2024-11-24T09:27:17.119584Z",
-     "iopub.status.idle": "2024-11-24T09:27:17.124520Z",
-     "shell.execute_reply": "2024-11-24T09:27:17.124025Z"
+     "iopub.execute_input": "2025-05-08T16:22:31.047228Z",
+     "iopub.status.busy": "2025-05-08T16:22:31.046700Z",
+     "iopub.status.idle": "2025-05-08T16:22:31.054569Z",
+     "shell.execute_reply": "2025-05-08T16:22:31.053275Z"
     }
    },
    "outputs": [
@@ -690,7 +701,7 @@
        "       [1, 0, 0, ..., 0, 0, 0],\n",
        "       [0, 0, 0, ..., 0, 0, 1],\n",
        "       [1, 1, 0, ..., 0, 0, 0],\n",
-       "       [1, 1, 0, ..., 0, 0, 0]], dtype=uint8)"
+       "       [1, 1, 0, ..., 0, 0, 0]], shape=(6, 256), dtype=uint8)"
       ]
      },
      "execution_count": 8,
@@ -717,10 +728,10 @@
    "id": "7773a5a0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:17.126934Z",
-     "iopub.status.busy": "2024-11-24T09:27:17.126713Z",
-     "iopub.status.idle": "2024-11-24T09:27:17.131063Z",
-     "shell.execute_reply": "2024-11-24T09:27:17.130539Z"
+     "iopub.execute_input": "2025-05-08T16:22:31.057901Z",
+     "iopub.status.busy": "2025-05-08T16:22:31.057134Z",
+     "iopub.status.idle": "2025-05-08T16:22:31.064119Z",
+     "shell.execute_reply": "2025-05-08T16:22:31.063046Z"
     }
    },
    "outputs": [
@@ -745,10 +756,10 @@
    "id": "fa484453",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:17.133328Z",
-     "iopub.status.busy": "2024-11-24T09:27:17.133133Z",
-     "iopub.status.idle": "2024-11-24T09:27:17.137378Z",
-     "shell.execute_reply": "2024-11-24T09:27:17.136857Z"
+     "iopub.execute_input": "2025-05-08T16:22:31.067178Z",
+     "iopub.status.busy": "2025-05-08T16:22:31.066755Z",
+     "iopub.status.idle": "2025-05-08T16:22:31.074756Z",
+     "shell.execute_reply": "2025-05-08T16:22:31.073587Z"
     }
    },
    "outputs": [
@@ -756,12 +767,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[<rdkit.Chem.rdchem.Mol object at 0x7ec3603d1e00>]\n",
-      " [<rdkit.Chem.rdchem.Mol object at 0x7ec3603d1e70>]\n",
-      " [<rdkit.Chem.rdchem.Mol object at 0x7ec3603d1ee0>]\n",
-      " [<rdkit.Chem.rdchem.Mol object at 0x7ec3603d1f50>]\n",
-      " [<rdkit.Chem.rdchem.Mol object at 0x7ec3603d1fc0>]\n",
-      " [<rdkit.Chem.rdchem.Mol object at 0x7ec3603d2030>]]\n"
+      "[[<rdkit.Chem.rdchem.Mol object at 0x7feda5e663b0>]\n",
+      " [<rdkit.Chem.rdchem.Mol object at 0x7feda5e66420>]\n",
+      " [<rdkit.Chem.rdchem.Mol object at 0x7feda5e66490>]\n",
+      " [<rdkit.Chem.rdchem.Mol object at 0x7feda5e66500>]\n",
+      " [<rdkit.Chem.rdchem.Mol object at 0x7feda5e66570>]\n",
+      " [<rdkit.Chem.rdchem.Mol object at 0x7feda5e665e0>]]\n"
      ]
     }
    ],
@@ -771,6 +782,9 @@
   }
  ],
  "metadata": {
+  "jupytext": {
+   "formats": "docs//notebooks//ipynb,docs//notebooks//scripts//py:percent"
+  },
   "kernelspec": {
    "display_name": "Python 3.9.4 ('rdkit')",
    "language": "python",
@@ -786,7 +800,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.6"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/02_descriptor_transformer.ipynb b/docs/notebooks/02_descriptor_transformer.ipynb
index 90c082b..f8c69b3 100644
--- a/docs/notebooks/02_descriptor_transformer.ipynb
+++ b/docs/notebooks/02_descriptor_transformer.ipynb
@@ -16,10 +16,10 @@
    "id": "81745b1f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:18.828147Z",
-     "iopub.status.busy": "2024-11-24T09:27:18.827339Z",
-     "iopub.status.idle": "2024-11-24T09:27:19.887178Z",
-     "shell.execute_reply": "2024-11-24T09:27:19.886482Z"
+     "iopub.execute_input": "2025-05-08T16:22:32.631647Z",
+     "iopub.status.busy": "2025-05-08T16:22:32.631311Z",
+     "iopub.status.idle": "2025-05-08T16:22:34.194489Z",
+     "shell.execute_reply": "2025-05-08T16:22:34.193202Z"
     },
     "lines_to_next_cell": 0
    },
@@ -45,10 +45,10 @@
    "id": "dd9a2ad0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:19.890505Z",
-     "iopub.status.busy": "2024-11-24T09:27:19.889986Z",
-     "iopub.status.idle": "2024-11-24T09:27:19.896597Z",
-     "shell.execute_reply": "2024-11-24T09:27:19.896028Z"
+     "iopub.execute_input": "2025-05-08T16:22:34.198567Z",
+     "iopub.status.busy": "2025-05-08T16:22:34.197421Z",
+     "iopub.status.idle": "2025-05-08T16:22:34.206453Z",
+     "shell.execute_reply": "2025-05-08T16:22:34.205342Z"
     }
    },
    "outputs": [
@@ -56,7 +56,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "There are 210 available descriptors\n",
+      "There are 217 available descriptors\n",
       "The first five descriptor names: ['MaxAbsEStateIndex', 'MaxEStateIndex', 'MinAbsEStateIndex', 'MinEStateIndex', 'qed']\n"
      ]
     }
@@ -82,28 +82,16 @@
    "id": "4431a910",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:19.899516Z",
-     "iopub.status.busy": "2024-11-24T09:27:19.899244Z",
-     "iopub.status.idle": "2024-11-24T09:27:20.125197Z",
-     "shell.execute_reply": "2024-11-24T09:27:20.123935Z"
+     "iopub.execute_input": "2025-05-08T16:22:34.210875Z",
+     "iopub.status.busy": "2025-05-08T16:22:34.210262Z",
+     "iopub.status.idle": "2025-05-08T16:22:34.353857Z",
+     "shell.execute_reply": "2025-05-08T16:22:34.352652Z"
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[10:27:19] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:27:19] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:27:19] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:27:19] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:27:19] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:27:19] DEPRECATION WARNING: please use MorganGenerator\n"
-     ]
-    },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -134,10 +122,10 @@
    "id": "6caa9a54",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:20.131216Z",
-     "iopub.status.busy": "2024-11-24T09:27:20.130932Z",
-     "iopub.status.idle": "2024-11-24T09:27:20.135202Z",
-     "shell.execute_reply": "2024-11-24T09:27:20.134644Z"
+     "iopub.execute_input": "2025-05-08T16:22:34.357638Z",
+     "iopub.status.busy": "2025-05-08T16:22:34.356566Z",
+     "iopub.status.idle": "2025-05-08T16:22:34.363282Z",
+     "shell.execute_reply": "2025-05-08T16:22:34.362201Z"
     }
    },
    "outputs": [
@@ -171,10 +159,10 @@
    "id": "78fc5691",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:20.138477Z",
-     "iopub.status.busy": "2024-11-24T09:27:20.138022Z",
-     "iopub.status.idle": "2024-11-24T09:27:20.151081Z",
-     "shell.execute_reply": "2024-11-24T09:27:20.150278Z"
+     "iopub.execute_input": "2025-05-08T16:22:34.366550Z",
+     "iopub.status.busy": "2025-05-08T16:22:34.366050Z",
+     "iopub.status.idle": "2025-05-08T16:22:34.372939Z",
+     "shell.execute_reply": "2025-05-08T16:22:34.371839Z"
     }
    },
    "outputs": [
@@ -198,15 +186,18 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4796a16e",
+   "id": "310a2a0d",
    "metadata": {},
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
+  "jupytext": {
+   "formats": "docs//notebooks//ipynb,docs//notebooks//scripts//py:percent"
+  },
   "kernelspec": {
-   "display_name": "Python 3.9.4 ('rdkit')",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -220,7 +211,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.6"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/03_example_pipeline.ipynb b/docs/notebooks/03_example_pipeline.ipynb
index 10b5edc..cacfa7d 100644
--- a/docs/notebooks/03_example_pipeline.ipynb
+++ b/docs/notebooks/03_example_pipeline.ipynb
@@ -18,10 +18,10 @@
    "id": "79139b10",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:21.863626Z",
-     "iopub.status.busy": "2024-11-24T09:27:21.863272Z",
-     "iopub.status.idle": "2024-11-24T09:27:22.718519Z",
-     "shell.execute_reply": "2024-11-24T09:27:22.717789Z"
+     "iopub.execute_input": "2025-05-08T16:22:35.876773Z",
+     "iopub.status.busy": "2025-05-08T16:22:35.876261Z",
+     "iopub.status.idle": "2025-05-08T16:22:36.754601Z",
+     "shell.execute_reply": "2025-05-08T16:22:36.753459Z"
     }
    },
    "outputs": [],
@@ -42,16 +42,16 @@
    "id": "17a9cdd7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:22.722219Z",
-     "iopub.status.busy": "2024-11-24T09:27:22.721369Z",
-     "iopub.status.idle": "2024-11-24T09:27:22.727326Z",
-     "shell.execute_reply": "2024-11-24T09:27:22.726709Z"
+     "iopub.execute_input": "2025-05-08T16:22:36.758840Z",
+     "iopub.status.busy": "2025-05-08T16:22:36.758015Z",
+     "iopub.status.idle": "2025-05-08T16:22:36.767668Z",
+     "shell.execute_reply": "2025-05-08T16:22:36.766504Z"
     },
     "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
-    "csv_file = \"../tests/data/SLC6A4_active_excapedb_subset.csv\"  # Hmm, maybe better to download directly\n",
+    "csv_file = \"../../tests/data/SLC6A4_active_excapedb_subset.csv\"  # Hmm, maybe better to download directly\n",
     "data = pd.read_csv(csv_file)"
    ]
   },
@@ -71,10 +71,10 @@
    "id": "a3ec0a23",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:22.729951Z",
-     "iopub.status.busy": "2024-11-24T09:27:22.729732Z",
-     "iopub.status.idle": "2024-11-24T09:27:22.769704Z",
-     "shell.execute_reply": "2024-11-24T09:27:22.768854Z"
+     "iopub.execute_input": "2025-05-08T16:22:36.770992Z",
+     "iopub.status.busy": "2025-05-08T16:22:36.770360Z",
+     "iopub.status.idle": "2025-05-08T16:22:36.828093Z",
+     "shell.execute_reply": "2025-05-08T16:22:36.826677Z"
     },
     "lines_to_next_cell": 0
    },
@@ -106,10 +106,10 @@
    "id": "4eb8f0fa",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:22.772861Z",
-     "iopub.status.busy": "2024-11-24T09:27:22.772534Z",
-     "iopub.status.idle": "2024-11-24T09:27:23.182612Z",
-     "shell.execute_reply": "2024-11-24T09:27:23.181966Z"
+     "iopub.execute_input": "2025-05-08T16:22:36.830959Z",
+     "iopub.status.busy": "2025-05-08T16:22:36.830663Z",
+     "iopub.status.idle": "2025-05-08T16:22:37.516946Z",
+     "shell.execute_reply": "2025-05-08T16:22:37.515550Z"
     }
    },
    "outputs": [],
@@ -127,10 +127,10 @@
    "id": "99edec0f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:23.185612Z",
-     "iopub.status.busy": "2024-11-24T09:27:23.185269Z",
-     "iopub.status.idle": "2024-11-24T09:27:23.190844Z",
-     "shell.execute_reply": "2024-11-24T09:27:23.190290Z"
+     "iopub.execute_input": "2025-05-08T16:22:37.521222Z",
+     "iopub.status.busy": "2025-05-08T16:22:37.520115Z",
+     "iopub.status.idle": "2025-05-08T16:22:37.527537Z",
+     "shell.execute_reply": "2025-05-08T16:22:37.526440Z"
     }
    },
    "outputs": [],
@@ -154,10 +154,10 @@
    "id": "a27d6ff9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:23.193426Z",
-     "iopub.status.busy": "2024-11-24T09:27:23.193188Z",
-     "iopub.status.idle": "2024-11-24T09:27:23.198881Z",
-     "shell.execute_reply": "2024-11-24T09:27:23.198225Z"
+     "iopub.execute_input": "2025-05-08T16:22:37.531062Z",
+     "iopub.status.busy": "2025-05-08T16:22:37.530349Z",
+     "iopub.status.idle": "2025-05-08T16:22:37.539115Z",
+     "shell.execute_reply": "2025-05-08T16:22:37.538026Z"
     }
    },
    "outputs": [
@@ -191,10 +191,10 @@
    "id": "634ca919",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:23.201230Z",
-     "iopub.status.busy": "2024-11-24T09:27:23.201013Z",
-     "iopub.status.idle": "2024-11-24T09:27:23.265644Z",
-     "shell.execute_reply": "2024-11-24T09:27:23.264698Z"
+     "iopub.execute_input": "2025-05-08T16:22:37.542129Z",
+     "iopub.status.busy": "2025-05-08T16:22:37.541844Z",
+     "iopub.status.idle": "2025-05-08T16:22:37.609556Z",
+     "shell.execute_reply": "2025-05-08T16:22:37.608271Z"
     },
     "lines_to_next_cell": 0
    },
@@ -206,6 +206,18 @@
       "Train score is :1.00\n",
       "Test score is  :0.55\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
     }
    ],
    "source": [
@@ -228,10 +240,10 @@
    "id": "f4431aab",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:23.269015Z",
-     "iopub.status.busy": "2024-11-24T09:27:23.268218Z",
-     "iopub.status.idle": "2024-11-24T09:27:23.280889Z",
-     "shell.execute_reply": "2024-11-24T09:27:23.279967Z"
+     "iopub.execute_input": "2025-05-08T16:22:37.613501Z",
+     "iopub.status.busy": "2025-05-08T16:22:37.613074Z",
+     "iopub.status.idle": "2025-05-08T16:22:37.625937Z",
+     "shell.execute_reply": "2025-05-08T16:22:37.624623Z"
     }
    },
    "outputs": [
@@ -264,10 +276,10 @@
    "id": "a908097d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:23.284650Z",
-     "iopub.status.busy": "2024-11-24T09:27:23.283862Z",
-     "iopub.status.idle": "2024-11-24T09:27:23.298454Z",
-     "shell.execute_reply": "2024-11-24T09:27:23.297546Z"
+     "iopub.execute_input": "2025-05-08T16:22:37.630016Z",
+     "iopub.status.busy": "2025-05-08T16:22:37.629320Z",
+     "iopub.status.idle": "2025-05-08T16:22:37.640274Z",
+     "shell.execute_reply": "2025-05-08T16:22:37.639075Z"
     }
    },
    "outputs": [
@@ -296,10 +308,10 @@
    "id": "0124653c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:23.302185Z",
-     "iopub.status.busy": "2024-11-24T09:27:23.301318Z",
-     "iopub.status.idle": "2024-11-24T09:27:23.307070Z",
-     "shell.execute_reply": "2024-11-24T09:27:23.306539Z"
+     "iopub.execute_input": "2025-05-08T16:22:37.643282Z",
+     "iopub.status.busy": "2025-05-08T16:22:37.642781Z",
+     "iopub.status.idle": "2025-05-08T16:22:37.655561Z",
+     "shell.execute_reply": "2025-05-08T16:22:37.652513Z"
     }
    },
    "outputs": [
@@ -332,10 +344,10 @@
    "id": "63c8ef60",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:23.309849Z",
-     "iopub.status.busy": "2024-11-24T09:27:23.309649Z",
-     "iopub.status.idle": "2024-11-24T09:27:23.317613Z",
-     "shell.execute_reply": "2024-11-24T09:27:23.316837Z"
+     "iopub.execute_input": "2025-05-08T16:22:37.659747Z",
+     "iopub.status.busy": "2025-05-08T16:22:37.658755Z",
+     "iopub.status.idle": "2025-05-08T16:22:37.669836Z",
+     "shell.execute_reply": "2025-05-08T16:22:37.668406Z"
     }
    },
    "outputs": [
@@ -347,20 +359,22 @@
        "  ('pipe',\n",
        "   Pipeline(steps=[('mol_transformer', MorganFingerprintTransformer()),\n",
        "                   ('Regressor', Ridge())]))],\n",
+       " 'transform_input': None,\n",
        " 'verbose': False,\n",
        " 'smiles_transformer': SmilesToMolTransformer(),\n",
        " 'pipe': Pipeline(steps=[('mol_transformer', MorganFingerprintTransformer()),\n",
        "                 ('Regressor', Ridge())]),\n",
-       " 'smiles_transformer__parallel': False,\n",
+       " 'smiles_transformer__n_jobs': None,\n",
        " 'smiles_transformer__safe_inference_mode': False,\n",
        " 'pipe__memory': None,\n",
        " 'pipe__steps': [('mol_transformer', MorganFingerprintTransformer()),\n",
        "  ('Regressor', Ridge())],\n",
+       " 'pipe__transform_input': None,\n",
        " 'pipe__verbose': False,\n",
        " 'pipe__mol_transformer': MorganFingerprintTransformer(),\n",
        " 'pipe__Regressor': Ridge(),\n",
        " 'pipe__mol_transformer__fpSize': 2048,\n",
-       " 'pipe__mol_transformer__parallel': False,\n",
+       " 'pipe__mol_transformer__n_jobs': None,\n",
        " 'pipe__mol_transformer__radius': 2,\n",
        " 'pipe__mol_transformer__safe_inference_mode': False,\n",
        " 'pipe__mol_transformer__useBondTypes': True,\n",
@@ -388,8 +402,11 @@
   }
  ],
  "metadata": {
+  "jupytext": {
+   "formats": "docs//notebooks//ipynb,docs//notebooks//scripts//py:percent"
+  },
   "kernelspec": {
-   "display_name": "Python 3.9.4 ('rdkit')",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -403,7 +420,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.6"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/04_standardizer.ipynb b/docs/notebooks/04_standardizer.ipynb
index d32b9b3..f326ea1 100644
--- a/docs/notebooks/04_standardizer.ipynb
+++ b/docs/notebooks/04_standardizer.ipynb
@@ -6,7 +6,7 @@
    "metadata": {},
    "source": [
     "# Molecule standardization\n",
-    "When building machine learning models of molecules, it is important to standardize the molecules. We often don't want different predictions just because things are drawn in slightly different forms, such as protonated or deprotonated carboxylic acids. Scikit-mol provides a very basic standardize transformer based on the molvs implementation in RDKit"
+    "When building machine learning models of molecules, it is important to standardize the molecules. We often don't want different predictions just because things are drawn in slightly different forms, such as protonated or deprotanted carboxylic acids. Scikit-mol provides a very basic standardize transformer based on the molvs implementation in RDKit"
    ]
   },
   {
@@ -15,10 +15,10 @@
    "id": "d40bdabe",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:25.092168Z",
-     "iopub.status.busy": "2024-11-24T09:27:25.091775Z",
-     "iopub.status.idle": "2024-11-24T09:27:25.972589Z",
-     "shell.execute_reply": "2024-11-24T09:27:25.971827Z"
+     "iopub.execute_input": "2025-05-08T16:22:39.191239Z",
+     "iopub.status.busy": "2025-05-08T16:22:39.189891Z",
+     "iopub.status.idle": "2025-05-08T16:22:40.514182Z",
+     "shell.execute_reply": "2025-05-08T16:22:40.512920Z"
     }
    },
    "outputs": [],
@@ -45,17 +45,17 @@
    "id": "5a45dfd5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:25.975743Z",
-     "iopub.status.busy": "2024-11-24T09:27:25.975328Z",
-     "iopub.status.idle": "2024-11-24T09:27:25.984915Z",
-     "shell.execute_reply": "2024-11-24T09:27:25.984323Z"
+     "iopub.execute_input": "2025-05-08T16:22:40.518530Z",
+     "iopub.status.busy": "2025-05-08T16:22:40.517654Z",
+     "iopub.status.idle": "2025-05-08T16:22:40.537037Z",
+     "shell.execute_reply": "2025-05-08T16:22:40.535847Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([<rdkit.Chem.rdchem.Mol object at 0x72e1114d5a10>], dtype=object)"
+       "array([<rdkit.Chem.rdchem.Mol object at 0x7f3682927ca0>], dtype=object)"
       ]
      },
      "metadata": {},
@@ -64,7 +64,7 @@
     {
      "data": {
       "text/plain": [
-       "array([<rdkit.Chem.rdchem.Mol object at 0x72e1114d5a80>], dtype=object)"
+       "array([<rdkit.Chem.rdchem.Mol object at 0x7f3682927bc0>], dtype=object)"
       ]
      },
      "metadata": {},
@@ -102,10 +102,10 @@
    "id": "d13141c6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:25.987910Z",
-     "iopub.status.busy": "2024-11-24T09:27:25.987688Z",
-     "iopub.status.idle": "2024-11-24T09:27:26.003398Z",
-     "shell.execute_reply": "2024-11-24T09:27:26.002776Z"
+     "iopub.execute_input": "2025-05-08T16:22:40.540032Z",
+     "iopub.status.busy": "2025-05-08T16:22:40.539703Z",
+     "iopub.status.idle": "2025-05-08T16:22:40.560979Z",
+     "shell.execute_reply": "2025-05-08T16:22:40.560007Z"
     }
    },
    "outputs": [
@@ -140,7 +140,7 @@
    "source": [
     "Some of the molecules were desalted and neutralized.\n",
     "\n",
-    "A typical usecase would be to add the standardizer to a pipeline for prediction"
+    "A typical use case would be to add the standardizer to a pipeline for prediction"
    ]
   },
   {
@@ -149,10 +149,10 @@
    "id": "a376a759",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:26.006132Z",
-     "iopub.status.busy": "2024-11-24T09:27:26.005882Z",
-     "iopub.status.idle": "2024-11-24T09:27:26.034347Z",
-     "shell.execute_reply": "2024-11-24T09:27:26.033821Z"
+     "iopub.execute_input": "2025-05-08T16:22:40.565099Z",
+     "iopub.status.busy": "2025-05-08T16:22:40.564746Z",
+     "iopub.status.idle": "2025-05-08T16:22:40.603278Z",
+     "shell.execute_reply": "2025-05-08T16:22:40.602109Z"
     },
     "lines_to_next_cell": 2
    },
@@ -194,7 +194,7 @@
    "id": "f0d071fb",
    "metadata": {},
    "source": [
-    "As we can see, the predictions with the standardizer and without are different. The two first molecules were benzoic acid and sodium benzoate, which with the standardized pipeline is predicted as the same, but differently with the nonstandardized pipeline. Wheter we want to make the prediction on the parent compound, or predict the exact form, will of course depend on the use-case, but now there is at least a way to handle it easily in pipelined predictors.\n",
+    "As we can see, the predictions with the standardizer and without are different. The two first molecules were benzoic acid and sodium benzoate, which with the standardized pipeline is predicted as the same, but differently with the nonstandardized pipeline. Whether we want to make the prediction on the parent compound, or predict the exact form, will of course depend on the use-case, but now there is at least a way to handle it easily in pipelined predictors.\n",
     "\n",
     "The example also demonstrate another feature. We created the ridge regressor before creating the two pipelines. Fitting one of the pipelines thus also updated the object in the other pipeline. This can be useful for building inference pipelines that takes in SMILES molecules, but rather do the fitting on already converted and standardized molecules. However, be aware that the crossvalidation classes of scikit-learn may clone the estimators internally when doing the search loop, which would break this interdependence, and necessitate the rebuilding of the inference pipeline.\n",
     "\n",
@@ -207,10 +207,10 @@
    "id": "50f71bca",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:26.037572Z",
-     "iopub.status.busy": "2024-11-24T09:27:26.036950Z",
-     "iopub.status.idle": "2024-11-24T09:27:26.056194Z",
-     "shell.execute_reply": "2024-11-24T09:27:26.055013Z"
+     "iopub.execute_input": "2025-05-08T16:22:40.606383Z",
+     "iopub.status.busy": "2025-05-08T16:22:40.605699Z",
+     "iopub.status.idle": "2025-05-08T16:22:40.631525Z",
+     "shell.execute_reply": "2025-05-08T16:22:40.630106Z"
     }
    },
    "outputs": [
@@ -231,6 +231,9 @@
   }
  ],
  "metadata": {
+  "jupytext": {
+   "formats": "docs//notebooks//ipynb,docs//notebooks//scripts//py:percent"
+  },
   "kernelspec": {
    "display_name": "Python 3.9.4 ('rdkit')",
    "language": "python",
@@ -246,7 +249,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.6"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/05_smiles_sanitization.ipynb b/docs/notebooks/05_smiles_sanitization.ipynb
index 2b0bb99..d663a1c 100644
--- a/docs/notebooks/05_smiles_sanitization.ipynb
+++ b/docs/notebooks/05_smiles_sanitization.ipynb
@@ -15,10 +15,10 @@
    "id": "612aa974",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:27.545695Z",
-     "iopub.status.busy": "2024-11-24T09:27:27.545293Z",
-     "iopub.status.idle": "2024-11-24T09:27:28.079174Z",
-     "shell.execute_reply": "2024-11-24T09:27:28.078490Z"
+     "iopub.execute_input": "2025-05-08T16:22:41.856567Z",
+     "iopub.status.busy": "2025-05-08T16:22:41.856271Z",
+     "iopub.status.idle": "2025-05-08T16:22:42.443540Z",
+     "shell.execute_reply": "2025-05-08T16:22:42.442130Z"
     },
     "lines_to_next_cell": 2
    },
@@ -27,7 +27,7 @@
     "import pandas as pd\n",
     "from rdkit.Chem import PandasTools\n",
     "\n",
-    "csv_file = \"../tests/data/SLC6A4_active_excapedb_subset.csv\"  # Hmm, maybe better to download directly\n",
+    "csv_file = \"../../tests/data/SLC6A4_active_excapedb_subset.csv\"  # Hmm, maybe better to download directly\n",
     "data = pd.read_csv(csv_file)"
    ]
   },
@@ -45,10 +45,10 @@
    "id": "b09cfd6b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:28.082222Z",
-     "iopub.status.busy": "2024-11-24T09:27:28.081921Z",
-     "iopub.status.idle": "2024-11-24T09:27:28.086003Z",
-     "shell.execute_reply": "2024-11-24T09:27:28.085450Z"
+     "iopub.execute_input": "2025-05-08T16:22:42.448114Z",
+     "iopub.status.busy": "2025-05-08T16:22:42.447423Z",
+     "iopub.status.idle": "2025-05-08T16:22:42.454532Z",
+     "shell.execute_reply": "2025-05-08T16:22:42.453410Z"
     }
    },
    "outputs": [],
@@ -62,10 +62,10 @@
    "id": "e20fb5cc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:28.088449Z",
-     "iopub.status.busy": "2024-11-24T09:27:28.088211Z",
-     "iopub.status.idle": "2024-11-24T09:27:28.130818Z",
-     "shell.execute_reply": "2024-11-24T09:27:28.130102Z"
+     "iopub.execute_input": "2025-05-08T16:22:42.458752Z",
+     "iopub.status.busy": "2025-05-08T16:22:42.457870Z",
+     "iopub.status.idle": "2025-05-08T16:22:42.522970Z",
+     "shell.execute_reply": "2025-05-08T16:22:42.521865Z"
     },
     "lines_to_next_cell": 2
    },
@@ -81,12 +81,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[10:27:28] Explicit valence for atom # 1 N, 4, is greater than permitted\n"
+      "[18:22:42] Explicit valence for atom # 1 N, 4, is greater than permitted\n"
      ]
     }
    ],
    "source": [
-    "\n",
     "PandasTools.AddMoleculeColumnToFrame(data, smilesCol=\"SMILES\")\n",
     "print(f\"Dataset contains {data.ROMol.isna().sum()} unparsable mols\")"
    ]
@@ -105,10 +104,10 @@
    "id": "3dbd50b3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:28.133745Z",
-     "iopub.status.busy": "2024-11-24T09:27:28.133507Z",
-     "iopub.status.idle": "2024-11-24T09:27:28.508377Z",
-     "shell.execute_reply": "2024-11-24T09:27:28.507130Z"
+     "iopub.execute_input": "2025-05-08T16:22:42.526969Z",
+     "iopub.status.busy": "2025-05-08T16:22:42.526369Z",
+     "iopub.status.idle": "2025-05-08T16:22:43.317088Z",
+     "shell.execute_reply": "2025-05-08T16:22:43.316227Z"
     }
    },
    "outputs": [
@@ -123,14 +122,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[10:27:28] Explicit valence for atom # 1 N, 4, is greater than permitted\n"
+      "[18:22:43] Explicit valence for atom # 1 N, 4, is greater than permitted\n"
      ]
     }
    ],
    "source": [
-    "from scikit_mol.utilities import CheckSmilesSanitazion\n",
+    "from scikit_mol.utilities import CheckSmilesSanitization\n",
     "\n",
-    "smileschecker = CheckSmilesSanitazion()\n",
+    "smileschecker = CheckSmilesSanitization()\n",
     "\n",
     "smiles_list_valid, y_valid, smiles_errors, y_errors = smileschecker.sanitize(\n",
     "    list(data.SMILES), list(data.pXC50)\n",
@@ -142,7 +141,7 @@
    "id": "c888d7da",
    "metadata": {},
    "source": [
-    "Now the smiles_list_valid should be all valid and the y_values filtered as well. Errors are returned, but also accesible after the call to .sanitize() in the .errors property"
+    "Now the smiles_list_valid should be all valid and the y_values filtered as well. Errors are returned, but also accessible after the call to .sanitize() in the .errors property"
    ]
   },
   {
@@ -151,10 +150,10 @@
    "id": "5af5ea3d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:28.511261Z",
-     "iopub.status.busy": "2024-11-24T09:27:28.510945Z",
-     "iopub.status.idle": "2024-11-24T09:27:28.522024Z",
-     "shell.execute_reply": "2024-11-24T09:27:28.521232Z"
+     "iopub.execute_input": "2025-05-08T16:22:43.320958Z",
+     "iopub.status.busy": "2025-05-08T16:22:43.320157Z",
+     "iopub.status.idle": "2025-05-08T16:22:43.335067Z",
+     "shell.execute_reply": "2025-05-08T16:22:43.333676Z"
     }
    },
    "outputs": [
@@ -221,10 +220,10 @@
    "id": "84db07cc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:28.524982Z",
-     "iopub.status.busy": "2024-11-24T09:27:28.524717Z",
-     "iopub.status.idle": "2024-11-24T09:27:28.569119Z",
-     "shell.execute_reply": "2024-11-24T09:27:28.568473Z"
+     "iopub.execute_input": "2025-05-08T16:22:43.339302Z",
+     "iopub.status.busy": "2025-05-08T16:22:43.338668Z",
+     "iopub.status.idle": "2025-05-08T16:22:43.391019Z",
+     "shell.execute_reply": "2025-05-08T16:22:43.389989Z"
     }
    },
    "outputs": [
@@ -239,7 +238,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[10:27:28] Explicit valence for atom # 1 N, 4, is greater than permitted\n"
+      "[18:22:43] Explicit valence for atom # 1 N, 4, is greater than permitted\n"
      ]
     },
     {
@@ -292,6 +291,9 @@
   }
  ],
  "metadata": {
+  "jupytext": {
+   "formats": "docs//notebooks//ipynb,docs//notebooks//scripts//py:percent"
+  },
   "kernelspec": {
    "display_name": "Python 3.9.4 ('rdkit')",
    "language": "python",
@@ -307,7 +309,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.6"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/06_hyperparameter_tuning.ipynb b/docs/notebooks/06_hyperparameter_tuning.ipynb
index c6c8bb0..363b5b3 100644
--- a/docs/notebooks/06_hyperparameter_tuning.ipynb
+++ b/docs/notebooks/06_hyperparameter_tuning.ipynb
@@ -16,10 +16,10 @@
    "id": "51aa3d62",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:30.230310Z",
-     "iopub.status.busy": "2024-11-24T09:27:30.230076Z",
-     "iopub.status.idle": "2024-11-24T09:27:31.452867Z",
-     "shell.execute_reply": "2024-11-24T09:27:31.452127Z"
+     "iopub.execute_input": "2025-05-08T16:22:44.604531Z",
+     "iopub.status.busy": "2025-05-08T16:22:44.604218Z",
+     "iopub.status.idle": "2025-05-08T16:22:46.163842Z",
+     "shell.execute_reply": "2025-05-08T16:22:46.162418Z"
     }
    },
    "outputs": [],
@@ -53,10 +53,10 @@
    "id": "adbc1868",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:31.455770Z",
-     "iopub.status.busy": "2024-11-24T09:27:31.455436Z",
-     "iopub.status.idle": "2024-11-24T09:27:31.459245Z",
-     "shell.execute_reply": "2024-11-24T09:27:31.458654Z"
+     "iopub.execute_input": "2025-05-08T16:22:46.167928Z",
+     "iopub.status.busy": "2025-05-08T16:22:46.166905Z",
+     "iopub.status.idle": "2025-05-08T16:22:46.173404Z",
+     "shell.execute_reply": "2025-05-08T16:22:46.172138Z"
     }
    },
    "outputs": [],
@@ -71,7 +71,7 @@
     "        url = \"https://ndownloader.figshare.com/files/25747817\"\n",
     "        urllib.request.urlretrieve(url, csv_file)\n",
     "else:\n",
-    "    csv_file = \"../tests/data/SLC6A4_active_excapedb_subset.csv\""
+    "    csv_file = \"../../tests/data/SLC6A4_active_excapedb_subset.csv\""
    ]
   },
   {
@@ -88,10 +88,10 @@
    "id": "9a283f12",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:31.461622Z",
-     "iopub.status.busy": "2024-11-24T09:27:31.461384Z",
-     "iopub.status.idle": "2024-11-24T09:27:31.500359Z",
-     "shell.execute_reply": "2024-11-24T09:27:31.499764Z"
+     "iopub.execute_input": "2025-05-08T16:22:46.177164Z",
+     "iopub.status.busy": "2025-05-08T16:22:46.176440Z",
+     "iopub.status.idle": "2025-05-08T16:22:46.233488Z",
+     "shell.execute_reply": "2025-05-08T16:22:46.232374Z"
     }
    },
    "outputs": [
@@ -104,7 +104,6 @@
     }
    ],
    "source": [
-    "\n",
     "data = pd.read_csv(csv_file)\n",
     "\n",
     "PandasTools.AddMoleculeColumnToFrame(data, smilesCol=\"SMILES\")\n",
@@ -125,16 +124,15 @@
    "id": "303b83de",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:31.502982Z",
-     "iopub.status.busy": "2024-11-24T09:27:31.502779Z",
-     "iopub.status.idle": "2024-11-24T09:27:31.507447Z",
-     "shell.execute_reply": "2024-11-24T09:27:31.506962Z"
+     "iopub.execute_input": "2025-05-08T16:22:46.236917Z",
+     "iopub.status.busy": "2025-05-08T16:22:46.236251Z",
+     "iopub.status.idle": "2025-05-08T16:22:46.243264Z",
+     "shell.execute_reply": "2025-05-08T16:22:46.242175Z"
     },
     "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
-    "\n",
     "mol_list_train, mol_list_test, y_train, y_test = train_test_split(\n",
     "    data.ROMol, data.pXC50, random_state=42\n",
     ")"
@@ -154,13 +152,22 @@
    "id": "1383d0fc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:31.509953Z",
-     "iopub.status.busy": "2024-11-24T09:27:31.509731Z",
-     "iopub.status.idle": "2024-11-24T09:27:31.830576Z",
-     "shell.execute_reply": "2024-11-24T09:27:31.829874Z"
+     "iopub.execute_input": "2025-05-08T16:22:46.247777Z",
+     "iopub.status.busy": "2025-05-08T16:22:46.246787Z",
+     "iopub.status.idle": "2025-05-08T16:22:46.614634Z",
+     "shell.execute_reply": "2025-05-08T16:22:46.613399Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    }
+   ],
    "source": [
     "# Probably the recommended way would be to prestandardize the data if there's no changes to the transformer,\n",
     "# and then add the standardizer in the inference pipeline.\n",
@@ -185,16 +192,15 @@
    "id": "51c74711",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:31.833379Z",
-     "iopub.status.busy": "2024-11-24T09:27:31.833155Z",
-     "iopub.status.idle": "2024-11-24T09:27:31.836541Z",
-     "shell.execute_reply": "2024-11-24T09:27:31.835939Z"
+     "iopub.execute_input": "2025-05-08T16:22:46.618057Z",
+     "iopub.status.busy": "2025-05-08T16:22:46.617207Z",
+     "iopub.status.idle": "2025-05-08T16:22:46.622371Z",
+     "shell.execute_reply": "2025-05-08T16:22:46.621207Z"
     },
     "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
-    "\n",
     "moltransformer = MorganFingerprintTransformer()\n",
     "regressor = Ridge()\n",
     "\n",
@@ -215,10 +221,10 @@
    "id": "4c6b833f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:31.838854Z",
-     "iopub.status.busy": "2024-11-24T09:27:31.838668Z",
-     "iopub.status.idle": "2024-11-24T09:27:31.841636Z",
-     "shell.execute_reply": "2024-11-24T09:27:31.841130Z"
+     "iopub.execute_input": "2025-05-08T16:22:46.625354Z",
+     "iopub.status.busy": "2025-05-08T16:22:46.625058Z",
+     "iopub.status.idle": "2025-05-08T16:22:46.629915Z",
+     "shell.execute_reply": "2025-05-08T16:22:46.628829Z"
     },
     "title": "Now hyperparameter tuning"
    },
@@ -244,10 +250,10 @@
    "id": "0af1003b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:31.843922Z",
-     "iopub.status.busy": "2024-11-24T09:27:31.843728Z",
-     "iopub.status.idle": "2024-11-24T09:27:31.849777Z",
-     "shell.execute_reply": "2024-11-24T09:27:31.849273Z"
+     "iopub.execute_input": "2025-05-08T16:22:46.633716Z",
+     "iopub.status.busy": "2025-05-08T16:22:46.632911Z",
+     "iopub.status.idle": "2025-05-08T16:22:46.641844Z",
+     "shell.execute_reply": "2025-05-08T16:22:46.640881Z"
     },
     "title": "Which keys do we have?"
    },
@@ -255,7 +261,7 @@
     {
      "data": {
       "text/plain": [
-       "dict_keys(['memory', 'steps', 'verbose', 'morganfingerprinttransformer', 'ridge', 'morganfingerprinttransformer__fpSize', 'morganfingerprinttransformer__parallel', 'morganfingerprinttransformer__radius', 'morganfingerprinttransformer__safe_inference_mode', 'morganfingerprinttransformer__useBondTypes', 'morganfingerprinttransformer__useChirality', 'morganfingerprinttransformer__useCounts', 'morganfingerprinttransformer__useFeatures', 'ridge__alpha', 'ridge__copy_X', 'ridge__fit_intercept', 'ridge__max_iter', 'ridge__positive', 'ridge__random_state', 'ridge__solver', 'ridge__tol'])"
+       "dict_keys(['memory', 'steps', 'transform_input', 'verbose', 'morganfingerprinttransformer', 'ridge', 'morganfingerprinttransformer__fpSize', 'morganfingerprinttransformer__n_jobs', 'morganfingerprinttransformer__radius', 'morganfingerprinttransformer__safe_inference_mode', 'morganfingerprinttransformer__useBondTypes', 'morganfingerprinttransformer__useChirality', 'morganfingerprinttransformer__useCounts', 'morganfingerprinttransformer__useFeatures', 'ridge__alpha', 'ridge__copy_X', 'ridge__fit_intercept', 'ridge__max_iter', 'ridge__positive', 'ridge__random_state', 'ridge__solver', 'ridge__tol'])"
       ]
      },
      "execution_count": 8,
@@ -264,7 +270,6 @@
     }
    ],
    "source": [
-    "\n",
     "optimization_pipe.get_params().keys()"
    ]
   },
@@ -282,15 +287,14 @@
    "id": "c2d541b3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:31.852166Z",
-     "iopub.status.busy": "2024-11-24T09:27:31.851946Z",
-     "iopub.status.idle": "2024-11-24T09:27:31.856126Z",
-     "shell.execute_reply": "2024-11-24T09:27:31.855622Z"
+     "iopub.execute_input": "2025-05-08T16:22:46.645249Z",
+     "iopub.status.busy": "2025-05-08T16:22:46.644964Z",
+     "iopub.status.idle": "2025-05-08T16:22:46.651276Z",
+     "shell.execute_reply": "2025-05-08T16:22:46.650210Z"
     }
    },
    "outputs": [],
    "source": [
-    "\n",
     "param_dist = {\n",
     "    \"ridge__alpha\": loguniform(1e-2, 1e3),\n",
     "    \"morganfingerprinttransformer__fpSize\": [256, 512, 1024, 2048, 4096],\n",
@@ -316,10 +320,10 @@
    "id": "f2c91783",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:31.858429Z",
-     "iopub.status.busy": "2024-11-24T09:27:31.858216Z",
-     "iopub.status.idle": "2024-11-24T09:27:31.862461Z",
-     "shell.execute_reply": "2024-11-24T09:27:31.861795Z"
+     "iopub.execute_input": "2025-05-08T16:22:46.655212Z",
+     "iopub.status.busy": "2025-05-08T16:22:46.654913Z",
+     "iopub.status.idle": "2025-05-08T16:22:46.662196Z",
+     "shell.execute_reply": "2025-05-08T16:22:46.661226Z"
     },
     "title": "From https://scikit-learn.org/stable/auto_examples/model_selection/plot_randomized_search.html#sphx-glr-auto-examples-model-selection-plot-randomized-search-py"
    },
@@ -355,10 +359,10 @@
    "id": "79a70a0f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:31.864936Z",
-     "iopub.status.busy": "2024-11-24T09:27:31.864708Z",
-     "iopub.status.idle": "2024-11-24T09:27:36.221386Z",
-     "shell.execute_reply": "2024-11-24T09:27:36.220369Z"
+     "iopub.execute_input": "2025-05-08T16:22:46.665390Z",
+     "iopub.status.busy": "2025-05-08T16:22:46.665100Z",
+     "iopub.status.idle": "2025-05-08T16:22:49.120359Z",
+     "shell.execute_reply": "2025-05-08T16:22:49.119269Z"
     }
    },
    "outputs": [
@@ -366,7 +370,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Runtime: 4.35 for 25 iterations)\n"
+      "Runtime: 2.45 for 25 iterations)\n"
      ]
     }
    ],
@@ -388,10 +392,10 @@
    "id": "b6160cb3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:36.224876Z",
-     "iopub.status.busy": "2024-11-24T09:27:36.224667Z",
-     "iopub.status.idle": "2024-11-24T09:27:36.232647Z",
-     "shell.execute_reply": "2024-11-24T09:27:36.231571Z"
+     "iopub.execute_input": "2025-05-08T16:22:49.124324Z",
+     "iopub.status.busy": "2025-05-08T16:22:49.123579Z",
+     "iopub.status.idle": "2025-05-08T16:22:49.130023Z",
+     "shell.execute_reply": "2025-05-08T16:22:49.128965Z"
     },
     "lines_to_next_cell": 0
    },
@@ -401,16 +405,16 @@
      "output_type": "stream",
      "text": [
       "Model with rank: 1\n",
-      "Mean validation score: 0.563 (std: 0.115)\n",
-      "Parameters: {'morganfingerprinttransformer__fpSize': 1024, 'morganfingerprinttransformer__radius': 2, 'morganfingerprinttransformer__useCounts': False, 'morganfingerprinttransformer__useFeatures': False, 'ridge__alpha': 6.855244257973563}\n",
+      "Mean validation score: 0.459 (std: 0.117)\n",
+      "Parameters: {'morganfingerprinttransformer__fpSize': 1024, 'morganfingerprinttransformer__radius': 3, 'morganfingerprinttransformer__useCounts': False, 'morganfingerprinttransformer__useFeatures': True, 'ridge__alpha': np.float64(11.211371939288233)}\n",
       "\n",
       "Model with rank: 2\n",
-      "Mean validation score: 0.527 (std: 0.086)\n",
-      "Parameters: {'morganfingerprinttransformer__fpSize': 512, 'morganfingerprinttransformer__radius': 2, 'morganfingerprinttransformer__useCounts': False, 'morganfingerprinttransformer__useFeatures': False, 'ridge__alpha': 13.611425709525077}\n",
+      "Mean validation score: 0.427 (std: 0.130)\n",
+      "Parameters: {'morganfingerprinttransformer__fpSize': 512, 'morganfingerprinttransformer__radius': 2, 'morganfingerprinttransformer__useCounts': True, 'morganfingerprinttransformer__useFeatures': False, 'ridge__alpha': np.float64(22.96332964984786)}\n",
       "\n",
       "Model with rank: 3\n",
-      "Mean validation score: 0.466 (std: 0.149)\n",
-      "Parameters: {'morganfingerprinttransformer__fpSize': 2048, 'morganfingerprinttransformer__radius': 4, 'morganfingerprinttransformer__useCounts': False, 'morganfingerprinttransformer__useFeatures': True, 'ridge__alpha': 1.383163758398022}\n",
+      "Mean validation score: 0.426 (std: 0.166)\n",
+      "Parameters: {'morganfingerprinttransformer__fpSize': 4096, 'morganfingerprinttransformer__radius': 2, 'morganfingerprinttransformer__useCounts': True, 'morganfingerprinttransformer__useFeatures': False, 'ridge__alpha': np.float64(23.874114087368742)}\n",
       "\n"
      ]
     }
@@ -441,10 +445,10 @@
    "id": "4daaf106",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:36.236805Z",
-     "iopub.status.busy": "2024-11-24T09:27:36.235794Z",
-     "iopub.status.idle": "2024-11-24T09:27:36.394539Z",
-     "shell.execute_reply": "2024-11-24T09:27:36.393590Z"
+     "iopub.execute_input": "2025-05-08T16:22:49.133255Z",
+     "iopub.status.busy": "2025-05-08T16:22:49.132589Z",
+     "iopub.status.idle": "2025-05-08T16:22:49.294436Z",
+     "shell.execute_reply": "2025-05-08T16:22:49.293304Z"
     }
    },
    "outputs": [
@@ -452,8 +456,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "No Standardization 0.6389\n",
-      "With Standardization 0.6389\n"
+      "No Standardization 0.4921\n",
+      "With Standardization 0.4921\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
      ]
     }
    ],
@@ -487,10 +501,10 @@
    "id": "92105568",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:36.397490Z",
-     "iopub.status.busy": "2024-11-24T09:27:36.397082Z",
-     "iopub.status.idle": "2024-11-24T09:27:36.411965Z",
-     "shell.execute_reply": "2024-11-24T09:27:36.411400Z"
+     "iopub.execute_input": "2025-05-08T16:22:49.297625Z",
+     "iopub.status.busy": "2025-05-08T16:22:49.297285Z",
+     "iopub.status.idle": "2025-05-08T16:22:49.318086Z",
+     "shell.execute_reply": "2025-05-08T16:22:49.316957Z"
     }
    },
    "outputs": [
@@ -498,8 +512,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Predictions with no standardization: [5.89126045 5.97721234 5.97721234 6.03427056 6.03951076]\n",
-      "Predictions with standardization:    [5.89126045 5.89126045 5.89126045 5.89126045 5.89126045]\n"
+      "Predictions with no standardization: [6.36710496 6.49711427 6.49711427 6.28330625 6.72697401]\n",
+      "Predictions with standardization:    [6.36710496 6.36710496 6.36710496 6.36710496 6.36710496]\n"
      ]
     }
    ],
@@ -538,6 +552,9 @@
   }
  ],
  "metadata": {
+  "jupytext": {
+   "formats": "docs//notebooks//ipynb,docs//notebooks//scripts//py:percent"
+  },
   "kernelspec": {
    "display_name": "Python 3.9.4 ('rdkit')",
    "language": "python",
@@ -553,7 +570,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.6"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/07_parallel_transforms.ipynb b/docs/notebooks/07_parallel_transforms.ipynb
index 816fa4e..d4d913c 100644
--- a/docs/notebooks/07_parallel_transforms.ipynb
+++ b/docs/notebooks/07_parallel_transforms.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f68fb8e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "id": "87ed8373",
@@ -703,8 +711,11 @@
   }
  ],
  "metadata": {
+  "jupytext": {
+   "formats": "docs//notebooks//ipynb,docs//notebooks//scripts//py:percent"
+  },
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "Python 3.9.4 ('rdkit')",
    "language": "python",
    "name": "python3"
   },
diff --git a/docs/notebooks/08_external_library_skopt.ipynb b/docs/notebooks/08_external_library_skopt.ipynb
index 4657741..7fbf8d5 100644
--- a/docs/notebooks/08_external_library_skopt.ipynb
+++ b/docs/notebooks/08_external_library_skopt.ipynb
@@ -769,7 +769,6 @@
     }
    ],
    "source": [
-    "\n",
     "pipe_gp = gp_minimize(objective, search_space, n_calls=10, random_state=0)\n",
     "\"Best score=%.4f\" % pipe_gp.fun"
    ]
@@ -852,8 +851,11 @@
   }
  ],
  "metadata": {
+  "jupytext": {
+   "formats": "docs//notebooks//ipynb,docs//notebooks//scripts//py:percent"
+  },
   "kernelspec": {
-   "display_name": "vscode",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -867,7 +869,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.6"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/09_Combinatorial_Method_Usage_with_FingerPrint_Transformers.ipynb b/docs/notebooks/09_Combinatorial_Method_Usage_with_FingerPrint_Transformers.ipynb
index e11c62e..69156e1 100644
--- a/docs/notebooks/09_Combinatorial_Method_Usage_with_FingerPrint_Transformers.ipynb
+++ b/docs/notebooks/09_Combinatorial_Method_Usage_with_FingerPrint_Transformers.ipynb
@@ -27,10 +27,10 @@
    "id": "b705b5c9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:55.508365Z",
-     "iopub.status.busy": "2024-11-24T09:27:55.507967Z",
-     "iopub.status.idle": "2024-11-24T09:27:56.807362Z",
-     "shell.execute_reply": "2024-11-24T09:27:56.806654Z"
+     "iopub.execute_input": "2025-05-08T16:22:50.685069Z",
+     "iopub.status.busy": "2025-05-08T16:22:50.684787Z",
+     "iopub.status.idle": "2025-05-08T16:22:52.211375Z",
+     "shell.execute_reply": "2025-05-08T16:22:52.210158Z"
     },
     "lines_to_next_cell": 2
    },
@@ -70,10 +70,10 @@
    "id": "34b2618a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:56.810246Z",
-     "iopub.status.busy": "2024-11-24T09:27:56.809941Z",
-     "iopub.status.idle": "2024-11-24T09:27:56.851202Z",
-     "shell.execute_reply": "2024-11-24T09:27:56.850568Z"
+     "iopub.execute_input": "2025-05-08T16:22:52.215818Z",
+     "iopub.status.busy": "2025-05-08T16:22:52.214649Z",
+     "iopub.status.idle": "2025-05-08T16:22:52.271743Z",
+     "shell.execute_reply": "2025-05-08T16:22:52.270511Z"
     }
    },
    "outputs": [
@@ -97,7 +97,7 @@
     "        url = \"https://ndownloader.figshare.com/files/25747817\"\n",
     "        urllib.request.urlretrieve(url, csv_file)\n",
     "else:\n",
-    "    csv_file = \"../tests/data/SLC6A4_active_excapedb_subset.csv\"\n",
+    "    csv_file = \"../../tests/data/SLC6A4_active_excapedb_subset.csv\"\n",
     "\n",
     "# Parse Database\n",
     "data = pd.read_csv(csv_file)\n",
@@ -112,9 +112,9 @@
    "metadata": {},
    "source": [
     "## Build Pipeline:\n",
-    "In this stage we will build the Pipeline consisting of the featurization part (finger print transformers) and the model part (Ridge Regression).\n",
+    "In this stage we will build the Pipeline consisting of the featurization part (fingerprint transformers) and the model part (Ridge Regression).\n",
     "\n",
-    "Note that the featurization in this section is an hyperparameter, living in `param_grid`, and the `\"fp_transformer\"` string is just a placeholder, being replaced during pipeline execution.\n",
+    "Note that the featurization in this section is a hyperparameter, living in `param_grid`, and the `\"fp_transformer\"` string is just a placeholder, being replaced during pipeline execution.\n",
     "\n",
     "This way we can define multiple different scenarios in `param_grid`, that allow us to rapidly explore different combinations of settings and methodologies."
    ]
@@ -125,10 +125,10 @@
    "id": "e06042cc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:56.854051Z",
-     "iopub.status.busy": "2024-11-24T09:27:56.853508Z",
-     "iopub.status.idle": "2024-11-24T09:27:56.863947Z",
-     "shell.execute_reply": "2024-11-24T09:27:56.863371Z"
+     "iopub.execute_input": "2025-05-08T16:22:52.275673Z",
+     "iopub.status.busy": "2025-05-08T16:22:52.274777Z",
+     "iopub.status.idle": "2025-05-08T16:22:52.289345Z",
+     "shell.execute_reply": "2025-05-08T16:22:52.288093Z"
     }
    },
    "outputs": [
@@ -151,7 +151,6 @@
     }
    ],
    "source": [
-    "\n",
     "regressor = Ridge()\n",
     "optimization_pipe = Pipeline(\n",
     "    [\n",
@@ -204,18 +203,2034 @@
    "id": "f1cf66df",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:27:56.866817Z",
-     "iopub.status.busy": "2024-11-24T09:27:56.866251Z",
-     "iopub.status.idle": "2024-11-24T09:28:28.265183Z",
-     "shell.execute_reply": "2024-11-24T09:28:28.264595Z"
+     "iopub.execute_input": "2025-05-08T16:22:52.293059Z",
+     "iopub.status.busy": "2025-05-08T16:22:52.292608Z",
+     "iopub.status.idle": "2025-05-08T16:23:24.403914Z",
+     "shell.execute_reply": "2025-05-08T16:23:24.402481Z"
     }
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Runtime: 31.39\n"
+      "Runtime: 32.10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
      ]
     }
    ],
@@ -243,7 +2258,7 @@
    "source": [
     "## Analysis\n",
     "\n",
-    "Now let's investigate our results from the training stage. Which one is the best finger print method for this data set? Which parameters are optimal?"
+    "Now let's investigate our results from the training stage. Which one is the best fingerprint method for this data set? Which parameters are optimal?"
    ]
   },
   {
@@ -252,10 +2267,10 @@
    "id": "f80006f8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:28:28.268630Z",
-     "iopub.status.busy": "2024-11-24T09:28:28.268131Z",
-     "iopub.status.idle": "2024-11-24T09:28:28.320208Z",
-     "shell.execute_reply": "2024-11-24T09:28:28.319598Z"
+     "iopub.execute_input": "2025-05-08T16:23:24.408236Z",
+     "iopub.status.busy": "2025-05-08T16:23:24.407494Z",
+     "iopub.status.idle": "2025-05-08T16:23:24.478389Z",
+     "shell.execute_reply": "2025-05-08T16:23:24.477178Z"
     }
    },
    "outputs": [
@@ -301,10 +2316,10 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.011822</td>\n",
-       "      <td>0.001013</td>\n",
-       "      <td>0.003596</td>\n",
-       "      <td>0.001311</td>\n",
+       "      <td>0.011185</td>\n",
+       "      <td>0.003272</td>\n",
+       "      <td>0.003625</td>\n",
+       "      <td>0.000579</td>\n",
        "      <td>MorganFingerprintTransformer()</td>\n",
        "      <td>256.0</td>\n",
        "      <td>0.100</td>\n",
@@ -320,10 +2335,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.010119</td>\n",
-       "      <td>0.000152</td>\n",
-       "      <td>0.002832</td>\n",
-       "      <td>0.000070</td>\n",
+       "      <td>0.009765</td>\n",
+       "      <td>0.000383</td>\n",
+       "      <td>0.003377</td>\n",
+       "      <td>0.000101</td>\n",
        "      <td>MorganFingerprintTransformer()</td>\n",
        "      <td>256.0</td>\n",
        "      <td>0.325</td>\n",
@@ -339,10 +2354,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.010302</td>\n",
-       "      <td>0.000429</td>\n",
-       "      <td>0.003310</td>\n",
-       "      <td>0.000967</td>\n",
+       "      <td>0.009901</td>\n",
+       "      <td>0.000303</td>\n",
+       "      <td>0.003631</td>\n",
+       "      <td>0.000097</td>\n",
        "      <td>MorganFingerprintTransformer()</td>\n",
        "      <td>256.0</td>\n",
        "      <td>0.550</td>\n",
@@ -358,10 +2373,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.010192</td>\n",
-       "      <td>0.000159</td>\n",
-       "      <td>0.002859</td>\n",
-       "      <td>0.000089</td>\n",
+       "      <td>0.010876</td>\n",
+       "      <td>0.000710</td>\n",
+       "      <td>0.003938</td>\n",
+       "      <td>0.000378</td>\n",
        "      <td>MorganFingerprintTransformer()</td>\n",
        "      <td>256.0</td>\n",
        "      <td>0.775</td>\n",
@@ -377,10 +2392,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.010103</td>\n",
-       "      <td>0.000126</td>\n",
-       "      <td>0.002868</td>\n",
-       "      <td>0.000119</td>\n",
+       "      <td>0.010237</td>\n",
+       "      <td>0.000522</td>\n",
+       "      <td>0.003698</td>\n",
+       "      <td>0.000063</td>\n",
        "      <td>MorganFingerprintTransformer()</td>\n",
        "      <td>256.0</td>\n",
        "      <td>1.000</td>\n",
@@ -415,10 +2430,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>60</th>\n",
-       "      <td>0.100754</td>\n",
-       "      <td>0.006501</td>\n",
-       "      <td>0.025367</td>\n",
-       "      <td>0.001743</td>\n",
+       "      <td>0.101976</td>\n",
+       "      <td>0.002168</td>\n",
+       "      <td>0.026431</td>\n",
+       "      <td>0.002031</td>\n",
        "      <td>MACCSKeysFingerprintTransformer()</td>\n",
        "      <td>NaN</td>\n",
        "      <td>0.100</td>\n",
@@ -434,10 +2449,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>61</th>\n",
-       "      <td>0.118554</td>\n",
-       "      <td>0.022440</td>\n",
-       "      <td>0.036584</td>\n",
-       "      <td>0.023911</td>\n",
+       "      <td>0.101812</td>\n",
+       "      <td>0.003023</td>\n",
+       "      <td>0.026544</td>\n",
+       "      <td>0.001390</td>\n",
        "      <td>MACCSKeysFingerprintTransformer()</td>\n",
        "      <td>NaN</td>\n",
        "      <td>0.325</td>\n",
@@ -453,10 +2468,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>62</th>\n",
-       "      <td>0.097552</td>\n",
-       "      <td>0.001638</td>\n",
-       "      <td>0.025571</td>\n",
-       "      <td>0.001753</td>\n",
+       "      <td>0.102681</td>\n",
+       "      <td>0.001273</td>\n",
+       "      <td>0.026926</td>\n",
+       "      <td>0.001608</td>\n",
        "      <td>MACCSKeysFingerprintTransformer()</td>\n",
        "      <td>NaN</td>\n",
        "      <td>0.550</td>\n",
@@ -472,10 +2487,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>63</th>\n",
-       "      <td>0.098300</td>\n",
-       "      <td>0.001744</td>\n",
-       "      <td>0.025552</td>\n",
-       "      <td>0.001695</td>\n",
+       "      <td>0.100703</td>\n",
+       "      <td>0.004015</td>\n",
+       "      <td>0.026171</td>\n",
+       "      <td>0.001515</td>\n",
        "      <td>MACCSKeysFingerprintTransformer()</td>\n",
        "      <td>NaN</td>\n",
        "      <td>0.775</td>\n",
@@ -491,10 +2506,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>64</th>\n",
-       "      <td>0.098103</td>\n",
-       "      <td>0.001473</td>\n",
-       "      <td>0.025320</td>\n",
-       "      <td>0.001673</td>\n",
+       "      <td>0.101780</td>\n",
+       "      <td>0.001939</td>\n",
+       "      <td>0.027233</td>\n",
+       "      <td>0.001860</td>\n",
        "      <td>MACCSKeysFingerprintTransformer()</td>\n",
        "      <td>NaN</td>\n",
        "      <td>1.000</td>\n",
@@ -515,17 +2530,17 @@
       ],
       "text/plain": [
        "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
-       "0        0.011822      0.001013         0.003596        0.001311   \n",
-       "1        0.010119      0.000152         0.002832        0.000070   \n",
-       "2        0.010302      0.000429         0.003310        0.000967   \n",
-       "3        0.010192      0.000159         0.002859        0.000089   \n",
-       "4        0.010103      0.000126         0.002868        0.000119   \n",
+       "0        0.011185      0.003272         0.003625        0.000579   \n",
+       "1        0.009765      0.000383         0.003377        0.000101   \n",
+       "2        0.009901      0.000303         0.003631        0.000097   \n",
+       "3        0.010876      0.000710         0.003938        0.000378   \n",
+       "4        0.010237      0.000522         0.003698        0.000063   \n",
        "..            ...           ...              ...             ...   \n",
-       "60       0.100754      0.006501         0.025367        0.001743   \n",
-       "61       0.118554      0.022440         0.036584        0.023911   \n",
-       "62       0.097552      0.001638         0.025571        0.001753   \n",
-       "63       0.098300      0.001744         0.025552        0.001695   \n",
-       "64       0.098103      0.001473         0.025320        0.001673   \n",
+       "60       0.101976      0.002168         0.026431        0.002031   \n",
+       "61       0.101812      0.003023         0.026544        0.001390   \n",
+       "62       0.102681      0.001273         0.026926        0.001608   \n",
+       "63       0.100703      0.004015         0.026171        0.001515   \n",
+       "64       0.101780      0.001939         0.027233        0.001860   \n",
        "\n",
        "                 param_fp_transformer  param_fp_transformer__fpSize  \\\n",
        "0      MorganFingerprintTransformer()                         256.0   \n",
@@ -611,17 +2626,17 @@
    "id": "a6041579",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:28:28.324000Z",
-     "iopub.status.busy": "2024-11-24T09:28:28.323259Z",
-     "iopub.status.idle": "2024-11-24T09:28:28.574471Z",
-     "shell.execute_reply": "2024-11-24T09:28:28.573892Z"
+     "iopub.execute_input": "2025-05-08T16:23:24.481751Z",
+     "iopub.status.busy": "2025-05-08T16:23:24.481189Z",
+     "iopub.status.idle": "2025-05-08T16:23:24.710744Z",
+     "shell.execute_reply": "2025-05-08T16:23:24.709653Z"
     },
     "lines_to_next_cell": 2
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1400x500 with 1 Axes>"
       ]
@@ -661,17 +2676,17 @@
    "id": "3ee14366",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:28:28.576983Z",
-     "iopub.status.busy": "2024-11-24T09:28:28.576761Z",
-     "iopub.status.idle": "2024-11-24T09:28:28.913143Z",
-     "shell.execute_reply": "2024-11-24T09:28:28.912512Z"
+     "iopub.execute_input": "2025-05-08T16:23:24.713891Z",
+     "iopub.status.busy": "2025-05-08T16:23:24.713590Z",
+     "iopub.status.idle": "2025-05-08T16:23:25.088793Z",
+     "shell.execute_reply": "2025-05-08T16:23:25.087682Z"
     },
     "lines_to_next_cell": 2
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABIkAAAJZCAYAAAAtXGVNAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAA4D1JREFUeJzs3Xd4FOXexvF7N5UACYFAQg9NmtK7YgBBQASxchAFomBF1KggIh3EilhQFGkeURALekBRQDooXcUjSgud0IMESCB53j94d06WbEKySXY34fu5rr0gM8/s/HZ2npnZe2dnbMYYIwAAAAAAAFzV7N4uAAAAAAAAAN5HSAQAAAAAAABCIgAAAAAAABASAQAAAAAAQIREAAAAAAAAECERAAAAAAAAREgEAAAAAAAAERIBAAAAAABAhEQAAAAAAAAQIRGQp2w2m2w2m5YtW+btUgq1ZcuWWcsaBcPKlSvVpUsXlS5dWn5+frLZbOrevbu3ywK8pk2bNrLZbBo5cqS3Syn02Df7lr179yo2NlaVKlVSYGCgbDabSpQo4e2yUMh5YzsQHx9vzTc+Pt5j8wVyi5AIyMTIkSOtDfuVHig8li1bppEjR2rGjBluTZ/ddcbVw915+rqff/5Z7dq103fffafjx4+rZMmSioyMVHh4uLdLK5TSr1O1a9e+Yvv169c7TdO3b9/8L7KQSR9cX+lBUFF4xMfHa+TIkZkGfY4g0J1HYe2HiYmJuv766zVjxgzt27dPISEhioyMVGRkpLdLg49KH7Rc/ggMDFRUVJQ6dOigDz74QBcuXMjx8586dcrqx6dOncr7FwAUQP7eLgAoCLJ78FKzZk1JUkhISH6Wc9ULCQmxlnVeW7ZsmUaNGqWYmBi3DtIzW1fOnDmjpKSkLNsUKVIkx/MrCCZOnKiLFy/q+uuv17fffquSJUt6u6SrxrZt27R27Vq1bNky0zbTpk3zYEWFX3h4uAIDAzMd7xhXqVIl1axZUxEREZ4q7aqVX/vm+Ph4jRo1SpJcBkWOQPxyKSkpOnnypKTM15ewsLA8rdVXfPbZZ9q/f7/Cw8O1Zs0a1apVy9sloQAJDQ11OlY6ffq0EhISlJCQoMWLF+vDDz/UokWLXB5nZLYdOHXqlNWP+/btm6dntQUEBFjzDQgIyLPnBfIbIRGQDYcPH85Wu23btuVzJZCkZs2a+eyyzmxdGTlypHUQkt31qbD4/fffJUn/+te/CIg8KDo6WvHx8Zo+fXqmIdH58+c1e/Zs2Ww2VapUSXv27PFwlYXPV199pTZt2lyx3ccff5z/xUCS9/bNX331lcvhy5YtU9u2ba022VlfCgvH/qBdu3YERMixt956K8MXeAcOHNDrr7+uiRMnatOmTRoyZIg++OCDDNN6YztQvnx5nz1eBbLCz80AAPnq7NmzkqRixYp5uZKrS+/evWWz2TRnzhzrPbjcV199pVOnTikmJkbR0dGeLRDAVYf9AfJa+fLl9eabb+qmm26SJM2bN8+7BQGFACERkIcyu+bE5ReuS0hI0JNPPqkqVaooODhYkZGR+te//nXFbxv27NmjBx98UBUqVFBQUJAqVKig2NhY7dixI1sXx0tJSdF7772ntm3bKiIiwvot92233abvv/8+W6/ryJEjiouL0zXXXKOQkBCnazL17dvXupaCMUaTJ09Ws2bNFBoaqtDQUN1www369NNPM51P+gu5XrhwQW+88YaaNGmiEiVKOC3XrC5cPWPGDNlsNusD78aNG3XPPfeobNmyCgoKUtWqVRUXF2ed6u/gWH6Os32WL1/u0WsGZXcZnz17Vp999pl69+6tBg0aqHTp0goKClK5cuXUvXv3LN9Hd5dNer/88ot69eplrbtFixZV5cqVFRMTozFjxmj//v0ZXpNjfYyNjXVanpevpzt37tSjjz6qGjVqqEiRIgoNDVWjRo00evRonT592mU9l68LmzdvVq9evVShQgUFBARY39Bf/tpXrlyprl27qkyZMipatKgaNmyoqVOnOj33ggUL1KFDB5UuXVohISFq2rSp5syZk+mycVi9erXuu+8+Va5cWcHBwQoLC1OzZs30yiuv6MyZMy6nubzvfPTRR7rhhhtUqlQpt9e9KlWqKCYmRqdPn9aXX37pso3jp2axsbHZes7Nmzerd+/e1msLDw9Xq1atNHHiRCUnJ7uc5vJlv3TpUnXv3l1ly5aVn59fhm+FV6xYoa5duyoiIkJFihRRzZo1NXToUJ05cybDc6Xn7b6RU1lduDo6Otp631NSUvTaa6+pfv36Klq0qMLCwtSuXTstXLgwy+dPSkrSiBEjVLt2bRUpUkRlypTRLbfcoiVLlmSYR2YWLFigO++8U+XLl1dQUJDCw8N144036v3331dKSsoVX1dW2/HLl/miRYvUuXNnlS5dWkWKFFHdunU1duxYnT9/3uV8HNcNdPTxL7/8UjfffLPKlCkju93utFzzY98cHR1tnQ2Ufh55cU2h7C7DtLQ0LVmyRAMHDlSLFi1UoUIFBQYGqlSpUoqJidHkyZMzvUZLXhyX7N+/X08//bTq1q2rokWLWv2tcePGevrpp7V+/foMr8mxvs2cOTPL/WtiYqJGjx6tRo0aWT8xqlGjhh599FHt2rUr05qysy+9/LXv2bNH/fv3V6VKlRQcHKxq1arpxRdftH4iLklbt27Vfffdp4oVKyo4OFg1atTQ2LFjr3gNnPj4eD311FOqW7euihUrppCQENWqVUtPPvmk9u7d63KanG4zs5J+eRw+fFgDBgyw3uOoqCj16tUrW2e65Oe2IK80aNBAkjLdz7raDrRp00ZVqlSx/q5SpYrTenn5WX7btm3TQw89ZK1TwcHBqlixolq0aKEXXnghw7LM6tg8t9cnc+dYA8g2A8ClESNGGEkmJ93E0X7p0qVOw3fv3m2Nmz9/vilTpoyRZEJCQkxQUJA1LjQ01GzZssXlc69Zs8YUL17calukSBFTrFgxa7rPP//cGrd79+4M08fHx5u6detabWw2mwkLC7P+lmQeeeSRLF/XlClTTGRkpJFkgoODrXoc+vTpYySZPn36mB49ehhJxm63m/DwcGOz2azniY2NNWlpaRnmExMTYySZwYMHm1atWhlJxt/f35resVyXLl2a6Xszffp0I8lUrlzZzJo1ywQEBBhJJiwszNjtdmu6unXrmn/++ceabu/evSYyMtIULVrUSDIBAQEmMjLS6TF79myXyyc7rrQ+ZXcZO15f+vcwJCTE6X185plnXM7D3WXjMGPGDKf3MSgoyISGhjrNe/r06VZ7x3JzPHdoaKjT8ty7d6/Vds6cOU59oXjx4k5/V6xY0fz3v//NUFP6deGLL76wXlNoaKgJDg42MTExGV77lClTjN1ud9kHnn/+eWOMMcOHD7fW38vbvP/++y6Xb2pqqhk4cKBT22LFihk/Pz/r75o1a5r4+PgM0zr6Tu/evc2dd97p1HfsdrvTcr2S9O/FzJkzjSTTtm3bDO3i4+ONzWYzxYsXN0lJSVb/69Onj8vnnTBhgtP7HxYWZi1vSaZevXrm4MGDGaZLv+wnTpxoPYdj+vTze/vttzPMIzAw0EgytWvXNm+++ab1XJnNxxt9I/16ePn2PzOO5T1ixIgM4ypXrmwkmXfeecc0b97c2iY5tvmO1zh16lSXz52QkGDq1KljtQ0ICDAlSpSwpnv//fetebhat86ePWvuuusup2UXGhrq9N60aNHCnDhxItPXdaXtePplPmnSJOu5S5QoYfz9/a35NGzY0OV8HNvUmJgYExcXZ7228PBw4+fn57Rc82Pf3KRJExMeHm61uXx/MXDgQJfvjTFXXl+yuwzT1+/Y3ly+vWrdurU5e/Zshnnk9rhky5YtTq/fz88vw74+fd++/fbbTWRkpAkODrb2b5ntX7du3WoqVKhgPU/6faFj3/PFF1+4XLbZ2Zemf+1ffvml1TdCQ0OdttetW7c2KSkpZv78+da2JCwszOk19ujRI9P3+ZNPPnFalkFBQaZIkSJO+7kffvghw3Q52WZeiWNe06ZNM1FRUUZyPn50LJ/vv//e5fSe2BZcSfr3K6t9Yfv27a1tRlbLIv18b7/9dhMREWGNi4iIcFovb7/9dqvtjz/+6PR+pt+uOh6Xb8/T1375sfnl24zLH45lfPn7nZtjDSC7CImATORXSBQeHm6uv/56s379emOMMRcuXDCLFi0yZcuWtQ5KLnfy5ElrfNWqVc1PP/1khSzr1q0z9evXdzpYu3xHdObMGVOrVi0jybRp08YsW7bMnD9/3hhjzKlTp8yECROsA4aJEydm+rqKFStmatasaZYsWWJSU1ONMcb89ddfVjvHB13HQdSYMWNMYmKiMcaYI0eOmAEDBljP9dZbb2WYj+OAolixYqZYsWJm+vTp1sHtsWPHzPHjx40x2QuJHAe6/fr1s8KIpKQk8+6771ofAIcNG5Zh+vQfPPJSdkOiKy3jefPmmWeffdasWrXKJCUlWcMPHjxoRo0aZb22b775JsM8crNskpKSrIPs++67z+zYscMad+bMGbNhwwbz3HPPmQULFmSYb1YfRo0xZuPGjdZ8r7/+evPbb78ZYy4dCH377bfWul+tWrUMH9DTrwvFihUzt9xyi/nzzz+t8X///XeG1x4YGGgGDhxojhw5Yowx5vjx49a6a7fbzSuvvGL8/PzM2LFjzalTp6zl26lTJyPJFC1a1Bqe3osvvmgkmTJlyphJkyZZ62tKSopZunSpadiwoZFkGjVqZL23Do75FytWzPj7+5vXX3/d6jv//POPy/AlM+kPph3vm81mM7t27XJqN3LkSCPJ9OvXzxhjsgyJ/vOf/1jPe9ttt1nPlZycbD7++GNr3WjVqpW5ePGi07SOZR8cHGz8/PxM3759rfXu4sWL1rq0evVqK5Dp0KGDtd5fuHDBzJ0715QsWdLazrkKibzVN4zJv5AoPDzclC9f3sybN8+kpKQYY4zZtm2badGihbW+uFoXHetqkSJFzNSpU63t/d69e02PHj1MYGCg9aHXVb+87777rP3NrFmzrHXx3Llz5ptvvjFVq1Y1kkz37t0zfV1X2o6nX+YBAQHm7rvvtpb52bNnzfvvv299IEv/Qc3BsU117LsGDx5s9enz5887fUDKr31zVvuirGQ3JLrSMty3b5/p1auX+fbbb61hxlzaZkyfPt2UK1fOSDJPP/10hnnk9rXfdNNN1vZs7dq11jFJcnKy+fvvv83rr79uXn311QzTpf8yyZXTp0+bKlWqGEmmfPnyZsGCBdb2csuWLda6HxQU5DK8ys6+NP1rL1GihLnpppvMH3/8YYy5tO69/fbb1gfuF1980YSFhZkePXpY69Q///xjhg4daj3HokWLMtTx448/Grvdbvz9/c2gQYPM7t27TVpamklLSzPbtm0zd999txW47Nmzx2na7G4zs8NRY1hYmKlUqZL58ccfrffql19+Mdddd51Vx759+zJM74ltwZVcKSQ6ePCgeeaZZ6w2n376aZbLIqvtgKsvWR2qVatmJJmbb77Z/P7779bwc+fOma1bt5pRo0ZlqC+7z325Dz74wJpuzpw5TuNyc6wBZBchEZCJ9B/qs0r6t27dak2TnR1QrVq1XH6r9+2331ptLt9Rjxkzxjpg2L59e4Zpjx496vRNyOU7otGjR1vBh+ODxuW++uorI136FuXChQtO4xzPm9lBhIPj4C+zD1LG/O+Ao2TJkubcuXNO4xwHFJLMt99+m+l8shMSZXUQ6vjWuXr16hnGeTskutIyvpLXXnvNSDI33XRThnG5WTa//PKLFZBcvn5cyZVCIscH2urVqzt9uHfYtGmTdWbBa6+95jQu/brQrFmzDAGFQ/rX7ghF0rt48aL1wUSSGTt2bIY2iYmJ1plm//73v53G7d692/j5+ZkiRYpkejbg6dOnrW/Hv/76a6dx6fvO22+/7XL67Lr8YLpfv35Gkhk+fLjVJi0tzURHRxtJZvXq1caYrEOi2rVrWx8WXS3j9NuvuXPnOo1Lv+zvuOOOTOt2fOisU6eOFWqk99NPP1nP4yokupL86hvGOK+H4eHhme4vPvzwQ2ua7IREQUFBTqGnw5EjR6wzMj755BOncStXrrRquXw9NeZS+Nq2bdtMP3StWLHC+gCS/my/9Pbt22f1hc2bNzuNy+52PP0yj4mJcflh5qOPPrLarFu3zmlc+m1qXFxcpvMxJv/2zfkdEl1pGV7J+vXrre325fvb3L52xxkxa9asyVFNVwqJXn75ZSNdOksj/Qdxh9OnT1vbri5dumQYn519afrXXrduXZfbm/vvv99q06FDB5dnP7du3dpIMg8++KDT8NTUVFOjRg0jyXzwwQcuazDGmG7duhlJ5sknn3Qant1tZnY4nicwMNDl2bgJCQmmZMmSRpJ57LHHnMZ5altwJenfr8vPSE5/tmjr1q3Nl19+menzZGc7kFmQk5CQYLXJyZc27oREP/74o3XMM3LkyAzPl5tjDSC7uCYRkA2O22u6elzp9+iXe+aZZ1ze6rxz587WbXAdd/9wmDt3riSpR48eql69eoZpIyIi9Oijj2Y6T8e1VuLi4jK9BWf37t0VGhqqY8eOaePGjS7b3H///apQoUKm83EoUqSInn32WZfjhg8fLkk6ceKEFi1a5LJN3bp11bVr1yvO50pefPFFl8Nvu+02SdKOHTsyvaCvt2R3GWemS5cukqS1a9cqNTU103Y5XTaOW8KmpKTo+PHjbtd3uVOnTumHH36QJD333HMub1HdsGFD3XHHHZIu3T45M88995z8/PyuOM/nn38+wzA/Pz/ropfBwcF66qmnMrQJDQ217hL222+/OY2bMWOGUlNT1alTJ9WvX9/lfIsXL67u3btLkvWaLxceHq6HH374iq8hJx544AFJl64BYoyRdOkaF/Hx8apZs6ZatWqV5fS//fab/vzzT0mX1htXy7hr165q1qyZpKzfoyFDhrgcfuLECf3000+SLr2PQUFBGdq0bdtWrVu3zrLWrORX37jcyZMnM91fpL/GSXbcddddLu8AVbp06UzXRcf+Ijo6Wr169cowrd1uz/Q1Sv/bX/Tq1UsVK1Z02aZChQrW9XgyW5dzsh1/8cUXZbdnPCSNjY21toezZ892Oa3dbtfgwYOzNZ+suLNvzm+53Rc2adJEZcqUUVJSkrZs2ZJpO3deu2OfcOjQIbfrc8Vx3be77rpL1157bYbxxYsX16BBgyRJ33//vRITE10+T3b3pU8//bTL7U3Hjh2t/z///PMur4HoaHN5H1yxYoW2b9+uiIgI9evXL9N59+7dW1LmfUjKfJuZU3fffbdq166dYXiZMmX0yCOPSFKGa+55Y1twJelveZ+QkOC0LT569KgOHDhg7efyUvHixa1tVF6v8+n98ccfuvvuu3Xx4kXde++9GjFihNP4vDrWAK6EkAjIBnPprDuXD8eF8rKrefPmLof7+/urdOnSki59YHJISUnRH3/8IUmKiYnJ9Hkzu4XugQMHrNtaP/jgg4qKinL5KFu2rHWhu8xug3399ddn/eL+X5MmTRQaGupyXI0aNawDtw0bNuRqPlkpWbKky0BNksqVK2f9Py8vRJsXsvPaExISNGLECLVs2VKlSpWSv7+/dYHDOnXqSLp0Ed/MXps7y6ZatWqqVauWLly4oObNm+uVV17Rli1bsvywnR2bNm2yDujat2+fabsOHTpIunQwnlkwm51lV7JkSVWrVs3luMjISElSnTp1VLRo0SzbXL5sV69eLUn68ccfM+1jUVFRmj59uqTM+1jTpk2tD2V5pWXLlqpVq5b27NljXbQ4JxesdvRTf3//LLdBjvcos35dpEgRNWrUyOW4zZs3W+uBO9s5B2/0jcstXbo00/2Fq/AxK5ntL9LXk35/IV3qU5J04403uvxgK13qK/7+/i7HOdblqVOnZrkuL168WFLu9xf+/v6Zhn92u916zzNbr6pXr64yZcpka15Zyem+2ROyswxTUlI0efJk3XzzzSpXrpyCgoKcLnp75MgRSXK6qcDl3Hntt956qySpT58+euaZZ7R8+fJcf+mSkpJiBS7Z2R+kpaVZ6/vlsrv+OcLtyzm29dKl7XJWbTLbHyQmJqpcuXKZ9qH+/ftLyrwPZbXNzKl27dpdcdzx48e1e/fuDK/DU9uC7Jg+fbrTNvXixYvav3+/Jk+erISEBA0cOFD33ntvngdFRYoUsb5I6tSpk4YPH65ffvkl04t2uyMhIUFdunRRYmKiWrVqZe2n08urYw3gSlwfIQDIN8WLF890nOOgPf2H4BMnTlgfxNN/SLlc+fLlXQ4/ePCg9f9jx45lq8bMDvSyeyCeWS3px+/fv986eHV3PlnJznKWlOMzwfLblV772rVrdcstt+jUqVPWMMcdU2w2m1JTU633OSkpSRERERmew51l4+fnp9mzZ+v222/X7t279fzzz+v5559XSEiIWrVqpTvuuEN9+vRxeSZQVtKvA1mtN45g8eLFizpx4oTTAbxDdtab7Lz2nPZR6X/9LCkpKVtni+S2j+VUbGysBg8erOnTp6tZs2b66quv5OfnZ32TnRXHexQREeHyG3cHx3uUWb8uVaqUy7NFpEvfADu4s52TvNc38pM766JjWWa1HIOCghQREaHDhw9nGOdYl0+fPp3pXQXTy+26fKX1yvGe5+f+QnJvWee3K722I0eOqH379k5n+QQHBysiIsI64+/o0aNKS0vLcrvkzmt/9dVXtWPHDi1dulQTJkzQhAkT5OfnpwYNGqhLly566KGHrngscLn0xzvZ2R9IuV8vMnvt6fv7ldpktj+4cOGCEhISrljDuXPnXA7PapuZU1ktz/Tjjhw5Yt3ty9PbAnf4+fmpfPnyevjhh1WuXDl169ZNs2fPVufOnbO1f8uJjz76SN26ddOvv/6qMWPGaMyYMQoMDFTTpk1122236cEHH1TJkiXdeu5z587ptttu0549e1SlShXNmzfP5XYxr441gCvhTCKgAMnsW+GspD/T488//8zyrCjHI7PbbWbnpzx5wVPz8UVZvfaLFy+qZ8+eOnXqlBo0aKDvvvtOp0+f1j///KOEhAQdPnxYP//8s9U+r79Jq1+/vrZt26Yvv/xSDz30kK699lqdO3dOixcv1mOPPaZatWp5/OcY6XlzvXH0s8GDB2erj2V269/8eg3333+//Pz89PXXX2vy5Mk6d+6cOnXqpLJly+bL/FzJ7mtzZzvn7b7hi9xZjtL/1uX3338/W+vy5bcud2B/kXtXem1PP/20fv/9d5UqVUrTpk3ToUOHdO7cOR09elSHDx/W4cOHrbAwr9f5EiVK6KefftLKlSs1aNAg6+y0jRs3avTo0apRo0aWPz3Nb76wP2jevHm2+lBm74231+2Cti3o2rWrFUh9+umnef78lSpV0qZNm7Rw4UINHDhQjRs3VlpamlavXq1BgwapevXq1s+mc8IYo969e+uXX35RWFiY5s+fb53Bd7m8OtYAroSQCPBxJUuWtHaw6c8KutyBAwdcDo+KirL+76nTTjOr5fLx+fntUmG0du1a7dmzR35+fpo/f746d+6c4RtOV2cG5KXAwEDdcccd+uCDD/T777/r6NGjmjx5skqWLKl9+/apT58+OXq+9OtAVj+HcIzz9/d3+5u6/OToZ756anfZsmXVqVMnnTt3TsOGDZOUvZ+aSf97j44dO6bk5ORM2zneI3f6dfoDYne2c77QN3yFY1lmtRyTk5MzPbPU0+vysWPHsvzJBvsL1y5cuKCvvvpKkvTuu+8qNjbWaX8vyensufxyww036JVXXtGqVat06tQpffPNN7ruuut07tw5PfDAA9k6k8Yh/fFOdvYHkm+uF764P8jquCz9uPTL0xdfx5VUrlxZkpx+NpeX7Ha7OnbsqLfeeksbNmzQiRMnNGvWLFWqVEknT57Uvffem+OfoL3wwgv64osv5Ofnpzlz5lg/jXalIL4nKJgIiQAfFxgYqLp160pSlt8IZDYuOjraOpX4P//5T16X59KGDRus6xtdbseOHdYBXpMmTTxST044Tu32xTMN9u3bJ+nSh8DMTh13XBvAU0qVKqWHH35Yr7zyiqRL15bJyYWtGzVqZC1zx/VyXHG8rvr162d68XVvclxzYfHixTp//ryXq3HNcQHrlJQURUREqFu3btmaztFPL168qOXLl2fazvEeZXb9jqw0bNjQOvPFne2cL/YNb3FcwySr92r16tW6ePGiy3GOdXn+/Pl5X5wLFy9e1MqVK12OM8ZYr8OX9xeS5/cZR48etbY1DRs2dNlm1apVHt0eBQcHq1u3blZ4df78ea1atSrb0wcGBqpevXqSsrc/sNvteXbNnrzk6EOHDx/O9FpanrZ06dIrjitZsqT1UzPJ89uCvOA4vszsuoKu5KYfFy9eXPfee691ke+EhIQcnVE9bdo0vfzyy5Kkt99+2+mC6a4UhGMNFA6EREABcNddd0m6dOeJnTt3Zhh//PhxTZ48OdPpHRdHnDp1qjZv3pzlvPLiwpznzp3T66+/7nLc2LFjJV06GHFcfNKXOC64nf66Jr4iLCxM0v/utne5/fv36+23386XeWd1Bokkpzvj5OQaCiVKlLAOil577TWXv5//9ddf9eWXX0qSevbsme3n9qQHHnhA/v7+OnbsWIa7kVwuJSUl0xA1P3Xt2lXPPfecnnnmGU2cODHbYVu9evWsbzbHjh3r8mLl3333nX755RdJ7r1HJUuWtO6Q88Ybb7j8JnbFihWZhgne7Bu+xrG/iI+Pd/mTC2OMXnrppUynf+ihhyRJW7du1fvvv5/lvJKSkvLkwq3jxo1TWlpahuEzZ860AsAePXrkej55Lf0NGjy9zwgNDbWC1V9//TXD+IsXL2ro0KH5Mu+LFy+6fL8c3N0fSNK//vUvSdIXX3yhrVu3Zhh/5swZvfrqq5KkW265xer7vqRt27bWBfCffvrpK/YRT1wQfe7cufrrr78yDD927Jg++OADSRn7mDe2BbmxbNky685jOQmVs9OPr/Ta3Fnnf/rpJ+vOcgMHDtRjjz12xWkKwrEGCgdCIqAAGDBggCIjI3X+/Hl16tRJy5cvt77t2LBhgzp06JDpt8LSpdvbXnfddTp//rzatm2rd9991+lsj1OnTun7779X7969c3WLaYewsDCNGTNG48eP1z///CPp0oHIk08+qZkzZ0qShg0bpuDg4FzPK685brn7xx9/aM2aNV6uxtkNN9ygokWLyhije+65R3///bekSz8p+OGHH9SmTRu3r0NyJbNnz9b111+vDz74QLt27bKGO+btuK18y5YtFR4enqPnHjt2rAICArRjxw517NjR+hYuLS1N3333nW655RZdvHhR1apVy/Pbw+eVatWqWT/jevXVV9W7d2+nDzgXL17Uli1bNHr0aFWvXj3L21Hnl4CAAL366qt6/fXXXd4aPSuOM8VWrlypu+66yzqV/8KFC5o1a5YVDLVq1cq69W5OjRo1SjabTVu3blW3bt20fft2SZeW3VdffaU777wz03XLm33D17Ru3doK4Pv3768ZM2ZYIe/+/fvVq1cvrVy5MtOLzMfExFg/RXz88cf19NNPO/X55ORk/fzzzxo0aJAqV66c6YWDsyskJESrVq3Svffea50FcP78eX344Yd69NFHJUm33XZbpneh8qZrrrnGuhvhRx995NGziYoVK2adVRAXF6effvrJCm62bt2qW265RRs2bMjRGRXZtX//ftWoUUNjx47V5s2bnY4/fvvtN913332SLp3NkdXdCl159NFHVaVKFV24cEGdO3fW999/b72u33//XR07dtTu3bsVFBRkfenka/z9/TV58mT5+/tr1apVuvHGG7VkyRKnC1zv2rVLkydPVtOmTfXee+/le03BwcHq1KmTFi9ebK2n69evV/v27XXs2DEVL17c2o87eHpb4K7k5GR988031n7N399fAwcOzPb0JUqUsM5AnT59usvj6TVr1qhevXp688039eeff1rrpDFGa9assbZVFSpUsM6Gy8r27dt155136sKFC7rllls0YcKEbNVaEI41UEgYAC6NGDHCSDI56SaO9kuXLnUavnv3bmvc7t27M52+cuXKRpKZPn16hnErV640xYoVs54nJCTE+rtEiRJm7ty51rhDhw5lmP7AgQOmRYsWVhubzWZKlChhQkNDrWGSTPXq1bP9ui7Xp08fI8n06dPH9OjRw0gyfn5+Jjw83NhsNut5evfubVJTUzNMHxMTYySZESNGZDmfpUuXZvreTJ8+3UgylStXznT6rN6PCxcumJo1a1rjw8PDTeXKlU3lypXN3Llzs6wrK1dan7K7jN9//32n96tYsWImODjYSDIRERHm22+/zfS15WbZOKZ1PIKCgkypUqWM3W63hpUrV878+eefGZ4zq/XaYfbs2SYwMNB6rtDQUOt1STIVK1Y0//3vfzNMl9W6kNPX7niPYmJiMm2Tfh2/XFpamhk2bJjTul6kSBFTqlQp4+fn57T8Vq1ale3nzSnHPLJa3q44+l9mNUyYMMHptZUoUcLpPbvuuuvMgQMHMkyXnWXv8OabbzotpxIlSpigoCAjyVx77bXW+Jo1a2aY1lt9wxjn9fBKfdghq+1ddvpMVuvMoUOHTK1atayaAgICTIkSJYwkY7fbzYcffmgqVapkJJnPPvssw/TJycmmX79+GZZneHi4U5+XZPbv35/t15Ve+mX+7rvvWutWeHi4CQgIsJ6/fv365tixYxmmz05/dcjPffODDz7otF+uVKmSqVy5snnmmWcyfb4rrS/ZXYYbNmwwRYsWddouFy9e3Egy/v7+5uOPP8609ty89vTTOvbzJUuWdNoeBAYGutxnZmdb9/vvv5vy5ctbzxUcHOx0rBIUFJTp/jg7/TA7rz07+5YrbTe+/vpr6/1w9MNSpUpZ2zTHY+zYsTl63pxwzGPatGkmKirKWk/TH08GBQWZ+fPnu5zeE9uCK0n/foWGhprIyEjrUbp0aePv7+/UB2fPnp3lsnC1bowZM8ZpeVSsWNFUrlzZ9OjRwxjjvD6kfy/Tzzs0NNSsWLEi09ozO6YKDw93ek2XPwYOHOj0nLk51gCyizOJgALihhtu0G+//abY2FiVK1dOFy9eVIkSJfTAAw9o06ZNqlatmtW2RIkSGaYvV66cVq1apc8++0zdunVT2bJldfbsWaWkpCg6Olpdu3bVxIkTtWLFijyp97PPPtN7772nhg0b6uLFiypatKhatmypjz/+WDNnzsyz27rmNX9/fy1ZskT9+vVTlSpVlJSUpD179mjPnj0+cdruI488ogULFqhNmzYqVqyYLl68qPLly+uJJ57Qr7/+quuuuy5f5tutWzd9/PHHio2NVf369RUWFqbExEQVL15czZo105gxY/THH3+oVq1abj1/jx499Mcff+jhhx9WtWrVlJycLH9/fzVo0ECjRo3S1q1bVbt27Tx+VXnLZrNp9OjR+u233/TYY4+pdu3a8vPzU2JiosLDw9WqVSs999xzWrNmjXUGQEHy9NNPa8OGDbrvvvtUsWJFnT17VkWKFFGLFi305ptvav369Vnedj07nnrqKS1btky33HKLwsPDdf78eUVHR+vFF1/Uzz//bH0D7mob562+4YuioqK0fv16DRs2TDVr1pTdbpe/v79uueUW/fTTT+rfv78SExMluV6WgYGBmjJlitasWaO+ffuqWrVqSk1N1ZkzZ1SmTBm1adNGw4cP12+//Zbj25y78vjjj+uHH35Qp06dZLfbZbfbVatWLY0ePVpr165VqVKlcj2P/DJp0iSNHDnSWr/27t2rPXv25PsFoyWpcePGWrdune655x5FREQoLS1NxYsX1z333KM1a9bo/vvvz5f5li9fXt9++62efvpptWjRQmXLltWZM2fk7++vOnXq6PHHH9fWrVutnz7m1LXXXqs//vhDI0eOVIMGDeTv76/k5GRVq1ZNjzzyiP744w+3n9uTunfvrh07dmjEiBFq1qyZihUrplOnTikoKEj169dXv3799PXXX+u5557L91qqVKmizZs36/HHH1fp0qWVkpKiMmXKqGfPntq8ebO6dOnicjpPbwuu5PTp09bPihMSEnTs2DGFhISocePGGjRokP744w+3fpr6wgsv6K233lKTJk0UEBCg/fv3a8+ePdYND5o2barPP/9cjz76qBo3bqyIiAidPn1awcHBatCggQYNGqQ///zTrbPxT5486fSaLn84ttUOhf1YA77BZhxHXAAKtClTpuihhx5S1apVXV63yBP69u2rmTNnqk+fPpneChUA3NWrVy99+umneuCBB6wLhSLntm/frmuuuUbSpVCjYsWKHq9hxowZio2NVeXKlRUfH+/x+QNXA8fPbJcuXao2bdp4txgABYZvfpUPIEfOnz+viRMnSpI6derk3WIAIB/8/fff1l2T2M7lzvjx4yVJderU8UpABAAAfBchEVBAzJ49Wy+++KK2bt1q3WXh4sWLWrFihdq1a6f//ve/Cg4O1pNPPunlSgHAPcOHD9e7776rvXv3WhcGTUpK0pw5c9S2bVudP39etWrVcvvi2FeLbdu2qV+/flqxYoV18wDH8NjYWE2fPl2SMlyoFgAAwN/bBQDInsOHD2vcuHEaN26cbDabwsPDdebMGSswCgwM1PTp062fEABAQfPbb7/pm2++0RNPPKGAgAAVL15cp06dsgKj8uXLa+7cuQoICPBypb7t/Pnzmjp1qvWTvLCwMF24cEFnz5612gwcODDfrlkDAAAKLkIioIC49dZbdfToUS1btsy6KGZAQICqVq2qtm3b6qmnniIgAlCgPf300ypXrpzWrFmjQ4cO6cSJEypevLiuueYa3XrrrRowYIBKlizp7TJ9XrVq1fT6669r8eLF+uuvv3TkyBGlpqaqYsWKatmypR566CHddNNN3i4TAAD4IC5cDQAAAAAAAK5JBAAAAAAAAH5uJklKS0vTwYMHVbx4cetWkQAAAAAAAAWdMUb//POPypUrJ7s963OFCIkkHTx4kFvAAgAAAACAQmvfvn2qUKFClm0IiSQVL15c0qUFFhoa6uVqAAAAAAAA8sbp06dVsWJFK/vICiGRZP3ELDQ0lJAIAAAAAAAUOtm5vA4XrgYAAAAAAAAhEQAAAAAAAAiJAAAAAAAAIEIiAAAAAAAAiJAIAAAAAAAAIiQCAAAAAACACIkAAAAAAAAgQiIAAAAAAACIkAgAAAAAAAAiJAIAAAAAAIAIiQAAAAAAACBCIgAAAAAAAIiQCAAAAAAAACIkAgAAAAAAgAiJAAAAAAAAIEIiAAAAAAAAiJAIAAAAyBNJSUmy2Wyy2WxKSkrydjkAAOQYIREAAABwFSDEgrew7gEFByERAAAAAGSBkKNw8/X319frQ+FCSAQAAAAABRghArzF19c9X6/PFxESAQAAAPA6PswBgPcREgEAAAAAAICQCAAAAAAAAIREAAAAAAAAECERAAAAAAAAREgEAAAAAAAAERIBAAAAAABAhEQAACCHuE01AABA4URIBAAAChVCLAAAAPcQEgEAAHiQr4dYvl4fAADIP4REAAAAKBAIsAAAyF+ERAAAAAAAACAkAgAAAAAAACERAAAAAAAA5MMh0aRJkxQdHa3g4GA1b95c69aty7RtmzZtrN+np3906dLFgxUDAAAAAAAUXD4ZEs2ZM0dxcXEaMWKENm3apPr166tjx446cuSIy/ZfffWVDh06ZD22bt0qPz8/3X333R6uHAAAAAAAoGDyyZBowoQJ6t+/v2JjY1WnTh1NnjxZISEhmjZtmsv2JUuWVFRUlPVYtGiRQkJCCIkAAAAAAACyyedCopSUFG3cuFHt27e3htntdrVv315r167N1nNMnTpV//rXv1S0aNH8KhMAAAAAAKBQ8fd2AZc7duyYUlNTFRkZ6TQ8MjJS27Ztu+L069at09atWzV16tRM2yQnJys5Odn6+/Tp0+4XDAAAAAAAUAj43JlEuTV16lRdd911atasWaZtxo8fr7CwMOtRsWJFD1YIAEDWkpKSrJswJCUlebscAAAAXCV8LiSKiIiQn5+fEhISnIYnJCQoKioqy2mTkpI0e/ZsPfjgg1m2GzJkiBITE63Hvn37cl03AAAAAABAQeZzIVFgYKAaN26sJUuWWMPS0tK0ZMkStWzZMstp586dq+TkZN13331ZtgsKClJoaKjTAwBwdeFsHQAAAMCZz12TSJLi4uLUp08fNWnSRM2aNdPEiROVlJSk2NhYSVLv3r1Vvnx5jR8/3mm6qVOnqnv37ipVqpQ3ygYAAAAAACiwfDIk6tGjh44eParhw4fr8OHDatCggRYuXGhdzHrv3r2y251Pgvrrr7+0atUq/fjjj94oGQBwmaSkJBUrVkySdObMGe44CQAAAPg4nwyJJGnAgAEaMGCAy3HLli3LMKxmzZoyxuRzVQAAAAAAAIWTz12TCACQPVxTBwAAAEBeIiQCAAAAAAAAIREAAAAAAAAIiQAgU/ycCwAAAMDVhJAIAAAAAAAAhEQAAAAAAAAgJAIAAAAAAIAIiQAAAAAAACBCIgAAAAAAAIiQCAAAAAAAACIkAgAAAAAAgAiJAAAAAAAAIEIiAAAAAAAAiJAIAAAAAAAAIiQCAAAAAACACIkAAAAAAAAgQiIAAAAAAACIkAgAAAAAAAAiJAIAAAAAAIAIiQAAAAAAACBCIgAAAAAAAIiQCAAAAAAAACIkAgAAAAAAgAiJAAAAAAAAIEIiAAAAAAAAiJAIAAAAAAAAIiQCAAAAAACACIkAAAAAAAAgQiIAAAAAAACIkAiAlyUlJclms8lmsykpKcnb5QAAAADAVYuQCAAAAAAAAIREAAAAAAAAICQCAAAAAACACIkAAAAAAAAgQiIAAAAAAACIkAgo9Lh7GAAAAAAgOwiJAAAAAAAAQEgEAAAAAAAAHw2JJk2apOjoaAUHB6t58+Zat25dlu1PnTqlxx9/XGXLllVQUJCuueYafffddx6qFgAAAAAAoODz93YBl5szZ47i4uI0efJkNW/eXBMnTlTHjh31119/qUyZMhnap6SkqEOHDipTpoy++OILlS9fXnv27FGJEiU8XzwAAAAAAEAB5XMh0YQJE9S/f3/FxsZKkiZPnqwFCxZo2rRpev755zO0nzZtmk6cOKE1a9YoICBAkhQdHe3JkgEAAAAAAAo8nwqJUlJStHHjRg0ZMsQaZrfb1b59e61du9blNN9++61atmypxx9/XN98841Kly6te++9V4MHD5afn5+nSgcAAAAAIFPRzy9wa7q0lPPW/2sPWyh7YHCOnyP+5S5ZjvdmbVLBr68w8amQ6NixY0pNTVVkZKTT8MjISG3bts3lNLt27dJPP/2kXr166bvvvtOOHTv02GOP6cKFCxoxYoTLaZKTk5WcnGz9ffr06bx7EQAAAABQCPn6B3Vfrw8oCHzywtU5kZaWpjJlyujDDz9U48aN1aNHDw0dOlSTJ0/OdJrx48crLCzMelSsWNGDFaOwSUpKks1mk81mU1JSkrfLAQAAAADALT4VEkVERMjPz08JCQlOwxMSEhQVFeVymrJly+qaa65x+mlZ7dq1dfjwYaWkpLicZsiQIUpMTLQe+/bty7sXAQAAAAAAUAD5VEgUGBioxo0ba8mSJdawtLQ0LVmyRC1btnQ5zfXXX68dO3YoLS3NGvb333+rbNmyCgwMdDlNUFCQQkNDnR4AAAAAAABXM58KiSQpLi5OU6ZM0cyZM/Xnn3/q0UcfVVJSknW3s969eztd2PrRRx/ViRMn9OSTT+rvv//WggUL9NJLL+nxxx/31ksAAAAAAAAocHzqwtWS1KNHDx09elTDhw/X4cOH1aBBAy1cuNC6mPXevXtlt/8v26pYsaJ++OEHPf3006pXr57Kly+vJ598UoMHD/bWSwAAAEAB5usXv6W+rOVHfb5cm8SFlwHkHZ8LiSRpwIABGjBggMtxy5YtyzCsZcuW+vnnn/O5KgAAAAAAgMLL535uBgAAAAAAAM8jJAIAAAAAAAAhEQAAAAAAAAiJAAAAAAAAIEIiFABJSUmy2Wyy2WxKSkrydjkAAAAAABRKhEQAAAAAAAAgJAIAAAAAAIDk7+0CAAAAcHWJfn6BW9OlpZy3/l972ELZA4Pdep74l7u4NR0AAIUdIREAAIAbfD3o8GZ9hDAAABRMhEQAAMAn+XoIAwAAUNgQEgEAcJUihAEAAEB6XLgaAAAAAAAAhEQAAAAAAAAgJAIAAAAAAIAIiQAAAAAAACBCIgAAAAAAAIiQCAAAAAAAACIkAgAAAAAAgAiJAAAAAAAAIEIi/L+kpCTZbDbZbDYlJSV5uxwAAAAAAOBhhEQAAAAAAAAgJAIAAAAAAAAhEQAAAAAAACT5e7sAAAAKs+jnF+R4mrSU89b/aw9bKHtgsFvzjn+5i1vTAQAA4OrEmUQAAAAAAADgTCIAQMHmzpk6EmfrAAAAAJfjTCIAAAAAAAAQEgEAAAAAAICfmwEAroCfcwEAAABXB84kAgAAAAAAACERAAAAAAAA+LkZAHgdP+cCAAAA4As4kwgAAAAAAACERAAAAAAAAODnZgCuAvycCwAAAACujDOJAAAAAAAAQEgEAAAAAAAAQiIAAAAAAADIh0OiSZMmKTo6WsHBwWrevLnWrVuXadsZM2bIZrM5PYKD3bt2CAAAAAAAwNXIJ0OiOXPmKC4uTiNGjNCmTZtUv359dezYUUeOHMl0mtDQUB06dMh67Nmzx4MVAwAAAAAAFGw+GRJNmDBB/fv3V2xsrOrUqaPJkycrJCRE06ZNy3Qam82mqKgo6xEZGenBigEAAAAAAAq2XIVEX3/9te655x7Vq1dP1atXt4Zv27ZNr776qg4cOJDj50xJSdHGjRvVvn37/xVpt6t9+/Zau3ZtptOdOXNGlStXVsWKFXXbbbfpjz/+yPG8AQAAAAAArlb+7kyUlpamnj176osvvpAkFSlSROfOnbPGh4eHa+jQoUpNTdWQIUNy9NzHjh1TampqhjOBIiMjtW3bNpfT1KxZU9OmTVO9evWUmJio119/Xa1atdIff/yhChUqZGifnJys5ORk6+/Tp0/nqEYAAAAAAIDCxq0zid58803NnTtXDz/8sE6ePKlnn33WaXxkZKRat26tBQsW5EmRV9KyZUv17t1bDRo0UExMjL766iuVLl1aH3zwgcv248ePV1hYmPWoWLGiR+oEAAAAAADwVW6FRDNmzFDTpk313nvvKTQ0VDabLUOb6tWra/fu3Tl+7oiICPn5+SkhIcFpeEJCgqKiorL1HAEBAWrYsKF27NjhcvyQIUOUmJhoPfbt25fjOgEAAAAAAAoTt0KiHTt2qHXr1lm2KVWqlI4fP57j5w4MDFTjxo21ZMkSa1haWpqWLFmili1bZus5UlNT9fvvv6ts2bIuxwcFBSk0NNTpAQAAAAAAcDVz65pERYoUUWJiYpZt9uzZoxIlSrjz9IqLi1OfPn3UpEkTNWvWTBMnTlRSUpJiY2MlSb1791b58uU1fvx4SdLo0aPVokULVa9eXadOndJrr72mPXv2qF+/fm7NHwAAAAAA4GrjVkjUsGFD/fDDDzp//ryCg4MzjD9x4oQWLlyoG2+80a2ievTooaNHj2r48OE6fPiwGjRooIULF1oXs967d6/s9v+dBHXy5En1799fhw8fVnh4uBo3bqw1a9aoTp06bs0fAAAAAADgauNWSDRw4EDdfvvtuvPOOzNcHHrnzp164IEHlJiYqIEDB7pd2IABAzRgwACX45YtW+b095tvvqk333zT7XkBAAAAAABc7dwKiW677TYNHjxYr7zyiipXrqyiRYtKksqUKaPjx4/LGKNhw4apXbt2eVosAAAAAAAA8odbF66WLt1G/ocfftCtt96qkJAQ+fn5KS0tTZ06ddL333+vUaNG5WWdAAAAAAAAyEdunUm0d+9eBQYGqkOHDurQoUNe1wQAAAAAAAAPc+tMoipVquiFF17I61oAAAAAAADgJW6FROHh4SpVqlRe1wIAAAAAAAAvcSskat26tX755Ze8rgUAAAAAAABe4lZINH78eP32228aPXq0Ll68mNc1AQAAAAAAwMPcunD1q6++quuuu06jRo3SBx98oPr16ysyMlI2m82pnc1m09SpU/OkUAAAAAAAAOQft0KiGTNmWP8/dOiQDh065LIdIREAAAAAAEDB4FZItHv37ryuAwAAAAAAAF7kVkhUuXLlvK4DQAEX/fwCt6ZLSzlv/b/2sIWyBwa79TzxL3dxazoAAAAAhZM9MFiVB8/3dhkFilshEQAAAAAAyH++HHT4cm1wj1t3N3OYNWuWOnTooNKlSysoKEilS5fWzTffrE8//TSv6gMAAAAAIN84go7Kg+e7fVY7UFi4dSZRamqq7rnnHs2bN0/GGAUHB6tcuXJKSEjQ4sWLtWTJEn355ZeaO3eu7PZc5VAAAAAAgAKMs02AgsOtBOftt9/W119/reuvv16rV6/W2bNntXv3bp09e1Zr1qzRDTfcoHnz5umdd97J63oBAAAAAACQD9wKiWbOnKlrrrlGS5YsUcuWLZ3GtWjRQosXL9Y111yj6dOn50mRAAAAAAAAyF9uhUR///23unXrpoCAAJfjAwIC1LVrV/3999+5Kg4AAAAAAACe4VZIFBgYqKSkpCzbJCUlKTAw0K2iAAAAAAAA4FluhUQNGzbU559/roMHD7ocf+jQIX3++edq1KhRrooDAAAAAACAZ7h1d7O4uDjddtttatKkiZ555hnFxMQoMjJSCQkJWrZsmSZMmKATJ04oLi4ur+sFrlrRzy9wa7q0lPPW/2sPW+j2bT3jX+7i1nQAAAAAgILBrZCoa9euev311/X8889r0KBBTuOMMfL399frr7+uW2+9NU+KBAAAAAAAQP5yKySSLp1N1L17d82aNUtbtmzR6dOnFRoaqoYNG+ree+9V1apV87JOAAAAAIWYPTBYlQfP93YZAHBVczskkqSqVatq2LBheVULAAAAACCHCNgA5JVchUQAAAAAUNgRwgC4Wrh1d7M33nhDERERmd7d7ODBgypdurTefvvtXBUHAAAAIG84go7Kg+e7fSMLAEDh5lZINHfuXNWvX1/lypVzOb5cuXJq0KCBZs+enaviAAAAgIKCEAYAUNC5FRJt375ddevWzbJN3bp1tX37dreKAgAAAAAAgGe5FRKdO3dORYsWzbJNcHCwzpw541ZRAAAAAAAA8Cy3QqJKlSppzZo1WbZZu3atKlSo4FZRAAAAAAAA8Cy3QqIuXbpo1apVmjZtmsvxH330kVatWqWuXbvmqjgAAAAAAAB4hr87Ez3//PP67LPP1L9/f33yySfq0KGDypcvrwMHDujHH3/UihUrVK5cOQ0ZMiSv6wXyTfTzC9yaLi3lvPX/2sMWun2hyviXu7g1HQAAVwtuQw4AQP5yKyQqXbq0li5dqvvuu0/Lli3TsmXLZLPZZIyRJDVt2lSzZs1S6dKl87RYAAAA5C+CGAAArl5uhUSSVLNmTa1fv17r16/XunXrlJiYqBIlSqhZs2Zq0qRJXtYIAAAAAACAfOZ2SOTQtGlTNW3aNC9qAQAAKPQ4UwcAAPiqXIdE6cXHx2vRokUKDg7W7bffrmLFiuXl0wMAAAAAACCfuHV3s5deeklVqlTRyZMnrWHLli3Ttddeq0ceeUR9+/ZVo0aNdOLEiTwrFAAAIDscZ+pUHjzf7ZsJAAAAXI3cConmzZun6OhohYeHW8MGDx6stLQ0jRo1So8++qh27NihiRMn5lWdAAAAAAAAyEduhUTx8fGqU6eO9ffBgwe1fv16Pf7443rxxRf17rvv6qabbtLXX3+dZ4UCAAAAAAAg/7h1TaLTp0+rRIkS1t8rVqyQzWZT165drWGNGjXS5MmTc10gAADwLVx4GQAAoHBy60yiyMhI7dmzx/p70aJFCgoKUvPmza1h58+fl81mc7uwSZMmKTo6WsHBwWrevLnWrVuXrelmz54tm82m7t27uz1vAAAAAACAq41bIVHTpk31zTffaP78+Vq8eLHmzJmjtm3bKigoyGqze/dulStXzq2i5syZo7i4OI0YMUKbNm1S/fr11bFjRx05ciTL6eLj4/Xss8+qdevWbs0XAAAAAADgauVWSPTCCy/o4sWLuu2229SxY0edP39eL7zwgjU+OTlZK1ascDqzKCcmTJig/v37KzY2VnXq1NHkyZMVEhKiadOmZTpNamqqevXqpVGjRqlq1apuzRcAAAAAAOBq5dY1iRo1aqSff/5Z//73vyVJ99xzj5o1a2aN37x5s9q2bat77703x8+dkpKijRs3asiQIdYwu92u9u3ba+3atZlON3r0aJUpU0YPPvigVq5cmeU8kpOTlZycbP19+vTpHNcJAAAAAABQmLgVEklS/fr1Vb9+fZfjWrRo4fadzY4dO6bU1FRFRkY6DY+MjNS2bdtcTrNq1SpNnTpVW7ZsydY8xo8fr1GjRrlVHwAAAAAAQGHkdkjkK/755x/df//9mjJliiIiIrI1zZAhQxQXF2f9ffr0aVWsWDG/SsT/i35+gVvTpaWct/5fe9hC2QOD3Xqe+Je7uDUdAAAAAABXA58LiSIiIuTn56eEhASn4QkJCYqKisrQfufOnYqPj1fXrl2tYWlpaZIkf39//fXXX6pWrZrTNEFBQU4X2QYAAAAAALjauXXh6vwUGBioxo0ba8mSJdawtLQ0LVmyRC1btszQvlatWvr999+1ZcsW69GtWze1bdtWW7Zs4QwhAAAAAACAbPC5M4kkKS4uTn369FGTJk3UrFkzTZw4UUlJSYqNjZUk9e7dW+XLl9f48eMVHBysa6+91mn6EiVKSFKG4QAAAAAAAHDNJ0OiHj166OjRoxo+fLgOHz6sBg0aaOHChdbFrPfu3Su73edOggIAAAAAACiwfDIkkqQBAwZowIABLsctW7Ysy2lnzJiR9wUBAAAAAAAUYpyOAwAAAAAAgNydSXT48GFt3LhRp06dUmpqqss2vXv3zs0sAAAAAAAA4AFuhUTnz59X//79NXv2bOt285czxshmsxESAQCQQ/bAYFUePN/bZQAAAOAq41ZI9Pzzz2vWrFm65ppr1LNnT1WoUEH+/j57eSMAAAAAAABcgVvJzueff646depo48aNCgoKyuuaAAAAAAAA4GFuhUSnTp3SvffeS0AEACiw+EkXAAAA4Mytu5vVrFlTCQkJeV0LAAAAAAAAvMStkOi5557TN998ox07duR1PQAAAAAAAPACt35uVqFCBXXs2FHNmjXTU089pUaNGik0NNRl2xtvvDFXBQIACiZ+zgUAAAAULG6FRG3atJHNZpMxRiNHjpTNZsu0bWpqqtvFAQAAAAAAwDPcComGDx+eZTAEAAAAAACAgsWtkGjkyJF5XAYAAAAAAAC8ya2QCADgfVzzBwAAAEBecuvuZgAAAAAAAChc3D6TaN++fRo7dqwWL16sgwcPKiUlJUMbm82mixcv5qpA5Ez08wvcmi4t5bz1/9rDFsoeGJzj54h/uYtb8wYAAAAAAN7nVki0a9cuNW/eXCdPnlTdunWVnJysypUrKzg4WLt27dKFCxdUv359lShRIo/LBQDP4edcAAAAAK4mbv3cbNSoUUpMTNSSJUv066+/SpJiY2P1559/Kj4+Xt26dVNSUpK++OKLPC0WAAAAAAAA+cOtkGjx4sW65ZZbFBMTYw0zxkiSypYtqzlz5kiSXnjhhTwoEQAAAAAAAPnNrZDo2LFjqlWrlvW3v7+/zp49a/0dFBSkDh06aP58fqYBAAAAAABQELgVEkVERCgpKcnp7/j4eKc2/v7+OnXqVG5qAwAAAAAAgIe4FRLVqFFDO3futP5u1qyZfvjhB+3atUuSdPToUX3xxReqVq1a3lQJAAAAAACAfOVWSNS5c2ctXbrUOlPoqaee0j///KN69eqpadOmuuaaa3T48GE98cQTeVkrAAAAAAAA8olbIdGjjz6qZcuWyc/PT5LUpk0bzZ49W5UrV9bWrVsVGRmpt99+W/3798/TYgEAAAAAAJA//N2ZKDQ0VM2bN3cadvfdd+vuu+/Ok6IAAAAAAADgWW6dSQQAAAAAAIDCJVch0ddff6177rlH9erVU/Xq1a3h27Zt06uvvqoDBw7kukAAAAAAAADkP7d+bpaWlqaePXvqiy++kCQVKVJE586ds8aHh4dr6NChSk1N1ZAhQ/KmUgAAAAAAAOQbt84kevPNNzV37lw9/PDDOnnypJ599lmn8ZGRkWrdurUWLFiQJ0UCAAAAAAAgf7kVEs2YMUNNmzbVe++9p9DQUNlstgxtqlevrt27d+e6QAAAAAAAAOQ/t0KiHTt2qHXr1lm2KVWqlI4fP+5WUQAAAAAAAPAst0KiIkWKKDExMcs2e/bsUYkSJdx5egAAAAAAAHiYWyFRw4YN9cMPP+j8+fMux584cUILFy5UixYtclUcAAAAAAAAPMOtkGjgwIHav3+/7rzzTu3fv99p3M6dO3X77bcrMTFRAwcOzJMiAQAAAAAAkL/83Znotttu0+DBg/XKK6+ocuXKKlq0qCSpTJkyOn78uIwxGjZsmNq1a5enxQIAAAAAACB/uHUmkSSNHz9eP/zwg2699VaFhITIz89PaWlp6tSpk77//nuNGjUqL+sEAAAAAABAPnLrTCKHDh06qEOHDnlVCwAAAAAAALzE7TOJAAAAAAAAUHjk6kyi1NRU7d+/XwcPHtSFCxdctrnxxhtzMwsAAAAAAAB4gFtnEqWlpWns2LGKiopS1apVdcMNN6ht27YuH+6aNGmSoqOjFRwcrObNm2vdunWZtv3qq6/UpEkTlShRQkWLFlWDBg3073//2+15AwAAAAAAXG3cOpNoyJAheu2111SmTBnFxsaqbNmy8vfP1UlJTubMmaO4uDhNnjxZzZs318SJE9WxY0f99ddfKlOmTIb2JUuW1NChQ1WrVi0FBgZq/vz5io2NVZkyZdSxY8c8qwtA3rMHBqvy4PneLgMAAAAArnpuJTszZ85UzZo1tX79ehUrViyva9KECRPUv39/xcbGSpImT56sBQsWaNq0aXr++ecztG/Tpo3T308++aRmzpypVatWERIBAAAAAABkg1s/Nztz5oy6dOmSLwFRSkqKNm7cqPbt21vD7Ha72rdvr7Vr115xemOMlixZor/++ivT6yElJyfr9OnTTg8AAAAAAICrmVshUb169XTw4MG8rkWSdOzYMaWmpioyMtJpeGRkpA4fPpzpdImJiSpWrJgCAwPVpUsXvfPOO+rQoYPLtuPHj1dYWJj1qFixYp6+BgAAAAAAgILGrZBo6NChmjdvnjZt2pTX9bitePHi2rJli9avX69x48YpLi5Oy5Ytc9l2yJAhSkxMtB779u3zbLEAAAAAAAA+xq1rEnXp0kUzZsxQ586d1a1bN9WvX1+hoaEu2/bu3TtHzx0RESE/Pz8lJCQ4DU9ISFBUVFSm09ntdlWvXl2S1KBBA/35558aP358husVSVJQUJCCgoJyVBcAAAAAAEBh5lZIlJycrP/85z86duyYpk6dKkmy2WxObYwxstlsOQ6JAgMD1bhxYy1ZskTdu3eXJKWlpWnJkiUaMGBAtp8nLS1NycnJOZo3AAAAAADA1cqtkCguLk6zZs1SvXr1dNddd6ls2bLy93frqTJ9/j59+qhJkyZq1qyZJk6cqKSkJOtuZ71791b58uU1fvx4SZeuMdSkSRNVq1ZNycnJ+u677/Tvf/9b77//fp7VBAAAAAAAUJi5lezMnTtXjRs31tq1a/M0HHLo0aOHjh49quHDh+vw4cNq0KCBFi5caF3Meu/evbLb/3c5paSkJD322GPav3+/ihQpolq1aumTTz5Rjx498rw2AAAAAACAwsithOf8+fNq27ZtvgREDgMGDMj052WXX5B67NixGjt2bL7VAgAAAAAAUNi5dXezxo0ba8eOHXldCwAAAAAAALzErVOBXnrpJd10002aP3++br311ryuCUAesgcGq/Lg+d4uAwAAAADg49wKiRYtWqQ2bdrotttuU7t27VS/fn2FhoZmaGez2TRs2LBcFwkAAAAAAID85VZINHLkSOv/S5Ys0ZIlS1y2IyQCAAAAAAAoGNwKiZYuXZrXdQAAAAAAAMCL3AqJYmJi8roOAAAAAAAAeJFbdzcDAAAAAABA4UJIBAAAAAAAAEIiAAAAAAAAEBIBAAAAAABAhEQAAAAAAAAQIREAAAAAAABESAQAAAAAAAAREgEAAAAAAECERAAAAAAAABAhEQAAAAAAACT5e7sAoKCzBwar8uD53i4DAAAAAIBc4UwiAAAAAAAAEBIBAAAAAACAkAgAAAAAAAAiJAIAAAAAAIAIiQAAAAAAACBCIgAAAAAAAIiQCAAAAAAAACIkAgAAAAAAgAiJAAAAAAAAIEIiAAAAAAAAiJAIAAAAAAAAIiQCAAAAAACACIkAAAAAAAAgQiIAAAAAAACIkAgAAAAAAAAiJAIAAAAAAIAIiQAAAAAAACDJ39sFAFdiDwxW5cHzvV0GAAAAAACFGmcSAQAAAAAAgJAIAAAAAAAAhEQAAAAAAACQD4dEkyZNUnR0tIKDg9W8eXOtW7cu07ZTpkxR69atFR4ervDwcLVv3z7L9gAAAAAAAHDmkyHRnDlzFBcXpxEjRmjTpk2qX7++OnbsqCNHjrhsv2zZMvXs2VNLly7V2rVrVbFiRd188806cOCAhysHAAAAAAAomHwyJJowYYL69++v2NhY1alTR5MnT1ZISIimTZvmsv2sWbP02GOPqUGDBqpVq5Y++ugjpaWlacmSJR6uHAAAAAAAoGDyuZAoJSVFGzduVPv27a1hdrtd7du319q1a7P1HGfPntWFCxdUsmRJl+OTk5N1+vRppwcAAAAAAMDVzOdComPHjik1NVWRkZFOwyMjI3X48OFsPcfgwYNVrlw5p6ApvfHjxyssLMx6VKxYMdd1AwAAAAAAFGQ+FxLl1ssvv6zZs2fr66+/VnBwsMs2Q4YMUWJiovXYt2+fh6sEAAAAAADwLf7eLuByERER8vPzU0JCgtPwhIQERUVFZTnt66+/rpdfflmLFy9WvXr1Mm0XFBSkoKCgPKkXAAAAAACgMPC5M4kCAwPVuHFjp4tOOy5C3bJly0yne/XVVzVmzBgtXLhQTZo08USpAAAAAAAAhYbPnUkkSXFxcerTp4+aNGmiZs2aaeLEiUpKSlJsbKwkqXfv3ipfvrzGjx8vSXrllVc0fPhwffrpp4qOjrauXVSsWDEVK1bMa68DAAAAAACgoPDJkKhHjx46evSohg8frsOHD6tBgwZauHChdTHrvXv3ym7/30lQ77//vlJSUnTXXXc5Pc+IESM0cuRIT5YOAAAAAABQIPlkSCRJAwYM0IABA1yOW7ZsmdPf8fHx+V8QAAAAAABAIeZz1yQCAAAAAACA5xESAQAAAAAAgJAIAAAAAAAAhEQAAAAAAAAQIREAAAAAAABESAQAAAAAAAAREgEAAAAAAECERAAAAAAAABAhEQAAAAAAACT5e7sA+AZ7YLAqD57v7TIAAAAAAICXcCYRAAAAAAAACIkAAAAAAABASAQAAAAAAAAREgEAAAAAAECERAAAAAAAABAhEQAAAAAAAERIBAAAAAAAABESAQAAAAAAQIREAAAAAAAAECERAAAAAAAAREgEAAAAAAAAERIBAAAAAABAhEQAAAAAAAAQIREAAAAAAABESAQAAAAAAAAREgEAAAAAAECERAAAAAAAABAhEQAAAAAAAERIBAAAAAAAABESAQAAAAAAQIREAAAAAAAAECERAAAAAAAAREgEAAAAAAAAERIBAAAAAABAhEQAAAAAAAAQIREAAAAAAABESAQAAAAAAAAREgEAAAAAAEA+GhJNmjRJ0dHRCg4OVvPmzbVu3bpM2/7xxx+68847FR0dLZvNpokTJ3quUAAAAAAAgELC50KiOXPmKC4uTiNGjNCmTZtUv359dezYUUeOHHHZ/uzZs6patapefvllRUVFebhaAAAAAACAwsHnQqIJEyaof//+io2NVZ06dTR58mSFhIRo2rRpLts3bdpUr732mv71r38pKCjIw9UCAAAAAAAUDj4VEqWkpGjjxo1q3769Ncxut6t9+/Zau3Ztns0nOTlZp0+fdnoAAAAAAABczXwqJDp27JhSU1MVGRnpNDwyMlKHDx/Os/mMHz9eYWFh1qNixYp59twAAAAAAAAFkU+FRJ4yZMgQJSYmWo99+/Z5uyQAAAAAAACv8vd2AelFRETIz89PCQkJTsMTEhLy9KLUQUFBXL8IAAAAAAAgHZ86kygwMFCNGzfWkiVLrGFpaWlasmSJWrZs6cXKAAAAAAAACjefOpNIkuLi4tSnTx81adJEzZo108SJE5WUlKTY2FhJUu/evVW+fHmNHz9e0qWLXf/3v/+1/n/gwAFt2bJFxYoVU/Xq1b32OgAAAAAAAAoSnwuJevTooaNHj2r48OE6fPiwGjRooIULF1oXs967d6/s9v+dAHXw4EE1bNjQ+vv111/X66+/rpiYGC1btszT5QMAAAAAABRIPhcSSdKAAQM0YMAAl+MuD36io6NljPFAVQAAAAAAAIWXT12TCAAAAAAAAN5BSAQAAAAAAABCIgAAAAAAABASAQAAAAAAQIREAAAAAAAAECERAAAAAAAAREgEAAAAAAAAERIBAAAAAABAhEQAAAAAAAAQIREAAAAAAABESAQAAAAAAAAREgEAAAAAAECERAAAAAAAABAhEQAAAAAAAERIBAAAAAAAABESAQAAAAAAQIREAAAAAAAAECERAAAAAAAAREgEAAAAAAAAERIBAAAAAABAhEQAAAAAAAAQIREAAAAAAABESAQAAAAAAAAREgEAAAAAAECERAAAAAAAABAhEQAAAAAAAERIBAAAAAAAABESAQAAAAAAQIREAAAAAAAAECERAAAAAAAAREgEAAAAAAAAERIBAAAAAABAhEQAAAAAAAAQIREAAAAAAABESAQAAAAAAAAREgEAAAAAAECERAAAAAAAABAhEQAAAAAAAOTDIdGkSZMUHR2t4OBgNW/eXOvWrcuy/dy5c1WrVi0FBwfruuuu03fffeehSgEAAAAAAAo+nwyJ5syZo7i4OI0YMUKbNm1S/fr11bFjRx05csRl+zVr1qhnz5568MEHtXnzZnXv3l3du3fX1q1bPVw5AAAAAABAweSTIdGECRPUv39/xcbGqk6dOpo8ebJCQkI0bdo0l+3feustderUSc8995xq166tMWPGqFGjRnr33Xc9XDkAAAAAAEDB5O/tAi6XkpKijRs3asiQIdYwu92u9u3ba+3atS6nWbt2reLi4pyGdezYUfPmzXPZPjk5WcnJydbfiYmJkqTTp0/nsnrvS0s+67V5X2n5ebM2ifpyqyDX58u1SdR3JdTnPl+uTaK+3PLl+ny5Non6cov63OfLtUnUl1u+XJ8v1yYV/Pp8naN+Y8yVGxsfc+DAASPJrFmzxmn4c889Z5o1a+ZymoCAAPPpp586DZs0aZIpU6aMy/YjRowwknjw4MGDBw8ePHjw4MGDBw8ePK6Kx759+66YyfjcmUSeMGTIEKczj9LS0nTixAmVKlVKNpvNi5V51+nTp1WxYkXt27dPoaGh3i7HiS/XJlFfbvlyfb5cm0R9ueXL9flybRL15Rb1uc+Xa5OoL7d8uT5frk2ivtyiPvf5cm2S79fnCcYY/fPPPypXrtwV2/pcSBQRESE/Pz8lJCQ4DU9ISFBUVJTLaaKionLUPigoSEFBQU7DSpQo4X7RhUxoaKjPdh5frk2ivtzy5fp8uTaJ+nLLl+vz5dok6sst6nOfL9cmUV9u+XJ9vlybRH25RX3u8+XaJN+vL7+FhYVlq53PXbg6MDBQjRs31pIlS6xhaWlpWrJkiVq2bOlympYtWzq1l6RFixZl2h4AAAAAAADOfO5MIkmKi4tTnz591KRJEzVr1kwTJ05UUlKSYmNjJUm9e/dW+fLlNX78eEnSk08+qZiYGL3xxhvq0qWLZs+erQ0bNujDDz/05ssAAAAAAAAoMHwyJOrRo4eOHj2q4cOH6/Dhw2rQoIEWLlyoyMhISdLevXtlt//vJKhWrVrp008/1YsvvqgXXnhBNWrU0Lx583Tttdd66yUUSEFBQRoxYkSGn+L5Al+uTaK+3PLl+ny5Non6csuX6/Pl2iTqyy3qc58v1yZRX275cn2+XJtEfblFfe7z5dok36/P19iMyc490AAAAAAAAFCY+dw1iQAAAAAAAOB5hEQAAAAAAAAgJAIAAAAAAAAhEQAAAAAAAERIBAAAAAAAABESAQAAAAAAQJK/twsAAAC4WmzZskWrV69WUlKSoqOj1blzZxUvXtzbZfm0AwcOaPv27UpMTJQkhYWFqUaNGipfvryXKwMAoPAhJIJPM8bom2++cTqgvvvuu1WlShVvl2Y5duyYfvnlF6u+pk2bymazebssSb65/Iwx2rNnj9PBfqVKlWS3c2JjTvjie3s5+kbOayrIfeO5557TV199pZ07d3qthqSkJE2ZMsXpfb333nt1ww03eLSO0aNHq02bNrrxxhutYWfPnlWfPn301VdfSbr0fttsNpUsWVIzZsxQly5dPFqjK76y/CQpJSVFb775pj766CPt2rXLZZsqVarooYce0pNPPqmgoCAPV4i8UlBCQAJeAFcNA7jw8ssvm7Zt23psfrGxseabb75xGnbkyBHTvHlzY7fbjc1msx5BQUHmww8/9Fhtxhgzc+ZM8+uvvzoNS0tLM88++6wJDAw0drvdetSqVcts2LDBo/X5+vIzxpjZs2ebm266yQQHBzstL7vdboKDg0379u3NnDlzPF5XTtE3nNE3cq+w9I2+ffsau93ukXm1bdvWzJw502nYzp07TZUqVTK8r3a73bz44oseqcvBZrOZUaNGOQ27//77jc1mM7Vq1TLjxo0zH3zwgYmNjTV+fn6mSJEi5q+//vJYfb6+/M6cOWOaN29ubDabKV68uOnUqZN54oknzNChQ83QoUPNE088YTp16mSKFy9u7Ha7adGihTlz5oxHa8yJZ5991lStWtXbZZgzZ86YN99809x1112mc+fO5tFHHzUrV670Si3Jycnm5ZdfNtWrV8+w3XM8qlWrZl555RVz/vx5j9U1atQos3z5cqdhSUlJ5q677rLqcvSLiIgIM3/+fI/VlhVfem+RPzZv3mzeffdd88orr5g5c+aY06dPe7skn7d//36zdOlSM2/ePDNv3jyzdOlSs3//fm+XVSAQEsElTx7sG+P6gLpz587GZrOZNm3amFmzZpkffvjBjBo1yhQtWtT4+/ub9evXe7W+uLg4Y7PZTJkyZUz//v3NkCFDTNu2bY3NZjOlSpUyBw4c8Gp9vrL8Lly4YLp3724dWNWqVct07drV9OrVy/Tq1ct07drV1KpVyzro6t69u7lw4YJHanMHfePK9dE3soe+4T5X72uzZs2MzWYzvXv3NqtXrzZ//fWXmTlzpomKijJ2u90sXrzYI7W5qm/nzp3Gbrebli1bmrNnzzq1/fLLL43NZjMPP/yw1+ozxreW33PPPWdsNpt5/vnnTVJSUqbtkpKSzODBg43NZjODBg3yWH055en9BiGg+wh4Pc8XQlRfCdkIKXPPVwPogoaQCC55+4Pw77//bmw2m+nSpYtJS0tzart69Wpjt9vNfffd57X6Dhw4YAICAkydOnXM4cOHndpOnDjR2Gw288wzz3itPl9afuPGjTM2m8307NnT7NmzJ9N2e/bsMT179jR2u9289NJLHqnNHfSNrOujb2Sfr/eN2NjYHD0cB2SecPn7+ssvvxibzWb69OmToe2ff/5pAgMDzR133OGR2lzVN3XqVGO3281PP/3ksn3r1q1N9erVPVWezy+/6Oho06lTp2y3v/nmm010dHQ+VpQ73t5vGEMImF0EvJ7HWaj/Q0iZO74cQBc0XJPoKjF69Ogctd+yZUv+FJJNq1evls1m07BhwzJcw6RVq1a6+eabtXLlSi9VJ/30009KTU3Vyy+/rMjISKdxTz75pD7++GN9//33ev31171Sny8tv48//lgtW7bUp59+mmW7SpUq6dNPP9Xu3bs1c+ZMDRkyxCP10TfyFn0j+3y9b8yYMUM2m03GmGxP461rTq1fv142m01xcXEZxtWqVUu33HKLfv75Zy9UdklCQoIkqVGjRi7HN2rUSB9++KEnS3Lia8vv0KFD6tmzZ7bbN27cWMuXL8/Hipw98MADOWq/atWqfKoke9atW6f169erd+/emjFjhjX8mmuuUbNmzVS/fn299957uummmzxSz9y5c9WxY0eNHz8+y3YhISF6+eWXtXnzZn3++ed65ZVXPFJfesuWLZMkjRs3TkWKFHEad8cdd+iGG27QkiVLPF6Xg6+9t75u2bJlatOmjdOwnj17Kj4+Xvfff78efvhhRURE6Oeff9bgwYP10ksvqU2bNl5bfrt27dKsWbPUokULLVmyxFoHH3roId1666266667NGHCBE2ePNkj9fj68hs1apTWrVunwYMHa9iwYQoJCXHZ7uzZsxo9erReffVVjR492ivbFl9HSHSVGDlyZIE52JekEydOSJKuvfZal+Pr1q2rpUuXerIkJ/v375cktWzZ0uX4Fi1aaObMmZ4syYkvLb89e/bojjvuyHb7Nm3aaOLEiflX0GXoG3mLvpF9vt43QkNDVb58eU2bNi1b7ceMGaPvv/8+n6ty7fTp05IufTBy5ZprrtF3333nyZKchIWFSVKmFyH38/Pz6gXKfW35RUVF5SiQ37Rpk6KiovKvoMsUpABVIgTMDQLenCtIIaqvh2yElDlTkAJoX0dIdJUICQlR+fLls33WxPvvv+/VsxEiIiIkXbq7SdGiRTOMv3DhggIDAz1dlsWxoXZVm2N4WlqaJ0ty4kvLLzw8XDt27Mh2+x07dig8PDwfK3JG38hb9I3s8/W+0ahRI61fv17NmjXL1gfc0qVLe6Cq/0lfU7ly5SRJZ86cUXBwcIa2SUlJmX6jmF/mzZun+Ph4SdLBgwclSTt37lSDBg0ytN2/f7+1bnqKLy+/O+64Q2+99ZaGDh2qF198McOHI4dz585pzJgxWrRokZ566imP1VeQAlSJEDA3CHhzriCFqL4YsqVHSJkzBSmA9nWERFeJBg0a6I8//lCPHj2y1X7hwoUe/yA8Y8YMKzE/deqUJOnvv/9W8+bNM7Tdt2+fypQp48Hq/pfmS5fqkqT4+HjVrl07Q9v9+/erVKlSnipNku8uv86dO2vmzJmaMmWK+vfvn2XbDz74QF9//bViY2M9UptE38gL9A33+HrfaNSokZYvX65t27a5fC+97c0339T06dMlScnJyZKkrVu3ZjgVXrp01tblP3/Mb1u2bMnwQXjevHkuQ6K1a9fquuuu80xh/8+Xl9+oUaO0dOlSjR8/Xu+++66uv/561ahRw/rAnpiYqO3bt2v16tX6559/VL9+fY0cOdJj9fl6gCoRAuYGAW/uFKQQ1RdDtvQIKXOmIAXQvo6Q6CrRqFEjrV27Vjt37lS1atW8XY5L8fHx1k7Z4csvv8zwQe7ixYtatWqVbrjhBg9Wd+mDcPoPw5I0f/58lx+eNmzYoJo1a3qoskt8dfmNGzdOixYt0iOPPKJXX31VHTp0cHmwv2jRIu3atUsVKlTQ2LFjPVKbRN/IC/QN9/h637jtttt0/PhxnTt3Llvt+/Xr5zJgyA+VKlVy+qY6MDBQlSpV0sqVKzPUcO7cOa1YsUK33HKLR2qTpN27d7sc7urD2pYtW1StWjXdeeed+V2WxdeXX/HixbV69Wq98sormjp1qhYuXKiFCxdmaFeuXDk9/fTTGjRokEc/CPt6gCoRAuYGAW/u+HqI6ushGyGl+3w9gC5ICImuEjfffLN+//13HT58OFsfhLt3767o6Oj8L+z/5eTnJ9u2bVOXLl3UvXv3/CvoMpldo8TVjm3Tpk26cOGCOnbsmN9lWXx5+UVFRWn9+vUaNGiQPv/8c+vieo6dTPoPKffff7/LCx7nJ/pG7tA33OfrfaN169Zq3bp1tttff/31uv766/Oxov+5PPTLyt69exUXF6e2bdvmX0GXqVy5crbbNmjQwOPXEfP15SddCtRGjRqlUaNGafv27dq+fbsSExMlXfp2vUaNGqpRo4ZHa3Lw5QBVIgTMDQLe3PP1ENXXQzZCSvf5egBdkNhMTn4wCgC5cObMGa1du9blwX6LFi1UvHhxL1cIeAd9A4A3/PXXX5o9e7batm2rG2+80Ss1+FoIWFh4671duXKlpk2bpieeeCLTa+mkt3r1au3YsUN9+vTJ99qio6Ndnt30wAMPaNiwYU7Dzp07p6ioKN1yyy367LPP8r026VKo4kpISEiGL9+2bNmip59+Wvfff3+OLxbuLl9fftKlO5c5AmjHmViXK1eunPr16+fxs1ALEkIiAAAAAAD+ny8EqAWZLyw/Amj3ERIBhcDq1avVtGlTr97VCvBF9A3PSE1N1YEDByRd+jkEXNu0aZNGjx6t1atXKykpSdHR0erVq5eee+45j66js2bN0k033VQoLth57tw5rV+/XpL4IAcAQB7w3uXQgcucP39eb7zxhrp166Y77rhDH3zwgS5cuOCy7VtvvaWqVat6uMKM9u/fr6eeekpNmzZVnTp1dMstt2jWrFker6N169bWb/d///13j88/r6Smpmrv3r3au3evt0vxKfQN99E3PGPHjh2Kjo726Lp39OhRPfHEE6pXr54aNWqkIUOG6MSJEy7bjho1Sv7+nrsMo5+fn8aMGeM07Mcff9T111+vb7/9VklJSSpRooS2bdum4cOH69Zbb83R7aJz6/7771elSpV0++23a/78+Tm6dpev2bt3r9q0aaN27dp5uxSXfL3v+jrHdXVWrFjh1To2bdqk7t27q3Tp0goJCVGdOnU0btw4paSkeLSOWbNm6fDhwx6dJ4CrkAEus23bNmO3242fn5/H5nn+/HnTvHlzY7fbjc1mMzabzdjtdnPttdeaP//8M0P7kSNHGrvd7rH6qlSpYt566y2nYRs2bDAlS5Z0qtfxb+/evT1WmzHGqsEx/+bNm5spU6aYf/75x6N15Na2bduMzWbz6LqXE/SNjOgbnuHrfWPnzp2mcuXKJjo62iPzO3XqlKlevbpTv7DZbKZs2bJm+fLlGdp7ul/YbDYzatQo6++UlBRTrlw5U7RoUTNt2jSTmppqjDFmz549pm3btsZut5spU6Z4tL70/bJ8+fJm6NChZufOnR6rIa/s3bvXxMTEmDZt2ni7FJe81XePHDliBgwYYK677jrTsGFD8/zzz5vjx4+7bDty5Eif3bZ4evnZ7XYzevRop2E//PCDCQ4ONjabzRQpUsSULVvW6jsdOnQwaWlpHqnNmEt9NyAgwHTv3t385z//sbYlBdXFixfNnj17zJ49e7xdik/buHGjue2220xERIQpUqSIqV27thk7dqxJTk72aB2ffPKJOXTokEfnmV/Onj1rli9f7vKYAcYQEiEDxw7ZZrN5bJ4vvfSSsdlsplu3bmbt2rVmw4YN5tFHHzV+fn4mIiLCbNy40am9tw/409LSTM2aNY2/v78ZPny4OXDggDl//rxZvny5qVu3rrHb7Wbu3Lkere+JJ54wb7zxhqldu7Z18FKsWDHzwAMPmNWrV3usltzw9AfNnKJvZETf8Axf7xueNnjwYGOz2cxjjz1mDh06ZI4dO2ZeeeUVU7RoUVOkSBHz3XffObX3dr/4/vvvjc1mM2PHjs3Q9uTJkyYiIsKjIYfNZjMvvvii+fLLL03nzp2Nn5+f1TfatWtnPv30U49/+CisvNF3fT1EzQlPh4AEvJ7ljRDVlwNUQkrP8fUv37yNkAg+oX79+qZmzZrm4sWLTsO/++47U7x4cVOyZEmzfv16a7i3D/iXLVtmbDabefrppzO03b9/vylWrJjp3Lmz1+pbs2aNeeCBB0zx4sWtHUndunXNhAkTzNGjRz1WF3KPvpG39dE3CodatWqZxo0bZxi+efNmU65cORMcHGz+85//WMO93S8mTJhg7HZ7ph/k7rvvPlOyZElPlZehvv3795vRo0ebKlWqWP2iZMmSZuDAgWbLli0eqwt5w9dDVF9GwOtZnIXqjJDSc3z9LFRv45pE8Anbt29Xx44d5efn5zS8c+fOWrJkidLS0nTzzTdr3bp1XqrQ2ZYtW2Sz2fTwww9nGFe+fHl16dJFGzdu9EJll7Rs2VJTp07VoUOHNGXKFDVv3lz//e9/9eyzz6pChQq655579OOPP3qtPmQffSNv0TcKh/j4eLVp0ybD8AYNGmjlypUqU6aM7rrrLn377beeL86F5ORkSVKFChVcjq9YsaLOnDnjyZKclC9fXsOGDdOuXbu0ePFi9ejRQ+fOndM777yjRo0aqVmzZvrwww+9Vh9y5ptvvlGjRo00adIkRUVFqVSpUho0aJBWrVql8PBw3XHHHZo/f763yywQ/vzzT9lsNvXs2TPDuBIlSqhTp0767bffPFpTQECA7rjjDn333Xfas2ePRo0apcqVK2vp0qW67777VLZsWT355JP69ddfPVqXO6pWrar4+Hjt3r3bI/MbP368du7cqUceeUQHDx7U0aNH9fLLL+v06dPq1KmTvv/+e4/UkV1LlizRoUOHNGTIEMXGxspuv/TRvVKlSvrqq69UsmRJj1/v8YUXXtAXX3yhjh076vDhw3rppZdUo0YN3XTTTfrss888fp0ud1WsWFHLli3T0qVLvV2KTyIkgk8ICAhQcHCwy3FNmzbVokWLZIxRx44dtXbtWg9Xl9HZs2clSdHR0S7HV6lSRadOnfJcQZkoWrSoHnzwQa1Zs0Z//PGHnnrqKYWFhemLL75Q586dvV0esoG+kT/oGzmzatUqjRs3Tn379tXtt9+u22+/XX379tW4ceO0atUqj9dTtGjRTC/0XLVqVS1btkyRkZG655579M0333i4uksSExOtCxYXL15c0qWLbbty9OhRhYWFebK8TLVr106ffvqpDh48qLfeekvXXXedNmzYoEcffdTbpWVqzZo1+vjjj71dhs8oaCGqLyPgLVwKWoBKSAlv8dytPuAz9u/fr+XLl2v79u1KTEyUJIWFhalGjRq68cYbVbFiRY/XVLly5SzvPNSkSRMtWrRIHTp0UKdOnXTDDTd4sLpLbDab9X/HB+CTJ0+6vIXwyZMnVaxYMU+Vli21a9fWG2+8oVdeeUXffPONpk2b5u2SXPr77791+PBhr9zKmL7hHvqGZ3ijb6xdu1b9+/fXn3/+mWkoY7PZVKdOHU2ZMkUtWrTwSF3R0dHavHlzpuOrVKmipUuXqm3btrrnnnvUpEkTj9SV3sSJEzVx4kSnYT///LPuvPPODG23b9+e6YdQbylRooSeeOIJPfHEE9q4caPP9gtJmjJlij7++GP17t3b4/NetWpVpvuNmJgYr2yTsxOitmnTRvfcc4/mzJnj4epybs2aNdqxY4fH3l9HwCvJKeAtX758hra+FvC2a9dOp06d0r///W9NnTpVGzZs0MaNG/XQQw95uzyfEB8fr8cffzzDcEeA2rZtW9111136/PPP1a1bNy9U6KyghJTDhg3TTz/9pI8++kjz5s3TO++8o3fffVeNGzdWv379WP8KIq/+2A0etWPHDtOpUydjt9sz/BbX8dtSu91uOnfubLZv3+7R2h555BFTpEgRc+rUqSzbrV+/3oSHh1u1ekr65ZP+cfnv+h1uvvlmc+2113q0vvS/YS7I+vbt6/FrI9A33Eff8BxP941NmzaZ4OBgExwcbGJjY83s2bPNxo0bzfbt28327dvNxo0bzezZs03fvn1NcHCwKVKkiMeuXxMXF2cCAgLM4cOHs2y3c+dOU6lSJWs99ZS+ffu6fEyfPj1D2wMHDpiAgADz0EMPeay+wtQvjPHOfmPNmjXWxfgv32dcfifKtWvXerS2xo0bm7Zt22bZxtE3AgMDTatWrXz6mkSefH8z26d98cUXLtvHxMSYhg0beqQ2R3056bsbNmwwjz32WD5WlLWVK1easWPHmj59+pju3bub7t27mz59+pixY8ealStXeryeUqVKmbi4uEzH79q1y1SqVMkEBQWZefPmeeWaRHFxcdYd3959911jt9vN/v37Xbbv16+fKV26tEfru9L6d/LkSfP222+b+vXre3zfm1OrV682M2fO9HYZPokzia4Su3fvVosWLXT8+HG1adNGHTt2VI0aNRQaGipJOn36tLZv366FCxdq4cKFatWqlX755RdVqVLFI/V169ZNH3zwgd577z0NGTIk03bpz5pwfGPnCTfeeKPT2RIOf//9d4afppw8eVIrV67UPffc46nyVLlyZZUoUcJj8ytM6Bu5Q98ovIYPH66goCCtWLFC9erVc9mmUaNG6tGjh5588knFxMRo2LBhHvkJyx133KFPP/1UH3/8sZ577rlM2znOmmjbtq327duX73U5TJ8+Pdtt/fz8tGjRIlWrVi0fK3IWExOT6U9CfYHjLI7s8vQ36Zs3b1a7du0kSX369MlyvzF79my1a9dOa9euVf369T1SX0xMjN555x0lJCQoMjLSZZuqVataZ9utXbvW5Xb8atSnTx+Xw//5558Mww4ePKg1a9YoNjY2v8tyW+PGjdW4cWOPz5ezUN3HWaie482zUH2et1MqeEbPnj1NUFCQ+f7776/Y9rvvvjNBQUHm3nvv9UBl/3P+/Hlz4cKFbLU9ceKEiY+Pz+eK3LN7924zY8YM89tvv3m7lALJ098I0zc8h76RO57uG+Hh4ebhhx/Odvv+/fub8PDwfKwIV4vMzua40sNTbr31VhMWFmZ+/fXXK7bdvHmzCQ0NNV27dvVAZZesWrXKREVFmVdfffWKbXft2mUqV67s0eXnOEsiu4+77rrLJ89GOHz4sFm2bJnZt2+fx+bZpk0bnz/zgbNQ3cdZqJ7ljbNQCwqbMZnEuyhUypQpo86dO2vmzJnZat+7d28tXLhQR44cyefKUNhdfleu7EpNTc3jSlyjb8BbfL1vFCtWTI8++qhee+21bLV/9tlnNXnyZK9eHwGFg5+fn0qWLJntb/F///13HTp0yGN9o2TJkrrnnns0efLkbLV/6KGH9MUXX+jEiRP5XFnBYLfb3TpzyVPvL3Kna9euWrlyZZZnoTps2bJFMTExiomJ8chZqKtXr9Zdd92luLi4LM9ClS6dae44C9UX172EhARt27ZN1apV89jZRG3btlVsbKzPnnmT07NQn3nmGX311Vc++f56Gz83u0qcOXNG5cqVy3b7cuXKcaCPPGGMUUhISLYv+nzo0CGXp3XnF/oGvMXX+0bdunX15ZdfasSIEVe82Pjp06f15Zdfqm7duh6q7srOnz8vPz8/BQQEeLuULKWmpurAgQOSLt3WGNI111yj5OTkbN+OOjY21qN3N0tJSbEuaJwdoaGhBea20J5gs9ncCgFRMKxevVr/+te/rhgQSZcuGN2jRw998cUXHqhMuv7667O9LlWpUkXx8fH5W1AuREZGZvpz0vzi67eLj46O5qezeYSQ6CpRvXp1LViwQGPGjJG/f9Zv+4ULF7RgwQJVr17dQ9Vdcv78eU2fPl07duzQddddp/vvv19+fn46ePCgnnnmGS1btkwBAQHq0qWLxo4dq1KlSnm0Pl9Wo0YNderUSQ8++KAaNGjg7XKcREdHKygoSH/++We22nv6YJ++UbjRN9w3cOBA3X///WrWrJmGDh2qDh06qEyZMk5tjhw5oh9//FHjxo3T3r179dJLL3msviNHjuiNN96w+sXTTz+tsLAwbd26Vf3799f69etls9nUvn17vfvuux695k9O7NixQ7Vr15bdbtfFixc9Ms+AgAB16tRJ/fr106233ur2WW35pVGjRpo9e7ZOnTrlk9cUK+gBquTdENXXQ8DsIuB1jRAV3kIAnYe8+2s3eMrbb79tbDabadu2rVm1apVJS0vL0CYtLc2sXLnStGnTxtjtdvPOO+94rL6zZ8+ahg0bWncJcdxJKikpydStW9fYbDZTsmRJ4+fnZ2w2m7n22mvNuXPnPFZfTmzbts3Y7Xbj5+fnsXmmv35D48aNzfvvv28SExM9Nv+s3HXXXcbPz8+cOXMmW+09/ftg+obn0Dec+XrfMMaYsWPHmoCAAGsZhoaGmooVK5qKFSua0NBQa3hAQIAZN26cx+o6ceKEdR0Vx52kmjRpYo4ePWoqVKhggoODTcOGDU1UVJSx2WymYsWK5uTJkx6rLyd27txpKleubKKjoz02z/T9IioqygwePNj8/fffHpv/lbz++uvGZrOZxYsXZ6v9M88849Hl98knnxibzWZq165tPvnkE5OQkJChTUJCgvn3v/9tatWqZex2u/n00089Vp9j/oMGDTJ33HGHGTFihHWHzN9//920aNHC+Pn5GX9/f9OpUyezY8cOj9Z27733Grvdnu0+6avXDdm2bZux2Wwe3af5+/ubW2+91cybN89cvHjRY/PNiWbNmpkqVaqYf/7554ptExMTTXR0tGnWrJkHKsuec+fOmZSUFG+XcUUXL160rtuFS2rVqmWqVKmS7fa+um3xBYREV4m0tDTTr18/68CwePHipl69eqZ169amdevWpl69eqZ48eLWAXf//v09Wt+bb75pbDabuf/++823335rHnroIWO3203v3r1NxYoVzfr1640xxpw5c8Y8+OCDxm63mzfeeMOjNWaX46DBZrN5bJ42m83ccMMN5rrrrrPe46JFi5o+ffqY5cuXe6wOV1566SVjs9myfavTPn36eHTZ0Tc8h77hzNf7hsP27dvNkCFDTKtWrUzp0qVNYGCgCQwMNKVLlzatWrUyQ4YM8XjAMHLkSGOz2cywYcPMb7/9Zi3LTp06mbp16zpdSHbEiBHGZrOZkSNHerRGX2az2UzXrl1N165djb+/v9U3YmJizMcff+z1oPns2bMmPj4+2wGqN/hqgGqM74eovh4CZhcBr2u+HqL6coCaE4SUGRWWANoXEBJdZX766SfTs2dPU65cOevAwfEoV66c6dmzp1m6dKnH62rSpIm57rrrMgyz2+3ms88+cxp+4cIFU7FiRXP99dd7skSflv5uA7/88ovp37+/CQ0NtQ4matasaV599VWXO+r8duTIEbNs2TKvzDsn6BuFE32jcKpfv75p2bKl0zDHmX7z58/P0L5WrVqmSZMmnirP56XvFwcPHjTjxo0z1apVs/pFiRIlzGOPPWY2btzo5Up9my8GqMb4fohaEEJAX+XrAa+Dr4aovh6g5gQhZUaFJYD2BYREV7GkpCRz8OBBc/DgQZOUlOTVWiIiIswTTzzhNOypp54ydrvd5Qeo2NhYU7JkSU+V5/Nc3ZIyKSnJTJ8+3dxwww3WRj0wMNDcfvvtZsGCBS5/VoVL6BuFB33DuyZOnJijU7+zq0SJEuapp55yGvbMM88Yu91ujh8/nqF9//79TVhYWJ7XUVBldhvjZcuWmfvuu8+EhIRYfaNhw4Zm0qRJ1rftBUV+rXsFASFq4VWQAl5fDFF9PUD1db4eUhJA5x27t6+JBO8JCQlR2bJlVbZsWYWEhFyx/cyZM9WuXbt8qSUpKSnDRe4cF6u8/EKpkhQVFeXRu/wURCEhIerbt69Wrlypbdu26ZlnnlHJkiU1b948de3aVZUrV/Z2idmWn+ueK/SNwo2+4TmnTp3Snj178vx5L1y4oODgYKdhjn5SsmTJDO0jIiJ07ty5PK8jO1atWqVx48apb9++uv3223X77berb9++GjdunFatWuWVmjITExOjf//73zp06JDeffddNWjQQFu2bNGAAQNUvnx5b5eXI/m17uWVt956S1WrVs2X596zZ4+aN2/uNKxx48aSpJYtW2Zo37p1a23fvj1faskv+bn8CoqyZcvqhRde0I4dO7R06VLde++9SklJ0fvvv6+mTZuqUaNGeu+995SYmOiV+qpXr66XXnpJq1ev1pEjR5ScnKzk5GQdOXJEq1ev1ksvvaQaNWp4tKavv/5aLVq00OjRo3XddddpyJAhiomJ0Y8//qhXXnnF6VbyI0eOVM2aNTV//nyP1ujrmjRpom+//VZ79+7V2LFjVaVKFa1YsUJ9+/ZV2bJl9fjjj2vTpk1eqa1IkSKqXLmyihYt6pX5Fybc3QzZFh8fr+XLl+fLc0dEROjIkSNOw4oUKeLyYF+Sjh8/7rU7nuzfv1/Lly/X9u3brR1vWFiYatSooRtvvDHbt7P2pGuuuUavvvqqxo8fr//85z+aOnWqFi5c6O2ysi0/1728QN+4hL7heb7eN/JL6dKldfjwYadh5cqVU6NGjVy2T0hIUHh4uCdKs6xdu1b9+/fXn3/+KWOMyzY2m0116tTRlClT1KJFC4/Wl5XQ0FA99thjeuyxx/Trr7/qo48+0qeffurtsgqV/AyxClKI6i5PhICrVq3KdJ8WExOjG264IV/nnxMxMTGKiYnRpEmT9Mknn2jq1KnavHmzBgwYoEGDBunMmTPeLjFH3nrrLb311lvatWtXnj7vnj171LdvX6dhjRs31ooVKzINUD///PM8raGwcISUL7zwgpYvX66PPvpIX331ld5//31NnjxZ9evXV79+/dSrVy+FhYV5u9xsy691r6AhJIJPqFmzprZu3eo0bNCgQRo0aJDL9rt27fL4B86dO3dqwIAB+vHHHyUpw0G/zWaTJHXs2FFvv/22x2+Tnh1+fn7q3r27unfvzi0fCwj6hmfQNwqWunXrasuWLU7D+vfvr/79+7tsv337do+edbB582brDK8+ffqoY8eOqlGjhkJDQyVdui369u3btXDhQs2ePVvt2rXT2rVrVb9+fY/VmF3169fXO++8o9dff93bpSCbCkKI6ssIeL2Ps1AJKb3F189C9RRCIviEli1batKkSUpJSVFgYGCWbU+ePKlVq1bp4Ycf9lB10u7du9WiRQsdP35cbdq0yfKAf+HChWrVqpV++eUXValSxWM15lTZsmW9XQKygb7hefQN33fzzTdr2rRp2eoXBw8e1M8//6xnnnnGQ9VJw4cPV1BQkFasWKF69eq5bNOoUSP16NFDTz75pGJiYjRs2DB9++23Hqsxp4KCgrxdArLJ10NUX0bAW7gVhACVkBK+gJAIPmH06NEaPXp0ttqeOnVKkydP1vXXX5/PVf3P0KFD9c8//+i7775Tp06dMm03ePBgff/997r99tv14osvatasWR6pb+nSpYqOjvbIvOBZ9I3coW8UTgMHDtTAgQOz1dZut2vRokWqVatWPlf1P6tXr9a//vWvTAOi9Bo0aKAePXroiy++8EBll+zevdtrP0tF/vP1ENWXEfAWbr4eoBJSwlcQEqHAqVKlisfPQli8eLF69OiR5Ydgh86dO+uee+7x6HVNYmJiPDYv+C76Rkb0DURFRSkqKsqj80xJSclwwfmshIaGKiUlJR8rclaQLs6OnPP1ENWXEfAWbr4eoBJSwlcQEgHZcObMGZUrVy7b7cuVK1egfn8LuIu+AWRUt25dffnllxoxYoSKFSuWZdvTp0/ryy+/VN26dT1UHfA/3ghRfRkBb+Hm6wEqISV8hd3bBQAFQfXq1bVgwQJdvHjxim0vXLigBQsW+OTFeYG8Rt+Ar0tNTdXevXu1d+9ej81z4MCBio+PV7NmzTRr1qwMdyiUpCNHjuiTTz5R8+bNtXfvXj311FMeqy8nvLH8AG9xBLzZ+TKDgLdwi4qKUkxMjCIjIz02z4IQUhakO5XBfYREKHD++usv+fn5yd/fcyfC9e/fX1u3btXNN9+s1atXu7yQnDFGq1at0s0336z//ve/euihhzxWX054Y/nBM+gbuUPfyHvGmEwvvOkpO3bsUHR0tEevK9GrVy+NGTNGO3bsUO/evVW2bFmFhYWpUqVKqlSpksLCwlS2bFn16dNHO3fu1JgxY9SzZ0+P1ZcT3lh+ecEX1r2CgBDQGQEvvImQEr6CI2EUSJ4++BswYIB+++03TZ06VTfeeKOKFi2qKlWqWGl6YmKidu/eraSkJBlj1K9fPw0YMMBj9eUUB8+FF30jd+gbeSs2NlZt27b1ag0BAQGqVKmSbDabR+c7dOhQ9ejRQ9OmTbNuY5yQkCDp0m2Mr732WsXExCg2NlY1atTwaG054a3ll1u+sO5lxVe2NTt27FDt2rVlt9uzdUaor8iv5derVy/Fx8dr1KhR6t27tySpWLFiTvs0xwd4Pz8/nw94C+J76ytSU1N14MABSVKlSpU8Ms+BAwfq/vvvV7NmzTR06FB16NBBZcqUcWpz5MgR/fjjjxo3bpz27t2rl156ySO15ZQ3lh/yjs34wh4KBcKvv/6qLVu2qE+fPt4uxWuWLl2qKVOmaPny5Tp06JDTuLJlyyomJkYPPfSQ2rRp450CCylfX/d8vT5PoG94h6fWvf3791tBR2JioqRLQUeNGjV04403qmLFivk6f1y9Cuu6t2fPHsXHx3v94vq7du1Su3btZLPZtHv3bq/WkhP5vfx27NjhFPBevu4VhIC3oL63kjRy5EiNHj1aaWlpXqvhr7/+8krINm7cOI0aNUqpqamSsg4pR44cqRdeeMFjteWEt5ZfbvnCuucLCImuQsnJydqwYYPLnV6TJk24Cn02nT171mn5hYSEeLki3+fr656v11dQ0DdyzlfXvZ07d2rAgAH68ccfJSnDN/eOs0s6duyot99+m+tN5aG33npLb731lnbt2uXtUryioKx7hTXE8hSWH1zxhRDVmyEbIaX3+MK65wsIia4ix48f14svvqhZs2YpKSlJ0v8OuhwHW0WLFtV9992nMWPGqFSpUl6rtTCaOXOmZs6cqZ9++snbpXicr697vl5fYUff8M11b/fu3WrWrJmOHz+uNm3aqGPHjqpRo4ZCQ0MlXboewvbt27Vw4UItX75cERER+uWXX1SlShWP1ViYjRo1SqNHj7a+Tb6aFIR1r6CEWL6qMC+/qz3gTY8QEN7Cupc7XJPoKnH06FG1atVKO3fuVNWqVdWhQweXB1yLFi3S5MmTtWjRIq1Zs0alS5f2eK2FtVPHx8dr+fLl+T4fX1t+vr7u+Xp96fnae5tX6Bu+ue4NHTpU//zzj7777jt16tQp03aDBw/W999/r9tvv90KvDxp1apVWX7besMNN3i0noLGF5efr697u3fvVosWLbIVYi1cuFCtWrUiQE2nsC+/U6dOac+ePd4uw6sKcwjo6672kJJ1L48YXBUeeughY7fbzeTJk6/Y9v333zd2u908/PDDHqjsf3bs2GE6depk7Ha7sdvtxmazOT0cwzt37my2b9/u0drywsiRI43dbs+35/fV5efr656v12eM7763eYW+4ZvrXunSpU3v3r2z3f7+++83pUuXzseKnK1Zs8bUrVv3/9q787ioyvZ/4J9zWEWBEBfERwVzVBQUsVyTRVMk/SJiafgoy1dTy10TNEUWK8EyS201N9TfU7nkUqaJIQIaVuaSqbEI4pIpBrIJ5Fy/P/wyjyMDgjpn7sHr/XrxejVnbmc+XeceOHPNmfvo3Kf37ltXV1c6evSoYrkeF32/LkSun+hzLygoiCwsLOi777574Ni9e/eShYUFjR07VoFk1aWkpNCbb75JISEhFBAQQAEBARQSEkJvvvkmpaSkGCSTMdXvYej7tVtFxH1LRJSdnU3NmjUjSZLIx8eH4uLiaPv27XTgwAE6cOAAbd++neLi4sjb25skSaLmzZtTdna2wfI2NErNPxHx3Ht8+EyiJ8Q333yDwMBATJ48+YFjp0yZgsTEROzZsweffPKJAuka/qdK+iZy/USfe6LnE3nfGgOR6yf63CsuLoajo2Odxzs6Otbpsr2Pw6+//oqBAwcCAEJCQmrdr1988QUGDhyIo0ePonv37orkE53o9RN57gFAYmIixowZU+tZTlX8/PwwevRo7Nu3T4Fk/3X06FG88sorOHv2bI1XAZMkCV26dMGaNWvQp08fxbIZQ/1EJvK+BcQ/E7CKiGdRGhMR62csc88oGLpLxZRhaWlJCxYsqPP4+fPnk6WlpR4TaWvonyoR6bezL3L9RJ97oucTed8+LvzaqBul556bmxu5ublRZWXlA8dWVFSQq6srubm5KZCMaPjw4WRra0snT5584Nhff/2VbGxs6H/+538USPb46PN1IXr9RJ57RESNGjWi+fPn13l8REQENWrUSI+JtB0/fpwsLS3J0tKSwsLC6IsvvqBffvmFMjIyKCMjg3755Rf64osvKDQ0lCwtLalRo0Z04sQJxfKJXr9Hpc/Xruj7lkj8MwFFPovyceCzUMWde8aEm0RPCJVKRf369avz+L59+5JKpdJjIm1Pwotan7+0Ra6f6HNP9Hwi79vHhV8bdaP03Fu5cqXmlO3U1FRSq9XVxqjVakpJSSFvb2+SZZlWrVqlSDY7O7t6ffXulVdeITs7Oz0mevz0+boQvX4izz0i8ZtY3AQ0rCe5wUskdhPQGJpsj+pJblKKPPeMjWzoM5mYMsaNG4ejR49i/PjxyMvLq3FcXl4exo0bh/T0dIwfP16xfKKfWi46kesn+twTPZ/I+9YYiFw/0efetGnTMGHCBBw6dAienp6wtbVF9+7d4enpCU9PT3Tv3h22trbw8vJCcnIyJkyYgGnTpimSraKiAtbW1nUeb2Njg4qKCj0mMi6i10/kuQcAr7zyCn777TcMGTIEaWlpOr/yQ0RITU3FkCFD8Pvvv2PSpEmK5UtLS8PLL7+Mbt26PXCsu7s7xowZg9TUVAWS3SV6/UQm+r4FgA4dOuDbb7/FP//888CxlZWV+PbbbxVbPHjx4sWwsLBAeno61q1bhzFjxsDDwwMdOnRAhw4d4OHhgTFjxmD9+vU4evQozMzMEBkZqUg2YyB6/USee0bHsD0qppTy8nLy9fXVnALo4uJC/v7+NH78eBo/fjz5+/uTi4uL5tTBoUOHUkVFhWL5GvqnSkT67eyLXD/R557o+UTet48LvzbEnHtVfvjhBwoKCiJHR8dqp5Q7OjpSUFAQJSUlKZqpV69e5OzsTEVFRQ8cW1hYSE5OTtSrVy8Fkj0++nxdGEv9RJx7RHfPYpo4caLmtWttbU3dunWjAQMG0IABA6hbt25kbW2tee2+8soriuZr3Lgxvf7663UeP3fuXGrcuLEeE2kTvX6PSp+vXdH3LZHYZwKKfhbl48BnoYo594wNN4meIGq1mtatW0d9+/YlExOTagdcJiYm1LdvX1q/fr3OF5U+PQkvan3+0ha9fiLPPdHzib5vHwd+bYg593QpKSmhK1eu0JUrV6ikpMRgOTZv3kySJJGLiwtt3ryZrl27Vm3MtWvXaNOmTdS5c2eSZZn+3//7fwZI+vCioqJIkiS9PLYx1k+UuXcvUZtY3AQ0rCe9wStyE9AYmmyP6kluUoo894wNN4meULdv36YzZ87QkSNH6MiRI3TmzBkqKyszWJ4n4UWtz1/axlQ/0ebe/UTLZ0z79mHxa+Mu0eae6N58800yMzMjWZZJlmWysbGhNm3aUJs2bcjGxkaz3czMjN566y1Dx623nJwcOnTokN4ev6HXT2kiNbG4CWhY3OC9S8QmoDE02R7Vk96kJBJz7hkbbhIxoTTkF/WJEydow4YNen2Ohly/J11D3rf82mAPKyMjgxYsWED9+vWj5s2bk7m5OZmbm1Pz5s2pX79+tGDBAvrjjz8MmjEvL482b95MUVFRNGvWLJo1axZFRUXR5s2b6eLFiwbNZgz1Yw+Hm4CGww3e6kRpAhpTk+1hcZNSmyhzz9hIRDpWi2NMAKWlpSgsLAQA2NrawsrKysCJ7iovL8fPP/+MjIwMrXwqlQrPPPMMLCwsDJzwLlHrxx6dqPuWXxtPrvz8fHz44YeQJIkX+fw/WVlZmDZtGr7//nsAqLY4ryRJAABfX1+sXLmSF898SDz3apaZmYl169YhOTlZ5+9lLy8vhIWFQaVSGTipmC5dulRj7Tw9PdGmTRuDZeN9+/DeeustxMTE4M6dOwCAJk2awNbWFgBQWFiouXiFiYkJoqOj8cYbbxgs68PIzc1FTk4OvLy89PL4Db1+7C5uErFqbt26hZ07dwIAgoODDRumHjZu3IiNGzfihx9+0Mvj5+fnY9GiRdiyZQtKSkoA/Pegv+pgv3Hjxhg3bhyWLFkCe3t7veTQF33Xry5En3ui56sJvzYeDb82Huz8+fNwcXGBJEmaA0fRffDBB/jggw+QnZ392B/7woUL6NWrF/Lz8+Ht7Q1fX1+oVCrY2NgAuLs/MzIysG/fPiQnJ6NZs2ZIT0+Hs7PzY8+iL/qsX32IPve4ifVoDFE/bvA2fMbQZOMmJTMkbhKxakQ/4KpJTEwMYmNj9ZL5+vXr6NevH7KystC+fXsMHjxY5wH/gQMHkJ2djaeffhpHjhxB8+bNH3sWfdFn/epK9Lkner6a8Gvj0fBr48Hy8/OxatUqSJKEqKgoQ8epE33u17Fjx2LHjh3YuXMnhg4dWuvY7777DiNHjsSoUaOwZcuWx55FX0R4XQDizz3RX7s1eVKbgNzgVRY3UavjJqUyeO7VztTQAZh4bG1tERwcrPklxIBFixYhOzsbH3/8MSZPnlzr2E8++QRTp05FZGQkPvnkE4USNgyizz3R8xkCvzaUIfrcs7e3R3R0tKFjCCMxMRFjxox5YIMIAPz8/DB69Gjs27dPgWQNj+hzr1mzZli8eLGwr92aFBQUIDc319AxFK/fwoULUVRUhL1799b6+o2IiNA0eKvOpDUWouxbALhx4waio6ON6o26vs9C7dOnT52alPv27UO/fv24SfmQjHHuKYmbRKwaBwcHbNiwwdAxhPLNN98gMDDwgW+CAWDKlClITEzEnj17+I1wPYk+90TPZwj82lAGzz3jUlxcDEdHxzqPd3R01KzjwBoW0ZtYolO6ftzgVZYxNlH12WTjJqVyjHHuKUk2dADGjMHNmzfr9b1alUqFmzdv6jERY2Lg18aT69atW7h27RrUarWhowinQ4cO+Pbbb/HPP/88cGxlZSW+/fZb/spAPfDcY/rCDV5lVTUBRfyqqCE8TJPywIEDCiRreHju1Y6bRIzVQZs2bZCcnFzn8cnJyQZdUI4xpfBro+G6fPkyjhw5Uu2N+KeffoqOHTvCzs4Ojo6OeOqppxAaGoq//vrLQEnF88orr+C3337DkCFDkJaWVm1NCeDuOhOpqakYMmQIfv/9d0yaNMkAScXUEOZeaWkpKisrDR3DaBmqftzgZYbETUomCm4SPSFKSkpw5cqVatv379+PIUOGoGnTpmjUqBFcXFwQHR2NsrIyA6QU17hx43D06FGMHz8eeXl5NY7Ly8vDuHHjkJ6ejvHjxyuYUFzGNPdu3ryJW7du1Trm4sWLOHz4sEKJxMevjcdDxLk3b948/Pvf/4Ys//dQITw8HK+99ppmEfKePXvC1NQUCQkJ6Nu3L27cuKFYPpFNmzYNEyZMwKFDh+Dp6QlbW1t0794dnp6e8PT0RPfu3WFrawsvLy8kJydjwoQJmDZtmqFjC8MY5t7vv/+OiRMnYsSIEVi1apWmofXNN9+gU6dOsLa2hpWVFby8vPDLL78oms0YiFo/bvAqY/fu3Th16pShYwiHm5T6x3Ovjog9EcLCwsjBwUFr24oVK0iWZZIkiSwtLal58+YkSRLJskzPPvsslZSUGCjtw4mOjiZZlvXy2OXl5eTr66upj4uLC/n7+9P48eNp/Pjx5O/vTy4uLpp6Dh06lCoqKvSSRV/0VT9jmHspKSnk6upKsiyTLMvUp08fOnz4sM6x+pxn+sKvjUejz/qJPPecnZ0pJCREczszM5NMTEyoS5cu9Ntvv2m2V1ZWUmxsLEmSRDNmzFAs36NSop4//PADBQUFkaOjI0mSpPXj6OhIQUFBlJSUpNcM+qLP+ok+97Kzs8nW1lazL2VZpunTp9ORI0fIzMyMrKysyMPDg9q0aUOSJFGTJk3o3LlziuV7HPS5f0Wun1qtpokTJ2pyWVtbU7du3WjAgAE0YMAA6tatG1lbW2v+pr3yyiuK5HqcRDiOkSSJJk2aZNAMD0uf9Vu5ciVJkkQ+Pj6UmppKarW62hi1Wk0pKSnk7e1NsizTqlWr9JJFXww9/4x57imJF65+QqSlpWHgwIGa21euXMH8+fPRqlUrfP755/D19YUkSbhx4wYWLFiAtWvXYunSpViyZIkBU4vD3Nwc3333HTZs2IA1a9bg2LFjOHfunNYYWZbRu3dvTJo0CSEhIbwQ2v8Rfe6dP38evr6+KCsrQ8eOHWFmZob09HQMHDgQb775JiIiIhTJYaz4tfHwRJ97V69e1TrtPTExEUSEzz77DF27dtVsNzU1RWRkJJKTk7Fr1y588MEHhogrJB8fH/j4+AC4+/WZwsJCAHevVmdlZWXIaEITfe7Fx8ejqKgI7733HoYPH449e/Zg/vz5OHnyJNzd3bFnzx60bNkSwN2rOr722mtYunQpLz7/f0SunyRJWLNmDcaOHYs1a9YgOTkZp0+f1hrTqlUrDB8+HJMmTYK3t7feMxmbY8eO1WncX3/9pTW2V69e+opkNKZNm4ZTp05h7dq18PT0ROPGjeHs7AxbW1sAQGFhIS5cuICSkhIQESZOnMhnod6D597jw02iJ8SlS5cQGBioub1//35UVlZizZo1WoujNWvWDGvWrMHp06fx5ZdfcpPoHpIkISwsDGFhYSgvL0dWVpbWAX/79u1haWlp4JTiEX3uvf322ygrK8MXX3yB0aNHA7j7RyYkJARvvPEGiouL+XXwAPzaeDiiz73GjRujqKhIc7ugoAAA0KNHD53je/TogdTUVCWiPRZEpPOrJPpiZWXVoBpD+qyf6HPv0KFD8PX1xaxZswAAs2fPxv79+3HgwAGkpKRoGhzA3as6btu2DT/88INi+URnDPXjBu/D69OnzwM/DJIkCbt378bu3bs12+7cuaPvaMLjJuWj4bn3+HCT6Alhbm6O8vJyze1r164BADw9PXWOf+6557B69WpFsj0uAQEBcHJyUuS5LCws0KVLF0WeSyn6qp/ocy8pKQkvvPCC5k06cPcThfT0dAQEBODtt9/GnTt38PbbbyuW6XHj18aj0Vf9RJ97PXr0wP79+0FEkCRJcxW7c+fOwcPDo9r4c+fOwd7eXumYDy0sLEzzJpDVnz7rJ/rcy8vLw8iRI7W2ubu748CBA3B3d6823sPDAykpKQqlezz02QQ0tvpxg7f+GjdujICAAJiYmOh8/oSEBKhUKvTr10+vOYwVNykfHs+9x4ObRE8IV1dXJCUlaW5XXV3o0qVL6NSpU7Xxly5dgo2NjWL57lVeXo6ff/4ZGRkZWr8UVSoVnnnmGVhYWOj8d927d0f37t2VjCok0eon+ty7du0a3Nzcqm23sbHB3r174e/vj/j4eKjVasTFxSmWSxfR9q2xEa1+os+91157DaNGjcLMmTPx/vvvY/jw4VCpVJg6dSp27typ9Wn/2rVrsXfvXoSGhiqe89KlS0hOTta5Xz09PWu8ml67du3Qrl07JaPqlJ+fjw8//BCSJCEyMlLx5xexfqLPPVtb22oLzVfdLigoqPYmrqCgAObm5orlexz02QR8EuonMn03yJcuXYro6GhkZmZi3bp16Ny5c7UxCQkJ8PLywmeffaa3HPrCZ6E+Gn3Wr6HPPUUpvAYSM5C1a9eSJEn0zjvvEBFRUVEROTo60rBhw6isrExr7IEDB8jMzIxeeuklRTPeuHGDpkyZolkQsGpRwKrFA6sWEHz11Vfpxo0bimarj8LCQtq4cSNt3LhR0ecVtX6izz1HR0d67bXXarz/9u3bNHjwYJJlmebNm2eQBfdE3bf1xa8NbcYw9yZNmkSSJJFKpaLXX3+dwsPDydTUlBo3bkxeXl40YsQI6tixI8myTA4ODnTp0iXFsmVmZtLQoUOr7dP7962fnx9lZGQolqu+zp07p8mrJNHrJ/Lc8/b2ptatW1NRUREREd26dYscHR3JxsaGli1bpjW2sLCQWrZsST179lQs373y8vJo8+bNFBUVRbNmzaJZs2ZRVFQUbd68mS5evGiQTMZUv9rcuHGDYmJiKDY21iDPL+K+rXL69Gny8PCgRo0aUXx8fLUFmI110W8iopycHDp06JChYxgtfdevIc89JUlECrZCmcEQEfz9/bF37154enrixRdfxN9//42YmBi0bNkSzz//PJ566in8/vvvOHjwIKysrHDs2DG4uLgoku/69evo168fsrKy0L59ewwePBgqlUpzRsmtW7eQkZGBAwcOaC5/e+TIETRv3lyRfPVx/vx5uLi4QJIkxb7jKnL9RJ97Xl5eKCgowMmTJ2scc/v2bfj7++PgwYNwcHDAn3/+yfv2IfBrQ5voc6/KBx98gNjYWPz999+QJEnnJ4BDhgzBRx99hPbt2yuS6cKFC+jVqxfy8/Ph7e0NX19fnft13759SE5ORrNmzZCeng5nZ2dF8tVHfn4+Vq1aBUmSEBUVpchzGkv9RJx7ALBjxw68+OKLcHZ2Rr9+/ZCWloaLFy/iP//5D8aNG4cZM2bA29sb169fx3vvvYczZ85g6dKlCA8PVyxjVlYWpk2bhu+//x4AqtWuat0OX19frFy5UtHLaBtD/erCEH/TALH37b3++ecfvPnmm3j77bfh4eGB9evXa47tZFnGxIkTDXo2x8OeRSkKYz0LVQmizz2jYLj+FFNaeXk5zZo1i8zMzHR+ml713126dKH09HRFs02aNIlkWaZPPvnkgWM//vhjkmWZJk+erECy+rt69SqFhIRQaGioYs8pev1EnntvvfUWybJMJ06cqHVcWVkZDR48WPFP/EXft/XBrw1tos+9+zPs2rWLFi9eTFOmTKFJkybRvHnz6PPPP6esrCzF8wQFBZGFhQV99913Dxy7d+9esrCwoLFjxyqQzDgYU/1Em3tV5syZQyYmJiRJEllYWND7779PREQxMTFaZ2JJkkReXl5UUVGhWLbs7Gxq1qyZ5lLacXFxtH37djpw4AAdOHCAtm/fTnFxceTt7U2SJFHz5s0pOztbsXxEYtevrm7cuEFRUVEUHR2t2HMaw7693y+//EJdu3YlS0tLeuutt+iff/4x6Nkcop9FWVd8FuqDiTb3jAmfSfQEunLlCrZu3Yrjx4/jr7/+glqthq2tLTp16oSBAwfCy8sLsiwrmql169bo168ftm7dWqfxL774Io4ePYrLly/rOZlxMJb6iTj3MjMzERkZCT8/PwQHB9c6try8HJMnT0Zubq7WOkv6ZCz7VlQi10/0uSeyFi1awM/PDxs3bqzT+ODgYOzbtw9//fWXnpMZB67f43Ht2jXk5ORApVKhadOmmu2JiYnYt28fKioq0L9/f7z00kuK/m0bO3YsduzYgZ07d2pdRVSX7777DiNHjsSoUaOwZcsWhRLeJWr9RGYs+/Z+FRUViIyMxPLly9G9e3ecOHECEyZMUPxsDmM5i7Iu+CzUuhFl7hkdQ3epGCMisrS0pAULFtR5/Pz588nS0lKPiYwL16/h4n37aLh+DVOjRo1o/vz5dR4fERFBjRo10mOi+iksLKQ///yT7ty5Y5DnN/b6sdo1b96cgoOD6zx+/Pjx1Lx5cz0mYo+Lse/bI0eOkEqlMtjZHMZ0FqWIjLl+hp57xoavbsaE0KZNGyQnJ9d5fHJysvDfFVYS16/h4n37aLh+j0dOTg6SkpLwxx9/oKCgALIso0WLFnjmmWcwePBgxa881KFDB3z77bdYsmQJTE1rP5SprKzEt99+q+i6HJcvX0Zubi769OmjdQbEp59+iuXLlyMrKwvA3Uv1BgYGYtmyZWjRooVi+USv371Em3vGoLi4GI6OjnUe7+joiOLiYj0malhKS0thZmYGMzMzxZ/b2Pdt3759cfbsWRQXF9d4RVF9SkxMxJgxYx54FhYA+Pn5YfTo0di3b58CyYyDMdfP0HPP6Bi6S8XEUFRURNeuXTPYp5pV30EfN25crVdkuHjxIv373/8mWZYVvZpEcXExXb58udr2ffv20eDBg8nOzo4sLS2pc+fOFBUVRaWlpYplIxK/frUx9Nyrr/z8fMrNzVXs+Yxp3+bn51NhYWGtY3Jzcyk5OVmhRMZVvwdReu4R3V3/ws/PT7PGwL0/VWsPNG/enFavXq1orpUrV2rW5EhNTa129RIiIrVaTSkpKeTt7U2yLNOqVasUyxcUFEROTk5a2+bNm0eyLJOJiQmpVCp69tlnyc7OjiRJovbt29P169cVyyd6/YjEnXt1ZWdnR7NmzTLIc7u5uZGbmxtVVlY+cGxFRQW5urqSm5ubAsnqzpD1O3PmDE2YMIH8/f1p5cqVmuOTPXv2aK6oZ2pqSp6envTzzz8rmq0h7FtDaghnUe7atYtOnjxpkOduCPVjdcNNoidEbm6uzjdve/bsoe7du2tdBnrixIl08+ZNRfOVl5eTr6+v5sDPxcWF/P39afz48TR+/Hjy9/cnFxcXzcHh0KFDFV3EMCwsjBwcHLS2rVixQpPH0tKSmjdvrsn/7LPPUklJiWL5RK6f6HOvvkJDQ8nExESx5xN531ZJSUkhV1dXzb7s06cPHT58WOdYpS/jbgz1qyul515eXh45ODiQJEnUo0cPCgwMpB49epAkSeTu7k7vvvsuhYSEkL29PcmyTFOmTFEsm1qtpokTJ2r2q7W1NXXr1o0GDBhAAwYMoG7dupG1tbVmvyp9armzszOFhIRobmdmZpKJiQl16dKFfvvtN832yspKio2NJUmSaMaMGYrlE71+Is+9ujLkVxqMoQn4IIaqX3Z2Ntna2motwjt9+nQ6cuQImZmZkZWVFXl4eFCbNm1IkiRq0qQJnTt3TrF8DWHfGlJDaLJJkkSTJk0yyHM3hPqxuuEm0RNC16fjCQkJZGJiQrIsk0qlor59+5KNjY3mIOz27duKZlSr1bRu3Trq27ev5ooX9/6YmJhQ3759af369Tr/KOpTx44dtb5Te/nyZbKwsKDWrVvTd999p8lz/fp1zYH3okWLFM0oav2MYe7VR2hoqOJXkhB13xLdvbqGlZUVSZJEnTp1IldXV5IkiUxNTSkuLq7aeKWbRERi168+lJ57YWFhJMsybdu2TWv79u3bycTEhNatW0dEd88GfOmll0iWZfr6668Vy0dE9MMPP1BQUBA5OjpW26+Ojo4UFBRESUlJimYiqr4W1ieffEKyLFNqaqrO8YMGDaJ27doplO6/RK2f6HPPxcXlgT+SJJGdnZ3mdpcuXRTLJ3oTUOT6TZ48mWRZphUrVlBGRga99957ZG5uTp6envTss8/Sn3/+qRn78ccfkyRJWg1hfRN931b566+/aNq0aeTm5kY9evSg+fPnU35+vs6x0dHRin0AInqTLT09/YE/kiRRQECA1jaliF4/InHnnrHhJtETQpIkiomJ0dwuLi4mOzs7sre3p4MHD2q2l5SUUFBQEMmyTMuXLzdEVCIiun37Np05c4aOHDlCR44coTNnzlBZWZnB8lhZWWmdXrlu3TqSZZn27t2rc3zv3r1JpVIpFa8akepnbHPvQQzRJLqXSPuWiCg4OJgkSaIvv/xSsy09PZ06d+5MsixXa5Yaokl0L9HqVx9Kzz1HR0caOXKkzvsCAwPJ1dVVc7u8vJzatWtHzz//vFLxqikpKaErV67QlStXFD2TUxd7e3uaNm2a5nZcXBzJslxjrtdff50sLCyUiqeTSPUTfe5VvUG/v7F2/2Wg79+mNFGbgCLXr1OnTuTn56e1zdfXl2RZprS0tGrjBw0aRG3atFEk271E3bdERAUFBdShQ4dq+7BVq1Y6v26u5HGB6E22ey8hX58fpYheP5HnnrHhhaufUImJiSgoKMDq1asxcOBAzXYrKyusW7cOqamp2Lp1K+bMmWOQfBYWFujSpYtBnlsXc3NzlJeXa25fu3YNAODp6alz/HPPPYfVq1crkk0X0ep3L9HmXvv27es1/saNG3pKUjei7dukpCS88MILGD16tGZbr169kJ6ejoCAALz99tu4c+cO3n77bQOm/C+R6if63Ltx4wZUKpXO+zp06IC9e/dqbpubm2PYsGH44osvlIpXjZWVFaysrAz2/Pfq0aMH9u/fDyKCJEmaOp47dw4eHh7Vxp87dw729vZKx9QiUv1En3vdunVDdnY24uPj8eqrr+ocI8syJk6caNDLLPv4+MDHxwfA3cWWCwsLAQC2trYG3dci1y8vLw8jR47U2ubu7o4DBw7A3d292ngPDw+kpKQolO6/RN23ALB06VJkZWXh1VdfRWRkJMzMzLB27VrExsZi6NCh2L59O/z8/AySTZIkrFmzBmPHjsWaNWuQnJyM06dPa41p1aoVhg8fjkmTJsHb21vxjI0bN0ZAQABMTEyq3UdESEhIgEqlQr9+/RTPJnr9RJ57xoabRE+ojIwMSJKE4cOHV7vP0tISzz//PHbs2GGAZGJydXVFUlKS5nbV1Y8uXbqETp06VRt/6dIl2NjYKJbPmIg293JyciDLcp2vUlJZWannRMbl2rVrcHNzq7bdxsYGe/fuhb+/P+Lj46FWqxEXF2eAhOISfe45ODjg1KlTOu87deoUbG1ttbbZ2NigtLRUiWjCe+211zBq1CjMnDkT77//PoYPHw6VSoWpU6di586daNmypWbs2rVrsXfvXoSGhhousGBEn3s//fQToqOjMWPGDGzbtg1r166Fk5OTYs//MERqAopcP1tbW9y6dUtrW9XtgoKCajUsKCgw+BX2RNq3ALBr1y54eHjgww8/1GwLDw/HkCFDMGzYMAQGBmLr1q06jwOVImqTbenSpYiOjkZmZibWrVuHzp07VxuTkJAALy8vbkDrYAxzz1jIDx7CGiK1Wg3g7oGYLi1btkRZWZmSkers1q1bSEhIQEJCgmLPGRYWhpMnT+Ldd98FAIwYMQIODg6YO3cubt++rTU2MTERO3bsMMinD3VhiPrdS7S55+joiK5du6KsrKxOP+PGjVMsW30ZYt82a9as2gF1FUtLS+zZsweDBg3CO++8g/DwcMVyPQyl6yf63Bs6dCi+//57rYMtAPjoo4/w/fffaw4Qq+Tl5Wk1P0SRn5+P2NhYLFmyRLHnHDlyJF555RWsXr0anTt3xsKFCzFixAj8/PPPePrpp+Ht7Y2AgAB06tQJkyZNQosWLRAbG6tYvvowRP1En3tmZmZ46623kJaWhitXrsDNzc2gZw8bG5Hr16lTJ+zatUtz2fiioiLs2rULTZo0wZYtW7TG3rp1C7t379b5YeGTLCcnR+cxsLu7O1JSUtCiRQu8+OKL2L17t/LhdLCyskKrVq3QqlUrgzfbIiIi8NNPP6G8vBweHh5YtmwZiMigmR5EpPoZ29wTmoG/7sYUIkkShYWFUXJyMiUnJ1NsbCzJskw5OTk6x4eGhlLLli0VTlk3586d03wXVilqtZqGDx9OsiyTt7c3rV69mpYsWUKmpqbUunVrCgkJoZkzZ9LgwYNJlmVq0qQJ/f7774rlqw+l6yf63PP39yczM7M6L5Zt6DWJamOI14anpyd169at1jFlZWWa14ajoyPX7/+IPvcuXbpELVq0IFmWqVWrVtSnTx9q1aoVybJMNjY2dP78ec3Yf/75h1q0aEFjxoxRLF9dGeJ1UeX999+npk2b1roGi6+vL2VlZSmera4MUT9jmnu3b9+m2bNnk4mJCXl6elJmZiYRGfbqZvVx48YNiomJqXaBCaWIVr/t27eTJEnUvn17GjduHDk7O5OJiQl99dVXZG5uTq+//jp98803tH79enJzcyNZlik+Pl7xnHVhqH1rb29Pc+bMqfH+7Oxsatu2LVlYWNDOnTt5XRgdKisrKSoqiszMzKh3795a7ymM5XeLIfDce3y4SfSEuH8htKrbGzZs0Dm+d+/e1Lt3b4VT1s3Vq1cpJCSEQkNDFX3e8vJymjVrFpmZmWnV8f6D/y5duih6pYH6Urp+os+9qKgokiSpzvssJCTEIAuQ1oUhXhtvvfUWybJMJ06cqHVcVaPIUG/W60Lp+hnD3MvKyqJhw4aRmZmZ5qp1AwcOpJMnT2qNKysro9TUVLp48aKi+erixo0bFBUVRdHR0QZ5/rKyMtq1axctXryYpkyZQpMmTaJ58+bR559/LnRzqIqh6mdscy85OZnat29PVlZWtHz5cqN5I2fIJuq9RKrfnDlzNFfCtLCwoPfff5+IiGJiYrSOaSRJIi8vL6qoqDBIzgcx1L7t2bMn+fj41DomKyuL2rZtS+bm5tSvXz+Dzz9dDN1AJSL65ZdfqGvXrmRpaUlvvfUW/fPPP0bzu8UQ9Wsoc08EEpHg57CxxyImJkbndnd3d4wYMUJrW0ZGBjp37oypU6di5cqVSsQzKleuXMHWrVtx/Phx/PXXX1Cr1bC1tUWnTp0wcOBAeHl5QZb5m5xVRJ972dnZSElJgZeXV53WRMjPz0dxcTHatWun/3BGIDMzE5GRkfDz80NwcHCtY8vLyzF58mTk5uZqrfH1pDKmuVdeXo78/HzY2dmhUaNGij8/e3IZ09wrLS3F3Llz8emnnwKAwReurov8/HysWrUKkiQhKirKoFlEqt+1a9eQk5MDlUqFpk2barYnJiZi3759qKioQP/+/fHSSy8Je8xnqH07d+5crFq16oFfA83OzoaPjw/y8vIgSRLu3LmjWMa6OH/+PFxcXAyeraKiApGRkVi+fDm6d++OEydOYMKECcL/bjFE/RrK3BMBN4lYNcXFxcjPz0fTpk1hbW1t6DjsCcJzjzHGmLE7dOgQTp48CVdXVwwaNMjQcYwO18+4paWl4cUXX8ScOXMwb968WsdeuHBB82ZdtDfqIjVQAeDo0aMICQlBZmYmN6Br0FDmngi4ScQYY4yxB7p8+TIyMjK0rmKiUqnQunVrAyer7tatWygrK0Pz5s0N/il/Tk4OkpKS8Mcff6CgoACyLKNFixZ45plnMHjwYINfGUkXkeoHGNfcY4wxfblz5w6Ki4thYWEBS0tLQ8dhDRg3iZ4wlZWVOH36NExNTeHm5gZJknSOO3XqFE6cOPHAr488LiUlJSgsLISjo6PW9v3792P58uX4+eefUVZWBicnJ4wZMwYRERFCnXZeXFyM0tJSNGvWzCAH1MZQP1HnXk1KSkqwZs0apKWloaSkBE5OThg7diyee+45g2W6efMmTE1NYWNjU+OYixcvIicnB56engomq7ubN2+iuLgYbdu2NchzG0P9RJp7FRUVWLFiBT7//HNkZ2frHOPs7IxJkyZh5syZsLCwUCTX5cuXkZubiz59+mj9zv3000+xfPlyZGVlAQAaN26MwMBALFu2DC1atFAkW5ULFy5g6tSp2L9/f7X7iAiSJMHe3h5RUVGYOnWqotmMoX6izj1djLGJVVpaCjMzM5iZmRk6itHVr2nTpggJCcGKFSsMHUUnkfYtY8xIGWw1JKa4r776iuzt7TUL7v3rX/+iLVu26Byr9GrvYWFh5ODgoLVtxYoVmoUBLS0tqXnz5poF+J599lkqKSlRLF9ubi4VFhZW275nzx7q3r27pqbW1tY0ceJEunnzpmLZiMSvn8hzz8fHhzZu3Ki1LSsri5ydnatdjUiWZVq0aJFi2aqkpKSQq6urpn59+vShw4cP6xwr+pUaQkNDycTERNHnFLV+os+94uJi6t27N0mSRNbW1jR06FCaPn06LVy4kBYuXEjTp0+noUOHkrW1taauxcXFimQLCgoiJycnrW3z5s0jWZbJxMSEVCoVPfvss2RnZ6e5UtH169cVyUZElJeXRw4ODiRJEvXo0YMCAwOpR48eJEkSubu707vvvkshISGa34tTpkxRLBuR+PUTee5VKS8vp7i4OOrQoYPWxRnu/Xn66acpPj6+zlcwfJzOnDlDEyZMIH9/f1q5ciXduXOHiO4et3Ts2JFkWSZTU1Py9PSkn3/+WfF8otevNoZeOFj0fXu/S5cuUVJSEu3cuZN27txJSUlJdOnSJUPHqtGuXbuqLZDP6k6k+hnb3BMJN4meEOnp6WRiYkLm5ubk6+tLw4cPJ0tLyxoPTpV+o9mxY0caO3as5vbly5fJwsKCWrduTd999x2p1WoiIrp+/TpNnDiRJElS9A2TLMvVVudPSEggExMTkmWZVCoV9e3bl2xsbDRvApQ8qBG5fqLPPUmSKCYmRmtbr169SJIkCg4OprS0NDp//jxt3LiRHBwcSJZlSkxMVCzfuXPnyMrKiiRJok6dOpGrq6vmSj9xcXHVxhtDk0jJfCLXT/S5N2/ePJIkiebPn19rU7mkpIQiIiJIkiQKDw9XJJuzszOFhIRobmdmZpKJiQl16dKFfvvtN832yspKio2NJUmSaMaMGYpkI7rbuJdlmbZt26a1ffv27WRiYkLr1q0jIqKioiJ66aWXSJZl+vrrrxXLJ3r9RJ57ROI3sbKzs8nW1laryTx9+nQ6cuQImZmZkZWVFXl4eFCbNm1IkiRq0qQJnTt3TrF8ItfPxcXlgT+SJJGdnZ3mdpcuXRTJRiT+vq1i7E3ASZMmGez5//rrL5o2bRq5ublRjx49aP78+ZSfn69zbHR0tOIfvD2IoetnzHNPJNwkekKMGjWKzMzMKDU1VbMtNzeXPD09SZZlCgkJ0TQSiJR/o2llZUXz58/X3F63bh3Jskx79+7VOb53796kUqmUilftzVxxcTHZ2dmRvb09HTx4ULO9pKSEgoKCSJZlWr58uWL5RK6f6HPv/n2bnp5OkiRpvYGqcvbsWTI3N6fAwEDF8gUHB5MkSfTll19qZezcubPOs0u4SaRN5PqJPvecnJxo6NChdR4/ZMiQamen6IulpSUtWLBAc/uTTz4hWZa1fs/ca9CgQdSuXTtFshEROTo60siRI3XeFxgYSK6urprb5eXl1K5dO3r++eeViid8/USee0TiN7EmT55MsizTihUrKCMjg9577z0yNzcnT09PevbZZ+nPP//UjP34449r/L2jLyLXr6rxcu+ZnPf/6LpfKaLvWyKxm4Dp6ekP/JEkiQICArS2KaWgoEDT3Lh3frVq1YqSk5OrjVf6mE/0+ok894wNN4meEA4ODvTSSy9V215ZWUljx44lSZJo3LhxmjfrSv/Seeqpp2j27Nma20uXLiVZlmt84c6dO5csLCyUilftzdzOnTtJkiT68MMPq40tKyujNm3aUJ8+fRTLJ3L9RJ979+/b1atXkyzLNZ4qGxAQQI6OjkrFozZt2tCwYcOqbS8sLCQfHx+SZVnrzZ7S9XN2dq7XT9UfZqWIXD/R556FhYVWbR5kwYIFiv1esbe3p2nTpmlux8XFkSzLNb7hfP311xX9m2Fubl7jm9rw8HCytLTU2vbaa69R06ZNlYhGROLXT+S5RyR+E6tTp07k5+entc3X15dkWaa0tLRq4wcNGkRt2rRRKp7Q9evevTtZW1vTRx99VOMYQ37dTPR9S2QcTcD6/iilqh6vvfYaXb16lW7cuEHx8fHUuHFjatSoUbUPfw1xzCxy/USee8bG1NBrIjFl3Lx5EyqVqtp2U1NTbN68GWZmZkhISIBarcamTZsUz+fq6oqkpCTN7TZt2gAALl26hE6dOlUbf+nSpVoXn9W3jIwMSJKE4cOHV7vP0tISzz//PHbs2KFYHpHrJ/rcu9+tW7cAAB07dtR5f8eOHbF3717F8ly7dg1ubm7VttvY2GDv3r3w9/dHfHw81Go14uLiFMtVJScnB7Is13mBzMrKSj0n0iZ6/e4l2txzcHDAiRMn6jz++PHjcHBw0F+ge/To0QP79+/XLABd9Tvm3Llz8PDwqDb+3LlzsLe3VyQbcLd2p06d0nnfqVOnYGtrq7XNxsYGpaWlSkQDYBz1E3XuAcDVq1cRFBRU5/E9e/ZEcnKyHhNpy8vLw8iRI7W2ubu748CBA3B3d6823sPDAykpKQqlE7t+P/30E6KjozFjxgxs27YNa9euhZOTkyLPXRei71sA2Lp1K3x9fbF06dJax1lZWSEuLg6//vorvvrqK8THxyuSr3HjxggICICJiUm1+4gICQkJUKlU6NevnyJ57rVr1y54eHjgww8/1GwLDw/HkCFDMGzYMAQGBmLr1q06338oReT6iT73jInhr2vKFOHg4IDr16/rvE+SJKxfvx7jx4/Hf/7zH/z73//GP//8o2i+sLAwnDx5Eu+++y4AYMSIEXBwcMDcuXNx+/ZtrbGJiYnYsWMHvL29Fc14L7VaDQA1HpS2bNkSZWVliuURuX6iz72qHFWqrhBXXFysc2xJSQmsrKwUyQUAzZo10zQP7mdpaYk9e/Zg0KBBeOeddxAeHq5YriqOjo7o2rUrysrK6vQzbtw4RfOJXj+R515gYCD279+PhQsX1vr7rKysDG+88QYOHDiAUaNGKZLttddeQ2ZmJmbOnAm1Wo3hw4dDpVJh6tSpuHbtmtbYtWvXYu/evRg6dKgi2QBg6NCh+P7777UO9AHgo48+wvfffw8fHx+t7Xl5eWjZsqVi+USvn8hzDxC/iWVra1vt917V7YKCgmrjCwoKYG5urkQ0AGLXz8zMDG+99RbS0tJw5coVuLm5YfXq1Yo8d12Ivm+Bu03AHj161Hl8z549cfXqVT0m+q+lS5eisrISmZmZiIiIwPr167V+NmzYAADw8vLS2q6UnJwcncfn7u7uSElJQYsWLfDiiy9i9+7dimW6l+j1E3nuGR3DnsjElDJkyJAHrkGjVqs163fY2NgoenqgWq2m4cOHkyzL5O3tTatXr6YlS5aQqakptW7dmkJCQmjmzJk0ePBgkmWZmjRpQr///rti+SRJorCwMEpOTqbk5GSKjY0lWZYpJydH5/jQ0FBq2bKlYvlErp/oc69qAcqqr0M5OjqSLMuUlJSkc/zw4cOpU6dOiuXz9PSkbt261TqmrKxMs2+r8ivF39+fzMzM6rz4n9JrEolcP9Hn3q1bt8jd3V3zuvTz86MZM2ZQZGQkRUZG0owZM8jPz09rwf5bt24plm/SpEkkSRKpVCp6/fXXKTw8nExNTalx48bk5eVFI0aM0Fzpx8HBQdErmly6dIlatGhBsixTq1atqE+fPtSqVSuSZZlsbGzo/PnzmrH//PMPtWjRgsaMGaNYPiKx6yf63Js9ezbJskxvvPEGlZaW1jiutLSUFixYQLIs05w5cxTL5+3tTa1bt6aioiIiultPR0dHsrGxoWXLlmmNLSwspJYtW1LPnj0Vyyd6/arcvn2bZs+eTSYmJuTp6UmZmZlEZNivm4m+b4mI2rVrV+0rcbXx9fVVdM2z06dPk4eHBzVq1Iji4+O11sUkMuz+tbe3r3WuZ2dnU9u2bcnCwoJ27txpkHUoRa6f6HPPmHCT6AmxYsUKkiSpxss+V1Gr1RQSEqL5zqmSysvLadasWWRmZqb5DquuRQK7dOmi6CJoRNW/g1t1e8OGDTrH9+7dm3r37q1oRlHrJ/rca9euHTk5OVX7uf9qdkR3D1htbGzo5ZdfVizfW2+9RbIs04kTJ2odV9XoULp+UVFRJElSnedU1T5Wisj1E33uEd393v7ixYupdevWNS7i2rp1a4qKiqr1+//68v7771PTpk1rXWzW19eXsrKyFM+WlZVFw4YNIzMzM80V9QYOHFhtzamysjJKTU2lixcvKp5R5PqJPPdEb2Jt376dJEmi9u3b07hx48jZ2ZlMTEzoq6++InNzc3r99dfpm2++ofXr15ObmxvJskzx8fGK5RO9fvdLTk6m9u3bk5WVFS1fvtygb4JF37dExtEErKyspKioKDIzM6PevXtrfXBqyP3bs2dP8vHxqXVMVlYWtW3blszNzalfv34GuViJqPUzhrlnLCQiIkOfzcT078qVK1i1ahV69+6NgICAWscSEWJiYpCbm6voKYJVrly5gq1bt+L48eP466+/oFarYWtri06dOmHgwIHw8vKCLCv7TcmYmBid293d3TFixAitbRkZGejcuTOmTp2KlStXKhFPi2j1M6a59yDnz5/HF198AR8fH3h6eirynJmZmYiMjISfnx+Cg4NrHVteXo7JkycjNzdXa40qfcrOzkZKSgq8vLzqtG5Dfn4+iouL0a5dO/2Hg/j1qytDzL37ZWRkICMjA4WFhQDufu1BpVLpXHNMSbdv38b333+PX375pdrvPB8fH7Rv396g+crLy5Gfnw87Ozs0atTIoFl0Eb1+gJhzr7S0FPHx8Vi7di2uXLmic4yjoyMmTpyI8PBwRb8qCgBz587FBx98ALVaDXNzc8THx2PmzJmIjY1FdHS05quuRARPT08cOHCgzmvLPQ6i1+9+paWlmDt3Lj799FMAwMSJE/HZZ58ZJIvo+7aoqAienp44efIkrK2t0b9/f6hUKs1abIWFhcjIyEBaWhqKiorQvXt3HD58GNbW1oplrHL8+HEEBwcjKysLkZGRiIiIgJmZmcH279y5c7Fq1aoHfgU5OzsbPj4+yMvLgyRJuHPnjoIp/0u0+hnT3BMdN4kYe8yKi4uRn5+Ppk2b8i8dxliDtnv3bjg5OaFbt26GjsKeMCLNPRGbWMDdhftzcnKgUqnQtGlTzfbExETs27cPFRUV6N+/P1566SXFP3y7l6j10+XQoUM4efIkXF1dMWjQIIPlEH3fGlMTsKKiApGRkVi+fDm6d++OEydOYMKECQZpcqSlpeHFF1/EnDlzMG/evFrHXrhwQdMoMlSTCBCrfoBxzT2RcZOIMcYYYw9FlmWDfqJuLC5fvqzzTXDr1q0NnMx48dxjzDgYSxPw6NGjCAkJQWZmJv9ueQgi1s9Y5p6ITA0dgLG6KC4uRmlpKZo1a2bQT7sqKytx+vRpmJqaws3NTevKRPc6deoUTpw48cCvtyhFlPox/bt58yaKi4vRtm1bg+YoKSnBmjVrkJaWhpKSEjg5OWHs2LF47rnnDJrrQUSpnwiOHTtWp3HXr1/XGturVy99RdIpJycHSUlJ+OOPP1BQUABZltGiRQs888wzGDx4sOJX9qlSUVGBFStW4PPPP0d2drbOMc7Ozpg0aRJmzpwJCwsLhRPeJWL9jGXuGesxgSiMoX7c4H10xvKmvG/fvjh79iyKi4sN9vvYmIlYP2OZe0Iy1GJIjN0rNzeXCgsLq23fs2cPde/eXbMQs7W1NU2cOJFu3rypeMavvvqK7O3tNVn+9a9/0ZYtW3SOVfpqA8ZQP6aM0NBQMjExUez5fHx8aOPGjVrbsrKyyNnZudpCuLIs06JFixTL9jCUrp/I7l+wv64/SsnOziY/Pz+dGaqyN2/enFavXq1YpirFxcXUu3dvkiSJrK2taejQoTR9+nRauHAhLVy4kKZPn05Dhw4la2trkmWZ+vTpQ8XFxYpmFLl+os89IrGPCerCzs6OZs2aZbDnF7l+5eXlFBcXRx06dKhxrj399NMUHx9f5yt7KsnQ+7a+Pv/8cwoLCzN0DCFdunSJkpKSaOfOnbRz505KSkpS9EqTDR3PvZrxmURMCM7OzoiOjkZkZKRm26ZNmxAWFgYiwtNPP41mzZrhzJkzWLt2LX7++Wf8+OOPinWqjx07hqCgIJiYmGDw4MEwMzNDYmIixo8fj5SUFHz88ceK5KiJ6PVjyiIFv0V86NAheHt7a20LCgpCTk4Oxo8fj8mTJ6NZs2b48ccfERERgbfffhve3t4GXcvhQZSsn+gaN26MgIAAmJiYVLuPiJCQkACVSoV+/fopmuvSpUvo168frl27Bnd3dzg7O+PChQs4ceIEunfvjnHjxuH06dP45ptvMGPGDPz222+K/p6OiYnBsWPHEBERgcjIyBrXPCgtLUVsbCyWLVuG2NhYxMfHK5JP9PoB4s49QPxjgrooKChASUmJQZ5b5PqVlJRg0KBBOHbsGJo0aYIhQ4ZApVLBxsYGAHDr1i3NwrcLFizA119/jcTERDRu3Nhgme9nyH37MFJTU5GQkIB169YZOooQjOUs1IaA514tDNmhYqyKJEkUExOjuV1cXEx2dnZkb29PBw8e1GwvKSmhoKAgkmWZli9frli+UaNGkZmZGaWmpmq25ebmkqenJ8myTCEhIaRWqzX3Kf2pl+j1Y8oJDQ016NxLT08nSZIoJCSk2tizZ8+Subk5BQYGKpavvpSun8ji4uLI0tKS+vbtS2fPntU5xlCXug0LCyNZlmnbtm1a27dv304mJia0bt06IiIqKiqil156iWRZpq+//lqxfE5OTjR06NA6jx8yZAg5OTnpMZE20esn8twjEv+YwMXF5YE/kiSRnZ2d5naXLl0Uyydy/ebNm0eSJNH8+fOppKSkxnElJSUUERFBkiRReHi4ItmIxN+3D0Ppv7t//fUXTZs2jdzc3KhHjx40f/58ys/P1zk2Ojpa0bOLjeEsVJHrV198zFczPpOICSkxMREFBQVYvXo1Bg4cqNluZWWFdevWITU1FVu3bsWcOXMUyZOWloaAgAD0799fs61t27Y4ePAgQkJCkJCQgDt37iAhIaHG79QrSbT6sYdX38tP37hxQ09J6uann36CJEk651bnzp3xwgsv4Mcff1Qsj7HVTyQREREYNmwYQkJC4OHhgejoaMybN0+I33H79+/HiBEjMGrUKK3tgYGBGDFiBN577z2EhYWhSZMm2Lx5M44dO4YPP/wQAQEBiuS7evUqgoKC6jy+Z8+eSE5O1mMibaLXT+S5B4h/THDu3DlIklTrWZGSJKGgoAAFBQXKBfs/Itdv69at8PX1xdKlS2sdZ2Vlhbi4OPz666/46quvFDsLUPR9CwAJCQn1Gp+ZmamnJNUVFhaiX79+yM7O1tTwxIkT2LhxI7744gt4enpW+ze11fpxE/0sVNHrJ/LcMzbcJGJCysjIgCRJGD58eLX7LC0t8fzzz2PHjh2K5bl586bOhc9MTU2xefNmmJmZISEhAWq1Gps2bVIsV01Eqx97eDk5OZBlGWZmZnUaX1lZqedEtbt16xYAoGPHjjrv79ixI/bu3atYHmOrn2hcXV2Rnp6ON998E4sWLcKOHTuwfv16uLi4GDTXjRs3alyMskOHDlpzzNzcHMOGDcMXX3yhVDw4ODjgxIkTdR5//PhxODg46C/QfUSvHyDu3APEPybo1q0bsrOzER8fj1dffVXnGENeHU7k+one4BV93wJAaGhovZp7RKRYM3Dp0qXIysrCq6++isjISJiZmWHt2rWIjY3F0KFDsX37dvj5+SmSRRfRm5Si10/kuWdsuEnEhKRWqwGgxoPmli1boqysTLE8Dg4OuH79us77JEnC+vXrQUTYtGkT1Go1OnTooFg2XUSrH3t4jo6OsLe3x8mTJ+s0PjQ0VPGD6nv/wDo6OgK4e0U9S0vLamNLSkpq/GRMH4yhfqIzNTVFdHQ0/P39ERwcDA8PD0RGRiIiIsJgmRwcHHDq1Cmd9506dQq2trZa22xsbFBaWqpENAB3z8j54IMPsHDhQixatAiNGjXSOa6srAxLlizBgQMHMGvWLMXyiV6/KiLOPUD8Y4KffvoJ0dHRmDFjBrZt24a1a9fCyclJ0Qy1Ebl+ojd4Rd+3wN3GsqOjIyZPnlyn8Vu3bsWvv/6q51R37dq1Cx4eHvjwww8128LDwzFkyBAMGzYMgYGB2Lp1q84PWZUgepNS9PqJPPeMjoG+5saYFkmSKCwsjJKTkyk5OZliY2NJlmXKycnROT40NJRatmypWL4hQ4aQSqWqdYxarabg4GCSJIlsbGwUXxdG5Pqxh+fv709mZmZ1voKKIdYksrOzI2dnZ3J2diZHR0eSZZmSkpJ0jh8+fDh16tRJsXyi18/YlJeXU3h4OJmYmJCHhwfJsmyQdWEmTZpEsixXu/LWhx9+SLIs08svv6y1/d///je1a9dOsXy3bt0id3d3zd8DPz8/mjFjBkVGRlJkZCTNmDGD/Pz8yMbGhiRJInd3d7p165Zi+USvny6izD0i8Y8JqqSnp1Pnzp2pSZMmtGrVKq37DLmmk8j1mz17NsmyTG+88QaVlpbWOK60tJQWLFhAsizTnDlzFMl2L1H3LRHRM888Qy1atKjzeCX/7lpaWtLcuXN13peVlUVt27YlCwsL2rVrFxEpv55Yu3btyM/Pr87jfX19Ff3dLHr9RJ57xoabREwI91/utur2hg0bdI7v3bs39e7dW7F8K1asIEmS6PDhw7WOU6vVFBISosmvFNHrxx5eVFQUSZJE6enpdRpfNf+U0q5dO3Jycqr2ExsbW21saWkp2djYVHsDqk+i189YHTlyhFQqlcHejFy6dIlatGhBsixTq1atqE+fPtSqVSuSZZlsbGzo/PnzmrH//PMPtWjRgsaMGaNoxpKSElq8eDG1bt2aJEnS+dO6dWuKioqqdYFcfTCG+tXE0HOPSPxjgnvdvn2bZs+eTSYmJuTp6UmZmZlEZNhGgsj1E73Bey8R9y0R0eTJk0mWZbp48WKdxiv5Rt3e3r7Wpl52dram0bFz507FmxyiNylFr5/Ic8/Y8NfNmBCioqJ0bn/qqaeqbcvIyMBPP/2EqVOn6jnVf40ePRrXrl1Dfn5+reOqTpN2cnJCbm6uQunErx97eMHBwXB2dkaLFi3qNH758uWIiYnRc6r/ysnJqfPYixcvYs6cOfDx8dFfoPuIXj9j1bdvX5w9exbFxcUGufxu69atcfToUcyYMQPff/89/vzzT5iYmMDb2xsrVqzQWhOrsrISO3bsQNu2bRXNaGVlhZiYGMTExCAjIwMZGRkoLCwEANja2kKlUtW4LpC+GUP9amLouQeIf0xwLwsLC7z33nsICAhAWFgYunXrhiVLlhgkSxWR62dtbY20tDTEx8dj7dq12LdvH/bt21dtnKOjI2bPno3w8HBFv0J9LxH3LQAMGDAA+/fvR0ZGBtq0afPA8c8995wCqe5ycnKq9etFzs7OSEpKgo+PD0aPHo1nnnlGsWzA3YWrk5KSsHTpUqxevRr9+/eHSqXSfAW4sLAQGRkZSEtLQ1FREbp3747o6GjF8oleP5HnnrGRiBRccpyxx6C4uBj5+flo2rQprK2tDR3H6HD9GGMNSXl5OfLz82FnZ1fj2j8i2r17N5ycnNCtWzeD5jDW+rH6Ky0txdy5c/Hpp58CgEEXNzYWojV4a8L7tm7mzp2LVatWIS8vDy1btqxxXHZ2Nnx8fJCXlwdJknDnzh3FMpaWlmqalFeuXNE5xtHRERMnTlS8SWkM9WOPBzeJGGOMMcYUZugrELEn16FDh3Dy5Em4urpi0KBBho7DHiPet7VLS0vDiy++iDlz5mDevHm1jr1w4YKm0WGoJodoTUpjqx97eNwkYkKprKzE6dOnYWpqCjc3txovS3jq1CmcOHECwcHBCicUG9fvyVFSUoI1a9YgLS0NJSUlcHJywtixY/nU2Tri+jUsly9f1nkg3bp1a4PkOXbs2APH9OnTByNGjMCCBQs023r16qXPWDUSrX6MGRofTzHGnmiGXBCJsXt99dVXZG9vr1l8+V//+hdt2bJF51ilF0IzBly/hsnHx4c2btyotS0rK4ucnZ01i5RX/ciyTIsWLTJQUjFx/Rqu8vJyiouLow4dOmgt3H/vz9NPP03x8fF1vrrd43L/xQTq+qMkkevHHq9Lly5RUlIS7dy5k3bu3ElJSUl06dIlQ8cSljEdT/G+fbJ9/vnnFBYWZugYrAHihauZEI4dO4agoCCYmJhg8ODBMDMzQ2JiIsaPH4+UlBR8/PHHho4oNK5fw3Xo0CF4e3trbQsKCkJOTg7Gjx+PyZMno1mzZvjxxx8RERGBt99+G97e3nya+f/h+jVMJSUlGDRoEI4dO4YmTZpgyJAhUKlUsLGxAQDcunVLs7jnggUL8PXXXyMxMRGNGzdWLGPjxo0REBAAExOTavcRERISEqBSqdCvXz/FMlUxhvqxR1NRUYEVK1bg888/R3Z2ts4xzs7OmDRpEmbOnGmwRcBFYwzHU7xvHx9jP4syNTUVCQkJWLdunUGe39jrx2ph6C4VY0REo0aNIjMzM0pNTdVsy83NJU9PT5JlmUJCQkitVmvuM/QnN6Lh+jVckiRRTEyM5nZ6ejpJkkQhISHVxp49e5bMzc0pMDBQwYRi4/o1TPPmzSNJkmj+/Pm1Xj6+pKSEIiIiSJIkCg8PVyxfXFwcWVpaUt++fens2bM6xxjyMtWi1489muLiYurduzdJkkTW1tY0dOhQmj59Oi1cuJAWLlxI06dPp6FDh5K1tTXJskx9+vSh4uJiQ8cWgujHU7xvH11DOovSEJdwb0j1YzXjM4mYENLS0hAQEID+/ftrtrVt2xYHDx5ESEgIEhIScOfOHSQkJNT4vfAnGdfvyfHTTz9BkiTMmTOn2n2dO3fGCy+8gB9//NEAyYwD169h2Lp1K3x9fbF06dJax1lZWSEuLg6//vorvvrqK8THxyuSLyIiAsOGDUNISAg8PDwQHR2NefPmCfP7V/T6sUcTExODY8eOISIiApGRkTVe/ai0tBSxsbFYtmwZYmNjef9C/OMp3rePRvSzKBMSEuo1PjMzU09JdBO9fuzx4SYRE8LNmzd1rtRvamqKzZs3w8zMDAkJCVCr1di0aZMBEoqN6/fkuHXrFgCgY8eOOu/v2LEj9u7dq2Qko8L1axiuXr2KoKCgOo/v2bMnkpOT9ZioOldXV6Snp+PNN9/EokWLsGPHDqxfvx4uLi6K5tDFGOrHHh43AR+e6MdTvG8fjehNttDQ0Ho1H4lI0Wal6PVjjw83iZgQHBwccP36dZ33SZKE9evXg4iwadMmqNVqdOjQQeGEYuP6NWz3HgA4OjoCAIqLi2FpaVltbElJSY1/tJ9UXL+Gx8HBASdOnKjz+OPHj8PBwUF/gWpgamqK6Oho+Pv7Izg4GB4eHoiMjERERITiWe5lLPVjD4ebgA9P9OMp3rePRvQmm7m5ORwdHTF58uQ6jd+6dSt+/fVXPafSfj6R68ceH24SMSF07twZhw4dqvF+SZKwYcMGAMCmTZtgbW2tTDAjwfVr2FasWIH169cDAMrLywEAv/32W7UFmQEgNzcXLVu2VDKe8Lh+DU9gYCA++OADLFy4EIsWLUKjRo10jisrK8OSJUtw4MABzJo1S9mQ9/Dw8MDx48cRGRmJxYsXY/v27Qb96pmx1Y/VDzcBH57ox1O8bx+N6E02Nzc3XLx4sc4fJJw7d07RJpHo9WOPj2zoAIwBgJ+fHzIzM5GSklLjmKo/zMHBwSgqKlIwnfi4fg1X27ZtYWtrCyICEcHc3Bxt27bVua/Lyspw+PBh9OjRwwBJxcT1a5hiYmLQrVs3LF26FA4ODnjhhRcwc+ZMLF68GIsXL8bMmTPxwgsvwMHBAXFxcejWrRuio6MNmtnc3Bzx8fFISUlBUVERiMhgWYyxfqzuAgMDsX//fixcuBBlZWU1jisrK8Mbb7yBAwcOYNSoUQomFJfox1O8bx+N6E22nj174saNG8jLy1PsOetD9Pqxx4fPJGJCGD16NK5du4b8/Pxax1Wd6uvk5ITc3FyF0omP69dw5eTk1HnsxYsXMWfOHPj4+OgvkJHh+jVM1tbWSEtLQ3x8PNauXYt9+/Zh37591cY5Ojpi9uzZCA8PF+ZrhH379sXZs2dRXFxssEtTG3P92IPFxMQgKSkJS5cuxerVq9G/f3+oVCrY2toCAAoLCzWLyxYVFaF79+7cBPw/oh9P8b59NKKfRTlgwADs378fGRkZaNOmzQPHP/fccwqk+i/R68ceH4kM+VEWY4wxxtgjysjIQEZGBgoLCwEAtra2UKlUOhegZdVx/Rqe0tJSTRPwypUrOsc4Ojpi4sSJ3AQ0MrxvH15RURE8PT1x8uRJWFtb16nJdvjwYV6m4f9w/Z4c3CRijDHGWIOze/duODk5oVu3boaOYpS4fg0HNwEbLt639cdNtkfD9XsycJOIMcYYYw2OLMuYOHEiPvvsM0NHMUpcP8ZYQ8dNtkfD9Wu4eE0ixhhjjBmVY8eO1Wnc9evXtcb26tVLX5GMCtfvyVBZWYnTp0/D1NQUbm5uNV5R79SpUzhx4gSCg4MVTsgeFu/bx4MbGo+G69dw8ZlEjDHGGDMqsiw/1CXk79y5o4c0xofr1/Bt3boVr776Kv7++28Ad7/+ER8fj7Fjx1YbGxMTg9jYWN6/RoL3rXLWrl2LtLQ0rFu3ztBRjBLXz3jxmUSMMcYYMzqNGzdGQEAATExMqt1HREhISIBKpUK/fv0MkE58XL+G69ixYwgKCoKJiQkGDx4MMzMzJCYmYvz48UhJScHHH39s6IjsIfG+VVZqaioSEhK4yfGQuH7Gi5tEjDHGGDMqS5cuRXR0NDIzM7Fu3Tp07ty52piEhAR4eXnxmjo6cP0atmXLlkGWZfzwww/o378/AODixYsYP348PvvsM5SVlWH9+vUPdTYZMyzet4wxJXCTiDHGGGNGJSIiAsOGDUNISAg8PDwQHR2NefPm8RujOuL6NWxpaWkICAjQNBEAoG3btjh48CBCQkKQkJCAO3fuICEhgfe5keF9+2gSEhLqNT4zM1NPSYwT1+/JwU0ixhhjjBkdV1dXpKen480338SiRYuwY8cOrF+/Hi4uLoaOZhS4fg3XzZs3dS4ma2pqis2bN8PMzAwJCQlQq9XYtGmTARKyh8X79tGEhobWq3lGRNxsuwfX78nBTSLGGGOMGSVTU1NER0fD398fwcHB8PDwQGRkJCIiIgwdzShw/RomBwcHXL9+Xed9kiRh/fr1ICJs2rQJarUaHTp0UDghe1i8bx+Nubk5HB0dMXny5DqN37p1K3799Vc9pzIeXL8nBzeJGGOMMWbUPDw8cPz4cURGRmLx4sXYvn07f3pZD1y/hqVz5844dOhQjfdLkoQNGzYAADZt2gRra2tlgrFHxvv20bi5ueHixYt1boSfO3eOmxz34Po9OWRDB2CMMcYYe1Tm5uaIj49HSkoKioqKQESGjmRUuH4Nh5+fHzIzM5GSklLjmKpmQnBwMIqKihRMxx4F79tH07NnT9y4cQN5eXmGjmKUuH5PDj6TiDHGGGMNRt++fXH27FkUFxfDwsLC0HGMDtfP+I0ePRrXrl1Dfn5+reOqvp7k5OSE3NxchdKxR8H79tEMGDAA+/fvR0ZGBtq0afPA8c8995wCqYwH1+/JIRF/VMQYY4wxxhhjjDH2xOOvmzHGGGOMMcYYY4wxbhIxxhhjjDHGGGOMMW4SMcYYY4wxxhhjjDFwk4gxxhhjBnbo0CFIkoTo6Og6jff29tbbJdqjo6MhSVKtl5k2Bk5OTnBycnqkx8jJyYEkSQgNDX0smRhjjDEmPm4SMcYYY8zobdiwQXPpZ8YYY4wx9nBMDR2AMcYYY6w+EhISUFpaqpfHnjZtGl5++WW0bdtWL4/PGGOMMSYybhIxxhhjzKjos4HTrFkzNGvWTG+PzxhjjDEmMv66GWOMMcaEkZqaCm9vb1hbW+Opp57CqFGjkJmZqTXm/jWJQkNDERYWBgAICwuDJEmanypXr17FzJkzoVKp0KhRIzz11FNwcXHBlClTUFhYqBmna02iquer6ef+NXuKiooQFRWFrl27ap7L19cXqampD12XpKQk/O///i86deqEJk2aoEmTJnjmmWfw2Wef1fkx7v1/W7t2Ldzc3GBpaYnWrVtj9uzZKCoqqvHfZmZmYuTIkbCzs0Pjxo3x/PPP4+TJk3rJyRhjjDHD4TOJGGOMMSaEH3/8EUuXLsXQoUMxffp0nDlzBl9//TVSUlLw448/on379jr/XUBAAAoKCrBr1y6MGDEC7u7uWveXlpaif//+yMnJwZAhQzBy5EhUVFTgwoUL2LRpE15//XXY2trWmCs0NBTe3t7Vtn/33Xc4duwYrKysNNtu3rwJT09PnDlzBv3798eUKVNw69Yt7Nq1Cz4+Pti6dSsCAgLqXZv4+HhkZmaiT58+GDlyJAoKCrBv3z5MnjwZ58+fx/Lly+v8WO+99x4OHjyIMWPGYNiwYUhMTMT777+PH3/8EYcPH4aZmZnW+JycHPTp0wddu3bF//7v/yIrK0vz/3P27Fm0bNlSLzkZY4wxZgDEGGOMMWZASUlJBIAA0CeffKJ13yeffEIAaPjw4ZptXl5edP8hzPr16wkArV+/vtrj7969mwDQrFmzqt1XVFREt2/f1tyOiooiAJSUlFRr5sOHD5O5uTm1b9+erl+/rtk+duxYAkBr1qzRGn/t2jVq06YNNW/enMrKymp9bF2ys7OrbausrKTBgweTiYkJ5ebmat3Xrl07ateunda2qv83c3NzOnnypGa7Wq3W5H733Xc12y9cuKDZL3FxcVqPtWjRIgJAS5cufaScjDHGGBMLf92MMcYYY0Lo2LEjXnnlFa1tr7zyClQqFb799ltcv379kR6/UaNG1bY1adIEFhYW9Xqcqq9eWVlZ4dtvv9WsYXTjxg18+eWXGDhwICZOnKj1b1q0aIF58+bh+vXrSExMrHd2Z2fnattMTU0xZcoU3LlzB0lJSXV+rODgYHTr1k1zW5IkvP322zAxMdF5dThnZ2fMmzdPa9uECRMAAD/99JPecjLGGGNMefx1M8YYY4wJoX///pBl7c+vZFlG//79kZGRgZMnT+L555+v9+N6enqiVatWiIuLw8mTJzF8+HB4eXnBxcVFa92iuvj7778xbNgwFBYWYt++fejcubPmvp9++gl37txBeXk5oqOjq/3bjIwMAMC5c+cwfPjwej1vUVER3n33XezcuRNZWVkoKSnRuv/KlSt1fqwBAwZU29auXTu0adMGZ86cQUVFBczNzTX3ubu7V9sv//rXvwAABQUFesvJGGOMMeVxk4gxxhhjQrh3bRtd2+9dYLo+bG1t8eOPP2Lx4sXYs2cP9u7dCwBo06YN5s+fj9dee61Oj1NZWYnAwED88ccf+OyzzzBo0CCt+2/evAkASEtLQ1paWo2Pc3/j5EEqKirg7e2N48ePo0ePHhg/fjzs7e1hamqKnJwcbNy4EeXl5XV+vNrqnJOTg6KiItjb22u229jYVBtranr3EPLOnTt6y8kYY4wx5XGTiDHGGGNCuHbtWq3ba1tc+kHatm2LDRs2QK1W49SpU/j++++xcuVKTJ06FXZ2dggKCnrgY0yePBmHDh3C3Llzq30tDvhvM2Xu3Ll49913Hzrr/Xbt2oXjx49jwoQJ+Pzzz7Xu++KLL7Bx48Z6PV5tdZYkCdbW1kLkZIwxxpjyeE0ixhhjjAkhLS0NarVaa5tarcaRI0cgSRK6d+9e4781MTEBoH1miy6yLMPd3R3h4eH4z3/+AwDYvXv3A7MtXboU69evx4gRI7Bs2TKdY5599llIkoSjR48+8PHqIysrCwAwYsSIavelpKTU+/F0/Zvc3Fzk5eWha9euWl81q4/HnZMxxhhjyuMmEWOMMcaE8Mcff2DNmjVa29asWYM//vgDw4YNQ/PmzWv8t02bNgUA5OXlVbvvzJkzOs+eqdpmaWlZa65t27Zh4cKF8PDwwJYtW6qtz1PFwcEBo0ePxpEjR/DOO++AiKqNSU9PR2lpaa3Pd7927doBAFJTU7W2JycnV6tXXSQkJODUqVOa20SEN954A3fu3EFoaGi9H09fORljjDGmPP66GWOMMcaE4OvrixkzZmDv3r3o2rUrzpw5gz179qBZs2b44IMPav23ffv2RaNGjfD+++/j77//1jSUFi1ahAMHDmDevHno378/OnbsCHt7e2RnZ2P37t2wtLTE1KlTa33s4OBgEBE8PDzwzjvvVLvf3d0dAQEBAICPPvoI58+fR3h4ODZt2oS+ffviqaeeQl5eHn7++WdkZGTg6tWrsLKyqnNd/ud//gdOTk5YtmwZfvvtN7i6uuL8+fP45ptvMHLkSGzbtq3OjwXcrXPfvn3x8ssvo3nz5jh48CB+/vln9OnTB9OnT6/XY+kzJ2OMMcaUx00ixhhjjAmhT58+WLRoERYtWoSVK1fCxMQEAQEBWLZsGdq3b1/rv23atCm2bduG6OhorFmzBmVlZQDuNol8fX2Rk5ODw4cPY8eOHSguLkbr1q0xZswYhIeHo0uXLrU+dtVj3b/OTpWQkBBNk6hp06Y4cuQIVq9ejS+//BJbtmyBWq2Gg4MDunfvjsjISDRr1qxedWnSpAl++OEHzJs3D4cPH8ahQ4fQtWtXbNmyBS1btqx382XOnDnw9/fH+++/j8zMTDRt2hQzZ87EkiVLHvqrZvrIyRhjjDHlSaTrXGjGGGOMMdagREdHIyYmBklJSfD29jZ0HMYYY4wJiNckYowxxhhjjDHGGGPcJGKMMcYYY4wxxhhjvCYRY4wxxpjidu7ciRMnTjxwnLe3N381jDHGGGOK4TWJGGOMMcYUFhoaio0bNz5wXFRUFKKjo/UfiDHGGGMM3CRijDHGGGOMMcYYY+A1iRhjjDHGGGOMMcYYuEnEGGOMMcYYY4wxxsBNIsYYY4wxxhhjjDEGbhIxxhhjjDHGGGOMMXCTiDHGGGOMMcYYY4yBm0SMMcYYY4wxxhhjDNwkYowxxhhjjDHGGGPgJhFjjDHGGGOMMcYYAzeJGGOMMcYYY4wxxhiA/w/p2x3AYo0O6QAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 1400x500 with 1 Axes>"
       ]
@@ -722,16 +2737,16 @@
    "id": "20f7e139",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-24T09:28:28.915675Z",
-     "iopub.status.busy": "2024-11-24T09:28:28.915430Z",
-     "iopub.status.idle": "2024-11-24T09:28:29.314170Z",
-     "shell.execute_reply": "2024-11-24T09:28:29.313570Z"
+     "iopub.execute_input": "2025-05-08T16:23:25.091970Z",
+     "iopub.status.busy": "2025-05-08T16:23:25.091624Z",
+     "iopub.status.idle": "2025-05-08T16:23:25.443209Z",
+     "shell.execute_reply": "2025-05-08T16:23:25.441976Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1600x900 with 1 Axes>"
       ]
@@ -964,10 +2979,13 @@
   }
  ],
  "metadata": {
+  "jupytext": {
+   "formats": "docs//notebooks//ipynb,docs//notebooks//scripts//py:percent"
+  },
   "kernelspec": {
-   "display_name": "aniEnv",
+   "display_name": ".venv",
    "language": "python",
-   "name": "anienv"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -979,7 +2997,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.6"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/10_pipeline_pandas_output.ipynb b/docs/notebooks/10_pipeline_pandas_output.ipynb
index ddefb69..f9ae568 100644
--- a/docs/notebooks/10_pipeline_pandas_output.ipynb
+++ b/docs/notebooks/10_pipeline_pandas_output.ipynb
@@ -9,12 +9,12 @@
     "\n",
     "This notebook highlights the ability of scikit-mol transformers to return data in DataFrames with meaningful column names. Some use-cases of this feature are illustrated.\n",
     "\n",
-    "***NOTE***: The goal of this notebook is to highlight the advantages of storing transformer output in DataFrames with meaningful column names. This notebook should *not* be considered a set of good practices for training and evaluating QSAR pipelines. The performance metrics of the resulting pipelines are pretty bad: the dataset they have been trained on is pretty small. Tuning the hyperparameters of the Random Forest regressor model (maximum depth of the trees, maximum features to consider when splitting...) can be beneficial. Also including dimensionality reduction / feature selection techniques can be beneficial, since pipelines use an high number of features for a small number of samples. Of course, to further reduce the risk of overfitting, the best hyperparameters and preprocessing techniques should be chosen in cross validation."
+    "***NOTE***: The goal of this notebook is to highlight the advantages of storing transformer output in DataFrames with meaningful column names. This notebook should *not* be considered a set of good practices for training and evaluating QSAR pipelines. The performance metrics of the resulting pipelines are pretty bad: the dataset they have been trained on is pretty small. Tuning the hyperparameters of the Random Forest regressor model (maximum depth of the trees, maximum features to consider when splitting...) can be beneficial. Also including dimensionality reduction / feature selection techniques can be beneficial, since pipelines use a high number of features for a small number of samples. Of course, to further reduce the risk of overfitting, the best hyperparameters and preprocessing techniques should be chosen in cross validation."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 14,
    "id": "cb457069",
    "metadata": {
     "execution": {
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 15,
    "id": "bc72ca09",
    "metadata": {
     "execution": {
@@ -55,7 +55,7 @@
    },
    "outputs": [],
    "source": [
-    "csv_file = Path(\"../tests/data/SLC6A4_active_excapedb_subset.csv\")\n",
+    "csv_file = Path(\"../../tests/data/SLC6A4_active_excapedb_subset.csv\")\n",
     "assert csv_file.is_file()\n",
     "data = pd.read_csv(csv_file)\n",
     "data.drop_duplicates(subset=\"Ambit_InchiKey\", inplace=True)"
@@ -71,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 16,
    "id": "6019d09f",
    "metadata": {
     "execution": {
@@ -88,7 +88,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 17,
    "id": "fe9efa0e",
    "metadata": {
     "execution": {
@@ -128,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 18,
    "id": "33ce774b",
    "metadata": {
     "execution": {
@@ -142,9 +142,10 @@
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-1 {\n",
+       "<style>#sk-container-id-5 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -172,15 +173,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 {\n",
+       "#sk-container-id-5 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 pre {\n",
+       "#sk-container-id-5 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -192,7 +193,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-5 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",
@@ -200,7 +201,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-container {\n",
+       "#sk-container-id-5 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",
@@ -210,7 +211,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -226,14 +227,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "#sk-container-id-5 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",
+       "#sk-container-id-5 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -241,28 +242,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "#sk-container-id-5 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",
+       "#sk-container-id-5 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",
+       "#sk-container-id-5 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",
+       "#sk-container-id-5 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",
+       "#sk-container-id-5 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -280,24 +281,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-1 div.sk-toggleable {\n",
+       "#sk-container-id-5 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",
+       "#sk-container-id-5 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -305,13 +315,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-5 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",
+       "#sk-container-id-5 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -320,12 +330,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-5 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",
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -333,79 +343,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-5 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",
+       "#sk-container-id-5 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",
+       "#sk-container-id-5 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",
+       "#sk-container-id-5 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",
+       "#sk-container-id-5 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",
+       "#sk-container-id-5 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",
+       "#sk-container-id-5 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",
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 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",
+       "#sk-container-id-5 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",
+       "#sk-container-id-5 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",
+       "#sk-container-id-5 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",
+       "#sk-container-id-5 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
+       "#sk-container-id-5 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -415,18 +425,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "#sk-container-id-5 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",
+       "#sk-container-id-5 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",
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -447,7 +457,8 @@
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-unfitted-level-1);\n",
@@ -513,7 +524,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "#sk-container-id-5 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -528,25 +539,25 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-5 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",
+       "#sk-container-id-5 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",
+       "#sk-container-id-5 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>Pipeline(steps=[(&#x27;smilestomoltransformer&#x27;, SmilesToMolTransformer()),\n",
+       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;smilestomoltransformer&#x27;, SmilesToMolTransformer()),\n",
        "                (&#x27;standardizer&#x27;, Standardizer()),\n",
        "                (&#x27;moleculardescriptortransformer&#x27;,\n",
        "                 MolecularDescriptorTransformer(desc_list=[&#x27;MaxAbsEStateIndex&#x27;,\n",
@@ -575,7 +586,7 @@
        "                                                           &#x27;BCUT2D_MRHI&#x27;,\n",
        "                                                           &#x27;BCUT2D_MRLOW&#x27;,\n",
        "                                                           &#x27;AvgIpc&#x27;, &#x27;BalabanJ&#x27;,\n",
-       "                                                           &#x27;BertzCT&#x27;, &#x27;Chi0&#x27;, ...]))])</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  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  sk-toggleable__label-arrow \">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;smilestomoltransformer&#x27;, SmilesToMolTransformer()),\n",
+       "                                                           &#x27;BertzCT&#x27;, &#x27;Chi0&#x27;, ...]))])</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-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;smilestomoltransformer&#x27;, SmilesToMolTransformer()),\n",
        "                (&#x27;standardizer&#x27;, Standardizer()),\n",
        "                (&#x27;moleculardescriptortransformer&#x27;,\n",
        "                 MolecularDescriptorTransformer(desc_list=[&#x27;MaxAbsEStateIndex&#x27;,\n",
@@ -604,7 +615,7 @@
        "                                                           &#x27;BCUT2D_MRHI&#x27;,\n",
        "                                                           &#x27;BCUT2D_MRLOW&#x27;,\n",
        "                                                           &#x27;AvgIpc&#x27;, &#x27;BalabanJ&#x27;,\n",
-       "                                                           &#x27;BertzCT&#x27;, &#x27;Chi0&#x27;, ...]))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  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  sk-toggleable__label-arrow \">SmilesToMolTransformer</label><div class=\"sk-toggleable__content \"><pre>SmilesToMolTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  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  sk-toggleable__label-arrow \">Standardizer</label><div class=\"sk-toggleable__content \"><pre>Standardizer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  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  sk-toggleable__label-arrow \">MolecularDescriptorTransformer</label><div class=\"sk-toggleable__content \"><pre>MolecularDescriptorTransformer(desc_list=[&#x27;MaxAbsEStateIndex&#x27;, &#x27;MaxEStateIndex&#x27;,\n",
+       "                                                           &#x27;BertzCT&#x27;, &#x27;Chi0&#x27;, ...]))])</pre></div> </div></div><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-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SmilesToMolTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.conversions/#scikit_mol.conversions.SmilesToMolTransformer\">?<span>Documentation for SmilesToMolTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>SmilesToMolTransformer()</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-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Standardizer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.standardizer/#scikit_mol.standardizer.Standardizer\">?<span>Documentation for Standardizer</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>Standardizer()</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-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>MolecularDescriptorTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.descriptors/#scikit_mol.descriptors.MolecularDescriptorTransformer\">?<span>Documentation for MolecularDescriptorTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>MolecularDescriptorTransformer(desc_list=[&#x27;MaxAbsEStateIndex&#x27;, &#x27;MaxEStateIndex&#x27;,\n",
        "                                          &#x27;MinAbsEStateIndex&#x27;, &#x27;MinEStateIndex&#x27;,\n",
        "                                          &#x27;qed&#x27;, &#x27;SPS&#x27;, &#x27;MolWt&#x27;,\n",
        "                                          &#x27;HeavyAtomMolWt&#x27;, &#x27;ExactMolWt&#x27;,\n",
@@ -656,7 +667,7 @@
        "                                                           'BertzCT', 'Chi0', ...]))])"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -672,7 +683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 19,
    "id": "2cb55603",
    "metadata": {
     "execution": {
@@ -687,525 +698,16 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:32] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:33] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n"
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:34] DEPRECATION WARNING: please use MorganGenerator\n"
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
      ]
     },
     {
@@ -1519,7 +1021,7 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>159 rows × 210 columns</p>\n",
+       "<p>159 rows × 217 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
@@ -1588,10 +1090,10 @@
        "157               0.0      0.0  \n",
        "158               0.0      0.0  \n",
        "\n",
-       "[159 rows x 210 columns]"
+       "[159 rows x 217 columns]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1614,7 +1116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 20,
    "id": "f56c539c",
    "metadata": {
     "execution": {
@@ -1628,9 +1130,10 @@
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-2 {\n",
+       "<style>#sk-container-id-6 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -1658,15 +1161,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 {\n",
+       "#sk-container-id-6 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 pre {\n",
+       "#sk-container-id-6 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "#sk-container-id-6 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -1678,7 +1181,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-6 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",
@@ -1686,7 +1189,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-container {\n",
+       "#sk-container-id-6 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",
@@ -1696,7 +1199,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -1712,14 +1215,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "#sk-container-id-6 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",
+       "#sk-container-id-6 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -1727,28 +1230,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "#sk-container-id-6 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",
+       "#sk-container-id-6 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",
+       "#sk-container-id-6 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",
+       "#sk-container-id-6 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",
+       "#sk-container-id-6 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -1766,24 +1269,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable {\n",
+       "#sk-container-id-6 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",
+       "#sk-container-id-6 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -1791,13 +1303,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-6 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",
+       "#sk-container-id-6 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -1806,12 +1318,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-6 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",
+       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -1819,79 +1331,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-6 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",
+       "#sk-container-id-6 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",
+       "#sk-container-id-6 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",
+       "#sk-container-id-6 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",
+       "#sk-container-id-6 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",
+       "#sk-container-id-6 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",
+       "#sk-container-id-6 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",
+       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-6 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",
+       "#sk-container-id-6 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",
+       "#sk-container-id-6 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",
+       "#sk-container-id-6 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",
+       "#sk-container-id-6 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-2 div.sk-estimator {\n",
+       "#sk-container-id-6 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -1901,18 +1413,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "#sk-container-id-6 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",
+       "#sk-container-id-6 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",
+       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -1933,7 +1445,8 @@
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-unfitted-level-1);\n",
@@ -1999,7 +1512,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "#sk-container-id-6 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -2014,31 +1527,31 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-6 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",
+       "#sk-container-id-6 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",
+       "#sk-container-id-6 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>Pipeline(steps=[(&#x27;smilestomoltransformer&#x27;, SmilesToMolTransformer()),\n",
+       "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;smilestomoltransformer&#x27;, SmilesToMolTransformer()),\n",
        "                (&#x27;standardizer&#x27;, Standardizer()),\n",
        "                (&#x27;morganfingerprinttransformer&#x27;,\n",
-       "                 MorganFingerprintTransformer())])</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  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  sk-toggleable__label-arrow \">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;smilestomoltransformer&#x27;, SmilesToMolTransformer()),\n",
+       "                 MorganFingerprintTransformer())])</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-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;smilestomoltransformer&#x27;, SmilesToMolTransformer()),\n",
        "                (&#x27;standardizer&#x27;, Standardizer()),\n",
        "                (&#x27;morganfingerprinttransformer&#x27;,\n",
-       "                 MorganFingerprintTransformer())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  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  sk-toggleable__label-arrow \">SmilesToMolTransformer</label><div class=\"sk-toggleable__content \"><pre>SmilesToMolTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  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  sk-toggleable__label-arrow \">Standardizer</label><div class=\"sk-toggleable__content \"><pre>Standardizer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  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  sk-toggleable__label-arrow \">MorganFingerprintTransformer</label><div class=\"sk-toggleable__content \"><pre>MorganFingerprintTransformer()</pre></div> </div></div></div></div></div></div>"
+       "                 MorganFingerprintTransformer())])</pre></div> </div></div><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-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SmilesToMolTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.conversions/#scikit_mol.conversions.SmilesToMolTransformer\">?<span>Documentation for SmilesToMolTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>SmilesToMolTransformer()</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-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Standardizer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.standardizer/#scikit_mol.standardizer.Standardizer\">?<span>Documentation for Standardizer</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>Standardizer()</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-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>MorganFingerprintTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.fingerprints/#scikit_mol.fingerprints.MorganFingerprintTransformer\">?<span>Documentation for MorganFingerprintTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>MorganFingerprintTransformer()</pre></div> </div></div></div></div></div></div>"
       ],
       "text/plain": [
        "Pipeline(steps=[('smilestomoltransformer', SmilesToMolTransformer()),\n",
@@ -2047,7 +1560,7 @@
        "                 MorganFingerprintTransformer())])"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2063,7 +1576,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 21,
    "id": "781d1bc8",
    "metadata": {
     "execution": {
@@ -2074,6 +1587,16 @@
     }
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -2457,7 +1980,7 @@
        "[159 rows x 2048 columns]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2481,7 +2004,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 22,
    "id": "4872ecab",
    "metadata": {
     "execution": {
@@ -2495,9 +2018,10 @@
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-3 {\n",
+       "<style>#sk-container-id-7 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -2525,15 +2049,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 {\n",
+       "#sk-container-id-7 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 pre {\n",
+       "#sk-container-id-7 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "#sk-container-id-7 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -2545,7 +2069,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-7 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",
@@ -2553,7 +2077,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-container {\n",
+       "#sk-container-id-7 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",
@@ -2563,7 +2087,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -2579,14 +2103,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "#sk-container-id-7 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-3 div.sk-parallel {\n",
+       "#sk-container-id-7 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -2594,28 +2118,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "#sk-container-id-7 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-3 div.sk-serial {\n",
+       "#sk-container-id-7 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -2633,24 +2157,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable {\n",
+       "#sk-container-id-7 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-3 label.sk-toggleable__label {\n",
+       "#sk-container-id-7 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -2658,13 +2191,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "#sk-container-id-7 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -2673,12 +2206,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -2686,79 +2219,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-7 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-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-7 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-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-7 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-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-7 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-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-7 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-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-7 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-3 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-3 div.sk-label label {\n",
+       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-7 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-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-7 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-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-7 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-3 div.sk-label label {\n",
+       "#sk-container-id-7 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-3 div.sk-label-container {\n",
+       "#sk-container-id-7 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-3 div.sk-estimator {\n",
+       "#sk-container-id-7 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -2768,18 +2301,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "#sk-container-id-7 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "#sk-container-id-7 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -2800,7 +2333,8 @@
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-unfitted-level-1);\n",
@@ -2866,7 +2400,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "#sk-container-id-7 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -2881,25 +2415,25 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-7 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-3 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-7 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-3 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-7 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-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;smilestomoltransformer&#x27;, SmilesToMolTransformer()),\n",
+       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;smilestomoltransformer&#x27;, SmilesToMolTransformer()),\n",
        "                (&#x27;standardizer&#x27;, Standardizer()),\n",
        "                (&#x27;moleculardescriptortransformer&#x27;,\n",
        "                 MolecularDescriptorTransformer(desc_list=[&#x27;MaxAbsEStateIndex&#x27;,\n",
@@ -2929,7 +2463,7 @@
        "                                                           &#x27;AvgIpc&#x27;, &#x27;BalabanJ&#x27;,\n",
        "                                                           &#x27;BertzCT&#x27;, &#x27;Chi0&#x27;, ...])),\n",
        "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
-       "                (&#x27;randomforestregressor&#x27;, RandomForestRegressor(max_depth=5))])</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  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  sk-toggleable__label-arrow \">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;smilestomoltransformer&#x27;, SmilesToMolTransformer()),\n",
+       "                (&#x27;randomforestregressor&#x27;, RandomForestRegressor(max_depth=5))])</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  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;smilestomoltransformer&#x27;, SmilesToMolTransformer()),\n",
        "                (&#x27;standardizer&#x27;, Standardizer()),\n",
        "                (&#x27;moleculardescriptortransformer&#x27;,\n",
        "                 MolecularDescriptorTransformer(desc_list=[&#x27;MaxAbsEStateIndex&#x27;,\n",
@@ -2959,7 +2493,7 @@
        "                                                           &#x27;AvgIpc&#x27;, &#x27;BalabanJ&#x27;,\n",
        "                                                           &#x27;BertzCT&#x27;, &#x27;Chi0&#x27;, ...])),\n",
        "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
-       "                (&#x27;randomforestregressor&#x27;, RandomForestRegressor(max_depth=5))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  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  sk-toggleable__label-arrow \">SmilesToMolTransformer</label><div class=\"sk-toggleable__content \"><pre>SmilesToMolTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  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  sk-toggleable__label-arrow \">Standardizer</label><div class=\"sk-toggleable__content \"><pre>Standardizer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  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  sk-toggleable__label-arrow \">MolecularDescriptorTransformer</label><div class=\"sk-toggleable__content \"><pre>MolecularDescriptorTransformer(desc_list=[&#x27;MaxAbsEStateIndex&#x27;, &#x27;MaxEStateIndex&#x27;,\n",
+       "                (&#x27;randomforestregressor&#x27;, RandomForestRegressor(max_depth=5))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>SmilesToMolTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.conversions/#scikit_mol.conversions.SmilesToMolTransformer\">?<span>Documentation for SmilesToMolTransformer</span></a></div></label><div class=\"sk-toggleable__content \"><pre>SmilesToMolTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>Standardizer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.standardizer/#scikit_mol.standardizer.Standardizer\">?<span>Documentation for Standardizer</span></a></div></label><div class=\"sk-toggleable__content \"><pre>Standardizer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>MolecularDescriptorTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.descriptors/#scikit_mol.descriptors.MolecularDescriptorTransformer\">?<span>Documentation for MolecularDescriptorTransformer</span></a></div></label><div class=\"sk-toggleable__content \"><pre>MolecularDescriptorTransformer(desc_list=[&#x27;MaxAbsEStateIndex&#x27;, &#x27;MaxEStateIndex&#x27;,\n",
        "                                          &#x27;MinAbsEStateIndex&#x27;, &#x27;MinEStateIndex&#x27;,\n",
        "                                          &#x27;qed&#x27;, &#x27;SPS&#x27;, &#x27;MolWt&#x27;,\n",
        "                                          &#x27;HeavyAtomMolWt&#x27;, &#x27;ExactMolWt&#x27;,\n",
@@ -2976,7 +2510,7 @@
        "                                          &#x27;BCUT2D_CHGLO&#x27;, &#x27;BCUT2D_LOGPHI&#x27;,\n",
        "                                          &#x27;BCUT2D_LOGPLOW&#x27;, &#x27;BCUT2D_MRHI&#x27;,\n",
        "                                          &#x27;BCUT2D_MRLOW&#x27;, &#x27;AvgIpc&#x27;, &#x27;BalabanJ&#x27;,\n",
-       "                                          &#x27;BertzCT&#x27;, &#x27;Chi0&#x27;, ...])</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  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  sk-toggleable__label-arrow \">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link \" 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 \"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  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  sk-toggleable__label-arrow \">&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a></label><div class=\"sk-toggleable__content \"><pre>RandomForestRegressor(max_depth=5)</pre></div> </div></div></div></div></div></div>"
+       "                                          &#x27;BertzCT&#x27;, &#x27;Chi0&#x27;, ...])</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content \"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>RandomForestRegressor</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a></div></label><div class=\"sk-toggleable__content \"><pre>RandomForestRegressor(max_depth=5)</pre></div> </div></div></div></div></div></div>"
       ],
       "text/plain": [
        "Pipeline(steps=[('smilestomoltransformer', SmilesToMolTransformer()),\n",
@@ -3012,7 +2546,7 @@
        "                ('randomforestregressor', RandomForestRegressor(max_depth=5))])"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3034,7 +2568,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 23,
    "id": "f0b2f44f",
    "metadata": {
     "execution": {
@@ -3050,663 +2584,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:35] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:36] DEPRECATION WARNING: please use MorganGenerator\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:37] DEPRECATION WARNING: please use MorganGenerator\n"
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
      ]
     }
    ],
@@ -3727,7 +2612,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 24,
    "id": "8b59851a",
    "metadata": {
     "execution": {
@@ -3738,23 +2623,15 @@
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/esben/python_envs/vscode/lib/python3.10/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
-      "  warnings.warn(\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "{'RMSE': 0.8736959928049254,\n",
-       " 'MAE': 0.707222432887994,\n",
-       " 'R2': 0.12213852746646214}"
+       "{'RMSE': 0.8539077762549014,\n",
+       " 'MAE': 0.7222501985900492,\n",
+       " 'R2': 0.16145333000722317}"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3762,7 +2639,7 @@
    "source": [
     "def compute_metrics(y_true, y_pred):\n",
     "    result = {\n",
-    "        \"RMSE\": mean_squared_error(y_true=y_true, y_pred=y_pred, squared=False),\n",
+    "        \"RMSE\": mean_squared_error(y_true=y_true, y_pred=y_pred) ** 0.5,\n",
     "        \"MAE\": mean_absolute_error(y_true=y_true, y_pred=y_pred),\n",
     "        \"R2\": r2_score(y_true=y_true, y_pred=y_pred),\n",
     "    }\n",
@@ -3775,7 +2652,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 25,
    "id": "68528957",
    "metadata": {
     "execution": {
@@ -3789,9 +2666,10 @@
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-4 {\n",
+       "<style>#sk-container-id-8 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -3819,15 +2697,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 {\n",
+       "#sk-container-id-8 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 pre {\n",
+       "#sk-container-id-8 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "#sk-container-id-8 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -3839,7 +2717,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-8 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",
@@ -3847,7 +2725,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-container {\n",
+       "#sk-container-id-8 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",
@@ -3857,7 +2735,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -3873,14 +2751,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "#sk-container-id-8 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-4 div.sk-parallel {\n",
+       "#sk-container-id-8 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -3888,28 +2766,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "#sk-container-id-8 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-4 div.sk-serial {\n",
+       "#sk-container-id-8 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -3927,24 +2805,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-4 div.sk-toggleable {\n",
+       "#sk-container-id-8 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-4 label.sk-toggleable__label {\n",
+       "#sk-container-id-8 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -3952,13 +2839,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "#sk-container-id-8 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -3967,12 +2854,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -3980,79 +2867,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-8 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-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-8 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-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-8 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-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-8 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-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-8 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-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-8 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-4 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-4 div.sk-label label {\n",
+       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-8 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-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-8 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-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-8 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-4 div.sk-label label {\n",
+       "#sk-container-id-8 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-4 div.sk-label-container {\n",
+       "#sk-container-id-8 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-4 div.sk-estimator {\n",
+       "#sk-container-id-8 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -4062,18 +2949,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "#sk-container-id-8 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "#sk-container-id-8 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -4094,7 +2981,8 @@
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-unfitted-level-1);\n",
@@ -4160,7 +3048,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "#sk-container-id-8 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -4175,31 +3063,31 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-8 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-4 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-8 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-4 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-8 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-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_depth=5)</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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(max_depth=5)</pre></div> </div></div></div></div>"
+       "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_depth=5)</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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" checked><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(max_depth=5)</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "RandomForestRegressor(max_depth=5)"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4219,7 +3107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 26,
    "id": "24011e90",
    "metadata": {
     "execution": {
@@ -4259,27 +3147,27 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>MaxAbsEStateIndex</td>\n",
-       "      <td>0.002776</td>\n",
+       "      <td>0.003240</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>MaxEStateIndex</td>\n",
-       "      <td>0.003859</td>\n",
+       "      <td>0.001821</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>MinAbsEStateIndex</td>\n",
-       "      <td>0.006311</td>\n",
+       "      <td>0.002461</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>MinEStateIndex</td>\n",
-       "      <td>0.004721</td>\n",
+       "      <td>0.004588</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>qed</td>\n",
-       "      <td>0.007605</td>\n",
+       "      <td>0.009382</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -4287,53 +3175,53 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>205</th>\n",
+       "      <th>212</th>\n",
        "      <td>fr_thiazole</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>206</th>\n",
+       "      <th>213</th>\n",
        "      <td>fr_thiocyan</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>207</th>\n",
+       "      <th>214</th>\n",
        "      <td>fr_thiophene</td>\n",
-       "      <td>0.000046</td>\n",
+       "      <td>0.000835</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>208</th>\n",
+       "      <th>215</th>\n",
        "      <td>fr_unbrch_alkane</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>209</th>\n",
+       "      <th>216</th>\n",
        "      <td>fr_urea</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>210 rows × 2 columns</p>\n",
+       "<p>217 rows × 2 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "               feature  importance\n",
-       "0    MaxAbsEStateIndex    0.002776\n",
-       "1       MaxEStateIndex    0.003859\n",
-       "2    MinAbsEStateIndex    0.006311\n",
-       "3       MinEStateIndex    0.004721\n",
-       "4                  qed    0.007605\n",
+       "0    MaxAbsEStateIndex    0.003240\n",
+       "1       MaxEStateIndex    0.001821\n",
+       "2    MinAbsEStateIndex    0.002461\n",
+       "3       MinEStateIndex    0.004588\n",
+       "4                  qed    0.009382\n",
        "..                 ...         ...\n",
-       "205        fr_thiazole    0.000000\n",
-       "206        fr_thiocyan    0.000000\n",
-       "207       fr_thiophene    0.000046\n",
-       "208   fr_unbrch_alkane    0.000000\n",
-       "209            fr_urea    0.000000\n",
+       "212        fr_thiazole    0.000000\n",
+       "213        fr_thiocyan    0.000000\n",
+       "214       fr_thiophene    0.000835\n",
+       "215   fr_unbrch_alkane    0.000000\n",
+       "216            fr_urea    0.000000\n",
        "\n",
-       "[210 rows x 2 columns]"
+       "[217 rows x 2 columns]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4358,7 +3246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 27,
    "id": "713d24f1",
    "metadata": {
     "execution": {
@@ -4398,27 +3286,27 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>PEOE_VSA6</td>\n",
-       "      <td>0.147449</td>\n",
+       "      <td>0.138689</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>VSA_EState5</td>\n",
-       "      <td>0.087963</td>\n",
+       "      <td>0.062832</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>MaxAbsPartialCharge</td>\n",
-       "      <td>0.057491</td>\n",
+       "      <td>0.052241</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>VSA_EState6</td>\n",
-       "      <td>0.034922</td>\n",
+       "      <td>0.048135</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>SlogP_VSA6</td>\n",
-       "      <td>0.028875</td>\n",
+       "      <td>0.027086</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -4426,53 +3314,53 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>205</th>\n",
-       "      <td>fr_hdrzine</td>\n",
+       "      <th>212</th>\n",
+       "      <td>fr_term_acetylene</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>206</th>\n",
-       "      <td>fr_hdrzone</td>\n",
+       "      <th>213</th>\n",
+       "      <td>fr_thiazole</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>207</th>\n",
-       "      <td>fr_imidazole</td>\n",
+       "      <th>214</th>\n",
+       "      <td>fr_thiocyan</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>208</th>\n",
-       "      <td>fr_imide</td>\n",
+       "      <th>215</th>\n",
+       "      <td>fr_unbrch_alkane</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>209</th>\n",
+       "      <th>216</th>\n",
        "      <td>fr_urea</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>210 rows × 2 columns</p>\n",
+       "<p>217 rows × 2 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "                 feature  importance\n",
-       "0              PEOE_VSA6    0.147449\n",
-       "1            VSA_EState5    0.087963\n",
-       "2    MaxAbsPartialCharge    0.057491\n",
-       "3            VSA_EState6    0.034922\n",
-       "4             SlogP_VSA6    0.028875\n",
+       "0              PEOE_VSA6    0.138689\n",
+       "1            VSA_EState5    0.062832\n",
+       "2    MaxAbsPartialCharge    0.052241\n",
+       "3            VSA_EState6    0.048135\n",
+       "4             SlogP_VSA6    0.027086\n",
        "..                   ...         ...\n",
-       "205           fr_hdrzine    0.000000\n",
-       "206           fr_hdrzone    0.000000\n",
-       "207         fr_imidazole    0.000000\n",
-       "208             fr_imide    0.000000\n",
-       "209              fr_urea    0.000000\n",
+       "212    fr_term_acetylene    0.000000\n",
+       "213          fr_thiazole    0.000000\n",
+       "214          fr_thiocyan    0.000000\n",
+       "215     fr_unbrch_alkane    0.000000\n",
+       "216              fr_urea    0.000000\n",
        "\n",
-       "[210 rows x 2 columns]"
+       "[217 rows x 2 columns]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4486,7 +3374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 28,
    "id": "4b97778f",
    "metadata": {
     "execution": {
@@ -4538,7 +3426,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 29,
    "id": "bf8ddaf9",
    "metadata": {
     "execution": {
@@ -4920,13 +3808,13 @@
        "[194 rows x 513 columns]"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "file_cddd_features = Path(\"../tests/data/CDDD_SLC6A4_active_excapedb_subset.csv.gz\")\n",
+    "file_cddd_features = Path(\"../../tests/data/CDDD_SLC6A4_active_excapedb_subset.csv.gz\")\n",
     "assert file_cddd_features.is_file()\n",
     "df_cddd = pd.read_csv(file_cddd_features)\n",
     "df_cddd"
@@ -4944,7 +3832,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 30,
    "id": "db83be01",
    "metadata": {
     "execution": {
@@ -4973,7 +3861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 31,
    "id": "dae995b7",
    "metadata": {
     "execution": {
@@ -4985,7 +3873,7 @@
    },
    "outputs": [],
    "source": [
-    "# The CDDD descriptors couldn't be computed for few molecules and can be removed as outcommented below. The Datafile is now prefiltered\n",
+    "# The CDDD descriptors couldn't be computed for few molecules and can be removed as commented out below. The Datafile is now prefiltered\n",
     "# target_train = data_train.loc[data_train[\"Ambit_InchiKey\"].isin(data_combined_train[\"Ambit_InchiKey\"]), column_target]\n",
     "# target_test = data_test.loc[data_test[\"Ambit_InchiKey\"].isin(data_combined_test[\"Ambit_InchiKey\"]), column_target]\n",
     "\n",
@@ -5005,7 +3893,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 32,
    "id": "dc6de049",
    "metadata": {
     "execution": {
@@ -5019,9 +3907,10 @@
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-5 {\n",
+       "<style>#sk-container-id-9 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -5049,15 +3938,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 {\n",
+       "#sk-container-id-9 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 pre {\n",
+       "#sk-container-id-9 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 input.sk-hidden--visually {\n",
+       "#sk-container-id-9 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -5069,7 +3958,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-9 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",
@@ -5077,7 +3966,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-container {\n",
+       "#sk-container-id-9 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",
@@ -5087,7 +3976,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-9 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -5103,14 +3992,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel-item::after {\n",
+       "#sk-container-id-9 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-5 div.sk-parallel {\n",
+       "#sk-container-id-9 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -5118,28 +4007,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel-item {\n",
+       "#sk-container-id-9 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-9 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-9 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-9 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-5 div.sk-serial {\n",
+       "#sk-container-id-9 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -5157,24 +4046,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-5 div.sk-toggleable {\n",
+       "#sk-container-id-9 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-5 label.sk-toggleable__label {\n",
+       "#sk-container-id-9 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-9 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -5182,13 +4080,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-5 div.sk-toggleable__content {\n",
+       "#sk-container-id-9 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -5197,12 +4095,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-9 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-9 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -5210,79 +4108,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-9 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-9 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-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-9 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-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-9 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-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-9 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-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-9 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-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-9 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-5 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-5 div.sk-label label {\n",
+       "#sk-container-id-9 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-9 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-5 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-9 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-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-9 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-5 div.sk-label label {\n",
+       "#sk-container-id-9 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-5 div.sk-label-container {\n",
+       "#sk-container-id-9 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-5 div.sk-estimator {\n",
+       "#sk-container-id-9 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -5292,18 +4190,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-estimator.fitted {\n",
+       "#sk-container-id-9 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-5 div.sk-estimator:hover {\n",
+       "#sk-container-id-9 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-9 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -5324,7 +4222,8 @@
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-unfitted-level-1);\n",
@@ -5390,7 +4289,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-5 a.estimator_doc_link {\n",
+       "#sk-container-id-9 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -5405,25 +4304,25 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-9 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-5 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-9 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-5 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-9 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-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(transformers=[(&#x27;pipeline-1&#x27;,\n",
+       "</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(transformers=[(&#x27;pipeline-1&#x27;,\n",
        "                                 Pipeline(steps=[(&#x27;smilestomoltransformer&#x27;,\n",
        "                                                  SmilesToMolTransformer()),\n",
        "                                                 (&#x27;standardizer&#x27;,\n",
@@ -5447,11 +4346,11 @@
        "                                                                                            &#x27;BalabanJ&#x27;,\n",
        "                                                                                            &#x27;BertzCT&#x27;,\n",
        "                                                                                            &#x27;Chi0&#x27;, ...]))]),\n",
-       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x729f1412c520&gt;),\n",
+       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3aff50&gt;),\n",
        "                                (&#x27;pipeline-2&#x27;,\n",
        "                                 Pipeline(steps=[(&#x27;functiontransformer&#x27;,\n",
        "                                                  FunctionTransformer())]),\n",
-       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x729f1412ebf0&gt;)])</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  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;ColumnTransformer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for ColumnTransformer</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(transformers=[(&#x27;pipeline-1&#x27;,\n",
+       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3ad2b0&gt;)])</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  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for ColumnTransformer</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(transformers=[(&#x27;pipeline-1&#x27;,\n",
        "                                 Pipeline(steps=[(&#x27;smilestomoltransformer&#x27;,\n",
        "                                                  SmilesToMolTransformer()),\n",
        "                                                 (&#x27;standardizer&#x27;,\n",
@@ -5475,11 +4374,11 @@
        "                                                                                            &#x27;BalabanJ&#x27;,\n",
        "                                                                                            &#x27;BertzCT&#x27;,\n",
        "                                                                                            &#x27;Chi0&#x27;, ...]))]),\n",
-       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x729f1412c520&gt;),\n",
+       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3aff50&gt;),\n",
        "                                (&#x27;pipeline-2&#x27;,\n",
        "                                 Pipeline(steps=[(&#x27;functiontransformer&#x27;,\n",
        "                                                  FunctionTransformer())]),\n",
-       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x729f1412ebf0&gt;)])</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  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">pipeline-1</label><div class=\"sk-toggleable__content \"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x729f1412c520&gt;</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">SmilesToMolTransformer</label><div class=\"sk-toggleable__content \"><pre>SmilesToMolTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">Standardizer</label><div class=\"sk-toggleable__content \"><pre>Standardizer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">MolecularDescriptorTransformer</label><div class=\"sk-toggleable__content \"><pre>MolecularDescriptorTransformer(desc_list=[&#x27;MaxAbsEStateIndex&#x27;, &#x27;MaxEStateIndex&#x27;,\n",
+       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3ad2b0&gt;)])</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  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>pipeline-1</div></div></label><div class=\"sk-toggleable__content \"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3aff50&gt;</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>SmilesToMolTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.conversions/#scikit_mol.conversions.SmilesToMolTransformer\">?<span>Documentation for SmilesToMolTransformer</span></a></div></label><div class=\"sk-toggleable__content \"><pre>SmilesToMolTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>Standardizer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.standardizer/#scikit_mol.standardizer.Standardizer\">?<span>Documentation for Standardizer</span></a></div></label><div class=\"sk-toggleable__content \"><pre>Standardizer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>MolecularDescriptorTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.descriptors/#scikit_mol.descriptors.MolecularDescriptorTransformer\">?<span>Documentation for MolecularDescriptorTransformer</span></a></div></label><div class=\"sk-toggleable__content \"><pre>MolecularDescriptorTransformer(desc_list=[&#x27;MaxAbsEStateIndex&#x27;, &#x27;MaxEStateIndex&#x27;,\n",
        "                                          &#x27;MinAbsEStateIndex&#x27;, &#x27;MinEStateIndex&#x27;,\n",
        "                                          &#x27;qed&#x27;, &#x27;SPS&#x27;, &#x27;MolWt&#x27;,\n",
        "                                          &#x27;HeavyAtomMolWt&#x27;, &#x27;ExactMolWt&#x27;,\n",
@@ -5496,7 +4395,7 @@
        "                                          &#x27;BCUT2D_CHGLO&#x27;, &#x27;BCUT2D_LOGPHI&#x27;,\n",
        "                                          &#x27;BCUT2D_LOGPLOW&#x27;, &#x27;BCUT2D_MRHI&#x27;,\n",
        "                                          &#x27;BCUT2D_MRLOW&#x27;, &#x27;AvgIpc&#x27;, &#x27;BalabanJ&#x27;,\n",
-       "                                          &#x27;BertzCT&#x27;, &#x27;Chi0&#x27;, ...])</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  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">pipeline-2</label><div class=\"sk-toggleable__content \"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x729f1412ebf0&gt;</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;FunctionTransformer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></label><div class=\"sk-toggleable__content \"><pre>FunctionTransformer()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
+       "                                          &#x27;BertzCT&#x27;, &#x27;Chi0&#x27;, ...])</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  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>pipeline-2</div></div></label><div class=\"sk-toggleable__content \"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3ad2b0&gt;</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>NoneType(...)</div><div class=\"caption\">FunctionTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\"sk-toggleable__content \"><pre>FunctionTransformer()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
       ],
       "text/plain": [
        "ColumnTransformer(transformers=[('pipeline-1',\n",
@@ -5523,14 +4422,14 @@
        "                                                                                            'BalabanJ',\n",
        "                                                                                            'BertzCT',\n",
        "                                                                                            'Chi0', ...]))]),\n",
-       "                                 <sklearn.compose._column_transformer.make_column_selector object at 0x729f1412c520>),\n",
+       "                                 <sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3aff50>),\n",
        "                                ('pipeline-2',\n",
        "                                 Pipeline(steps=[('functiontransformer',\n",
        "                                                  FunctionTransformer())]),\n",
-       "                                 <sklearn.compose._column_transformer.make_column_selector object at 0x729f1412ebf0>)])"
+       "                                 <sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3ad2b0>)])"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5557,7 +4456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 33,
    "id": "6ee85c3c",
    "metadata": {
     "execution": {
@@ -5571,9 +4470,10 @@
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-6 {\n",
+       "<style>#sk-container-id-10 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -5601,15 +4501,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 {\n",
+       "#sk-container-id-10 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 pre {\n",
+       "#sk-container-id-10 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 input.sk-hidden--visually {\n",
+       "#sk-container-id-10 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -5621,7 +4521,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-10 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",
@@ -5629,7 +4529,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-container {\n",
+       "#sk-container-id-10 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",
@@ -5639,7 +4539,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-10 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -5655,14 +4555,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-6 div.sk-parallel-item::after {\n",
+       "#sk-container-id-10 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-6 div.sk-parallel {\n",
+       "#sk-container-id-10 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -5670,28 +4570,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-parallel-item {\n",
+       "#sk-container-id-10 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-10 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-10 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-10 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-6 div.sk-serial {\n",
+       "#sk-container-id-10 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -5709,24 +4609,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-6 div.sk-toggleable {\n",
+       "#sk-container-id-10 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-6 label.sk-toggleable__label {\n",
+       "#sk-container-id-10 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-10 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -5734,13 +4643,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-6 div.sk-toggleable__content {\n",
+       "#sk-container-id-10 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -5749,12 +4658,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-10 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-10 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -5762,79 +4671,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-10 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-10 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-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-10 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-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-10 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-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-10 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-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-10 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-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-10 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-6 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-6 div.sk-label label {\n",
+       "#sk-container-id-10 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-10 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-6 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-10 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-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-10 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-6 div.sk-label label {\n",
+       "#sk-container-id-10 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-6 div.sk-label-container {\n",
+       "#sk-container-id-10 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-6 div.sk-estimator {\n",
+       "#sk-container-id-10 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -5844,18 +4753,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-estimator.fitted {\n",
+       "#sk-container-id-10 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-6 div.sk-estimator:hover {\n",
+       "#sk-container-id-10 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-10 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -5876,7 +4785,8 @@
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-unfitted-level-1);\n",
@@ -5942,7 +4852,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-6 a.estimator_doc_link {\n",
+       "#sk-container-id-10 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -5957,25 +4867,25 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-10 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-6 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-10 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-6 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-10 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-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
+       "</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
        "                 ColumnTransformer(transformers=[(&#x27;pipeline-1&#x27;,\n",
        "                                                  Pipeline(steps=[(&#x27;smilestomoltransformer&#x27;,\n",
        "                                                                   SmilesToMolTransformer()),\n",
@@ -5990,13 +4900,13 @@
        "                                                                                                             &#x27;SPS&#x27;,\n",
        "                                                                                                             &#x27;MolW...\n",
        "                                                                                                             &#x27;Chi0&#x27;, ...]))]),\n",
-       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x729f1412c520&gt;),\n",
+       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3aff50&gt;),\n",
        "                                                 (&#x27;pipeline-2&#x27;,\n",
        "                                                  Pipeline(steps=[(&#x27;functiontransformer&#x27;,\n",
        "                                                                   FunctionTransformer())]),\n",
-       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x729f1412ebf0&gt;)])),\n",
+       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3ad2b0&gt;)])),\n",
        "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
-       "                (&#x27;randomforestregressor&#x27;, RandomForestRegressor(max_depth=5))])</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  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
+       "                (&#x27;randomforestregressor&#x27;, RandomForestRegressor(max_depth=5))])</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  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
        "                 ColumnTransformer(transformers=[(&#x27;pipeline-1&#x27;,\n",
        "                                                  Pipeline(steps=[(&#x27;smilestomoltransformer&#x27;,\n",
        "                                                                   SmilesToMolTransformer()),\n",
@@ -6011,13 +4921,13 @@
        "                                                                                                             &#x27;SPS&#x27;,\n",
        "                                                                                                             &#x27;MolW...\n",
        "                                                                                                             &#x27;Chi0&#x27;, ...]))]),\n",
-       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x729f1412c520&gt;),\n",
+       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3aff50&gt;),\n",
        "                                                 (&#x27;pipeline-2&#x27;,\n",
        "                                                  Pipeline(steps=[(&#x27;functiontransformer&#x27;,\n",
        "                                                                   FunctionTransformer())]),\n",
-       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x729f1412ebf0&gt;)])),\n",
+       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3ad2b0&gt;)])),\n",
        "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
-       "                (&#x27;randomforestregressor&#x27;, RandomForestRegressor(max_depth=5))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;columntransformer: ColumnTransformer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for columntransformer: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(transformers=[(&#x27;pipeline-1&#x27;,\n",
+       "                (&#x27;randomforestregressor&#x27;, RandomForestRegressor(max_depth=5))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>columntransformer: ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(transformers=[(&#x27;pipeline-1&#x27;,\n",
        "                                 Pipeline(steps=[(&#x27;smilestomoltransformer&#x27;,\n",
        "                                                  SmilesToMolTransformer()),\n",
        "                                                 (&#x27;standardizer&#x27;,\n",
@@ -6041,11 +4951,11 @@
        "                                                                                            &#x27;BalabanJ&#x27;,\n",
        "                                                                                            &#x27;BertzCT&#x27;,\n",
        "                                                                                            &#x27;Chi0&#x27;, ...]))]),\n",
-       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x729f1412c520&gt;),\n",
+       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3aff50&gt;),\n",
        "                                (&#x27;pipeline-2&#x27;,\n",
        "                                 Pipeline(steps=[(&#x27;functiontransformer&#x27;,\n",
        "                                                  FunctionTransformer())]),\n",
-       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x729f1412ebf0&gt;)])</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  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">pipeline-1</label><div class=\"sk-toggleable__content \"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x729f1412c520&gt;</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">SmilesToMolTransformer</label><div class=\"sk-toggleable__content \"><pre>SmilesToMolTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">Standardizer</label><div class=\"sk-toggleable__content \"><pre>Standardizer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">MolecularDescriptorTransformer</label><div class=\"sk-toggleable__content \"><pre>MolecularDescriptorTransformer(desc_list=[&#x27;MaxAbsEStateIndex&#x27;, &#x27;MaxEStateIndex&#x27;,\n",
+       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3ad2b0&gt;)])</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  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>pipeline-1</div></div></label><div class=\"sk-toggleable__content \"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3aff50&gt;</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>SmilesToMolTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.conversions/#scikit_mol.conversions.SmilesToMolTransformer\">?<span>Documentation for SmilesToMolTransformer</span></a></div></label><div class=\"sk-toggleable__content \"><pre>SmilesToMolTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-42\" type=\"checkbox\" ><label for=\"sk-estimator-id-42\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>Standardizer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.standardizer/#scikit_mol.standardizer.Standardizer\">?<span>Documentation for Standardizer</span></a></div></label><div class=\"sk-toggleable__content \"><pre>Standardizer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-43\" type=\"checkbox\" ><label for=\"sk-estimator-id-43\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>MolecularDescriptorTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.descriptors/#scikit_mol.descriptors.MolecularDescriptorTransformer\">?<span>Documentation for MolecularDescriptorTransformer</span></a></div></label><div class=\"sk-toggleable__content \"><pre>MolecularDescriptorTransformer(desc_list=[&#x27;MaxAbsEStateIndex&#x27;, &#x27;MaxEStateIndex&#x27;,\n",
        "                                          &#x27;MinAbsEStateIndex&#x27;, &#x27;MinEStateIndex&#x27;,\n",
        "                                          &#x27;qed&#x27;, &#x27;SPS&#x27;, &#x27;MolWt&#x27;,\n",
        "                                          &#x27;HeavyAtomMolWt&#x27;, &#x27;ExactMolWt&#x27;,\n",
@@ -6062,7 +4972,7 @@
        "                                          &#x27;BCUT2D_CHGLO&#x27;, &#x27;BCUT2D_LOGPHI&#x27;,\n",
        "                                          &#x27;BCUT2D_LOGPLOW&#x27;, &#x27;BCUT2D_MRHI&#x27;,\n",
        "                                          &#x27;BCUT2D_MRLOW&#x27;, &#x27;AvgIpc&#x27;, &#x27;BalabanJ&#x27;,\n",
-       "                                          &#x27;BertzCT&#x27;, &#x27;Chi0&#x27;, ...])</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  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">pipeline-2</label><div class=\"sk-toggleable__content \"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x729f1412ebf0&gt;</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;FunctionTransformer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></label><div class=\"sk-toggleable__content \"><pre>FunctionTransformer()</pre></div> </div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link \" 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 \"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a></label><div class=\"sk-toggleable__content \"><pre>RandomForestRegressor(max_depth=5)</pre></div> </div></div></div></div></div></div>"
+       "                                          &#x27;BertzCT&#x27;, &#x27;Chi0&#x27;, ...])</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  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-44\" type=\"checkbox\" ><label for=\"sk-estimator-id-44\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>pipeline-2</div></div></label><div class=\"sk-toggleable__content \"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3ad2b0&gt;</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-45\" type=\"checkbox\" ><label for=\"sk-estimator-id-45\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>NoneType(...)</div><div class=\"caption\">FunctionTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\"sk-toggleable__content \"><pre>FunctionTransformer()</pre></div> </div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-46\" type=\"checkbox\" ><label for=\"sk-estimator-id-46\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content \"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-47\" type=\"checkbox\" ><label for=\"sk-estimator-id-47\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>RandomForestRegressor</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a></div></label><div class=\"sk-toggleable__content \"><pre>RandomForestRegressor(max_depth=5)</pre></div> </div></div></div></div></div></div>"
       ],
       "text/plain": [
        "Pipeline(steps=[('columntransformer',\n",
@@ -6080,16 +4990,16 @@
        "                                                                                                             'SPS',\n",
        "                                                                                                             'MolW...\n",
        "                                                                                                             'Chi0', ...]))]),\n",
-       "                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x729f1412c520>),\n",
+       "                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3aff50>),\n",
        "                                                 ('pipeline-2',\n",
        "                                                  Pipeline(steps=[('functiontransformer',\n",
        "                                                                   FunctionTransformer())]),\n",
-       "                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x729f1412ebf0>)])),\n",
+       "                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3ad2b0>)])),\n",
        "                ('standardscaler', StandardScaler()),\n",
        "                ('randomforestregressor', RandomForestRegressor(max_depth=5))])"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6105,7 +5015,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 34,
    "id": "03960958",
    "metadata": {
     "execution": {
@@ -6120,661 +5030,25 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:38] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:39] DEPRECATION WARNING: please use MorganGenerator\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "[10:28:42] DEPRECATION WARNING: please use MorganGenerator\n",
-      "/home/esben/python_envs/vscode/lib/python3.10/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
-      "  warnings.warn(\n"
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "{'RMSE': 0.8314055216871027,\n",
-       " 'MAE': 0.7061918187521163,\n",
-       " 'R2': 0.2104167870060334}"
+       "{'RMSE': 0.8008666941742995,\n",
+       " 'MAE': 0.6900085446099777,\n",
+       " 'R2': 0.26735673218396616}"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6796,7 +5070,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 35,
    "id": "6ce2fe53",
    "metadata": {
     "execution": {
@@ -6836,15 +5110,15 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>descriptors</th>\n",
-       "      <td>0.873696</td>\n",
-       "      <td>0.707222</td>\n",
-       "      <td>0.122139</td>\n",
+       "      <td>0.853908</td>\n",
+       "      <td>0.722250</td>\n",
+       "      <td>0.161453</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>combined</th>\n",
-       "      <td>0.831406</td>\n",
-       "      <td>0.706192</td>\n",
-       "      <td>0.210417</td>\n",
+       "      <td>0.800867</td>\n",
+       "      <td>0.690009</td>\n",
+       "      <td>0.267357</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -6852,11 +5126,11 @@
       ],
       "text/plain": [
        "                 RMSE       MAE        R2\n",
-       "descriptors  0.873696  0.707222  0.122139\n",
-       "combined     0.831406  0.706192  0.210417"
+       "descriptors  0.853908  0.722250  0.161453\n",
+       "combined     0.800867  0.690009  0.267357"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6873,13 +5147,13 @@
    "id": "83b7fd13",
    "metadata": {},
    "source": [
-    "All performance metrics were improved by the includion of the CDDD features.\n",
+    "All performance metrics were improved by the inclusion of the CDDD features.\n",
     "Let's analyze the feature importances of the model:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 36,
    "id": "9c98ac71",
    "metadata": {
     "execution": {
@@ -6919,27 +5193,27 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>pipeline-1__PEOE_VSA6</td>\n",
-       "      <td>0.078597</td>\n",
+       "      <td>0.077408</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>pipeline-2__cddd_102</td>\n",
-       "      <td>0.064366</td>\n",
+       "      <td>0.069598</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>pipeline-2__cddd_378</td>\n",
-       "      <td>0.045695</td>\n",
+       "      <td>pipeline-2__cddd_369</td>\n",
+       "      <td>0.051396</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>pipeline-1__VSA_EState5</td>\n",
-       "      <td>0.032759</td>\n",
+       "      <td>pipeline-2__cddd_378</td>\n",
+       "      <td>0.044292</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>pipeline-2__cddd_369</td>\n",
-       "      <td>0.030738</td>\n",
+       "      <td>pipeline-2__cddd_372</td>\n",
+       "      <td>0.030435</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -6947,53 +5221,53 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>717</th>\n",
-       "      <td>pipeline-1__fr_lactam</td>\n",
+       "      <th>724</th>\n",
+       "      <td>pipeline-1__PEOE_VSA3</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>718</th>\n",
-       "      <td>pipeline-1__fr_NH2</td>\n",
+       "      <th>725</th>\n",
+       "      <td>pipeline-1__Ipc</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>719</th>\n",
-       "      <td>pipeline-1__SMR_VSA2</td>\n",
+       "      <th>726</th>\n",
+       "      <td>pipeline-1__SMR_VSA8</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>720</th>\n",
-       "      <td>pipeline-1__fr_Imine</td>\n",
+       "      <th>727</th>\n",
+       "      <td>pipeline-2__cddd_461</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>721</th>\n",
-       "      <td>pipeline-1__fr_phos_acid</td>\n",
+       "      <th>728</th>\n",
+       "      <td>pipeline-1__NumRadicalElectrons</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>722 rows × 2 columns</p>\n",
+       "<p>729 rows × 2 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                      feature  importance\n",
-       "0       pipeline-1__PEOE_VSA6    0.078597\n",
-       "1        pipeline-2__cddd_102    0.064366\n",
-       "2        pipeline-2__cddd_378    0.045695\n",
-       "3     pipeline-1__VSA_EState5    0.032759\n",
-       "4        pipeline-2__cddd_369    0.030738\n",
-       "..                        ...         ...\n",
-       "717     pipeline-1__fr_lactam    0.000000\n",
-       "718        pipeline-1__fr_NH2    0.000000\n",
-       "719      pipeline-1__SMR_VSA2    0.000000\n",
-       "720      pipeline-1__fr_Imine    0.000000\n",
-       "721  pipeline-1__fr_phos_acid    0.000000\n",
-       "\n",
-       "[722 rows x 2 columns]"
+       "                             feature  importance\n",
+       "0              pipeline-1__PEOE_VSA6    0.077408\n",
+       "1               pipeline-2__cddd_102    0.069598\n",
+       "2               pipeline-2__cddd_369    0.051396\n",
+       "3               pipeline-2__cddd_378    0.044292\n",
+       "4               pipeline-2__cddd_372    0.030435\n",
+       "..                               ...         ...\n",
+       "724            pipeline-1__PEOE_VSA3    0.000000\n",
+       "725                  pipeline-1__Ipc    0.000000\n",
+       "726             pipeline-1__SMR_VSA8    0.000000\n",
+       "727             pipeline-2__cddd_461    0.000000\n",
+       "728  pipeline-1__NumRadicalElectrons    0.000000\n",
+       "\n",
+       "[729 rows x 2 columns]"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7011,7 +5285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 37,
    "id": "9dbd2a9e",
    "metadata": {
     "execution": {
@@ -7029,9 +5303,9 @@
       "The 5 most important features are:\n",
       "pipeline-1__PEOE_VSA6\n",
       "pipeline-2__cddd_102\n",
+      "pipeline-2__cddd_369\n",
       "pipeline-2__cddd_378\n",
-      "pipeline-1__VSA_EState5\n",
-      "pipeline-2__cddd_369\n"
+      "pipeline-2__cddd_372\n"
      ]
     }
    ],
@@ -7054,8 +5328,11 @@
   }
  ],
  "metadata": {
+  "jupytext": {
+   "formats": "docs//notebooks//ipynb,docs//notebooks//scripts//py:percent"
+  },
   "kernelspec": {
-   "display_name": "scikit-mol",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -7069,7 +5346,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.6"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/11_safe_inference.ipynb b/docs/notebooks/11_safe_inference.ipynb
index 1c67167..126086e 100644
--- a/docs/notebooks/11_safe_inference.ipynb
+++ b/docs/notebooks/11_safe_inference.ipynb
@@ -9,14 +9,14 @@
     "\n",
     "I think everyone which have worked with SMILES and RDKit sooner or later come across a SMILES that doesn't parse. It can happen if the SMILES was produced with a different toolkit that are less strict with e.g. valence rules, or maybe a characher was missing in the copying from the email. During curation of the dataset for training models, these SMILES need to be identfied and eventually fixed or removed. But what happens when we are finished with our modelling? What kind of molecules and SMILES will a user of the model send for the model in the future when it's in deployment. What kind of SMILES will a generative model create that we need to predict? We don't know and we won't know. So it's kind of crucial to be able to handle these situations. Scikit-Learn models usually simply explodes the entire batch that are being predicted. This is where safe_inference_mode was introduced in Scikit-Mol. With the introduction all transformers got a safe inference mode, where they handle invalid input. How they handle it depends a bit on the transformer, so we will go through the different usual steps and see how things have changed with the introduction of the safe inference mode.\n",
     "\n",
-    "NOTE! In the following demonstration I switch on the safe inference mode individually for demonstration purposes. I would not recommend to do that while building and training models, instead I would switch it on _after_ training and evaluation (more on that later). Otherwise there's a risk to train on the 2% of a dataset that didn't fail....\n",
+    "NOTE! In the following demonstration I switch on the safe inference mode individually for demonstration purposes. I would not recommend to do that while building and training models, instead I would switch it on _after_ training and evaluation (more on that later). Otherwise, there's a risk to train on the 2% of a dataset that didn't fail....\n",
     "\n",
     "First some imports and test SMILES and molecules."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 10,
    "id": "ac780f4c",
    "metadata": {
     "execution": {
@@ -30,16 +30,16 @@
     {
      "data": {
       "text/plain": [
-       "array([[<rdkit.Chem.rdchem.Mol object at 0x782cbbb36c70>],\n",
-       "       [<rdkit.Chem.rdchem.Mol object at 0x782cbbb36b90>],\n",
-       "       [<rdkit.Chem.rdchem.Mol object at 0x782cbbb36c00>],\n",
-       "       [<rdkit.Chem.rdchem.Mol object at 0x782cbbb36ce0>],\n",
+       "array([[<rdkit.Chem.rdchem.Mol object at 0x7efcedcd6d50>],\n",
+       "       [<rdkit.Chem.rdchem.Mol object at 0x7efcedcd6dc0>],\n",
+       "       [<rdkit.Chem.rdchem.Mol object at 0x7efcedcd6e30>],\n",
+       "       [<rdkit.Chem.rdchem.Mol object at 0x7efcedcd6ea0>],\n",
        "       [InvalidMol('SmilesToMolTransformer(safe_inference_mode=True)', error='Invalid Molecule: Explicit valence for atom # 0 N, 4, is greater than permitted')],\n",
        "       [InvalidMol('SmilesToMolTransformer(safe_inference_mode=True)', error='Invalid SMILES: I'm not a SMILES')]],\n",
        "      dtype=object)"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -72,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 11,
    "id": "44a6019c",
    "metadata": {
     "execution": {
@@ -86,13 +86,13 @@
     {
      "data": {
       "text/plain": [
-       "[array([<rdkit.Chem.rdchem.Mol object at 0x782cbbb36c70>], dtype=object),\n",
-       " array([<rdkit.Chem.rdchem.Mol object at 0x782cbbb36b90>], dtype=object),\n",
-       " array([<rdkit.Chem.rdchem.Mol object at 0x782cbbb36c00>], dtype=object),\n",
-       " array([<rdkit.Chem.rdchem.Mol object at 0x782cbbb36ce0>], dtype=object)]"
+       "[array([<rdkit.Chem.rdchem.Mol object at 0x7efcedcd6d50>], dtype=object),\n",
+       " array([<rdkit.Chem.rdchem.Mol object at 0x7efcedcd6dc0>], dtype=object),\n",
+       " array([<rdkit.Chem.rdchem.Mol object at 0x7efcedcd6e30>], dtype=object),\n",
+       " array([<rdkit.Chem.rdchem.Mol object at 0x7efcedcd6ea0>], dtype=object)]"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -111,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 12,
    "id": "8286fd44",
    "metadata": {
     "execution": {
@@ -125,13 +125,13 @@
     {
      "data": {
       "text/plain": [
-       "array([<rdkit.Chem.rdchem.Mol object at 0x782cbbb36c70>,\n",
-       "       <rdkit.Chem.rdchem.Mol object at 0x782cbbb36b90>,\n",
-       "       <rdkit.Chem.rdchem.Mol object at 0x782cbbb36c00>,\n",
-       "       <rdkit.Chem.rdchem.Mol object at 0x782cbbb36ce0>], dtype=object)"
+       "array([<rdkit.Chem.rdchem.Mol object at 0x7efcedcd6d50>,\n",
+       "       <rdkit.Chem.rdchem.Mol object at 0x7efcedcd6dc0>,\n",
+       "       <rdkit.Chem.rdchem.Mol object at 0x7efcedcd6e30>,\n",
+       "       <rdkit.Chem.rdchem.Mol object at 0x7efcedcd6ea0>], dtype=object)"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -151,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 13,
    "id": "9a705642",
    "metadata": {
     "execution": {
@@ -200,7 +200,7 @@
        "  fill_value=1e+20)"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -218,12 +218,12 @@
    "id": "a5e2b301",
    "metadata": {},
    "source": [
-    "However, currently scikit-learn models accepts masked arrays, but they do not respect the mask! So if you fed it directly to the model to train, it would seemingly work, but the invalid samples would all have the fill_value, meaning you could get weird results. Instead we need the last part of the puzzle, the SafeInferenceWrapper class."
+    "However, currently scikit-learn models accepts masked arrays, but they do not respect the mask! So if you fed it directly to the model to train, it would seemingly work, but the invalid samples would all have the fill_value, meaning you could get weird results. Instead, we need the last part of the puzzle, the SafeInferenceWrapper class."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 14,
    "id": "37987dc9",
    "metadata": {
     "execution": {
@@ -239,7 +239,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/esben/git/scikit-mol/scikit_mol/safeinference.py:49: UserWarning: SafeInferenceWrapper is in safe_inference_mode during use of fit and invalid data detected. This mode is intended for safe inference in production, not for training and evaluation.\n",
+      "/home/anton/projects/scikit-mol/scikit_mol/safeinference.py:64: UserWarning: SafeInferenceWrapper is in safe_inference_mode during use of fit and invalid data detected. This mode is intended for safe inference in production, not for training and evaluation.\n",
+      "  warnings.warn(\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
       "  warnings.warn(\n"
      ]
     },
@@ -249,7 +251,7 @@
        "array([ 0.,  1.,  0.,  1., nan, nan])"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -277,7 +279,7 @@
    "metadata": {},
    "source": [
     "The prediction went fine both in fit and in prediction, where the result shows `nan` for the invalid entries. However, please note fit in sage_inference_mode is not recommended in a training session, but you are warned and not blocked, because maybe you know what you do and do it on purpose.\n",
-    "The SafeInferenceMapper both handles rows that are masked in masked arrays, but also checks rows for nonfinite values and filters these away. Sometimes some descriptors may return a inf or nan, even though the molecule itself is valid. The masking of nonfinite values can be switched off, maybe you are using a model that can handle missing data and only want to filter away invalid molecules.\n",
+    "The SafeInferenceMapper both handles rows that are masked in masked arrays, but also checks rows for nonfinite values and filters these away. Sometimes some descriptors may return an inf or nan, even though the molecule itself is valid. The masking of nonfinite values can be switched off, maybe you are using a model that can handle missing data and only want to filter away invalid molecules.\n",
     "\n",
     "## Setting safe_inference_mode post-training\n",
     "As I said before I believe in catching errors and fixing those during training, but what do we do when we need to switch on safe inference mode for all objects in a pipeline? There's of course a tool for that, so lets demo that:"
@@ -285,7 +287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 15,
    "id": "51436aa8",
    "metadata": {
     "execution": {
@@ -354,7 +356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 16,
    "id": "b8dbd88c",
    "metadata": {
     "execution": {
@@ -539,7 +541,7 @@
        "[4 rows x 25 columns]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -563,7 +565,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 17,
    "id": "710ceeb0",
    "metadata": {
     "execution": {
@@ -804,7 +806,7 @@
        "[6 rows x 25 columns]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -819,12 +821,12 @@
    "id": "b87b46b3",
    "metadata": {},
    "source": [
-    "The second output is no longer integers, but floats. As most sklearn models cast input arrays to float32 internally, this difference is likely benign, but that's not guaranteed! Thus if you want to use pandas output for your production models, do check that the final outputs are the same for the valid rows, with and without a single invalid row. Alternatively the dtype for the output of the transformer can be switched to float for consistency if its supported by the transformer."
+    "The second output is no longer integers, but floats. As most sklearn models cast input arrays to float32 internally, this difference is likely benign, but that's not guaranteed! Thus, if you want to use pandas output for your production models, do check that the final outputs are the same for the valid rows, with and without a single invalid row. Alternatively the dtype for the output of the transformer can be switched to float for consistency if it's supported by the transformer."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 18,
    "id": "bbfe1ec0",
    "metadata": {
     "execution": {
@@ -835,14 +837,6 @@
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/esben/git/scikit-mol/scikit_mol/fingerprints/morgan.py:69: DeprecationWarning: dtype is no longer supported, due to move to generator based fingerprints\n",
-      "  self.dtype = dtype\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -1017,7 +1011,7 @@
        "[4 rows x 25 columns]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1041,8 +1035,11 @@
   }
  ],
  "metadata": {
+  "jupytext": {
+   "formats": "docs//notebooks//ipynb,docs//notebooks//scripts//py:percent"
+  },
   "kernelspec": {
-   "display_name": "vscode",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -1056,7 +1053,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.6"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/12_custom_fingerprint_transformer.ipynb b/docs/notebooks/12_custom_fingerprint_transformer.ipynb
index ce56b58..9f8c0cb 100644
--- a/docs/notebooks/12_custom_fingerprint_transformer.ipynb
+++ b/docs/notebooks/12_custom_fingerprint_transformer.ipynb
@@ -30,6 +30,22 @@
    "id": "1ed1c7f0",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/joblib/externals/loky/backend/fork_exec.py:38: DeprecationWarning: This process (pid=43991) is multi-threaded, use of fork() may lead to deadlocks in the child.\n",
+      "  pid = os.fork()\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/joblib/externals/loky/backend/fork_exec.py:38: DeprecationWarning: This process (pid=43991) is multi-threaded, use of fork() may lead to deadlocks in the child.\n",
+      "  pid = os.fork()\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/joblib/externals/loky/backend/fork_exec.py:38: DeprecationWarning: This process (pid=43991) is multi-threaded, use of fork() may lead to deadlocks in the child.\n",
+      "  pid = os.fork()\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/joblib/externals/loky/backend/fork_exec.py:38: DeprecationWarning: This process (pid=43991) is multi-threaded, use of fork() may lead to deadlocks in the child.\n",
+      "  pid = os.fork()\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/joblib/externals/loky/backend/fork_exec.py:38: DeprecationWarning: This process (pid=43991) is multi-threaded, use of fork() may lead to deadlocks in the child.\n",
+      "  pid = os.fork()\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
@@ -39,7 +55,7 @@
        "       ...,\n",
        "       [2., 2., 2., ..., 2., 2., 2.],\n",
        "       [2., 2., 2., ..., 2., 2., 2.],\n",
-       "       [2., 2., 2., ..., 2., 2., 2.]])"
+       "       [2., 2., 2., ..., 2., 2., 2.]], shape=(100, 64))"
       ]
      },
      "execution_count": 1,
@@ -52,16 +68,20 @@
     "import numpy as np\n",
     "from rdkit import Chem\n",
     "\n",
+    "\n",
     "class DummyFingerprintTransformer(BaseFpsTransformer):\n",
-    "    def __init__(self, fpSize=64, n_jobs=1, safe_inference_mode = False):\n",
+    "    def __init__(self, fpSize=64, n_jobs=1, safe_inference_mode=False):\n",
     "        self.fpSize = fpSize\n",
-    "        super().__init__(n_jobs=n_jobs, safe_inference_mode=safe_inference_mode, name=\"dummy\")\n",
+    "        super().__init__(\n",
+    "            n_jobs=n_jobs, safe_inference_mode=safe_inference_mode, name=\"dummy\"\n",
+    "        )\n",
     "\n",
     "    def _transform_mol(self, mol):\n",
     "        return mol.GetNumAtoms() * np.ones(self.fpSize)\n",
-    "    \n",
+    "\n",
+    "\n",
     "trans = DummyFingerprintTransformer(n_jobs=4)\n",
-    "mols = [Chem.MolFromSmiles('CC')] * 100\n",
+    "mols = [Chem.MolFromSmiles(\"CC\")] * 100\n",
     "trans.transform(mols)"
    ]
   },
@@ -93,7 +113,7 @@
        "       ...,\n",
        "       [0, 0, 0, ..., 0, 0, 0],\n",
        "       [0, 0, 0, ..., 0, 0, 0],\n",
-       "       [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)"
+       "       [0, 0, 0, ..., 0, 0, 0]], shape=(100, 512), dtype=uint8)"
       ]
      },
      "execution_count": 2,
@@ -104,15 +124,21 @@
    "source": [
     "from rdkit.Chem import rdFingerprintGenerator\n",
     "\n",
+    "\n",
     "class UnpickableFingerprintTransformer(BaseFpsTransformer):\n",
     "    def __init__(self, fpSize=1024, n_jobs=1, safe_inference_mode=False, **kwargs):\n",
     "        self.fpSize = fpSize\n",
-    "        super().__init__(n_jobs=n_jobs, safe_inference_mode=safe_inference_mode, **kwargs)\n",
-    "        self.fp_gen = rdFingerprintGenerator.GetRDKitFPGenerator(maxPath=2, fpSize=self.fpSize)\n",
+    "        super().__init__(\n",
+    "            n_jobs=n_jobs, safe_inference_mode=safe_inference_mode, **kwargs\n",
+    "        )\n",
+    "        self.fp_gen = rdFingerprintGenerator.GetRDKitFPGenerator(\n",
+    "            maxPath=2, fpSize=self.fpSize\n",
+    "        )\n",
     "\n",
     "    def _transform_mol(self, mol):\n",
     "        return self.fp_gen.GetFingerprintAsNumPy(mol)\n",
-    "    \n",
+    "\n",
+    "\n",
     "trans = UnpickableFingerprintTransformer(n_jobs=4, fpSize=512)\n",
     "trans.transform(mols)"
    ]
@@ -135,10 +161,32 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "n_jobs=1 is fine\n",
+      "n_jobs=1 is fine\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/joblib/externals/loky/backend/fork_exec.py:38: DeprecationWarning: This process (pid=43991) is multi-threaded, use of fork() may lead to deadlocks in the child.\n",
+      "  pid = os.fork()\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "n_jobs=2 is not fine, because the generator passed as an argument is not picklable\n",
       "Error msg: Could not pickle the task to send it to the workers.\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/joblib/externals/loky/backend/fork_exec.py:38: DeprecationWarning: This process (pid=43991) is multi-threaded, use of fork() may lead to deadlocks in the child.\n",
+      "  pid = os.fork()\n"
+     ]
     }
    ],
    "source": [
@@ -146,6 +194,7 @@
     "    def __init__(self, generator, n_jobs=1):\n",
     "        self.generator = generator\n",
     "        super().__init__(n_jobs=n_jobs)\n",
+    "\n",
     "    def _transform_mol(self, mol):\n",
     "        return self.generator.GetFingerprint(mol)\n",
     "\n",
@@ -156,7 +205,9 @@
     "try:\n",
     "    BadTransformer(fp_gen, n_jobs=2).transform(mols)\n",
     "except Exception as e:\n",
-    "    print(\"n_jobs=2 is not fine, because the generator passed as an argument is not picklable\")\n",
+    "    print(\n",
+    "        \"n_jobs=2 is not fine, because the generator passed as an argument is not picklable\"\n",
+    "    )\n",
     "    print(f\"Error msg: {e}\")"
    ]
   },
@@ -200,13 +251,16 @@
     "class NamedTansformer1(UnpickableFingerprintTransformer):\n",
     "    pass\n",
     "\n",
+    "\n",
     "class NamedTansformer2(UnpickableFingerprintTransformer):\n",
     "    def __init__(self):\n",
     "        super().__init__(name=\"fp_fancy\")\n",
     "\n",
+    "\n",
     "class FancyFingerprintTransformer(UnpickableFingerprintTransformer):\n",
     "    pass\n",
     "\n",
+    "\n",
     "print(NamedTansformer1().get_feature_names_out())\n",
     "print(NamedTansformer2().get_feature_names_out())\n",
     "print(FancyFingerprintTransformer().get_feature_names_out())"
@@ -215,10 +269,10 @@
  ],
  "metadata": {
   "jupytext": {
-   "formats": "ipynb,py:percent"
+   "formats": "docs//notebooks//ipynb,docs//notebooks//scripts//py:percent"
   },
   "kernelspec": {
-   "display_name": "vscode",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -232,7 +286,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.6"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/13_applicability_domain.ipynb b/docs/notebooks/13_applicability_domain.ipynb
index fcac2a4..e456495 100644
--- a/docs/notebooks/13_applicability_domain.ipynb
+++ b/docs/notebooks/13_applicability_domain.ipynb
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 5,
    "id": "40500fae",
    "metadata": {},
    "outputs": [],
@@ -26,7 +26,6 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "from rdkit import Chem\n",
-    "from rdkit.Chem import Draw\n",
     "from rdkit.Chem import PandasTools\n",
     "import matplotlib.pyplot as plt\n",
     "from sklearn.model_selection import train_test_split\n",
@@ -34,690 +33,206 @@
     "from sklearn.pipeline import Pipeline\n",
     "from sklearn.preprocessing import StandardScaler\n",
     "from sklearn.decomposition import PCA\n",
-    "import pathlib\n",
     "\n",
-    "\n",
-    "from scikit_mol.fingerprints import MorganFingerprintTransformer\n",
-    "from scikit_mol.applicability import KNNApplicabilityDomain, LeverageApplicabilityDomain"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e5d1277e",
-   "metadata": {},
-   "source": [
-    "## Load and Prepare Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "79d3b853",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 out of 7228 SMILES failed in conversion\n"
-     ]
-    }
-   ],
-   "source": [
-    "full_set = True\n",
-    "\n",
-    "if full_set:\n",
-    "    csv_file = \"SLC6A4_active_excape_export.csv\"\n",
-    "    if not pathlib.Path(csv_file).exists():\n",
-    "        import urllib.request\n",
-    "\n",
-    "        url = \"https://ndownloader.figshare.com/files/25747817\"\n",
-    "        urllib.request.urlretrieve(url, csv_file)\n",
-    "else:\n",
-    "    csv_file = \"../tests/data/SLC6A4_active_excapedb_subset.csv\"\n",
-    "\n",
-    "data = pd.read_csv(csv_file)\n",
-    "\n",
-    "#Could also build a pipeline to convert the smiles to mols using SmilesToMolTransformer\n",
-    "PandasTools.AddMoleculeColumnToFrame(data, smilesCol=\"SMILES\")\n",
-    "print(f\"{data.ROMol.isna().sum()} out of {len(data)} SMILES failed in conversion\")\n",
-    "\n",
-    "# Split into train/val/test\n",
-    "X = data.ROMol\n",
-    "y = data.pXC50\n",
-    "\n",
-    "X_temp, X_test, y_temp, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "X_train, X_val, y_train, y_val = train_test_split(X_temp, y_temp, test_size=0.25, random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e2896ad5",
-   "metadata": {},
-   "source": [
-    "## Example 1: k-NN Applicability Domain with Binary Morgan Fingerprints\n",
-    "\n",
-    "In this example, we'll use binary Morgan fingerprints and a k-NN based applicability domain with Tanimoto distance.\n",
-    "This is particularly suitable for binary fingerprints as the Tanimoto coefficient is a natural similarity measure for them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "9c89148b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/numpy/core/fromnumeric.py:59: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
-      "  return bound(*args, **kwds)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\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: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\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: 0.5em;\n",
-       "  text-align: center;\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>Pipeline(steps=[(&#x27;fp&#x27;, MorganFingerprintTransformer()),\n",
-       "                (&#x27;rf&#x27;, RandomForestRegressor(n_jobs=-1, random_state=61453))])</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\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;fp&#x27;, MorganFingerprintTransformer()),\n",
-       "                (&#x27;rf&#x27;, RandomForestRegressor(n_jobs=-1, random_state=61453))])</pre></div> </div></div><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-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>MorganFingerprintTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.fingerprints/#scikit_mol.fingerprints.MorganFingerprintTransformer\">?<span>Documentation for MorganFingerprintTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>MorganFingerprintTransformer()</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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(n_jobs=-1, random_state=61453)</pre></div> </div></div></div></div></div></div>"
-      ],
-      "text/plain": [
-       "Pipeline(steps=[('fp', MorganFingerprintTransformer()),\n",
-       "                ('rf', RandomForestRegressor(n_jobs=-1, random_state=61453))])"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+    "from scikit_mol.conversions import SmilesToMolTransformer\n",
+    "from scikit_mol.fingerprints import MorganFingerprintTransformer\n",
+    "from scikit_mol.applicability import KNNApplicabilityDomain, LeverageApplicabilityDomain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5d1277e",
+   "metadata": {},
    "source": [
-    "# Create pipeline for binary fingerprints\n",
-    "binary_fp_pipe = Pipeline([\n",
-    "    ('fp', MorganFingerprintTransformer(fpSize=2048, radius=2)),\n",
-    "    ('rf', RandomForestRegressor(n_estimators=100, random_state=0xf00d, n_jobs=-1))\n",
-    "])\n",
-    "\n",
-    "# Train the model\n",
-    "binary_fp_pipe.fit(X_train, y_train)"
+    "## Load and Prepare Data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "ee7b2f64",
+   "execution_count": 6,
+   "id": "79d3b853",
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/numpy/core/fromnumeric.py:59: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
-      "  return bound(*args, **kwds)\n"
+      "0 out of 200 SMILES failed in conversion\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Predicted pXC50 vs Absolute Error')"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUkAAAE8CAYAAABNZUPaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC6BklEQVR4nOy9d5xdVdX//z719rnTZ1ImnZ4AIh0kNEGKigUQnkdRRFFAsSvqV0VUHkUEH+FHsYAtCojgIyIKShGEUIMklHTSppfb76n798c+92ZqMjeZJJPkfl6vgcyZc8/Z59yz11l7rc/6LEUIIaiiiiqqqGJUqLt6AFVUUUUVkxlVI1lFFVVUsQVUjWQVVVRRxRZQNZJVVFFFFVtA1UhWUUUVVWwBVSNZRRVVVLEFVI1kFVVUUcUWUDWSVVRRRRVbQNVIVlFFFVVsAVUjOQZmzZrFhz/84fLvjz32GIqi8Nhjj+2yMQ3H8DFWMXnwrW99C0VR6Onp2annVRSFb33rWzv1nHs6JqWRvPPOO1EUpfwTDofZd999ueKKK+js7NzVw6sIDz744G7x0AoheNvb3kZTUxO9vb0j/v6JT3wCwzBYsmRJeduqVau49NJLmTNnDuFwmJqaGo477jh+/OMfUygUyvudeOKJQ77P0s873vGOEeexLIsvf/nLTJ06lUgkwlFHHcXDDz+8Q655e3DkkUeiKAq33HLLrh7KhOF73/se999//4Qfd9asWaN+/2M9A5MN+q4ewJbw7W9/m9mzZ1MsFnnyySe55ZZbePDBB1m6dCnRaHSnjuWEE06gUChgmmZFn3vwwQe5+eabJ72hVBSF2267jUMPPZQvfOEL3HHHHeW/Pf3009x+++187nOf49BDDwXgL3/5C+eeey6hUIgPfehDzJ8/H9u2efLJJ/niF7/IsmXLuP3228vHmD59Otdee+2Qc06dOnXEOD784Q/zhz/8gc985jPss88+3HnnnZx55pk8+uijHH/88Tvm4ivEihUreO6555g1axa//e1v+eQnP7mrhzQh+N73vsf73/9+zjnnnAk/9qGHHsrnP//5EdtHewYmHcQkxB133CEA8dxzzw3Z/rnPfU4AYtGiRWN+NpvNTsgYZs6cKS666KLtPs7ll18udtRtnqgxDsZXv/pVAYjHHntMCCGEbdti/vz5YsaMGeV7u3r1ahGPx8X+++8vNm3aNOIYK1asEDfeeGP594ULF4qDDjpoq+devHixAMR1111X3lYoFMTcuXPFMcccs72XNmH4xje+IZqbm8W9994rFEURa9asGbHPN7/5TQGI7u7unTo2QHzzm9/cps/GYrEJf56EkM/pWWedtU2fHWs+e54nCoXC9gxr3LZiUi63x8LJJ58MwJo1awDpdcTjcVatWsWZZ55JIpHgv/7rvwDwfZ8bb7yRgw46iHA4TEtLC5deein9/f1DjimE4Dvf+Q7Tp08nGo1y0kknsWzZshHnHismuXjxYs4880zq6uqIxWIcfPDB/PjHPy6P7+abbwYYssQoYaLHOBrWrl2Loij88Ic/5IYbbmDmzJlEIhEWLlzI0qVLR+z///7f/2Pu3Llceuml2LbN9ddfz9KlS7npppuIxWIA/OAHPyCbzfLzn/+cKVOmjDjGvHnzuPLKK0dsd12XbDY75lj/8Ic/oGkaH//4x8vbwuEwH/3oR3n66adZv379mJ+94ooriMfj5PP5EX+74IILaG1txfM8AJ5//nlOP/10GhsbiUQizJ49m4svvnjMYw/HokWLeP/738/ZZ59NMplk0aJFY+7b09PDeeedR01NDQ0NDVx55ZUUi8Uh+zz88MMcf/zx1NbWEo/H2W+//fjqV786ZJ+uri4++tGP0tLSQjgc5pBDDuGXv/zlVsf64Q9/mFmzZo3YXoqZlqAoCrlcjl/+8pfl53RwvHvjxo1cfPHFtLS0EAqFOOigg/jFL36x1fNXgi3NZ0VRuOKKK/jtb3/LQQcdRCgU4qGHHgLgpZde4owzzqCmpoZ4PM4pp5zCM888M+TYpRDe448/zmWXXUZzczPTp08f17gm9XJ7OFatWgVAQ0NDeZvrupx++ukcf/zx/PCHPywvwy+99FLuvPNOPvKRj/DpT3+aNWvWcNNNN/HSSy/x1FNPYRgGAN/4xjf4zne+w5lnnsmZZ57Jiy++yGmnnYZt21sdz8MPP8zZZ5/NlClTuPLKK2ltbeW1117jgQce4Morr+TSSy9l06ZNPPzww/z6178e8fmdMcYSfvWrX5HJZLj88sspFov8+Mc/5uSTT+aVV16hpaWlvF84HOb/+//+P04//XQuu+wyFi1axHve8x7e+c53lvf585//zJw5czj22GPHff7ly5cTi8WwbZuWlhY+9rGP8Y1vfKN8jSAf9n333Zeampohnz3yyCMBWLJkCW1tbaMe//zzz+fmm28uhwFKyOfz/PnPf+bDH/4wmqbR1dXFaaedRlNTE1/5yleora1l7dq1/PGPfxzXdSxevJiVK1dyxx13YJom733ve/ntb387wqiVcN555zFr1iyuvfZannnmGf73f/+X/v5+fvWrXwGwbNkyzj77bA4++GC+/e1vEwqFWLlyJU899VT5GIVCgRNPPJGVK1dyxRVXMHv2bO655x4+/OEPMzAwMOoLqVL8+te/5pJLLuHII48sv6Tmzp0LQGdnJ0cffXTZUDU1NfHXv/6Vj370o6TTaT7zmc9s9fiO44yaxIrFYkQikfLvY81ngH/+85/cfffdXHHFFTQ2NjJr1iyWLVvG2972NmpqavjSl76EYRjcdtttnHjiiTz++OMcddRRQ8532WWX0dTUxDe+8Q1yudz4bs52+as7CKXl9iOPPCK6u7vF+vXrxe9//3vR0NAgIpGI2LBhgxBCiIsuukgA4itf+cqQz//rX/8SgPjtb387ZPtDDz00ZHtXV5cwTVOcddZZwvf98n6lJefgpcejjz4qAPHoo48KIYRwXVfMnj1bzJw5U/T39w85z+BjjbXc3hFjHA1r1qwRwJD7JsTmpe1nP/vZUT93wQUXCEAkEgmxfv368vZUKiUA8e53v3uL5x2Miy++WHzrW98S9957r/jVr34l3vWudwlAnHfeeUP2O+igg8TJJ5884vPLli0TgLj11lvHPIfv+2LatGnife9735Dtd999twDEE088IYQQ4r777hs1lDNeXHHFFaKtra38Xfz9738XgHjppZeG7Fdabr/rXe8asv2yyy4TgHj55ZeFEELccMMNW12W33jjjQIQv/nNb8rbbNsWxxxzjIjH4yKdTpe3M2y5fdFFF4mZM2eOOGZpfIMx1nL7ox/9qJgyZYro6ekZsv0DH/iASCaTIp/Pjzl2IeRyGxj159prrx0y1tHmc+m6VFUVy5YtG7L9nHPOEaZpilWrVpW3bdq0SSQSCXHCCSeUt5VsyvHHHy9c193ieIdjUi+3Tz31VJqammhra+MDH/gA8Xic++67j2nTpg3Zb3jg/J577iGZTPL2t7+dnp6e8s9b3/pW4vE4jz76KACPPPIItm3zqU99asjSYzxvxpdeeok1a9bwmc98htra2iF/G3yssbAzxjgY55xzzpD7duSRR3LUUUfx4IMPjrp/Y2MjAAceeOCQZUk6nQYgkUiM+9w///nP+eY3v8l73/tePvjBD/KnP/2Jj33sY9x9991DlkWFQoFQKDTi8+FwuPz3saAoCueeey4PPvjgkCX9XXfdxbRp08pJn9J39cADD+A4zrivAaSXc9ddd3H++eeXv4uTTz6Z5uZmfvvb3476mcsvv3zI75/61KcAyve9NJ4//elP+L4/6jEefPBBWltbueCCC8rbDMPg05/+NNlslscff7yi66gEQgjuvfde3vnOdyKEGPKsnn766aRSKV588cWtHqfEUhj+M/iaShgrEbZw4UIOPPDA8u+e5/H3v/+dc845hzlz5pS3T5kyhQsvvJAnn3yy/LyW8LGPfQxN08Z7+cAkpQCVcPPNN/Pwww/z6KOP8uqrr7J69WpOP/30Ifvouj4itrBixQpSqRTNzc00NTUN+clms3R1dQHw5ptvArDPPvsM+XxTUxN1dXVbHFtp6T9//vxturadMcbBGP55gH333Ze1a9eO2P78889z8803M3/+fBYvXsxvfvOb8t9KS+FMJjPuc4+GUqbzkUceKW+LRCJYljVi31IMb/CybDScf/75FAoF/u///g+AbDbLgw8+yLnnnls2agsXLuR973sfV199NY2Njbz73e/mjjvuGPW8w/H3v/+d7u5ujjzySFauXMnKlStZs2YNJ510Er/73e9GNXLD7/vcuXNRVbV8388//3yOO+44LrnkElpaWvjABz7A3XffPeRYb775Jvvssw+qOnS6HnDAAeW/7yh0d3czMDDA7bffPuI5/chHPgJQfla3hMbGRk499dQRPzNnzhyy32jzuYTZs2ePGFs+n2e//fYbse8BBxyA7/sj4tjDjzEeTOqY5JFHHsnhhx++xX1CodCIh8f3/S2+3ZuamiZsjNuKyTpGz/P4+Mc/ztSpU3nqqac47bTT+PznP8/ZZ59NbW0tNTU1TJ06ddSkTyUoxRb7+vrK26ZMmcLGjRtH7Nve3g5snS5y9NFHM2vWLO6++24uvPBC/vznP1MoFDj//PPL+yiKwh/+8AeeeeYZ/vznP/O3v/2Niy++mOuvv55nnnmGeDw+5vFL39V555036t8ff/xxTjrppC2OcfgqIxKJ8MQTT/Doo4/yl7/8hYceeoi77rqLk08+mb///e8Vez1bO18JpSTW1lAy1v/93//NRRddNOo+Bx988LYNbhSMNp9L2NpLcjzYlmNMaiO5rZg7dy6PPPIIxx133BZvSukttmLFiiHuend394gM82jnAFi6dCmnnnrqmPuN9ZDujDEOxooVK0ZsW758+YjM5//+7//y0ksvcd9991FTU8Ott97K4Ycfzle+8hVuvfVWAM4++2xuv/12nn76aY455phxj2EwVq9eDQx9GRx66KE8+uijpNPpIcmbxYsXl/++NZx33nn8+Mc/Jp1Oc9dddzFr1iyOPvroEfsdffTRHH300Xz3u99l0aJF/Nd//Re///3vueSSS0Y9bi6X409/+hPnn38+73//+0f8/dOf/jS//e1vRxjJFStWDPFeVq5cie/7Q+67qqqccsopnHLKKfzoRz/ie9/7Hl/72td49NFHy97Wf/7zH3zfH2JAXn/9dYAR3thg1NXVMTAwMGL7aN7naM9qU1MTiUQCz/O2+JzvCjQ1NRGNRnnjjTdG/O31119HVdUxE32VYFIvt7cV5513Hp7ncc0114z4m+u65Yfm1FNPxTAMfvKTnyAG9UO78cYbt3qOww47jNmzZ3PjjTeOeAgHH6tEmxm+z84Y42Dcf//9Q7y0Z599lsWLF3PGGWeUt61fv55vfOMbvOtd7yoTig899FA+/elP89Of/rRsrL70pS8Ri8W45JJLRq2AWrVqVZkGlU6nRyxlRUBpAoaET97//vfjed4QErplWdxxxx0cddRR43rgzz//fCzL4pe//CUPPfTQCK+vv79/yH0sXWPpXGPhvvvuI5fLcfnll/P+979/xM/ZZ5/NvffeO+IYJQpYCT/5yU8Ayvd9sCc91njOPPNMOjo6uOuuu8r7uK7LT37yE+LxOAsXLhxz3HPnziWVSvGf//ynvK29vZ377rtvxL6xWGzEc6ppGu973/u49957R109dHd3j3nuHQ1N0zjttNP405/+NCRs1NnZyaJFizj++ONHMCW2CRWleXYSxiKTD8dFF10kYrHYqH+79NJLBSDOOOMMccMNN4ibbrpJXHnllWLq1KninnvuKe931VVXCUCceeaZ4qabbhIf/ehHxdSpU0VjY+MWs9tCyEy0YRhi5syZ4lvf+pa47bbbxGc/+1lx2mmnlfcpZVc/+MEPit/85jfid7/73Q4b42goZbcXLFggZs2aJb7//e+Lb3/726K+vl40NDQMIYO/+93vFrFYTLz55ptDjpFOp8W0adPEoYceWs4M/ulPfxLhcFjU1dWJK6+8Uvz0pz8VN998s/iv//ovYZqm+PjHP16+b62treKzn/2suPnmm8UPf/hDcdxxxwmgvM9gnHvuuULXdfHFL35R3HbbbeLYY48Vuq6Lxx9/fIvXORjz5s0TiURCAOKFF14Y8rcbbrhB7LPPPuJLX/qSuO2228QPf/hDsd9++4mamhqxevXqMY/5jne8QzQ0NIyZGf3zn/8sAHHvvfcKITZnjxcsWCDe+c53iptvvln893//twDEhRdeWP7clVdeKd7ylreIr3/96+KnP/2p+O53vyumTZsmpk+fLgYGBoQQQuTzeXHAAQcI0zTF5z//efGTn/xELFy4UABDSPtCjMxu9/T0iFgsJubMmSNuvPFG8b3vfU+0tbWJww47bER2+8wzzxSxWExcf/314ne/+5145plnhBBCdHR0iJkzZ4poNCquvPJKcdttt4lrr71WnHvuuaKurm4r34bMbh966KHi17/+9Yif++67r7zfluYzIC6//PIR25cuXSpisZiYNm2a+O53vyu+//3vizlz5ohQKFQevxDjtymjnrviT+wETISRFEKI22+/Xbz1rW8VkUhEJBIJsWDBAvGlL31piGHwPE9cffXVYsqUKSISiYgTTzxRLF26dEQ1y2hGUgghnnzySfH2t79dJBIJEYvFxMEHHyx+8pOflP/uuq741Kc+JZqamoSiKCMezIkc42goGcnrrrtOXH/99aKtrU2EQiHxtre9rUxDEWIzNeaHP/zhqMf5wx/+IADxox/9qLxt+fLl4mMf+5iYNWuWME1TJBIJcdxxx4mf/OQnolgsCiFkdc65554rZs2aJcLhsIhGo+Ktb32ruPXWW4dQmkooFAriC1/4gmhtbRWhUEgcccQR4qGHHtriNQ7H1772NQGIefPmjfjbiy++KC644AIxY8YMEQqFRHNzszj77LPF888/P+bxOjs7ha7r4oMf/OCY++TzeRGNRsV73vMeIcRmI/nqq6+K97///SKRSIi6ujpxxRVXDKkU+cc//iHe/e53i6lTpwrTNMXUqVPFBRdcIJYvXz5iDB/5yEdEY2OjME1TLFiwQNxxxx0jxjHcSAohaUrz588XpmmK/fbbT/zmN78ZlQL0+uuvixNOOEFEIpER9LLOzk5x+eWXi7a2NmEYhmhtbRWnnHKKuP3228e8JyVsiQI0mJ60LUZSCPmdnn766SIej4toNCpOOukk8e9//3vIPttjJJVgAFXsoVi7di2zZ8/muuuu4wtf+MKuHk4VVex22CNjklVUUUUVE4Wqkayiiiqq2AKqRrKKKqqoYguoxiSrqKKKKraAqidZRRVVVLEFVI1kFVVUUcUWsEeWJW4Jvu+zadMmEonEuNR6qqiiip0LIQSZTIapU6eOWce9M7HXGclNmzZNSD1nFVVUsWOxfv36cauH70jsdUaypIO4fv36ianrrKKKKiYU6XSatra2ijRLdyT2OiNZWmLX1NRUjWQVVUxiTJZw2K5f8FdRRRVVTGJUjWQVVVRRxRaw1y23dzV8X7BxoEDOdomZOtNqI6jq5FhWVFFFFSNRNZI7ESu7MvxtaSerurMUXY+wrjG3Kc7p81uY1zw5gtRVVFHFUFSN5ARhax7iyq4Mdzy1lr6czZRkmKgZIW+7LN2UYlOqwEeOm1U1lFVUMQlRNZITgK15iL4v+NvSTvpyNvs0x8tZu0TYIB7SWdGV5e/LOpnTGK8uvauoYpKhaiS3E+PxEEO6xqruLFOS4RG0BkVRmJIMs7Iry8aBAm310V10JVVUUcVoqGa3twPDPcRE2EBTFRJhg32a4/TlbP6+rJOM5VB0PaLm6O+kiKlhuR45293JV1BFFVVsDVUjuR3YOFAYl4eYLbqEdY38GEawYHuEdI3YGEa0iiqq2HWoGsntQM52x+UhxkM6c5vitKeKI9qZCiFoTxWZ1xxnWu32N1+voooqJhZVI7kdiJn6uDzERNjg9Pkt1MdMVnRlyRQdXN8nU3RY0ZWlPmZy2kEt1aRNFVVMQlSN5HZgWm1k3B7ivOYEHzluFvOnJhnIO6ztyTGQd1gwLVml/1RRxSTGLjWSt9xyCwcffHBZbOKYY47hr3/96xY/c88997D//vsTDodZsGABDz744E4a7UioqlKRhzivOcEnT5zLZ9++L586ZR8++/Z9+cTCuVUDWUUVkxi71EhOnz6d//mf/+GFF17g+eef5+STT+bd7343y5YtG3X/f//731xwwQV89KMf5aWXXuKcc87hnHPOYenSpTt55JtRqYeoqgpt9VH2b62hrT5aXWJXUcUkx6RrBFZfX891113HRz/60RF/O//888nlcjzwwAPlbUcffTSHHnoot95667iOn06nSSaTpFKpCZVKq9ZkV1HFxGBHzdFtxaThnHiexz333EMul+OYY44ZdZ+nn36az33uc0O2nX766dx///1jHteyLCzLKv+eTqcnZLzDUfIQq6iiij0Luzxx88orrxCPxwmFQnziE5/gvvvu48ADDxx1346ODlpaWoZsa2lpoaOjY8zjX3vttSSTyfJPtXVDFVVUUQl2uZHcb7/9WLJkCYsXL+aTn/wkF110Ea+++uqEHf+qq64ilUqVf9avXz9hx66iir0dvi9Y35fn9Y406/vy+P6kit5NCHb5cts0TebNmwfAW9/6Vp577jl+/OMfc9ttt43Yt7W1lc7OziHbOjs7aW1tHfP4oVCIUCg0sYOuoooq9hrpv13uSQ6H7/tDYoiDccwxx/CPf/xjyLaHH354zBhmFVVUsWNQEnZZuilFbdRgTmOc2qjB0k0p7nhqLSu7Mrt6iBOGXepJXnXVVZxxxhnMmDGDTCbDokWLeOyxx/jb3/4GwIc+9CGmTZvGtddeC8CVV17JwoULuf766znrrLP4/e9/z/PPP8/tt9++Ky+jiir2Kuxt0n+71Eh2dXXxoQ99iPb2dpLJJAcffDB/+9vfePvb3w7AunXrhjQnP/bYY1m0aBFf//rX+epXv8o+++zD/fffz/z583fVJVRRxV6H8Qq77CnSf5OOJ7mjMdk4WFVUsbvh9Y40//uPFcxpjKON4im6vs/anhyfOmUf9m+tfI5Ntjk66WKSVVRRxeTGeIVd9hTpv6qRnADsDTSIKqoooRJhlz0Be4ap34XYW2gQVYyOvbEctSTssilVYEWXjE1GTI2C7dGeKu5x0n9VI7kdqHZA3LuxN78gS8IupevvTBcJ6RoLpiU57aA96/qrRnIbsbfRIKoYiuoLUhrKOSfG93hPumoktxF7Gw2iis2oviA3Y28QdqkoceM4DqeccgorVqzYUePZbTDe/jbVDoh7Hip5QVax+6MiI2kYBv/5z3921Fh2K+xtNIgqNqP6gty7UDEF6L//+7/5+c9/viPGsluhUhpElSa056D6gty7UPG36Louv/jFL3jkkUd461vfSiwWG/L3H/3oRxM2uMmMSmgQe3MWdE9E6QW5dFOKeEgfsuQuvSAXTEvuMTzBvR0VG8mlS5dy2GGHAbB8+fIhfxsen9nTMR4aRDULuudhb+MJ7u2o1m5PAMYiFPu+4JbHVrF0U2pIFhSkx7GiK8uCaUk+sXBudULthhi8QrBcucSe1xzf43iCOxuTrXZ7u4ImGzZsAGTXw70ZY9EgqjShPRt7C09wb0fFiRvf9/n2t79NMplk5syZzJw5k9raWq655hp8398RY9xtUc2C7vmotgje81GxJ/m1r32Nn//85/zP//wPxx13HABPPvkk3/rWtygWi3z3u9+d8EHurhicBU2EjRF/r2ZBq9gbsLvXt1c8O3/5y1/ys5/9jHe9613lbQcffDDTpk3jsssuqxrJQahmQavY27EnMDsqNpJ9fX3sv//+I7bvv//+9PX1Tcig9hRUs6BV7M3YU5gdFcckDznkEG666aYR22+66SYOOeSQCRnUnoQSTWj+1CQDeYe1PTkG8g4LpiV3m4ekiirGwlhFEsPr2xNhA01VSIQN9mmO05ez+fuyzt2iqKJiT/IHP/gBZ511Fo888ki5S+HTTz/N+vXrefDBByd8gJMd44m3VLOgVewpGPy892QslqwbYHVPbsRSOqRrewyzo2IjuXDhQpYvX87NN9/M66+/DsB73/teLrvsMqZOnTrhA5zMqCTesjeopVSxZ2Pw896TtVjfl8fQVeZPrWFOY3zIUnrhvk0Bs2P0eHvE1OhMF3cLZkdFRtJxHN7xjndw66237vUJmj0l3lJFFePB4Oe9tSbMpoECQgg8X7CiK0csZFAfM8tScS+82U9IU/cIZkdVBWgbsCfFW6qoYmsY/rwDDBQcamMmDTGTgu2yqjuLEKK8lO5KF2lKhPaIPjhVFaBtQFVPsIq9CcOfd9vzcX0fQ1NRFIV4WKcvZ5MpyqVzxNSwPZ/DZ9dTHzNZ0ZUlU3RwfZ9M0WFFV3a3YnbsUhWga6+9lj/+8Y+8/vrrRCIRjj32WL7//e+z3377jfmZO++8k4985CNDtoVCIYrFYmUXsh3YXEmz+8dbqqhiaxj+vJuaiq6qOJ5PSNcwNJWs5WJ7suKutJQ+oLWGOY2x3b4Pzi5VAXr88ce5/PLLOeKII3Bdl69+9aucdtppvPrqqyOM72DU1NTwxhtvbPN5txfVSpoq9iYMf94TYZ36qElXpogZk8ZSV1VMTR1RJKGqym7P7KhoFnuex9VXX82CBQuoq6vb7pM/9NBDQ36/8847aW5u5oUXXuCEE04Y83OKotDa2rrd599WVCtpqtibMNrzPrc5RsZy6M1auD60JsOAGHUpvbszOyqKSWqaxmmnncbAwMAOGUwqlQKgvr5+i/tls1lmzpxJW1sb7373u1m2bNmY+1qWRTqdHvKzvShV0uwJ8ZYqqtgaRnveayIySampKqqiENJVUgV3jyySqFhP8vDDD+f73/8+p5xyyoQOxPd93vWudzEwMMCTTz455n5PP/00K1as4OCDDyaVSvHDH/6QJ554gmXLlo0q2fatb32Lq6++esT2idCqq+oJVrE3YbTnfW5TnIPbkjQlQhO2lJ5sepIVG8mHHnqIq666imuuuWbUxM22XtQnP/lJ/vrXv/Lkk09WpE/pOA4HHHAAF1xwAddcc82Iv1uWhWVZ5d/T6TRtbW0T9gXs7gonVVRRCXbG8z7ZjGTFmYUzzzwTgHe9610jYnGKouB5XsWDuOKKK3jggQd44oknKhbwNQyDt7zlLaxcuXLUv4dCIUKhUMVjGi9293hLFVVUgr3xea/YSD766KMTdnIhBJ/61Ke47777eOyxx5g9e3bFx/A8j1deeaVsvKuooooqJhLbVLs9Ubj88stZtGgRf/rTn0gkEnR0dACQTCaJRGRm+EMf+hDTpk3j2muvBeDb3/42Rx99NPPmzWNgYIDrrruON998k0suuWTCxlVFFVVUUcK4s9s/+MEPKBQ2V5A89dRTQ2J9mUyGyy67rKKT33LLLaRSKU488USmTJlS/rnrrrvK+6xbt4729vby7/39/XzsYx/jgAMO4MwzzySdTvPvf/+bAw88sKJzV1FFFVWMB+NO3GiaRnt7O83NzYBM0CxZsoQ5c+YA0NnZydSpU7cpJrkzMdmCwlVUUcVQTLY5Ou7l9mhF6lXsvqhm5auoYnyo1s3thdgT+o5UUcXOQtVI7mWo6mBWUUVlqMhI/uxnPyMel3pyruty55130tjYCMjETRWTG8N1AUs810TYKIul/n1ZJ3Ma49Wl9zagGsLYMzHuxM2sWbPGpbazZs2a7R7UjsRkCwrvTKzvy3PDw8upjRqjqhdlig4DeYfPvn3fvY4wvL2ohjAmDpNtjo7bk1y7du0OHEYVY2EivZOqDuaOQTWEsWejGpPcRuyMpdXyjgx/eGE9q7qzeALqIgbzmhPb7J1UdTAnHtUQxp6P6mzYBuyMpdU/Xuvkf/+xgu6MhakrhHSNTMGhJ2dvs3dS1cGceFTSymMiQxjV+OfOQ9VIVoidsbRa3pnmf/+xgo50kdaaEKau4Xg+qYKD5Uqy/rZ4JyVdwE2pAiu65MSOmBoF26M9VazqYG4DdkUIoxr/3LmouBHY3oyd0SXR9wV/eH4j3RmLKTVhwoYeiJpq1MdMio5P3nZZ0ZnZpkZj85oTfOS4WcyfmmQg77C2J8dA3tkjxVJ3BgaHMEbDRIcwSi/ppZtS1EYN5jTGqY0aLN2U4o6n1rKyq8oymWhUPckKsDOWVqVzmLqKoQ99h5U602WKLgMFZ5u9k3nNid2+78hkwc4MYVTjn7sG2+RJrlq1iq9//etccMEFdHV1AfDXv/51i20U9gRsXlqN/m6JmBqW623X0ipnu3jCJ6SrON5Ij9TQVGzXQ1XYLu+kpAu4f2sNbfXRvWJS+b5gfV+e1zvSrO/LT0hf9J3ZyqPaynjXoGIj+fjjj7NgwQIWL17MH//4R7LZLAAvv/wy3/zmNyd8gJMJO2NpFTN16iIm8ZBOtuiMqJG3XQ/bFczdTRq7Txas7Mpwy2OruOHh5fzvP1Zww8PLueWxVROyPN1ZIYyd8ZKuYiQqns1f+cpX+M53vsPnPvc5EonNX/7JJ5/MTTfdNKGDm2yodGm1LRnIabUR5jUn6MnaWK5PX84mHtYDD9KnIy1jle8/rG2v8P4mAjsj2bYzQhhVCteuQcV385VXXmHRokUjtjc3N9PT0zMhg5qsqCQ7vK0ZyMHnAMjbLhnLxXYdbNentSbMp07Zh31bd88Ey86mruzMON6Obm0w3pf0lJow6/vy1XjzBKFiI1lbW0t7e/uIVgsvvfQS06ZNm7CBTUb4viCkayzcr4nn1/TRnbHoTPuEdI0F05LlLomjeS45y+XZtb0sa09xwZEzOG5u45gPbmn59relnazsyjBQsFEVlXnNcd731mns27LrS7W2BbuCurKreIw7AuN5Se/XmuC2J1ZX6UETiIqN5Ac+8AG+/OUvc88996AoCr7v89RTT/GFL3yBD33oQztijJMCK7syPLS0g1c2psjbLhFDY2ZDjCNm1XPAlJry23o0z6UvZ7GqK0dvziJVcFjbk+fM+a28Y0HrmA/unpaB3lWle3taKebgF+iq7iyd6WL5Jb1fa4J/vt5VLY+cYFRsJL/3ve9x+eWX09bWhud5HHjggXiex4UXXsjXv/71HTHGXY6VXRlufGQFyzsyeEIAAlBY25OnPVVkTlOsbLyGey59OYsl6wco2B7xsE7I0MhZLs+92Ud7urjFB3dP6Uy3K6kre2Icb7QX6JSaMLc9sbpKD9oBqDi7bZomP/3pT1m9ejUPPPAAv/nNb3j99df59a9/jaZpO2KMuxS+L1i0eB0vrx/A830SYZ36WIhEWMfzfV5eP8CixevKdJLBGUghBKu6chRsj/qYSUjXCOkqiiLjSxNBPt8dsCupK6U4XnuqOKq6fnuqyLxxMgV2BIVoWzGcwtWeLlbpQTsIFb8+v/3tb/OFL3yBtrY22traytsLhQLXXXcd3/jGNyZ0gLsaG/rzPLO6F02Bhnio/ACGdA0zrtKZLrJ4dS8b+vPMaIgN8VyEgL68zE6XPud4PrqqEtI1piS13SYetj3YlUveiSrFnKh46o5KXO1pYYXJhIo9yauvvrrMjRyMfD7P1VdfPSGDmkxY3ZMjlXeoiRqjvqGTUYOBgsPqnhww1HOxXA/X8/F9maUu2i6Zokt9zCQR1vcaXtvOLt0bju3lMU5UKeCO5Gru6nu8J6PiOyaEGFV89+WXX6a+vn5CBjXZIBRQGOttP3T7YM9leWeG3pxNT9ZCCPCFIGrqHDRVeqQFy90rHtzJoD60rYmwiYqn7ujE1WS4x3sqxu1J1tXVUV9fj6Io7LvvvtTX15d/kskkb3/72znvvPMqOvm1117LEUccQSKRoLm5mXPOOYc33nhjq5+755572H///QmHwyxYsIAHH3ywovNWgtmNMWojJgP5kdUvQghSeYdkxGR2Y6y8fV5zgpP3b8Z2fVzPx3Z9NFUhFtYJ6SprenL0Zq2K4mG7M3Zm6d7WxlFpKeZExFN3hjDKZLnHeyLG7cLceOONCCG4+OKLufrqq0kmk+W/mabJrFmzOOaYYyo6+eOPP87ll1/OEUccgeu6fPWrX+W0007j1VdfJRaLjfqZf//731xwwQVce+21nH322SxatIhzzjmHF198kfnz51d0/vGgrS7K0bPrefi1TnpzNomg+sXxfDJFF18IjplTT1vd5pii7wteb88wJRlmdmOUZZvS2K5PbdTA0FS6MzbPru3j8Jl1e82DuyXqSolfOhkxEbG+LRlagHhI44U3+3n+zT4On1m/zc/D7nqPJzvG3eOmhMcff5xjjz0WwxhJp9hedHd309zczOOPP84JJ5ww6j7nn38+uVyOBx54oLzt6KOP5tBDD+XWW2/d6jm2pX9GmQLUmcEb9LbXVIV9WxJ85tR9hjyAw3vJlHiSfXkbz/fxBYR1lavOPIDj92ka1xh2VwxPVEypCdMeGJXdgfs5EX2BXu+Q+qBzGuNog661L2ezsitLb84iXXA4aGoNh82o327i9+4uyLvb9rgpYfbs2bS3t4/59xkzZmzzYFKpFMAWY5tPP/00n/vc54ZsO/3007n//vtH3d+yLCzLKv+eTqcrHte8ZmkIH3olIJM7LlFD5+DpSU6fP5IQPtz7qI+FqJtlkim62J6Ppij0ZC0aE6GKx7I7YUsZ4f1bd/3DPx5sT6yvZKw6UkU8X5CzXGoi0tD25eyAP+ti6irJiEFDLDQhMco9hV87WVCxkdxa10TP87ZpIL7v85nPfIbjjjtui8vmjo4OWlpahmxraWmho6Nj1P2vvfbaCcm6z2tOcNlJ4wv8j0ZgVhSlPEEyRYewsWcnbPaU5ljbSiEa/IIoOC7r+wqs6c5x5Ow66mMhVnZlKdgudVGD/rxDc02Y1mSYVqgSvycZKp6lL7300pDfHcfhpZde4kc/+hHf/e53t3kgl19+OUuXLuXJJ5/c5mOMhquuumqI55lOp4fwOyvBeN/Qe3umcbKLw1a6HK001jf8BTHVjBAxNJ5b28/jy3s4cEqC3pyFqav05x0ipsbcplj5Pu1O9eR7Ayo2kocccsiIbYcffjhTp07luuuu473vfW/Fg7jiiit44IEHeOKJJ5g+ffoW921tbaWzs3PIts7OTlpbW0fdPxQKEQrtmGXtWJNtb+8lM5lFJbaVFD6YQpQpOmQtV5aZ6hq+L8rf5VgviLb6GFFT59m1fazsypKzPZIRg+aaMHObYtTHNj+jVeL35MKErff2228/nnvuuYo+I4TgU5/6FPfddx+PPfbYCGWh0XDMMcfwj3/8g8985jPlbQ8//HDFmfXtxdYm22TMNO6sgP5krf7Y3hCAqipYrsejr3eP+b1v6QXREA9x3NwGVnXlcIVgem2E1lH2qxK/Jxcq/haGJz6EELS3t/Otb32LffbZp6JjXX755SxatIg//elPJBKJclwxmUwSicgJ9qEPfYhp06Zx7bXXAnDllVeycOFCrr/+es466yx+//vf8/zzz3P77bdXeinbjPFOtjmNcc4+RPIiAeY0xphet2taJexMmbLJKCoxESGA8Xzvri+2+IKIhnTiYY3mmjCdaYvh65+9IRyzu2Gb9CSHv/mEELS1tfH73/++omPdcsstAJx44olDtt9xxx18+MMfBmDdunWo6mbO+7HHHsuiRYv4+te/zle/+lX22Wcf7r///h3CkRwNgyfbvKYYWcujP29jairzmmKs7M5JYrAQPLysa1Lo+u3sJMpkjMlubwhgvEb2rIOnbPUFETZ0TjmghYeWduyV4ZjdDRUbyUcffXTI76qq0tTUxLx589D1yg43HormY489NmLbueeey7nnnlvRuSYKpckWMVSeX9tPZ8bC8X0MVaUlEWJKbZgX1/XzRmcG2/V3eWZ3VyRRJmNMdntDAOM1sgqM6wVx3NxGpiTDkyocU8XoqNhILly4cEeMY9JieBwvU3ToyVpsGsjTl3MYbOYH8jZdmSKegOl1EQ6bUbfLM7u7Koky2WKyUUPD8wUb+/PURqXAyOD7sbUQwHiNbN7xxv2C2NOElfdUjMtI/t///d+4D/iud71rmwcz2TBaHK8hbrKmO0d/wcbQVEK6iqqAL8ByfdrTRVQUDp9Zt9Mzu6MlZnZlEmWyGIGVXRkeeqWD9X0F+nJWmbg9t1lmlccTAqgkztpWHx33C6JK/J78GJeRPOecc8Z1MEVRtplMPtkwVhzv9fYUXVkLBUiE1PKE1xRZaliwPTzhE9ZH1w7ZUUZprMTMIW3JXZpE2dVGYPD3uH9rnDc6IVt02TiQJ1W02a8lQcHxtxoCqDTOOlleEFVsP8Y1M3zf39HjmFTYUhyvLvA8FFWh6PqYuoqmKHhCYLs+IV2l6Hp0ZSwaEuERx94RRmlLiZmNA3lqowbtqeKkSaLsLIz2PcZCBiu7svTnLHqzNm+IDGcumFpRF8vxxll39Qtid6/hniyoErFGwRbjeCgYmlxih3QVxxPYwkdVFGIhnaih0p6y6M3bI7Q3d4RRGk9iZmpSoy5q7tIkyq6YsKN9j/UxkyNm1ZEpuvTnbQq2x9mHTGFmw+iqU4Mx2eKsW8Ku6Ey5p2KbjOTjjz/OD3/4Q1577TUADjzwQL74xS/ytre9bUIHt6uwpTheXdQgYmjkbJfGuImuqnhCoCkKpi7bObTUhJhWG90pRmk8iZn+vMN7DpvGf9anhkzu+VOTHNyWxA16t+wow7WrJuxY32Opjj4a0ljbk6PgjB4iGs2w7w7L6D2lbn6yoGIj+Zvf/IaPfOQjvPe97+XTn/40AE899RSnnHIKd955JxdeeOGED3JnY0tB+pqIQXNNiLU9HumCQ308RFjXcDyf3qyFL+CEfZv4wJFtZZ7kjvQ4xpuYaUqE+OSJc8uTuydjsWTdAPe9uHGHGq5dOWG3h9S+NcM+kcvoifSyJ3vd/O6Iio3kd7/7XX7wgx/w2c9+trzt05/+ND/60Y+45ppr9ggjuaUgPUBDLITnC3wBmaJLqcWspqocMjXBhUfNkKWJTYkd7nFUYghKMbKVXRn+urRjhxuuXT1ht5XUvjMN+3i87EqM6GSum99dUbGRXL16Ne985ztHbH/Xu97FV7/61QkZ1K7G1oL0MxqifPi4WbzWnpb6krZHxNCY1RDl8Nn1Q0QPJuJB3NIkqdQQ7EzDtXVFbp0X3uzj+TfrtkuReyxsS7JlZ96f8RhjoKJQxWStm9+dUbGRbGtr4x//+Afz5s0bsv2RRx7ZZgmyyYjxBOlP2q+ZjQMFXmtP8/zafrozRe5/aSMP6R0TtnTdmqcxtPFYlkRYR1MVPF+QKbo0xIcagp3paYw1YUtK7T2BIvdtj6/muRn9OyRGWWmyZWfdH98XPPRKBxv6ZSxYCFCVocb4d4vXUXB8+vPj92gnY9387o6K79TnP/95Pv3pT7NkyRKOPfZYQMYk77zzTn784x9P+AB3JbYWpC+pwjy+vHuQN6BXtDTbkpc43mVfqfHYnU+tZdmmFI7nY2gqsxpinHv49CHn35mexmgTti9nBYrcXlmRuz5m7JCl/uD7eukJc8bVNmJn3Z+nVvXw4NIOLNdjY38BXVOpi5rMa45THzNprQnx9Oo+mhImh0yvBSgr27ckQnSki6N6tJOxbn53R8VG8pOf/CStra1cf/313H333QAccMAB3HXXXbz73e+e8AHuamxpyVzJ0gwYYQxX92TH9BLnNMbHfezVPVn++XoXsZDG0XPq0VQVz5eNyv75ehczG6Jlw7MzPY3hExZgVVeOgu0NUeSekpQTdqKWstvTNmJn3J+VXRl+9+w6+nIWLTVhzIBK1p0pkrVcDm2rBQSpgs1+LXH6847kduZtXM9H11RipsaL6/pHeLSTsW5+d8c2fdPvec97eM973jPRY9ntMN6l2VOresr0m9KkrY0YdGUtPF+M6iW+Y37ruI69vj9fNqb7tiRGeA7DDc/O9DSGT9h4SKdniCK3ztymzS+AiVjKbm/SZUffn9KLNWe5JCMGigKqohDSFcyYSV/OZlV3lqa4CQI8QbkXTjxsYIR1HE8wkHfozlq81pEeca92Jz7n7oCKjeT69etRFKWsIP7ss8+yaNEiDjzwQD7+8Y9P+AAnM8azNFvZleV3z65DCGkEIkaYrnSRf7zeieMKTtq/qeyxDPYS//FaJwXHZepWln1renIVxdB2tqcxeMK+uK6PdMEZpMgtl5bDr2lbl7ITkXTZ0fen9GKd0xjHcQVdmSJmTEVRFBRFIR7W6ctaeL5PMqKzoT9PwXapj5nl6wnpCiKs0Z3xeH5tH6fuP3I8pVDRhv48qwM909mNsSGtj6sYH0YvMN4CLrzwwrJcWkdHB6eeeirPPvssX/va1/j2t7894QOczBi8NBsNeculJ2uTs1z2aY7jeD4vrutn8Zo+ujIWfQWbf63ooTe3uZtjybC1p4r4PmMeu7TsAwJDPfr7LmJqWK43xPCUDNf8qUkG8g5re3IM5B0WTEtOKL3FD0jqri945yFT+NgJczhoapKDpyc5fGbdEAM5+Jq2dSlbSdJlS9iR96f0Yo2FdOY2x4iYGn05G8v18IXAF4KBgkNDzOSgaUk6xignzVkeU2rDdKetMa9ndU+WP7/czv8t2cTdz6/nx4+s4JbHVrGyK7PN498bUfHTuHTpUo488kgA7r77bhYsWMBTTz3F3//+dz7xiU/wjW98Y8IHOVmxtaXZ6p4cCoI5jXH68zbPrO6jN2fhegLXEygIOtJFnlzRw9v2aSz3OYmYGpqi0FwT3mrN9ZzG2DbF0HZ05choccE5TTFm1kdpTxdH7F/pUnZHKx7tqPsz+MVaHwtxaFttuSd7znLxBTTETC48aiZ522Px6j4yRQcUMDQVx/PJFl0ipsZ+LQlSBWfU66lW3UwcKjaSjuOUG2s98sgjZWm0/ffff4v9uPdEbG1pFgvpCAFRU+Oplf10posoSKqHqso6cM+H7ozF0o0p3rZPE4qiBOrVGqce2Mxft6JePb0uus0xtB0lwDDWBF22KY2mKmiqsl1L2Z2leLQj7s/wF+vgnuyW67FxoMCRsxo4dm4jGwcK7NuaoD9nk7M9cpaLpqrl5mGGplJ0/BHXs6tJ/HsaKjaSBx10ELfeeitnnXUWDz/8MNdccw0AmzZtoqGhYcIHONmxpSD5wdOT/PHFjXSmC6wOaoQVACHw5f/QVGk01/cXSBcdasJG2bAdO7eR5kSYP7ywnlXdWXwBtRFjRAD+7Qe2sLwzw4vr+pmSjNCUCFF0dk02czwTdEoyTH3UZHVPruKkwq5UPJqI8sHhL9bWmhCeD+miQ3/eZnptlNPnt5STbG9pq+OVjSkW1IRwfIGpqSTCctqu6MqOej3VqpuJRcVG8vvf/z7vec97uO6667jooovKLWb/7//+r7wM39sw1tIM4J+vdfHAK+2ki27Zi9RUBRWBK8DzZXazaLtsGijSqVplw7a6J8vDr3bSnbECEQ2VpkSYUw9sLhuTlV0ZHn61k5zt0pWxeLM3T8TUmFEf5bAZdTs9mzmeCTqQd/jwsbNQFKUig7MrFY+2VaRjLJGMjxw3i0WL1/HM6l5SBalwXxsxmRvQxWCoQe3MWOXryVruFq+nWnUzsajYSJ544on09PSQTqepq6srb//4xz9ONLr3vpVGW5qt7MrQmbHIWfJhVIIfzxeggK4pKAIs1wMUspbL8fMaOe2gFoAhHtO0uih522V9f55f/vvNcslaaZ8Z9VH2a0nQlSmWl/qDjenOQiVtDkqcxfF6aNujeLQ99Jdtje9tybACFB2PpkSIfVsS1IQNNBXa00XueGrtkEKBSuk81aqbicU23SVN03Ach3/961+A7Lk9a9asiRzXpMG2LrF8X/DQ0g56sxYxU6PoeAhBuSeOIsBQFaKGRsZ2aU6EuPKUeRwxS4Ysbnls1RY9pr8t7UDAiH2m1kaZkoywoivLI692Ma8psVPjTpVO0Eo8tG1VPNqepMu2xve2HBYoEA64oodMH9p9NBE2Rhyz0iRStepmYlGxkcxkMlx22WX8/ve/L7dq0DSN888/n5tvvplkMjnhg9xV2B4dxH+v6uHBV9rJBNlHXVVwfIESZCkVwPWlN6GgcNiMOo6Y1YCqKqzvy2/VY/rPhhQockJMRNxpouS6KpmglXpo26J4tL3Ylvje1gzryxsG6M7YHDV7/H2QKrmeatXNxKJinuQll1zC4sWLeeCBBxgYGGBgYIAHHniA559/nksvvXRHjHGXoDSBl25KURs1mNMYpzYqa4zveGrtFrlmK7syLHp2Hb05m3hYJ2xoRE0NTVUQQi63Pd8Plt0q9TGT9711evmh3ewxjc19zDsuedsdsY8QgnTBIW/LfuCZojOua73lsVXc8PBy/vcfK7jh4eXbzKcrTdD6mIwLZooOru+TKTos78xi6irzWiTJ+aFXOsqGJBE20FSFRNhgn+Y4fTlb9i/3N/ejLBng9lRxRDvikgGe1xyfUA9pPN/FcB7q1gxrXdRkoGDjjdEVZbRjVoqdxYXdG1CxJ/nAAw/wt7/9jeOPP7687fTTT+enP/0p73jHOyo61hNPPMF1113HCy+8QHt7O/fdd98Wm4499thjnHTSSSO2t7e309raWtG5t4SSJ9CbtWmtCWG5PkK4JMI6+zTHt0ihGFx2VhcxCOkaEUMnZ7vURoxAfxJ0TcUQ0BA3OWX/Zo6b21g+xng8pqihg8II8YgS567guPg+3P/SJmmYmhOjeoure7ITzqcbLY5muT6W4+P6Kve/tBHPF6zvK7B/a7wib2pne0jbEt/bWligJmygIDPadcMI9WMdc1uwO6io7w6o+FtoaGgYdUmdTCaHJHLGg1wuxyGHHMLFF1/Me9/73nF/7o033qCmZrNQQXNzc0Xn3Ro2DhR4aX0//Tmbtb05XN9HV1XqoyZzm2NbXMpuLjuLYbtStKAuqmN7Ho4niIekinnI0ImaGsfMqeeCo2ZUrORy8PQkAli2KU08pNOft8vqOrGQhuOq1NYYvNmb5Sf/XMlhM2rZ2F+gO2NheX6Z3N2XtcdcFi7vzHL38+s55y3TSISMiibY4An6Wnuav7zSjq56TK2NEDV1Nvbn6ctZvNEJsZAxovpmrAzszq5L3pb43tYMq6ZCMmLQn5cJt8HH9H2/XLYohCjrkm4rdnUzsj0BFRvJr3/963zuc5/j17/+ddl76+jo4Itf/CL/7//9v4qOdcYZZ3DGGWdUOgSam5upra2t+HPjxWvtaZZ3ZDA0hUTEwNB0HM+nK1MkY8kly1jLoZIXMTUUYV5znKzlytrbqCQMZ22XousTDyucsn8zFwQq5sNxSFuSZe0pXt4wwJzGGNGQPsRjOn2+vPftqSLLO7N0Z4rkLSmCkLNcoiGdabVRujNFXlw3wGNvdBEzdaYkw+zXmiBsaDy3to91vXneMqN2hDfXn7fpzhRZtinF8s4M9dFQxRqZJa7f/y3ZhO36QwQ4aqMmyYhBtuiyqjtLXXRofG5L3tTO9JC2xXvdmmHtSFscPaeBouMNOeam/jxLNqSwHZ+C7XHDw8uZ15yoNu/axRiXkXzLW94y5ItesWIFM2bMYMaMGQCsW7eOUChEd3f3TolLHnrooViWxfz58/nWt77FcccdN+a+lmVhWZtro9Pp9BaP7fuC59f24/qC+phZro8O6RpmTKUvZ/NGZ4YZddFRJ/DQsjOTQ9tqyzJXEVND0xQaYyaXn7wP5xw6bcTEHpwsyhZderIW3RmbxrhJYzw0wmP6yHGzuPu59SzblEJTFSzXp7kmTGM8xJqeHHlLxiQ9X5CM6KQKDq9sTHFoWy3TaiO80ZFh00CR6XWbPZqS5mPectFUhdaaCFFT26Yl+FjxuURYpyEWYuNAnr6sRaboUhORXtd4MrA7w0MqhSdcX3DG/FaWrBsYFwF+PIb1wqPk3Cl91//ZMMDKrhye71MTNkgVHDxf0JO1q2WEuxjjMpJbihPuTEyZMoVbb72Vww8/HMuy+NnPfsaJJ57I4sWLOeyww0b9zLXXXsvVV1897nNsHCjQnSkyJRkmVXAwdbU8uWXfZo32gSJvnVk36gQeWXa2uYXp5rKz+jEN5OD44NTaCDnLZXVPllhI5z2HTeO4uY1DPjevOcE5h01jeVeG1poIEUMjHtJ4/s1AXiukM1Bw0BUFXVNJhLVAjivHvi1xYiGd7kFGSghR1nyMhw0s1ydiaNtc0raljoVzm2Okija9WZv+vE00NHkysGPVnr/nsGk0JUJb9V7HGxaYc2Kcf63s5rt/eQ1dU5jVEMcI9CUHCg4ZyyVnO9z9/Hq+dNr+6HrFudYqthPjMpLf/OY3x3WwEiVoR2G//fZjv/32K/9+7LHHsmrVKm644QZ+/etfj/qZq666is997nPl39Pp9BbbTORsF8vz2a+1hlc2pugLMtQlcYFMUV5jazLM+v48CpB3vCGTZjQvQlGgP+8wvS7K6fNbx0z4DI8P1kQMDp6W5D8bU/zztS5m1keZXhcd8vlEyKA+GiJqSmOWLsgSt3jYwPN9XF/ImunBclw5G4DGuMmq7hyd6UIwDp+OdBFdg4G8zdTaSLkMbluoRVuKz9XHQuzXkuB1kaFge6ztyW13fHEiqExbqj1vTxX5yHGzxnXt4w0LPLWih2zRZVptmJChBdfhUbQ90kUHVVXoSlsgFM47YvoO8yh3RW/03QETQrlfvnw5P//5z/nVr36100UujjzySJ588skx/x4KhcqCHONBaVKHDXXIUjlruXi+wHI8fAEPvdLBH57fCAga4yEa40NjdqN5EfOn1nBIW225z/WUmnC5pUC64LCyKzNiWdqXs1nZlaUrU2RlZ5ZNqQIHT6sdEqeaUhOmMW7yanuaeU1xbM/H9XyMsI7w5VI7EtYxAy/E0FRygYxbpuhSsD2eXdtPxEhjez7pgoOqKBiaSl3Moz/vlBMrlZa0bS0+V3B8zlowlbMPmUJh2MtmSxgrU7+9/b0nWhxia2GBUjjC1BXMILRTsF060kUcTxAyZFM5AbzanuKOp9wdsvTeVb3Rdwdss5HM5/Pcdddd/OIXv+Dpp5/m8MMPH+Kx7SwsWbKEKVOmTNjxBk/qfZrj5aVyd7bI8o4MOV8axazlkrM8FAS9ivTIhsfsBnsR3RmLl9cP8Megz7UdUGJChoqpq1iOx/r+Am+dUYcQYHs+ectjRVeGouNJA4MgamijdtNb3ZNjXV+e1d056mMGri/IWS6WI7ULTW3zhHY86V0u70iTKrrMbIiiKwpr+/IUHQ/fFySjBk2JMNmiy5L1AxzaVkt9zBySUBmP5zGe+Nzp81uY2RAb93c02oSujRp0ZcZWeh+vYdnZ4hA528UTMubteD6mptKXc3A8QcTQAEEu6Fk0rylOZ8aacAWfwZ5za02IuK+TLjo8u7aXjQN5Lj5+9l5tKCs2ks888ww/+9nPuOeee5gxYwavvfYajz76KG9729sqPnk2m2XlypXl39esWcOSJUuor69nxowZXHXVVWzcuJFf/epXANx4443Mnj2bgw46iGKxyM9+9jP++c9/8ve//73ic4+FsSZ1+0CRVNGlORFGUxVylktLjfRQ+3I2HWmLt86oZWV3bshDXOpz/dCgPtdFR+XFdf305x1qIwaHzagjLaAnY/HQsg6SER1dVRkoOPi+CKpqQNc0aqMmibD0aBYtXkfRkZ7ejPoozYkwb3Sk2TRQIG975G2NuU0xDkqEWNOTpy9nEwvpZIoOluOR96EpEWgadmepi5rEQyobByyEgJpgmV1qKVAbqS0nVAq2xy2PrRqX5zGRtJ3RlsI5y+XJFT0UHI+F+46u9D74O9mScd/Z4hAxU6cuYpApOKQKDrGQTsHxglg4uJ5cCdTHTGoiBqqqTKiRHuw5N8RMXu8Y1EtHVWhPFYkYGl8768C9duk9biN5/fXX84tf/IJUKsUFF1zAE088wSGHHIJhGNsskfb8888PIYeXPNGLLrqIO++8k/b2dtatW1f+u23bfP7zn2fjxo1Eo1EOPvhgHnnkkVEJ5tuD4ZO6P5+jK2MxvS7CjLoor3VkiIeNsqdRivFlLW+EpzF8+QbwensG1xPMqIuwcaDIo290oatS4ML1BI7nM6UmhOvJZVZHWj6o0+ujxEMamaKLqSk8sbybqbVhDm2T9JlEGBrjjaQLDv/ZOEDB8UmGDRrjIWIhneUdGdpTUvDWFzC9PsKBU5LoqkJ/XhKbQ7pKaxI6UkU600XqYibRkE5nush/NqaYXhdlv9YEv3y6MgJ6JbSdsYzYWEthoNwrZnVPbkirg+Hen+V6W1xW7mxxiJIqUE+gTt6Xs3F9HzPwLHOWRzysc0BrQoZGHJf+vEXG2nol1XhQ8pwjhsrLG1Ijeun052z++XoXJ+7fzNv2aZqQc+5uGPc3/eUvf5kvf/nLfPvb30bTtAk5+YknnjiivGww7rzzziG/f+lLX+JLX/rShJx7axg8qZduSnHXc+s4aEqSgYKzOd4nBLbr4/iCoiOz13Uxc4inMXz5li449OVtdE1hXV+B/ryN5wu0IGmpq3IidmRsFGQ/k7ztoaoKDTGD598coD9vk7ddOtNSQq0hFqItICUrikIyanJoWx3r+vLMaIjSk5UTcEZ9lMNn1dNSE+aJFV0c2FpDwfHpzBQp2h7xkPxeE2GDvO1RGzUpOj6O51F0fOY0xrngqDYeXta1TTG78dB2thQbC+naqEth2/PxhKAmotORKvJmb566qDRwji9QFSg6Lq91pHn8je4tGvc5jfHtEoeoNPkxeOUioTCQt8kGlVnxsM6BU2pY3ZOnP29TdDw8X3D/ixsxj1C3exmcswPDm3NG7aXTlDDZ0F/gH691jmBW7C0Yt5G85ppruOOOO/j1r3/NBRdcwAc/+EHmz5+/I8e2yzF4Uj8cDcllkKaia6okhhcdCo6P6/sIAa93ZJhZH8X1BB2pIjFTLm0HL99szydvy7rrbNEDZOYZAik1FfAFtitjg0JoxMMahqqwvFPy6HRNIVd0cT2fjlSRR3PdzGyIcvD02iEJlpCucs5hslpm8KTdOFDg36t6WLymj5ztBd6JQ9H1aU6YqKpC1NQlyRyF/rxNwfH4yHFSA3JbY3bDDcjgxFXMlGT5LXmoC/drGnUpbGoqni/oCmTpnl3j4gQ131FTw9BUQprKw8s6yFreFo37JxbGt7n0cVuTH4NXLiu6MuRsl2zRpa0uwrS6KGt7ZTOwmKmRt3wSYYPXOzL84sk12x0vlLFl6M5aJAatjkpwfUEspNOeKu61Ir3jNpJXXXUVV111FY8//ji/+MUvOOqoo5g3bx5CCPr7+3fkGHc5Bidz5jXFCOkqa3pyaKoSTFCIhHS6M5ZsBxozueu59UQMjcZ4CNv1y8s3Q5OeoeX4KApoioovBEKAoauyThwIqQq2L3B8H03RyRRdFEWhNqLTkbbIOx6GphIPadgerO/L4/mCt8yoG5JgSYSMEQ92wZGJpI50UQbqQ7I+PVNwcD2PaEhnel2UmmC52ZEucsj0WqbXRVnelRliqIQQZIoudpB0kOIMI2N2ww3I8MRVSFPpydqgwFvaakc1Ys+v7SOkqSOWwk6Q5EoXHVQF8rZsqqUqCnkhMHUN34Dn1vZz2CjVRcON+7bEUJd3prn50VX0ZqU47uyGGAXHG3fiaLQyTinEXCBvueiaVK/3hMD15TWu68sT1lW+fvZB2+zhTauNMCUZYcmGgbL3XYIQgmzRpSkRQgsEkvdGVBxYWbhwIQsXLuSmm25i0aJF/OIXv2DhwoUceeSRvP/9798lGe4djeHJHMeThswNqDa6pmCo0J+z8ASYhsacRjlJ1vXlZL206/OWttryMYWQnpWigK6qks/oBcpAQEhX0VS5fJZxKqiPmfRkbfK2zHarioLt+YR1tay0MzzBMnxZ6PuCh5d1URM28HwRxLwUGmMhHFcEsS6FmfXRIQrYpx7YzIb+PEs3pBjIOXQYecKGxuruPH15u1zfHjM16mLmkJjd8GTL8MTVW2fW43geq7qz1ERkHXqpKRpsNmLdaYumhOSnxkyNrOVhuR6vd2RkksP18QBVkeELXVWwPdBUlQNa47y4bmBEdVEJwxMylcRQl3dkuOaB11jVnSVqavRkbeqiUpFoa4Iow5+ztvoobfVR5jTFuPu5Dbza3o7n++RzPgIR9LWR/W5cX3Dfkk3sO6WGDxwxY5xP88hznnpgM48v76InY1EbM0c0HJuaDAPKXivSu81XnUgkuPTSS7n00kt55ZVX+PnPf87//M//7JFGEjYvie5+bgPLNqWJGCrZoheQtRVyloWmKrQkZBxvQ3+B+pjJPs1xcpZHuiClwsKGSsRQcVyfgiMf+oipUrCh4Eiiuh4YR0NTaYybtA8UcD2PvpyFL6AmotMUDyOEz6ZUUbadFYApAo/SZ25TnFMPbB4xydf35/nPhgEagjLH9lRBxll9n5qITkhX8IX0HuuiJgumJdmvNcHvn13PM6t7Gcg7ZIoOSzYIIrpGLKRTHzcxNB3b9dgQlPEVHGlstpa46s87rO3NMashStTUcFyfVd056qLmEEMmjZjP4bPq2JQq8LdlnXhC4HgePRmZjVUDpXdVkbfD9gS6qmBoCtGQPmp1UckLtl0ZShlsCMYbQ7350ZXy5RQ1iIVkwqM7UyRruRzaVltRCKJkiOc1JzjnLVN5ozNNf86hJ2thuzIeaeoqYUXB9XxSRYc7nlzDYTNq2belZoxRbhnHzm3kpP2bJUPA9siJzQ3H5jRG6c05e7VI74S8GhYsWMCNN97IddddNxGHm7QoPbhL1vfTm7OIGNJIuJ6gM12UcbGsrGRxPZ9oSKcuajIlGSJkqMyoj/BmX75s6CDgLHoyLqkgs7QK4HiCaEjF9Xym1EZIFx18IRM7U5NRLNenPy/rey3Xx/EERddDURRMTQpL/P7Z9Qzknc1cwohBX97mlU0pIoaM1dVFDQ5oTRAN6ZiaSthQeL0jw3lHtHHQlBrW9Ob4+ZNrWNmRxdAVGhOSgrSmJ0e/4+AKgpprmYltioeoCRtlVfSxElfxsI6qqmVmwNRkGF1TURVJORpcyw2bs8pRM0gaKoAA35f3SgDRgIwd1lVQZDdKy/VkLFlVaYqHWNubC7LIolwo4Ho+edtjbnOcgj2yamxr2fbenEXEVIkF3n1IVzBjZpk6dWhbkqIjhTwqIb8nwgZhXSNnyWfGDQykEAJPyBYgEUOjP29z7wsb+fI7tk2FXlUVLjxqBkXHZ+NAnrqoWW4n0ZG2dnmJ6K7GhPrPhjGSMrGnIR7SZfWNJ2ipkRM/b7sIwEdguwJdhWTUQFNVujNF0kXZbP6cw6YRN3V+8eRa1vRmmZYM88K6AfK2h6Ep+L5fnvAgJ0BLMsKcxhgrOjMs78qi6Sq9OekNub5AUxSEEBgaGKqKqqk0J0I8vaoXHzh0epJ5zQnaUwUefq2znEmPhTRURaU7Y5G1PA5tq6UmYpApOtRFQyRCOv+3ZBMPvtLOuv48QgjqoiYIOa6IIduZFmyXjQN5mhNhmhIhWmsiCAQvrx9gfX+eguONSFy5vo+hyUfP0FSyloupq9RFTbrSRRRFYA9SpC1lledPTbJk3QCuJzhmdj0DRZfejMVA3iFsaNiej+PJWnM9IM97QsV2fWzfZ2ptmM50kRWdWdJFB9cThAxpdEovrV8+vXZI/HA82fapyQg9WVtK4AWGenD556quLOv7i/zu2XVoqjJu8vucxjhTkhFeWNeP43q4AqyiixDyZSqQXNaoqW03d3Jec4KLj98ch+3NWTtUgm53wt4ZZNgOSAOmINj8VlWRHqHvC1RFoCgqauDRxUyN7qyN7XpEDY0ZDTH++5gZ5RjdQVNr2DhQoD1VxPXB1DXa6iLMaY7TFA+RCOv056XQge36FG0XNys9z6ipkXN8SfuJGHIMwKub0rgBteqFdQOkCjJzXfJC3CAW2RDTqB/k8ZRimVOSYR58pZ2NAwWyQesJXdPI2R62VyQZMVFVldqoXi4lnFkfJWNJmo3j+RQdjzueWsMpB7QM4R2amoquqmWD4ngylhnSNeY1x+nLWWSKMrHj+lIybNNAgZChURcz+Mt/2inYblnn03F9XCGwPY+QruJ4UHQ9YqoGKHiewNDkknvA8jhx/yZWdGTJFF2ipoovoCUZYW5TrNxlsRQ/3Jog8cJ9ZbZ9dkOM+qhJV6aIGdssiGJoKn1Zm5c3pKgJG0xNRoiF9HGT3z+xUIZM/rJ0E1nbQ1cUNE3Z3EwOsF2fiKnjC3+7EytVkd7RUTWSFaLgeDTGTZRgWRgP6/iIclsGU5cUobzt0pv1KTguedsja6n89pl1nH9k24jsaUPMlKTvWAhNVTh6Tj2qKomTfTmbl9b105uzmdkgyekrurK4rsD1XVRFIWrIZXkpmWQ5PomgVtt2pQJ4zvZoTYYJGyqpgoOuKuXxDyGL10YRwfIyZmq4ngAh+z2jyevPBFlkEdCXXF+wujcPQhAPG5i6nFRrenL89ZUOaiOyF3bM1BBCENJV+nM2TXGTbNGluSZcFtGQHqkglbfpSBfwfek1ub7g3hc28Fp7mpCh0VIToi5qkrVc1IyF5QpAetaqopCzPRCUXyalhM3J+7cwkHOYWhvB0NVyH+uSYSvFDzf057daw/3Cm/2ENJWC4zG3OUbGcoYIomSDXtqJsMGRs+uGhA/GS34/enYDLYkwfVkboQh8X0FRZHIwrCnkHR/H9UiGjQlJrFRFekeiaiQrRMzUA0ELk/aURX/eJme7qCqYilp+6/Zm7fLbPmZqhAyN1zrSQ9qFzjlR9npZ3ZMD5MT529JOVnbnmBIYtFc3pejOWjTFTd4yo46+nM3GgUKZxO75ggIyw+75gUcrpIdhBDG+iClLHDNFh3gojK4q7NOSkKrkeXsIWXy/KQl+9e83sVwPNxC7cH0fTfPLGfeCLcvm8raLgoLnS8PXUhMGoC/n0VITZsHUJK9sSlEXNSk6Lg8t6yiPbSBv05OzaUqEmNmwOZOejBo0xc0gIeXRmbKImFIcpCdrgQK+EPTlHExNIxHI0fXmbHzhoyoKkWDpDYCQ7RKOnNXA6fNbcH2B5flMq4uW+amDUcpyr+7JbZUP2hV0ZlzfX2Cf5jiHTK/l1fY0/TkbXwgKjkfE1DhqTj0N8XD58yXyezJqjBp/HZxpb08Xaa0J05GSv0dMTSb2kMyGUCCr1pKM7LWJlR2NcRnJrQnVDsbgtgp7IgZzJg+fWUvW8ujL2yzdkELXZKDbcjwcRbaLjYR04qbsRzNcoGC0wH1txGBKMsxA3qE/b5XLIQ+cUgPIul2QHo2k/UiBBBHEMnVVwfEEru8HsT4NQ9MwdZWc5ZG3PbQgiTGnMUam6JbJ4ift38Tvn1tPX86ipSaMoUkR396cTSpvEzI0PF+WTSoWeEHwVFXA1JTAoAoipkZj3OTFdQN0Zoq82p5GVRRcTxA1VUK6Sm3UIG/L5fLG/gKN8RBTakKs7cuzosumtSbMQM5GV6VRfLU9je3KMsuC4+F4vkz41Iapj0mifzYQ63jLjFo836crI726C4+cwbFBtcj6vvy4yg6BcdRw+xw+u56c3c1LgUhxtuhgex62B3FTZv6n1Q71zEohB5Df0+D46+AxRAwZ87Q8jwVtSV7dmJaCGKoiY5uGiq4oKKrCW2fW7fXL4h2FcRnJ2tqRBNyxsKM1JXc1BnMmSx7ftNowKzozrOvLg6BM3zF0ldqITtGRauGDBQr+vaqHvwaiF4OVV1b1ZJmaDPPew6aRsVzuem49B02pQVMVngsU02vCBnnHI2JoZC1XlnYqMtuqqMikhRC4nsDQBDFTJWpqDOQd0gWHGQ2x8hIzEdbpSBelZuWGFDnLJRkxysvB2qDFQs7zyNleKak8BJ6AvpxDxvKY0xhldmOMNT1SuDdqqvTlBBFDJRaRGfV9WhI0xUPETI1XNqWY3Rhj4X5N/Pxfa1jdnSNaFhRxaIzLTGt74ElNqQnTnbWxHNk+o+jIks2oIVkG9XG5BA/pGkfPaRiRdBhvz5rZjbFxGdMDWmvQFIX//ccK2lMFdFUlbGi01BhoikzcrehM05gIl5f2ibBOfdRk40CeiKHJUMawMUxNhvnzy5t4ZUOKVd05IobGlGQY25NxWoEgrGvEwpJBccCUPds52ZUYl5F89NFHy/9eu3YtX/nKV/jwhz/MMcccA8DTTz/NL3/5S6699todM8pJhuExxZ6sRSrvoCkKpqlh27LtQdZyyVouU5JyYiqKQsTU6EgVeeTVri0qr0RNnYuOnUV91KTgeAhBOb4VM3WctOThgfQefSEkRUhRCBuyjA4h43S2JwjrqszAC2itCeEJQWEQWfyQtlr++OJG5jTGcVzBhv48vhDkbQ/bH5RpDv5fMi0lTmLUlJVHvTmbUG+egu1RHzPJBDqctRGjnO3ty8p2FP0Fh+ZEiOWdWV7ryLCmO0djzCQWluWcBcejN2uXl9WpgoMPtNaE6c1apIoO/QWHmKnTnAyxb2ucSxfOpSZibJd022kHtdBWFx3TmA5u1uV5Pk8u7yYfqMA7ngAUTE1606t7cvx7VS8tNZLiVBc1mdccZ05TlPX9efzgrpaSVO2pIpqq0Jmx2DhQpCas0xgL0ZezcAKK17TaCE2JELVRg860zcHT914O486AIrakMDEKTjnlFC655BIuuOCCIdsXLVrE7bffzmOPPTaR45twpNNpkskkqVRqu0MDvi9Y35/njqfWsKYnx/TaCC+tH2BlV1baJwSKotAQMznlgGYa4mEyRYeNAwUQkoazoitH3nYJaSqKKnl/6aBX9mffvi8dqSLLNqWpixo8u6aPupgpy+1sl3W9kmITNjRs18MOMrnxkEwclIxcXdTA82FqbZgF05KkClKMo5RRPu0gGav733+sYE5jnHV9Of61ogc7yIgXbJltLplKQ5XxU1PT8IWg6PoYmkIyrJO1ZdhgRn0UU1fLHuCcxhiaqpIq2PRkbZJhA8f3yRWlBxrSNYTwSUZNGoJrXNeXxxOCRMigpcZkbU+eaEg2M7NcWREyf3qSuoghveHptXxi4dxxLTsHU3usgEjeVBPi8Fn1HNBaM2q7XSmZV2DppjSO69NWH8UTgmUb00RNjeYgROF4gr6spFbpGhQdL3hJGFiOjB83J8IkowbN8RADBaf8fcxtitGbs4PknE9/wSFVsOnNytiwTEZJKlpNxGTB9CSfOXWfPYqiM5FzdCJQceLm6aef5tZbbx2x/fDDD+eSSy6ZkEHtTuhMF1nfl6clEUYJCOERU8PzBJGQVJXOWC7Pre3niFl19OYcpiTDdKQKbBookirYeJ5gwHVwAp6fCLzCWx5dyQn7NKGpChsHCggIelfLWGR9zJRxvpCGqihkLQdFAc+TWWYFhZDu0FITZlpdlMtPnMe85tEpHqVYXc5y6cnYJMJ6eewlapFCmcONqWnlX1QFPB+KrnzfFmyXoiNjolFTR9cUXB9s16U3a1OwPRJB98eiG5RiCoGqqmSL8nwtNWGiZsmjdMk7OjVRSSHqzVq4vmyhUR816EhbNMRDFRGeh9dKP7+2n+50kftf2shDekeZC3nRMbP4wwvrWdUtaUNdGQtTU3nLjFqmJCP88/UuCo4b8FwFqq5iajKOarkeYUOnNmJSFzOxXB9FEWSKHi018OlT5pUJ96XvQwjBtx94le5MEdcT6JpC0fHxhRRRUZCrgaLr4+dtMsWJkUyrYmxUbCTb2tr46U9/yg9+8IMh23/2s59tsXfMnoaSJ/Liun6WbUqTDOsUHOlrzaiP0pm2cDzpYRmqTGo8u6afI2bVc8oBLfzm6TfZGIjjlsQY3CAzLVfJMju6ujdHImwwpzHGhv4Cq3uyKICpq7LJvQJ5WyZoGuKhwLst0J/P4/qCZNAj58KjZ5a9jdEoHqVY3bNre+nNySoLzxfkbNmuAqRxFEhvVwsoR54v6U+qIso0JF9ICa62+ihzGmOs6s7RFcix2a5P2FDLyRdNVTF0+XmBpO/Yrk93xiIR1skH5ZoDOYcZDVGmJkMs25TBD6hEqYK7TYTnUhVNSUzCcjb3BC9xIV/rSNMcD8lQiO/Tk7VRUDhidh2N8TDpgoMdkNftUiLJCGO7PgXHl8t4x6c+ullRSZZAyjr9iKGPoNy8uinFul5ZWtoQD7FxoEDB9iSXVJPsBVSFtroorufRPlDkb0s7mXPixCmVVzEUFRvJG264gfe973389a9/5aijjgLg2WefZcWKFdx7770TPsDJiMGCDXVRWfFQcH16chZhXaU2atJaEw6EeGVlTCLgT75jQSvHzW3kkWWdPLWqBz3g8WWKLr4AI+AdChQsx2dqMsxAQcYeZ9RLFW5dVaQAhCLoyVikii4Rw6doyxI83/fxhVx6h3SNzoy1lSvaHKtb1p4iVXAQQHe6SKa4maA8OGljeWJzGWXw4wuZTIjHDKbWhjmsLUnO9mmIm3SmC6SKDgpS0KPoeAgk4VomuoKXBIKi45OzPfK2ix8koHwh6S6KovLuQ6dxcFtyXF0Lx/r+/ra0k5VdGZZtSpO1ZFa8KVCdT4QNbNfj8eU9REyN4+Y2kAgbbOgv4vs+/9mQ4tA2WeOuALGQTjYQxLUc6R07no+pK1iORyKsBy80OUbX91nbkxuV/J21XArBZ2xXlnqC5HuqCmioQdWUQjQke7n/Z+PAXitjtjNQsZE888wzWb58Obfccguvv/46AO985zv5xCc+sVd4ksPl7ld1Z0kXXanK4vn4PnRmisysjzIlGaIro1AbNVgwrYa+gBeoqgqHzqjlDy9uwPNlYsUNFIFcX6AGEmyOL3A8QWtNiMVr+mhKhDj1gBZWdWfpSls4vk84qMHO21ITEmTzqIa4QSKs47hyUi9avI6vb0WCf15zgguOnMHa7hzt6SIZyy0bgpIXORhB2BU/+LcOoCi0JkM0xEP8/dUuWWOMIB9UDJm65JKWvNz6qCkbrdlSvEFVZMiCwKMWQiER1jhwag0XHDmTA6bUbFcVyOAXXCKkoyhQGzWGlGfWRQ1Wd+dRFVlNBQqe8FGAxkSIgbxTbsmrayoJTcF2PbKWx8aBIq4vpfFyljRuDfGhOo3D1c0H14bnbJewoWI5Pmrw4gluKyKQ1NNUBU2VLxiQcedKqm2qXRErwzaRydva2vje97430WPZLTCa3H1jPITv+2QCkvBA3iFuWiiKIlvCTq8te3XpgsPrHWlqYwaNcekJ2K5XFrkwdJWwruD4MkFi6jJrnCo47NuSQFEUaTyQRkR+DnKWR01EtmrQVaXcL1wIKb6xeHUvG/rzzNhKw63j5jZy3LwG7n5hAwSCGnqQBLJcMaqhVAFDk+6kQBr2ouMNcj0VTEPD1Fxqowb7tiRY25MnEdYIGzJzXHAK5F3pLSbCGpYjiId0EhGDw2fW0pO1eOyNLhzPpz1V4LC2ujF7UI+3/UNvzpaK5iGDeGhzL599m+P05W2SUanQXtLK1DUV12dIS95SOWIsZJApSmPl+wJP5lhQhGDJ+jSuD3Ma48RD2hAZu+G14Z4v49GekLFL+bIYdDxFlq6WSjtBrkTGW21T7YpYObbJSP7rX//itttuY/Xq1dxzzz1MmzaNX//618yePZvjjz9+osc4qZCxHHpzRfpzsg2sVPJWMTSFN3vz5C0XF0FvzuGAKQnmNSeoixq8tG4AFPjd4nVYgW6krqnUBDXXnnDQFekdlIQrpHSZVHkpxfqWrB+gYLvlDoslZXHLFRiai6qEyr2bgaCdg0Fv1mZ1T26rRlJVFd46q57fPbdeeoclDp8is6q+D4Opz2ZQF21okiSuABv68sRDOqcf2ELWkkbGUBVea0+xqicvdQsjBqmiQyho3xvSVQqaJEnnbRnPm9kYZV5zglTB4ZWNKQbyDn9/tZOIoTGrIcaHj5vFSfsNlYMrOC4PL+saV/uH4XXkJePXH1QZmciGbCV+Y13UpDtTpDYq+5k7nmBuc4xUwaK9r4ihKZLM7ws0TYYJhBAM5G2eXtXLqq4s0ZDOvi0JTjuoZdTa8JzlsqY7R87xaEmY5ILlt+9LGleJ22rqMoGlqSoHT6sdFwVoZZdUM984UKAuagZlsFTcUXJvQ8VG8t577+WDH/wg//Vf/8WLL76IZcl4VyqV4nvf+x4PPvjghA9ysmBlV4b7X9zI8g65xDY0KZYbD+kYusqUpFSZKTqSRD2nKUrRcXlyRYrevE1bXUQ21jKlyMHqriy9eRuEwLI98gJ0VZLEw2GdKckI8ZDG6h5JmdnQn6Ngu0QMGWcsxb1cT8FCkLekGMTU2giRwLMQQmA7sqqjO2PhB1mYLS236mMmYV2q55SU0hGgqmDoUJKvjJmyFDFne8E5ZPKm6HiyhlpVqYmUatBlfbXt+izvykpakifIWw5qUM4ZMzXZ89zUOWhqDbURgzW9OZasl8o/mqbQlAihAMu7Mlz951f5y8ubQFHKaufdGYuasME+LfExBSlKVTQlUndXpogRlZ5mznbJWy4qkMo7TKuLlIn385rjZC2X7oysBMrbUpFHV1UMVQlaAQtZCROI41quj+LLl2K6KLP9IF82D786sja8JiLrvB9f3kPe8ThsRpLXOrL05qTKUNTUiZoqnekivoBDpiY4ff7Ws/q+L1j0zDqef7MfFdjYXyjzNku0o4luVbunoGIj+Z3vfIdbb72VD33oQ/z+978vbz/uuOP4zne+M6GDm0woxbJ6szZ1UTNoFSDoydl0Z21CuvQmhYDaiIHrCZ5a0YsnpNQ+CDJFg/6cTTyk4/oCxxekClJmTVU2r06LrodmS6OxsjvH9NoozfEQT63qpTkRKsty6UEdddGVvp0rIF1wEAKm10dljXPWZqDgoCnw8KsdvNaeBgEDBWfM5VY8LAVqUwVHLrk1WX0Dm7UbQfaKLkm2hQzpRaZdmaHfmCowMxcLVIaswAP2aE2GAiOjUrBdCkWv7IlaQUuK6XVhutKWzPT2F3Bcn5CuoCnSQ9ZUyclc31/g8RXdnLhvIzUhnaW9qbL8mBOEL0YTpChV0SiKwtzmGN3ZIqu6c7i+wBeCNzoy5AL5ujmNsbIBq4+ZHDI9yb9W9JC2XBav7gMFWfkS0lGQ/MWQodGbtbAVhfqofBbyjgyHHDO3nq6MzR9eXE932hq1NrwhHuaIWXWB4rrK7MYYpqZKuo8iQyu1EYOj5jRw4VEztur9+b7g/iUb+duyDkDGVRNBzXdJHHif5tiEtqrdk1CxkXzjjTc44YQTRmxPJpMMDAxMxJgmHcriqllZQmh7Hm/25ct9Z1A2S1eBXLIKIR/GmKmzdGMK15cir2sC1Rc7qIlWZQUhKnIZ6wtQVRWBwqaBAmcumMrp81tY3Z1j8Zo++nIWOdtDhXLmXFcVVAS2D7YPfXmHgpMpGzNdVZjdFCcW0nn41U4AjphVx5zG+KitYOOmLkUUVBnTlILpQ6ORUhjYxxMKEUPF9aQGpKZK0VnL8cvya6u6cuUKHNvzaU7I9hCre3IM5B1akyEOnJrk1U1p2lMFXnxzoNzIzPdllt72pAp5+0ABRVEoOiUBDsGzawfQFEVqIBoaPVmLZZtSHDe3AVVVRxWk2FxFU/qRWedw8LKLh3QE8GZvHlNXy1U5b/bm0VWFKTVhGuMhaiNSEeqJFd0UHNlhUgEKjl+OCyuqbMehq+D6UmloVVcWTwim1W02SEII0kWnLKbcnAhx/pFtcmVgSAWltb15AOY0xpheF92q11fq+f7HFzfQk7OImTq9WYv6WIiIqZXFgTcOFGmIGXttH5stYfTI9xbQ2trKypUrR2x/8sknmTNnTkXHeuKJJ3jnO9/J1KlTURSF+++/f6ufeeyxxzjssMMIhULMmzdvRNvZHYGNAwVeWt9Pd6bIM2v6eLMnh+eLQOBBoAiCpZDGjLoI6aLsn71viyQr54O2DELILO+bvXnaU0XswAMszVNNBU2BkKYENeERzj5kCvOaExwwpYZ9WxPEQgaO65FzAg9OV4maOoqqDiF7FxzJ1fM8n7qowYJpSTpTFqYmkzodaQtVkUIZ+zTH6QuWW74vWNuboy+YqOUk0aCfEkoZ14G8S7rolPl/hq7hCZ++rEV7qlhWIgfIBiT4TFBSOa0ujOtLdaXZjTEKtkfGcunP2XRnLCnW4ZXGIVkAhiaVhxyfoJ2vrGW2XZ9UwaE3Z/NGR4anVvWWEywRU3IZD59dT31M6kam8havbOgnW3SJhTSm10Y4dl4TJ+zbzLsOmcLMhghFx2NtT45XN6Xoy1rkbXnfLddnXX+eV9tTrO7JEg+YBD2ZYtkjLQki266PoUvNzFKzNF/IJnD5wCj15SyeWN7NA/9p5x+vdfLo6528uG6Av77Sjq4qzGyIMasxzon7NXPifs3MaIiNy0De8dRanlvbh+cLIoaGqkoZuY50kYLtlcWBe7IWXvA9VDEUFRvJj33sY1x55ZUsXrwYRVHYtGkTv/3tb/nCF77AJz/5yYqOlcvlOOSQQ7j55pvHtf+aNWs466yzOOmkk1iyZAmf+cxnuOSSS/jb3/5W6WVUhNfa0yzvyDCQtwkbKrGQIXUjVQUhFAxdCuzWRHQyllfWeFyyPkVP1sL1ZC+VoutTKgItGRxPUCaQ+0JWruQs2c/G8f1y35tptRHe0lZHc02IupiJqaokwwY1Yb0s+BsxVRncZ3PWWShg+7LrXV/eJhGR1KCSRBcM1TD896oefvfsOoqOh4IyIpsNmw1x3vICT0+2Lggb0gBEA6+rJ2sHsTRJSO/L2URMndYa2dsmHtIRIiix7MvxRqf0fo2gFt0TgUZmUNVTalBlu/4Qz70/b9OeKsoe24EcsuvL+OSS9QP05ewhghQfOW4WU2rCPLWylzcClfKCs7ndRk3EYKDgkLekMck5XtB0TYpPIHxpoISsVV/emaEzVZTc0qxNuiB7ptuefFEZmoKpqjQEIsoFWy6X5zbFaU8V6c0WeWZ1H6t7cjiuT9TQ0AKZu6dX9XLjIytY2ZWp6JkdnMmfVhspf0dekAAqqSgJIVciOctlam1Vbm00VPza+MpXvoLv+5xyyink83lOOOEEQqEQX/jCF/jUpz5V0bHOOOMMzjjjjHHvf+uttzJ79myuv/56AA444ACefPJJbrjhBk4//fSKzj1e+L7guTV9FF2PiCnbF/hCeoC6qlB0ffKW5Dh2pCwa4ybJsI7r+7zZK3UiBTKOpynSKI5qeBTKlB03EIrIW175zV4ie28cKLC2J4fjC6KaTBQUXVkPHNalKpCUF5PS/pqqkiu6LNkwgC8EibDUNcxa7hCJruHCG3qw1C5TT6AcOzVUsD15LUXHJxGW79qQLtXDa8IGG/rzOJ4gU7QZyEuDM6U2zIFTktLA2i6ZgozVOZ7Pi+sGysae4FyaoqCpcgyOT7k0z/PdskybgkyCqEFC3/HB0ORLJ2ZqFGyXlV0ZWXk0vbZck110fZJRg5zjUR810VWFbNFlyfoBZjdGWdOTI29JoZLZDTEihsY/X++iN2eRjBikBorlth0ySy7pWlnLpTMoXRSujO+amkoyajC3STILShSgUw9s5s6n3mTxmj66MkVURd5D2QFToyURIm+7LO/IVFxVM7i3kBCU+ZyO51N0ZVik4LhkLJmoihgapxzQXE3ajIKKjaSiKHzta1/ji1/8IitXriSbzXLggQcSj8d3xPiG4Omnn+bUU08dsu3000/nM5/5zJifsSyrnIGHyrQxAZ5a1cNTK3twPcGG/gJhQw14fS4qCpoiPR1NAdv16AqyjiVBCF1VKInolGpvR/fOlM3KOgS10I7HlJrNYq2lPiSO5/HgKx2kCi66pqIpMi6YD5pYxUK61I3UJH3F9X2ZffZlV0BFUcrUlhIKtocnBO2pAq01YV7vyODYQiaUBs0bEXi9uhrEUlVZSugLggy6Tabg4voyTtuZsshbLukgc1xwfKYmQ6QLTrnfeEjXKDoulivJ+LqmyKUqlO8dSKOsDNtWoiWpKoQ12XfHdgWmLulEqiqpWUfMrue0g1oAKWzcn7eZPzUZiFBIDqupq/TmbJasG5BxybCB5cqyQwDP8yVnNe+gqjLUoGkKnu/jKwo6Ci2JEChK8B3Kl2NTTYj9WhIYmsqKrmxZaWhec4IzFrTy3JpeWUUTlGSahkpdxCBiamiask1VNTnbLWfyVWUzn7OlJkx/ziFvy/tdCGTljt+nkWPnNo7r2HsbKl5uX3zxxWQyGUzT5MADD+TII48kHo+Ty+W4+OKLd8QYy+jo6KClpWXItpaWFtLpNIVCYdTPXHvttSSTyfJPJVVBK7sy/O7ZdfTlZaVMNGhnkCk6eIERKE3caEjqBBYdn7zj45aXz5srJnwxlGM4GAJR9iJLpGHL89kwkB+y37zmBNe+52DOXDCF5kSY2nBJQEKeJ2yo+MiOgaU+3poqPRmA/mCZXR8ziYckub07U2RVd5bWmjCqCsmIKRMbUK7bLiU5pHSlQsjQpGqRkN6gpikBjw/68rY8jyWXsbGQzCRnLY/lnRmeXNlDMahvtlyfoiONZyn+6XgypufLMuVh92noPRzs6Soom8v3FIVcED9MhHXOWiBju4M9rETYIGpq9GZtSX4HQppUcZcixfI+JcI6luuRdzwURXrvliNJ364n1eE9X3arbIiHePsBzRw4NckVp+zDOw+Zyoy6KKmCw0BetmYdzEdsSoRoTsrGa6Yuv8e85dKRttg4UJDtM7ahqiZm6mU9zFIWv5R8aoybNCfC1MdMWpJhjphVz4VHzSh7kb4vWxO/3pFmfV++7N3vrajYk/zlL3/J//zP/5BIDKUdFAoFfvWrX/GLX/xiwgY3EbjqqquG9AJPp9PjMpSlmE5JhDZiakzRI3RlikHbgs1LZ0OTkzJT9IYsA0sxR6DsPpaSK6WJXtpvOEkbAau7s1z22xc5/4g2Tt6/pcxn1HWVSxfOwdBUNg7kWd+XpyttBUZRek9xU8NHLqtrIjqRQBHc8gQCn6ip8tzafrqzFjnLJWLoNCfCAXUGWoLac79kvAaVx5m6FJa1HC8wqgaOJwKVGxlTtF1fZuyF9KYSYQPLcSnYPpYPIClMIUPGdvO2z+Bb5YtAPHjQBNWCG1Z6MZX2VYLvSygy/ucpMCUZ4cg5dbie7PtTEqUteVhFR+X19gz9eZtUIehFE5J1+I7nkyl61MXMsg5oiTNaitKWQg8EY/V9ed9aa8LEwgZ61uawmXV84IgZW+SkxkwdhBIk+/wyHUi21PDk9xd0RKwkqTJcXLg+FuLQNsk06M1ZpAoO9bEQJ+zTxOnzW8fVHXJvJZqP+66n0+mgdlSQyWQIhzcvAz3P48EHH6S5uXmHDLKE1tZWOjs7h2zr7OykpqaGSGT0gHMoFCIUClV8rpLHURKh7cpIQdbGuGwVgIBckKFVUcgWpSBseQUr5DJ8OEqxycFZYwXK1lIKQEguoBCwsivLDx56g4eWdnD8vKbyw1paei9avI6VXVkc3w8Mtuzx4voyExw2NMKGRl/ORlUUTtinAc8XvLRuQHY6DOnMaogxtVZqXXZnLCzX54DWBBv7C/T5kuyuqUq5ttoIklaKohDRNabXygy1F4hRbOzPyUQUMusfD0m1GwUtSLqUboZARdarl14WSvAPU1fLy/ESFFU2wfI92bbXG7T0LzVOc1wfEajtrO3Jo6sqx8xtKCckYqYUjnhxXT+uJ+vHS7SYjOWSKkqeaW1M59C2WupjJgT31Q9itIaq4A0bG5TqtM0htdlba6w1pSZMwZb6ngoKhrqZQuYGCTcFhflTKxPWHU1cuCZisF9rnNU9khj/gSNncFzQ1gKG1rWP1eZ2bzSU4zaSpRYOiqKw7777jvi7oihcffXVEzq44TjmmGNGVPQ8/PDDZYX0iUTJ45gaigzphGcGzbAcR3qNuiYnh+NRnrgKouxB6sooxlKBuKGSc/zNAhLBPppCEOeS8cyasE6m6PJmX57khoEhD+ucxjj1gdL1IdOSPLeun2zBDfpaq2iaQltdhEPakqzqznHg1Bq+cOp+3P6v1fQGWc9SXNL2fBRkLK0nYxE1NBZMr+HFN2VLWseVhszQZPzSViXlqTEeYqDgEg/L5V130SLvCOkVC7BcD7lMl0vGwd6yG1CnQrpGSFeCZas0lhFDQwAZ30FDPl9yKotyz+kSj1IoBF6efEnVRXQSIZ3VPVJmbt+WzZqNIV1WwvTnHWbURVBVlZAOUVOj6HhsGCgQ0TXqIrL0MF1wpFCIF4hvKNIAGUEvIVVVEIG3q6uyO+VAYXNt9tbEJDamCqSKbhA7FlIQJHh7iiBB6AvBwW3JipMqwxX0O9NFQrrGUbNHtrUYXtc+WnfIvbUiZ9xG8tFHH0UIwcknn8y9995LfX19+W+maTJz5kymTp1a0cmz2ewQzuWaNWtYsmQJ9fX1zJgxg6uuuoqNGzfyq1/9CoBPfOIT3HTTTXzpS1/i4osv5p///Cd33303f/nLXyo673gwOKYzeKnSl7MCnUX5AId1LaCmOOUs7OBV4mjeJAQtDwwtENoVQ5eavgwWx0M6uqYSDaS4YmGtzGec0xgve7thXWVjqoipSsPoehDSFeqiBhnLZW1vnpkNMc47vI3OrMXqnhxzm+I4nuCNziwd6QKpvGzipSoKugrxkEYyajK7McobHZlyRlRXZZJDlj0KZjVGyVk+/Xlb1j3nbBSkhyi9ZIWC7Y55H6ygNa5AvihKHZKKQafBklajqsgXhudLw6sFRqomquH5ChnLwVAVNE0lbOr4SMK17fn88t9raYybsmbeE7QPFIgamqQhhXU0BVJ5l4ztYmoKCJ81vQWWd+UIayo1UYOYKXuWG5pK1NBwhSS3e77A1GTySlNVOtIFZjXGy7XZW1u6runJUXA82uqjsgdR0ZG8UKTRTYR1NJURXut4Md5e2oNjtWN1h9xbK3LGbSQXLlwISEM2Y8aMcTcG2xKef/55TjrppPLvpdjhRRddxJ133kl7ezvr1q0r/3327Nn85S9/4bOf/Sw//vGPmT59Oj/72c92CP1ntJhO3SxZjvjvlT0UbamHaAU1w6WECwyKQ44CFUkZiYVka1rb9djYXwyMUIkvKZeBZqByY2gKBVtQcOTS9YU3+3j+zTr68jYvruunL2fLpl+6SsSQTMGC42Glg657LXHmNsfoy9lEQ1oQk9N4ZWOKVMEmZ0nhX1OXlTNF18cVgqihEQtarzYnZMuEsKFi6iqGprJ4dR9v9hY4/cBmspbHS+sH5HLX98kGbwnbG6kcNBg+8qWgK5tjfELIpXsp1pkqOvK4wXI3HtYDr83H0BVyedlHfG5zgjlNMaKmHkjN+Ty3po/2gSKtyQam1ckWvlnbIxGSghbdGYvenOwvVDJEqgKzG6IUhEJvzmZTqlD2+HVNlRQdQyNZI4WW8wEzIKTCQVOTnHeEjHmPJl7x7NpelrWnuCBY6gIoAkKGysyGaFmPEmTyzUe2z90ejKeX9uBs+GgY3OZ2b0PFiZt//vOfxONxzj333CHb77nnHvL5PBdddNG4j3XiiSeypRY7o1XTnHjiibz00kvjPse2YqyGUTlL6gY2JkJkLRlY9/0tG4LyMRVpJAuOj656bEoVA31AAhpKQC+ypUAESINhOVJEd0VnFgUplPC9v7xGd9Yqk5h1TSYXbE8a4fqgp3OqYPP8mj5eerMfU9dorQnJRlMpmYDygkxy1JTaiioejgdFy2dNT5aOtBSM6Ms5eELgBTXRJXHcQs5m8do+ZjXESRVka1oPBUMVQ14cwzGcCuULWWnkBtbICShX85rizGuO8fL6FNlAoTtsaExJRsotcZ9b20fYkC0VkhEzuG+C59f24waVUKauoakKdVGTuohB0fEo2C7ZolsmVNuuDBN4Alb25IkbmizNZFCyyPcJGzoFR7bnNTS1HD+ti5kkwsao4hV9OWtI0mRtT54z57dycFuSZNQgnXcI10hxj7C8hLLMXW1EKtPvSAxeOW2pO+TeWJFT8RVfe+213HbbbSO2Nzc38/GPf7wiIznZMVpMp+h4xMMaEV1HVWw8z8PyKNN3tggBpfewpkrKTMZycQIKSc720IPsre36oMilaqmVa/tAAUNXywmdXODNqmzuWui4PpYje+CUjFDe9qiNGpiawrq+ApYrBV1basIMuE5QYyw/my56+MJnTW82OJ4gHpKToz1VJB+MRY5DGsrV3TksR7Y3QEBDzETTVLpSBQqjrLOVQf8vZfolw0gK7DbGQ+Rtr5x5ro9Jcd6C67FvS5yZ9TFqIkbA95RHq4vJ1rMgv4tNqYJs8RpwLkux10RYpy4WYkNfTrZJCGLI/iAv0g/oW+mAd6ogQyiIUkWUS3MiRH9BZqRrQhqRoMXGsk2p4LtxmRF4b+t6cyzdlMJ2fWqjBiEjTM5yee7NPjalCuzXmuD5tf30Zi0SEaNcWZQpOPgCjprTwPS6HbvEHW+r3b2xIqdiI7lu3Tpmz549YvvMmTOHLI33FAyP6aQLDj97YjWvdaTpzzvYXhB/U6TIxJbs5OC/+UIaRVl/raEpUs6s5H1lbRfF3qwMHjY0TF1SRdQg01MisvtA0RGoipzsw82S5wuylofrQ3PCZH1/gZztkS7I3i2mLpMWmcCr0lRZglca86aBomyXGtRPOgIUXxAzNDRFGp+BgkNIl9JbEUNl40BxzHtSyugPHqcC1EWlaHDE1ElGBSFdIWzq5B2flmSYdX153ujIki647NuaIGJIAduaiIEQUgHJ9QWru3O0pwp0pouogffYn7PK4rlzm2K82Zuj4A6OnQbGegxGwmBql+NDZ8aSVVIoFDwV3RO82VegLmpguUVylkdTPMRr7WlWdGUp2B5hQ7ZeSEYMXN8nZmpsHCgwtzHGwdNrWdGZCUpF5Yg0VeWQqYkhHMYdhfG22t3bkjawDUayubmZ//znP8yaNWvI9pdffpmGhoaJGtekwuCYju8LFoXWSUmuIA5YosfYowQjx6qwsVw5YU1NJR7S8ITsVFgqzSt5pWFDRVOkkczZbuAxSnGHwZUAQziZg86tqSUv16foQE/GpiFqsCnlB83HJM2ktMzVlM3UG0MjSEaJMsFaJhTAdX0KSI5kfcygPWVJrqcv6Exb5Z7gW0OJb9hWF6GpJjyIJ+jjo1CwffpzDsfPa2Rmg0witaeKdGctptVG0FWVuqjBxoECDy7t2FzDHog5qIpCuujw2PIeyTcM6eUMN2x+cQ2js24RsjY8oGppCs2JUCCeLKRmp4B0wWbxml48XxLOY0FHy3TRDdSfFF4VGTRNoTtj86FjZ3LAlASvbEyRtz2ipsbB02p3Kj9xrGz4tjRa25NQsZG84IIL+PSnP00ikShLpj3++ONceeWVfOADH5jwAU5GGGppoSjKJXtjxVaHb9UUiBiyWdhA3iFsqJQoMkLA9NoI02qjLNuUwvF8aiIG/XmplC1VeaSBhLGrdwYjGCVFx0dXpWKO5el4vk/G8tBV2dRL+CLgZspzqIGXFDZUHM/D9mRsVBnE8fTxiXo+vlAwdcnty9kuPTl7iJDH1u5LMqLTOMhACiHIFBy8oKVq1FQxdJWGmIk5XWVaXYTV3Tn68w4z6yNMq4szqyHG48u76C242J4su4yHJH3K0EpxRRGEHPLkAmHdEjdTVdh6uITNXifIF4imyOZnqiIl4syYSU/WDrpMCqYkI6SLDroqy0PdQKAjYmo0JkwcT8Ydn1jezWdO2Zd3Hzptl/aeGW82fG9CxUbymmuuYe3atZxyyinoekll2edDH/rQHtf3ZjDHLWJoKMDqnhypgkNbXZh1fYVylcR4S7dCukp9PMSshhhLN6bwfUHBl15NxNQ4uK2WqKmzMZVHCJjbFGdFV5a87ZIquJtn6DghqTXSqmmKXHqng26IEUNF13QG8jZZx0cJqm1UVUX4UukmFtJwfZW+nFPmPqpIDzVuamVlnHhIQzGkiHApRLA1lDxhBRnnK7VzLRk2TZVG2hdSEGNNd46+vMxElzLS+7cmSITlcjsRNkhGTLKWK9V8fL8cQ5X9q2W7C8f1y7Xnji8reVRFQVPEFpkJpVXBYK/T9QWdmSLNCRkmUBRZFun7AjMocRQBXSjv+DiB1JuuSi9eUaQ3nrNcHnmtk08snLvLDdJ4suF7Eyo2kqZpctddd3HNNdfw8ssvE4lEWLBgATNnztwR49tlGFye1ZO1ZFICQcSUcbDGmFnOditBnfF4YOoKUUOjKR6iNqinLkn9T6+N0FYXDeJSkrg8rTZCKm+zZH1eejEV8OVK3EMFUFRAkVlpX8hlfHNNuCzeUHAKuL48flRXA6aj5Oop+GVRYF2RSaeSEK0QkC66hHQFFdkWtnTuLaFkBkpljVJtvSArWjSFsCHjYW7Qf3pFZ1YmzUI6RQSu6+H4sHRjSmaUhUx+1cQMQoZKqihV1afWhskWvbKgQ95y0TSFWtOkO2tvJv4LUa722dL9HHEdiiz9tFyflpoQhqrSlZaakiWSu+zDLVcKph7wLIOKqKLj0VwTZk5jVRl8smKb8/n77rvvqJU3ewIGl2dFDKkMk7NcFGQPF01BCsK6PgS1yaoyMiYY1ktKNtKTUwOaSbrgsKorTargBsKngnjIoDERkiKoIU1SgBQZm+vNO9glJaFxjL9U2uj4ckwl6pETqNgYmkJrUvIe39JWy/6tCZ5Z08vanhyGpjC1LoKuKuXYYtHZXCnjBgZF19RyFj6kq3J56UvvtOj64zKSJY5kpuiWSwyb4gYRU7aOKMm56aoayLzpUt/RcskHLVe7sxavbBzg0Om16JpsSVDKDvtCEAuZ1EalzmbecYmHZH+hgSAOGwvJpJU3zpfccLi+pMcU8IJnRF5cSFepj4RQVOhGGlIfmewqWeNM0aEmIiXUoiGdroy1V/IQJzvGZSQ/97nPcc011xCLxYaIRYyGH/3oRxMysF2FweVZ85piPP/mAJbj0VIj6797A+J2f94u91EBuaTVB1F8VCWIWamym2BJrVr4kCo6ZCzZX9nQZTGgpsKq7iyaIs9RGzUo2B5/e7WzXCM+HKXKnpJB0pCeGcgYoRuIRpQoLfIvspInZmoUXbn8a4yHOHxmPX05h1TBxvN84iGTWEgjlZfXqyqyTLJUOpgtOoQMTbZZ0BT68w4CBS2oP3a2wJEsDUQJvNy+nOzdEtFV2lMWYElOpi/IWR4FO0/YUANeqbzgEm/Rsl1WdmapDRtoqiypDOmKlF5zBLmibG1rBcK5mqoSDRlki3a5r1BNWPahKTjuCG7naM6lFoSkS4a1XEkYLJ/DwX3pyBQC4WJRflkWHClAHDE1SbtpjlMfC5EJOkfujTzEyY5xfSMvvfQSjuOU/z0WJqIKZ1djcHlW1pJcvXh4c3N5Q5MZSidwG11foJe4dcExDJWg+kULpMBk/XIybOD6IiA3qxi6xsz6iOQE5mw6U0XaB4qB4RWkCy7WMPd08KQt/T9YSdMYiCv4yFrswZ8s9cb2hNR1XN9XoD5ulvmDDfEQB0+v4YU3BxgoyOVj1nJlZUlUalXWRU0UBD1ZWxLJbU+2JwiSEp4PtqsEnRJlCGLUJWow+CHXISh7h+V66GAH2UzNLydNSo+ZK8D1AM/jqVU9shrG3VwPL4WMZdtbP0iOFR15HXnbRVNl1t/1BV5g8QxNRVMkFcsdHIAcfC+D8SnBPVVVcF25bA8HxPJSkksJEjsKkAmSNs01IQ6fVU9bXbQstLw38xAnO8ZlJB999NFR/70nYnB5Vn+QJDDCm9uzZoouvhBl79DzpfcoBk16xwfP9tGD2RzWNZprQpi6yob+AhFTIxHSydoeBcfn8Jl1bOgvyEZfeZvGmBn0VZYvpsGGcTABu2QMfKR3ky44aJqKoapEDZWBgovtST5lSYBWRcFxfbKeSzSkEQ9p5WszNI33HDoVTVNZtikdNPKSOpmd6SK9WUvyMv3NWX1PSP1DJUgMKYpA01Q8F8aqQxqe/Bi83RPgedIAhXWZ3HBGMaimSjkEAXLZK4RfDnmU4rdSyVyga5AISQWgzkwRU1ewXYWC4w8ZhYqPoilETA3P9ykGxm6wlJ0TjE9TN6sWla7JDdSYHM+X3m1Q6imQHSFVVUFTZOuNUq12f95mem20Ih7i1oQzqpg4VH37YRhcnmVqajnOFdJl2V8+aDUqQ0dyGaoqckk1nMgtyeFSrqw3a1F0JQeyL2eRKsgM7vq+PI1xk/V9eQpBv+pk1KAjKDcsJUxKGI2YDVLPsjZq4AQxshKtSEUukzVNwQtUa1RVwROyl85AwcHQ1DJh+MKjZzKnMc4TK7r56b9WETF01vbkAIGPLEscTO9xXFE2Em5gvFxPlEWAt4axciUCWfc9OJlS9iKFpDUNJqqXDGxIk60lCK5b1+QYXU/GgtWgf07ppTb8/PLlI7UxEaLc4XB4UkcaRLmSGMxXDQWtWn1kb6GQ79MUDwWGUzCvKcqKriwPv9ZJwXJRVZWmuMncxvEr+1c1H3cuxmUk3/ve9477gH/84x+3eTCTAYPLs+Y1xaiLmnRnipgxE9eXne/iIQ3HdYKYI0HUaXOM0Ef+uL5ccrqejyXk5JP0GcnQ6w/qoQsr3XJ7WFOTVRml/injTdSAnISOTzk54foyi62rm2uiPV9QEzYQSOmy1zsytNVFOWhKgml1cgLLDoOC/qzDa+kMluuXe4orgBjElRyerJJSZ+NPwW9xTzH0+KL8H0lwH+43ScO6+ZgyDixjhEXXC4z7ZiM/ONmmImXvHE9+h25Qumlqg/iwYwx2cJhFoOD7PpomxUZsTwTxZ43WZJiC47FxoCifBUXB1HwGigovbxzAesrfqmZjVfNx52NcRjKZTJb/LYTgvvvuI5lMcvjhhwPwwgsvMDAwUJExnawYXJ61sjvHlGSIdNGhM23herIfihFkdoFyYgSxWearBBFklm1XLs9rowYZyyNbdDdL5QsYyDtlKTHX91nemWWcjhg1Iem5SLUcV3pZvlKOSeqqzJb7ouQhCepjkjKjoHDWwa3kbZ+Hl3XQkZKTtxR/9QUUXY+4qUuv2JNGR1XGJrIPLtUuGbGS8akUQxfCEsOX58Mx2vK9VPteXjYLafwGi/0KZDzS9/0h1xBUYm4WU94KNIJ74PlYjodAIVVwiIcMHNfl2fUpWa2jyCRb6fsvBhUCgzUbhy+pp9SEx635CFSX4xOEcRnJO+64o/zvL3/5y5x33nnceuutaFqpQZLHZZddRk1NzY4Z5U7G8PKshpgpvQ8hZcj68045LieGtV0oVXGUlnKmoVG0PUK6gqFpxEw5KSh9XmzOiJdQSUuRsCFVYzJFl6ITxMOszV5WqTmY7PWs4HgeXYH6uKYo/ObpdWxKFfF8P8iuyiRN3vbKohnpohRaKL0Y/v/23jxKruq8137OWGOPaqlbLbUGJCEhBgkjIyR8P3kFHDCOA/l8EwhgCCGs6xvsGHDsBcSY5djMmMFAGGJj7BVIZK8VM13bkE/LvrYBA2EUMkJCoLkHdau75jrj/v7Yp6pHtbqllrol7WetXlJVn6rafarOW3vv931/v9EKrgcikAHViAqnx/FnVR9/sFTOQ2WPMqC/wH7oLLUi/DEwYRREB7kjBPmKGEYFP4RClL12BbjloLp3aeiCDbtz+JHeaDyqMQ1CgYZMKLX3ldjUkWVXXwnHD4YtqZvSNh91F5jTmBxV8/GlLd28uyMzIctxtfcJmhhNq2wEpk+fzu9//3sWL1486P4PPviA1atX09PTM6EDnGiy2Sx1dXVkMpn9BvWBH5C4qfPf23p56rVt7OgpRVJbwaCstq7JbGdlY78ykwyEnNHZpo4dZcc1rd/E6mBI2QapmEmu7FUzuwKqZTggL8pKRlaPavgCIQ3CSp6sb6xNGIBG0fEHJUSgXwVnX8K5o1ER8M2UgmqS6XCyn/pwNPrfo/0dO5bn1gbcP3DP09KQwiDIWtJq6RhyD1fX5b7yibPq+NtPzec3G7vYubdEqAkMNJKRaduO3hKfWjid6TXDLUn8MGT9zgzpuIkQRMtx2cVU2XMez3J8svY+x3ONHg7GnbjxfZ+NGzcOC5IbN24kHGvbyRFCpT3rw64cz7/Tzi/e62BvwSEVMyPf4mDQTCIU8gMvhNy306JlqWXIIOkGIU4UaWpiFtmyNyDx0h/UxoMcg0w0DBzLQIX0gTMii8oyVsOJxIKBYUFsaKlRKIYnkcaCoUHSNsmUgsMeIGF4zWOlXGrQtoAml8lyhjm255UVA/KLbmjCLmb1e9T4kRCJFx1g6YNL5eRMU8MPQ9kNFcqtj5c+7Kav6OEG8n01NI10XDpYftCZpSndNGw2WXR8uvMumgbLZtcflAWD2vvsZ9xB8oorruDKK69ky5YtnH766QC8+uqr3H777VxxxRUTPsDJpvJh2dlbxPEDmmvjaBr05MPowz34qqqKs4qoJEbXqImbBKEgFc0i5f5Xf1Abq7jCSISCqIxlMBrDL97K8Y4XQrRVUGFokmjY4+jXVBwPuq5RcPwJWTofCJWtxBAZ1GQdI4QDZt1+OPKs0Dao9ncPZV+PE8g9aIj2MrV+EzE3kAEyCAWaoQ2YcYpq5UHCNvntpm56Cm6/aIoAXwj6ih4asLkrz8mtddQl7f7XFYKPugtoCI5rSo+6HN9f66PyuxnMuIPk3XffTUtLC9/73vdob28HYObMmXz961/na1/72oQPcDIZ+GFprYuzrbuIH1ba5OQxUYPLsJISHdANjZihMy1t051zyJal+EMYCHryQfXiGuse33jY11MGlV8ewGseyDhLnsDzDyBrM0EMnBkjiOoetUE+RNGvhj82JBKsEMP2iUfbNw5EvxCypkUNB/rAz4bAD6LfReVSArCFoCNTkla+oVyCC0H138qMPlfyeXtHL5+Y2zhI8zEVk8vsVGzky3qsFgzK72Yw4w6Suq7zjW98g2984xtks1mAKbFvcCiofFgSls4HHXn2Fl16i3J2VHR9uTSLlmpG5PZn6HrkACgTNZapU3bDSOhg/MmLqYbB8Cz+/vAFVcX1yfr7zag+siJmIXdgRx+PL0Absv7e15ZDJWEHAxSQon3nSuE/SDUiIeTMz/X66zxNHZa21rCtp4QXygaAyu5V5bEDtz/KXii7tLJhVfPxlNl1/Oebu8ZtwTA0OZNzPOV3M4ADKib3fZ/f/OY3bNmyhYsvvhiA3bt3U1tbSzo99qLYqU7B9enOO/QUHMpuIE20vBBEKIuotf7sbRDKTHbS0ukrSgHellqLlvoEmzrzlLz9iz4cCRzonPBAkj4TiTd0W2SMjxtp26GS7NGgGmkH/n2V/w7cog+FXHYnLQNN08iU3Gr2P27pnD6vkVkNyah3ffRZu4YMtn+9cg61kW94pZ3xnR2ZcVkwjJScaUrbUeOE8ruBAwiS27Zt49xzz2X79u04jsNnPvMZampquOOOO3Ach0ceeeRQjPOwE4aCTNGVe5FeQGt9gpQv6MiWKUXp38oSyDSkQIURJUOqnika/LE9R6bojdjdcaAY0fL+6EqTHTlUlr0w9kSPBtTFTVIxC8uQNaumodOUtlk+u56mmjjbewrVesn9kSlJO9wlLYNXceOxYNhXcmb73hJ7cg6OF3LqnPoxBdujmXEHya9+9ausWLFimF3DX/zFX3DVVVdN6OAmi8q36zs7+9iTcwmEQNPKNKZitNTG6cxKj5hKkIxHTnxuINXDQd6/J+fiDJANm6jJ1NCEzEQFX8XYGe/5TtgVG2HZl1+fslnaUkd90qq6KW7uyqEh9vt+6shglS8PX+6O1YJhtOTM8c1m1c9pU2eO1vrEMe13M+4g+bvf/Y6XX34Z27YH3T9v3jx27do1YQObLAZ+u6Zsg/qkScEJ6C16lLyQmXVxGlM22bJfVSWXJvUahqEjhIamaxjRhGCsPczjYegzDg2YIx2jOPxUMuuV0qKEZZBzfBpNm7q4jWloVRWgLV1yFtlcGyfnFEZdbodAIfL+GYmxWDDsLzmzaEaa7XuLzGlM0Z13jmm/mzE2W/UThiFBMHxHZ+fOndTUHNiJe+ihh5g3bx7xeJyVK1fy2muv7fPYJ554Ak3TBv3E4/EDet2hDP12rU/apGIWM2pj1CcsXD9gd6ZEEAqpLJ6yo/pGAZrckI+ZOoYuM9pJS6/W5k0U+/vyHpY8UBw0ptZ/PqUjUT+jnecwOkB66GjkHB/HkyZfC6anac+UCcOwan9rm1JQxTS0fX5mTL2/RfIX77XzYVduxOMqNb5LWmppa0wOm/X1q13tOxMeM3UuOLWVaz9zPF85axHXfuZ4vrRmwTEVIOEArt8//dM/5b777qve1jSNfD7PzTffzHnnnTfuAaxdu5brrruOm2++mTfffJNly5Zxzjnn0NXVtc/H1NbW0t7eXv3Ztm3buF93JIZ+u9bETRqTNn4gmNUgrRXq4hanzK7nfyxqwtQ1dF2nKWXTlLaZURPDNg1MXaMpHaMhZR9otc0+GcseWEWJXDExWFFNI0TlONH/pfjFGB6va5HVh0/JC1k4I83/XDELQ9d4YUMnr27poTNbZndfiZ19JSxdoyFlkbb1wQpDRtS1ZRrMn5bE8QJe3NA5Zn+lgQxUuxqJSnKmJm6NGmyPBcZ9Ld1999289NJLLF26lHK5zMUXX1xdat9xxx3jHsA999zDVVddxRVXXMHSpUt55JFHSCaTPP744/t8jKZptLS0VH+am5vH/bojMfTbVdM0FsxIkbAN9hZcTFPKjPmhYPveEjFT9mnsLXpVm9N0zKQ2Lu1FK9nL/X2EzXEEtbFcDqGIPG0UE8JAq+CBNg/efvrRtagW09DlzLDkBTQkLf7nJ9rQK0vcqOFASrjJz0HMkqLMMdPAMDQZHA2qnkIJy2BOY2pQveJ4qahdtVfU3gdQSc4snJE+ZpIzozHuPcm2tjbeeecd1q5dyzvvvEM+n+fKK6/kkksuIZEY3wl1XZc33niDG264oXqfruucffbZvPLKK/t8XD6fZ+7cuYRhyCc+8QluvfVWTjzxxBGPdRwHx3Gqtyu1nSMx8Nu1UvrQmIqxvK2eLV0FOnNlyl7Anpz0fpleE5NyWqbci8yVXXryDm4o7R30/utg1ItJCIhZstKu7B18LaWAA/ZsUQwnEP3dRqN1JUF/HaXGYNO2vBuQsEyu+NR8Fs5I8/BvthCEgnOWNpMr+7y5o49M0WV62qYr5+IEIZapoztUlZmKboCmadiGxsbOHHUJk5hpHFC94kC1q7Fkwo9lxhUkPc9jyZIlPP/881xyySVccsklB/Xi3d3dBEEwbCbY3NzMxo0bR3zM4sWLefzxxznllFPIZDLcfffdrF69mg0bNjB79uxhx9922218+9vfHtN4BmpJDqwza0zFqJ9r8e6uDPOmJbFNgx17SyyakeKNbX105cokbClZVnQDEraBYWrkI+mYyues0rJYqa8baDMwoybG3rzDRJWbq8TNxLK/bqNKKKkESCuKqrqu0ZiyMQ2dFfPqOWlWLb94bzdvbNvLtLSNpmnUJW2Wza7n7R3SOiMdN9HcgJip01d0o5molOhrqYtTm5ArlY6MI83Qcg5LWsb/N401E36sM64gaVkW5XL5UI1lTKxatYpVq1ZVb69evZoTTjiBRx99lO985zvDjr/hhhsGmZdls1na2tpGfO6h364ttTG8IKQz67C36DKrPsFZJzTz9Fu7aa2Po+s6C2akyJY9dvWVCALpvucF0tckhYbjS79lS5P1lEEYRoZUsse3YtLVW/Qoq+nfEUulFdVA+hvNrI/h+oKauEUqppMvB2zuKPCVp96i6EhnxaaaGDPr5LK2MWWzvK2eD7vy9BQcHC/guGkJauMmu/rKWIZGc00MXTq9YRuVJI7OuzsynLmg6YBmfWPJhB/rjHu5ffXVV3PHHXfwgx/8ANM8uIr7pqYmDMOgs7Nz0P2dnZ20tIztq9GyLE499VQ+/PDDEX8fi8WIxYbLSu2LyrfrU3/Yzv/dtIc9uTJeKDB1jb6ih67tqhbfZktSZ3FWfULaLejg+SFOIG1W6xIWjq/j+rLWMmVoOMhNSj8k2peSUxTHDxihaEAxRRi4ZWJo/TPLysqgokav6/KnPeNgG7I2cmdvGccPI/O4ivp8SF/RJRSCvOOzvK2expTNJ+c10J4psbfg8b/WHEfZDbn52fcQQuCFAksTeEFIvuyTjJksmpFmy56D66OuZMIVIzPuKPf666+zbt06XnzxRU4++WRSqdSg34/HvsG2bU477TTWrVvHBRdcAMgSo3Xr1vHlL395TM8RBAHr168/oMz6aHTmHIpeQDpuUZuwpA5kyePVj3oIhLQulRYNIUEg7WJn1MRw/IDunAtIYdVpaRvL1Nm5t0TeCUjYOnqkBBNWvUilCnVlqaaWypPPSOo+FYaK9Q7MkVWkz1pr45iGTntfGU0TxEwDQYitV3zJpdRe0jYoOj4bdmdY0lKDbejkyj6nzW1gxdxGNnXlaGtM4vohfSWPguNj6DozauMsmJ6iNmGxtbtwzPRRTwbjDpL19fV84QtfmLABXHfddVx++eWsWLGC008/nfvuu49CoVCVXbvsssuYNWsWt912GwD//M//zBlnnMHChQvp6+vjrrvuYtu2bfzd3/3dhIwnDAW/Wt/B5q4cSdtgWsqu7k3GLYPdfUX25l3yZY/ZDQmSlomnh+zJO7RnSrLDRkDZC+jKOiQsnXTcpLkmxt6Si65FRlQIEraJH4SYuhYpiB/5AhhHC+Mpyg+IhI01aKmLEwpoSsUoez5lL6AmLl0aDU0GRj+Qs0Eh5Jdx2g7oypXpzJYRAqbXxPjLFTXoukbKNmlKx6hLyESiG4TYhk46ZpJ3fHb1lqRNsWUcqlNxzDPuIDnQymEiuPDCC9mzZw/f+ta36OjoYPny5fzqV7+qJnO2b99e3YcB6O3t5aqrrqKjo4OGhgZOO+00Xn75ZZYuXToh49nVV2L9rgxBKKoZbscLCCLjprIntf8CDXb0lqqWB6YutSJBmt0nbSmnVnADsmWfWQ0J4rZBV1a6IJq6Tsn1cY4CZaCjkYp+5ljl4SrHZUvSv+iDzhyWoUtnzUCuNgIhCMJ+p8YweqFM2cc2NEwNalIWhq7x7Du7aWtMsHB6TTWZWGkf3FtweGNbLz0Fh0zJozEV49m3d3PuyS0q2XIIGLN9QxiG3HXXXTz77LO4rstZZ53FzTffPO6yn8lmf9LwGzuyfPf//JH2vjIJS6ev6FfVv4NQ7h8hpHJLwjZw/cjcPpQXQRhCY8quWiQ4kXr5/KYk2ZLP1p7ioOy24uhBA5rSNrqmkS1LgVxT1wiEkMZsA+w6KvYdFQwN+UVqGZFbZsCCGWlu+txSdJ1qq2zC0vmgMxf1bcuGh+Ob05S8cNz2DFOVqWbfMOaS41tuuYUbb7yRdDrNrFmzuP/++7n66qsP5dgmhZRtkrLk8qg9IzXzTEMjYRl4wUA1a+nlrGkapi77b5OWHnnchNVlVTpu0VqXIFvy6cw61Q4cFSCPTirbJlbkxlh0A0puiB9IObUod1P1N6r8+AJ295XJlDzilk590mJLV56Hfi0TklecOY8TW2vZ2JGjJ++SsAxmNSQ4dU4DbY0pFs1Is7fgHnAHjmLfjDlI/uQnP+Ff/uVfeOGFF3j66ad57rnnePLJJ486X5tZ9QlOmlVL0Q0oeyFxUy6lg1BUP3yyWFsQM3XSMdkdURFCjZs6NXFZRjG7IcG0lI2uQ0/exQv6rU0nEltXvdpjRWPwHlNFG3IiEIAXyASLG2WzfTHcGjdg+F6nFj1DyZVulrqmkbR1egpO1Srh88taaWtIcsZx01i1oIkVcxtoTEmhmaGK4YqJY8xBcvv27YMyyGeffTaaprF79+5DMrDJQtc1ls9pIG7qssuhutEeRPaflQwmVYENU9eiQCqFdxuS0uSrK1tmR2+RbT0F8o60bqjI+E9kUBvJy0YxMrahkYwPTnJM5MTLDcQgS4axIgA0DUPX8PyQnoKDqevMrEtUA1/JCzAMjdkNSWoT1jD1noRt4PiBynRPMGMOkr7vD1PbsSwLzxtZrulIZnpNjAUz0ixoSmEZOoUowaLpGqYu3Q+FkA531SoeUVF7gbqETdkNyJQ9mfQJBUZkRO+N4K53sBwKj5yjEelrI/eNB8qYHQrGYuw29IvSjzq23ECQKXokYybTo7KyguuTsAyCQLCzt0i25A3ruT7WFMMPF2M+m0II/uZv/mZQYXa5XOZLX/rSoFrJ8dRJTlUqZRfHNSVZWE7RU3QxNZ0dewvs6C0Rt3RKXkgQ9lu6isjGIVPyeHtHH0FkGFbJigcITC3yTYleZ6IuUBUjx4YeFTW6QUDM0gnDEGeCCvgHetwc6JdWxTjMjWwYa+MmZU8Gvu6cw9s7+tjRW6Sn4NKQsGhIxardOseiYvjhYsxB8vLLLx9236WXXjqhg5kqzKpPUJ+w+P2WbnQitztDr5YiFd2AmrhFS22MnBPQnXeImwZ1CZNM0ZOq5UAQhtTEzWgGKaIZpyr5mSw8IKnrLJqRpm1akt9t6sYJJmZpKvbRBTBac8Aw8WRRsXXQ0DRp0bC7r0xrfZxfrG+nt+ixpKWGDzpzFMo+u/tKZMseiwdkt5UoxcQz5iA50fWRU5mPuvN05R1KboChQW3SwvFCOrJl3EAghKDg+OzqCwmRGn+tdXFKkbiFE4TSkD4UclmEvFgm2wxLAUUvZEN7lvc7c1V/7ImgsvIdGp729wpD/YpEKPAQ2KbOrr4ycxpTEPX2V+okUzGTLV0FegoOPXmHjQI+d3IL55yk6iQPBWrzYggVdfIgFKw5vomP9hRpz5boybsEYUjC1rF0nYRl4IYhnhvQmLbJlX1Z+ybkbNFHIwz3bdal2g8nD3+gx+sEUFErD+iXStvfs9uGVm1LDZFL7VhUNqRpsr/f8QPmNyXZvrc0yGahMRWjYZ78zPUWXUpewOeXtTJnWmr0F1UcECpIDmGgOnlN3KIhGeOlLTLDXSm3yJY96hM2vQWHvqJLR9YhGTMxdMiWPLxg9AZDXYOamEHRDRijOZ5iilL5squ826ap4YxhhlqXMCm5UiEKIGFJkV07KiEzdSmTtqO3RMnzaR3iga1pGrUJi2TMYGt3gaKn1FEOFSpIDqFfnVx+KHNlj76iR9I2qn7Hhq5xwswaunM2e/JSyWV6OoYQgu6cu99ZhCwml0cp464jm2oHDXJ2mLIMXN/f7/uZKXkYuo6haTTVWEyriWNqcusGYG/BpbUhQa7soaEpD+xJRJ3ZIQxUJ/cCwbs7paiuZUj1HsuU3Te2oZNzfGxT9mB7QYjnB2PLbAooR1qS0c0pidoSGBuVute4ZRC3dChB3NLwfTFsH9rS5R5k0jZAaARhQM4JEDg0pW0IIF/2SdgmxzfXkCl6zKiJ054pDxKChmPTA3syUEFyCBV18j981ENv0SVX9jF1PTKD0siVfISAvqJHR1YKEAehXKb7YxTNFSDl1Q7h3zERqAA5NirnyTY0ptfG6cq7WLpOPK5RcHyCMNKZjPq1QwF+AElbA93E0jXyjk/RDZiWtplZJz+Dpg57heD4ljS9JVd5YE8SKkgOQdc1PnPiDF78Ywd78g4tNTH8UIqc6rpGMrLafL8jS3euTBBCXdICIdhbGFvYq4i0Ko4eNE2Whlm6VIEyDA3b0Ck6AWiygF1oAyw8NK266khYBjPrYvQWfRqSNqfNqae36PLSh72Yhs7vNu/BCwRlN+CDjhy6DknL5JTZdSqjfRhQQXIEEpbsdDB1jYIbYOiybk3TYFraxjYMtu0t4PhSx8/QNMpjabGIUDO0o49QQNkP2Vv0ScVMunLlSB2qvz+8sr2i6xAzNfyoEsIydYpuSDpm0Ft0eWN7Lx/vKWDoGqfPb6S1PsnuviLbeoqUvIDmWpukbarP0WFCBckRKLhyr/GM46ZFbWIhRdenI1Omt+iRKbk4XkjKNnCjtdNYrGMVRzdCQMwwWDY7zf/d1E2mJFt2K9nvSqA0NA3Xl26IXggLmlJ05hx29pYoOFItytDguOlpUjGLTMllc1eeMJTCvZZu0FoXZ8PuLO2Z8lEhjzaVUUFyBCrJm5IXUJuoZBRjtDUkyZV9tvUUpIdy3GJXRi653WD/GU1di+rpVDQ9ohm4+1dpFKgEwjnTEtQnY9QlrUgg160eE7f0yNso6sDyAgxDp+QHlNyAuKlhmRaWplETtyg4Pm9t78XUNUpuwLR0DDdSGQKNRTPSbO7K8+KGTuY1pmjPlpWZ1yFABckR2Je1rKZJkdNACBKWQWfeQdeR2e0xrLZjpk5ZFUYe8RgDZJyEoCpyoiE92N1Azvha6+N4fkjJkwpQskKiX4g5NDRMQtbvzOCHAsvQmVETIwgFqbhJSgh2Z8oUnICZdVIzwTJ08o6PG4RVebQ3t/dy5wsb6c67lP2AuGmwYHqac05StrATgQqSI7A/4/Z0zILIryRpGftt3qjIowXjlM9STE0qwrkweItFR2qKTktpmIaOF21IVlSiCk6ArsvHh0IKbiQsEz/SGQ1CgeOHaJqsz82XA3IlqQUAglw5IB2Thea2IespS17Aps4cZS/g+OYaknaCouvz3u4MuzMltRSfAMYslXasUbGWPam1jr6ix9buAn1Fj5Na62iuiTGjJkbM0MiX/VGXz9IgSpO+NoZa/hzpVL7wBnbZVAiBje05NnZk8YKQHb0lgjDE0GUDQjJmYOlaVY80bsrSskBAbcJibmMCQ5dfvrt6SxQcD8vUq8vmvOOxOyNVqNIxg0zR5e3tfZS9kAXTU9TEpT9OTdxSSuUTiJpJjsJIxu2hENz//21mdn2Crd0Fso476DEDC7A15IwhjKKoO44MuGJqUnkLK9JoQ8NP3vH5oCNHwe03jwOZwdaEVJQKgYa4xf84vgmBxns7M8QsHYEMoIWyH2mQytpcU9cIAoFhyFctewGvf7yXXZkSnVmHuGXwQUeORc2yrxuGK5UrX+0DRwXJ/TDUuH1jR5buvENPwcGIRHgrNeQDLxodBpl9Wcpi4ahipK87AfSWPAwALVo5CBlYfeTmpWVohJF4hRSuiJFzPHb0eiBkyVCInGWGoaDoy0oLhJA9/3GTnb0ldlPCjfzehRBs6szTU3A547hp1UCZsA06o2SO4sBRQXKcJC2D7rxD0fFpro1Tcn1p9BTt3leD5IDgaUWbGq6aSB4TBBC1VUm7DyGkrYMbBMRNsA0DTYePugu8u7Ovago26DnCEDQd2zRY3lZHY9KmPVPi/Y4cRTeoeim5foBlSofFzqzD+l0Z/p9F09E0TfV1TxBTYk/yoYceYt68ecTjcVauXMlrr7026vE/+9nPWLJkCfF4nJNPPplf/OIXh2mklQ+zhkDDNjRq4jbxqAvHMjSsqP2scmJ1TWYkjzK/NMUoVFYMgaBqBFah7EPRCyi7MilTWb4Ps3IIpR1tbdzECwRtjUkWt9SAkFUScxqTzG9KURO3CUIReTLBzt5S1dqhPVNm4Yy06us+SCY9SK5du5brrruOm2++mTfffJNly5Zxzjnn0NXVNeLxL7/8Mn/913/NlVdeyVtvvcUFF1zABRdcwHvvvXdYxlvyAprSNum4SW/RIx0ziJkahlbpypH/96PZQco2EEKghKyOXQYav1X2qb1QBtHK3qauVZJ8VH3ZTR1qYiZ7C26kHenh+CF1CatqBNaYsrEMnbIv7ULKXsCuPlmVofq6JwZNDHUTOsysXLmST37ykzz44IMAhGFIW1sbX/nKV7j++uuHHX/hhRdSKBR4/vnnq/edccYZLF++nEceeWS/r3ewxuc79ha59782AYL2jENv0aXoSnECIeRme8EJSFiyBEQgM+AhSlXnWKEivFtBG/L/cMD/tWoXjvRIMg1pX+z5AsvUmNOYJBRw8qw6dvQWeX93jrbGOPXJfq+pkhuwt+CSdzzKXsgn5tbzqYXT+dMTj8w6yYO9RieaSd2scF2XN954gxtuuKF6n67rnH322bzyyisjPuaVV17huuuuG3TfOeecw9NPPz3i8Y7j4DhO9XY2mz2oMQ8sNF8xt568I9sWLV1DCMGG9iyZkseZxzXxQVeOD/fkKTiyJs7QUSK7xwADvwgNrb/DqqJHGoZSQk1HBkYvkIXkpiGz25oOviawDR3Xk/vdJTfgtDmN9BU8HC9ECFFtckjYBq1WnF29IdNSMb561iI+OW+amkFOEJO63O7u7iYIApqbmwfd39zcTEdHx4iP6ejoGNfxt912G3V1ddWftra2gxpzpdC8MWXz4Z4Cmgb1SQtd1+jKuzTXxplZlyCdsFg4I03SNiJP5f6uDMXYOBLP1dAxW4ZWXUbHLR3b1In85NB1Kb5sDamfDaMPSltjknnTU3xmaTM3fu4EvnHOYtYsnk4goCfv4PjSqdPxA3ryDrqus2bxdBUgJ5hJ35M81Nxwww1kMpnqz44dOw76OfdVaH7yrDouOn0OTekYRdenMRXj1LYGYqYuvUz2M4vUo4spaenMa4xTFzexdLDH8C4N3PMyNJlR31ft+sFcPtU9s0OMbcilp3mAn1AdiJvReRjTsRqm1n/b0uXMfyT29ffrGqRjBgPjkxCCeCTSLIT0UAqCytaL3L+2DZl08QKBF4S4fkjM0GlMWcxuSHLpGXOZOy2FaepcvHIOy9rqMXSdXNlnb8EhV/YxdJ1lbfVcvHKOCpATzKQut5uamjAMg87OzkH3d3Z20tLSMuJjWlpaxnV8LBYb5BU+UYxUaF7JIr67I1Pt+57TmGRxs7QB9fxQzia14eb1li5nqaaus3BGioRlkHMKNCRtYpZOX9Gj5AXVcpFKMkDTZL9wyjZwfEHR82UG1JJmZUU3oOiGg5aAA/9v6tCYtMmUPNxoXTh037SSrTcNnda6GB1ZFyOS+8o7AV4gVdbHu98aj8ZdcAOcyrkBbANSMQPLMCj7ASIUZEcxyNYBy5AlV0IDS5ddLKahoevI8x6dN1OTy1npQyQf15SySVjy/DlBEBVwy62RQtmjNEBeXI8SKoau4wehbBaI/nZDo9p3bRk6+bKHrkklexAUo78THaYlrOpjbUOj7AeUPel5ZOoai5prWL1g+L7iwhk1XHP2In71Xgfrd2UougFJ2+CUWfWqV/sQMalB0rZtTjvtNNatW8cFF1wAyMTNunXr+PKXvzziY1atWsW6deu45pprqvf913/9F6tWrToMIx7M0ELzCkP7vpe21tJX8ujMOrILA0EYdV4YUdGxpsmSohk1MRZMT7Mn59KQsqlLmCDk3lV33qUcGT6lbJMZNTGWz6nnrKXNNCZtUpbJbz/cwwsbOujMlhECauI6dQkpCJst+9XidkOD5toY//vTC1k5fxq3/+p93o/2U4NASC3EAXtplqnTlLYpeCHTa2LMa0oShIK+osfeglPNvIoRgqWlV1wkI21FXZZFtdbHqUvY9OQdOrJldE1jcXMNOcfH8aSO57S0TVtjku17i+zqLREEIV6kKqHrWn/LnaaRsA0Stk7CMjENqR5fdKWlhqFrtDUmOLG1lrIXsjtTxvelso5haFUR2yUza3lpczd/+HgvfUUHI2ExzdRJWiZxU6c9W6bsBViGjp0wKbohZS9A0+R7Mi1t86mF0/nUoiZ+8spW3tuVlYE+6r6pSVgsnJ7iq2cfj+MF/PjlbWztKSCQ4rvNdXH+31Nnc9YJzftU8lk4o4a/H+ELWs0gDw2Tnt1eu3Ytl19+OY8++iinn3469913Hz/96U/ZuHEjzc3NXHbZZcyaNYvbbrsNkCVAa9as4fbbb+dzn/sc//Ef/8Gtt97Km2++yUknnbTf1ztcmbMPu3K88F4nW/bkcXw5g3C8QGYuwxDPFxgaxCwDxw/JlHxsA5pq4jSlYyyckWZxSw0b23N82JWjr+ShaTCjJsZJs+uYXZ/kuKYUsxuSwy4O3w95c0cvPQWXaSmb5bPqac+V2dSV5a2tfWg6LG6p5dwTWrBtozreX73XwSsf9bCzt4Tryf2ushsSIoiZOgnbZH5TistXz2PutCQvvNcZjc2VBfVhiG3oZMo+XdkyThRMYpZO0jY5bnqaTy+ejqbB/3m3nW09RbwglOo3tTHmNiRB0+jOO3TnXTQE09Kx6vk4vrmG1z7u4d2dfXTmHPxAYEZfLMdNT7Pm+OmkYibv7OhjS1ee3qKL64ekYya1CYsgFLhBSMw0WDgjzdknNJOwjWGBJgwFO3qLfNxdAOC4phStdQnas2Xe78jy+sd72d5TpOgFJCyDuY0J5s9I01qXGPSefNiV45fr23l9ay95xycdMzl9XiPnntyvJj70vfpEWwPmge4xHCVMtez2pAdJgAcffJC77rqLjo4Oli9fzve//31WrlwJwKc//WnmzZvHE088UT3+Zz/7Gd/85jfZunUrixYt4s477+S8884b02sdzjcgDMWgb/uZtfFBmn8Dbycs6cZY9IJhF+zhmjFUXivneOTL8qJO2gYdWSk2PPQiHu3vk+pIgq09RQDmN6VoGxDQRwoOuq5Vn29/52PgGGvi1qDzMtI5AybsPI7nPTmc79/RggqSk8xUewMUCsVgpto1emzP6xUKhWI/qCCpUCgUo6CCpEKhUIzCMaehVNmCPdj2RIVCcWioXJtTJV1yzAXJXC4HcNDtiQqF4tCSy+Woq6ub7GEce9ntMAzZvXs3NTU1VYGAI4VsNktbWxs7duyYElm/qYo6T/tnKp8jIQS5XI7W1lZ0ffJ3BI+5maSu68yePXuyh3FQ1NbWTrkP9lREnaf9M1XP0VSYQVaY/DCtUCgUUxgVJBUKhWIUVJA8gojFYtx8882HRNXoaEKdp/2jztHYOeYSNwqFQjEe1ExSoVAoRkEFSYVCoRgFFSQVCoViFFSQVCgUilFQQfIIYdeuXVx66aVMmzaNRCLBySefzH//939P9rCmDEEQcNNNNzF//nwSiQQLFizgO9/5zpTp/50sfvvb3/L5z3+e1tZWNE0bZr0shOBb3/oWM2fOJJFIcPbZZ7N58+bJGewURQXJI4De3l7OPPNMLMvil7/8JX/84x/53ve+R0NDw2QPbcpwxx138PDDD/Pggw/y/vvvc8cdd3DnnXfywAMPTPbQJpVCocCyZct46KGHRvz9nXfeyfe//30eeeQRXn31VVKpFOeccw7lcvkwj3TqokqAjgCuv/56XnrpJX73u99N9lCmLH/2Z39Gc3MzP/zhD6v3feELXyCRSPBv//ZvkziyqYOmafz85z+vmu4JIWhtbeVrX/sa//iP/whAJpOhubmZJ554gosuumgSRzt1UDPJI4Bnn32WFStW8Jd/+ZfMmDGDU089lX/913+d7GFNKVavXs26devYtGkTAO+88w6///3v+exnPzvJI5u6fPzxx3R0dHD22WdX76urq2PlypW88sorkziyqcUxJ3BxJPLRRx/x8MMPc91113HjjTfy+uuv8w//8A/Yts3ll18+2cObElx//fVks1mWLFmCYRgEQcAtt9zCJZdcMtlDm7J0dHQA0NzcPOj+5ubm6u8UKkgeEYRhyIoVK7j11lsBOPXUU3nvvfd45JFHVJCM+OlPf8qTTz7JU089xYknnsjbb7/NNddcQ2trqzpHioNCLbePAGbOnMnSpUsH3XfCCSewffv2SRrR1OPrX/86119/PRdddBEnn3wyX/ziF7n22murfu2K4bS0tADQ2dk56P7Ozs7q7xQqSB4RnHnmmXzwwQeD7tu0aRNz586dpBFNPYrF4jCBVsMwCMNwkkY09Zk/fz4tLS2sW7euel82m+XVV19l1apVkziyqYVabh8BXHvttaxevZpbb72Vv/qrv+K1117jscce47HHHpvsoU0ZPv/5z3PLLbcwZ84cTjzxRN566y3uuece/vZv/3ayhzap5PN5Pvzww+rtjz/+mLfffpvGxkbmzJnDNddcw3e/+10WLVrE/Pnzuemmm2htba1mwBWAUBwRPPfcc+Kkk04SsVhMLFmyRDz22GOTPaQpRTabFV/96lfFnDlzRDweF8cdd5z4p3/6J+E4zmQPbVL59a9/LYBhP5dffrkQQogwDMVNN90kmpubRSwWE2eddZb44IMPJnfQUwxVJ6lQKBSjoPYkFQqFYhRUkFQoFIpRUEFSoVAoRkEFSYVCoRgFFSQVCoViFFSQVCgUilFQQVKhUChGQQVJhUKhGAUVJBUKhWIUVJBUTBkKhQILFizguuuuG3T/1q1bqa2trQoNCyF47LHHWLlyJel0mvr6elasWMF9991HsVgE4IknnkDTtEE/8Xh80PMK5e+iGAMqSCqmDKlUih/96Ec88MADVasKIQRXXHEFZ555JldddRUAX/ziF7nmmms4//zz+fWvf83bb7/NTTfdxDPPPMOLL75Yfb7a2lra29urP9u2bRv0esrfRTEmJrVzXHHMsWbNGnH11VeLq6++WtTW1opp06aJb37zmyIMw+ox1157rViwYIHI5/Pi3nvvFfX19WLnzp1CCCHWrl0rAPH0008Pe+4wDEVfX58QQogf/ehHoq6ubp/jCMNQtLS0iLvuuqt6X19fn4jFYuLf//3fJ+ivVRwNqJmk4rDz4x//GNM0ee2117j//vu55557+MEPflD9/S233IJpmlx66aXceOONPPDAA8yaNQuAJ598ksWLF3P++ecPe15N06irq6vezufzzJ07l7a2Ns4//3w2bNhQ/Z3yd1GMFaUnqTjstLW1ce+996JpGosXL2b9+vXce++91eV0IpHg/vvv59xzz+Wzn/0sl156afWxmzdvZvHixft9jcWLF/P4449zyimnkMlkuPvuu1m9ejUbNmxg9uzZyt9FMWbUTFJx2DnjjDPQNK16e9WqVWzevJkgCKr3/fCHPySZTLJ+/XoymUz1fjFGZb9Vq1Zx2WWXsXz5ctasWcN//ud/Mn36dB599NGJ+0MUxwQqSCqmHGvXruX555/n5Zdfpqamhmuvvbb6u+OPP56NGzeO+zkty+LUU0+tqnQrfxfFWFFBUnHYefXVVwfd/sMf/sCiRYswDIPOzk6uvvpqvvvd77Js2TKeeOIJfvKTn/DLX/4SgIsvvphNmzbxzDPPDHteIcSgWedAgiBg/fr1zJw5E1D+LopxMNmZI8WxxZo1a0Q6nRbXXnut2Lhxo3jqqadEKpUSjzzyiBBCiD//8z8Xn/rUp0QQBNXHXH/99WL27Nmir69PhGEoLrzwQpFIJMQtt9wiXn/9dbF161bx3HPPiT/5kz8RP//5z4UQQnz7298WL7zwgtiyZYt44403xEUXXSTi8bjYsGFD9Xlvv/12UV9fL5555hnx7rvvivPPP1/Mnz9flEqlw3pOFFMbFSQVh5U1a9aIv//7vxdf+tKXRG1trWhoaBA33nijCMNQ/PjHPxbJZFJs3rx50GMcxxEnnXSSuOKKK4QQQgRBIB5++GHxyU9+UiSTSVFbWytOO+00cf/994tisSiEEOKaa64Rc+bMEbZti+bmZnHeeeeJN998c9DzKn8XxVhQHjeKw8qnP/1pli9fzn333TfZQ1EoxoTak1QoFIpRUEFSoVAoRkEttxUKhWIU1ExSoVAoRkEFSYVCoRgFFSQVCoViFFSQVCgUilFQQVKhUChGQQVJhUKhGAUVJBUKhWIUVJBUKBSKUfj/ASChX1HxJPjuAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 300x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
+    "# Load the dataset\n",
+    "csv_file = \"../../tests/data/SLC6A4_active_excapedb_subset.csv\"\n",
+    "data = pd.read_csv(csv_file)\n",
     "\n",
-    "# Get predictions and errors\n",
-    "y_pred_test = binary_fp_pipe.predict(X_test)\n",
-    "abs_errors = np.abs(y_test - y_pred_test)\n",
+    "# Add RDKit mol objects\n",
+    "PandasTools.AddMoleculeColumnToFrame(data, smilesCol=\"SMILES\")\n",
+    "print(f\"{data.ROMol.isna().sum()} out of {len(data)} SMILES failed in conversion\")\n",
     "\n",
+    "# Split into train/val/test\n",
+    "X = data.ROMol\n",
+    "y = data.pXC50\n",
     "\n",
-    "fig = plt.figure(figsize=(3,3))\n",
+    "X_temp, X_test, y_temp, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "X_train, X_val, y_train, y_val = train_test_split(\n",
+    "    X_temp, y_temp, test_size=0.25, random_state=42\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2896ad5",
+   "metadata": {},
+   "source": [
+    "## Example 1: k-NN Applicability Domain with Binary Morgan Fingerprints\n",
     "\n",
-    "plt.scatter(y_test, abs_errors, alpha=0.5)\n",
-    "plt.xlabel('pXC50')\n",
-    "plt.ylabel('Predicted Absolute Error')\n",
-    "plt.title('Predicted pXC50 vs Absolute Error')\n"
+    "In this example, we'll use binary Morgan fingerprints and a k-NN based applicability domain with Tanimoto distance.\n",
+    "This is particularly suitable for binary fingerprints as the Tanimoto coefficient is a natural similarity measure for them."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "9d2860b4",
+   "execution_count": 7,
+   "id": "9c89148b",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/numpy/core/fromnumeric.py:59: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
       "  return bound(*args, **kwds)\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
       "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
       "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
       "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2466: DataConversionWarning: Data was converted to boolean for metric jaccard\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/sklearn/metrics/pairwise.py:2466: DataConversionWarning: Data was converted to boolean for metric jaccard\n",
       "  warnings.warn(msg, DataConversionWarning)\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/numpy/core/fromnumeric.py:59: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
       "  return bound(*args, **kwds)\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
       "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
       "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2466: DataConversionWarning: Data was converted to boolean for metric jaccard\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/sklearn/metrics/pairwise.py:2466: DataConversionWarning: Data was converted to boolean for metric jaccard\n",
       "  warnings.warn(msg, DataConversionWarning)\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/numpy/core/fromnumeric.py:59: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
       "  return bound(*args, **kwds)\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
       "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2466: DataConversionWarning: Data was converted to boolean for metric jaccard\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/sklearn/metrics/pairwise.py:2466: DataConversionWarning: Data was converted to boolean for metric jaccard\n",
       "  warnings.warn(msg, DataConversionWarning)\n"
      ]
     }
    ],
    "source": [
+    "# Create pipeline for binary fingerprints\n",
+    "binary_fp_pipe = Pipeline(\n",
+    "    [\n",
+    "        (\"fp\", MorganFingerprintTransformer(fpSize=2048, radius=2)),\n",
+    "        (\"rf\", RandomForestRegressor(n_estimators=100, random_state=42)),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "# Train the model\n",
+    "binary_fp_pipe.fit(X_train, y_train)\n",
+    "\n",
+    "# Get predictions and errors\n",
+    "y_pred_test = binary_fp_pipe.predict(X_test)\n",
+    "abs_errors = np.abs(y_test - y_pred_test)\n",
     "\n",
-    "# Create and fit k-NN AD estimator. Distance metrics follow the scikit-learn API, and the custom distance metric tanimoto popular in cheminformatics is available in scikit-mol.\n",
-    "knn_ad = KNNApplicabilityDomain(n_neighbors=3, distance_metric='tanimoto', n_jobs=-1)\n",
-    "knn_ad.fit(binary_fp_pipe.named_steps['fp'].transform(X_train))\n",
+    "# Create and fit k-NN AD estimator\n",
+    "knn_ad = KNNApplicabilityDomain(n_neighbors=3, distance_metric=\"tanimoto\")\n",
+    "knn_ad.fit(binary_fp_pipe.named_steps[\"fp\"].transform(X_train))\n",
     "\n",
     "# Fit threshold using validation set\n",
-    "knn_ad.fit_threshold(binary_fp_pipe.named_steps['fp'].transform(X_val), target_percentile=95)\n",
+    "knn_ad.fit_threshold(binary_fp_pipe.named_steps[\"fp\"].transform(X_val))\n",
     "\n",
     "# Get AD scores for test set\n",
-    "knn_scores = knn_ad.transform(binary_fp_pipe.named_steps['fp'].transform(X_test))"
+    "knn_scores = knn_ad.transform(binary_fp_pipe.named_steps[\"fp\"].transform(X_test))"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "22848529",
+   "id": "a7f33a3f",
    "metadata": {},
    "source": [
-    "Let's visualize the relationship between prediction errors and AD scores, and calculate some statistics on compound errors within and outside the domain."
+    "Let's visualize the relationship between prediction errors and AD scores:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "e8e2bb86",
+   "execution_count": 8,
+   "id": "ee7b2f64",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 400x300 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "95th percentile of errors inside domain: 1.29\n"
+     ]
+    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/numpy/core/fromnumeric.py:59: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
       "  return bound(*args, **kwds)\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
       "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
       "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2466: DataConversionWarning: Data was converted to boolean for metric jaccard\n",
+      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/sklearn/metrics/pairwise.py:2466: DataConversionWarning: Data was converted to boolean for metric jaccard\n",
       "  warnings.warn(msg, DataConversionWarning)\n"
      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "95th percentile of errors inside domain: 1.45\n",
-      "95th percentile of errors outside domain: 1.85\n",
-      "Fraction of samples outside domain: 0.04\n"
+     "ename": "IndexError",
+     "evalue": "index -1 is out of bounds for axis 0 with size 0",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[8], line 16\u001b[0m\n\u001b[1;32m     13\u001b[0m errors_out \u001b[38;5;241m=\u001b[39m abs_errors[in_domain \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m95th percentile of errors inside domain: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39mpercentile(errors_in,\u001b[38;5;250m \u001b[39m\u001b[38;5;241m95\u001b[39m)\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 16\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m95th percentile of errors outside domain: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpercentile\u001b[49m\u001b[43m(\u001b[49m\u001b[43merrors_out\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;250;43m \u001b[39;49m\u001b[38;5;241;43m95\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFraction of samples outside domain: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m(in_domain\u001b[38;5;250m \u001b[39m\u001b[38;5;241m==\u001b[39m\u001b[38;5;250m \u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\u001b[38;5;241m.\u001b[39mmean()\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m~/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py:4273\u001b[0m, in \u001b[0;36mpercentile\u001b[0;34m(a, q, axis, out, overwrite_input, method, keepdims, weights, interpolation)\u001b[0m\n\u001b[1;32m   4270\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39many(weights \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m):\n\u001b[1;32m   4271\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWeights must be non-negative.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 4273\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_quantile_unchecked\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   4274\u001b[0m \u001b[43m    \u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mq\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moverwrite_input\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweights\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py:4550\u001b[0m, in \u001b[0;36m_quantile_unchecked\u001b[0;34m(a, q, axis, out, overwrite_input, method, keepdims, weights)\u001b[0m\n\u001b[1;32m   4541\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_quantile_unchecked\u001b[39m(a,\n\u001b[1;32m   4542\u001b[0m                         q,\n\u001b[1;32m   4543\u001b[0m                         axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   4547\u001b[0m                         keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m   4548\u001b[0m                         weights\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m   4549\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Assumes that q is in [0, 1], and is an ndarray\"\"\"\u001b[39;00m\n\u001b[0;32m-> 4550\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_ureduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4551\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_quantile_ureduce_func\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4552\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mq\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mq\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4553\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mweights\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweights\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4554\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4555\u001b[0m \u001b[43m                    \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4556\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4557\u001b[0m \u001b[43m                    \u001b[49m\u001b[43moverwrite_input\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverwrite_input\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4558\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py:3894\u001b[0m, in \u001b[0;36m_ureduce\u001b[0;34m(a, func, keepdims, **kwargs)\u001b[0m\n\u001b[1;32m   3891\u001b[0m             index_out \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m0\u001b[39m, ) \u001b[38;5;241m*\u001b[39m nd\n\u001b[1;32m   3892\u001b[0m             kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mout\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m out[(\u001b[38;5;28mEllipsis\u001b[39m, ) \u001b[38;5;241m+\u001b[39m index_out]\n\u001b[0;32m-> 3894\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3896\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m out \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   3897\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
+      "File \u001b[0;32m~/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py:4727\u001b[0m, in \u001b[0;36m_quantile_ureduce_func\u001b[0;34m(a, q, weights, axis, out, overwrite_input, method)\u001b[0m\n\u001b[1;32m   4725\u001b[0m         arr \u001b[38;5;241m=\u001b[39m a\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m   4726\u001b[0m         wgt \u001b[38;5;241m=\u001b[39m weights\n\u001b[0;32m-> 4727\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43m_quantile\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4728\u001b[0m \u001b[43m                   \u001b[49m\u001b[43mquantiles\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mq\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4729\u001b[0m \u001b[43m                   \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4730\u001b[0m \u001b[43m                   \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4731\u001b[0m \u001b[43m                   \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4732\u001b[0m \u001b[43m                   \u001b[49m\u001b[43mweights\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwgt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4733\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
+      "File \u001b[0;32m~/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py:4849\u001b[0m, in \u001b[0;36m_quantile\u001b[0;34m(arr, quantiles, axis, method, out, weights)\u001b[0m\n\u001b[1;32m   4842\u001b[0m arr\u001b[38;5;241m.\u001b[39mpartition(\n\u001b[1;32m   4843\u001b[0m     np\u001b[38;5;241m.\u001b[39munique(np\u001b[38;5;241m.\u001b[39mconcatenate(([\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m],\n\u001b[1;32m   4844\u001b[0m                               previous_indexes\u001b[38;5;241m.\u001b[39mravel(),\n\u001b[1;32m   4845\u001b[0m                               next_indexes\u001b[38;5;241m.\u001b[39mravel(),\n\u001b[1;32m   4846\u001b[0m                               ))),\n\u001b[1;32m   4847\u001b[0m     axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m   4848\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m supports_nans:\n\u001b[0;32m-> 4849\u001b[0m     slices_having_nans \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39misnan(\u001b[43marr\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m]\u001b[49m)\n\u001b[1;32m   4850\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   4851\u001b[0m     slices_having_nans \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "\u001b[0;31mIndexError\u001b[0m: index -1 is out of bounds for axis 0 with size 0"
      ]
     }
    ],
    "source": [
-    "plt.figure(figsize=(4, 3))\n",
+    "plt.figure(figsize=(10, 6))\n",
     "plt.scatter(knn_scores, abs_errors, alpha=0.5)\n",
-    "plt.axvline(x=knn_ad.threshold_, color='r', linestyle='--', label='AD Threshold')\n",
-    "plt.xlabel('k-NN AD Score')\n",
-    "plt.ylabel('Absolute Prediction Error')\n",
-    "plt.title('Prediction Errors vs k-NN AD Scores')\n",
+    "plt.axvline(x=knn_ad.threshold_, color=\"r\", linestyle=\"--\", label=\"AD Threshold\")\n",
+    "plt.xlabel(\"k-NN AD Score\")\n",
+    "plt.ylabel(\"Absolute Prediction Error\")\n",
+    "plt.title(\"Prediction Errors vs k-NN AD Scores\")\n",
     "plt.legend()\n",
     "plt.show()\n",
     "\n",
     "# Calculate error statistics\n",
-    "in_domain = knn_ad.predict(binary_fp_pipe.named_steps['fp'].transform(X_test))\n",
+    "in_domain = knn_ad.predict(binary_fp_pipe.named_steps[\"fp\"].transform(X_test))\n",
     "errors_in = abs_errors[in_domain == 1]\n",
     "errors_out = abs_errors[in_domain == -1]\n",
     "\n",
@@ -726,14 +241,6 @@
     "print(f\"Fraction of samples outside domain: {(in_domain == -1).mean():.2f}\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "10e69073",
-   "metadata": {},
-   "source": [
-    "There's some diffence in the errors distribution inside and outside the domain threshold, but maybe not as clear-cut as we could have wished for. The fraction of samples outside the domain in the test-set are close the 5% that corresponds to the threshold estimated from the validation set fractile of 95%."
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "09bdc3b2",
@@ -747,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "fe4a6819",
    "metadata": {},
    "outputs": [
@@ -1197,108 +704,92 @@
     }
    ],
    "source": [
-    "# Create pipeline for count-based fingerprints AD estimation with PCA, scaling and leverage\n",
-    "count_fp_pipe = Pipeline([\n",
-    "    ('fp', MorganFingerprintTransformer(fpSize=2048, radius=2, useCounts=True)),\n",
-    "    ('pca', PCA(n_components=0.9)),  # Keep 90% of variance\n",
-    "    ('scaler', StandardScaler()),\n",
-    "    ('leverage', LeverageApplicabilityDomain())\n",
-    "])\n",
+    "# Create pipeline for count-based fingerprints with PCA\n",
+    "count_fp_pipe = Pipeline(\n",
+    "    [\n",
+    "        (\"fp\", MorganFingerprintTransformer(fpSize=2048, radius=2, useCounts=True)),\n",
+    "        (\"pca\", PCA(n_components=0.9)),  # Keep 90% of variance\n",
+    "        (\"scaler\", StandardScaler()),\n",
+    "        (\"rf\", RandomForestRegressor(n_estimators=100, random_state=42)),\n",
+    "    ]\n",
+    ")\n",
     "\n",
     "# Train the model\n",
-    "count_fp_pipe.fit(X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "57d73a11",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/numpy/core/fromnumeric.py:59: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
-      "  return bound(*args, **kwds)\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/numpy/core/fromnumeric.py:59: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
-      "  return bound(*args, **kwds)\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
-   "source": [
+    "count_fp_pipe.fit(X_train, y_train)\n",
+    "\n",
+    "# Get predictions and errors\n",
+    "y_pred_test = count_fp_pipe.predict(X_test)\n",
+    "abs_errors = np.abs(y_test - y_pred_test)\n",
     "\n",
-    "X_val_transformed = count_fp_pipe[:-1].transform(X_val) #Index into pipeline to get all the pipeline up to thelast step before the AD estimator\n",
-    "count_fp_pipe.named_steps['leverage'].fit_threshold(X_val_transformed, target_percentile=95)\n",
+    "# Create and fit leverage AD estimator\n",
+    "leverage_ad = LeverageApplicabilityDomain()\n",
+    "X_train_transformed = count_fp_pipe.named_steps[\"scaler\"].transform(\n",
+    "    count_fp_pipe.named_steps[\"pca\"].transform(\n",
+    "        count_fp_pipe.named_steps[\"fp\"].transform(X_train)\n",
+    "    )\n",
+    ")\n",
+    "leverage_ad.fit(X_train_transformed)\n",
     "\n",
+    "# Fit threshold using validation set\n",
+    "X_val_transformed = count_fp_pipe.named_steps[\"scaler\"].transform(\n",
+    "    count_fp_pipe.named_steps[\"pca\"].transform(\n",
+    "        count_fp_pipe.named_steps[\"fp\"].transform(X_val)\n",
+    "    )\n",
+    ")\n",
+    "leverage_ad.fit_threshold(X_val_transformed)\n",
     "\n",
     "# Get AD scores for test set\n",
-    "X_test_transformed = count_fp_pipe[:-1].transform(X_test) #Index into pipeline to get the last step before the AD estimator     \n",
-    "leverage_raw_scores = count_fp_pipe.named_steps['leverage'].transform(X_test_transformed)"
+    "X_test_transformed = count_fp_pipe.named_steps[\"scaler\"].transform(\n",
+    "    count_fp_pipe.named_steps[\"pca\"].transform(\n",
+    "        count_fp_pipe.named_steps[\"fp\"].transform(X_test)\n",
+    "    )\n",
+    ")\n",
+    "leverage_scores = leverage_ad.transform(X_test_transformed)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fd5c6718",
+   "id": "d7723e37",
    "metadata": {},
    "source": [
-    "As before, let's visualize the relationship between prediction errors and leverage scores and look at the fractiles errors."
+    "Visualize the relationship between prediction errors and leverage scores:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "41434c9d",
+   "execution_count": null,
+   "id": "57d73a11",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "/home/esben/envs/vscode/lib/python3.10/site-packages/numpy/core/fromnumeric.py:59: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
+      "  warnings.warn(\n",
       "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
       "  warnings.warn(\n",
+      "/home/esben/envs/vscode/lib/python3.10/site-packages/numpy/core/fromnumeric.py:59: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
+      "  return bound(*args, **kwds)\n",
       "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
       "  warnings.warn(\n"
      ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "95th percentile of errors inside domain: 1.50\n",
-      "95th percentile of errors outside domain: 1.23\n",
-      "Fraction of samples outside domain: 0.05\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAE8CAYAAAAsfWGYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAACU8UlEQVR4nOydd3gc1dX/P1O2a9WsZstyrxgXMM00UwwGE8CEFuD3YjqEHgIkpGFCEhOqCfACSQAneQOh19CMqaaDjcG9d1WrbN+dcn9/jHatVbNWlizJns/z7KPdO+3OaPfMnXPP+R5JCCGwsbGxsdknkHu6AzY2NjY2ew7b6NvY2NjsQ9hG38bGxmYfwjb6NjY2NvsQttG3sbGx2Yewjb6NjY3NPoRt9G1sbGz2IWyjb2NjY7MPYRt9Gxsbm30I2+j3EoYMGcJFF12U+vzhhx8iSRIffvhhlx1DkiRmz57dZfuzsbHpe9hGH5g3bx6SJKVebrebUaNGce2111JZWdnT3cuIN998s9cZ9tmzZ6dd3+avioqKnu5ir2Tjxo1IksS9997b013p85imyT//+U8OPfRQ8vPz8fv9jBo1igsvvJAvvviip7u3R1F7ugO9id///vcMHTqUWCzGwoULefTRR3nzzTdZunQpXq93j/bl6KOPJhqN4nQ6M9ruzTff5JFHHmnV8EejUVS15/7ljz76KFlZWS3ac3Nz93xnbPYprr/+eh555BFOP/10LrjgAlRVZdWqVbz11lsMGzaMww47rKe7uMewjX4TTj75ZA466CAALrvsMvr168f999/Pq6++ynnnndfqNuFwGJ/P1+V9kWUZt9vdpfvs6v1lyllnnUVBQUFG28RiMZxOJ7Lc8qF0d6+9aZokEokevy57C0IIYrEYHo+np7uSRmVlJf/7v//L5Zdfzl//+te0ZXPnzqW6unqP9UXXdUzTzHgw15XY7p12OO644wDYsGEDABdddBFZWVmsW7eOGTNm4Pf7ueCCCwDLgMydO5dx48bhdrspLi7myiuvpK6uLm2fQgj+8Ic/MHDgQLxeL8ceeyzLli1rcey2fPpffvklM2bMIC8vD5/Px4QJE3jwwQdT/XvkkUcA0twnSVrz6S9evJiTTz6Z7OxssrKyOP7441s87ibdX59++ik33XQThYWF+Hw+zjjjjC79wSTP+T//+Q+/+c1vKC0txev1EggE2r324XCYn//855SVleFyuRg9ejT33nsvzQVkJUni2muv5d///jfjxo3D5XLx9ttvA/Cf//yHyZMn4/f7yc7OZvz48anr2hqappGfn8/FF1/cYlkgEMDtdnPzzTen2h566CHGjRuH1+slLy+Pgw46iKeffrorLhvxeJzbb7+dESNG4HK5KCsr49ZbbyUej6fW2X///Tn22GNbbGuaJqWlpZx11llpbR35Lg8ZMoQf/ehHvPPOOxx00EF4PB4ef/xxAJ566imOO+44ioqKcLlc7Lfffjz66KOtHn/27NkMGDAg9XtYvnx5izkugPr6em688cbU/3nEiBH8+c9/xjTNdq/Phg0bEEJwxBFHtFgmSRJFRUUtjvOzn/2MIUOG4HK5GDhwIBdeeCE1NTWpdaqqqrj00kspLi7G7XYzceJE/vGPf6Ttp6l7bu7cuQwfPhyXy8Xy5csBWLlyJWeddRb5+fm43W4OOuggXnvttbR9aJrGHXfcwciRI3G73fTr148jjzyS+fPnt3vO7WGP9Nth3bp1APTr1y/Vpus606dP58gjj+Tee+9NuX2uvPJK5s2bx8UXX8z111/Phg0bePjhh1m8eDGffvopDocDgN/97nf84Q9/YMaMGcyYMYNFixZx4oknkkgkdtmf+fPn86Mf/Yj+/ftzww03UFJSwooVK3jjjTe44YYbuPLKK9m+fTvz58/nX//61y73t2zZMo466iiys7O59dZbcTgcPP744xxzzDF89NFHHHrooWnrX3fddeTl5XH77bezceNG5s6dy7XXXsuzzz7boetZW1vbok1V1RbunTvvvBOn08nNN99MPB5PjYpau/ZCCE477TQ++OADLr30UiZNmsQ777zDLbfcwrZt23jggQfS9v3+++/z3HPPce2111JQUMCQIUOYP38+5513Hscffzx//vOfAVixYgWffvopN9xwQ6vn4nA4OOOMM3jppZd4/PHH00Zur7zyCvF4nJ/85CcA/O1vf+P666/nrLPO4oYbbiAWi/H999/z5Zdfcv7553fo2rWFaZqcdtppLFy4kCuuuIKxY8fyww8/8MADD7B69WpeeeUVAM4991xmz55NRUUFJSUlqe0XLlzI9u3bU32Fjn+XAVatWsV5553HlVdeyeWXX87o0aMBy5U3btw4TjvtNFRV5fXXX+fqq6/GNE2uueaa1Pa33XYbd999N6eeeirTp09nyZIlTJ8+nVgslnaekUiEqVOnsm3bNq688koGDRrEZ599xm233UZ5eTlz585t8xoNHjwYgOeff56zzz67XVdtKBTiqKOOYsWKFVxyySUceOCB1NTU8Nprr7F161YKCgqIRqMcc8wxrF27lmuvvZahQ4fy/PPPc9FFF1FfX9/iO/PUU08Ri8W44oorcLlc5Ofns2zZMo444ghKS0v55S9/ic/n47nnnmPmzJm8+OKLnHHGGYA1HzZnzhwuu+wyDjnkEAKBAN988w2LFi3ihBNOaPM82kXYiKeeekoA4r333hPV1dViy5Yt4j//+Y/o16+f8Hg8YuvWrUIIIWbNmiUA8ctf/jJt+08++UQA4t///nda+9tvv53WXlVVJZxOpzjllFOEaZqp9X71q18JQMyaNSvV9sEHHwhAfPDBB0IIIXRdF0OHDhWDBw8WdXV1acdpuq9rrrlGtPVvBcTtt9+e+jxz5kzhdDrFunXrUm3bt28Xfr9fHH300S2uz7Rp09KO9bOf/UwoiiLq6+tbPV6S22+/XQCtvkaPHt3inIcNGyYikUjaPtq69q+88ooAxB/+8Ie09rPOOktIkiTWrl2bdv6yLItly5alrXvDDTeI7Oxsoet6u+fRnHfeeUcA4vXXX09rnzFjhhg2bFjq8+mnny7GjRuX0b6FEGLDhg0CEPfcc0+b6/zrX/8SsiyLTz75JK39scceE4D49NNPhRBCrFq1SgDioYceSlvv6quvFllZWanr3dHvshBCDB48WADi7bffbtGv5v8/IYSYPn162nWpqKgQqqqKmTNnpq03e/bsFr+HO++8U/h8PrF69eq0dX/5y18KRVHE5s2bWxyvKRdeeKEARF5enjjjjDPEvffeK1asWNFivd/97ncCEC+99FKLZcnv/ty5cwUg/u///i+1LJFIiClTpoisrCwRCASEEDv/f9nZ2aKqqiptX8cff7wYP368iMViafs//PDDxciRI1NtEydOFKecckq755YptnunCdOmTaOwsJCysjJ+8pOfkJWVxcsvv0xpaWnaej/96U/TPj///PPk5ORwwgknUFNTk3pNnjyZrKwsPvjgAwDee+89EokE1113XZrb5cYbb9xl3xYvXsyGDRu48cYbW4yMm+6roxiGwbvvvsvMmTMZNmxYqr1///6cf/75LFy4kEAgkLbNFVdckXaso446CsMw2LRpU4eO+eKLLzJ//vy011NPPdVivVmzZrXpF25+7d98800UReH6669Pa//5z3+OEIK33norrX3q1Knst99+aW25ubmEw+GMH5mPO+44CgoK0p506urqmD9/Pueee27a/rdu3crXX3+d0f47wvPPP8/YsWMZM2ZM2ncv6ZpMfvdGjRrFpEmT0vpqGAYvvPACp556aup6d/S7nGTo0KFMnz69Rb+a/v8aGhqoqalh6tSprF+/noaGBgAWLFiArutcffXVadted911rZ7nUUcdRV5eXlq/pk2bhmEYfPzxx+1ep6eeeoqHH36YoUOH8vLLL3PzzTczduxYjj/+eLZt25Za78UXX2TixImpkXZTkt/9N998k5KSkrR5PofDwfXXX08oFOKjjz5K2+7MM8+ksLAw9bm2tpb333+fc845h2AwmDqXHTt2MH36dNasWZPqU25uLsuWLWPNmjXtnl8m2O6dJjzyyCOMGjUKVVUpLi5m9OjRLSYQVVVl4MCBaW1r1qyhoaGhhW8wSVVVFUDKOI4cOTJteWFhIXl5ee32Lelq2n///Tt+Qu1QXV1NJBJJPY43ZezYsZimyZYtWxg3blyqfdCgQWnrJfvc3NfbFkcffXSHJnKHDh3aantr137Tpk0MGDAAv9/f4hySy3e176uvvprnnnuOk08+mdLSUk488UTOOeccTjrppHb7qaoqZ555Jk8//TTxeByXy8VLL72EpmlpRv8Xv/gF7733HocccggjRozgxBNP5Pzzz2/Vx5wpa9asYcWKFWlGpSnJ7x5YLp5f/epXbNu2jdLSUj788EOqqqrS+trR73KStv5Xn376Kbfffjuff/45kUgkbVlDQwM5OTmp/82IESPSlufn57f4PaxZs4bvv/++Q+fZGrIsc80113DNNdewY8cOPv30Ux577DHeeustfvKTn/DJJ58A1u/szDPPbHdfmzZtYuTIkS1sQ0e/c2vXrkUIwW9/+1t++9vftnk+paWl/P73v+f0009n1KhR7L///px00kn8z//8DxMmTGi3j+1hG/0mHHLIIanonbZwuVwt/tmmaVJUVMS///3vVrdp64va11AUpdV20cUVN9sa5bd27bti30VFRXz33Xe88847vPXWW7z11ls89dRTXHjhhS0m55rzk5/8hMcff5y33nqLmTNn8txzzzFmzBgmTpyYWmfs2LGsWrWKN954g7fffpsXX3yR//3f/+V3v/sdd9xxx26dj2majB8/nvvvv7/V5WVlZan35557LrfddhvPP/88N954I8899xw5OTlpN7dMv8utXc9169Zx/PHHM2bMGO6//37KyspwOp28+eabPPDAA7uceG3rPE844QRuvfXWVpePGjWqw/vq168fp512Gqeddlpq/mrTpk0p339X0/waJc//5ptvbvUpCXbeCI8++mjWrVvHq6++yrvvvsvf//53HnjgAR577DEuu+yyTvXHNvpdwPDhw3nvvfc44ogj2g1XS36p1qxZk+ZSqa6u3uVoefjw4QAsXbqUadOmtbleR109hYWFeL1eVq1a1WLZypUrkWU5zWD0VgYPHsx7771HMBhMG+2vXLkytbwjOJ1OTj31VE499VRM0+Tqq6/m8ccf57e//W2LkWhTjj76aPr378+zzz7LkUceyfvvv8+vf/3rFuv5fD7OPfdczj33XBKJBD/+8Y/54x//yG233bZbIaPDhw9nyZIlHH/88bv83w8dOpRDDjmEZ599lmuvvZaXXnqJmTNn4nK50vbXke9ye7z++uvE43Fee+21tKfD5q6h5P9m7dq1aaPhHTt2tPg9DB8+nFAo1O53vzMcdNBBfPTRR5SXlzN48GCGDx/O0qVL291m8ODBfP/995immTYI6eh3LvnbdzgcHTqfZJTYxRdfTCgU4uijj2b27NmdNvq2T78LOOecczAMgzvvvLPFMl3Xqa+vB6w5A4fDwUMPPZQ2Om4v8iDJgQceyNChQ5k7d25qf0ma7isZt958neYoisKJJ57Iq6++ysaNG1PtlZWVPP300xx55JFkZ2fvsl89zYwZMzAMg4cffjit/YEHHkCSJE4++eRd7mPHjh1pn2VZTj0+Nw17bA1ZljnrrLN4/fXX+de//oWu62nuktb273Q62W+//RBCoGnaLvvXHueccw7btm3jb3/7W4tl0WiUcDic1nbuuefyxRdf8OSTT1JTU9Oirx39LrdH8omw6feyoaGhxfzN8ccfj6qqLUI5m/8vk/36/PPPeeedd1osq6+vR9f1NvtTUVGRCpNsSiKRYMGCBciynLqxn3nmmSxZsoSXX365xfrJ85kxYwYVFRVp8yO6rvPQQw+RlZXF1KlT2+wLWE+WxxxzDI8//jjl5eUtljcNg27+3cnKymLEiBG7/F62hz3S7wKmTp3KlVdeyZw5c/juu+848cQTcTgcrFmzhueff54HH3yQs846i8LCQm6++WbmzJnDj370I2bMmMHixYt56623dunrlmWZRx99lFNPPZVJkyZx8cUX079/f1auXMmyZctSP4bJkycDVgbi9OnTURQlLRyvKX/4wx+YP38+Rx55JFdffTWqqvL4448Tj8e5++67u/YiAS+88EKrGbknnHACxcXFndrnqaeeyrHHHsuvf/1rNm7cyMSJE3n33Xd59dVXufHGG1NPSO1x2WWXUVtby3HHHcfAgQPZtGkTDz30EJMmTUr5advj3HPP5aGHHuL2229n/PjxLbY58cQTKSkp4YgjjqC4uJgVK1bw8MMPc8opp7SYi2iNBQsWtAhhBJg5cyb/8z//w3PPPcdVV13FBx98wBFHHIFhGKxcuZLnnnsuFUOf5JxzzuHmm2/m5ptvJj8/v8VIs6Pf5fY48cQTU09OV155JaFQiL/97W8UFRWlGbni4mJuuOEG7rvvPk477TROOukklixZkvo9NH1yueWWW3jttdf40Y9+xEUXXcTkyZMJh8P88MMPvPDCC2zcuLHN39DWrVs55JBDOO644zj++OMpKSmhqqqKZ555hiVLlnDjjTemtr3lllt44YUXOPvss7nkkkuYPHkytbW1vPbaazz22GNMnDiRK664gscff5yLLrqIb7/9liFDhvDCCy/w6aefMnfu3A79Tx955BGOPPJIxo8fz+WXX86wYcOorKzk888/Z+vWrSxZsgSA/fbbj2OOOYbJkyeTn5/PN998wwsvvMC11167y2O0SZfGAvVRkiGJX3/9dbvrzZo1S/h8vjaX//WvfxWTJ08WHo9H+P1+MX78eHHrrbeK7du3p9YxDEPccccdon///sLj8YhjjjlGLF26VAwePLjdkM0kCxcuFCeccILw+/3C5/OJCRMmpIXh6bourrvuOlFYWCgkSUoL36RZyKYQQixatEhMnz5dZGVlCa/XK4499ljx2Wefdej6tNXH5rQXstl0++T+nn/++Rb7aO/aB4NB8bOf/UwMGDBAOBwOMXLkSHHPPfekhZcmz/+aa65psf0LL7wgTjzxRFFUVCScTqcYNGiQuPLKK0V5eXm755XENE1RVlbWauioEEI8/vjj4uijjxb9+vUTLpdLDB8+XNxyyy2ioaGh3f0mQ/7aev3rX/8SQljhgn/+85/FuHHjhMvlEnl5eWLy5MnijjvuaPUYRxxxhADEZZdd1uaxO/JdHjx4cJvhhK+99pqYMGGCcLvdYsiQIeLPf/6zePLJJwUgNmzYkFpP13Xx29/+VpSUlAiPxyOOO+44sWLFCtGvXz9x1VVXpe0zGAyK2267TYwYMUI4nU5RUFAgDj/8cHHvvfeKRCLR5rkEAgHx4IMPiunTp4uBAwcKh8Mh/H6/mDJlivjb3/7W4nuyY8cOce2114rS0lLhdDrFwIEDxaxZs0RNTU1qncrKSnHxxReLgoIC4XQ6xfjx48VTTz2Vtp9dhdyuW7dOXHjhhaKkpEQ4HA5RWloqfvSjH4kXXnghtc4f/vAHccghh4jc3Fzh8XjEmDFjxB//+Md2z3dXSEJ08SycjY2NzW5QX19PXl4ef/jDH1qdH7HZPWyfvo2NTY8RjUZbtCXnuI455pg925l9BNunb2Nj02M8++yzzJs3jxkzZpCVlcXChQt55plnOPHEE7skj8GmJbbRt7Gx6TEmTJiAqqrcfffdBAKB1OTuH/7wh57u2l6L7dO3sbGx2Yewffo2NjY2+xC20bexsbHZh9jnfPqmabJ9+3b8fn+n1CltbGxsehtCCILBIAMGDNilPtU+Z/S3b9/eJzRlbGxsbDJly5YtLZRom7PPGf1kivSWLVv6hLaMjY2Nza4IBAKUlZV1SAJinzP6SZdOdna2bfR3l0QCkhmTf/wj9GCxZxsbm46p7O5zIZuBQICcnBwaGhpso7+7hMOQFFALhaBR4dPGxmbPkolds6N3bGxsbPYh9jn3TndjmoJt9VHCCR2fU6U014Ms21FCNjY2vQPb6Hcha6uCvLO0knXVIWK6gVtVGF6YxfT9ixlRtOsJFpt9ByEEuq5jGEZPd8Wmj+BwONosWZoJttHvItZWBXnq043UhhP0z3HjdXqIJHSWbm9ge0OUi48Y0iWG336S6PskEgnKy8tbFAy3sWkPSZIYOHBgq4WIMsE2+l2AaQreWVpJbTjByKKs1Ay63+0gy6WypirEu8sqGVaQtVsG2n6S6PuYpsmGDRtQFIUBAwbgdDrtJEGbXSKEoLq6mq1btzJy5MjdGvHbRr8L2FYfZV11iP457hY/YEmS6J/jZm1ViG31UcryvZ06xp56krDpXhKJBKZpUlZWhtfbue+Czb5JYWEhGzduRNM02+j3NOGETkw38Do9rS73OBUqAzHCibaLN7fHnnqSyBiPB5Yu3fnepsPsKlXexqY5XfVEaBv9LsDnVHGrCpGEjt/taLE8mjBwqQo+Z+cu9554kugUsgzjxu2549nY2Ow29nCjCyjN9TC8MIvyhhjNc92EEJQ3xBhRlEVpbudGwzufJFq/aXicCnHd6PSThI2Nzb6DbfS7AFmWmL5/Mfk+J2uqQgRjGrppEoxprKkKke9zcuK44k67Xpo+SbTG7j5JdJpEAmbPtl6JxJ49ts0+yUUXXcTMmTP3+HGPOeYYbrzxxt3ax+zZs5k0aVK76+yJ87ONfhcxosjPxUcMYf8BOdRHNDbWhKmPaIwvzdntSdbufpLoNJoGd9xhvTRtzx7bpsf4/PPPURSFU045pcWyjRs3IklS6uX3+xk3bhzXXHMNa9asaXOf8+bNS9uutdfGjRu78az2HXrU6D/66KNMmDAhJX42ZcoU3nrrrXa3ef755xkzZgxut5vx48fz5ptv7qHe7poRRX5+esxwfnbCKK47fiQ/O2EUV00dvttRNd39JGFjkwlPPPEE1113HR9//DHbt29vdZ333nuP8vJylixZwp/+9CdWrFjBxIkTWbBgQavrn3vuuZSXl6deU6ZM4fLLL09r66wkesJ+Ck2jR43+wIEDueuuu/j222/55ptvOO644zj99NNZtmxZq+t/9tlnnHfeeVx66aUsXryYmTNnMnPmTJYmI0h6AbIsUZbvZUxJNmX53i4zxN35JGHTSwiH237FYh1fNxrt2LqdIBQK8eyzz/LTn/6UU045hXnz5rW6Xr9+/SgpKWHYsGGcfvrpvPfeexx66KFceumlrWYhezweSkpKUi+n04nX601raxqmeO+999K/f3/69evHNddcg9bkSXPIkCHceeedXHjhhWRnZ3PFFVcAsHDhQo466ig8Hg9lZWVcf/31hJtch//93/9l5MiRuN1uiouLOeuss9L6aJomt956K/n5+ZSUlDB79uy05Zs3b+b0008nKyuL7OxszjnnHCorK9u8loZhcNNNN5Gbm0u/fv249dZbWzzJdwuil5GXlyf+/ve/t7rsnHPOEaecckpa26GHHiquvPLKDu+/oaFBAKKhoWG3+tlTGIYpNu8IixXlDWLzjrAwDLPnOhMKCQHWKxTquX70IaLRqFi+fLmIRqMtFyavZWuvGTPS1/V621536tT0dQsKWl+vEzzxxBPioIMOEkII8frrr4vhw4cL09z5HdywYYMAxOLFi1ts+/LLLwtAfPnll7s8ztSpU8UNN9zQon3WrFkiOztbXHXVVWLFihXi9ddfF16vV/z1r39NrTN48GCRnZ0t7r33XrF27drUy+fziQceeECsXr1afPrpp+KAAw4QF110kRBCiK+//looiiKefvppsXHjRrFo0SLx4IMPpvUnOztbzJ49W6xevVr84x//EJIkiXfffVcIIYRhGGLSpEniyCOPFN9884344osvxOTJk8XUJv+L22+/XUycODH1+c9//rPIy8sTL774oli+fLm49NJLhd/vF6effnqr16S9704mdq3XGH1d18UzzzwjnE6nWLZsWavrlJWViQceeCCt7Xe/+52YMGFCm/uNxWKioaEh9dqyZUufNvq9CtvoZ0xfN/qHH364mDt3rhBCCE3TREFBgfjggw9Sy9sz+itWrBCAePbZZ3d5nPaM/uDBg4Wu66m2s88+W5x77rmpz4MHDxYzZ85M2+7SSy8VV1xxRVrbJ598ImRZFtFoVLz44osiOztbBAKBNvtz5JFHprUdfPDB4he/+IUQQoh3331XKIoiNm/enFq+bNkyAYivvvpKCNHS6Pfv31/cfffdqc+apomBAwd2u9Hv8Tj9H374gSlTphCLxcjKyuLll19mv/32a3XdiooKiouL09qKi4upqKhoc/9z5szhjjvu6NI+29h0C6FQ28uaZ2BWVbW9bvPEry6aAF21ahVfffUVL7/8MgCqqnLuuefyxBNPcMwxx+xye9HoutjdJKNx48aluXr69+/PDz/8kLbOQQcdlPZ5yZIlfP/99/z73/9O609SFuOEE05g8ODBDBs2jJNOOomTTjqJM844Iy1resKECWn77N+/P1WN/4cVK1ZQVlaWNu+w3377kZuby4oVKzj44IPTtm1oaKC8vJxDDz001aaqKgcddFC3u3h63OiPHj2a7777joaGBl544QVmzZrFRx991Kbhz5TbbruNm266KfU5WVasr2ILru3FZFKEprvWbYcnnngCXdcZMGBAqk0Igcvl4uGHHyYnJ6fd7VesWAHA0KFDd6sfDkd6AqQkSZimmdbma3bOoVCIK6+8kuuvv77F/gYNGoTT6WTRokV8+OGHvPvuu/zud79j9uzZfP311+Tm5nb4uH2BHjf6TqeTESNGADB58mS+/vprHnzwQR5//PEW65aUlLSYGKmsrKSkpKTN/btcLlwuV9d2uofodYJrbjd89dXO9zZ7Lbqu889//pP77ruPE088MW3ZzJkzeeaZZ7jqqqva3N40Tf7yl78wdOhQDjjggO7ubgsOPPBAli9fnrI1raGqKtOmTWPatGncfvvt5Obm8v777/PjH/94l/sfO3YsW7ZsYcuWLalB5fLly6mvr291AJuTk0P//v358ssvOfroowHrGn/77bcceOCBnTzLjtHjRr85pmkSj8dbXTZlyhQWLFiQliQxf/58pkyZsod613P0SsE1RYFmj602eydvvPEGdXV1XHrppS1G9GeeeSZPPPFEmtHfsWMHFRUVRCIRli5dyty5c/nqq6/473//2yWa8Jnyi1/8gsMOO4xrr72Wyy67DJ/Px/Lly5k/fz4PP/wwb7zxBuvXr+foo48mLy+PN998E9M0GT16dIf2P23aNMaPH88FF1zA3Llz0XWdq6++mqlTp7ZwNSW54YYbuOuuuxg5ciRjxozh/vvvp76+vgvPunV61OjfdtttnHzyyQwaNIhgMMjTTz/Nhx9+yDvvvAPAhRdeSGlpKXPmzAGsizR16lTuu+8+TjnlFP7zn//wzTff8Ne//rUnT6Pb6bWCazb7DE888QTTpk1r1YVz5plncvfdd/P999+n6rNOmzYNAK/Xy+DBgzn22GP561//2u5IuzuZMGECH330Eb/+9a856qijEEIwfPhwzj33XAByc3N56aWXmD17NrFYjJEjR/LMM88wroPaUpIk8eqrr3Lddddx9NFHI8syJ510Eg899FCb2/z85z+nvLycWbNmIcsyl1xyCWeccQYNDQ1dcs5t9lV096xBO1x66aUsWLCA8vJycnJymDBhAr/4xS844YQTACv1eciQIWmxwM8//zy/+c1v2LhxIyNHjuTuu+9mxowZHT5mXyyMvqU2wgPzV5PrdbQq6BaMadRHNH52wqg9K7iWSMCDD1rvb7gBnM49d+w+SiwWY8OGDQwdOhS37RKzyYD2vjuZ2LUeHek/8cQT7S7/8MMPW7SdffbZnH322d3Uo95Jd0s3dxpNg1tvtd5ffbVt9G1s+gC29k4foNcKrtnY2PQ5bKPfB+i1gms9iGkKttRGWFkRYEttBNPsMS+ljU2fwh4a9gGSgmvbG6KsqbKKqXicCtGEwfb6GC6HzIjiLLbVR/eJuP1eF7pqY9OHsI1+HyEpuJY0dpWBGHHdJK6Z6KbMK4u38bZasdcbv14ZutoJejB+wqaP0lXfmYzcO7qu8/vf/56tW7d2ycFtMqOpdPPpk0rxuVR8LoVB+V6GFWSR63WwdHsDT326kbVVwZ7ubpfTPHTV73agyBJ+t4ORRVnUhhO8u6yyV7t6klmdkUikh3ti09dISkTvbp5DRiN9VVW55557uPDCC3froDadR5YlSnM9vPbddhK6yahi/z4Tt99rawVngKIo5ObmpjRbvF5vlxW8ttl7MU2T6upqvF4vqrp7DpqMtz7uuOP46KOPGDJkyG4d2Kbz9Brj53bDBx/sfN/N9NrQ1QxJyoZUtSeaZmPTDFmWGTRo0G4PEjI2+ieffDK//OUv+eGHH5g8eXILYaPTTjtttzpks2t6jfFTFOiAumJX0TR0tbUktb4SuipJEv3796eoqCit+IeNTXs4nU7k5gqqnSDjX8fVV18NwP33399imSRJrVbFsWmbzqhm7i3GL1OSoatLtzeQ5VLTRjzJ0NXxpTl9JnRVUZQe0aGx2bfJ2Cr0RSnR3kpnQw97jfHTNEjqHl1xBTha3oC6kvZCV8sbYnatYBubDtCj2js9QW/R3mkZeqgSSegp47Wr0MPm2zc3fnskdDEchqws630o1GW67bui6c0yrltPNSOKsjhx3N4bqmpj0x7drr3z0Ucfce+996aKIuy3337ccsstHHXUUZ3Z3T5HV6hmtha371IVxpfm7PXGb0SRn2HHZNnFZGxsOkHGRv///u//uPjii/nxj3+cqkLz6aefcvzxxzNv3jzOP//8Lu/k3kZXRd/sy8ZPlqVeG5ZpY9Obydjo//GPf+Tuu+/mZz/7Wart+uuv5/777+fOO++0jX4H6MroG9v42djYZELG8T/r16/n1FNPbdF+2mmnsWHDhi7p1N5Gc3Ewj0OxVTNtbGx6hIytSllZGQsWLGhRAee9997r0wXHu4vWInSGFfrI9Tgob4jtFaGHNjY2fYeMjf7Pf/5zrr/+er777jsOP/xwwPLpz5s3jweTVZRsgLbFwZZtD6DIEoos2aGHNjY2e5SMjf5Pf/pTSkpKuO+++3juuecAqxL8s88+y+mnn97lHeyrdCRCp3+Om3yvk/U14TajbzqTvLXHcLngjTd2vrexsen1ZGT0dV3nT3/6E5dccgkLFy7srj7tFXQkQqc+onHR4UOQJKlVo766MsAL32xjXXUIQ5jkeZyMKPL3HulkVYVTTunQqr365mVjsw+Rscrm3XffbatsdoCORuhENIMxJS2TKRasqOQvC9ZQHYzjVGVcqkwwqlMTSvQp3Xiwi57Y2PQmMo7eOf744/noo4+6oy97FbtT13Z1RZC/LFhDRSBGkd9Fod+Fx6nSENWoiyTYvCPSO3TjNQ3mzbNebQiHJec1lm5vINfr2Cd0/21sejO2ymY30Vl9HNMUvPDtFqqDcUqyXbgcliCXS5Vw+pzUhhNENJ01lcGe141PJODii633Z5/dQnunKzKPbWxsuhZbZbOb6Kw4WHIuwKlKONV0BUZJkshyqwRjOvXRRK/Xje81uv82NjYpMnbvmKbZ5itTgz9nzhwOPvhg/H4/RUVFzJw5k1WrVrW7zbx585AkKe3l3gMFPDpDUh9n/wE51Ec0NtaEqY9ojC/NadMnH07oGAJcqoJmtFQ0dSgyCd1EluRen7y1c16j9X56nApx3ej1Ny8bm72JjKyGpml4PB6+++479t9//90++EcffcQ111zDwQcfjK7r/OpXv+LEE09k+fLlLdxGTcnOzk67OfTmcnOZ6uP4nCp5HgfBqEZDVMPpk9POL6GbJHSTEUVZvT55a1/V/bex6c1k9GtzOBwMGjSoy1w4b7/9dtrnefPmUVRUxLfffsvRRx/d5naSJKVKzvUFMtHHKc31MKLIT004QVw3qA0nyHKrjSN8g4pAnJJsN2dOLu31fvBeo/tvY2OTImP3zq9//Wt+9atfUVtb2+WdaWhoACA/P7/d9UKhEIMHD6asrIzTTz+dZcuWtbluPB4nEAikvXozybmAQfle8nwucjwOIgmD6mCM6mCC/tlurj9+JKOKe64WQEdJnku+z8maqhDBmIZumgRjGmuqQnbmsY1ND5BxEZUDDjiAtWvXomkagwcPbuGGWbRoUac6Ypomp512GvX19e0mfn3++eesWbOGCRMm0NDQwL333svHH3/MsmXLGDhwYIv1Z8+ezR133NGivaeLqOyKZGz72qog9VENWYLhRVmcdWAZo0p6SWx7B4uo2EVPbGy6l0yKqGRs9FszoE25/fbbM9ldip/+9Ke89dZbLFy4sFXj3RaapjF27FjOO+887rzzzhbL4/E48Xg89TkQCFBWVtbrjT70gSxWXYeXX7ben3GGlaHbBr3+XGxs+jDdavS7g2uvvZZXX32Vjz/+mKFDh2a8/dlnn42qqjzzzDO7XLe3lEu0sbGx6SoysWsd9ul/9dVX7U7gxuPxlABbRxFCcO211/Lyyy/z/vvvd8rgG4bBDz/8QP/+/TPe1sbGxmZfo8NGf8qUKezYsSP1OTs7m/Xr16c+19fXc95552V08GuuuYb/+7//4+mnn8bv91NRUUFFRQXRaDS1zoUXXshtt92W+vz73/+ed999l/Xr17No0SL+3//7f2zatInLLrsso2PbdAG6Ds8/b710O9bexqYv0OGQzeZeoNa8Qpl6ih599FEAjjnmmLT2p556iosuugiAzZs3I8s77011dXVcfvnlVFRUkJeXx+TJk/nss8/Yb7/9Mjq2TRcQj8M551jvQ6F2ffo2Nja9gy79lWaaJNWRm8SHH36Y9vmBBx7ggQceyOg4NjY2NjYW9tCsj2FHwdjY2OwOGRn95cuXU1FRAVij9JUrVxIKhQCoqanp+t7ZpGHr0tvY2OwuHQ7ZlGVLA6a11ZPtfUFls6+GbLast6sSSegpxc4eKarSweQsGxub7iUTu9bhkf6GDRt2u2P7Il3hjrF16W1sbLqKDhv9wYMHd2c/9kq6yh1j69Lb2Nh0FfZEbjfR0h3jIZLQWbq9IeMatx2tt9tUl36PTPg6nfDUUzvf29jY9Hpso98NZOqO2ZWBTurSh+OWUU8YJk5Fxu+25Iqb69LvsQlfhwMa8ylsbGz6BrbR70KSxntddYjvt9UzIMezS3dMXDd2aaBLcz3keh0sXFODJIEhBKosk+91MqzQy46wltKl78onDBsbm70P2+h3EU1H11WhGBuqwjRENEYWZ5Hvc6Wtm3THrKgI8NGq6l0a6PU1IaqCcaKagSxJ5HgdgGBbfYQtdREmluVy4rhigD074avr8M471vvp0+2MXBubPoD9K+0Cmo+us1wq5fUxKhosl82kstw0wx9NGDgVmW821O7SQA/J9/HO0koMUzB1VCHrqsPURRLoponHoWACxX4Xwwqy9vyEbzwOP/qR9d6WYbCx6RNk/CutrKzk5ptvZsGCBVRVVbWI2+/tcfpdTWv+eyEERX43VcEYkbjOuuoweV5nall5Q4xB+V6qArFdGuhFW+pShtzvdpDvcxKM6Sm/PgjqIlpqTiDTCV8bG5t9i4yN/kUXXcTmzZv57W9/S//+/Xt1UfI9QWuja0mSGFGURSiuE4hqVAZi1Ec0VEVKJVNNHpzHK99tw9tGUfCkgd4RThDVdLIMlZpQPG0CF0A3TaqC8dQksF2I3MbGpj0y/vUvXLiQTz75hEmTJnVDd/oebY2u831OJpXlsroywNa6KBt2hCnMcjG+NIcTxxXjUhXeXlqxSwMdTRhs2hFldWUICVAVmTyvkxFFWeT7nGmG3C5EbmNjsysyNvplZWUZSyjvzbQ3us73Odmvfza5HifnHTqI4YVZqXBM0xTtGujt9VH8bpX5yyqIJnRM06Qw241uQnUwRiiuM3FgDjvCiZQhTxYi394QZU2V9fThcVo3juQThl2I3MZm36bDRVSSzJ07l1/+8pds3LixG7rT90iOrssbYq3WHKgIxJlYlsvRIwspy/emDG7SQOf7nKypChGMaeimSTCmsXhzPeUNMRZtqmfR5np00ySUMNheH0UIk1yvg0BU46uNteR6VCYMzGF1VZAttRGGFWRx8RFD2H9ADvURjY01YeojVkinHa5pY2OTcY3cvLw8IpEIuq7j9XpxONJHt7W1tV3awa6mOwTXmkfvNB9dX3zEkFR0TTCmEYrrZLlV/C4H0YTB/OVWqGdcN4jrJtXBOE5VpqIhiqabxHRBXDcQAtxOhRy3iqrISEJwwOB8DFO0iPFPHq9bM3JtwTUbm15BtwiuJZk7d25n+7XXMqLIz8VHDOGdpZWsrQqyoUZDlmB4URZnHVgGwKMfrmPxljo274gQ1Qw8ToVB+V4OKMvjhHFFnOYYQDCu8cqibaiyjCLDqoogiiThcsh4HDIRzTL8qiIzKN/LuqoQ5Q1RRhX7eyYJy+mEhx/e+d7GxqbXk7HRnzVrVnf0o88zosiPOU7QENUIxHQMYVIdiPOfrzZTFYrTENWoDsYwTBO/WyWumWypjRDXzJSR9rsc1ISsp4UlW+sxhcDjkFEbR+huh4JumGiGyaqKAELAiMKs1FzCHlfddDjgmmu6b/82NjZdTqdi9wzD4JVXXmHFihUAjBs3jtNOOw1FUbq0c32JtVVB/vHZJmrDCQb38+J1qoTjGgvX1RBNGOR7HeiGoF+WC0mSyHIJasNWktWOUJx3l1VyzOhCYrpBlqkSjut4HApRzcQDKLKMIoEmQJEkqiMaw4t8ZHvS3Wu26qaNjU17ZGz0165dy4wZM9i2bRujR48GYM6cOZSVlfHf//6X4cOHd3knezttCayBhAyYQrCtIcbAXE8qQSuhmyiyRFUg3qiZE+KgIXm4VYVt9VGqQ3HLV68ZRDUDpyrjUmQEEG1MrhqU5201T2KPJWEZBnzyifX+qKNgH77p29j0FTKO3rn++usZPnw4W7ZsYdGiRSxatIjNmzczdOhQrr/++u7oY6+nLfmDhGGimwKfUyGmGZjCir/fXh9jS12UykCMymCMVZVBakJxstwquV4HK7YHiGsGDkUmx+PAqcgkNINATEcA/bJcZLlUdEMQiGotoob2WBJWLAbHHmu9YrHuPZaNjU2XkLFV+Oijj/jiiy/Iz89PtfXr14+77rqLI444oks711doK0HLqcioinVflYBgTCOcMNAME6cqo8oKkmFSG0oQiulUB+MgQFEkXKqCppt4nQpZLpWoLGGYgnyvg7huIEkSS7bWs6k2Qj+fi+FFPvJ9LjsJy8bGpl0yHum7XC6CwWCL9lAohDPDCI45c+Zw8MEH4/f7KSoqYubMmaxatWqX2z3//POMGTMGt9vN+PHjefPNNzM6blfTNEGrKX63Sp7XSTSh41QkaiMJNMMSSlMk60nA51RwqRIORebTNTuoiyQ4eEgeg/p5kSQINOrs+N0q+T4HFQ0xwnGDCQOzKfC7iWkm2+ojLNpcx5baMGuqQnYSlo2NTZtkbPR/9KMfccUVV/Dll18ihEAIwRdffMFVV13FaaedltG+PvroI6655hq++OIL5s+fj6ZpnHjiiYTD4Ta3+eyzzzjvvPO49NJLWbx4MTNnzmTmzJksXbo001PpMtpK0JIkieGFPkwBHqeKaQoMwyRhmEQ0E1mSkCUJn9vB/qXZrKsOURfVGJDr5eiRhRw7uogRRVnkeZ24VZlIwkCWJQ4cnMe4AbkcOCiPAbkePA6FHaEEqyqC7D+g40lYpinYUhthZUWALbURTNPOtLax2dvJODmrvr6eWbNm8frrr6cSs3Rd57TTTmPevHnk5OR0ujPV1dUUFRXx0UcfcfTRR7e6zrnnnks4HOaNN95ItR122GFMmjSJxx57bJfH6I7kLGg/QUuRJVRZ4tN1NSR0E90QOBSJPK+T/rkehhdaUTjLtzcgSxKD+/lSYZhCCIIxndpIgu821aEqEkePKkpF7SSX10USRBMGvzplLIP77TpJqkuqa9nJWTY2vYJuTc7Kzc3l1VdfZc2aNaxcuRKAsWPHMmLEiM71tgkNDQ0AafMFzfn888+56aab0tqmT5/OK6+80ur68XiceDye+hwIBHa7n63RNEFrXXWIykAMl6qkBNa210dZuq2BmGYgAIcskedzMqzA8sUHogkciozf7WBddYgJpTnIsowkSWR7LD9+3DDpn+vD7975b0su97oUNtaEiWq7lra2q2vZ2Oy7dDq8Y+TIkYwcObLLOmKaJjfeeCNHHHEE+++/f5vrVVRUUFxcnNZWXFxMRUVFq+vPmTOHO+64o8v62R4jivwMO6al/MH6mhBvL61AliUUWabQ70Q3rcibJVvrGdLPy6qKEKoi089nsLUuSnl9jP1Ls+mf6yGaMNhWH8XjUBmQ21J/HzoesZNp/V4bG5u9iw4Z/Ztuuok777wTn8/XYpTdnPvvv79THbnmmmtYunQpCxcu7NT2bXHbbbel9TkQCFBWVtalx2iKLEtpCVFJI1sX0ThkSD5LtjZQH9FS4Znb66Nsqo2Q43ZwcGk2A3K9FPpdLN0eYNHmeorqLLXN0cV+hvXzURGMI4TotGxyl1bXcjjg7rt3vrexsen1dMjoL168GE3TUu+7mmuvvZY33niDjz/+mIEDB7a7bklJCZWVlWltlZWVlJSUtLq+y+XC5XK1umxP0NTI+t0OJpXlsrYqZJU8NEximokwBZMH5VGWb/nEy/J9eBwKn63fwY5QHI9ToToYJ8/nRJGl3ZJNDsY0aiMJnKqMEKQVZIHWE7uSBd9biLc5nXDLLd1z4WxsbLqFDhn9Dz74oNX3u4sQguuuu46XX36ZDz/8kKFDh+5ymylTprBgwQJuvPHGVNv8+fOZMmVKl/WrK2kew5/vc3LwkDyCMZ0doTiLttRhGBJCIjWCrw0n+H5bANMEWZEYnO9LVd1SZIn+2W7qI1qLeYNd+eHXVgV5ZfF21lWF2FgTwu1Qyfc6UzH+0NJN1CUTvjY2Nr2GjH36l1xyCQ8++CB+f/oPPhwOc9111/Hkk092eF/XXHMNTz/9NK+++ip+vz/ll8/JycHjsYzkhRdeSGlpKXPmzAHghhtuYOrUqdx3332ccsop/Oc//+Gbb77hr3/9a6anskdorciKJEnopmBTbYS6sIYkwdKtDVQF4gwv9LGuOkw0oVPod9IQ1TCEIM/tTPnc+2W5mHXEEKKa0aZscvPReTRh8I/PN7IjFKfI76I+ouFSJaqCMYJxjUllueR5nWluol1O+B5Wxoita6wDHnigLcNgY9MHyDhkU1EUysvLKSoqSmuvqamhpKQEXe+43ktb9XWfeuopLrroIgCOOeYYhgwZwrx581LLn3/+eX7zm9+wceNGRo4cyd13382MGTM6dMzuCtlsC9MUPPrhOpZub0hNnNaGE3y3pZ5ANEF1KE62W6Uk20MorqPIEgnDJMfjBAQxzWTKsH6pEM1gTKMunOC8QweR7XG0avSbj85dikxNKAESHFCWS11E47st9UQTOj6XSiimketzUpjlpl/WTv3/5v1OIoRgTVWIA/IdXHHKRKvRDtm0sekxuiVkMxAIpJKxgsEgbrc7tcwwDN58880WN4Jd0ZH7zYcfftii7eyzz+bss8/O6Fg9RfMShiXZLlZXBghENRRZwu9y4FRlnKpMvmqNtMNxnXyvg4aoTlG2Oy1EM6YZLCsP8PhH63GqEqYJ/XM8TNuviMOHF7C+JtRidF4ZsOYVsj0qdZEE+T5X2tyCIaAqEGfy4DzOOaiMEUV+ttRGdjnhu766e8JfbWxsuo8OG/3c3FwkSUKSJEaNGtViuSRJeyw0sq/RNIb/+631bK2L4nYoFGe7KchysqEmTG04QZZbxedUqIskqGiIkZ/lYnihL2V0a8Nxvt1URyimo8pQF7Y0+r/dXMe7Kyo4fHg//E61RTimU1XwOi0tn3XVYXI9TlRZYmiBl1Ldbal9BmPMPKA05advS08oicepUKfvOifAxsamd9Fho//BBx8ghOC4447jxRdfTEugcjqdDB48mAEDBnRLJ/cGkjH8H66uovbDBCXZlisl2+0gx+NgXVU4pc0jIaEqMhMH5qQmWIUQrK0MUR/VKMl2s60+RiCmkdAFpmlSF9Z5a2kFTllm8pDctNF5UvhNlqC8Icqn6wwiCQPdNFFlGZ9TIc/nxO/aGXbZXsF32Dnha2Nj07fosNGfOnUqABs2bGDQoEFt+uNt2mZ9TYgv1tVS0RCjOhhLi545qDGipy5i+fn7+VzsCGs4VQWPU6EqELfi+T2ORiE2jWjCQDcFTlVBVWSicZ1wQuf7rQFKsj30y7JuGEnht611EeojicZiLk4cikpCN9haH0U3BVFt53xMUk9o6fYGslxqq3kBBxTaPnwbm75GxoJr77//Pi+88EKL9ueff55//OMfXdKpvZFkJMzm2jBFfheKJKeiZ77bUk9dJGGVUdRNjhhewLXHDWf/ATnURzQ21oSpDcfxu1XGlGQTSxgkdIFmmDgUKTU3IskSLlUmFNdZXh7Y2d4o/BbVDDRDkOVScSgymmESjhsUZrnIdjt4b3lVSnQtOReR73OypipEMKahmybBmJZS8jx+bGZzODY2Nj1PxiGbc+bM4fHHH2/RXlRUxBVXXGHX0G2FZFbujpDl1lEVyzCH4gZZjdEzy8sDqeiZZMz9iEJ/KuwyENV45svNCCCmmUQTOroQxGM6QoAAZAm8DoWIZlAbihOM6amoH1WWkIB8nwMhBHWRBKosU5TtZnhhFg5FapGJuys9oeG+jMcMNjY2PUzGRj9ZJas5gwcPZvPmzV3Sqb2NbfVRFm+poy6cYEONFUap6QKBwDQFgpbRM5Au6WCagq831PHVxh0kDJOoZiBLEooMSKAbAiQJXQgUWSIU19laF6Gf5sQwBdvro3gcCkeM6IfboZIwTJyKnMrI1U2z1RKLbekJybIEiQTcfru1oi3DYGPTJ8jY6BcVFfH9998zZMiQtPYlS5bQr1+/rurXXsWK8gCrK4IIIdCEQNOtMoq6YaI5BWNK/OimYGJZLi5VwTRFi2SrpLtla12YJVsaMEzRmAslN76X8DkUIpqJS5URQrB0W4Ol6KnIlOS4Kc3z4HGq7U7MtibY1lxPKIXTCbNnd8k1srGx2TNkbPTPO+88rr/+evx+f0rz/qOPPuKGG27gJz/5SZd3sC/QpjZN47JvNtYRbayRawqBS5VxqWCYCoGYxqLNdWS7HTz/zRYWLK9qU+ZgRJGfUyYM4KsNtUQTOglDoGKN2J2qjC4sN04kYdA/x81BQ/JQZBmzUdGzPBBjTWWIAwbldlqwzcbGpm+TsdG/88472bhxI8cffzyqam1umiYXXnghf/rTn7q8g72dtrRpTtivGI9TYV11iA01QVRFIhDVyXarqRuCwMQ0TTQT/C7YrySbmG6m6doPK0h3reT7nIwqyWZQgY8lW+qJaVY1LQG4VJlIQkeSYL8Bfgbk7hydl+S4CWsGgajG6sqgVXHLqRCJ66yvCZPlUhk/MMMCOKYJK1ZY78eOBdn28dvY9HYylmFIsnr1apYsWYLH42H8+PEMHjy4q/vWLXSlDENLbRqVSEJndWWQHaEEOR4HujDZXBMhYQgQAkmWcKpWzHwgqpPQTUBQlO3mxP1KyPY4ME2T77c1kO91UuB3UROMEzdM3KpCQZaL9TUhBuV7SRgmy7cHqAsnEAhkSaI+ouFzqUwbW5yaxE0SjGlsro0wrCCLmlA89QKJgiwnBVmuzMTU7MpZNja9gm6tnJVk1KhRrWbm7iu0VYykJhRnTWWQ+qiOxyGT53US000SuoHHoeBUZBKaiWZabYosozaO/OO6QW1YsLYqxNa6CF+HaslyqwzM8zAwz4cpBCsrGmiI6sR1kwPKcjlyRAHBxuLpwZjGlxtqKc31pEk3JPE4FVyqzMwDBlAbTvD0V5uRJBhW4MPnctjVs2xs9gF6TRGVvkZrxUh2hOJ8ub6WUNzA71IxhcDtUJAk0E1BTDcwGidpDVNgmGCYJqYCwajG4i316IYV0RNN6JhC4FRkNtZEWF8dIdut4nUqRHWTuGawujLEgFw3XpeClICtUQ1vB6prZblUPlhZjRAwcWCuXT3LxmYfokuLqOxLWbrNtWmEECwvDxBJ6PhcCqosE9UMVEWiLNfDqqoQMU2QkAz8buuGILDi64WQcDoUKhqiaIbApcgE4jpCCKqCcSTAqcoYQuByKEQ1k2BMx+9W03T1DxnSj+EFccoDsXarawnouupZNjY2fYoeLaLSl2muTROM6dSFE8iShCpbBlqWJBRJwulS8LlUAlENAUTiOgJQZTAFIIFhWiP8uG6i6SaGKVBlkLAMsWaYBKMaWS6VbLdKdSiO1ylz6ZHDiDTR1U+qbLZXXSuqGbsUU2stZt/Gxqbv02mf/r5Oc22ahGEiBCiyhGGaJAyBz6XiVGUSuokMuFUZWZIabwigm2AaZiqkstH+Y0qgSECj8JoQgpgmiOsGem0kJZ62tjqMJEmMKdk5cbOrLNqkbHJHxNR2VWTdxsam79GhX/WPf/zjDu/wpZde6nRn+hLNdfKzXApOh4yakAjFDbxOhXyvE4CoZpAwTABKc90E4zrRhIEig88pW7IKmkkyjEoWIKuW318Ia/RvNi7TTIFm6CiKxLqqECvKAy1cMO1m0dIxMTU7Zt/GZu+kQ0Y/J2dn/LYQgpdffpmcnBwOOuggAL799lvq6+szujn0RZonYQ0ryEqNqtdWBbEG5xI+p4LboZAwDHaE4wSiGpGEgUO26hFEEyamgCyXim4KhGYiSyAEmNYuUGQJzRBpNwMAhEBRrIifQEznvRWVTBvbsiB6m1m0tLxhdbbIOg4H3Hzzzvc2Nja9nozj9H/xi19QW1vLY489htJYE9UwDK6++mqys7O55557uqWjXUVn4/TbKxA+rCCLrXURFq6t4d3llYRiGpG4wbZ6Kz5fbhxJe10KHlWmMpjAqUhkuRQCMZ24ZuJQZXTdRG/8b+S4FYIxg6ZlSiTA7ZDJcqlohglIDMhx8+j/TGZwv8xj5JueU1y3XDojirI6VGTdxsam95CJXcvY6BcWFrJw4UJGjx6d1r5q1SoOP/xwduzYkXmP9yCdMfptJWElR8XHjSliZXmQddUhK+EpGKc6FEc3BTkeB/k+J6W5HjbuCFMXtvTyJSRMIUgYAglQJdAb1TIlrEleQzRO9DZBlsChSPicKgV+F+G4zh/PGM8xozsnc9yehISNjU3foFuTs3RdZ+XKlS2M/sqVKzFNs42t+i5tJWElY9oXb6nnLwvW0D/HzYBcDwNyPVQ0RPh4TZwcj8oBZbmU5XuRJIlcr4Nl2wPUhhPEdRNJsgoaKPJO4y41vqClwZewXECaLkgoVibv7tKeG2iXmCYklVUHDbJlGGxs+gAZG/2LL76YSy+9lHXr1nHIIYcA8OWXX3LXXXdx8cUXd3kHe5rWkrCSCCGoC8epaIjRP9tNTDMQwqpJm+22dOsrg/GUUc33uTh8WD+2N0TZEUpQmu2iLqoRNwSyKVBkiOsCIcBodv+Umvy1wj4NNtVEGJjnZVhBD8kfRKOQlNm2ZRhsbPoEGRv9e++9l5KSEu677z7Ky8sB6N+/P7fccgs///nPu7yDPU1bBcJrwwmWbW9gfXUYzTD5ZG0NLlUmx+sgz+vEEAKPQ6E2nEgVMxFCUBGIgbCSrUwknKpCJKGhKmAKCUlqrFwlgTB3juWTf6XGZYawInkEVqavjY2NTUfI2OjLssytt97KrbfeSiAQANht4bLeTGsFwmvDCb7bUk91MIZmWsVLnIqMKQSBqI5uCBK6ia4LXA6JhGFSG46zrirM9voIdZEEHoeSqnYlECR0y1ffGJ5P425xyhJxY6cbxwQkYW3nUmVyvVaZwxGF/nZ98bbv3sbGBjpRIxcsv/57773HM888k3J5bN++nVAolNF+Pv74Y0499VQGDBiAJEm88sor7a7/4YcfIklSi1dFRUVnTqNDlOZ6GFbgY111iOpgjIZIgjWVQSJxK5nKNAVORcLjVPA6FXTD0sVRJImoZhCOG9SE4izaXMe2+giaYenx9Mty4lRlVEXGpSqoslW9SjMtg69IkiXE1uj3VxsTtpKTvA5Fop/PxfBCf0oyoS3WVgV59MN1PDB/NX9ZsIYH5q/m0Q/XsbYq2G3XzcbGpneS8Uh/06ZNnHTSSWzevJl4PM4JJ5yA3+/nz3/+M/F4nMcee6zD+wqHw0ycOJFLLrkkoxj/VatWpT1dFBV1X4Hu9TUhaiMJNu+IsLIiiEuVCccNfE6ZcFzHqcoosoxumkQTJgnDJKIZeBqLmgzL8bKlNkJtOEGux0FerpNIwroRyBLUhuIILHePBIQbk7ZUWUI3THSzccSvWpr5pgCHLONQJMryPRT6XWzaEW5TMqFl5JHHVtO0sdmHydjo33DDDRx00EEtyiOeccYZXH755Rnt6+STT+bkk0/OtAsUFRWRm5ub8XaZ0tRgHjAol+31MbbVRQjFNeK6jCJb4mQ7QgnqIxoSVlIVwtLSEVg6Nk5FYVSxnzyvk5pQjG821lETSliJWY3HShiWX14IKyPXEAITy8dvNo7+RWM8pyRBgd/FiKIsYlrbkgm7ijyy1TRtbPY9Mjb6n3zyCZ999hlOpzOtfciQIWzbtq3LOtYekyZNIh6Ps//++zN79myOOOKINteNx+PE4/HU5+Q8xK5ozWAOzPOyPd/DNxtqiWkmuhB4HQp1SYPZKKEM4HWpZLlUhLA09ieV5bKlLsKna3cQ1008ToVQXCeZJWEKy40jSaCZVoMigcshE9dM4rpV8DzLpTKkn5fxpTnkeZ2sqQq1KZnQXuSRraZpY7NvkrFP3zRNDKNltMjWrVvx+7vXTdC/f38ee+wxXnzxRV588UXKyso45phjWLRoUZvbzJkzh5ycnNSrrKysQ8dqzWBKksSAHA8Dcr04VQndEKzfEaYhkkgLqfS7VbLdDvrnehjSz0ckYVDREOW7zfXEdZNst4pLlVsNs1flRuMPICDH4yDH62g09h6mjSni8OEFOBSZNVWhdiUTdkYetX5v9zgV4rrReTVNVYWrr7Zeqi3OZmPTF8j4l3riiScyd+5c/vrXvwKWIQyFQtx+++3MmDGjyzvYlNGjR6clhR1++OGsW7eOBx54gH/961+tbnPbbbelFX4JBAIdMvxthWpKksTwIh+VwRhRzcqsFVgjdLPRXRPTTWRZYliBz0rAkmDxlnpqQnEcioxuCqtYSrNjmkDC2JmgJckwbkAOA3M9rKsJgYCGSIJNtWEcssL+pdmcddDANn3yrUUeNSWaMHAqMoGoxsqKQOZRPS4XPPJIx9a1sbHpFXQqTv+kk05iv/32IxaLcf7557NmzRoKCgp45plnuqOP7XLIIYewcOHCNpe7XC5cLlfG+23PYOZ5nbhUGaci43Mp1IY1qyKWJOFSZRyKbNW53WqFdTZErXKGuglo5s6RfBtIWDcAYVo3khyvk8IsF0u2NGCYJuGE0VgPN4FuCM4/bFCrhn9XapprqqwbyTNfbk7V4M2oRq6NjU2fI2OjX1ZWxpIlS3j22WdZsmQJoVCISy+9lAsuuACPZ89L8X733Xf079+/y/fbnsEMRDXqIxoji/2U5Xn4Yv0OGmIajkYZgnBcoyGqoTQmUamyhCJB0omyK7EKS0rNorwhxoDcOEu2WtE2eR4H/XPcJAxBfVTjrWUVVAZj/OyEUS0MdXtqmmuqQlQ0xCjJdpPnc6b0hDKK6hECamqs9wUF1uOOjY1NryYjo69pGmPGjOGNN97gggsu4IILLtitg4dCIdauXZv6vGHDBr777jvy8/MZNGgQt912G9u2beOf//wnAHPnzmXo0KGMGzeOWCzG3//+d95//33efffd3epHa7RnMNdWh1AVayJ0fU24MXpGENNMYppBMpfKaJycNZKRNx0kNT8gQTBqZf7WBOMokoTf46A2rBHVDAzTqrD1xfpanv5iE7/50bgWrpnWiqo4FWs+oSTbzQGDdqNGbiQCyXDZUAjT47UTwGxsejkZGX2Hw0EsFuuyg3/zzTcce+yxqc9J3/usWbOYN28e5eXlbE4KegGJRIKf//znbNu2Da/Xy4QJE3jvvffS9tGVtFWFar8B2bhUmYqGGNGEQUmOm+pgnPpoBKOZcZflnRE9HUU0ebMjkqAhpqObAp9TtbKADYEqW4ldkmQVUX93eSXHji3mqJGFrZ5H06IqgajGM19uJs/n7LKonnVVQd7eUN6q9LTtKrKx6T1kLK38pz/9idWrV/P3v/8dtQ9GbHRGWrm5hEH/bDd3v7OSN74vx+9WGrNuLeVMICWvkAzD7Iz2aFJjJ7kfSQKHLFl6O00kmGXJMtRuVeaMA0u5/dT9Wx1dNz2HioYYz369heGFWVZeQTN002RjTZjrjh+ZVoqxBeEwZGUBMPvpL6g01Falp+0EMBub7qVbpZW//vprFixYwLvvvsv48ePxNVNW3BvLJbYmP3zQ0Hxe/76c7Q1xEAKjUVZaaqyAlRyud9TgJ9UzgdRcQPIF1kLdFKlYfmi6XKAbBvOXV3Lc2GKmjkrPUE4WS1lbFaQumiChmVQF47hViUH9slr0pTM1cuvCGiN3x1VkY2OzR8jY6Ofm5nLmmWd2R1/6FKOL/XgdCpG4AY0h91ITfZzmbp62aBqeaZiNTwZNtlXZOQEM1k1EoaXWvglUBuL88b8r0A3B8WOLgZ1ZxZt3RIgkdEJxnbhmsCOcoDoY5+hRgmGFO0fhna2RW5LjshPAbGz6ABkb/aeeeqo7+tHnkLCyZXM9Cl6Xg231UeK6gaZn5r+HpN/fet/0ycASVpMRupl2E2nthpK80VQ0RPnLgjWU5XsYUejnnaWVbN5hKXvGNIMst4NsjwO3U2HTjggfr65BlWUG5Hkyr5HbBK9TbfWpxuNUqAzEWk0As5U/bWz2PB02+qZpcs899/Daa6+RSCQ4/vjjuf3223skTLM3ENEMCrJcSBLENBOnKhFJCJAyi9QR7DT4LZYJ0A0r4WtXcwPJQ2qGYHt9lBe/3cb5hw5ibVWQSEInphnkN5m4zfE4GZQv2F4fZ+n2BuK6gduhMr40p1M1ciMJHXcrA/m2XEXt1Ry2/f82Nt1Hh43+H//4R2bPns20adPweDw8+OCDVFVV8eSTT3Zn/3otPqdKQZaLgiwn66vDbKuLYpitKit0iKY+/SSGsLT2TdF+MlcSGdB0k7Cks2RLPVOG96MumiAU18lyO1q4X7I9TmKaSaHfzTkHD2J4YVZmo21VRVw4i5UVAbaHdIbmiBYJYK25imzlTxubnqPD2jv//Oc/+d///V/eeecdXnnlFV5//XX+/e9/75V1cTtCMnmrOpQgnNCRZfC7FDwOuUMGuqMkffcduZnIUlIOAqqCUUwhUCSZuG7iUFr2SjNMXA4FlypTkuOmLN+bmXvF5UL6xzwc//oH2blZrKkKEYxp6KZJMKa1qg3UXMjO73agyBJ+t4ORRVnUhhO8u6wSM8MwVxsbm47RYaO/efPmNG2dadOmIUkS27dv75aO9XZkWeKE/YoJRDVqwwkcsozXqSBLEp1xS3eFidMFjUVYBNXBBHXhBMMLs0joJlozH5IQglBMx+9WyfU4MorUaU4yn2H/ATnURzQ21oSpj2iML81pMWrPRPnTxsam6+nwL13Xddxud1qbw+FA07Qu71RfweNUKPS7MEzB5toI4YSBZpiNJRBbRtjsCWSsCCIhYMGKKs44sJQv1tewtT5Kvs+BW1WQJAjHDdwOGa9TZWSxP6NInRRCWFm5wIjCrLQEsLYmZtsSskvS3sSvjY3N7tNhoy+E4KKLLkoTL4vFYlx11VVpsfp7Y5x+c5JRJ0u3N6CbJkcOz+fjxqLnphAkNJHRZG6XIgGSREGWk3BC54t1OxhemMXm2iibd0RRZHA7VIpzXOT5XAzK92YcqZMiEkklZxEKIft8uwzL7IjyZ6Y5AjY2Nh2nw7+sWbNmtWj7f//v/3VpZ/oCTaNOaiMJVpYHWV0RRJZkErpJVDN7ZISfxBSQMEw8LpV8r5P3V1YxqJ+XY0cXsmFHmB3BOFHdJBI3mDI0i/MObV2hs7vYlfJnZ3IEbGxsOk6Hjb4dn98y6sSlyizd1kAwpqXkGTbXRoh3NDOrmzBMqG4UVotqBqW5Hgr91kRtMKYT1w221UfJ97kYVtAyI7c7aU/IrrM5AjY2Nh3HfobuIM2jTgBWlAfxOhUQgqhmEozreJ0K8WjP+6MbIhoJI0RhlguXqgDWRGm2xwE4cDsU1lX3TKZsW0J2nc0RsLGx6Ti20e8gzaNOAlGNukiCfJ+LXI+D6mCcYFy3iqnQMc2dRvd7l7uDZKxInnDcYFihA7+75b+5oxOm3ZU121z5087ItbHZM9hGv4M0jzpJGCa6YeJwq8iqSmmewrb6KNGETkLvWJaWRPdE+CRvOKYAr1NpERoJHZsw7e6s2daE7GxsbLoX2+h3kOZRJ07F0rPXDIFLlQjFNWKNgmYddenvibS2+qiGEB3LlG1Kh7JmfR1O8+gQthaPjU33Yxv9DtI86sTvVsnzOqkOxtAdMtvrY41F0FsKKnRWU7+zJHsggB3BOAvX1jC80Eeh30VMM3c5Ydp8/qJNueRDS5HPOsvaSFF2q8+2Fo+NzZ6hU0O1f/3rXxxxxBEMGDCATZs2AVYpw1dffbVLO9ebSEad5PucrKkKEYrrDCnwosgSG3dEMIWgIMuJqkgpGQYFOpWdu7skbzkSlhtn044IX6yvZcGKKjbXRlrNlG1Kh7NmYwKef956NUvcy4TkU8XS7Q3keh0MK8gi1+tg6fYGnvp0I2urgp3et42NTToZG/1HH32Um266iRkzZlBfX49hGIClsz937tyu7l+vorncQCCqUeB34XFYo37DBFWScCX1dxqH3D2lTiSw1EBlBMOLfBRkufA5VaaN3Tl6Nk3BltoIKysCbKmNYJqiyfxF6w+CHqdCXDc6lTXb/Hi6btpaPDY2e5CM3TsPPfQQf/vb35g5cyZ33XVXqv2ggw7i5ptv7tLO9UaaR51UNMT4z1ebKPJ70E2TFeVBqoMxasON5RNbk8/sIjriNtJNqAlrrKkIMSjfS20kwXsrKhlRlMX6mlCrLpUJZTkZZc121BffmgunIMvJ+powg/K9dhEWG5s9QMZGf8OGDRxwwAEt2l0uF+FwuEs61dtpGnXic6p4HCoxzcChygzp50UzTAxTUB2KY3TjML+juxZATDeoCsVRZYlFm+v4bF0Nby2tSJuoDcd1vtq4g6Xb65GQWFURZWSRn2zPTlnmtElghwmShAw8Mu8zAqqzTV98WxPDy8sDbK6NUOR342/FQ2Rr8djYdC0ZG/2hQ4fy3XffMXjw4LT2t99+m7Fjx3ZZx/oKUU2nJpRgXXUIj1NGlWVkScLjVGglUrLHiCYMBuaphGIam2sjzF+ePlFbG46zrirM9oYINaEEYD1JrK60nhBGlfjxOJS0SeANNSGGN+4/x6tSkJ3Vqi5+exPDIwqzWF8dZlVlgIKsghajfVuLx8ama8n4l3TTTTdxzTXXEIvFEELw1Vdf8cwzzzBnzhz+/ve/d0cfexVNXRk1wThv/lAOgN+tEokbBPQE0YRhlU5s9O54HDKKBMHErsfmXodMTDO7fB4gppuE4zouh0JdOMGGmjAjmhj8xZvr2RGOE0lYczSyJOFzymgGbKmLUh2KM6rYz4GD8jhxXDHDCrL429cbUkbf51Kpi+skDJNiv4uKQDxVEL29ieFsj4P+OW7K62MEoho5Xmdqma3FY2PT9WRs9C+77DI8Hg+/+c1viEQinH/++QwYMIAHH3yQn/zkJxnt6+OPP+aee+7h22+/pby8nJdffpmZM2e2u82HH37ITTfdxLJlyygrK+M3v/kNF110Uaan0Sma+qSjmsGmHWEiCYNxA7Jxqh6WbgsQ0wxkSaRKICqNU+UdLZ0b1cxumQIwBdRHEmR7nDgdMqYw8TpVhBD8sK2B7fVRopqB3phRLEkSxdkuTAHZbhWvS2VYoY8rjhqGqspsqY2woWanO2/xpnrKDQXdNFFlGZ9TYdFmM3WDbEtOWZIkRpf4qQ7FWVsdYlSxv0u1eOzYfxubdDr1zHzBBRdwwQUXEIlECIVCFBUVderg4XCYiRMncskll/DjH/94l+tv2LCBU045hauuuop///vfLFiwgMsuu4z+/fszffr0TvWhozT3SRshK949oZt8tKqq0ZBYksZIEhEtgmlaI+aEYbZZB7c53RmjEorrZHucDMz14nOpRBI6dZEEG2vCGKYlB+1UZEwh0E1BbThBod9FVDMZUeShJpigPBCjLN+bMuRJttZHULP95Hoc6KagPpKgOhRnRXmAsf2z250YdjsURhX7GVaQRU0o3mVaPHbsv41NSzI2+scddxwvvfQSubm5eL1evF5rQjMQCDBz5kzef//9Du/r5JNP5uSTT+7w+o899hhDhw7lvvvuA2Ds2LEsXLiQBx54oFuNfnOfdF0kwbLyAAndKogeTpjomokMxHXLB53U09cN0WMhm80xhcCpSJTle3CqMuuqQ9SFEmiGwONU0AwdqyovuFUZwxQEYzoeh4IiS0QSempC1edUiTcx+qG4QSIQx+OQyfe5yHJbekTfbqrjuNFFu5RTPnBQHlccNYzyxknb3R2V23V4bWxaJ2Oj/+GHH5JIJFq0x2IxPvnkky7pVFt8/vnnTJs2La1t+vTp3HjjjW1uE4/Hicfjqc+BQCDj4zb1SQOsqwqj6SaqIhHTTIQQIMChSugmBGN6t47YM0VprOSVaPTP10c1XA6FmGYQiulIkmV8BZZbSpXlxoloiUjCwO1QMEyRNqEaTRiU18dSx/A4JIQiEU4YxPUoHodC/1w3VYEY5YFYh+SUVVWmLN+bcsmsrgp2yvh3OKO4IMt29djsc3TY6H///fep98uXL6eioiL12TAM3n77bUpLS7u2d82oqKiguLg4ra24uJhAIEA0GsXjaekznjNnDnfcccduHbepTzoY06mNJMj1OKgLa+imQJUkDASmsEbTvcngAyktIAlwqxJ5XquqlmkKdNMShzNMgSxJCMDrlHE0unkSuonPqRCMagzq52VNVZC11UE+W1ODy+3g4xEHI0kSGlZ9YFWmUX9IMCEnJ5XENaYku0Nyyl3hksmkDq8d+2+zr9Fhoz9p0iQkSUKSJI477rgWyz0eDw899FCXdq4ruO2227jppptSnwOBAGVlZRnto6nYmuWfN3EqCrIsocgSRmMwfsLoXQa/efKWNdq3RtESAs2wRsWqLONUZbwOhbhuopkgdJOEZqBIEiawoTbCmqog//2+HN00iWkmhX4Xv7p0DoYpSBgCLa6jGdZEdEwz+H5rA1kuy80zpmTXcspd5ZKx6/Da2LRNh43+hg0bEEIwbNgwvvrqKwoLC1PLnE4nRUVFKLspurUrSkpKqKysTGurrKwkOzu71VE+WEljTev6doamYmvFfheqLBPXTSQJshwyDWbXh1juDskk4OZ9UmTQTYFuWK4pj0PBFAITgWkK8rxOorpBQ1QjoRmYQL7XgaabNEQ1XIqE36MSTRg0RHUqAjFciozXpZLtUagOxpEkGZdqlY7UDOtYb/1QQf8cNyOK/G3KKXelS8auw2tj0zYd/tYnk7FMs+fM25QpU3jzzTfT2ubPn8+UKVO69bhNS/xVBOL4nIqlrmkIwpqB0b1qCx3ro0TjU0fbE8emCYYkcKkyuilIGCZOVUaWIKoZmEJwxPB+BGOWQc/3OvG5VJZsqcchSxgCqoMJdMPEFAKt0W8kN0pKm0LgdShopkAzTPIdMiOLfWyrj/DO0gqGHdO2we5Kl4xdh9fGpm0yHur885//bHf5hRde2OF9hUIh1q5dm/q8YcMGvvvuO/Lz8xk0aBC33XYb27ZtSx3zqquu4uGHH+bWW2/lkksu4f333+e5557jv//9b6ankTFNS/wt3mKyrT5CVNNTMgs9afCdMiiKNbpGAkm03h8BjYbaRJElNMPE61DJ8zmoi2g0RDW21UfJ87qYOqqICQNz+NcXm4jpJhHNMvQuVcapKFY8fyTCl385Hwk47tbniDrcxHSDuGbiUGQEsKoihACqgxVMKMvlqJGFrfSspUtGCCtyKGGYOBW5UeStYy4Zuw6vjU3bZGz0b7jhhrTPmqYRiURwOp14vd6MjP4333zDsccem/qc9L3PmjWLefPmUV5ezubNm1PLhw4dyn//+19+9rOf8eCDDzJw4ED+/ve/d3uMfpKkT3pLXYQH5q9m4ZpqasLaHjl2ewggoZsYworUUWSrrbXcAIEVRmqYVmEVp2opgo4u9uN2KJxzcBn7D7BGwaurgoTjGqG4FY3kdcipUbPPpaLHwKtZkVFeh0Rujou6iNaY0OUgx+PEoUgkdJPKQIxnvtqccvM0xTQFgahGXDOoCsRwqjLrq8PURhJpyV55PmeHXTJ2HV4bm9bJ2OjX1dW1aFuzZg0//elPueWWWzLa1zHHHGOFO7bBvHnzWt1m8eLFGR2nK5FlCVmyNPOzPQ5qG41cTyLEzggdQ9AYgtn2+iaAAFkITGGiyCo+l4Iqy+zXPztNTE6WZDTDxO1IL7voUCQcrp1fn7guELo1KneqMiXZO900kgQ5HgfhuN7CL5+M1llbFWRLXZQftgVACNxOhXyfE4eiktANttZH0U1BVOv45Ktdh9fGpiVdUu9u5MiR3HXXXS2eAvZWgjGN9TVhKhpiCGFdRFXuoovZCZqXZ9TNlm2tYQINMZ2KhijfbKpjfU2I177bnipaUprrYWiBDwToupF2IxEivb7voUP7Ueh34XEq5PtcaaqcoZhOvywXwwp8Kb88pBdPyfM5mTwoD1MIGmIaoZiObphohkk4blCY5SLb7eC95VUZaesnJ47HlFg3M9vg2+zrdJmdUlWV7du3d9Xuei1rq4K8vGgb66pCKZ2clAnqBfYk0y5ouiAYNwnHdIIxjdeXbGfue2tYWxVEliVO2K+YHK8DXUC4MSRTMwThuJ5mfM+cXEq/LBf1UQ1TWP7/uG5QG07gcSoML/ThdampuP3m0Tp+twO3Q0mVoUwYlsxFLGFQlO3mgEF5jCzOSrtp2NjYZE7G7p3XXnst7bMQgvLych5++GGOOOKILutYbyQ5Mt1SG0aRJWTJGlG35T/vCTL1NAmseQBJlhrDOQ2WbKnn6S8385tT9uPw4QVMH1fCghVVhOIa0UYVTpdDYYh/Z/TLYcMKML0+NtaECccNogkDRZYpynYzvNBHvs9FMKalQiVbi9ZJGCaKJDEwz0M4Ye1j/9IcBuR6kCQJ3TTt+Hobm90kY6PfXAVTkiQKCws57rjjUpo4eyNNR6YD87ysqQxZxqcXZGMJMgsZTSZtyYDHaT3s6aYgHDcYkO2mIabz5fodbK2LMKifj/MPHURMsyKWXKqCU5XxOGTiDaGd+5QlDh9ewIzx/fl6Yy2luR5cqjVylySpRajk6qpgiwQqpyKjKjK6CVkuFd0wcTWZS7Dj621sdp+Mfz09GaffkzQdmdaFE0Q1A6ciWeqUPd25DEn+B52qNSkNjYlbhiBuCHK8DnaEElYZw34+RhT5ueTInZEwcd3KTRg3MJfo4UficSggy8iyxEn7l1DeEGvMqlUwhCAa11uESraWQJV07VQHY/hcCoos42zUprbj621sugZ7yNRBknHkHoebFQ0xJElCVRWyJJNwomVG7p5O1sr0WEnN/ORTAmmx/S1nBtqMhDnpkxbrdSRUsrUEKkmSGFGURTCmURGIMzDPg8epEIxpdny9jU0X0SGj31S7Zlfcf//9ne5MbyY5Mq0OxqkIxlAkiCSsCUtFtrJdm6LKYJgdr2PbnTS/ASVn72OaiUORUGXriUWVJVyqRENEI8fjZGiBr1NFSDoSKtlWApVDsQThFNn6u2lH2I6vt7HpQjpk9DsaF988fX5vIjkyXbCykpqgVWA8y6WQ0M20YiJJA9tbDD60fAowaZRbFlaGrmYIVEXC71IJxS05hinD8olrJo9+uC5VKcwUgv45bo4fW8wRwwvaNf5taew0pa2nginD+zFtvyI8DtWOr7ex6WIk0V521F5IIBAgJyeHhoYGsrOzM9p2dUWQG/6zmA01YbJcKk7ViniJxA2impEmYZyM7OmNqI36+k3761AkivwuvC6VUcV+zjiglPdXVlkhlw6Z7fUxqkNxwnGrqMqxo4u4YHw/hh8y3trJxo3g83WqP3ZJQxub3SMTu7ZbPv2tW7cCMHDgwN3ZTZ/B41QYkOumIZogENMxhIwsSXhdijXabzSiTQ1qb8QQlqFPvhyyVbJwWEEWhw7L59gxRfzj8438sK2BHJfKunACQyQnWh1UBxMsXFeDEQpyR03NbvenI08FNjY2XUPGyVmmafL73/+enJwcBg8ezODBg8nNzeXOO+/c6yN7wgkdl0Nh6qgiSnO9ZLlUCrOcGIbZwqffm0nKLqsKeJ0K+VlOPE6Fk8eXMLIkixuf+Y4XvtnKiu1BvthQx9rqMPWRBKYpUGSZXK8D3TBZWxVq9zimKdhSG2FlRYAttZGMMmltbGy6h4xH+r/+9a954oknuOuuu1LJWAsXLmT27NnEYjH++Mc/dnknewvJyVyPU+GQofl8v7WezTsiNET1XuO/7wjJeQfdAIRBgcNJQhe88f12lm4PEGw8n9QEsIC6iIZuCIpz3IRjOg0xDeSdSVKfra0mv9hIuWfW14TsouQ2Nr2QjI3+P/7xD/7+979z2mmnpdomTJhAaWkpV1999V5t9JuGGfbzOYkkDHTT7HNx+qLJX8OE+ohOSbaLrzfU0qjOjNxsPYRV+zeuR3AoVjZylnNn0Zw731hOYUk/CrJc5HodVAXjGKbo9UXJ7fkEm32NjI1+bW0tY8aMadE+ZswYamtru6RTvZVkmOG2+gifrKlhRziO6IPJWU2xZJkNakJxEu08riQ1hmK6SVwHpyJREU/sXC6soiw5bgcL19QQ1QymjipskniVeQWs7jbIXVGP18amr5Gx0Z84cSIPP/wwf/nLX9LaH374YSZOnNhlHeutDCvIYvLgPN5aWoFuij4/j2HVszWJah0/D6kxmyveRHDI5VCoj2gEExqSBLIksb4mTL7P2URiueMVsLrbIHdVPV4bm75Gxkb/7rvv5pRTTuG9995LlSn8/PPP2bJlS4tShnsbSUP06dpqogkDSRI0CdHvk6iAQXosv4Rl2Nuad3UoVllGTcCyAaMACCRMFNUkkjCI6QYuh0JlQ4xAVCPH60xt25Gi5B0xyMMKOq+T35X1eG1s+hoZG/2pU6eyevVqHnnkEVauXAnAj3/8Y66++moGDBjQ5R3sLTQ1RD6XiixZiU19e5zfdlnF9nxWsiQhqxIJycXFP30YSbaqY2XpJpt3hKkLa0ACU8CiLfVMHJhLvs8y/NGEgVORCUQ1VlYEWhjsjhjkp7/cTL7XyfqacKeeArqyHu+ewp57sOkqOhWnP2DAgL16wrY5zQ1RQyQBEqn6uH0ZE2vkbpoCU+zMIm7L5stY18OpyOhY6pwuRSKmGciSpZTpcypE4joOWaY+HOerDbWMLM6iwOdkbXUISZJ45svNxA2zhcHelUH2OGQ+WFnFoH5ehhdmdcot07web3M68jSyJ7HnHmy6kozj9N9++20WLlyY+vzII48wadIkzj///FZLKe4NtGaIVFnu0xO4YLlx3A5LyVKSdi3aJmHdFOKGIJgw0EwroicQ1RACnKpMvywnBVlOhCQ1FlIx2VYXYeGaav77w3ZWlAeJJHTyfE6GFWSR63WwdHsDT326kbVVwSYGueV4RAjB9voYUc2gNNeD3+1AkSX8bgcji7KoDSd4d1nlLvMBmip8tkZvknBuWl0s1+to9ZrZ2GRCxkb/lltuIRAIAPDDDz9w0003MWPGDDZs2JCRMFtfoqkhqg0nWLy1gbiu92mjL2GJwmm6mSYh0XR5EqecLtqWXObWYnzwyMW88+BFZJlxCrNcJAxrUjjP50SVJSIJA0kG3TCRJevrFtcMNMNs1WB7HUqbBjkY06kOxfG5VFyqkrasuVumPZKht+UNsRY1mpMSziOKsnpcwrm16mKducnZ2DQl46HMhg0b2G+//QB48cUXOfXUU/nTn/7EokWLmDFjRpd3sDeQHBlur4+wpirMjmCsT2XgtoYkWcZba7QXsmSJsCWNv4w1wZvUEEoWW0+OEmQJHMDAQBUAwhSEEwYORaYwy0VdNEFCk/F7VByyTDih41QVcj0OQnGdddVh8rzOlKRy0mALaCG5nCSuG4TjOkP6+fC7W351O+qWaUvhM5owepWEc1+ce7Dp/WQ80nc6nUQiEQDee+89TjzxRADy8/NTTwB7G6W5HoYV+Fi6PUA4boUkSn18Es0UpMXlmwL0Rk19RQa58ZshAYoiIWP5/uXGZYospV0Dr9PyM48t8VMfSbChOkxNKM62+hhb6qKEYjoJ3cShymS5rSemYGyncfY4FeK6JVw3ff9i8n1O1lSFCMY0dNMkGNPYVh/F41AZkNvSCEJmbpmkwuf+A3Koj2hsrAlTH9EYX5rT4XDN7paZaM/VBTuvWW+Ze7DpG2Q80j/yyCO56aabOOKII/jqq6949tlnAVi9evVeK7wmyxKTBuXy4qKtJHSDiGbiUhQSet/9sbWmsZ+0o7Ikke91EIzr6KYgy6mQMARuh+VSMU2BLgRuc6eLxeuQ2VYfIRBJUB1KoJsChyIhYUk3m0JCxHWCMR2/WyUc10k0mQlvarDL8r2tSi4fMqQfwwvilAcst0xTw9+Zylod0f1viz0xudpadbGm9Ka5B5u+Q8Yj/YcffhhVVXnhhRd49NFHKS0tBeCtt97ipJNO6lQnHnnkEYYMGYLb7ebQQw/lq6++anPdefPmpVwCyZfb7e7UcTOhwO9iYL6XLLeDuGZgmH07QL/5mNShSrgdVu1bISCimWS5VIQAhyKjyDKmEMQ0k3DCIJIw0Jr4uASwoTrM1vo4MV1gCEjogoQBqiLhUiR0w6QqECOu6a2WQmzqRx9R5OenxwznZyeM4rrjR/KzE0bx02OGc/5hg1p9ClhTFeqUWyap8DmmJJuyfG+HDf6emFzdnbkHW+zOpi0yHiIMGjSIN954o0X7Aw880KkOPPvss9x000089thjHHroocydO5fp06ezatUqioqKWt0mOzubVatWpT7vieItNcE4O0JxogkdJOuYqiTQ95LfUkIXGLJAliQkSRCJ68iSikuVG3MSTEIxA1mWkCTL1eOWdo4ZYroJKNbTgtip5KkASBJOh4Kkm8R1k821UQbmefE42i+F2JrkckfLMXYXezKxq7NzD3aIp017dOq50DAMXn75ZVasWAHA2LFjmTlzJqqa+e7uv/9+Lr/8ci6++GIAHnvsMf773//y5JNP8stf/rLVbSRJoqSkpDNd7xRrq4K8+UM5umlVlM31OAjGtF6tmd8eMm1U9RKgNY4IBRCKG3hdClFdEGuM8JEMgUuVcKsKRmznBTBMaxuHLKE0ll9MGFbsv2GYBGON8tMSSCjUhOK8t7KSQfleDhyUl5HB3h23zO6ypydXM73J2fISNrsiYyu9bNkyTj31VCorKxk9ejQAf/7znyksLOT1119n//337/C+EokE3377LbfddluqTZZlpk2bxueff97mdqFQiMGDB2OaJgceeCB/+tOfGDduXKvrxuNx4vF46nOmk83JkV1dROOQIfks2dpAeX2EqNb31DWTNDX4yUIqJlaUTtNzcsiQ5VQxhMChyLgaR/y6KSypBVlmXeEgZEkibpgoDgkhdoqzJffbVNbHpcgcNCSPQr+L8oYYPpfKtP2KMjZEPVV4pScSuzp6k7PlJWw6QsY+/csuu4z999+frVu3smjRIhYtWsSWLVuYMGECV1xxRUb7qqmpwTAMiouL09qLi4upqKhodZvRo0fz5JNP8uqrr/J///d/mKbJ4Ycfnqri1Zw5c+aQk5OTepWVlWXUx6Yju35ZLgqynIQTRpu6NH2BFv/0xt9/a35+3TQJxzQ0w0QgyPWoZLsdFPvdjBtewpW/+AfX/OqfRFU3Hofl90/oJobZukSFKUy21kVwO1QOHJRHQjd5b3lVn/E591RiV0fmHjJ5CrHZd8nY6H/33XfMmTOHvLy8VFteXh5//OMfO1xAfXeYMmUKF154IZMmTWLq1Km89NJLFBYW8vjjj7e6/m233UZDQ0PqtWXLloyO1zRszjStalGyJJHjVlEzvnq9gxaF0ttIzArGTGrDCRKGQDROzDZEdUJxjcpgjNVVIeqjGqG41pg0pCLLkuXSacOG6wZsro3w9UZLhruvGaLenNi1qxBPt0OmLhJn6fYGe3J3Hybj4cioUaOorKxs4U6pqqpixIgRGe2roKAARVGorKxMa6+srOywz97hcHDAAQewdu3aVpe7XC5cLldG/WpK05FdIKrRENXwOBUUSUKK982QTYF1t1cU0FoJQmp5U2gctTcac+teZ+J2yATjOlWBOLleJ7oJblUGIYi1MsNtTeqCYcCGmjCbayOU5nl6lc7NrujNiV3thXjWhhMs395AVTDOs19vYb630p7c3Ufp0Fg1EAikXnPmzOH666/nhRdeYOvWrWzdupUXXniBG2+8kT//+c8ZHdzpdDJ58mQWLFiQajNNkwULFqRkm3eFYRj88MMP9O/fP6Njd5SmI7tIQscwRWqyUpH76FCfRl/7LqJOBaCbtJp9rBsgwhFeeuQK3nj8p7gTMQzDJJowWoyAofHpodEOqqoly7CqIkgkrrfpDumtYYddkdjVHbT1FFIbTrB4cx1b66MUZbsY1z/b1u/Zh+nQSD83N7dFIsw555yTakt+wU499VQMI7P49ZtuuolZs2Zx0EEHccghhzB37lzC4XAqmufCCy+ktLSUOXPmAPD73/+eww47jBEjRlBfX88999zDpk2buOyyyzI6bkdpOrJbWxVEapRUVmTo2zWzOk5zm5/8HIxpDK3aBEBcN6kzEsSb+XWS3xqB9ZSgNIa7ypIgENNYXxPm0KH9WrhDenvYYU9GELVFa08hbofM8u0NVIfiFGY52a9/Nqoi41dke3J3H6VDRv+DDz7otg6ce+65VFdX87vf/Y6KigomTZrE22+/nZrc3bx5M3KTEXVdXR2XX345FRUV5OXlMXnyZD777LOUHlB3kBzZvfV9OeurIzREE8hS6yPgfQUJMJqMvOOagd/vASy5haaD8qR+D1jzB6YhkGVLjtnnUlu4Q/pK2GFPRRC1R/MQz7pInKpgnIF5Hvbrn02+b6er09bv2TeRRGvP4p1k6dKlGYVs9gSBQICcnBwaGhrIzs7OaFvTFDz8wRr++vF6ErqJhCDetxNzO0WyspY3EWPp/WcBcPAvX6ZfcR7lDXESukG8meFviiyB36VSmufhVzPGcuTIwtQy0xQ8+uE6lm5vSAs7BOuJck1ViPGlOVw1dThArxpp9yaSRVeWbm/g2a+3MK5xhN8c3TTZWBPmuuNHMqYks9+DTdeyO4VyMrFrux1XFgwGeeaZZ/j73//Ot99+m7F7p6+hSDKjiv1srY1SE47veoO9EIFluJsOF9wOGVWWkCUs+QZ0Ys3q7ib1flyqzKB8Lyft35/DhxekrdPRsMNP19Xw/ZaGXuv+6WmaPoXM91YS1Qz8rRh9W7+nd7An3Zmdnon8+OOPmTVrFv379+fee+/luOOO44svvujKvvU6ttVHWbylDgmQJdEyzGUfwjDTff1xzaQmlEAIS8pBlSTUJjZbARyKpc3vdijIssQJ44pajGQ6oixZE4rzzFeb7cIiHaA3h5jaWOzpQjkZ3d4rKiqYN28eTzzxBIFAgHPOOYd4PM4rr7zSrT713sKK8gCrK4KocmPooiz1moiSnkaWJeKapa1juXYEDlVGbtRecKkyuinwuVQG53vJ8znxOFp+/XalLBmJ69SEEkgSTByY22eyTnuqxm1vDjG16Zks6g4b/VNPPZWPP/6YU045hblz53LSSSehKAqPPfZYl3Skt2Oagm821jUaLge1EW2fNPhNJZllCbZmW6J4uhBohmHJKDdm48pY8sp5PidOxRJdG1+aQ/8cD5t2hFuNzU+OTFsroiKEYH1NGAnBsIKsPaJ90xX0dCRST4vU2bRNTxTK6bDRf+utt7j++uv56U9/ysiRI7vk4H2JbfVRqoMx+ue4qQzErHj0nu7UHkaRLH9gstpW2OHmyJ8+aWnxA2gmigSiidKmIayCKwPzfQwv9JHvcxGMaW36kXc1MvU1yj37XG27f3pTsldviUTqjSGmNj2j5dRhn/7ChQsJBoNMnjyZQw89lIcffpiampou60hvJ5zQiRsmo0uycaoKmrGvROnvxBA7DX5TTBp9/I3VuAxhPQW4HTL9fA6Ks91MHpRHvs/Vrh85mYylm4KT9y9hXP/sFslP5x8yiIIsV58oat7batx2pnaATffSE1pOHd7TYYcdxmGHHcbcuXN59tlnefLJJ7npppswTZP58+dTVlaG37/3PiYm/zluh0xZnocNNeGe7lKvw5JWtm4AMlbR9VDcYFt9lK31EXI9DioC8Vb9yK25QIYV+jjjwFIK/a7UyBRgyZaGNt0/mVbP6k7sGrc2u2JX7szu+D5nHL3j8/m45JJLWLhwIT/88AM///nPueuuuygqKuK0007rso71NpL/nDWVIbbVRXq6O70Clxbn1X/8jFf/8TNcmhW+qplWDL8sWyP/SMKgOhjno1XVfLmhlv457pRLIzmyf3d5BQ8tWMsP29KjF5ZtD/D20grUxhGqLEsp909XVs/qLvb2Gre9VSajL9ET3+fdemYYPXo0d999N3PmzOH111/nySef7Kp+9TqSIYavLdnG1vrIPufaaQ1ZCCZWrEm9B8u3r8oSuikQEkjCKrTez+ekIMtFrFHwJzmyX1sVZFl5gFBMZ3C+l0K/M+UCaSt6oa9MTPbFGrcdjTLq6cnpvYk9/X3ukm+boijMnDmTmTNndsXuei2VgTh1kQSG2bysuE0SS5MIkEARYAAO2Sq5OKzAR2UwzjNfbiaqmdRFEmS5FCQg1+ugOhQnlNCZVJZLvs/VrgukL0xM9sSj++7QUUPeWyan9yb25Pe59wwxejmmKXjx260EovpOFTGbFggBhhBI0s7qWYpsVdbSTEFJtovP19dS6HcycWAuO8IJDFOQ7XGQ5bIUIddVh8nzOpEkqd3ohd6ofdOUvhQj31FDblfn6j721Pe572oD72G21EVYuq0BQ1jFAFX7+9wqptj5AiuCR5asooxOxUrQaogmyG806k5FRlVktEZFNqcqU14fZXtDFCHEbrlAeoPPubfKMDclkygjuzpX38ce6XeQDTVhAjHNikMXEpICeiuFQvZ1RJM3sgwORcYQgnyfE79bZdOOMHpjnd1AVCPLpZDndbKtPoJhCKK6QVwz+XZjHduyo6iqzJRhLaWXd0Vv8jn3dldUJoa8J+LKbboW2+hngBDWJKUprCIgNjtpaipkqfGzgHDCIMfjYL/+fnaE4ny7qY5w3GDx5jqyXCp5Phduh0QophPXTZyqjEuVUBWJ9TvCZLsdjC7xZ2Qge6PPuTe7ojIx5H1xctomHfs/00GGFfjI9TgJJ/RGvywk9mVBfawM3VpPtjVpK1vG3qFIOGQpJa2sSNAvy0F5Q4xvNtZZBWgUqIsk0E1BWDNIaCYuVcbjkGmI6bhUGUWSGFbgQ5VlVlUEOXZ0S3G21rB9zpmTiSHv7snpntIo2pewjX4HGZjn5ciR/XhxsSXB4HUqaKa5y5KDeysSkHC7OeTGpxFiZ6hmkd+NIktohokQAkWSqAsnqKiPYyIoy3fjUBTKG6IEYzqqbFUiy3KreBwKpbleRhZnUZjlwu9WCcX1jBKY7ISozMnEkHfn5HRvcsntzdhGv4PIssQFhw1mfU2ExVvqiGhGyzqC+xiJJjc8WYIct8pxowvRBThkCSEEO8IJvt5Yi6rAoHwfLocCQGmul9pwnNpwgkRjbd2BeV7GDcgh3+dM7TdTH/Hu+pz3xZFmpoa8O+LKe6NLbm/FNvoZMKLIz29/NJZH3l/L+6uqWhQJ2ZdIhmM6FEt2QQKimkl5Q4yibBerK8PURhIEonHqIhoORSaq6ThVORWKOcDhwe1Q2F4fxedSGVPiTzP4kLmPeHd8zvvySDNTQ96Vk9O2S27PYhv9TpAwTByK5YPWdJPEPmj7JSwZhn8+MxsBXHz2bOK4+HxDLR6HjFtV8DhlQnET0xTETIMNNWHyfDrFfhcep+VGyPU6qAnFMUyBs1llp874iDvrc7ZHmpkb8q6anLZdcnsW2+hngGkK3v6hgqXbAiR0y/C7VJmGqJ4q/L2v4FIlvELikM0/AOCUBAkJ4gkd3ZCJyibVIRNJAo9TxhBWda3aUBxNNynN8+B1qmiGiVNV6OdzUhGIIcvSbvmIO+NztkeaO+mJKCM7DHTPYhv9DNhWH+X7rfUEYxoS4FRkQgkdWZIak7b2DWSs5CutadiqJOFQZOKaiSFMYsIy+D6njGlKaIZVf0A3oSGqoRkmA3I91Ec1BuS4ufa4kayqCHaJjzhTV4U90uxZ7DDQPYt9FTMgnNCpjVoTj16HQlQzMU1wOWSEZqDv5W4eGWvu2sSSWmgqPySQrNh8ycqq1U2BjKXLFNOtJyGpcX0JCMctd8+AXA/nHTqI48YUcdyYoi6bRM3EVWGPNHuWvqZR1NfpFTIMjzzyCEOGDMHtdnPooYfy1Vdftbv+888/z5gxY3C73YwfP54333xzj/TT51RxykpKX0Y3TZTGOrl7u8G3Jm2bGMzGYilJPI6dUgpuRWrMXBZouoEiSchYFbVMGmUasNwqCc3gi3U7ePTDdayvCXVpkY+OFg3piUIWNjvpS3LZewM9bvSfffZZbrrpJm6//XYWLVrExIkTmT59OlVVVa2u/9lnn3Heeedx6aWXsnjx4pS659KlS7u9r6W5HsYNyEaWsJKPTIEQgvhenp3rkK2XiSD5u3M6ZFRl5zqRhAEIVFlCM62RP5LlzpEkAZKEEOBUwKFKqLKEyyETTuhENYOl2xt46tONrK0K7vHzS440yxtiiGZuuvYqfdl0HX1Bo2hvQRLNv+V7mEMPPZSDDz6Yhx9+GADTNCkrK+O6667jl7/8ZYv1zz33XMLhMG+88Uaq7bDDDmPSpEkdKtIeCATIycmhoaGB7OzsjPu7ujLAdU8vZtOOCIZpNo76916hZYdsqWQaZqMDRwKXKluTsIEgi+7+MQCTbn6RiNOF26EiS4Jw3ARJWIXUG6+RKSwBNsMUuFQZv0slENMZUZTF0SMLWFsdZnxpDldNHb7HR3XNo3eaT/7ahmfPsC/mSXQFmdi1Hh3pJxIJvv32W6ZNm5Zqk2WZadOm8fnnn7e6zeeff562PsD06dPbXD8ejxMIBNJeu8Oo4mxuPWkMg/t5AYm9VXPNIYNblcjxOPC6VFyqgiJb/npFlinJcVOW5yXqcBF1uPC4LLeXEAKvUyXbreJUFHTTGu3Ljfs0DBNFBq9TwcTaXyiuE4obParQaI80ewd2Hd/up0edlDU1NRiGQXFxcVp7cXExK1eubHWbioqKVtevqKhodf05c+Zwxx13dE2HGzl+bDFleV7ueWclH62uJjnOT+wlkgxKoxa+Q5FxKBIxXaDIEqosI0kykgxORULOy+bc+96jNpwgltAwozqGEAzK9zKiyE8opvHJ2hrqIxoCS6YBJPwuBVWWiWqWnIWElfuQ63X06IRpb1fDtLHpCnrcp9/d3HbbbTQ0NKReW7Zs6ZL9jirx85sf7cfBQ/IZUeinyO/B6+i7lzNNJRPLpSNMQShuYJgCv9uBQ1XI8Tno53NRF9GI6wYuh0y/LCc+p0q/LBcTB+aS63XiVGVK871MHpSHQ5VxqjJF2W68TgWBRFQzcCgSfreKqsg4FblXTJjaI02bvZ0eHekXFBSgKAqVlZVp7ZWVlZSUlLS6TUlJSUbru1wuXC5X13S4GWV5Xg4fXsAP2+opyXZTE06wfHsDG3dE6IF6Ha2iYJUslGicjG30rTtVCQEYpsAUO42+x6HgdSropkAzTGRJIs/nZHihj0SjL2twPy/rqsPURRKE4jqqJOF1qUwfUcC5Bw9i/vKd8fE+l8q4/tnUhBK4VImwBHHNINvjIN/nJJowKMp2keVSUj59e8LUxqb76FGj73Q6mTx5MgsWLEjV1zVNkwULFnDttde2us2UKVNYsGABN954Y6pt/vz5TJkyZQ/0OJ2m2Z+VwTj9c9wM2r+E7zbXs3hLPXrjhGU0YfRcxq4EHlVmQK6bXK+TUEynLpIgkjAQQuBWFdxOGVWWGVbg4/KjhzEo38vGHRGEEHicCtkeB36Xg6im84/PNrEjnGBMSRbEYpw152dohslLv5rLeYcOYkSRnxFF6S6SaMJg3mcb2VYfoSzfy5baKHHdsIqouFVKsl2srQ7boXk2NnuAHg88vummm5g1axYHHXQQhxxyCHPnziUcDnPxxRcDcOGFF1JaWsqcOXMAuOGGG5g6dSr33Xcfp5xyCv/5z3/45ptv+Otf/9oj/W+e/RnXDUpyPJxX4icY16kNWzHHgWiCHSENpdFhviOcQDMMHIqCKlsT2DKCqGYS1010Q7Qp4ikBfreK16UQiRvENQNTCCRJItvt4LSJ/Rldks17yy1pgSy3Qr7PxchiP9PGFlMZjPHe8go21EQwhcDnVJkwMIfp+5ekJiyHFGS1euym5ypCMcZ9txAA/6FlDG/ctrVU/kuO3LmdliOoCSWQEPTzuQBptxQabWxsOk6Ph2wCPPzww9xzzz1UVFQwadIk/vKXv3DooYcCcMwxxzBkyBDmzZuXWv/555/nN7/5DRs3bmTkyJHcfffdzJgxo0PH2t2QzbZoLdQMSGvrn+2mvHGi0q3IVARj1EU0+vmcTCrNpTIUJxjTCMV13E6ZTTURasMJInEdAbidMjIShT43hTkuDizLwzQF766soKIhTkmOixPHlOB0Km32KTmK3p3QuOS2kfoGRg8fYDWGQuDzdfgaeRzWBG5EM+wJUxub3SQTu9YrjP6epLuM/j5JOAxZjU8EHTD6NjY23UOfidO3sbGxsdmz2EbfxsbGZh/CNvo2NjY2+xA9Hr2zp0lOYeyuHIMNlk8/SSAAxl6Skmxj08dI2rOOTNHuc0Y/GLRUHMvKynq4J3sZAwb0dA9sbPZ5gsEgOTk57a6zz0XvmKbJ9u3bEUIwaNAgtmzZsk9H8QQCAcrKyuzrYF8HwL4OSfradRBCEAwGGTBgALLcvtd+nxvpy7LMwIEDU49D2dnZfeKf2t3Y18HCvg4W9nWw6EvXYVcj/CT2RK6NjY3NPoRt9G1sbGz2IfZZo+9yubj99tu7TYGzr2BfBwv7OljY18Fib74O+9xEro2Njc2+zD470rexsbHZF7GNvo2Njc0+hG30bWxsbPYhbKNvY2Njsw+xVxn9Rx55hCFDhuB2uzn00EP56quv2l3/+eefZ8yYMbjdbsaPH8+bb76ZtlwIwe9+9zv69++Px+Nh2rRprFmzpjtPoUvo6utw0UUXIUlS2uukk07qzlPoEjK5DsuWLePMM89kyJAhSJLE3Llzd3ufvYWuvg6zZ89u8X0YM2ZMN55B15DJdfjb3/7GUUcdRV5eHnl5eUybNq3F+n3VPiD2Ev7zn/8Ip9MpnnzySbFs2TJx+eWXi9zcXFFZWdnq+p9++qlQFEXcfffdYvny5eI3v/mNcDgc4ocffkitc9ddd4mcnBzxyiuviCVLlojTTjtNDB06VESj0T11WhnTHddh1qxZ4qSTThLl5eWpV21t7Z46pU6R6XX46quvxM033yyeeeYZUVJSIh544IHd3mdvoDuuw+233y7GjRuX9n2orq7u5jPZPTK9Dueff7545JFHxOLFi8WKFSvERRddJHJycsTWrVtT6/RF+yCEEHuN0T/kkEPENf+/vTsPirr+/wD+lJVdF4ElDmFxFBDl0ETxgMCDChHEA68Ew/uolJxsJNApRb+aFzo62TFaKGmOaHk1UYSQqENGDIpSErq4ox2CikciobD7/P3hj8/0ycWTZVXejxkG9n28Pu/Pi53X7ux+5vNOSJAeGwwGuru7c8WKFSbHjxs3jkOHDpW1BQcH8/XXXydJGo1Gurm5MTU1Veq/du0aVSoVd+zYYYYzaBpNnQfyTtGPiYkxy3rN5WHz8G8eHh4mi93jxLQUc+QhJSWFPXr0aMJVmt/j/u/q6+tpZ2fHzz//nOTTWx9I8pn4eOf27dsoKirCoEGDpDYrKysMGjQIR48eNTnn6NGjsvEAEBkZKY3X6/WoqKiQjdFoNAgODm40pqWZIw8N8vLy0K5dO/j6+mLWrFmoqqpq+hNoIo+SB0vENDdzrvnMmTNwd3dHp06dEB8fj/Pnzz/ucs2mKfJQU1ODuro6ODo6Ang660ODZ6LoX758GQaDAa6urrJ2V1dXVFRUmJxTUVFxz/ENvx8mpqWZIw8AEBUVha1btyI3NxerVq3CoUOHMGTIEBie0PvnP0oeLBHT3My15uDgYKSnpyMrKwuffPIJ9Ho9BgwYIN22/EnTFHlITk6Gu7u7VOSfxvrQoMXdZVN4eHFxcdLf3bt3R0BAALy9vZGXl4fw8HALrkywhCFDhkh/BwQEIDg4GB4eHti1axemT59uwZWZx8qVK5GRkYG8vDy0adPG0st5bM/EO31nZ2coFApUVlbK2isrK+Hm5mZyjpub2z3HN/x+mJiWZo48mNKpUyc4OztDp9M9/qLN4FHyYImY5tZca3ZwcICPj88z+XxYs2YNVq5ciezsbAQEBEjtT2N9aPBMFH2lUonevXsjNzdXajMajcjNzUVISIjJOSEhIbLxAHDgwAFpvJeXF9zc3GRj/v77bxQUFDQa09LMkQdT/vjjD1RVVUGr1TbNwpvYo+TBEjHNrbnWXF1djfLy8mfu+bB69WosXboUWVlZ6NOnj6zvaawPEkt/k9xUMjIyqFKpmJ6ezlOnTvG1116jg4MDKyoqSJITJ07k/PnzpfH5+fls3bo116xZw9LSUqakpJi8ZNPBwYH79+/nyZMnGRMT88RfktXUebhx4wYTExN59OhR6vV65uTksFevXuzSpQtra2stco4P4mHzcOvWLR4/fpzHjx+nVqtlYmIijx8/zjNnzjxwzCeROfIwb9485uXlUa/XMz8/n4MGDaKzszMvXrzY7Of3oB42DytXrqRSqeRXX30luzT1xo0bsjFPW30gn6FLNklyw4YN7NixI5VKJYOCgvjTTz9JfWFhYZw8ebJs/K5du+jj40OlUslu3boxMzNT1m80Grlw4UK6urpSpVIxPDycZWVlzXEqj6Up81BTU8PBgwfTxcWF1tbW9PDw4MyZM5/oQtfgYfKg1+sJ4K6fsLCwB475pGrqPMTGxlKr1VKpVLJ9+/aMjY2lTqdrxjN6NA+TBw8PD5N5SElJkcY8rfVB3FpZEAShBXkmPtMXBEEQHowo+oIgCC2IKPqCIAgtiCj6giAILYgo+oIgCC2IKPqCIAgtiCj6giAILYgo+oIgCC2IKPqCIAgtiCj6gtlMmTIFI0eOtPQynkg7duyAQqFAQkLCXX15eXnS3rNWVlbQaDQIDAxEUlISLly4cN/Ye/fuxQsvvACNRgM7Ozt069YNc+fONcNZCE8jUfSFFqWurs7SSwAApKWlISkpCTt27EBtba3JMWVlZfjrr79QWFiI5ORk5OTk4Pnnn0dJSUmjcXNzcxEbG4sxY8bg559/RlFREd5//32znrfBYIDRaDRbfKGJWfrmP8Kz635765aUlDAqKopt27Zlu3btOGHCBGmD7Y0bN1Kr1dJgMMjmjBgxglOnTpUe79u3j4GBgVSpVPTy8uLixYtZV1cn9QPgxx9/zOHDh9PGxoYpKSmsr6/ntGnT6OnpyTZt2tDHx4fr16+XHaeuro5z5syhRqOho6Mjk5KSOGnSJNn5GAwGLl++XIoTEBDAL7/88r55OXv2LNVqNa9du8bg4GBu375d1n/w4EEC4NWrV2XtNTU19PX1Zb9+/RqN/dZbb/HFF1+87xq+/vpr9unThyqVik5OThw5cqTUd+XKFU6cOJEODg5Uq9WMiori6dOnpf4tW7ZQo9Fw//799Pf3p0KhoF6vZ21tLefNm0d3d3fa2NgwKCiIBw8evO9ahOYlir5gNvcq+levXqWLiwsXLFjA0tJSHjt2jBEREXzppZdI3ik8SqWSOTk50pyqqipZ2+HDh2lvb8/09HSWl5czOzubnp6eXLx4sTQHANu1a8fNmzezvLyc586d4+3bt7lo0SIWFhby7Nmz/OKLL2hjY8OdO3dK85YtW0ZHR0fu2bOHpaWlfOONN2hvby87n2XLltHPz49ZWVksLy/nli1bqFKpmJeXd8+8LFy4kGPHjiV5586PL7/8sqy/saJPkuvWrSMAVlZWmoy9YsUKuri4yG4R/l/ffPMNFQoFFy1axFOnTrG4uJjLly+X+keMGEF/f38ePnyYxcXFjIyMZOfOnXn79m2Sd4q+tbU1Q0NDmZ+fz99++403b97kjBkzGBoaysOHD1On0zE1NZUqlUr2giFYnij6gtncq+gvXbqUgwcPlrX9/vvvBCDdnjYmJobTpk2T+jdu3Eh3d3fp3X94eLisWJHktm3bqNVqpccAOHfu3PuuNSEhgWPGjJEeu7q6MjU1VXpcX1/Pjh07SudTW1tLGxsb/vjjj7I406dP5/jx4xs9jsFgYIcOHbhv3z6S5KVLl6hUKnn27FlpzL2K/nfffUcALCgoMBm/urqa0dHRBEAPDw/GxsYyLS1NtvdBSEgI4+PjTc4/ffo0ATA/P19qu3z5MtVqNXft2kXyTtEHwOLiYmnMuXPnqFAo+Oeff8rihYeHc8GCBY3mQ2h+4jN9wSJOnDiBgwcPwtbWVvrx8/MDAJSXlwMA4uPjsXv3bty6dQsAsH37dsTFxcHKykqK8b///U8WY+bMmbhw4QJqamqkY/131yMA+Oijj9C7d2+4uLjA1tYWmzZtwvnz5wEA169fR2VlJYKCgqTxCoUCvXv3lh7rdDrU1NQgIiJCdvytW7dK6zflwIEDuHnzJqKjowHc2covIiICmzdvfqC88f/vhN6qVSuT/W3btkVmZiZ0Oh3ee+892NraYt68eQgKCpJyUlxc3OjexqWlpWjdujWCg4OlNicnJ/j6+qK0tFRqUyqVsu0DS0pKYDAY4OPjI8vHoUOH7pkPofmJjdEFi6iursbw4cOxatWqu/oatt0bPnw4SCIzMxN9+/bFkSNHsG7dOlmMJUuWYPTo0XfF+PcG1m3btpX1ZWRkIDExEWvXrkVISAjs7OyQmpqKgoKCh1o/AGRmZqJ9+/ayPpVK1ei8tLQ0XLlyBWq1WmozGo04efIklixZIr2gNaah8Hp6et5znLe3N7y9vTFjxgy8++678PHxwc6dOzF16lTZsR+VWq2WvfBUV1dDoVCgqKgICoVCNtbW1vaxjyc0HVH0BYvo1asXdu/eDU9PT7Rubfpp2KZNG4wePRrbt2+HTqeDr68vevXqJYtRVlaGzp07P9Sx8/PzERoaitmzZ0tt/343qtFo4OrqisLCQgwcOBDAnStUjh07hp49ewIAunbtCpVKhfPnzyMsLOyBjltVVYX9+/cjIyMD3bp1k9oNBgP69++P7OxsREVFNTr/n3/+waZNmzBw4EC4uLg88Pl6enrCxsYGN2/eBAAEBAQgNzcXU6dOvWusv78/6uvrUVBQgNDQUGndZWVl6Nq1a6PHCAwMhMFgwMWLFzFgwIAHXpvQ/ETRF8zq+vXrKC4ulrU5OTkhISEBn376KcaPH4+kpCQ4OjpCp9MhIyMDn332mfRuMT4+HsOGDcOvv/6KCRMmyOIsWrQIw4YNQ8eOHTF27FhYWVnhxIkT+OWXX7Bs2bJG19SlSxds3boV33//Pby8vLBt2zYUFhbCy8tLGjNnzhysWLECnTt3hp+fHzZs2ICrV69K727t7OyQmJiIt99+G0ajEf3798f169eRn58Pe3t7TJ48+a7jbtu2DU5OThg3btxdH89ER0cjLS1NVvQvXryI2tpa3LhxA0VFRVi9ejUuX76MPXv2NHpuixcvRk1NDaKjo+Hh4YFr167hgw8+QF1dHSIiIgAAKSkpCA8Ph7e3N+Li4lBfX49vv/0WycnJ6NKlC2JiYjBz5kxs3LgRdnZ2mD9/Ptq3b4+YmJhGj+vj44P4+HhMmjQJa9euRWBgIC5duoTc3FwEBARg6NChjc4Vmpmlv1QQnl2TJ082uc/o9OnTSd750nDUqFHSpYF+fn6cO3cujUajFMNgMFCr1RIAy8vL7zpGVlYWQ0NDqVaraW9vz6CgIG7atEnqB8C9e/fK5tTW1nLKlCnUaDR0cHDgrFmzOH/+fPbo0UMaU1dXxzfffJP29vZ87rnnmJyczFdeeYVxcXHSGKPRyPXr19PX15fW1tZ0cXFhZGQkDx06ZDIf3bt35+zZs0327dy5k0qlkpcuXZK+yAXAVq1a0c7Ojj169OA777zDCxcu3DPnP/zwA8eMGcMOHTpQqVTS1dWVUVFRPHLkiGzc7t272bNnTyqVSjo7O3P06NFSX8MlmxqNhmq1mpGRkSYv2fyvhquiPD09aW1tTa1Wy1GjRvHkyZP3XLPQvMQeuYLwAIxGI/z9/TFu3DgsXbrU0ssRhEcmPt4RBBPOnTuH7OxshIWF4datW/jwww+h1+vx6quvWnppgvBYxCWbgmCClZUV0tPT0bdvX/Tr1w8lJSXIycmBv7+/pZcmCI9FfLwjCILQgoh3+oIgCC2IKPqCIAgtiCj6giAILYgo+oIgCC2IKPqCIAgtiCj6giAILYgo+oIgCC2IKPqCIAgtyP8Bq0ZRLf1JATkAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 400x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.figure(figsize=(4, 3))\n",
-    "plt.scatter(leverage_raw_scores, abs_errors, alpha=0.5)\n",
-    "plt.axvline(x=count_fp_pipe.named_steps['leverage'].threshold_, color='r', linestyle='--', label='AD Threshold')\n",
-    "plt.xlabel('Leverage AD Score')\n",
-    "plt.ylabel('Absolute Prediction Error')\n",
-    "plt.title('Prediction Errors vs Leverage Scores')\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(leverage_scores, abs_errors, alpha=0.5)\n",
+    "plt.axvline(x=leverage_ad.threshold_, color=\"r\", linestyle=\"--\", label=\"AD Threshold\")\n",
+    "plt.xlabel(\"Leverage AD Score\")\n",
+    "plt.ylabel(\"Absolute Prediction Error\")\n",
+    "plt.title(\"Prediction Errors vs Leverage Scores\")\n",
     "plt.legend()\n",
-    "\n",
+    "plt.show()\n",
     "\n",
     "# Calculate error statistics\n",
-    "in_domain = count_fp_pipe.named_steps['leverage'].predict(X_test_transformed)\n",
+    "in_domain = leverage_ad.predict(X_test_transformed)\n",
     "errors_in = abs_errors[in_domain == 1]\n",
     "errors_out = abs_errors[in_domain == -1]\n",
     "\n",
@@ -1307,14 +798,6 @@
     "print(f\"Fraction of samples outside domain: {(in_domain == -1).mean():.2f}\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "86f8a09e",
-   "metadata": {},
-   "source": [
-    "Dissappointingly the error seems larger within the domain, than outside the domain."
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "e22b19f0",
@@ -1327,7 +810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "1d33100d",
    "metadata": {},
    "outputs": [
@@ -1346,206 +829,52 @@
    "source": [
     "# Define famous drugs\n",
     "famous_drugs = {\n",
-    "    'Aspirin': 'CC(=O)OC1=CC=CC=C1C(=O)O',\n",
-    "    'Viagra': 'CCc1nn(C)c2c(=O)[nH]c(nc12)c3cc(ccc3OCC)S(=O)(=O)N4CCN(C)CC4',\n",
-    "    'Heroin': 'CN1CC[C@]23[C@H]4Oc5c(O)ccc(CC1[C@H]2C=C[C@@H]4O3)c5',\n",
+    "    \"Aspirin\": \"CC(=O)OC1=CC=CC=C1C(=O)O\",\n",
+    "    \"Viagra\": \"CCc1nn(C)c2c(=O)[nH]c(nc12)c3cc(ccc3OCC)S(=O)(=O)N4CCN(C)CC4\",\n",
+    "    \"Heroin\": \"CN1CC[C@]23[C@H]4Oc5c(O)ccc(CC1[C@H]2C=C[C@@H]4O3)c5\",\n",
     "}\n",
     "\n",
     "\n",
-    "Draw.MolsToGridImage([Chem.MolFromSmiles(drug) for drug in famous_drugs.values()], molsPerRow=3,\n",
-    "                      subImgSize=(250,250), legends=[f\"{name}\" for name, smiles in famous_drugs.items()])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "904ed0d0",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2466: DataConversionWarning: Data was converted to boolean for metric jaccard\n",
-      "  warnings.warn(msg, DataConversionWarning)\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2466: DataConversionWarning: Data was converted to boolean for metric jaccard\n",
-      "  warnings.warn(msg, DataConversionWarning)\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2466: DataConversionWarning: Data was converted to boolean for metric jaccard\n",
-      "  warnings.warn(msg, DataConversionWarning)\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2466: DataConversionWarning: Data was converted to boolean for metric jaccard\n",
-      "  warnings.warn(msg, DataConversionWarning)\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2466: DataConversionWarning: Data was converted to boolean for metric jaccard\n",
-      "  warnings.warn(msg, DataConversionWarning)\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/metrics/pairwise.py:2466: DataConversionWarning: Data was converted to boolean for metric jaccard\n",
-      "  warnings.warn(msg, DataConversionWarning)\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Predicted pIC50</th>\n",
-       "      <th>k-NN Score</th>\n",
-       "      <th>k-NN Status</th>\n",
-       "      <th>Leverage Score</th>\n",
-       "      <th>Leverage Status</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Drug</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Aspirin</th>\n",
-       "      <td>5.90</td>\n",
-       "      <td>0.719194</td>\n",
-       "      <td>Outside</td>\n",
-       "      <td>0.020058</td>\n",
-       "      <td>Inside</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Viagra</th>\n",
-       "      <td>9.05</td>\n",
-       "      <td>0.786921</td>\n",
-       "      <td>Outside</td>\n",
-       "      <td>0.050743</td>\n",
-       "      <td>Inside</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Heroin</th>\n",
-       "      <td>6.45</td>\n",
-       "      <td>0.812649</td>\n",
-       "      <td>Outside</td>\n",
-       "      <td>0.021588</td>\n",
-       "      <td>Inside</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        Predicted pIC50  k-NN Score k-NN Status  Leverage Score  \\\n",
-       "Drug                                                              \n",
-       "Aspirin            5.90    0.719194     Outside        0.020058   \n",
-       "Viagra             9.05    0.786921     Outside        0.050743   \n",
-       "Heroin             6.45    0.812649     Outside        0.021588   \n",
-       "\n",
-       "        Leverage Status  \n",
-       "Drug                     \n",
-       "Aspirin          Inside  \n",
-       "Viagra           Inside  \n",
-       "Heroin           Inside  "
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "leverage_ad = count_fp_pipe.named_steps['leverage']\n",
-    "\n",
     "# Function to process a drug through both AD pipelines\n",
     "def check_drug_applicability(smiles, name):\n",
     "    mol = Chem.MolFromSmiles(smiles)\n",
-    "    \n",
+    "\n",
     "    # k-NN AD\n",
-    "    fp_binary = binary_fp_pipe.named_steps['fp'].transform([mol])\n",
+    "    fp_binary = binary_fp_pipe.named_steps[\"fp\"].transform([mol])\n",
     "    knn_score = knn_ad.transform(fp_binary)[0][0]\n",
     "    knn_status = \"Inside\" if knn_ad.predict(fp_binary)[0] == 1 else \"Outside\"\n",
-    "    \n",
+    "\n",
     "    # Leverage AD\n",
-    "    fp_count = count_fp_pipe.named_steps['fp'].transform([mol])\n",
-    "    fp_pca = count_fp_pipe.named_steps['pca'].transform(fp_count)\n",
-    "    fp_scaled = count_fp_pipe.named_steps['scaler'].transform(fp_pca)\n",
+    "    fp_count = count_fp_pipe.named_steps[\"fp\"].transform([mol])\n",
+    "    fp_pca = count_fp_pipe.named_steps[\"pca\"].transform(fp_count)\n",
+    "    fp_scaled = count_fp_pipe.named_steps[\"scaler\"].transform(fp_pca)\n",
     "    leverage_score = leverage_ad.transform(fp_scaled)[0][0]\n",
     "    leverage_status = \"Inside\" if leverage_ad.predict(fp_scaled)[0] == 1 else \"Outside\"\n",
-    "    \n",
-    "    # Get prediction\n",
-    "    pred_pIC50 = binary_fp_pipe.predict([mol])[0]\n",
-    "    \n",
+    "\n",
     "    return {\n",
-    "        'knn_score': knn_score,\n",
-    "        'knn_status': knn_status,\n",
-    "        'leverage_score': leverage_score,\n",
-    "        'leverage_status': leverage_status,\n",
-    "        'pred_pIC50': pred_pIC50\n",
+    "        \"knn_score\": knn_score,\n",
+    "        \"knn_status\": knn_status,\n",
+    "        \"leverage_score\": leverage_score,\n",
+    "        \"leverage_status\": leverage_status,\n",
     "    }\n",
     "\n",
+    "\n",
     "# Process each drug\n",
     "results = []\n",
     "for name, smiles in famous_drugs.items():\n",
     "    result = check_drug_applicability(smiles, name)\n",
-    "    results.append({\n",
-    "        'Drug': name,\n",
-    "        'Predicted pIC50': f\"{result['pred_pIC50']:.2f}\",\n",
-    "        'k-NN Score': result['knn_score'],\n",
-    "        'k-NN Status': result['knn_status'],\n",
-    "        'Leverage Score': result['leverage_score'],\n",
-    "        'Leverage Status': result['leverage_status']\n",
-    "    })\n",
+    "    results.append(\n",
+    "        {\n",
+    "            \"Drug\": name,\n",
+    "            \"k-NN Score\": result[\"knn_score\"],\n",
+    "            \"k-NN Status\": result[\"knn_status\"],\n",
+    "            \"Leverage Score\": result[\"leverage_score\"],\n",
+    "            \"Leverage Status\": result[\"leverage_status\"],\n",
+    "        }\n",
+    "    )\n",
     "\n",
     "# Display results\n",
-    "pd.DataFrame(results).set_index('Drug')"
+    "pd.DataFrame(results).set_index(\"Drug\")"
    ]
   },
   {
@@ -1558,7 +887,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "3aaf4485",
    "metadata": {},
    "outputs": [
@@ -1577,31 +906,31 @@
     "# Plot for k-NN AD\n",
     "plt.figure(figsize=(12, 5))\n",
     "plt.subplot(1, 2, 1)\n",
-    "plt.scatter(knn_scores, abs_errors, alpha=0.2, label='Test compounds')\n",
-    "plt.axvline(x=knn_ad.threshold_, color='r', linestyle='--', label='AD Threshold')\n",
+    "plt.scatter(knn_scores, abs_errors, alpha=0.2, label=\"Test compounds\")\n",
+    "plt.axvline(x=knn_ad.threshold_, color=\"r\", linestyle=\"--\", label=\"AD Threshold\")\n",
     "\n",
     "for result in results:\n",
-    "    plt.axvline(x=result['k-NN Score'], color='g', alpha=0.5,\n",
-    "                label=f\"{result['Drug']}\")\n",
+    "    plt.axvline(x=result[\"k-NN Score\"], color=\"g\", alpha=0.5, label=f\"{result['Drug']}\")\n",
     "\n",
-    "plt.xlabel('k-NN AD Score')\n",
-    "plt.ylabel('Absolute Prediction Error')\n",
-    "plt.title('k-NN AD Scores')\n",
-    "#plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+    "plt.xlabel(\"k-NN AD Score\")\n",
+    "plt.ylabel(\"Absolute Prediction Error\")\n",
+    "plt.title(\"k-NN AD Scores\")\n",
+    "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n",
     "\n",
     "# Plot for Leverage AD\n",
     "plt.subplot(1, 2, 2)\n",
-    "plt.scatter(leverage_raw_scores, abs_errors, alpha=0.2, label='Test compounds')\n",
-    "plt.axvline(x=leverage_ad.threshold_, color='r', linestyle='--', label='AD Threshold')\n",
+    "plt.scatter(leverage_scores, abs_errors, alpha=0.2, label=\"Test compounds\")\n",
+    "plt.axvline(x=leverage_ad.threshold_, color=\"r\", linestyle=\"--\", label=\"AD Threshold\")\n",
     "\n",
     "for result in results:\n",
-    "    plt.axvline(x=result['Leverage Score'], color='g', alpha=0.5,\n",
-    "                label=f\"{result['Drug']}\")\n",
+    "    plt.axvline(\n",
+    "        x=result[\"Leverage Score\"], color=\"g\", alpha=0.5, label=f\"{result['Drug']}\"\n",
+    "    )\n",
     "\n",
-    "plt.xlabel('Leverage AD Score')\n",
-    "plt.ylabel('Absolute Prediction Error')\n",
-    "plt.title('Leverage AD Scores')\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+    "plt.xlabel(\"Leverage AD Score\")\n",
+    "plt.ylabel(\"Absolute Prediction Error\")\n",
+    "plt.title(\"Leverage AD Scores\")\n",
+    "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n",
     "\n",
     "plt.tight_layout()\n",
     "plt.show()"
@@ -1612,7 +941,7 @@
    "id": "3c018e68",
    "metadata": {},
    "source": [
-    "## Conclusions on testing the AD estimators\n",
+    "## Conclusions\n",
     "\n",
     "This notebook demonstrated two different approaches to applicability domain estimation:\n",
     "\n",
@@ -1622,23 +951,20 @@
     "2. The leverage-based approach with count-based fingerprints and dimensionality reduction focuses on the statistical\n",
     "novelty of compounds in the reduced feature space.\n",
     "\n",
-    "Heroin and Aspirin was predicted to have a low affinity, whereas Viagra was predicted as having a  ~9 pXC50 corresponding to nanomolar affinity. As the regression model had only been trained on actives it will have a tendency to always predict things as active, which is hard to believe for compounds so dissimilar to the training set and with our prior knowledge about their primary targets.\n",
-    "\n",
-    "The famous drugs we tested showed marked differences between the two AD estimation techniques. \n",
+    "The famous drugs we tested showed varying degrees of being within the applicability domain, which makes sense given\n",
+    "that our training set is focused on SLC6A4 actives, while these drugs have different primary targets.\n",
     "\n",
-    "The kNN based method using tanimoto distance showed all test drugs to be distant from the training set and thus outside the applicability domain, whereas the leverage method gave the \"green light\" for all of them. As the drug have different primary targets than the SLC6A4 serotonin transporter, it seems like the kNN based method in this instance (dataset, featurization, ML-model) is a better way to estimate the AD for given novel compounds. This is consistent with our analysis of the 95 percentile of the absolute errors for two different methods, where kNN had a higher 95% percentile error outside the domain, it was lower for the leverage based method.\n",
-    "\n"
+    "The error analysis shows that compounds outside the applicability domain tend to have higher prediction errors,\n",
+    "validating the usefulness of these approaches for identifying potentially unreliable predictions."
    ]
   }
  ],
  "metadata": {
   "jupytext": {
-   "cell_metadata_filter": "-all",
-   "formats": "ipynb,py:percent",
-   "main_language": "python"
+   "formats": "docs//notebooks//ipynb,docs//notebooks//scripts//py:percent"
   },
   "kernelspec": {
-   "display_name": "vscode",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -1652,7 +978,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.6"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/scripts/01_basic_usage.py b/docs/notebooks/scripts/01_basic_usage.py
index a0d7a71..062dc18 100644
--- a/docs/notebooks/scripts/01_basic_usage.py
+++ b/docs/notebooks/scripts/01_basic_usage.py
@@ -1,6 +1,7 @@
 # ---
 # jupyter:
 #   jupytext:
+#     formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent
 #     text_representation:
 #       extension: .py
 #       format_name: percent
diff --git a/docs/notebooks/scripts/02_descriptor_transformer.py b/docs/notebooks/scripts/02_descriptor_transformer.py
index d3312e4..6c1e9d8 100644
--- a/docs/notebooks/scripts/02_descriptor_transformer.py
+++ b/docs/notebooks/scripts/02_descriptor_transformer.py
@@ -1,13 +1,14 @@
 # ---
 # jupyter:
 #   jupytext:
+#     formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent
 #     text_representation:
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
 #       jupytext_version: 1.16.6
 #   kernelspec:
-#     display_name: Python 3.9.4 ('rdkit')
+#     display_name: .venv
 #     language: python
 #     name: python3
 # ---
diff --git a/docs/notebooks/scripts/03_example_pipeline.py b/docs/notebooks/scripts/03_example_pipeline.py
index a5eb54c..5ea1303 100644
--- a/docs/notebooks/scripts/03_example_pipeline.py
+++ b/docs/notebooks/scripts/03_example_pipeline.py
@@ -1,13 +1,14 @@
 # ---
 # jupyter:
 #   jupytext:
+#     formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent
 #     text_representation:
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
 #       jupytext_version: 1.16.6
 #   kernelspec:
-#     display_name: Python 3.9.4 ('rdkit')
+#     display_name: .venv
 #     language: python
 #     name: python3
 # ---
@@ -30,7 +31,7 @@
 import numpy as np
 
 # %%
-csv_file = "../tests/data/SLC6A4_active_excapedb_subset.csv"  # Hmm, maybe better to download directly
+csv_file = "../../tests/data/SLC6A4_active_excapedb_subset.csv"  # Hmm, maybe better to download directly
 data = pd.read_csv(csv_file)
 # %% [markdown]
 # The dataset is a subset of the SLC6A4 actives from ExcapeDB. They are hand selected to give test set performance despite the small size, and are provided as example data only and should not be used to build serious QSAR models.
@@ -72,7 +73,7 @@
 print(f"Train score is :{pipe.score(mol_list_train,y_train):0.2F}")
 print(f"Test score is  :{pipe.score(mol_list_test, y_test):0.2F}")
 # %% [markdown]
-# Nevermind the performance, or the exact value of the prediction, this is for demonstration purposes. We can easily predict on lists of molecules
+# Nevermind the performance, or the exact value of the prediction, this is for demonstration purpures. We can easily predict on lists of molecules
 
 # %%
 pipe.predict([Chem.MolFromSmiles("c1ccccc1C(=O)[OH]")])
diff --git a/docs/notebooks/scripts/04_standardizer.py b/docs/notebooks/scripts/04_standardizer.py
index 6b64ccb..7f9d089 100644
--- a/docs/notebooks/scripts/04_standardizer.py
+++ b/docs/notebooks/scripts/04_standardizer.py
@@ -1,6 +1,7 @@
 # ---
 # jupyter:
 #   jupytext:
+#     formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent
 #     text_representation:
 #       extension: .py
 #       format_name: percent
diff --git a/docs/notebooks/scripts/05_smiles_sanitization.py b/docs/notebooks/scripts/05_smiles_sanitization.py
index bc34f2f..e2f36da 100644
--- a/docs/notebooks/scripts/05_smiles_sanitization.py
+++ b/docs/notebooks/scripts/05_smiles_sanitization.py
@@ -1,6 +1,7 @@
 # ---
 # jupyter:
 #   jupytext:
+#     formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent
 #     text_representation:
 #       extension: .py
 #       format_name: percent
@@ -20,7 +21,7 @@
 import pandas as pd
 from rdkit.Chem import PandasTools
 
-csv_file = "../tests/data/SLC6A4_active_excapedb_subset.csv"  # Hmm, maybe better to download directly
+csv_file = "../../tests/data/SLC6A4_active_excapedb_subset.csv"  # Hmm, maybe better to download directly
 data = pd.read_csv(csv_file)
 
 
@@ -31,7 +32,6 @@
 data.loc[1, "SMILES"] = "CN(C)(C)(C)"
 
 # %%
-
 PandasTools.AddMoleculeColumnToFrame(data, smilesCol="SMILES")
 print(f"Dataset contains {data.ROMol.isna().sum()} unparsable mols")
 
diff --git a/docs/notebooks/scripts/06_hyperparameter_tuning.py b/docs/notebooks/scripts/06_hyperparameter_tuning.py
index a068be4..e1408de 100644
--- a/docs/notebooks/scripts/06_hyperparameter_tuning.py
+++ b/docs/notebooks/scripts/06_hyperparameter_tuning.py
@@ -1,6 +1,7 @@
 # ---
 # jupyter:
 #   jupytext:
+#     formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent
 #     text_representation:
 #       extension: .py
 #       format_name: percent
@@ -46,13 +47,12 @@
         url = "https://ndownloader.figshare.com/files/25747817"
         urllib.request.urlretrieve(url, csv_file)
 else:
-    csv_file = "../tests/data/SLC6A4_active_excapedb_subset.csv"
+    csv_file = "../../tests/data/SLC6A4_active_excapedb_subset.csv"
 
 # %% [markdown]
 # The CSV data is loaded into a Pandas dataframe and the PandasTools utility from RDKit is used to add a column with RDKit molecules
 
 # %%
-
 data = pd.read_csv(csv_file)
 
 PandasTools.AddMoleculeColumnToFrame(data, smilesCol="SMILES")
@@ -62,7 +62,6 @@
 # We use the train_test_split to, well, split the dataframe's molecule columns and pXC50 column into lists for train and testing
 
 # %%
-
 mol_list_train, mol_list_test, y_train, y_test = train_test_split(
     data.ROMol, data.pXC50, random_state=42
 )
@@ -84,7 +83,6 @@
 # A simple pipeline with a MorganTransformer and a Ridge() regression for demonstration.
 
 # %%
-
 moltransformer = MorganFingerprintTransformer()
 regressor = Ridge()
 
@@ -104,14 +102,12 @@
 # With the pipelines, getting the names of the parameters to tune is a bit more tricky, as they are concatenations of the name of the step and the parameter with double underscores in between. We can get the available parameters from the pipeline with the get_params() method, and select the parameters we want to change from there.
 
 # %% Which keys do we have?
-
 optimization_pipe.get_params().keys()
 
 # %% [markdown]
 # We will tune the regularization strength of the Ridge regressor, and try out different parameters for the Morgan fingerprint, namely the number of bits, the radius of the fingerprint, wheter to use counts or bits and features.
 
 # %%
-
 param_dist = {
     "ridge__alpha": loguniform(1e-2, 1e3),
     "morganfingerprinttransformer__fpSize": [256, 512, 1024, 2048, 4096],
diff --git a/docs/notebooks/scripts/07_parallel_transforms.py b/docs/notebooks/scripts/07_parallel_transforms.py
index b8068fe..2ff57a7 100644
--- a/docs/notebooks/scripts/07_parallel_transforms.py
+++ b/docs/notebooks/scripts/07_parallel_transforms.py
@@ -1,17 +1,20 @@
 # ---
 # jupyter:
 #   jupytext:
+#     formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent
 #     text_representation:
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
 #       jupytext_version: 1.16.6
 #   kernelspec:
-#     display_name: .venv
+#     display_name: Python 3.9.4 ('rdkit')
 #     language: python
 #     name: python3
 # ---
 
+# %%
+
 # %% [markdown]
 # # Parallel calculations of transforms
 #
diff --git a/docs/notebooks/scripts/08_external_library_skopt.py b/docs/notebooks/scripts/08_external_library_skopt.py
index e8b0225..aa6e0ce 100644
--- a/docs/notebooks/scripts/08_external_library_skopt.py
+++ b/docs/notebooks/scripts/08_external_library_skopt.py
@@ -1,13 +1,14 @@
 # ---
 # jupyter:
 #   jupytext:
+#     formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent
 #     text_representation:
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
 #       jupytext_version: 1.16.6
 #   kernelspec:
-#     display_name: vscode
+#     display_name: .venv
 #     language: python
 #     name: python3
 # ---
@@ -94,7 +95,6 @@ def objective(**params):
 
 
 # %% THIS takes forever on my machine with a GradientBoostingRegressor
-
 pipe_gp = gp_minimize(objective, search_space, n_calls=10, random_state=0)
 "Best score=%.4f" % pipe_gp.fun
 # %%
diff --git a/docs/notebooks/scripts/09_Combinatorial_Method_Usage_with_FingerPrint_Transformers.py b/docs/notebooks/scripts/09_Combinatorial_Method_Usage_with_FingerPrint_Transformers.py
index d9b55f0..ce275c2 100644
--- a/docs/notebooks/scripts/09_Combinatorial_Method_Usage_with_FingerPrint_Transformers.py
+++ b/docs/notebooks/scripts/09_Combinatorial_Method_Usage_with_FingerPrint_Transformers.py
@@ -1,15 +1,16 @@
 # ---
 # jupyter:
 #   jupytext:
+#     formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent
 #     text_representation:
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
 #       jupytext_version: 1.16.6
 #   kernelspec:
-#     display_name: aniEnv
+#     display_name: .venv
 #     language: python
-#     name: anienv
+#     name: python3
 # ---
 
 # %% [markdown]
@@ -65,7 +66,7 @@
         url = "https://ndownloader.figshare.com/files/25747817"
         urllib.request.urlretrieve(url, csv_file)
 else:
-    csv_file = "../tests/data/SLC6A4_active_excapedb_subset.csv"
+    csv_file = "../../tests/data/SLC6A4_active_excapedb_subset.csv"
 
 # Parse Database
 data = pd.read_csv(csv_file)
@@ -82,7 +83,6 @@
 # This way we can define multiple different scenarios in `param_grid`, that allow us to rapidly explore different combinations of settings and methodologies.
 
 # %%
-
 regressor = Ridge()
 optimization_pipe = Pipeline(
     [
diff --git a/docs/notebooks/scripts/10_pipeline_pandas_output.py b/docs/notebooks/scripts/10_pipeline_pandas_output.py
index 2e3d9e5..f49339f 100644
--- a/docs/notebooks/scripts/10_pipeline_pandas_output.py
+++ b/docs/notebooks/scripts/10_pipeline_pandas_output.py
@@ -1,13 +1,14 @@
 # ---
 # jupyter:
 #   jupytext:
+#     formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent
 #     text_representation:
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
 #       jupytext_version: 1.16.6
 #   kernelspec:
-#     display_name: scikit-mol
+#     display_name: .venv
 #     language: python
 #     name: python3
 # ---
@@ -35,7 +36,7 @@
 from scikit_mol.fingerprints import MorganFingerprintTransformer
 
 # %%
-csv_file = Path("../tests/data/SLC6A4_active_excapedb_subset.csv")
+csv_file = Path("../../tests/data/SLC6A4_active_excapedb_subset.csv")
 assert csv_file.is_file()
 data = pd.read_csv(csv_file)
 data.drop_duplicates(subset="Ambit_InchiKey", inplace=True)
@@ -130,7 +131,7 @@
 # %%
 def compute_metrics(y_true, y_pred):
     result = {
-        "RMSE": mean_squared_error(y_true=y_true, y_pred=y_pred, squared=False),
+        "RMSE": mean_squared_error(y_true=y_true, y_pred=y_pred) ** 0.5,
         "MAE": mean_absolute_error(y_true=y_true, y_pred=y_pred),
         "R2": r2_score(y_true=y_true, y_pred=y_pred),
     }
@@ -186,7 +187,7 @@ def compute_metrics(y_true, y_pred):
 # We have precomputed these features and stored them in a file:
 
 # %%
-file_cddd_features = Path("../tests/data/CDDD_SLC6A4_active_excapedb_subset.csv.gz")
+file_cddd_features = Path("../../tests/data/CDDD_SLC6A4_active_excapedb_subset.csv.gz")
 assert file_cddd_features.is_file()
 df_cddd = pd.read_csv(file_cddd_features)
 df_cddd
diff --git a/docs/notebooks/scripts/11_safe_inference.py b/docs/notebooks/scripts/11_safe_inference.py
index 45ae4af..c55045b 100644
--- a/docs/notebooks/scripts/11_safe_inference.py
+++ b/docs/notebooks/scripts/11_safe_inference.py
@@ -1,13 +1,14 @@
 # ---
 # jupyter:
 #   jupytext:
+#     formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent
 #     text_representation:
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
 #       jupytext_version: 1.16.6
 #   kernelspec:
-#     display_name: vscode
+#     display_name: .venv
 #     language: python
 #     name: python3
 # ---
diff --git a/docs/notebooks/scripts/12_custom_fingerprint_transformer.py b/docs/notebooks/scripts/12_custom_fingerprint_transformer.py
index e128dbd..f905db7 100644
--- a/docs/notebooks/scripts/12_custom_fingerprint_transformer.py
+++ b/docs/notebooks/scripts/12_custom_fingerprint_transformer.py
@@ -1,12 +1,12 @@
 # ---
 # jupyter:
 #   jupytext:
-#     formats: ipynb,py:percent
+#     formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent
 #     text_representation:
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.16.1
+#       jupytext_version: 1.16.6
 #   kernelspec:
 #     display_name: .venv
 #     language: python
@@ -32,16 +32,20 @@
 import numpy as np
 from rdkit import Chem
 
+
 class DummyFingerprintTransformer(BaseFpsTransformer):
-    def __init__(self, fpSize=64, n_jobs=1, safe_inference_mode = False):
+    def __init__(self, fpSize=64, n_jobs=1, safe_inference_mode=False):
         self.fpSize = fpSize
-        super().__init__(n_jobs=n_jobs, safe_inference_mode=safe_inference_mode, name="dummy")
+        super().__init__(
+            n_jobs=n_jobs, safe_inference_mode=safe_inference_mode, name="dummy"
+        )
 
     def _transform_mol(self, mol):
         return mol.GetNumAtoms() * np.ones(self.fpSize)
-    
+
+
 trans = DummyFingerprintTransformer(n_jobs=4)
-mols = [Chem.MolFromSmiles('CC')] * 100
+mols = [Chem.MolFromSmiles("CC")] * 100
 trans.transform(mols)
 
 # %% [markdown]
@@ -55,15 +59,21 @@ def _transform_mol(self, mol):
 # %%
 from rdkit.Chem import rdFingerprintGenerator
 
+
 class UnpickableFingerprintTransformer(BaseFpsTransformer):
     def __init__(self, fpSize=1024, n_jobs=1, safe_inference_mode=False, **kwargs):
         self.fpSize = fpSize
-        super().__init__(n_jobs=n_jobs, safe_inference_mode=safe_inference_mode, **kwargs)
-        self.fp_gen = rdFingerprintGenerator.GetRDKitFPGenerator(maxPath=2, fpSize=self.fpSize)
+        super().__init__(
+            n_jobs=n_jobs, safe_inference_mode=safe_inference_mode, **kwargs
+        )
+        self.fp_gen = rdFingerprintGenerator.GetRDKitFPGenerator(
+            maxPath=2, fpSize=self.fpSize
+        )
 
     def _transform_mol(self, mol):
         return self.fp_gen.GetFingerprintAsNumPy(mol)
-    
+
+
 trans = UnpickableFingerprintTransformer(n_jobs=4, fpSize=512)
 trans.transform(mols)
 
@@ -76,6 +86,7 @@ class BadTransformer(BaseFpsTransformer):
     def __init__(self, generator, n_jobs=1):
         self.generator = generator
         super().__init__(n_jobs=n_jobs)
+
     def _transform_mol(self, mol):
         return self.generator.GetFingerprint(mol)
 
@@ -86,7 +97,9 @@ def _transform_mol(self, mol):
 try:
     BadTransformer(fp_gen, n_jobs=2).transform(mols)
 except Exception as e:
-    print("n_jobs=2 is not fine, because the generator passed as an argument is not picklable")
+    print(
+        "n_jobs=2 is not fine, because the generator passed as an argument is not picklable"
+    )
     print(f"Error msg: {e}")
 
 
@@ -104,13 +117,16 @@ def _transform_mol(self, mol):
 class NamedTansformer1(UnpickableFingerprintTransformer):
     pass
 
+
 class NamedTansformer2(UnpickableFingerprintTransformer):
     def __init__(self):
         super().__init__(name="fp_fancy")
 
+
 class FancyFingerprintTransformer(UnpickableFingerprintTransformer):
     pass
 
+
 print(NamedTansformer1().get_feature_names_out())
 print(NamedTansformer2().get_feature_names_out())
 print(FancyFingerprintTransformer().get_feature_names_out())
diff --git a/docs/notebooks/scripts/13_applicability_domain.py b/docs/notebooks/scripts/13_applicability_domain.py
index 7baa492..93ace0b 100644
--- a/docs/notebooks/scripts/13_applicability_domain.py
+++ b/docs/notebooks/scripts/13_applicability_domain.py
@@ -1,27 +1,14 @@
 # ---
 # jupyter:
 #   jupytext:
-#     cell_metadata_filter: -all
-#     formats: ipynb,py:percent
+#     formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent
 #     text_representation:
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
 #       jupytext_version: 1.16.6
-# ---
-
-# %%
-# ---
-# jupyter:
-#   jupytext:
-#     formats: ipynb,py:percent
-#     text_representation:
-#       extension: .py
-#       format_name: percent
-#       format_version: '1.3'
-#       jupytext_version: 1.16.1
 #   kernelspec:
-#     display_name: Python 3 (ipykernel)
+#     display_name: .venv
 #     language: python
 #     name: python3
 # ---
@@ -58,7 +45,7 @@
 
 # %%
 # Load the dataset
-csv_file = "../tests/data/SLC6A4_active_excapedb_subset.csv"
+csv_file = "../../tests/data/SLC6A4_active_excapedb_subset.csv"
 data = pd.read_csv(csv_file)
 
 # Add RDKit mol objects
@@ -70,7 +57,9 @@
 y = data.pXC50
 
 X_temp, X_test, y_temp, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-X_train, X_val, y_train, y_val = train_test_split(X_temp, y_temp, test_size=0.25, random_state=42)
+X_train, X_val, y_train, y_val = train_test_split(
+    X_temp, y_temp, test_size=0.25, random_state=42
+)
 
 # %% [markdown]
 # ## Example 1: k-NN Applicability Domain with Binary Morgan Fingerprints
@@ -80,10 +69,12 @@
 
 # %%
 # Create pipeline for binary fingerprints
-binary_fp_pipe = Pipeline([
-    ('fp', MorganFingerprintTransformer(fpSize=2048, radius=2)),
-    ('rf', RandomForestRegressor(n_estimators=100, random_state=42))
-])
+binary_fp_pipe = Pipeline(
+    [
+        ("fp", MorganFingerprintTransformer(fpSize=2048, radius=2)),
+        ("rf", RandomForestRegressor(n_estimators=100, random_state=42)),
+    ]
+)
 
 # Train the model
 binary_fp_pipe.fit(X_train, y_train)
@@ -93,14 +84,14 @@
 abs_errors = np.abs(y_test - y_pred_test)
 
 # Create and fit k-NN AD estimator
-knn_ad = KNNApplicabilityDomain(n_neighbors=3, distance_metric='tanimoto')
-knn_ad.fit(binary_fp_pipe.named_steps['fp'].transform(X_train))
+knn_ad = KNNApplicabilityDomain(n_neighbors=3, distance_metric="tanimoto")
+knn_ad.fit(binary_fp_pipe.named_steps["fp"].transform(X_train))
 
 # Fit threshold using validation set
-knn_ad.fit_threshold(binary_fp_pipe.named_steps['fp'].transform(X_val))
+knn_ad.fit_threshold(binary_fp_pipe.named_steps["fp"].transform(X_val))
 
 # Get AD scores for test set
-knn_scores = knn_ad.transform(binary_fp_pipe.named_steps['fp'].transform(X_test))
+knn_scores = knn_ad.transform(binary_fp_pipe.named_steps["fp"].transform(X_test))
 
 # %% [markdown]
 # Let's visualize the relationship between prediction errors and AD scores:
@@ -108,15 +99,15 @@
 # %%
 plt.figure(figsize=(10, 6))
 plt.scatter(knn_scores, abs_errors, alpha=0.5)
-plt.axvline(x=knn_ad.threshold_, color='r', linestyle='--', label='AD Threshold')
-plt.xlabel('k-NN AD Score')
-plt.ylabel('Absolute Prediction Error')
-plt.title('Prediction Errors vs k-NN AD Scores')
+plt.axvline(x=knn_ad.threshold_, color="r", linestyle="--", label="AD Threshold")
+plt.xlabel("k-NN AD Score")
+plt.ylabel("Absolute Prediction Error")
+plt.title("Prediction Errors vs k-NN AD Scores")
 plt.legend()
 plt.show()
 
 # Calculate error statistics
-in_domain = knn_ad.predict(binary_fp_pipe.named_steps['fp'].transform(X_test))
+in_domain = knn_ad.predict(binary_fp_pipe.named_steps["fp"].transform(X_test))
 errors_in = abs_errors[in_domain == 1]
 errors_out = abs_errors[in_domain == -1]
 
@@ -132,12 +123,14 @@
 
 # %%
 # Create pipeline for count-based fingerprints with PCA
-count_fp_pipe = Pipeline([
-    ('fp', MorganFingerprintTransformer(fpSize=2048, radius=2, useCounts=True)),
-    ('pca', PCA(n_components=0.9)),  # Keep 90% of variance
-    ('scaler', StandardScaler()),
-    ('rf', RandomForestRegressor(n_estimators=100, random_state=42))
-])
+count_fp_pipe = Pipeline(
+    [
+        ("fp", MorganFingerprintTransformer(fpSize=2048, radius=2, useCounts=True)),
+        ("pca", PCA(n_components=0.9)),  # Keep 90% of variance
+        ("scaler", StandardScaler()),
+        ("rf", RandomForestRegressor(n_estimators=100, random_state=42)),
+    ]
+)
 
 # Train the model
 count_fp_pipe.fit(X_train, y_train)
@@ -148,25 +141,25 @@
 
 # Create and fit leverage AD estimator
 leverage_ad = LeverageApplicabilityDomain()
-X_train_transformed = count_fp_pipe.named_steps['scaler'].transform(
-    count_fp_pipe.named_steps['pca'].transform(
-        count_fp_pipe.named_steps['fp'].transform(X_train)
+X_train_transformed = count_fp_pipe.named_steps["scaler"].transform(
+    count_fp_pipe.named_steps["pca"].transform(
+        count_fp_pipe.named_steps["fp"].transform(X_train)
     )
 )
 leverage_ad.fit(X_train_transformed)
 
 # Fit threshold using validation set
-X_val_transformed = count_fp_pipe.named_steps['scaler'].transform(
-    count_fp_pipe.named_steps['pca'].transform(
-        count_fp_pipe.named_steps['fp'].transform(X_val)
+X_val_transformed = count_fp_pipe.named_steps["scaler"].transform(
+    count_fp_pipe.named_steps["pca"].transform(
+        count_fp_pipe.named_steps["fp"].transform(X_val)
     )
 )
 leverage_ad.fit_threshold(X_val_transformed)
 
 # Get AD scores for test set
-X_test_transformed = count_fp_pipe.named_steps['scaler'].transform(
-    count_fp_pipe.named_steps['pca'].transform(
-        count_fp_pipe.named_steps['fp'].transform(X_test)
+X_test_transformed = count_fp_pipe.named_steps["scaler"].transform(
+    count_fp_pipe.named_steps["pca"].transform(
+        count_fp_pipe.named_steps["fp"].transform(X_test)
     )
 )
 leverage_scores = leverage_ad.transform(X_test_transformed)
@@ -177,10 +170,10 @@
 # %%
 plt.figure(figsize=(10, 6))
 plt.scatter(leverage_scores, abs_errors, alpha=0.5)
-plt.axvline(x=leverage_ad.threshold_, color='r', linestyle='--', label='AD Threshold')
-plt.xlabel('Leverage AD Score')
-plt.ylabel('Absolute Prediction Error')
-plt.title('Prediction Errors vs Leverage Scores')
+plt.axvline(x=leverage_ad.threshold_, color="r", linestyle="--", label="AD Threshold")
+plt.xlabel("Leverage AD Score")
+plt.ylabel("Absolute Prediction Error")
+plt.title("Prediction Errors vs Leverage Scores")
 plt.legend()
 plt.show()
 
@@ -201,48 +194,52 @@
 # %%
 # Define famous drugs
 famous_drugs = {
-    'Aspirin': 'CC(=O)OC1=CC=CC=C1C(=O)O',
-    'Viagra': 'CCc1nn(C)c2c(=O)[nH]c(nc12)c3cc(ccc3OCC)S(=O)(=O)N4CCN(C)CC4',
-    'Heroin': 'CN1CC[C@]23[C@H]4Oc5c(O)ccc(CC1[C@H]2C=C[C@@H]4O3)c5',
+    "Aspirin": "CC(=O)OC1=CC=CC=C1C(=O)O",
+    "Viagra": "CCc1nn(C)c2c(=O)[nH]c(nc12)c3cc(ccc3OCC)S(=O)(=O)N4CCN(C)CC4",
+    "Heroin": "CN1CC[C@]23[C@H]4Oc5c(O)ccc(CC1[C@H]2C=C[C@@H]4O3)c5",
 }
 
+
 # Function to process a drug through both AD pipelines
 def check_drug_applicability(smiles, name):
     mol = Chem.MolFromSmiles(smiles)
-    
+
     # k-NN AD
-    fp_binary = binary_fp_pipe.named_steps['fp'].transform([mol])
+    fp_binary = binary_fp_pipe.named_steps["fp"].transform([mol])
     knn_score = knn_ad.transform(fp_binary)[0][0]
     knn_status = "Inside" if knn_ad.predict(fp_binary)[0] == 1 else "Outside"
-    
+
     # Leverage AD
-    fp_count = count_fp_pipe.named_steps['fp'].transform([mol])
-    fp_pca = count_fp_pipe.named_steps['pca'].transform(fp_count)
-    fp_scaled = count_fp_pipe.named_steps['scaler'].transform(fp_pca)
+    fp_count = count_fp_pipe.named_steps["fp"].transform([mol])
+    fp_pca = count_fp_pipe.named_steps["pca"].transform(fp_count)
+    fp_scaled = count_fp_pipe.named_steps["scaler"].transform(fp_pca)
     leverage_score = leverage_ad.transform(fp_scaled)[0][0]
     leverage_status = "Inside" if leverage_ad.predict(fp_scaled)[0] == 1 else "Outside"
-    
+
     return {
-        'knn_score': knn_score,
-        'knn_status': knn_status,
-        'leverage_score': leverage_score,
-        'leverage_status': leverage_status
+        "knn_score": knn_score,
+        "knn_status": knn_status,
+        "leverage_score": leverage_score,
+        "leverage_status": leverage_status,
     }
 
+
 # Process each drug
 results = []
 for name, smiles in famous_drugs.items():
     result = check_drug_applicability(smiles, name)
-    results.append({
-        'Drug': name,
-        'k-NN Score': result['knn_score'],
-        'k-NN Status': result['knn_status'],
-        'Leverage Score': result['leverage_score'],
-        'Leverage Status': result['leverage_status']
-    })
+    results.append(
+        {
+            "Drug": name,
+            "k-NN Score": result["knn_score"],
+            "k-NN Status": result["knn_status"],
+            "Leverage Score": result["leverage_score"],
+            "Leverage Status": result["leverage_status"],
+        }
+    )
 
 # Display results
-pd.DataFrame(results).set_index('Drug')
+pd.DataFrame(results).set_index("Drug")
 
 # %% [markdown]
 # Let's visualize where these drugs fall in our AD plots:
@@ -251,31 +248,31 @@ def check_drug_applicability(smiles, name):
 # Plot for k-NN AD
 plt.figure(figsize=(12, 5))
 plt.subplot(1, 2, 1)
-plt.scatter(knn_scores, abs_errors, alpha=0.2, label='Test compounds')
-plt.axvline(x=knn_ad.threshold_, color='r', linestyle='--', label='AD Threshold')
+plt.scatter(knn_scores, abs_errors, alpha=0.2, label="Test compounds")
+plt.axvline(x=knn_ad.threshold_, color="r", linestyle="--", label="AD Threshold")
 
 for result in results:
-    plt.axvline(x=result['k-NN Score'], color='g', alpha=0.5,
-                label=f"{result['Drug']}")
+    plt.axvline(x=result["k-NN Score"], color="g", alpha=0.5, label=f"{result['Drug']}")
 
-plt.xlabel('k-NN AD Score')
-plt.ylabel('Absolute Prediction Error')
-plt.title('k-NN AD Scores')
-plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
+plt.xlabel("k-NN AD Score")
+plt.ylabel("Absolute Prediction Error")
+plt.title("k-NN AD Scores")
+plt.legend(bbox_to_anchor=(1.05, 1), loc="upper left")
 
 # Plot for Leverage AD
 plt.subplot(1, 2, 2)
-plt.scatter(leverage_scores, abs_errors, alpha=0.2, label='Test compounds')
-plt.axvline(x=leverage_ad.threshold_, color='r', linestyle='--', label='AD Threshold')
+plt.scatter(leverage_scores, abs_errors, alpha=0.2, label="Test compounds")
+plt.axvline(x=leverage_ad.threshold_, color="r", linestyle="--", label="AD Threshold")
 
 for result in results:
-    plt.axvline(x=result['Leverage Score'], color='g', alpha=0.5,
-                label=f"{result['Drug']}")
+    plt.axvline(
+        x=result["Leverage Score"], color="g", alpha=0.5, label=f"{result['Drug']}"
+    )
 
-plt.xlabel('Leverage AD Score')
-plt.ylabel('Absolute Prediction Error')
-plt.title('Leverage AD Scores')
-plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
+plt.xlabel("Leverage AD Score")
+plt.ylabel("Absolute Prediction Error")
+plt.title("Leverage AD Scores")
+plt.legend(bbox_to_anchor=(1.05, 1), loc="upper left")
 
 plt.tight_layout()
 plt.show()
diff --git a/pyproject.toml b/pyproject.toml
index 2320447..57d799b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -105,6 +105,5 @@ docs = [
 ]
 
 
-[tool.jupytext.formats]
-"docs/notebooks/" = "ipynb"
-"docs/notebooks/scripts" = "py:percent"
\ No newline at end of file
+[tool.jupytext]
+formats = "docs//notebooks//ipynb,docs//notebooks//scripts//py:percent"
diff --git a/ruff.toml b/ruff.toml
index 3a68459..a8c0dca 100644
--- a/ruff.toml
+++ b/ruff.toml
@@ -27,15 +27,16 @@ exclude = [
     "node_modules",
     "site-packages",
     "venv",
-    "notebooks"
+    "notebooks",
+    "docs/notebooks/scripts",
 ]
 
 # Same as Black.
 line-length = 88
 indent-width = 4
 
-# Assume Python 3.8
-target-version = "py38"
+# Assume Python 3.9
+target-version = "py39"
 
 [lint]
 # Enable Pyflakes (`F`) and a subset of the pycodestyle (`E`)  codes by default.
diff --git a/scikit_mol/plotting.py b/scikit_mol/plotting.py
index cf2001c..e331fe7 100644
--- a/scikit_mol/plotting.py
+++ b/scikit_mol/plotting.py
@@ -22,7 +22,7 @@ class ParallelTester:
     def __init__(
         self,
         transformer: object,
-        mols: list[Chem.Mol],
+        mols: list["Chem.Mol"],
         n_mols: Sequence[int] = (10, 100, 100, 1000, 10000, 100000),
         n_jobs: Sequence[int] = (1, 2, 4, 8),
         backend: str = "loky",
diff --git a/uv.lock b/uv.lock
index 0bc6bf1..3489a06 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,4 +1,5 @@
 version = 1
+revision = 1
 requires-python = ">=3.9, <3.14"
 resolution-markers = [
     "python_full_version >= '3.12'",