diff --git a/README.md b/README.md
index 7476da8d..33bda88d 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
 [![Develop test](https://github.com/ATOMScience-org/AMPL/actions/workflows/pytest.yml/badge.svg)](https://github.com/ATOMScience-org/AMPL/actions/workflows/pytest.yml)  [![Linter](https://github.com/ATOMScience-org/AMPL/actions/workflows/lint.yml/badge.svg)](https://github.com/ATOMScience-org/AMPL/actions/workflows/lint.yml)    [![Documentation Status](https://readthedocs.org/projects/ampl/badge/?version=stable)](https://ampl.readthedocs.io/en/latest/?badge=stable)
  [![codecov](https://codecov.io/gh/ATOMScience-org/AMPL/graph/badge.svg)](https://codecov.io/gh/ATOMScience-org/AMPL)
 
-![GitHub Release](https://img.shields.io/github/v/release/ATOMScience-org/AMPL)  [![License](http://img.shields.io/:license-mit-blue.svg)](https://github.com/ATOMScience-org/AMPL/blob/master/LICENSE)   [![LinkedIn](https://custom-icon-badges.demolab.com/badge/LinkedIn-0A66C2?logo=linkedin-white&logoColor=fff)](https://www.linkedin.com/company/atomscience) [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?logo=YouTube&logoColor=white)](https://www.youtube.com/channel/UCOF6zZ7ltGwopYCoOGIFM-w) [![DOI](https://zenodo.org/badge/220339090.svg)](https://doi.org/10.5281/zenodo.20802004)
+![GitHub Release](https://img.shields.io/github/v/release/ATOMScience-org/AMPL)  [![License](http://img.shields.io/:license-mit-blue.svg)](https://github.com/ATOMScience-org/AMPL/blob/master/LICENSE)   [![LinkedIn](https://custom-icon-badges.demolab.com/badge/LinkedIn-0A66C2?logo=linkedin-white&logoColor=fff)](https://www.linkedin.com/company/atomscience) [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?logo=YouTube&logoColor=white)](https://www.youtube.com/channel/UCOF6zZ7ltGwopYCoOGIFM-w) [![DOI]([https://img.shields.io/badge/DOI-10.5281%2Fzenodo-blue.svg)](https://doi.org/10.5281/zenodo.20802004)
 
 | [Install](#install) | [Docker](#install-with-docker) | [Tutorials](#ampl-tutorials) |  [Features](#ampl-features) | [Pipeline parameters](atomsci/ddm/docs/PARAMETERS.md) | [Docs](https://ampl.readthedocs.io/en/latest/) |
 
diff --git a/atomsci/ddm/examples/tutorials/09_kfold_cross_validation.ipynb b/atomsci/ddm/examples/tutorials/09_kfold_cross_validation.ipynb
new file mode 100644
index 00000000..5c9fe04d
--- /dev/null
+++ b/atomsci/ddm/examples/tutorials/09_kfold_cross_validation.ipynb
@@ -0,0 +1,1414 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3b585fad",
+   "metadata": {},
+   "source": [
+    "# _k_-Fold Cross Validation\n",
+    "\n",
+    "In this tutorial, we will perform _k_-fold cross validation on the data that we prepared in **Tutorial 1, \"Data Curation\"**, using the\n",
+    "**[AMPL](https://github.com/ATOMScience-org/AMPL)** modules, classes and functions listed below.\n",
+    "\n",
+    "- [parameter_parser](https://ampl.readthedocs.io/en/latest/pipeline.html#pipeline-parameter-parser-module)\n",
+    "- [ModelPipeline](https://ampl.readthedocs.io/en/latest/pipeline.html#pipeline.model_pipeline.ModelPipeline)\n",
+    "- [split_dataset](https://ampl.readthedocs.io/en/latest/pipeline.html#pipeline.model_pipeline.ModelPipeline.split_dataset)\n",
+    "- [compare_splits_plots](https://ampl.readthedocs.io/en/latest/utils.html#module-utils.compare_splits_plots)\n",
+    "- [split_response_dist_plots](https://ampl.readthedocs.io/en/latest/utils.html#module-utils.split_response_dist_plots)\n",
+    "\n",
+    "In tutorial 02, we performed a a 3-way split of the curated dataset (train, validation, test). However, this method has limitations: it reduces the number of samples available for training, and the results can depend on which compounds happen \n",
+    "to end up in each subset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b05b3e02",
+   "metadata": {},
+   "source": [
+    "**_k_-fold Cross Validation** addresses this by splitting the data into _k_ equal subsets (\"folds\"). The model is trained on _k_ − 1 folds and validated on the remaining fold. This process is repeated _k_ times, with each fold serving as the validation set exactly once. Performance is then averaged across all _k_ iterations to give a more robust estimate of how well the model generalizes to new data."
+   ]
+  },
+  {
+   "attachments": {
+    "image-2.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "398a7c40",
+   "metadata": {},
+   "source": [
+    "![image-2.png](attachment:image-2.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c055e3f",
+   "metadata": {},
+   "source": [
+    "Importantly, there remains a true, holdout test set that the model will not see until after hyperparameter searching and model selection. The full workflow for training a model with cross validation is as follows:\n",
+    "\n",
+    "1. Set aside a held-out test set that the model does not see during training or hyperparameter tuning (i.e. a 20% split of the data).\n",
+    "2. Run hyperparameter search using _k_-fold cross validation on the remaining data (i.e. the remaining 80%).\n",
+    "3. Evaluate each hyperparameter combination across all _k_ folds and select the best combination based on the average validation performance.\n",
+    "3. Retrain a single production model using the selected best hyperparameters on 100% of the training and validation data (i.e. the full 80% training / validation partition).\n",
+    "4. Evaluate final model performance on the held-out test set.\n",
+    "\n",
+    "The final model is used for predictions and deployment, compared to the _k_ cross-validation models which are only used to estimate model performance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04327d43",
+   "metadata": {},
+   "source": [
+    "This approach can be more computationally expensive than the 3-way split, but makes better use of available data, which is an advantage when working with small datasets.\n",
+    "\n",
+    "> Note: During cross-validation, neural network models and tree-based models (Random Forest and XGBoost) follow slightly different internal training implementations. Neural networks determine the optimal training duration from the cross-validation results and then retrain the final production model for best epoch +1 epochs using the complete train/validation dataset. Random Forest and XGBoost models similarly retrain a single final model on the complete train/validation dataset after cross-validation, but do not require epoch selection.\n",
+    "\n",
+    "> *Why +1? When you add more data (the held-out validation fold), the model can usually learn slightly longer before overfitting. The +1 is a heuristic approximation of this. Some people use a small multiplier instead (e.g., optimal_epochs * (k/(k-1))).*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0d295988",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-10-12T20:07:57.240956Z",
+     "start_time": "2026-10-12T20:07:57.228174Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Same set up\n",
+    "dataset_file = 'dataset/SLC6A3_Ki_curated.csv'\n",
+    "odir = 'dataset'\n",
+    "\n",
+    "# Set for less chatty log messages\n",
+    "import logging\n",
+    "logger = logging.getLogger('ATOM')\n",
+    "logger.setLevel(logging.INFO)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2de0e1a1",
+   "metadata": {},
+   "source": [
+    "## Performing a Split\n",
+    "We will split our data with a k-fold scaffold split. To do this, we only need to specify \"k_fold_cv\" as the `split_strategy`. For more information on scaffold splitting, refer to tutorial notebook 2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "bc9a3ada",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-10-12T20:08:05.965721Z",
+     "start_time": "2026-10-12T20:08:05.946100Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "params = {\n",
+    "    # dataset info\n",
+    "    \"dataset_key\" : dataset_file,\n",
+    "    \"response_cols\" : \"avg_pKi\",\n",
+    "    \"id_col\": \"compound_id\",\n",
+    "    \"smiles_col\" : \"base_rdkit_smiles\",\n",
+    "    \"result_dir\": odir,\n",
+    "\n",
+    "    # splitting\n",
+    "    \"previously_split\": \"False\",\n",
+    "    \"splitter\": 'scaffold',\n",
+    "    \"split_strategy\": \"k_fold_cv\",\n",
+    "    \"num_folds\": \"5\", # default is 5\n",
+    " \n",
+    "    # featurization & training params\n",
+    "    \"featurizer\": \"computed_descriptors\",\n",
+    "    \"descriptor_type\" : \"rdkit_raw\",\n",
+    "    \"previously_featurized\": \"True\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c92a7353",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-10-12T20:08:09.221134Z",
+     "start_time": "2026-10-12T20:08:08.164220Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No normalization for SPS. Feature removed!\n",
+      "No normalization for AvgIpc. Feature removed!\n",
+      "Skipped loading modules with transformers dependency. No module named 'transformers'\n",
+      "cannot import name 'HuggingFaceModel' from 'deepchem.models.torch_models' (/Users/echun/repos/AMPL/.venv-mchip/lib/python3.10/site-packages/deepchem/models/torch_models/__init__.py)\n",
+      "Skipped loading modules with pytorch-lightning dependency, missing a dependency. No module named 'pkg_resources'\n",
+      "Skipped loading some Jax models, missing a dependency. No module named 'jax'\n",
+      "INFO:ATOM:atomsci.clients package missing, is currently unsupported for non-ATOM users.\n",
+      "ATOM users should run 'pip install clients --user' to install.\n",
+      "WARNING:ATOM:The global seed is being set to 3965484093, for reproducibility. Note that this action will synchronize the randonmess across all libraries which may impact the randomness of other parts of the pipeline.\n",
+      "INFO:ATOM:Initiating ModelPipeline with seed 3965484093\n",
+      "INFO:ATOM:Using prefeaturized data; number of features = 200\n",
+      "INFO:ATOM:Dataset split table saved to /Users/echun/repos/AMPL/atomsci/ddm/examples/tutorials/dataset/SLC6A3_Ki_curated_5_fold_cv_scaffold_c9d39947-530d-4850-a695-04a33bca62ae.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "from atomsci.ddm.pipeline import model_pipeline as mp\n",
+    "from atomsci.ddm.pipeline import parameter_parser as parse\n",
+    "\n",
+    "pparams = parse.wrapper(params)\n",
+    "MP = mp.ModelPipeline(pparams)\n",
+    "split_uuid = MP.split_dataset()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ddc69627",
+   "metadata": {},
+   "source": [
+    "Again, the dataset split table is saved as a .csv in the same directory as the `dataset_key`. \n",
+    "\n",
+    "The name of the split file starts with the `dataset_key` and is followed by the `split_strategy` (k_fold_cv), `split type` (scaffold), and the `split_uuid` (a unique identifier of the split)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f642f0f9",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-10-12T20:08:29.281466Z",
+     "start_time": "2026-10-12T20:08:29.241802Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'dataset/SLC6A3_Ki_curated_5_fold_cv_scaffold_c9d39947-530d-4850-a695-04a33bca62ae.csv'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# display the split file location\n",
+    "import glob\n",
+    "import os\n",
+    "dirname = os.path.dirname(params['dataset_key'])\n",
+    "split_file = glob.glob(f\"{dirname}/*{split_uuid}*\")[0]\n",
+    "split_file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc9ce1d9",
+   "metadata": {},
+   "source": [
+    "### Format of the Split File\n",
+    "The split file consists of three columns: `cmpd_id` is the compound ID; `subset` tells you if the compound is in the train (train_valid) or test set and `fold` contains the fold index."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "086d6ae2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-10-12T20:08:33.428753Z",
+     "start_time": "2026-10-12T20:08:33.410209Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cmpd_id</th>\n",
+       "      <th>subset</th>\n",
+       "      <th>fold</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CHEMBL577501</td>\n",
+       "      <td>train_valid</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>CHEMBL204490</td>\n",
+       "      <td>train_valid</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>CHEMBL566208</td>\n",
+       "      <td>train_valid</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>CHEMBL1945248</td>\n",
+       "      <td>train_valid</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CHEMBL345689</td>\n",
+       "      <td>train_valid</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         cmpd_id       subset  fold\n",
+       "0   CHEMBL577501  train_valid     0\n",
+       "1   CHEMBL204490  train_valid     0\n",
+       "2   CHEMBL566208  train_valid     0\n",
+       "3  CHEMBL1945248  train_valid     0\n",
+       "4   CHEMBL345689  train_valid     0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Explore contents of the split file\n",
+    "split_df = pd.read_csv(split_file)\n",
+    "split_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1d7f8600",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-10-12T20:11:00.138980Z",
+     "start_time": "2026-10-12T20:11:00.118270Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "subset\n",
+       "train_valid    1637\n",
+       "test            182\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Show the numbers of compounds in each split subset\n",
+    "split_df.subset.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c2917ba",
+   "metadata": {},
+   "source": [
+    "### Visualizing Scaffold Splits\n",
+    "For _k_-fold cross validation, you would ideally visualize the **[Tanimoto distance](https://en.wikipedia.org/wiki/Jaccard_index#Tanimoto_similarity_and_distance)** distribution for each fold separately to confirm that scaffold splitting is creating structurally diverse splits across folds. We can manually loop over folds to visualize this. We can also visualize the distribution of input values in each fold. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5cf51185",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-10-12T20:10:29.454424Z",
+     "start_time": "2026-10-12T20:10:22.805417Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import atomsci.ddm.utils.compare_splits_plots as csp\n",
+    "\n",
+    "# read the dataset\n",
+    "df = pd.read_csv(dataset_file)\n",
+    "\n",
+    "# read the split file\n",
+    "split = pd.read_csv(split_file)\n",
+    "split_type = params['splitter']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "41602287",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-10-12T20:11:24.094431Z",
+     "start_time": "2026-10-12T20:11:24.081638Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "subset       fold\n",
+       "test         0       182\n",
+       "train_valid  0       327\n",
+       "             1       327\n",
+       "             2       327\n",
+       "             3       328\n",
+       "             4       328\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# show the counts in each fold\n",
+    "split[['subset','fold']].value_counts().sort_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "bc576b44",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import importlib\n",
+    "importlib.reload(csp)\n",
+    "# plot Tanimoto distance histograms for all folds\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n",
+    "\n",
+    "ss = csp.SplitStats(\n",
+    "    df, split,\n",
+    "    smiles_col='base_rdkit_smiles',\n",
+    "    id_col='compound_id',\n",
+    "    response_cols=['avg_pKi']\n",
+    ")\n",
+    "ss.dist_hist_kfold_plot(n_bins=10, ax=axes[0])       # n_bins=10 is default, can be changed to 20\n",
+    "ss.dist_hist_kfold_tvv_plot(n_bins=10, ax=axes[1])\n",
+    "\n",
+    "fig.suptitle(f\"K-fold Tanimoto Distance using {split_type} split\")\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7fb79a75",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-10-12T20:11:47.803924Z",
+     "start_time": "2026-10-12T20:11:47.648588Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualize distributions of the response values (pKi) in each subset\n",
+    "import atomsci.ddm.utils.split_response_dist_plots as srdp\n",
+    "split_params = {\n",
+    "    \"dataset_key\" : dataset_file,\n",
+    "    \"smiles_col\" : \"base_rdkit_smiles\",\n",
+    "    \"response_cols\" : \"avg_pKi\",\n",
+    "    \"split_uuid\": split_uuid,\n",
+    "    \"split_strategy\": \"k_fold_cv\",\n",
+    "    \"splitter\": 'scaffold',\n",
+    "}\n",
+    "srdp.plot_split_subset_response_distrs(split_params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "33a5a8a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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Ui79D0e0mjl/UThK5c/sn0tdOVXEwf9/UdbDFh7okEayILhnxjVl8SL/yyit48sknZTAkgg3x82ADg4Mliv+JgErk9TSX5yCK74lRS6J4X7RvrX+G+LAXI57EhaGpRN8/Q1xgxbkTFxhRxPDvf/+7vHiIC4dYH22EVnPEhUucBzFpqrioi4upSFgWFyvxzX3/fCHR0iQKDorzK/KtxGOLVihxH6IQXVchWm9EEC26RkVeiph/TRQjFMFNbave/udGjEAUo6xEt44Ilp5//nkZ3IuuKhFsH06BPlHwUHRFipa8V199Fe+9954MhkQA1V7Bgij0eOedd8qWRxGIi9ZBESQK99xzj0xkFgGz+JsXXy7E34HYT7xX9u/KFZXKxWfFwbT8UtehEGPa2/sgiKjliCq04tu5+FAXXSadkUhMFaODRM7SwXbbxYqLLrpIBjQiEBowYECrPY4ILkWQtf+0EV3Ftm3bZD6gGO4/a9as9j4cakNs8SHqYkSLkujqEN1RLT0XVUsTw6tFkFOfaOkRU0yIZOVY7oIQc67tT3RZiXpL4oLdmkFPLBC1skTX6m233dbeh0JtjDk+RF3Qs88+K7slRK6M6FbryN+6RfeY6J7LycmRQZAYMSRaM0SdI9GlFqtE4rDIoRGj5MQEruKciC4m0V0jurHo8InJYkXwKLoFxbml2MKuLiJqNyJZVuRriJYMMdxY5GOJkV6ipUeMUDrYKtRdkSjIKLq0RC6VCAjFuRAj5kQeS1OTyLakrt7VRbGLgQ8RERHFDOb4EBERUcxg4ENEREQxg8nN+xGjYEQBLFFHo73nnCEiIqKDI6rziHw4Ucy0uUKqDHz2I4KejjwKhoiIiJomCsOKQqhNYeCzH9HSU3vios0yTURERB2PmMJGNFzUXsebwsBnP7XdWyLoYeBDRETUuRwoTYXJzURERBQzGPgQERFRzGDgQ0RERDGDOT5ERERdpByL3+9HV6XRaORcdX8WAx8iIqJOTgQ8u3btksFPVxYXF4e0tLQ/VWePgQ8REVEnL9xXWFgoW0PEcO7mivd15ufodrtRUlIib6enpx/2fTHwISIi6sSCwaAMCkTFYqPRiK7KYDDInyL4SUlJOexur64XFhIREcWQUCgkf2q1WnR1xn2BXSAQOOz7YOBDRETUBcTC/JKKFniO7OoiImoB4VAIIV8AiISh0uugbIHRJ0TU8tjiQ0T0J5MuvZV2VO/IRemqDbDvyoe/ygGfw8nzSp3uvXz11VcjISFBtqysWbOm2f0XLlwo96uqqmpynzfffFOOxOpI2OJDRHSYPC434PVhz1cLEfL66tYrVCpkTz0GiAA6q5nnlzqFb775RgYqIqDJyclBUlISuiK2+BARHQZftQPw+FCwaHmDoEeIhELI//5XRAIB+S2aqDPYsWOHHCY+fvx4WStHre6abSMMfIiIDiPoyf1mMQJOF7xllVH3Cfn8CHp9CHq8PL/U4V166aW44YYbkJubK7uvevToAZ/PhxtvvFEOHdfr9Tj66KPx22+/NXs/osUoOztbjr4644wzUF5ejo6GgQ8R0SHwO1wo/GUl/NUORA5QJTccCAHKrj/Shjq/Z599FnPmzEFWVpYshigCnDvvvBOffPIJ3nrrLaxatQq9e/fGlClTUFFREfU+li1bhiuuuALXX3+9zA+aOHEiHn74YXQ0DHyIiA5BOBCAK7+o5kYkAqVW0+S+WpsZGr2e55c6PJvNBovFIosCim4u0WLz0ksv4cknn8S0adMwcOBAvPrqq7KI4GuvvdZk8DR16lQZMPXt21e2FolAqaNh4ENEdAjCwZpicULl5p1IHjYw6n5xA3pB2UVzJCg28n0CgQCOOuqoBpOEjhkzBps2bYr6O2L92LFjG6wbN24cOhoGPkREh0Cl1dS18rj2FiPk8yHjmNHQ2ixyndqoR9LoIUg4oh+0FhPPLVEHw8CHiOgQKLQaJNVr5Slbuxnl67YgYWBvdJ92HHqccjxMOd1giLPyvFKn1atXLzkFxi+//FK3TrQAidwf0e0VzYABA2SeT31Lly5FR8N2WCKiQ6A1GmDNyYJSrULZ6o1y1JZIeBYjvSzdM6GxmKCLgakDqGszmUy49tprcccdd8iChmKk1hNPPCEnQxUJzNGInB7RNfb3v/8d06dPx7fffitrA3U0DHyIiA6RzmqBsrcG5qw0OVWFKFio0muZyExdymOPPYZwOIyLL74YDocDo0aNksFMfHx81P2PPPJImQA9e/ZszJo1CyeccALuu+8+PPTQQ21+7M1RRFhdqwG73S6z26urq2G1sqmaiIg6Nq/Xi127dqFnz56y3k6sPlf7QV6/meNDREREMYOBDxEREcUMBj5EREQUMxj4EBERUcxg4ENEREQxg4EPERERxYxOE/jMnTsXo0ePlpOopaSk4PTTT8eWLVsaDXObOXMmEhMTYTabcdZZZ6G4uLjdjpmIiIg6lk4T+CxatEgGNaL89fz582Xp7MmTJ8PlctXtc8stt+CLL77ARx99JPcvKCjAmWee2a7HTURERB1Hp6ncvH/Z6zfffFO2/KxcuRLHHnusLFj02muv4b333sPxxx8v93njjTfk3CEiWBIVJYmIiCi2dZoWn/2JQEcQc4gIIgASrUCiRHat/v37y/lFlixZ0m7HSURERB1Hpwx8xNwhN998s5wMbfDgwXJdUVGRnEk2Li6uwb6pqalyW1N8Pp8sc11/ISIiorbx4osvokePHnIKirFjx2L58uWt+nidMvARuT7r16/HBx980CJJ02Juj9qlW7duLXKMRB1VMBiUM4mLxW93tvfhEFEHEQ4GEfJ6EHQ55U9xu7V9+OGHuPXWW+XEpqtWrcLQoUMxZcoUlJSUtNpjdrrA5/rrr8eXX36JBQsWICsrq259Wloa/H4/qqqqGuwvRnWJbU255557ZLdZ7ZKXl9eqx0/UnkSgU/n7Fuz+/Ads//Ar5H3/Cxx5hfAxACKKaSG/H87cHajeugH2HZvlT2fuTrm+Nc2bNw9XXXUVLrvsMgwcOBAvv/wyjEYjXn/99VZ7zE4T+IhJ5EXQ8+mnn+LHH3+UM7PWN3LkSGg0Gvzwww9168Rw99zcXIwbN67J+9XpdHIW1/oLUVcNeoqWrkbJinUIerxynbesErlfL5I/iSg2hYNBuPJ3Ieh0NFgfdNrhyt/dai0/orFC5OfWz81VKpXydmvm5qo7U/eWGLH13//+V9byqc3bEd1TBoNB/rziiitkk5lIeBYBzA033CCDHo7oIgJCPj8cu/dGPRXFy9ZAnxgHnc3CU0UUYyLBQKOgp37wI7ZD3fLhQllZGUKhkMzFrU/c3rx5MxDrgc9LL70kf06YMKHBejFk/dJLL5X/fvrpp2W0KAoXiqRl0U/4j3/8o12Ol6ijcZeUN7kt4HAhHGj9/nwi6ngioVDz28PNb+9s1J2pq+tAREa4yA4XCxE1pNJpmz0lCmWn6fkmohakUKma365sfvvhSkpKgkqlajTDwoFyc/8sftIRxQhDUjwUquh/8qbMNCg1neZ7EBG1IIVaA7U5en6rWC+2twZRgkbk59bPzRXlasTt5nJz/ywGPkQxQqHRIHPikYBC0WC92mRE+vjh0FpM7XZsRNR+lGo1TFk9GgU/4rZYL7a3FpGX++qrr+Ktt97Cpk2bcO2118qpqMQor9bCr3hEMUJrMkCRkoReZ02FfVceAk43TOkpMKQmMqmZKMaptFqYs3NkIrPI6RHdW6KlpzWDHuHcc89FaWkpZs2aJQctDRs2TE5RtX/Cc0ti4EMUQzRmI0SjtT7B1t6HQkQdjFIEOa0c6EQjStWIpa0w8CFqAwGnC+FQuOaPzqiHStM6feZERNQ8Bj5ErSjo88Ff7UTxsrVwF5ZAqdUgfkAvuaiMBqjb4dsVEVEs46cuUSvyVzmw6/MfRD0GeTvsD6B87Wa49hbLROOw0QDtAYaZExFRy+GoLqJW4ne6ZUtPbdBTn5giQiwRrw8hf4CvARFRG2HgQ9SK1VDdRaVNbhfbxJxZ4S5WFZWIqCNj4EPUWhT7Rkk09cen0SDg8jSqq0NERK2HgQ9RK1Hr9Yjrn9PkdlN6MtR6HcJ+zpFFRNRWGPgQtRKVVoPEwX2hi29cMydl1BFwF5fJujrhAHN8iIjaCkd1EbUirdWMbpOPhre8Es7cQjlRqCkzFd6KasT17QmR9qwx6PkaEBG1EQY+RK1MZ7NAqdPCmJKEkGjdiURkK5CYjE+lUUPNwIeIqM2wq4uoDWj0uprpIkxGqPQ6OUu63maBxmjg+SeimLV48WKceuqpyMjIgEKhwGeffdbqj8nAh6iN835EsMOAh4g6mqDXB1+VHe6ScvlT3G5tYib2oUOH4sUXX0RbYVcXERFRjAs43di7aDlce4vq1pky05B53BjZWt1apk2bJpe2xBYfIiKiGBb0+hoFPYK4Lda3RctPW2LgQ0REFMNCXl+joKeWWC+2dyUMfIiIiGJY6ADzBXa1+QQZ+BAREcX4oIs/s72zYeBDREQUw1R6nUxkjkasF9u7EgY+REREMUyt18nRW/sHP7WjusT21uJ0OrFmzRq5CLt27ZL/zs3NbbXH5HB2IiKiGKcxG5E1aZxMZBY5PaJ7S7T0tGbQI6xYsQITJ06su33rrbfKnzNmzMCbb77ZKo/JwIeIiIigboNAZ38TJkxAJCJmLWw77OoiIiKimMHAh4iIiGIGAx8iIiKKGQx8iIiIKGYw8CEiIqKYwcCHiIiIYgYDHyIiIooZDHyIiIgoZjDwISIiopjBwIeIiIhiRqcKfBYvXoxTTz0VGRkZUCgU+Oyzzxpsv/TSS+X6+svUqVPb7XiJiIgourlz52L06NGwWCxISUnB6aefji1btqC1darAx+VyYejQoXjxxReb3EcEOoWFhXXL+++/36bHSERE1Bn5XF7YiypQvqtQ/hS3W9OiRYswc+ZMLF26FPPnz0cgEMDkyZPltb41dapJSqdNmyaX5uh0OqSlpbXZMREREXV27goHfnt7Poo35datSx3QHaMvPgHGBEurPOY333zT4LaYjV20/KxcuRLHHnssWkunavE5GAsXLpQnrl+/frj22mtRXl7e3odERETUYflc3kZBj1C8aQ9+e/v7Vm/5qVVdXS1/JiQktOrjdKoWnwMR3VxnnnkmevbsiR07duDee++VLURLliyBSqWK+js+n08utex2exseMRERUfvyOdyNgp76wY/YrjPpW/UYwuEwbr75Zhx11FEYPHhwqz5Wlwp8zjvvvLp/H3HEERgyZAh69eolW4EmTZrUZHLVgw8+2IZHSURE1HEEPL4DbPe3+jGIXJ/169fj559/bvXH6nJdXfXl5OQgKSkJ27dvb3Kfe+65Rzav1S55eXlteoxERETtSWPQHWC7tlUf//rrr8eXX36JBQsWICsrC62tS7X47C8/P1/m+KSnpzebDC0WIiKiWKSzGGUis+jW2p9YL7a3hkgkghtuuAGffvqp7JkRaSptoVO1+DidTqxZs0Yuwq5du+S/c3Nz5bY77rhDDovbvXs3fvjhB0yfPh29e/fGlClT2vvQiYiIOiSdSS9Hb4kgpz45quuSE1otv0d0b73zzjt47733ZC2foqIiuXg8HrQmRUSEXJ2EiAgnTpzYaP2MGTPw0ksvyeJHq1evRlVVlSxyKOoBPPTQQ0hNTT3oxxDJzTabTXZ7Wa3WFn4GRERELcvr9cqGANFiotcffpDic3llIrPI6RHdW6KlpzWTmkWR4WjeeOMNWZD4UJ/rwV6/O1VX14QJE2TTWFO+/fbbNj0eIiKirkJn0rf66K362qvdpVN1dRERERH9GQx8iIiIKGYw8CEiIqKYwcCHiIiIYgYDHyIiIooZDHyIiIgoZjDwISIiopjBwIeIiIhiBgMfIiIiihkMfIiIiChmMPAhIiKiNifm2BwyZIicV0ss48aNw9dff93qj9up5uoiosZz3QQ9XkTCYURCEYRDQSiUSig1amhNRp4uIjpoLocbzkoHPE4vDGYDzPFmmCyt9zmSlZWFxx57DH369JGfZW+99RamT58uJxsfNGhQqz0uAx+iTsrvcCEcCIgpjhHy+uDaWwx/tRPaOAuMqUmIBMPQ2cztfZhE1AlUllTivSc+xOYVW+rW9R/dDxfccS7iU+Jb5TFPPfXUBrcfeeQR2Qq0dOlSBj5E1JDP7kTF+q1QatUwpiQhb/4viIRCddtFi0/WpPFQqACtmcEPETXf0vPefkGPsPm3LXjvyQ9x6axLWrXlRwiFQvjoo4/gcrlkl1drYo4PUScT9Hrhr7bDW14FW89uKFi8vEHQI4QDQRT+shJhfwgBr6/djpWIOj5npaNR0FM/+BHbW8u6detgNpuh0+nw17/+FZ9++ikGDhyI1sTAh6gTCfh8iASDUKkjSBnVFyqdGolD+svurkb7iq4wfwB+JwMfImqax+lt9vR4XM1v/zP69euHNWvWYNmyZbj22msxY8YMbNy4Ea2JgQ9RJ+FzuOAtLoRj52Z4SwvhKy2CY+cW6KwaZE4YE/V3IoigaNMemThIRBSNwaxvfrup+e1/hlarRe/evTFy5EjMnTsXQ4cOxbPPPovWxMCHqBPwVDsR9nsRqCprtC1gr4QuzgSttWEuj0Ktgkqvw57lmxHw+NvwaImoMzHHW2QiczRivdjeVsLhMHy+1m2l5qguog4k6A/CXmVH0B+SyX5KpRImvVb2ZIXs5U3+nr+6HHF9e6Bkxfq6dSmjh6C6qBI5Rw2qyfOJRKBtxW9uscjlcqOyvAoajRpxNgu0Bj0UUbodiToyk8UoR2+JRGaR09NwVNd5rZbYfM8992DatGnIzs6Gw+HAe++9h4ULF+Lbb79Fa2LgQ9RB+D1+VJVXy0TCDUs3QWfQYtC4Qah2eWBLsomvQk3+biQUhNZmlbk+ujgrkkcNhspkRvHmPOxYvA6r/70IcVnJGHTKWOgtJqi0apgSrW36/LqSQCCIvbkF+Pc7/8XyJauRnJKAcy85E3369kB6RgqUan60UucSnxIvR2/JOj4ur+zeEi09rTmaq6SkBJdccgkKCwths9lkMUMR9Jx44oloTYoIO/8bsNvt8gWorq6WlSSJ2kIwGERVSRU+evYTbFy2ucG2U644CeOnjEbYXYWQvXFXl6BNSIbLGUHIE4BSo0IEChRtzMW2H1c32nfkBZNQvrsIOROGYE9REZKSEpCUmggTCx4eFE+1A7v3FODSv9wIj6dh0uel15yHcy86DRlZ6bKQJFFb8Hq92LVrF3r27Am9Xh+zz9V+kNdv/mUSdQAupwsbl21qFPQIX772P3h9fij1ZihUUVoSlEro4pOw+LnPse7LZTCnJkBj1GP7gjVRH2vDl0uQ0jcTv732DcryS/CXk67ED18vRkV5ZWs8tS7FUV6N6rJKPHL/042CHuHNVz5AZYUdoWCgXY6PiA6MgQ9ROxJTTTgq7LBXOqCyanH+rPMx/frTkJSZ1GC/hZ/8BIVaA3VCJlSmP77JqEwWmLv3RcmOIhx97ckYf8WJMMabEfD4mhzJ5bW7odZq4CiuRGZKsuweu//2x1CYX9zqz7ezBz2iLc1ebcfvazY1ud+6NZvgbsXhv0T057AjmqidhAJBVJVXYsEPSzBv7stwOlxyfXaPTPztgVuw+L1FyN+2V66rLK0ClAoEQ4BCFw+tJREqtQpQquB3O2AxBRHy+2CIT5f5JSpN83/atd0w3gonEhJt2Jvnwfv/9ynufvBGmM2mNnj2nYujwoFgKAxFKCBqBDRLLUbTideGWm0AgKfSIQN4pVoJrckArVkPnbFrd/FQy2HgQ9ROvA43Nm/aiTn3PtVgfe7uvbj1+ll44ZW5ePO+t+S6PsN7y2Ge+Su3whBnRvoRPaHQqBCsLEbY74fabIEpqzuUWp3cXyQuq3UaBH2Nu1ysGYlwllXLf4th8FWVdvnv/D0FcLs8DHz243G4oEYYynAISq0GZqMBQ0cMwtpVG6K+riPGDIHBwItwa/3N7PplAzZ8uRRh8S1AvIctRoy7chpMyTaYEpiXSQfGri6idhAKhuBwuvDc3/8VdbvL6cbqVevQvX824lPjkZSRiIAvgKShvdD7uKHyA16MzjJl9YC5Z28YUjOg0v0xlFpvM+LIK6ZBoWw4tFqt12LI6Udh24I1MCZYUFpdLR9L6D+oD4Oe/XirHKjauB27P5uP3Z9/j52ffItwYQn+NucmGIyGRq/bX2+8BBaLiaO6WknZ9gKs++yXuqBH8DncWPz8Zwj5AvDuey8TNYctPkTtIOjxy66Tndt2N7nP5s07cOrkSUjNTsGmZZvQs183OOxVjbqsFFG+v6jUaqT074bJ91+E3Us2wlFUCVtmEpJ6ZWDdf3+RXTE9Tx2Nm258QO6v1Wlx9gWnwmhqfDGPVV67C/Yde1CxtmE+T/X2PTCHwvjgvy/h4/e+xIrf1iIxOQGXXnUuevbujuTUhvlZ1DI8dpds6YlGBEL5q7cj5+gjeLrpgBj4ELUDMceWIhxGVnYGdjQR/PQb2Av9R/bFvBufwy1Pz8SGFVvQb0Tfg34MkcBsS0/EkDOOhqfahZA/AGdJFYb85TisXbcZN8ycheKiUnkMDzx+B9IyU1rwGXZuoYBf5vOU/x594kbnrjxk98/BRScfhwv+Mg3m5ARoDToYmB/VaoJeP5wi160JVfmlnJqFDgoDH6J2IIISq9WCa2+5FLdfV9PqUp9ogZk4cTxWL1qDG5+6VnZKZ/bKrClkeIhE95cxrmY6C0tKvLw4DNIp8fQ/H5LbbHFWZHZLZ8XhfVwOF5Q+N8KBEMKBpoelB5xuKMIRxKcmIaBUMOhpdQpY0xJQmVsSdWtcphih2PpHQZ0fc3yI2onMtxncHzNvuRzqeqOw4hNs+McbjyExOR7jTxoLpVoFrcGAzF4ZLRKciPsQrTyDhw7AoCH95b87yzQLoksjFAyipKgUe3bmIW/PXvj9gRbJuaoorUTetnw5o723eG9NN2K0IoQKBQxZ6fAYdAimJaHS6cLnH3+LPbvy4PdzTrTWotZr0HfSiKjbRCXyjKE5bPGhg8IWH6J2YrAYoVIpZaXfaacdj+LiMuh0WiQmxiMx0QaVSgWNQQ9rUhxivYvDY3fLn4UbdssyAGkDsqFWALPufRJnnHsSho8cjMzsjMN+DFEuYN0v6/Ht2/Nx45NXQh8KIuR1wtorG9X1uiJFqQDT2KH4z+c/YOG811Bd5UBaegquvvES/OPpN3DmuSdj5NihUHPKihZnsJpgTrXhiNOPwqavl9eNWDQmWjHi3AnyiwTnoqODwcCHqB1pjXq5xCXFIzunG1+L/QRFXlJZNQp+34X1n/9at37T/5bLb/hzH78bUyZegIuuOBsXXn62DEIOVXWFHWsX/47PXvpc3q6t+yhmvU8c3AsBlxvughLZ+hM3YQx+/mkVTjn5eJxx0glQqpRwBwN48bk3ccpZk/HUIy/h2X89gvSMVL6WrcCcHI9QIISxV0xFJBiuaZFTKmDLTJRfEhhw/jnVVQ5Zwd1hd8JiNSMhMR62uLabmf2xxx6TE5fedNNNeOaZZ1rtcRj4EFGHHskT8gcbBD21CtbuRGr/bFw58yK8+sLbOP0vJx3UfQaDIZQWl8kpJ9QqlbyQzn/vh7rtG5Zvwdhx3RFyO+EpykPK8L5QjBoMpV6Lymo3jho5FK6SKuxYtQ3hYBjpg3tg1uyb8Oln32LaaZNQUlTGwKeV6Ex6pPTJgqvcjoDXLwNPMT2Lwdp6E2nGiqKCEjxw1xP4dfFvdevGHzsaDzx+J9IyWn/gw2+//YZXXnlFTlTa2pjjQ0QdmALbF//e5NYdi3/HheefhjfffRoepxvhYLDZe6sor8Jb//wQZ0+9HKdPugRnTrkM7771Cc64cTrUmppqy99/uAgepRlKjVbMKQJfeRF8FUWyBIA6CGz5doWc7V7UlKnYXSSHWC979WucddZJciSe1+Nr8bNADYkCnXGZSTLZmUFPy7T0PLBf0COI22K92N6anE4nLrzwQrz66quIj49Ha2PgQ0Qdkhh9JvJ6fE5Pk/v4nF75rb97VgZSTFbYi6vgqXIg5PPBUe1A3p4C7Ny+ByXFZTLx+IP/+xTPPv6KbMoX/D4//v3Of/F/b3+M4849Tq7zOD14/o5XUexUI2ROgTYxFaZuPeB3++EsrkTpvmlE6nOVVSP/ty3o3rMbMrulteJZIWp5FeWVjYKeWmJ9a09gPHPmTJx88sk44YQT0BYY+BBRhxT0+WXdoeReTSctJ/fJxK5fNsohznuWbsLCeR+jcMMeuKpceGTWMzjluAtky87FZ1yHndv24JvPf0BGVlqjUWw/LViKjL6Zdevt5Xa8cMer+Oec9+BVGRFWawClUhbJa0reiq3QK9VISGr9b6xELcmx74tAU5wH2P5nfPDBB1i1ahXmzp2LttKpAp/Fixfj1FNPRUZGzfDbzz77rNE3xFmzZiE9PR0Gg0FGj9u2bWu34yWiQyeSiUV1ZEdegZxsVcwtpo+SwyGG+fefMgo7Fq3Fine+l5WqtUad/LerzI6hQwbUDW8+ZsJYZKYn4aVXZuO1Nx7GZ9++hgsvO7PB/ZWVlUNvqpnrTDBajLjwzvPx1NyXsXXzLihVzU88Kh5JdIcVF9QMta8qr5kPjaijs1hr6nw1xXyA7YcrLy9PJjK/++670Ovbbn67ThX4uFwuDB06FC+++GLU7U888QSee+45vPzyy1i2bBlMJhOmTJkCr9fb5sdKRIcX9OxduBzQ6eAMq6CPM2HjV8swZsYUOYqrdu6xxJx0HD3zNBSu31U3rHnztytqpiyI1OT+HH30aLn++lsvw9VXnIZA/g4YvNVysfkqcdmF03D7vX+te+yktEScecuZmHLZZJxz+9m4+YUbsGzFGqRlJOPpuS9jx+489Bw3sMlj7z6mP/758ruYPulinDH5Ujw253kZANWKhMMI+X1yOVAuElFbSkiMl4nM0Yj1YntrWLlyJUpKSjBixAg5Ik8sixYtktdx8e9Q6I852WI28Jk2bRoefvhhnHHGGY22iW92Yvjbfffdh+nTp8vM8P/7v/9DQUFBo5YhIuqYHLkFSBjcB4GIAlplBH6E0Ouc8SgPeND75DE44W8XYuIdf8GAk8bIYediWHut6r1lMCXVzM5tL6yAal9V6pNOPhZqV+PWF7XHjkmTxsp5tsSolb35Rbjp+vvx0RdfIaVnCj58/7945P55cvLWlcvW4uqL74Ah1Yak3o273kQtGXW3eLz7xifydjAQxP8+m4+/XnIH8nbvlcGOu6RI5h6FA355O+jzwe9tOn/pUPi9PgQ9XgTcbjgrqlu1a4K6HlucRY7e2j/4kaO6nriz1Ya0T5o0CevWrcOaNWvqllGjRslEZ/FvUcusNXSZ4ey7du1CUVFRg+Qom82GsWPHYsmSJTjvvPOi/p7P55NLLbvd3ibHS0QNBb0+uIvLYMhMR1CngD0UxEsPv4oF3/2CuHgr/vbwraiqrMbC73+Rs8ifedY09DtjHDZ/ugSRUFgWrwv6a1pSzClxCITCOHn6JJgVAYSbONkm+HHBjDNw9MRxKC0qw4uvz0ViciJee+lddO+ZhTvunykDo3MvPh22eCteeP4tXHHleehZYseOn9bJStJZI/pAmxmPa664q1Hl4L15hdi4bgsS9KLysAEBRzU0ZitCPhcUUEBttiDo9UKh0RzWh3wgEIAiGESgsgyB6kpZVVobnwiNPg72CjusCTWBINGBpGWk4PHnZ8tEZhE4m9ugjo/FYsHgwYMbrBM9NYmJiY3Wt6QuE/iIoEdITW1YOEzcrt0WjUioevDBB1v9+IjoACIR6JMSoLGasXdvIebNfQWrlv8uA4KHnroHjz/wHHJ3/zGi6uvPf5ABySW3nYOKjXmAP4Tc5Zvltr7HD0eFz4Mpp0yEItx0c7kiFMT5l5wOvUGPDKsGYbcDXo0CQ4YPxIf/9xnsdgeOOm4spp8zFe+98QlUKjUqHQ7Mnv0kTpl+Ao46ZhSsiTZMnzQDXm/0Yew/fvczJp1wpAx6xPB45+6GeYeG9CyorXGHFPi4quxQKxUizoEzbyciwT+m7fAWFyBQVQFDVg5cdhdMVtNB3y/FNlucpU0LFraXTtXV1RpElcjq6uq6RSRbEVHbU+l10NkscPn98PuDMugRJpw4Hgu++7lB0FPrw7c/w54d+VizdgfSR/RG+a5CjLjgeJQ6HTj/9L9iwfxfodA1XdxOZTCh+Pfd2PnLBij1NoSNKXCWOZGZkoyExDgUF5biPx98iWsvuQNnX3AqXE4XvvrsexQVluCYiaORog8i7HbJ1qCmpKQlIeByQqnWwFde2mi7pzBfVFU8qHPkd7rgraiCWqFA5cbt8FWUNQh6aoV8XoS9LoQDzCWizmXhwoWtWrW5SwU+aWk1tTOKi4sbrBe3a7dFo9PpYLVaGyxE1PbESE19QpycYbui7I+6IcdNOgrffrmgyd9b8OMvcNvdWPPzeky861zMX7ECF54zU3Y7vf3aR6j0hQFF9MlGw2oTLKlxSO6diV9f+RKLnvoYq1/9FqXfrMXMS87FZVeeK3d1uzx45/WPccqZk+Hz+XH5NechWRdGxOeFWR3GRZef3eTxnXbmZKh0WhmkNMVXXoJwONxkF6Df6Yav2oGK9VtRtm4rvGWV0FrNCHmaLiznr66EWtNlPuKJWkyX6erq2bOnDHB++OEHDBs2rC5fR4zuuvbaa9v78IjoIGhMBigqq5GUkli3TlRUFoUGm+L1+aAyxOHHjxYiZIzgpWffglqtgtFkhMvpxqy7nsKTz94DndeOkNcLlV4Pld4AhcGGgEIJrVYLnVqFY2eegoDHi0hYgdxV27H582U47fLJyM0vxI6tu/DzgmW46a6r5UiTvn2yEXbX5APqbHGYPHk8liz+Db/+tKLBsd3z4I1ItBmh1OgQjtIyUyvs9yMSCsLtCsDt8EClVsMaZ4K3vArFS1fDU1YJtVGP+P69kDygN4qWroY5MxVQKZsNJBXRAj6iGNepAh9R1nr79u0NEppF5ndCQgKys7Nx8803y1Ffffr0kYHQ/fffL2v+nH766e163ER08FIzU+Fye9Gteyby9uyVXV5HTxyLH775Ker+xx57JBb930K4ql1ISEzA3Q/ehLT0ZDk9RWJSPPJzCzDz6tl4/Z2nEHJ6sXdTHgzxgCHTiOoqO1KTbfCWFDboMuoxNB3dRp2PyjIHzpw2Fb5JPsRnJEClUEKj1cBo0AEOIKQ3I6LWQufIw+zZ16K40olff1oJi9WE8UePhEUZgtlkkPepNhjh90fPA1JZ41C4p1TODr9rw25YEyw44byJSLHq4CmtkPsEXR6UrlwPX2U1rD2yULl5B1JG9oPfF71chzYhCeFWGhVD1Jl1qsBnxYoVmDhxYt3tW2+9Vf6cMWMG3nzzTdx5552y1s/VV1+NqqoqHH300fjmm2/atDASEf05oqUiMdGGp1+ag1uunSVzap5+5SEs/XmlbMGpb/iowdCEVXBUOJDWPRVZ3dPxxCMvYNuWnVBr1Jh6ykRMP2syXn/7Kaz96CcUrt2JXiePhiXZim+++BGjxg5GpNSJyH4J0GJmdnW8Bt+8+z027kuYFsd10uVTccTYAYhLtMEdcOP37UXoBxWMkQh0nipkG5XImT5edrOFPPuG0EdSZA0ffXIq/PaqP6Z/30ep06OkxINnbnoB4VBNd1d1WTXemPM2jjp5LMaN7Q3n5j++8Nl35iFxSH94S0V3oBpKvQHh/YbFa6w2qLR66Az87CPq1IHPhAkTGg0XrU98MM2ZM0cuRNR5xYmCaWEFHn3iHtgdTnidXrz18Qt4783/YPGPv8JkMmL6mVMxaEA/fPT3j+XvHH/BRGzesl0GPWIo+nP/nAOLMgitEihatwsFa3ag17RRWLhmDaZmJ+KDdz7DiZNGI+KtinoMIXs5Jp51dF3gIz57vnrta/Tslw2VIgK/2oh7bpuLjz9/6Y9fCocRdLv+uC2GXSlVCEUApUIBS05fuPP3yORjMQmqW2dFWKHFh0+8Wxf01PfLV8swfuromvup99nnK6+CJacb8uYvQebEMdBaEhDy1tTu0SUkQ6HVQm2oaWmi2NHc9bGriLTAc+xUgQ8RxY645Dg5Aamj0onta3egaEsBrr7uYlxyxV9QsGMvln6+DO/9Z5WsVXPWjWdgT2EB9uzOk8PCn33lQcQFHDJvJmJLxbYff4XGoIPfpEZyWhI8Hi/m/P1uWIxaBJso7B4JhRpMYVHr56+WIn1YJsKIwOv2ori0EvqMdPg8Pmh1GmjhQ0QUTBStQPGJUGo1UEaAgKNKjGaHsVtNBeo9e4pww4x78LdZN2HvjoImz8PuzblIt5rhr66XyKxQIGXkYFiy0lG+bitUOh3iB+RAlxAHpU4rq952FQF/AIUFxSgrrZCFIU1mI+IT42HR6eRrHfD6EfIFoTXpoLeaoNZpEGtqSyGIiXjFdE1dmdtd0+qr0Rz+69x1/jqIqMsRQU1YEUFAF0ZaWgp8fi8qKquQnpOBv9x6NsLhiExkDgUD6DM0Bzdccx9OmHoMrCox4mrfUG6FEj6HR87ltWnrDvTq2xN33/iQHEX11rtPoKnB7gq1Bo7yeq03+1SX26Eo0MjA58G5d2LP6lx88NC/4ff4ZaA2atJwTLvoeOiUfuiS0xBw2OEpyK37fW9JAdzmJFx10a0oK6mom4ajKaLLDt4/vuUqlEqoDTrkffcT0o4ZhYzjxsppOlR6LdQ6LboSp92B35auxZx7n0J5aQW0Oi2On3IUbr/rOkSCYSx57VtZwqD2vOQcMxi9Jg2DKc4i501TKmMjuVsEukajEaWlpTIg6IrPOxKJyKBHTHERF3doda/2x8CHiJr9sBH5KXIGTqWiXT5QRQLyrdfNQu++PXDVDRcjKTkR119xF/42+yZkmG1YP38VPJVOxHVLxnPzZsETCUAdcP5RrTnsR1KfTChUSgwbcQSuvOAW2XIgFJVVoVe8BpFA4xFXEWMc5r/YeLqbrH6ZWLh8Ga695TJsXLQBP3/6S9020V21/LuVqC534IzrpiN39Q4kptigNcYB7pouNZXBiPz8Uhn0CKtXb0CvITnY8fvORo8lgqLsPpmo/mVfDSOFApnHHwmV0YCs48fJ1iStpXUmkGxvojtwz45c3Hz1ffJ9eMW1F2LaycdDq9JAo1Thl5e+gKP4j7IH4n26d/V2WPukY29RKfw+H+LjbYizWaFRqaAzGbpsa5BI8xCTc4sBP3v27EFXFhcX12yJmoPBwIeIGpHJuQE/EAr+MaGmQiGL8Il8lfLyavy+eiPKyyoxdMQgpGemyhFULamsoByRSFg23c9+4g4MHzoY8VYzPKEA5r0wB57NBVjxyfy6/Uu35mPRtnyMu+pkqNMS4C+rqdgedlVh0EmjsHXRenz/9aK6oEf42+1P4J9vPYY4tRchz77EaaUS2sQUrF2xR46wqk9v0qP3yN545KnnccMtV2JZvbnC6tuycisqSyrxz7+9JoPGY08/ChOnj4bJYoBSq0Pu0h/r9v33u5/h6RceQuGuIrgdDZO3z7xuOoxmPRR9e0JrM8PasxvUJqOctb4rE0P/q4qK8Oo/3pPvxdvuuRYnTjkOG5ZshNqoQU5ORoOgRzDEmdHrrHG4+29PYNvmnXUBwSlnTMZ1N86AweGBIcEMg6VrVrIWZRnEiGbR3dVVaQ5zapf9de2/HiI6JH5/AA67EzazAb6yIvgqy2WuikKpgi4xWbZWqHR6uOwOPHr/07IbRnwYXX3DxRgzfgR0eh2SkhP+VMtQ6d4ybF29Db98sQR+jw+Dxg3CsaeOg8IfxJKXv4Qx0YIhZx6D7xd93fiXI8Dajxfj2BvPaJCroww6kDPhCPzzrv812L1wbzFmnHcr/nrDxRh31HAkJyeguLgc/3v3Wxw1bjS69euGvC011dxFq8zZ10/H1l275cXU7XAhsG9m+GiqSqthMBtkccXcrXkwJUxGoLwIusQU9OyZWbdfZUU15sx+Cn+bfTPyN+Zj79a9sCXbcNQp4xCfGof45Hige+OJUbsyEWx7fQFs2bRDTjQ7eeoEvDP3PegMOqSNyIQlt/HFr/vk4bjtlofk/Gi1RND0xX++lfdxyrixSOmd1WUDH0H83XEU84Ex8CGKUSHRvaNQoKysUuZP+Lx+xMVbkJKaBF95cYNKw2K4t7e05qItiuL17JWF+T+/h6A/AIVGix3bczHnnqewfesuzLj6XJw8/QQkpyYdVtDz4byPZItJreLcEiz/ZjmumXMZPJUOqPVaOAorarrfonBXOGTCqwjSwvtaccIeJ3QaLXr36YEVS9c02F9UiX509nM48uiRuOK6C7H0pxU48qjhSMu04oq7z4Q/pBTFpKFRBqH0VmBA/xx89tE3sNgsskWhqVEmJptJBm7HTh+PSWePhyIckN0xAWc1MtISkJWdIWsMCTu37cEVF92C4aOOwOV/vQBDRw6GQaeEztS63VhyRneXR9YI0iXGQaFWQ6VVt3+OSDgMjTKCrOx0DBt5BIr2FCN3Sx7OueNsPPLIc3jq8Xsa7C66sJwhX4Ogp76P3/sc5154GtZ+shjjrjwJxviuPx8VNa3rZUARUbNCfjEFgh3uwjw5tNqkVaJ7jwz07dMNiSYNIi4nfAo9NBk5UJkaXiB8FaUy1ycSDCHoqIZSJFKGQ8jJiMfzL/wN3yx8G+tXb8TD9z2Nin2F9w5WMBhE8Z7iBkFPLWe1Cz99tRQZw3sj6PEdsKtHXLj3VAfhMcRDY42D2mKDIxjB6eee1ORF/ewLToPP48Vf//oX9E3QQmsvg8JRBp27BFp3CRTOCkSCQVlLaOCgvghHwhgwtn/U+4pPjUdCShxuevpanHLZZOiUIQTsVTLPSLRAGf1O/OO1R3DEsAF1vyMKIw4dPgCDhvRBOH8rgtWHdv4OVcDpRsHPK+H2h2APRLDo8yX44d8LkL81HxVFrfvYByICME3Ag6tmXoTR44Zh5Q+r5Hq1Vo3CvUUorqyEMfGP6YV0FgPy85qejFpMICumGinfUYiQn/OXxTq2+BDFEI/DhVBVCfyiC2vfzOCiNUMZCcFVUgQ3jPj+wx+xa+Me2JKsmHzhJKSnpQD2kpo7EK0b4QhCiCBitMBTmAvUL56nUOLRx2/DzTc8gpLCYtjizFBpDm6kkegSWr2wYWtMfWt/Wocx916IvOVbYE2Lh0qjQijQeOb1hB6pUOs1SEqMw4ZNO1FcUIxIOILBIwbJyssPPXU3Hpv9nOzSE/R6Ha65aQZW//Y7PnrvCyxe/pEcBh+NqL2TEB8P9/Zq/Ou713HFg5fCXu5A/rb8un3ikm245pErYFE6EXQ64CtyQ5+UKi/mYkJSfUq6PP+2iB1P/f02OPxheWG2Wkyw6dXQKYOQZ7QVa7KIrqSS1ethGtQfP3+5BN+/90Pdtq9e/wajTxyJky+fBovFAK256UleW4vIJdPFJ6GH3g9HQCEDHrleoZAB4uNz/4Fnn38Qe75aAXthhRy1163bH0Hk/gwGvXydlWrVAUfRUdfHwIcoRjirnYj4PAjsC3pEt1XY54XapIanpAAVbjWev/05hII1wUR5YTlevvtfOPGCSTjq+P5Q7BuVpFCrYLd7oXZVNQx6hEgYzj078MiTd+C7/y1CTk7mQQc+8r6buSgpVYqaEWYA8tfuwKiLT8TyN75t0NWkNeow7JzjsHtbPjJ6pKJfvx5wVDtwzLGj8evPKzHrzicwYvQQzJp7O1QqJYLBEDIyU7H0l5V4941PZC6I3e6B0WxF0FkzF1d9mqQ0/OP+t5G7uSbv5405b+Hky09C4uXTaqbMSItHXKIZGlcpgvumpwj7fXAX5MqAR20yy2HXolqzOPdaTzXkrGRisJE3AFNmPzh311Rp1sb9MV9ZSwu6vbLic3lRRYOgp9Zv81di4NgB6NMzGQplMjTGtq0NI86RPikFyqoKaAIRHDl1DFZ+vwqblm7ByaedgM8+/ho3XHc/brjpMgyeMhxhfwimtDhk98hE7u59I+DqOefC0wCHD9mj+0G7bwoRil3s6iKKEeLCjH0Tawoai03m8YjE5YDShA+e/k9d0FPf9+//iIBCD6XJCkVcKvwhwOd1A57GgYEkRmKpFUhJT0EgSvDQFKPViBHHj2hy+4gJw1G2uaYezvrPfkU4FMIJ916APhOHIWNoLwyefhSOu+UsrPjpd/zzb6/Lua+SbWZMO2EMfB43nnjoRfm7q377HXfMfAC3XTsb99z0MC4/72b06ZcjWwUe+fu9ePvRDwBrMnSilUZZk0QrAhV1ajcsX7ixLugRPE4vPn7uP3ht9hswWPSITzRDVVUgg539yRyphCS4C/JgTO8GbXxiTUVm8Q3UaJZVneWcYaGgfG1U2sbFE1uKOHeG7llY/N+fm9xn4ceLEdTpEfJEn1+sTVp9ElNgTUxAUmYSRhw/HGsXrsUpp5yI0UcOR0lxGe6/90mcd+71uPHWB+B0ufHkiw9gwOC+dfchRgCdee5JuOjSs7D5698wYNoYGRxTbGOLD1EXIioJi0q3wUgI1RV2lJaUy64BMeFn/ra96JtjqeuyES0O4sIrWlH8wYhMII1GtKjs2VaAisJyrF+6CQmp8fjLTacj4Cht8jjCAT/698+Bcl/gcLBF2JIzkzD8uKFYvWhtg23xKXGYcOYxWPLCf+vWVewuwaqVW2BLtCF5QDby80vwxl//A6+rphTz4s9+Rt/7L4Bz1xZUB3RwOlyNh+yHQnIRo9Fe+OdcLP5gMXI35+L7fy/CiedPhCbTDI84p1Agb3sh/vPiH49fX9AfxLbV25GVnSQDvyZOpHxMQ1oGvKWFUBlNMthRqtQy0dxdlC/3MXXrCY3ZKvOnWovIkQp7vXBVNS7QWD+vShSIdOQVQJ8YF3WfoNeHkM8vk7xlhWqNGsoWnBhV3K9Kq0VSeiJOvepk2QX32/crccVl5+Hmu65Gfn6hHEUokqBT01NkMvnz/3wElZXV8nWzxVthNhjgL3dg3JXTYEmO/jwotjDwIeoCHFUOBFw+eQEWF9ewWoFH7n8Gy35ZKbdPmnosph5zLPoNzgAc1TXdQ/vmf1KIC9W+loemeFxe/Pq/5agqrZKja0YePxQ9UrUywIlGXNS19kJo03oc0vMQgc+pV5+CEZOGY8mXS+H1+DDk6CMw5OjBcOaVYeylk2WrlKiQbMlMxC8Pv4uFH0eftT0lKxm+ypqRaaJbqzmKEPD2nHfqus2MZiMCgTDWrt0g6/6Ii6pWq5FBpAgsozHsK5DXbOpsOCxbfjTWeIS1Rnj8YRg0IahFENS9N0TClWjpaG1qowEaXwB9hveW04FEI4bvqwJ+BMONc41CwSACdhc85ZWymKI/HEZJeSWMFhPi4qywJcTJoKUlieBHLD0G9UAkFIbRYsQRIwY22EdvMsglJTOlpvVy3yGoslJa9Fioc2PgQ9TJ2cvsqNxThM3/W47qvWVyqG73owfjkUfvxCMPP48F83/Gkp9+w3nnnobiQjsSDTX5JUqtVgY8YrSR0aRHdr9uMqjZnwgyREAigh5RwG/SeROhN5tQ5lPBaIqDThWCwl0pL+pyf50BCihhTEiC4jBaLcRjiVyZ7v2zZSBiS7RCpVbDYjUh4PbJqSbEdATVdjey+mRi25o/Zi6vL7NXOiKBmm4aq0kviyyKuj37i0+wybmeaoMecd/9RvaVLUEiIHrw7r/LROgXXnsMI08YgaX/W9boPsRFXgRnZUVVSNDqonZ1iSkwghEFnGozyvdWYPu2NXL2eI1535D1FmwpOZgcGtHKNXLSCCz+9OeabtB6RIAnXufiRb+ix8kTG2wT5ynocMNbXomKdVvgq7RDazXB0CMLSxf+hnXrt+LGO65EakbrBBtm68HV4RGvI1E0ikgsTOd6COx2O2w2G6qrq2G1/jFckqijCfn9CAcCCDgdCPpD8HtDWPufX1G+q2ZYb7ex/dHnxBE4/qhz5O2b77gaxoAGU86bCHXII4b2QGMwyWHtxqzuKCvz4pmbXpSTbdZ3yhUnoWBXITYu24TLZl2C7975HjvW/TG9Qt8RfXD+zadDaS+Ww99FV46YukFnMNS0JrWSgj1iaHII8657BsEoo7vuef0O2HQhWZNI5Ojsqgri6ovvkMOaa4kCjI8/fT9Wf7FSjhwaN30c9Da97AqymIxY8vlS9BjSE75QAKFICIMH98e/7ntd1haq77zbzkHe1nwMGtUPfYb1gGfvbkR0BlnlWszqJeoJec2JuOPmubKqcHbPLMz7xxxkdc+QgWV7Ea91eWEF/vvy59j02xYZ1PQ6IgdnzJwOTXUFQi4PUscMbTCyy+90w7ErH0VLaoaY12fo1xP/98UC6Ix63Hj7lTBZu+Z0GtS5r98MfA7zxBG1F3eVA2qNEp6ifIRc9WbsViqhScjAb+//hLJte2Uz/4Tb/4I5Dz+HxT8ukfVrnnrhQaQnJcNe5cCg0X2hVCuhFq0+VeWydcYd1Mi5pkQrSlxKHI49/Wis+H4lfvrsFzm8efOKLVHnlBowuj8uuec8aMRcSCoVfO6abjcx5Fzk4LQG0QpTVVIlWyvee/wDVJZU1RUOPOv605HVOxPJaTbYt2+WeTcKnQHVCgO+/Xox1q/dgn4De2HKKcfDJEY3FZajqKIMD97zd9j3zYJutphw/yO3omhdAX5fvE4+t8vnXIpQJIzygnLkbsiFwWrA6BNGYsV3qzB8/CBs/uwXpI/pC1W6Fe+88TGKC8swZtwwnHLmFKz/fTOWLP4Nx04ch/6D+yA9I7VumHZ7E615IggSgY9GqYRn2w6YszNgTEuC1mxqEGyLgoe7PvteBt3RWpIUwwfi4vNvwaffvYFuPbLa+JlQLLMf5PW7Y/zVEdFBcVXYEXB6EQy7GgY9gqgKXF6AYWeOx/ePfyQrG4v5jMYeNVIGPqKLaMvWHTj+pGPg9/plDoTOqIMiHIY6LVN216hDIfQ/egBKvJXYunUX4jdsqisoKLqVvno9yjQRADb9thmVBWXwbdqKjGNHw5NfhEAEUMfZZH0eS4IFZlvLfvu3WM3yQq01aHHNY1fJ6SNEorbRaoJSTKjqd8uEYUuP3rJVK+T1wAoPLjj9WPjOmYJPPvoW2zftxOihgxBRRXD7zAcaDI0XydB33fgQ3vn0Hxg0ZiDMNhNEDefsvtky16T38N7QqFXwVntgsRqx47sVsPVKw/rCPDxxXc0IstpRZG+//jHe/Oh5nDj1WNm9KLqSOpK4fUm/QdGK6PXDMnYotKbG9XtCXj/C/kDUoEcQ51+/r5q1x1OTZE7U0TDwIeokvC4PqvJKoTNpofQ2nKCxTiQMlTIMc0ocnCVV0JsNKNxbU8Zfp9Pi5NNPlAGOmPOoTr3uKJ1ajV59eyA+0SYrFIuE3kH9++KF216G3xto/vg8fvgrq7H7qwXocdIE7PriRznKJ37cSJQXBqDVaaHVH3xNn4NhtVnk4vP5aooZiuKK/iA0ejV8xeUIupxweb3QJaXIOcZEns3SX9fg6cdflfV8pk46Dh6XBx+8+98mp554658f4ta7/ioL4MWnxMuuKZH8XCuvOh89+2dj1S8b0HP6GFw37YpG9yHO5UN/m4dnXpwFg1GPyooAEpMTWmTCxZakFnlfYmmCCGxE9enmiO5N8bw4ZxR1VKzjQ9RJ+B0eWYG4pnpyE0OmhUgQOpMBGoMOpmQb/vvxt8jsloZX3n1K/jwYCYnxcgi8GM2U2TsTd/3rNlgTmp/fSG+ouWCK6SzsewpgykyVrQOONRvgqqiWI89aS9gXRNDphbfSCVdpFbYvWoegMg6mHv1kdWpRJ0etN8ITCON/Xy7Ajm27MXXaRNm943V7sHNHTX2gaHZu3wOH243E9MSo+Tgmi1EOHorPTsHmjdtly1o0v6/aAKfHD1/+TvhcLqxfu1lO09GZiKHlYq42bRO5OyqdFsXllTjrvJOR2EpdnER/Flt8iDqJoNcvgx53pQtWqxZhf/Sh5FBpZR7QkVedhLBKgVfe/jviE+OQ2S39sB5XDNMWS3V5NfqP7ofNv21ptM8R4wchUlmTYyN4istk7RdXfhH81Q5YBxrg9zRxvH+CCDJEy9ae5Zuxd80OmTPTY/wgpPTJgr2gHO5KBzKH9aqbn8uq1eL2v12Ho44dg8qiSpTYCzFs3CD06p2NjesaPy+hZ69s+B1euB0eGC2Nq/4mpCcgEgjCnGxDaahxknWjFhOFAsaIH2/8+38yj0iMNjO2cWXkw6U26uF3uZB5/Djs+WoBwoFgg/wew7CBWPDDr7jkyr/AEKWrjKgjYOBD1EmotBo4S6tRmVeCuKlDEa5oPBO1UquDUqvHsTeeAbVRB5PNjISUhD/92KJQHaqqcdZV0/CpQomNyzfJ9eIiPnj8QJx64SRULllRt7/GYqr5nX0Uol5QC8/47XF74KtyYfEz/4HXXjMLu1Cxuxgp/buh76QRKN60B4k90mCs11oluphOOXMyVi1cg/ff/BDjp47GX6+7BGOPHI7Skgp8/O+v6mb5Fs/vnPNOlTV+/vrY1bK2TTQGqwm2rGSk9W56CLeoDm0SOVUhNVQBL0aMGow1K9ajsrwKo44cJveprKiWNXJE/pIYbt4R6eJsMvjJOWMyHHmFMsjVxttg7JYOVyCA8y45HbZ4W4sWMiRqSQx8iDoJfZwJ9qJyOd9Q/u95yDoiCyFHmZwxXFCbrTBmdEMYSqjFrOktNExa5L449uxFwaLlcp6uk08biVMumSRzekQROb1GgeKFy+Ss47Xi+vRA7nc1hQWVajWUOi30ppa9kHvsHmydv7JB0FOrZHMeco4ajPTBPeGxuxoEPrUyeqZh0Jj+8Hn9iPhD6JWVjX45vTBl6gT8vm4THn3wOdx+73XY9NMmeN0+fPvOfFw+e4asZbQ/Y5wJ3cf2RyAUwGVXn4s3/vlhg+1i2Px9D92EOKsJzspCOR2Fy+VGfFICsrMzkb9zr6wbVFFVjTdeeR9JKQmYcdW5spVOVLTuSMRQf0OcDUGfH3F9usPWu7ucY02t06H5zlCijqFj/UURUZNEbZSB08Zi03e/oduIvijYWIjEnqnQxemhMxtEpUGo9S3fZRJ0eVCy/Pe6/B3Hxm1120TnVbfJR4st8rZo1Uk7agSqd+TKfQXLwD7QWcyIS2q56QLcTrcMeIp+39XkPnt/34HeE4ZFLUothsCLLrspF0zCmp834Jt358vh97Vzhl181/n4z1ev4d0nP8TOfY8hhrz7fYGogY8Q0aqghxoXzTgdI8cMweuvfCjnkxo+ajAuveocJOkVNYUNw2G4FTp4fAEcM7g/Fny0CBuXbMLgsQMw5sSRmPPQbXD7fbjlull4eN69sqWoI1LrWjZRnaitMPAh6kTEaK2BJx8pE50Te2XIBGbRmqIxmVqtEJ6YRyrYzNBk0aWVNekomX+ktZlRvn4rqrbshNZmQdyQ/tAnJcDUwomuIkgR85I1Ny2CSMJVqlU1QeF+RKJ1fIIVRbkl+PKNhkP0xfD7f81+E3f981YcecKousCnW58sOfy/KWazCZWVVXK6hlFDctD/sRsRDEWgUwEaJWBIToEzd5eIYLEzrwRTp07Ey3f+E4FAEFf87SIUr9mBFa98JScQTRvcEy+//CiWr16HtPQU2fVFRC2DgQ9RJ2OKt8ilrchcjX3zekUjQg+lXgtdfJws8pc8bCCShg5ARAybt5hafM4mQRxKIBRG+tAc7P5lQ9R9uo/pLwso7t/NVV5WicryaiRmJuGDZz6O+ruixtHy+SsxbFQ/OZVH3rZ8nHjhCdAdYDh+fHwc3C4PQnoL4m3xYhp0KKBA0OeFs7wMTq0V69ZtQ2JqEtYs/h3lRRW47tErsfGTn+Au/2Mm+8Lfd6J0Sx4m3Ho2PA43Ax+iFsTh7ETULJVeB3P3jOgfIBq1HMKsVChlnocghjrrbBboreZWCXpqh5AbbUYkD+kZNX8nfUiObB0zxDcMvNwuN15/6T3k5e6V9WjKCsqbfIzC3UWybtLxZx2Dvz52FZKzkg7q2IwmAyw2CzR6AzRiGL3RBKXJAntEi+27C6A16NGjRzbW/7wemb0yEKxyNgh6agV9AWz9YTX0zdTVIaJDx8CHiA44mixt7DDZdbV/obqM48agene+DH5EAm9bEcPWLXFmqE0GDDznOPSdNhqJPdOR0q8bRl82BUPPPgbGRIucL2z/1p733vgE77z9iZzEMi07tcnHyO6bhYqdhejRLxuurXvhLKpEsImZ2Q9Eq9UiOTVJ1kZaufx3hMMh2TomJmKt3NF4dF6tki15CDg88DoaJ3AT0eFhVxcRHZBowcmeegw8pZVyVm6N0QCN1YyqrbuQMnIwlPUrQR8iZ7ULAZ9fJg2LekEHSyRLi/o8LrUS+rgcpA/tBa1BB41BA3MTOTEV5VVy9JQYRr5g4RJMu3QKXrjtpUb7iSklBo8ZILugRJfTrp/XY8/STZh057myUOHhELV6evftiTvunykLFx512jis/2UD1HFN5+9oDFp5vJFQMwUrieiQsMWHiA6KzmaFISUR1pxuUOn1MvcnZfQQGQBpDmOET1W5Het+WY9/3f863nr4XXzy/KfYszlXjtg6WNYEK9J7pCOjZzpSe6QhPjW+yaBHENNO1Jo7+xmEtQpceNd5MJj/GKkl7uOaRy7Htv8tR79JI7Dz5/VyfTgYwvqvlsJV+ecqUItgTbQA9RjYA8FgCMmDeza5b/exA6C3GGsSqYioRXB29v1wdnai1lddWoVfvlgKg1GH7F4ZcBSWQ2PUyVFhKr0ICrq3yii10pIyXHr2jcjbs7du3U9rPoff5Ud1WbUstBiodmPvsk3IHN5bDpnf+v2qun1VWjUm3X0+4jISW+R4yosr4Hd6kL9sC3YtrikZUCu5bxYGnjQGhngLrKnxLfJ4RF3ZwV6/Gfgc5okjosPjd/tQUVKJstwSlK3aJosN1hLDz4ddcDySemcgLqV1LvbbtuzElefdLKskC+OPGYV75tyCJQuW4ajxI6CJKOF3eWVLj8ixqc8QZ8JR15+OhKzkFj0mZ4Udfrsbe37bgpAvgNQB2TDEmaE1G+Ts83qrqUUfjyiWr9/M8SGiNhXwB+S8X97CigZBT2130up3fsAJf7ug1R5fFAT84MtXsXnjNmzfvAt9+ufILrDFi5fh8/9+h78//jcsfa1hbZ9a3cYOgNPlxZ+fBKQhc4IVSLBCH29GOBSW50F0H2pNOqg6WOVmos6Of1FE1KZEi0ZcvAUbl/3Y5ESeIqE4PvPgho8fDjExqFgmniiqTte4+e5rcPGZM+GNBNH7xJHYPn9lg9+J75mGbqP6ovlpSP8co42FColaGwMfImpTojVDq9ch4P5jEtP9uSv+XALx4ejVtwc+/PKf+ObzH3HSSROR2DsDpZvzEA4EEN8rE4YEM8rKqtC9b3abHxsRtRwGPkTUpkS9n2AoBFtGIqqbKCCY0jerzV8VlUqFHr2ycdm158PpdCMcCKF7kk0OtQ+FwnJC1qRuKdAbo8/VRUSdAwMfImpTImm3uqgMR5xxNH5+8b+NtpuSbIjrdni1clqCTq+TCxF1TV2qjs8DDzwgy9PXX/r379/eh0VE9Ygh4WLSUnOyDeOuPhnGxJrRF+LvNXNoLxx305kwxjPXpTNwVNllXSOX3YlIE3O5EXU0Xa7FZ9CgQfj+++/rbqs5IoKow9GZDHIxJVplJeSQPyiHsotifaJaMXVsjko7wt4AnCVVKFi3UxazzB7dD/o4EyxJce19eESxFfiIQCctLa29D4OIDoJKo4Y5ycZz1YkEPD6EXD6s/c/PKN64p279jkW/o8f4gRhw0liodGoYLaw9RB1Tl+rqErZt24aMjAzk5OTgwgsvRG5ubrP7+3w+WfSo/kJERNF53V6UbMlvEPTU2v3rRrhKqmQBSEdpFXxuL08jdThdKvAZO3Ys3nzzTXzzzTd46aWXsGvXLhxzzDFwOJoeGjt37lxZ6bF26datW5seMxFRZxL2BbF76cYG6xRKJXodewQm3Xk2TPEG6M06QKOQNZtCgWC7HStRzE1ZUVVVhe7du2PevHm44oormmzxEUst0eIjgh9OWUFE1Fh1QRl+eekLOEur64KeY68/FQZDCEFnNbDvkqI026BPzYBSqYLWwBIA1Po4ZQWAuLg49O3bF9u3b2/yROl0OrkQUftxO9xwOzywOx1QqVVyBvP4xDiYmSfS4aj1WqT0z4azdJ283XfSMBj0QQQdNYFQrbCzGr5IBPr0tq/JRBQzXV37czqd2LFjB9LT09v7UIioCS67C7u37sG69Ztx23WzcerEi3DJ2dfj0w+/QklBCfxON4KexrkiorG6CzdYd1hiwtQ+E4dCY6z5wpg9qk9NS08UIZcdCIfb+AiJYmhU1+23345TTz1Vdm8VFBRg9uzZshrr+eef396HRkRNsFc6sH3XHtx321x5W/zNjhg1GMnxNhTs2Qtdn+5Q+QMIl1VAH2dBJAz47U5Ubd0p94/v3wu6eBs0RgPPcSsJBYNyDjWlUgEEg9CYtDj+9nOw4ctlUKog5y9TarQI6qwIhGtqqGkVQSg81YiEmONDHUuXCnzy8/NlkFNeXo7k5GQcffTRWLp0qfw3EXU8wUAQ3oAP8x59Sd4WE4c+948HoCmvQqCwBNiZC7dSAUtmKjQmI4IeP0p++x2uvcV192HfkQdzt3RkHDeGwU8LCodC8FU74Ni9F77yKuhTEmDKSoc/GIJBr4FCq8CIvxwLpUYJp7sK5R41Pn7qY+Rv31tTPHZ0X5xxzckwaTQteVhEf1qXCnw++OCD9j4EIjoEoVAI1dUOeL0+XHzF2bjmr+ej8Ltf4HF76vapWLsZjp35yDp+HDyl5Q2CnlrOvEK4i0phy+EEoi1BdCGK85n79WLZ0iPYd+VBt20PMiaMRemazXDlFkChUiJz4pFwR0x47tZnEQ6F635/0/ItyNu6F7e+eCPioRBDaaBQqqDSMdGZ2leXCnyIqHMJh8NQKIAP//sPxOvV8OzZi2C9oKdWwOGEu7gUvsqm62xVrN8Gc2Y6VDq2MPxZ3moH8n9YUhf06FMSgW7p8GvU2L0rH3q9Fgq1CgGHC46SSnz98S91QU99zion1v28HmPG58BfWSq7wwwZ3aDQ6iH6yLQ6VummTpDcPGPGDCxevLh1joaIYoqY9bxvn+6w+u1QKhRw5hVB1cTF0L4rHzqbpcn7Cos8FJEARH9KJBxB2OtHyFtT5kPXMwubA0FcfuW9OPfUa3De2dfjlnvnwdcrG9rEOET0euxct6vJ+9u6ejuCqprXNBzww7VnByI+DxShADwOJyqKK/mKUccOfER9mxNOOAF9+vTBo48+ir1797bOkRFRl6fTqRFx2gG1Ft6IBoGMLHgzs2E7agzMfXMa7Cvmg9JYm54GwZrTrcmgiQ5+OoqyXYUIB4KIH9wHGScchUBqIu65/TFUVvwxcmvX9j24+rK7oezTU47asjQzqaw1wQyVouHoO0/xXgRE1XwxYi8cQFlJBUfoUccNfD777DMZ7Fx77bX48MMP0aNHD0ybNg0ff/wxAoFA6xwlEXU5ooVGXDQDfh+Kq4DH//osXrz7X3jt4XfxxPUvYOmKXbCNHFK3v7VXNlQ6HdSmxqO31CYjrD27yaRaOnyuCgfse0sR0WqhSUmD2+GHOaTBjz/9GzNvvrThvk43fvp5BVBZiQlnHdfkfY47+UgEq0obrFPpDFBrtTCFPTDBB5M6BL/bjcrSSnjtTlSXVqI4twS5W/JQlFsMj6tx9ydRu1VuXrVqFd544w3861//gtlsxkUXXYTrrrtOtgh15cqPRPTnhAJ+BLwBVJdX47Gr5iEYEIOiGzrn+unIVPmhNuiRMvoI5H33CzKOHgn77nzZ9SVyZm29uiN+QC8o9TpWCP4TxLxa5TsKodap4al0YtUHC2ULkKBQKtD3xJHY5anCrHuerPud46ccg1v+Mg2GnB7437s/YuUPq+q2id857epTMHx8PyiqCuvWq00WaOMTEPaL19WIoNtZt16h0aKqwo1lXy/Hgo8Xwe/1Q6lSYuTxw3HSZdOQlJH4Z54idXH2g7x+/6nk5sLCQsyfP18uovbGSSedhHXr1mHgwIF44okncMstt/yZuyeiLkyhUMLvD2L90s1Rgx7hh48XY+bcK+HaW4aqSifSjx8Hd34BtFYLup1wFJRakcgcgaugBImDOueXrY4i6A1Aa9Ah6A9g+Vvz6xKba/N+tny7AiMuPgE9e3eXXV1CTk43RHx++HbuxikzJuP4v0zA1tXboNFq0HdEn5paPvWCHkGflIKQ34ew3wdnSb1tpcXQJmdiyVcrMf+9H+pWi6Tp3+avhL3CgQvvPA/xqfFtcTqoCzvkri7RnfXJJ5/glFNOkYUCP/roI9x8882yYOBbb72F77//Hv/+978xZ86c1jliIuoSlGq1iH5QtKekyX0qi6vgLHNg5QeLkPvrZnz22jfQZGbCkCq++dcMuc6b/yss3dLa9Ni7pEgECpUCRRv3NAh66tvxw2qce96p8t9qtQonnTIR8f1yYEhOQPG3C+H6dRmG9E7GkZOGIynFCrPNKF/jOkolIhGFzNfyV1U0un+XJ4iFH0cfPLNl5VY4q10t9Wwphh1yi4+Y/kEMQRWFApcvX45hw4Y12mfixIlyniwiouZo9Rp065uFpV8vi7o9IycdxngzxsyYLAOd7uMGoLqsGsEtW2RRPbXRgKwTxkNjbjrpmQ6O3maC1+6Cq7zpkgFiW+qwLDmH2uPP/A1Grw+FK9cj5PPL7SIpWmM2onrnHphSbFDqNLD26o+g2yVrNvmhhEKjgreocS0mwevxI+BvOle0OLdYvl+I2jTwefrpp3HOOedAr2+6CJUIenbtanp4IxGRoDca0H9UX5htpqjf5k+5fBp+f38BqvaW1c0E3nfySOQceyTUyjBUIgnXbGJScwsQk8PqLUbYMhKxd3X0iZ1tGUnoO7AXPvrqVWBvCSpXbWiwXVTQNiQlQO12I+CogNKvR8gfQLU/gvvu+js2rt2Mn1Z92uQ0FlpRH0ihaHKElzmu6dFjRK3W1XXxxRc3G/QQER2KlG4puH7edQ2+yYtA6MK7zoMnt6Qu6BFEF8yWb35D6dZ8hCC6S0plFw21DLVBi/TBPeQM7NEMnj4OFrMFmd0zkTykH7qfPBG2vj1h69MD2dOOQ/LIwQh4PIiEPDKHR20yI6Iz4oO3/4uVS9fA4/EiP78IanP0xFOdIoABY/tH3ZaYloC4ZBtfamr/UV1dDUd1EbW9UDCEom35CCuUckJMBEPwldux9qPo+R5x3ZIx/C8TYND5YEjPgkrD+j0txV3lhLvcjhXvfA97YU0ejtakx7BzjkN8ThrMCVbZOlQrHAwhFAgiHAgg7HPLYFQkQ+uTUqGxxqG4qAyXnXsTCvdNNRKfEIfvf30fzh1bEAk3TGpXaDQIWjLw+oP/h/xt+XXr41PiMOP+i5HePQ1Gq7HFnit1LW0yqouIqCWIC2lCeiJ++7/58vaw8ybg9yUbm9zfVWZHwOuDOd4iRrRTCzLGmaEzG3DUdach6PUjEopAY9RBo9dAH6WApFKtkgsMOoT8WmjFBUehgEqrq9tHo/njUlNZUYWrL7kL/3jtEQTKixB01OQUqUwWwJwIv9OHyx+YgYriChTtLkZ8chwS0mtaexj0UEtgi89+2OLz54gGxIj4xi7mJFSpmXtBh8TncNe0OFTY4SiqxO+f/hJ1v6TeGeh7wgik9Baztpv5PuvAvG4v3nvzP3jm8VcarLdYzbju5ktxxjlTRcozvN4AQsEwdAatDGjFMHav2weVSgmd8Y8giujPXr8POceHKJpgMIiQz4uA0w5vZRmce3bAV1Yi63UQHSydxYj4binIHNobGcN6Q93EhKN9Jg6DJSUeap2OQU8HpzfqMeXUieg7oFeD9Q67E78tW4vKaiccXi/iUuKRmJEogx5BFC40WgwMeqjFscVnP2zxOXRBnw8Rvw+ekkIZ/Kj0eugTUxBwVCPoccPcvVeDZm+ig+F1e+AqrsbyN7+FY99ElqILZtApR8KcEgdbZiIMNo7y6Szy9hRg3ZqN+OKTb6HVaXDW+aeiZ69sGE1GJCaxKCG13fWbgc9hnjgS3VphOduyv7oansK8RqfEmNENfnsVdAlJ0MWx1DwdumAgAE+VEwG3X+abqHQa6Ew6aAx6GQRR5yJq+VRX2mu6whUKmehM1FKY3Eyt38oTDMihxJ6ivVH38RQVwJjVHf6KMjm6Q6n8YyQI0cFQazSwJLM1oKsQUxslsHWH2hlHddEhC/n9CPs8sqaKHFETiV7eXgxVlbNlKxQQ/xEREbU3Bj50SMKhEMIBH9wFeTCmZTachycahQK6hGRZcZcOTVF+kcyL2LhuKzKz09F/YG8kpyRAZ2QXDxHR4WLgQ4dE5PSIDnpRlbW29qUYth6tBL1CXTMiR6XnhfpQeFxulJdV4vor7sHObTWzYAsmsxEvv/UkBgzsDa2R1dOJiA4Hv4bToRHdW7KRRwF/Vbms1iqSmGW2Yn0KBUzdekCp00Ol44iugyUmaHQ73Hj8gecbBD2Cy+nGzMvvRklJeYd41wYDQQS8NZNTEhF1FmzxoUOjVMpaPRpbHALVldBY4mT3l7lHbwTsVQj5vVDp9NAlpIhMRqi1nErgUIhZrqurnVi8YGnU7fZqB/L27EVWj8x2e+d6HW54qp1Q67RwOdxyndagkxNIip9ERB0ZAx86JEq1RtbnMaZ3Q8jrgbsgF9q4BKg0Gmjj4uX2iFIJNev2HJagLwCf19vk7NRCRXlNTZv24HN6UL6nCGqTAUu+/RU/f7kEzioXegzsjlMunyonHBWF6IiIOip2ddGhvWHUapgysuEpLoA5Owem7BwoVCoZBImfEYWCQc+fILoRjQY9rLaa6rXR9Orbs93etWKOLH2cBR+98Bm+fns+HJVOGaTt2rAbL9z+Cgp3F8PnZrVuIuq4GPjQIRNdWcaM7JpWCbVGzsKsjU+C2mCCWsek2z9DY9BBG4jIOYyiGX/MKCQktF+LSiAYRGVJFbas3Npom3g//Ocf/4WzyinzfzoSUXrB53DBW1EFT1kFfFUOBP2B9j4sImoH7OqiwyK6tsRCLUuEC9a0eBx95AjMfvQ2vPjMGygrqYBer8MZ556ES68+HykZye122o02E9Y2M2t60e4iBHwBFBeWyiH4HUEoEEDA4Ubp6g2w78yTRTfVRgNSxwyBIS0ZWouJ830RxRAGPkQdiNFogDcCJKUlYdKEcRg9dhj8wSB0Oi3i46wwx7fvNCpqrQYmq7HR+rFTR2PclNFQqWsmlgxFQugogi4P8n9cAl9F1R/r3B7sXbgMGRPGIqJQQm9p/JyIqGti4EPUwehNBsBkgNrrg0lhla0RHWl0XO/hveXM2eFQGKnZKbjpiavhLy+Hu6AEaqMeygSzrDPkqrLDYDHLfduLGHHotzsbBD31la5Yh+ypCbBXVMOaYGvz4yOitsccH6IOSqvXQaPTdaigx2AzQWfU4cK7zofRYsSNj1+Jgvk/oeiXVbDvykPFhm3Y89UCuPKLoAyG4KhytuvxhoNheJsZBRdwuhH0eKFSckoVoljBwKeDEQmX9uIKFG3eg5Jt+agurpDfrIk6irikOPQZ3hv3vnYbKlZvkIHD/oqWrpE1ibTKCLye9hvlpVQroTE33Y0lRyJGwogEgwgHO1ZCNhG1DnZ1dQCiOT4imuQ9fuxesgm7l26ERq+Dq6xajpQZdelkJOVkQG/iiCnqGOKT4+CtrJYtO1FFIrKlxWLKgL3SAX07FTZUqlQwJCfWBDihxnlHtt7ZCPsCUBkMsjgnEXV9DHzakQhqwj4fRKk6MdeVAmFkDs2B1qSHs6QKvScMkfutfvcHjL9+OgMf6lgiB9gcDMld2rsTKaJSIXvKMcj97id5TLUMKYmw9uwGpUYNfzgMJQMfopjAwKcdiYk+xVXBU5CLkD8If8SEn/7xhfx3LXNKHEZfPBl7V++AMdEKg4GtPtQxqPVaaOOs8FfZo243pCXJwMcYZRRYW9JbTPApFcg5/US4i8vliC59gk22tCo0GkR0Glg44z1RzGDg044F1UJ+P7zFexH0eoDELFQXV2LYlVOgCkVg31OCXb9ulC0/W39cjcxhOfD7/Ax8qMMQtXDSjxqBPf9bJLu26rP0yIJar4MvEIItrn2H4As6kxGOUBiGjBQowiGZNye6vwLhiCwhII6ViGKDItLcpEAxyG63w2azobq6GlZr631ghwN+hIMhhIN+FJZW48Wn30CvXtmYdvJxMGqV8sNYYzKjam8FFj7zH0y663woLTo5tFmr0zIAog4hFAjKoeIlK9bBU1wOlUGPhMF9YOmWjlAE0FqMUKv5/YqIOs71u0sGPi+++CKefPJJFBUVYejQoXj++ecxZsyYDhX4hPw+eIr2ojKkwQWnX4db774KR4/qB5XH0WA/fWoGoDEi4AtDoVWhorwCJqMWJrMRKo0Wap0OKl5YqJ0FfX45iksSuTIqFXScqZ2I2tDBXr+73DCGDz/8ELfeeitmz56NVatWycBnypQpKCkpQUcRDofgKSlEKBjCfz/5BmaLCWNGDWoU9Aje4gKoNUqseGc+1rz3I0xKLXSIwJu7A57cnTJPSJTkF/kKRO1FrdNCZzXXLGYjgx4i6rC6XOAzb948XHXVVbjsssswcOBAvPzyyzAajXj99dfRUUQCQfgry+FT6bBg/q845/xTYFbs+7Ychb+iFD2PHICiDXuw4O8foWKvA6buvWHMzIavvBSewnyE3E4Efd6aiUOJiIgoqi7V+e73+7Fy5Urcc889devEENUTTjgBS5Ysifo7Pp9PLvWbylpbJBySyaBiXiOLxYykpDhEAk3PFB0OBqAx/pF8qVIp4K8sQ8D+Rxl+f1U5NGYrDJnZnCGdiIgoFlp8ysrKEAqFkJqa2mC9uC3yfaKZO3eu7BOsXbp169bqx6nYVy9EG/LjkqvOwdo1mxDWND2qRG0yo+D3XfLfYki7NcXcIOipFXDaEXQ5EQo2HUQRERHFsi4V+BwO0TokEqFql7y8vFZ7rLKSchQVlsohvtq4BOiT0zBk2AB4vX64FDpA0bjUm0Kpgtochz3LNsvb3Ub2gSLoarZbDMz3ISIi6vpdXUlJSVCpVCguLm6wXtxOS0uL+js6nU4uram8rFLW7ZEzWgf8NUPSUzIQdjth1Wtw/XXnwh0MwpKSBYW9HCGPu66lx5CWhVUf/VJ3XxqtGpFm5u4Sj8M0HyIiohho8dFqtRg5ciR++OGHunXhcFjeHjduXLsck9vlgtvpwndfLcCTc17AD1//hIrySjjsLhRUOLF7bxkqXH6oFUpRFAW6xBSYu/eSi9pkgbvShT1LN9XdX8nWvdBYbE0+nsjzUXCmaSIioq7f4iOIoewzZszAqFGjZO2eZ555Bi6XS47yamuiMvOOrbtx5QW3weP2yHWLf1yK/oN6Q6FQ4sWnXsPK5b/L9RqtBhfOOAPnnz8NOleFXGfM7C5bsHKOOQKO4krYMhLR69gjoI0zwVdZ2ighWqFWyy40pUbT5s+ViIioM+hygc+5556L0tJSzJo1SyY0Dxs2DN98802jhOe2UFpagdtnPlgX9AgXX/kXVFU68OK817Bz25669QF/AG+++m8YjAZccskp0Gh18DuqoUtIwoCpI2tK7Ie80BgUcOXthqVnX3hLixCorkIEEWit8dAlJsugRwRVREREFAOBj3D99dfLpT2JXJvKimoU7m2YbzRoaD+4nZ4GQU99b/3r3zjtrMlIMZqg0mrh2LFZ9NdBoVLD1K0n3IV5CPu8cOzcBlNmFvTJqTWzZCuVUKo1UKpUbfQMiYiIOp8uGfh0BKKQYDAYjLIB2JtX2OTvuV0eeLw+mQCt0ulhSMuUU1NAqZK5O7r4RATdLig1Wii1ekCh4gSLREREB4l9Iq1Yqyc+Ia7RZKIioElNT27y98QEpHq9Dv7qmjyfkM8ngyhRoVkETWJ6CtH9JYbCqw1GOVcXERERHRwGPq1EBDjx8RZcf/sVDdb/uvg3xMXZkJoWPfg54y8nITHeBrXRLPN19InJMsAxZmVDKVqAktPkqC7RDUZERESHhoFPKzJZrThp+iQ8++rD6NMvB2qNGr/9ugoZ2Wl4/PlZyMrOaLD/pKnH4MqZF8paPSrRmmMwQa03yC4vlVoDlUYjAx4RVBEREdGhU0Q4q+VhTWt/qHOIVVdWIxgIQa1WwhZnlevKy6pRXWWHw+FEWkYqEhJschsTlImIiFrn+s3k5jYqrJic2rBrS6vXw2y1yvwdtuAQERG1DXZ1tTMGPURERG2HLT6dhBzZ5fbA7nBBrVYjISm+vQ+JiIio02Hg0wkEPB5UVNmhjgAmjQZqrRoBtxvBUBgGi7m9D4+IiOiAHGVViIRExV3AmGiRX+LbAwOfDi7o8aKyygmzSo2wzyeHuNt35Mn6PuasdPjCEehslvY+TCIioqgc5dXwVjqx4YslKN9VBL3FiN4ThyFzaA7MyXFoawx8Ojivzw+jWoWwx4uAw4XcrxfXbSv/fQsMKYnIOn4ctFa2/BARUccSCoXgLKrEzy/8V6ZsCK5yO9Z+vBhl2/diyFnHwNLGwQ+Tmzs4MX1FyOGEyqBD4S8rG28vKUfFxu0Ih0LtcnxERERNce8LcqJVztm7ZgcCbh/aGgOfDk6j1UCh0cCxe2+T+1Ru2oGA848Z4ImIiDqCoC8Ie2HNFEzRlG7LRyAQaNNjYldXJ6gBpAiH4fQ2HRWH5ZumcTRNRETUnpQqJaBo+hKl0esQCbft9YstPh2cTrT46HQwZaY2uY/I81FqGMMSEVHHotJpkDawR9RtCqUCSb0y5OTcbYmBTwen0mqgVKugT4iDLj5KCW4x2/uRw6AxGtrj8IiIiJpkTrRi6NnHQG8zNdo28oJJUOhUaGucq6sN5upqCQG/H2GPD2VrNqF6+x5EQiHok+KRNm449InxMkAiIiLqiOzFlSjdmo/izbkwxlvQfWx/qI06WBJtbX79ZuBzmCeuvQR9foQ8XogEeaVWDa3J2N6HREREdEDhUBjhSBgKcf1Sq1p8yiZOUtpFqXVauRC1NWelAy67CyX5ZTCY9IhPiYMtOQ5q5pcR0UEmOov/2hszYonogKpKKvHN/32HX79aVlePw2gx4qqHLkP3AdnQMBgnok6i/UMvIurQ/F4f1iz+Hb98ubRBETK3w41/3PlPVJVURS1ORkTUETHwIaJmOSudmP/ej1G3BfwBbF6xBcFm6kwREXUk7OoioiaJlpxwOAx7hb3JfYr2lCASCAGsqNCphYMBWQw1EgpCoVJDoVZDpWE+IXU9DHyIKCp3pUOUiYLFasDd/7wZuVv3Yvn8lQj4gigvqoCzyin3yxnUHQGXG1pr4zod1DmE/D4483ZDZ7VBqdUh5PNBKYKecFgGQUo1LxXUdfDdTESNuCod0OrU8JYUIOCogiESwcC+Now45iL4PV543AGUlzrw6ctfovvA7tDZjHKiXKWq7YuR0Z8jWnlE0GNMTYe7MB8hj7tum0pvgDm7F8IiApY1NBRQKvkaU+fGOj6drI4PUWsLBUIIB/xw5e1A2OdttN2cnSMvkKI5yJCVA0RC8JeXQG00QZeQzNaBTibocSMcDMJbUoiQ1wNdQhLUBiMiiCBgr0bI54U+JR0hjwdaW5wYkyzrsbgcIkBSwGAxQm/Ut/fTIALr+BDRYREtOsqwP2rQI3hKi+TF0VNcAF9xPgypGdAnpSAciciLpFJt5pnvTCKRukJypm494C0thre0SAa22rgEGFIzoVCp4CrdBbXJBHuVHT99sRQrvl8tg6NRxw/HcWcdg/jUBCiVHC9DHR+7uoioAZVGg0BFeZNnRXSFqJLT5L+DLgcU4mKnVEK1b0h70OtFSKGATqfjme0MRC9WKAh9chqce7bXdGkJkQj8leUIupwwd+8FQ2o6qivdeOHOV1FRVFn36ws+XozVi9bipmevR1JGUvs9D6KDxPCciBpQaVRQapqe+02hVDWs26NQIGCvgmPXNti3bYRj5xZEHFUI+ryIhEM8ux1YKBBA0OWGQqOBr7zkj6CnnrDfJ1vyVCYz1v26sUHQU6uqtBqrF6yB1xO9lZCoI2HgQ0QNqNQqaKxxTZ4VbXwi/FUVDf7tKdqLSDAg14mf7oI8+MpL5YWVOnhrj2zxCSPgdDS5W6C6EgF/GKsW/d7kPit/XA1nRc1IP6KOjIEPETWmUMLUrWej1SKBWWO2IOColrdFMrO3rDjqGfSVlcjh0GK0F3VMSpUaGqNJ5nOJPJ6miJo+SrUSGm3TLYEanQYKZctOOknUGpjjQ0SNPxhkfk4E1j4D5cieSCQsg56w3w9n7i7ZNSKSmiG6spqcriIih0or1BqAw9w7JJHULHO01Gro4hNrkpqj0FjiEAn6cez08di+dkfUfY478xhYEzkSljo+tvgQUVRqnR7QaKCJT4DWFg+FRg212QJr7/4wpGRAbTAdcI4ueWHl+e3QVDq9HL4uAh+1sfGIPBHgihY+T2kxco7oiUFHDmi0T7+RfdFjYI9mW4SIOgq2+BBR0x8QorVmn1DAj0goVNNCIIa9O+3QGIxQ6nQI+xrP1aXUauVor+YSpanjBLmhYFB2b4YDPtnKJ1470a0pavwodZB5XwqtDuffdo6cpuTXr8SktcC4k8ciJSsZiemJ7f00iA4KAx8iOihy3qZ9MYzovpLdWAoFTFk94Ny9Qw6JriXyRcR6Zb3AiTo2lZiWQszPpdNBbbIgFAxAEaqZskLkfMm5u9Rq2JJ1sCXHI2dIDkLhEPR6Fi+kzqVLdXX16NGjpmm93vLYY4+192ERdTliagq1Xi8vktDoYO3VX7YW6JPSZMBj6dkPCo2uZjt1OuKzU63RQqWv6QaTr/V+83WJbi0GPdQZdbkWnzlz5uCqq66qu22xWNr1eIi6Oo3o0hLBkE6HiCUouz9EEUQioo6oywU+ItBJS6upKktEbUe2srJri4g6uC7V1SWIrq3ExEQMHz4cTz75JILBP/IOovH5fHJis/oLERERdU1dqsXnxhtvxIgRI5CQkIBff/0V99xzDwoLCzFv3rwmf2fu3Ll48MEH2/Q4iYiIqH0oIgcqxNHO7r77bjz++OPN7rNp0yb079+/0frXX38d11xzDZxOZ5MTJooWH7HUEi0+3bp1Q3V1NaxWFuMiIiLqDMT122azHfD63eEDn9LSUpSXNz1TtJCTkwPtvgTL+jZs2IDBgwdj8+bN6NevX4ueOCIiIuo4Dvb63eG7upKTk+VyONasWQOlUomUlJQWPy4iIiLqfDp84HOwlixZgmXLlmHixIlyZJe4fcstt+Ciiy5CfHx8ex8eERERdQBdJvAROTwffPABHnjgAZmz07NnTxn43Hrrre19aERERNRBdJnAR4zmWrp0aXsfBhEREXVgXa6ODxEREVFTGPgQERFRzGDgQ0RERDGDgQ8RERHFDAY+REREFDMY+BAREVHMYOBDREREMYOBDxEREcUMBj5EREQUMxj4EBERUcxg4ENEREQxg4EPERERxQwGPkRERBQzGPgQERFRzGDgQ0RERDGDgQ8RERHFDAY+REREFDMY+BAREVHMYOBDREREMYOBDxEREcUMBj5EREQUMxj4EBERUcxg4ENEREQxg4EPERERxQwGPkRERBQzGPgQERFRzGDgQ0RERDGDgQ8RERHFDAY+REREFDMY+BAREVHMULf3AVBsclY6EPQFoFIpYUmOa+/DISKiGMHAh9qUvawKnkonNn65FFW5pTDEmdD3xJFI7J0BW0o8Xw0iImpVDHyozXhdHlTllWLpP78CIjXrAh4fVvzffPQ85gj0PXEEgx8iImpVzPGhNuOxu/D7R4vrgp76dv20DiF/kK8GERG1Krb4UJvwu70Iev1wVzia3KcyrwRamxFmi4mvChF1CJFwGAGvD5FQGAqlEiqtGiqNpr0Pi2KhxeeRRx7B+PHjYTQaERcXPRk2NzcXJ598stwnJSUFd9xxB4JBtiJ0BKFgSH5oNEetVSMUCrfZMRERNcdX7YC7pByuvEIE7E6E/X4EPT4EnG6euE6s07T4+P1+nHPOORg3bhxee+21RttDoZAMetLS0vDrr7+isLAQl1xyCTQaDR599NF2OWb6g9aoh9vlQUKPVFTsLm50apRqFazpiTCZjTxtRNTufFV25M3/Gb5Ke906rdWMbpOPRkilgsLjhdqgb9djpC7e4vPggw/illtuwRFHHBF1+3fffYeNGzfinXfewbBhwzBt2jQ89NBDePHFF2XQRO1LpVZBr9diyF+Og9aoa7hRAQy7YCIiaiXUalV7HSIREfxOl2zp2btoeYOgR/Dbncj/YYn4po1QgL0JnVWnafE5kCVLlsigKDU1tW7dlClTcO2112LDhg0YPnx41N/z+XxyqWW3N3yjU8sxWE0IhsI49razkbdmOxy5ZdDFGdFj3CAo9WokpCTwdBNRu/A7nAi4PChfuwnxA/vAU1wWdT9fZTXCwZCsQUadU5cJfIqKihoEPULtbbGtKXPnzpWtSdT6lCoVbElx8NrdyDn6CAT8fijVauj1OugN+7UCERG1Eb/LA3dhKfYuXCZv2/r0bHb/sD8AlY4Jzp1Vu4asd999NxQKRbPL5s2bW/UY7rnnHlRXV9cteXl5rfp4BOitRlhsZiQkJyAu3sqgh4jaVSQQQOEvK+tuK0VrjkLR5P4it0ep1bbR0VGXavG57bbbcOmllza7T05OzkHdl0hqXr58eYN1xcXFdduaotPp5EJERLE5XD3o8SJcL2fHkVsAW+/uqN62u9H+lh5ZUGjUUOt53eis2jXwSU5OlktLEKO9xJD3kpISOZRdmD9/PqxWKwYOHNgij0FERF1LJBJpVFS1cvNOZE4YK7viq7buQiRUU45DBEPJIwZBYzHJHgnqnDpNjo+o0VNRUSF/iqHra9asket79+4Ns9mMyZMnywDn4osvxhNPPCHzeu677z7MnDmTLTpERNRk7qFKr4VSq5G5O1IkIvN9bDnZyJo0DhqTEQq1CiqdDhojh7B3dp0mLX3WrFlyZNbs2bPhdDrlv8WyYsUKuV2lUuHLL7+UP0Xrz0UXXSTr+MyZM6e9D52IiDowhUaDjGNGNczriURQvTMXkXAEKoMO+ngbg54uQhGR7XxUfzi7zWaTic6im4yIiLo+n92JkNeHyo3bZP0eXbwNCYP7QqnTQsdpdLrU9bvTdHURERG1Fp3VjJBBh+RRQ0TGs2wF0jCBuUti4ENERCRSJjQaTkAaAzpNjg8RERHRn8XAh4iIiGIGAx8iIiKKGQx8iIiIKGYw8CEiIqKYwcCHiIiIYgYDHyIiIooZDHyIiIgoZjDwISIiopjBwIeIiIhiBgMfIiIiihkMfIiIiChmMPAhIiKimMHAh4iIiGIGAx8iIiKKGQx8iIiIKGYw8CEiIqKYwcCHiIiIYgYDHyIiIooZDHyIiIgoZjDwISIiopjBwIeIiIhiBgMfIiIiihkMfIiIiChmMPAhIiKimMHAh4iIiGKGur0PgIhaRiQSQcDlBiKAUquBWqflqSUi2g8DH6IuwO90wb4jDxUbtiLkC8CYmYKUEYOhNOigMxnb+/CIiDoMBj5EnVzA7cHeBcvgLiypW+fcvReuvEJ0P2kCEA5DZzG36zESEXUUzPEh6uT8VY4GQU+tSCiM0pXrEXC44XW62+XYiIg6GgY+RJ2cI3dvk9tcBSUI+wNQhEJtekxERB0VAx+iTk6p0TS5TaFSIhIKIhKOtOkxERF1VAx8iDo5a4/MprflZMO5txhQtOkhERF1WJ0m8HnkkUcwfvx4GI1GxMXFRd1HoVA0Wj744IM2P1aitqQy6pE86ohG6zUWM2y9sqGLsyLIl4SIqHON6vL7/TjnnHMwbtw4vPbaa03u98Ybb2Dq1Kl1t5sKkoi6Co3BAFufHjBnpqJy806E/H6Y0lOgNuhljk/cgF5QqjvNdxwiolbVaQKfBx98UP588803m91PBDppaWltdFREHYPOYkLQoEPKqMEI+YMI+rxQ6/TQJMZBoVZDazK09yESEXUIXe5r4MyZM5GUlIQxY8bg9ddfl9Vsm+Pz+WC32xssRJ2RWq2GxmSEPt4KfWI8NGYTjHFW6Bj0EBF1vhafgzFnzhwcf/zxMg/ou+++w3XXXQen04kbb7yxyd+ZO3duXWsSUVehbmakFxFRLFNEDtQk0oruvvtuPP74483us2nTJvTv37/utujquvnmm1FVVXXA+581a5bM+cnLy2u2xUcstUSLT7du3VBdXQ2r1XrQz4WIiIjaj7h+22y2A16/27XF57bbbsOll17a7D45OTmHff9jx47FQw89JAMbnU4XdR+xvqltRERE1LW0a+CTnJwsl9ayZs0axMfHM7AhIiKizpXjk5ubi4qKCvkzFArJoEbo3bs3zGYzvvjiCxQXF+PII4+EXq/H/Pnz8eijj+L2229v70MnIiKiDqLTBD4iX+ett96quz18+HD5c8GCBZgwYQI0Gg1efPFF3HLLLXIklwiI5s2bh6uuuqodj5qIiIg6knZNbu7MyVFERETU+a7fXa6ODxEREVFTGPgQERFRzGDgQ0RERDGDgQ8RERHFjE4zqqut1OZ6c84uIiKizqP2un2gMVsMfPbjcDjkTzFtBREREXW+67gY3dUUDmffTzgcRkFBASwWCxQKBTq72rnHxHxlHJ7P88H3CP9m+DnCz9aueq0RLT0i6MnIyIBS2XQmD1t89iNOVlZWFroa8UZk4MPzwfcI/2b4OcLP1q58rWmupacWk5uJiIgoZjDwISIiopjBwKeL0+l0mD17Nmeo5/nge4R/M/wc4Wdrq+lM1xomNxMREVHMYIsPERERxQwGPkRERBQzGPgQERFRzGDgQ0RERDGDgU8X9sgjj2D8+PEwGo2Ii4uLuo+oTr3/8sEHHyBWz0dubi5OPvlkuU9KSgruuOMOBINBxIoePXo0ej889thjiCUvvviiPA96vR5jx47F8uXLEaseeOCBRu+H/v37I1YsXrwYp556qqwELJ77Z5991qhS8KxZs5Ceng6DwYATTjgB27ZtQyyfk0svvbTRe2bq1KnoSBj4dGF+vx/nnHMOrr322mb3e+ONN1BYWFi3nH766YjF8xEKhWTQI/b79ddf8dZbb+HNN9+UH2yxZM6cOQ3eDzfccANixYcffohbb71VDstdtWoVhg4diilTpqCkpASxatCgQQ3eDz///DNihcvlku8BEQxH88QTT+C5557Dyy+/jGXLlsFkMsn3i9frRayeE0EEOvXfM++//z46lAh1eW+88UbEZrNF3SbeAp9++mkkljR1Pv73v/9FlEplpKioqG7dSy+9FLFarRGfzxeJBd27d488/fTTkVg1ZsyYyMyZM+tuh0KhSEZGRmTu3LmRWDR79uzI0KFD2/swOoT9PyvD4XAkLS0t8uSTT9atq6qqiuh0usj7778fiQWIcv2YMWNGZPr06ZGOjC0+hJkzZyIpKQljxozB66+/LptvY9GSJUtwxBFHIDU1tW6d+PYmJt/bsGEDYoXo2kpMTMTw4cPx5JNPxkxXn2jpW7lypeyuqD93n7gt3huxSnTdiG6NnJwcXHjhhbI7mIBdu3ahqKiowftFzBMlukdj+f0iLFy4UKYK9OvXT7awl5eXoyPhJKUxTnRrHH/88TKn5bvvvsN1110Hp9OJG2+8EbFGfIjVD3qE2ttiWywQr/uIESOQkJAgu/vuuece2VQ9b948dHVlZWWyuzPae2Dz5s2IReIiLrp7xQVMvA8efPBBHHPMMVi/fj0sFgtiWe1nQrT3S6x8XjTVzXXmmWeiZ8+e2LFjB+69915MmzZNBoMqlQodAQOfTubuu+/G448/3uw+mzZtOugExPvvv7/u3+Ibvui/Fd/yO0vg09Lnoys6lHMk8ltqDRkyBFqtFtdccw3mzp3bKUrRU8sSF6z67wcRCHXv3h3//ve/ccUVV/B0UyPnnXde3b9FC7p43/Tq1Uu2Ak2aNAkdAQOfTua2226TWfPNEU3Sh0t8sD300EPw+Xyd4kLXkucjLS2t0Qie4uLium2d1Z85R+L9ILq6du/eLb/1d2Wiu1d8I619zWuJ25359W9JYjRk3759sX37dsS62veEeH+IUV21xO1hw4a145F1LDk5OfJvS7xnGPjQYUlOTpZLa1mzZg3i4+M7RdDT0udj3Lhxcsi7GMEj+qeF+fPnw2q1YuDAgeis/sw5Eu8HkedSez66MtG6NXLkSPzwww91IxvD4bC8ff3117f34XUIohtcdF9cfPHFiHWiK0cEP+L9URvoiHxAMbrrQCNpY0l+fr7M8akfHLY3tvh0YSIJsaKiQv4UuQviIib07t0bZrMZX3zxhfx2cuSRR8qaJeIi/+ijj+L2229HLJ6PyZMnywBHfKiLYaqin/6+++6Tyd+dJRD8M0QfvPjQnjhxoszfELdvueUWXHTRRTIYjgWiq2/GjBkYNWqUTPZ/5plnZPfvZZddhlgkPgtEzRbRvVVQUCCH+YtWsfPPPx+xEujVb90SCc3ic0PkwGVnZ+Pmm2/Gww8/jD59+shASKQOiETwrloS5EDnRCwiD+yss86SQaEIku+88075GSsGinQY7T2sjFqPGFYoXuL9lwULFsjtX3/9dWTYsGERs9kcMZlMctjqyy+/LIfwxuL5EHbv3h2ZNm1axGAwRJKSkiK33XZbJBAIRGLBypUrI2PHjpVD/fV6fWTAgAGRRx99NOL1eiOx5Pnnn49kZ2dHtFqtHN6+dOnSSKw699xzI+np6fJcZGZmytvbt2+PxArx2RDtM0N8ltQOab///vsjqampchj7pEmTIlu2bInE6jlxu92RyZMnR5KTkyMajUaWx7jqqqsalAjpCBTif+0dfBERERG1BdbxISIiopjBwIeIiIhiBgMfIiIiihkMfIiIiChmMPAhIiKimMHAh4iIiGIGAx8iIiKKGQx8iIiIKGYw8CEiIqKYwcCHiIiIYgYDHyLq0kpLS+WEiWIC3lq//vqrnI1dzKxNRLGFc3URUZf3v//9T86YLQKefv36YdiwYZg+fTrmzZvX3odGRG2MgQ8RxYSZM2fi+++/x6hRo7Bu3Tr89ttv0Ol07X1YRNTGGPgQUUzweDwYPHgw8vLysHLlShxxxBHtfUhE1A6Y40NEMWHHjh0oKChAOBzG7t272/twiKidsMWHiLo8v9+PMWPGyNwekePzzDPPyO6ulJSU9j40ImpjDHyIqMu744478PHHH2Pt2rUwm8047rjjYLPZ8OWXX7b3oRFRG2NXFxF1aQsXLpQtPG+//TasViuUSqX8908//YSXXnqpvQ+PiNoYW3yIiIgoZrDFh4iIiGIGAx8iIiKKGQx8iIiIKGYw8CEiIqKYwcCHiIiIYgYDHyIiIooZDHyIiIgoZjDwISIiopjBwIeIiIhiBgMfIiIiihkMfIiIiChmMPAhIiIixIr/Bx7CzYUWvm3IAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 566.375x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# generates all k-fold diagnostic plots: train/test and train/valid Tanimoto distance\n",
+    "# histograms, UMAP visualization, and subset fraction box plot, all per fold\n",
+    "ss.make_all_plots()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5652e7a",
+   "metadata": {},
+   "source": [
+    "## Training a Model\n",
+    "Now we can use our dataset and scaffold split to train a model with _k_-fold cross validation to predict the $pK_i$'s.\n",
+    "\n",
+    "We will train a random forest model using the following inputs:\n",
+    "\n",
+    "1. The curated **[SLC6A3](https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL238)** dataset from **Tutorial 1, \"Data Curation\"**.\n",
+    "2. The split file generated in this notebook.\n",
+    "3. **[RDKit](https://github.com/rdkit/rdkit)** features calculated by the **[AMPL](https://github.com/ATOMScience-org/AMPL)** pipeline."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "339115fa",
+   "metadata": {},
+   "source": [
+    "Here, we will use `\"split_uuid\": \"e5ae743e-485d-42a6-a1cd-482a88b4d0df\"` which is saved in `dataset/` as a convenience for these tutorials."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "1c8abfd4",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-10-12T20:12:14.057462Z",
+     "start_time": "2026-10-12T20:12:08.811867Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:ATOM:The global seed is being set to 2459145597, for reproducibility. Note that this action will synchronize the randonmess across all libraries which may impact the randomness of other parts of the pipeline.\n",
+      "INFO:ATOM:Initiating ModelPipeline with seed 2459145597\n",
+      "INFO:ATOM:Using prefeaturized data; number of features = 200\n",
+      "WARNING:ATOM:Previous dataset split restored\n",
+      "INFO:ATOM:Wrote transformers to dataset/SLC6A3_models/SLC6A3_Ki_curated/RF_computed_descriptors_scaffold_regression/e92d04e2-51bf-45b1-af3d-c34d718cdcb5/transformers.pkl\n",
+      "INFO:ATOM:Fitting random forest model\n",
+      "INFO:ATOM:Using max_invalid_pred_frac=0.0100 for invalid prediction handling.\n",
+      "INFO:ATOM:Using max_invalid_pred_frac=0.0100 for invalid prediction handling.\n",
+      "INFO:ATOM:Using max_invalid_pred_frac=0.0100 for invalid prediction handling.\n",
+      "INFO:ATOM:Transforming response data\n",
+      "INFO:ATOM:Transforming feature data\n",
+      "/Users/apaulson/repos/AMPL/atomsci/ddm/pipeline/transformations.py:326: RuntimeWarning: invalid value encountered in divide\n",
+      "  X = np.nan_to_num((X - self.X_means) * X_weight / self.X_stds)\n",
+      "INFO:ATOM:Transforming response data\n",
+      "INFO:ATOM:Transforming feature data\n",
+      "INFO:ATOM:Transforming response data\n",
+      "INFO:ATOM:Transforming feature data\n",
+      "INFO:ATOM:Fold 0: training r2_score = 0.948, validation r2_score = -0.076, test r2_score = 0.553\n",
+      "INFO:ATOM:Transforming response data\n",
+      "INFO:ATOM:Transforming feature data\n",
+      "/Users/apaulson/repos/AMPL/atomsci/ddm/pipeline/transformations.py:326: RuntimeWarning: invalid value encountered in divide\n",
+      "  X = np.nan_to_num((X - self.X_means) * X_weight / self.X_stds)\n",
+      "INFO:ATOM:Transforming response data\n",
+      "INFO:ATOM:Transforming feature data\n",
+      "INFO:ATOM:Transforming response data\n",
+      "INFO:ATOM:Transforming feature data\n",
+      "INFO:ATOM:Fold 1: training r2_score = 0.950, validation r2_score = 0.448, test r2_score = 0.611\n",
+      "INFO:ATOM:Transforming response data\n",
+      "INFO:ATOM:Transforming feature data\n",
+      "/Users/apaulson/repos/AMPL/atomsci/ddm/pipeline/transformations.py:326: RuntimeWarning: invalid value encountered in divide\n",
+      "  X = np.nan_to_num((X - self.X_means) * X_weight / self.X_stds)\n",
+      "INFO:ATOM:Transforming response data\n",
+      "INFO:ATOM:Transforming feature data\n",
+      "INFO:ATOM:Transforming response data\n",
+      "INFO:ATOM:Transforming feature data\n",
+      "INFO:ATOM:Fold 2: training r2_score = 0.938, validation r2_score = 0.620, test r2_score = 0.614\n",
+      "INFO:ATOM:Transforming response data\n",
+      "INFO:ATOM:Transforming feature data\n",
+      "/Users/apaulson/repos/AMPL/atomsci/ddm/pipeline/transformations.py:326: RuntimeWarning: invalid value encountered in divide\n",
+      "  X = np.nan_to_num((X - self.X_means) * X_weight / self.X_stds)\n",
+      "INFO:ATOM:Transforming response data\n",
+      "INFO:ATOM:Transforming feature data\n",
+      "INFO:ATOM:Transforming response data\n",
+      "INFO:ATOM:Transforming feature data\n",
+      "INFO:ATOM:Fold 3: training r2_score = 0.950, validation r2_score = 0.473, test r2_score = 0.561\n",
+      "INFO:ATOM:Transforming response data\n",
+      "INFO:ATOM:Transforming feature data\n",
+      "/Users/apaulson/repos/AMPL/atomsci/ddm/pipeline/transformations.py:326: RuntimeWarning: invalid value encountered in divide\n",
+      "  X = np.nan_to_num((X - self.X_means) * X_weight / self.X_stds)\n",
+      "INFO:ATOM:Transforming response data\n",
+      "INFO:ATOM:Transforming feature data\n",
+      "INFO:ATOM:Transforming response data\n",
+      "INFO:ATOM:Transforming feature data\n",
+      "INFO:ATOM:Fold 4: training r2_score = 0.950, validation r2_score = 0.479, test r2_score = 0.429\n",
+      "INFO:ATOM:Transforming response data\n",
+      "INFO:ATOM:Transforming feature data\n",
+      "/Users/apaulson/repos/AMPL/atomsci/ddm/pipeline/transformations.py:326: RuntimeWarning: invalid value encountered in divide\n",
+      "  X = np.nan_to_num((X - self.X_means) * X_weight / self.X_stds)\n",
+      "INFO:ATOM:Transforming response data\n",
+      "INFO:ATOM:Transforming feature data\n",
+      "/Users/apaulson/repos/AMPL/atomsci/ddm/pipeline/transformations.py:326: RuntimeWarning: invalid value encountered in divide\n",
+      "  X = np.nan_to_num((X - self.X_means) * X_weight / self.X_stds)\n",
+      "INFO:ATOM:Using max_invalid_pred_frac=0.0100 for invalid prediction handling.\n",
+      "INFO:ATOM:Wrote model tarball to dataset/SLC6A3_models/SLC6A3_Ki_curated_model_e92d04e2-51bf-45b1-af3d-c34d718cdcb5.tar.gz\n"
+     ]
+    }
+   ],
+   "source": [
+    "# importing relevant libraries\n",
+    "pd.set_option('display.max_columns', None)\n",
+    "from atomsci.ddm.pipeline import model_pipeline as mp\n",
+    "from atomsci.ddm.pipeline import parameter_parser as parse\n",
+    "\n",
+    "# Set up\n",
+    "dataset_file = 'dataset/SLC6A3_Ki_curated.csv'\n",
+    "odir='dataset/SLC6A3_models/'\n",
+    "\n",
+    "response_col = \"avg_pKi\"\n",
+    "compound_id = \"compound_id\"\n",
+    "smiles_col = \"base_rdkit_smiles\"\n",
+    "split_uuid = \"e5ae743e-485d-42a6-a1cd-482a88b4d0df\"\n",
+    "\n",
+    "params = {\n",
+    "        \"prediction_type\": \"regression\",\n",
+    "        \"dataset_key\": dataset_file,\n",
+    "        \"id_col\": compound_id,\n",
+    "        \"smiles_col\": smiles_col,\n",
+    "        \"response_cols\": response_col,\n",
+    "\n",
+    "        \"previously_split\": \"True\",\n",
+    "        \"split_strategy\": \"k_fold_cv\",\n",
+    "        \"splitter\": 'scaffold',\n",
+    "        \"split_uuid\": split_uuid,\n",
+    "        \"split_only\": \"False\",\n",
+    "\n",
+    "        \"featurizer\": \"computed_descriptors\",\n",
+    "        \"descriptor_type\" : \"rdkit_raw\",\n",
+    "        \"model_type\": \"RF\",\n",
+    "        \"verbose\": \"True\",\n",
+    "        \"transformers\": \"True\",\n",
+    "        \"rerun\": \"False\",\n",
+    "        \"result_dir\": odir\n",
+    "    }\n",
+    "\n",
+    "ampl_param = parse.wrapper(params)\n",
+    "pl = mp.ModelPipeline(ampl_param)\n",
+    "pl.train_model()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2435350",
+   "metadata": {},
+   "source": [
+    "### Performance of the Model\n",
+    "**[AMPL](https://github.com/ATOMScience-org/AMPL)** has several popular metrics to evaulate regression models; \n",
+    "**Mean Absolute Error (MAE)**, **Root Mean Squared Error (RMSE)** and $R^2$ (R-Squared).\n",
+    "In our tutorials, we will use $R^2$ metric to compare our models. The best model will have the highest\n",
+    "$R^2$ score."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db52f817",
+   "metadata": {},
+   "source": [
+    "When using _k_-fold cross validation, we are actually training one model per each of the _k_ folds. The train and validation performance metrics returned are the average of the _k_ models.\n",
+    "\n",
+    "After model selection, AMPL retrains a single production model, whose performance is then evaluated separately on the held-out test set. The test metrics reported come from this model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "629d0db1",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-10-12T20:18:52.418346Z",
+     "start_time": "2026-10-12T20:18:52.386758Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found data for 7 models under dataset/SLC6A3_models/\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Model Performance\n",
+    "from atomsci.ddm.pipeline import compare_models as cm\n",
+    "perf_df = cm.get_filesystem_perf_results(odir, pred_type='regression') # printed statement will show all models in odir\n",
+    "perf_df = perf_df[perf_df.split_strategy == 'k_fold_cv']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "cd9052e3",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-10-12T20:18:54.776329Z",
+     "start_time": "2026-10-12T20:18:54.757866Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# save perf_df\n",
+    "perf_df.to_csv(os.path.join(odir, 'kfold_perf_df.csv'), index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "9306a354",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-10-12T20:18:55.400797Z",
+     "start_time": "2026-10-12T20:18:55.382130Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "perf_df = pd.read_csv('dataset/SLC6A3_models/kfold_perf_df.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "3f0ebcfa",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-10-12T20:18:56.037333Z",
+     "start_time": "2026-10-12T20:18:56.024998Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model_uuid</th>\n",
+       "      <th>split_uuid</th>\n",
+       "      <th>best_train_r2_score</th>\n",
+       "      <th>best_valid_r2_score</th>\n",
+       "      <th>best_test_r2_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>e92d04e2-51bf-45b1-af3d-c34d718cdcb5</td>\n",
+       "      <td>e5ae743e-485d-42a6-a1cd-482a88b4d0df</td>\n",
+       "      <td>0.947267</td>\n",
+       "      <td>0.388938</td>\n",
+       "      <td>0.597357</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             model_uuid                            split_uuid  \\\n",
+       "0  e92d04e2-51bf-45b1-af3d-c34d718cdcb5  e5ae743e-485d-42a6-a1cd-482a88b4d0df   \n",
+       "\n",
+       "   best_train_r2_score  best_valid_r2_score  best_test_r2_score  \n",
+       "0             0.947267             0.388938            0.597357  "
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# View the perf_df dataframe\n",
+    "\n",
+    "# show most useful columns\n",
+    "perf_df[['model_uuid', 'split_uuid', 'best_train_r2_score', 'best_valid_r2_score', 'best_test_r2_score']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "138cca7d",
+   "metadata": {},
+   "source": [
+    "### Finding the Top Performing Model\n",
+    "\n",
+    "This may or may not be the _k_-fold cross validated regression model we just created. \n",
+    "If you have models from the previous tutorials, those may have a better `best_valid_r2_score`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "0a89acbb",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-10-12T20:18:58.986546Z",
+     "start_time": "2026-10-12T20:18:58.974249Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "model_uuid                                              e92d04e2-51bf-45b1-af3d-c34d718cdcb5\n",
+       "model_path                                 dataset/SLC6A3_models/SLC6A3_Ki_curated_model_...\n",
+       "ampl_version                                                                           1.7.0\n",
+       "model_type                                                                                RF\n",
+       "dataset_key                                /Users/apaulson/repos/AMPL/atomsci/ddm/example...\n",
+       "features                                                                           rdkit_raw\n",
+       "splitter                                                                            scaffold\n",
+       "split_strategy                                                                     k_fold_cv\n",
+       "split_uuid                                              e5ae743e-485d-42a6-a1cd-482a88b4d0df\n",
+       "model_score_type                                                                          r2\n",
+       "feature_transform_type                                                         normalization\n",
+       "weight_transform_type                                                                    NaN\n",
+       "model_choice_score                                                                  0.448497\n",
+       "best_train_r2_score                                                                 0.947267\n",
+       "best_train_rms_score                                                                0.281882\n",
+       "best_train_mae_score                                                                0.204538\n",
+       "best_train_num_compounds                                                                1637\n",
+       "best_train_invalid_prediction_count                                                        0\n",
+       "best_train_invalid_prediction_fraction                                                   0.0\n",
+       "best_train_invalid_prediction_threshold                                                 0.01\n",
+       "best_valid_r2_score                                                                 0.388938\n",
+       "best_valid_rms_score                                                                0.922362\n",
+       "best_valid_mae_score                                                                0.726648\n",
+       "best_valid_num_compounds                                                                1637\n",
+       "best_valid_invalid_prediction_count                                                        0\n",
+       "best_valid_invalid_prediction_fraction                                                   0.0\n",
+       "best_valid_invalid_prediction_threshold                                                 0.01\n",
+       "best_test_r2_score                                                                  0.597357\n",
+       "best_test_rms_score                                                                 0.758328\n",
+       "best_test_mae_score                                                                 0.626366\n",
+       "best_test_num_compounds                                                                  182\n",
+       "best_test_invalid_prediction_count                                                         0\n",
+       "best_test_invalid_prediction_fraction                                                    0.0\n",
+       "best_test_invalid_prediction_threshold                                                  0.01\n",
+       "max_epochs                                                                               NaN\n",
+       "best_epoch                                                                               NaN\n",
+       "learning_rate                                                                            NaN\n",
+       "weight_decay_penalty_type                                                                NaN\n",
+       "weight_decay_penalty                                                                     NaN\n",
+       "layer_sizes                                                                              NaN\n",
+       "dropouts                                                                                 NaN\n",
+       "rf_estimators                                                                          500.0\n",
+       "rf_max_features                                                                         32.0\n",
+       "rf_max_depth                                                                             NaN\n",
+       "xgb_gamma                                                                                NaN\n",
+       "xgb_alpha                                                                                NaN\n",
+       "xgb_lambda                                                                               NaN\n",
+       "xgb_learning_rate                                                                        NaN\n",
+       "xgb_max_depth                                                                            NaN\n",
+       "xgb_colsample_bytree                                                                     NaN\n",
+       "xgb_subsample                                                                            NaN\n",
+       "xgb_n_estimators                                                                         NaN\n",
+       "xgb_min_child_weight                                                                     NaN\n",
+       "model_parameters_dict                      {\"rf_estimators\": 500, \"rf_max_depth\": null, \"...\n",
+       "feat_parameters_dict                                                                      {}\n",
+       "Name: 0, dtype: object"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Top performing model\n",
+    "top_model=perf_df.sort_values(by=\"best_valid_r2_score\", ascending=False).iloc[0]\n",
+    "top_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "1873f6a9",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-10-12T20:18:59.906261Z",
+     "start_time": "2026-10-12T20:18:59.889580Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'dataset/SLC6A3_models/SLC6A3_Ki_curated_model_e92d04e2-51bf-45b1-af3d-c34d718cdcb5.tar.gz'"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Top performing model path\n",
+    "top_model.model_path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b69ea2f",
+   "metadata": {},
+   "source": [
+    "## Application of the Trained Model\n",
+    "With _k_-fold cross validation, the final saved model is trained on 100% of the train/validation data using the hyperparameters selected during cross-validation. This is different than the train_valid_test workflow, where the final model is only trained on the training data.\n",
+    "\n",
+    "As a best practice, the final model should be evaluated on the held-out test set to verify that its performance is consistent with the cross-validation estimate. Small improvements are expected because the final model is trained on more data, but substantial deviations from the averaged cross-validation metrics should be looked into.\n",
+    "\n",
+    "For more information on applying a trained model, please refer to tutorial notebook 4."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "e65e377c",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-10-12T20:19:01.192226Z",
+     "start_time": "2026-10-12T20:19:01.166561Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# ignore sklearn future warnings\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore',category=FutureWarning)\n",
+    "\n",
+    "from atomsci.ddm.pipeline import predict_from_model as pfm\n",
+    "from sklearn.metrics import r2_score"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "618ce790",
+   "metadata": {},
+   "source": [
+    "### Creating The Test Dataset\n",
+    "First, create a test set by selecting the test data from the curated dataset.\n",
+    "Here we are using the pre-featurized dataset to save time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "d7385054",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-10-12T20:19:03.468839Z",
+     "start_time": "2026-10-12T20:19:03.428389Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>compound_id</th>\n",
+       "      <th>base_rdkit_smiles</th>\n",
+       "      <th>avg_pKi</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>CHEMBL561878</td>\n",
+       "      <td>O=C(c1ccccc1Oc1ccccc1)N(C1CCC1)[C@H]1CCNC1</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>CHEMBL17157</td>\n",
+       "      <td>CC(C)(C)c1ccc(C(O)CCCN2CCC(C(O)(c3ccccc3)c3ccc...</td>\n",
+       "      <td>6.692504</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CHEMBL3663914</td>\n",
+       "      <td>CN1Cc2cc(CN3CCOCC3)ccc2C(c2cc3ccccc3s2)C1</td>\n",
+       "      <td>7.450997</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>CHEMBL3663910</td>\n",
+       "      <td>COc1ccc(C2CN(C)Cc3ccccc32)cn1</td>\n",
+       "      <td>5.554863</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>CHEMBL3321789</td>\n",
+       "      <td>OC1(c2ccc(Cl)cc2)CC2CCC(C1)N2CCCOc1ccc(F)cc1</td>\n",
+       "      <td>6.207608</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     compound_id                                  base_rdkit_smiles   avg_pKi\n",
+       "2   CHEMBL561878         O=C(c1ccccc1Oc1ccccc1)N(C1CCC1)[C@H]1CCNC1  5.000000\n",
+       "3    CHEMBL17157  CC(C)(C)c1ccc(C(O)CCCN2CCC(C(O)(c3ccccc3)c3ccc...  6.692504\n",
+       "4  CHEMBL3663914          CN1Cc2cc(CN3CCOCC3)ccc2C(c2cc3ccccc3s2)C1  7.450997\n",
+       "5  CHEMBL3663910                      COc1ccc(C2CN(C)Cc3ccccc32)cn1  5.554863\n",
+       "7  CHEMBL3321789       OC1(c2ccc(Cl)cc2)CC2CCC(C1)N2CCCOc1ccc(F)cc1  6.207608"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "split_file_path = 'dataset/SLC6A3_Ki_curated_5_fold_cv_scaffold_e5ae743e-485d-42a6-a1cd-482a88b4d0df.csv'\n",
+    "curated_data_path = 'dataset/scaled_descriptors/SLC6A3_Ki_curated_with_rdkit_raw_descriptors.csv'\n",
+    "\n",
+    "split_data = pd.read_csv(split_file_path)\n",
+    "\n",
+    "curated_data = pd.read_csv(curated_data_path)\n",
+    "\n",
+    "test_ids=split_data[split_data.subset == 'test'].cmpd_id.unique()\n",
+    "test_data = curated_data[curated_data.compound_id.isin(test_ids)]\n",
+    "\n",
+    "# show most useful columns \n",
+    "test_data[['compound_id', 'base_rdkit_smiles', 'avg_pKi']].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "498f3d1f",
+   "metadata": {},
+   "source": [
+    "### Performing Predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "24e5a404",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-10-12T20:20:30.724928Z",
+     "start_time": "2026-10-12T20:20:29.313579Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Standardizing SMILES strings for 182 compounds.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/apaulson/repos/AMPL/atomsci/ddm/pipeline/predict_from_model.py:184: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  input_df['orig_smiles']=input_df[smiles_col]\n",
+      "/Users/apaulson/repos/AMPL/atomsci/ddm/pipeline/predict_from_model.py:185: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  input_df[smiles_col] = std_smiles\n",
+      "INFO:atomsci.ddm.utils.model_version_utils:dataset/SLC6A3_models/SLC6A3_Ki_curated_model_e92d04e2-51bf-45b1-af3d-c34d718cdcb5.tar.gz, 1.7.0\n",
+      "INFO:atomsci.ddm.utils.model_version_utils:Version compatible check: dataset/SLC6A3_models/SLC6A3_Ki_curated_model_e92d04e2-51bf-45b1-af3d-c34d718cdcb5.tar.gz version = \"1.7\", AMPL version = \"1.7\"\n",
+      "INFO:ATOM:Featurization = ComputedDescriptorFeaturization with rdkit_raw descriptors\n",
+      "WARNING:ATOM:The global seed is being set to 2459145597, for reproducibility. Note that this action will synchronize the randonmess across all libraries which may impact the randomness of other parts of the pipeline.\n",
+      "INFO:ATOM:Initiating ModelPipeline with seed 2459145597\n",
+      "WARNING:ATOM:Formatting already featurized data...\n",
+      "WARNING:ATOM:Done\n",
+      "INFO:ATOM:Evaluating current model\n",
+      "INFO:ATOM:Transforming response data\n",
+      "INFO:ATOM:Transforming feature data\n",
+      "/Users/apaulson/repos/AMPL/atomsci/ddm/pipeline/transformations.py:326: RuntimeWarning: invalid value encountered in divide\n",
+      "  X = np.nan_to_num((X - self.X_means) * X_weight / self.X_stds)\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>compound_id</th>\n",
+       "      <th>base_rdkit_smiles</th>\n",
+       "      <th>avg_pKi_actual</th>\n",
+       "      <th>avg_pKi_pred</th>\n",
+       "      <th>avg_pKi_std</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CHEMBL561878</td>\n",
+       "      <td>O=C(c1ccccc1Oc1ccccc1)N(C1CCC1)[C@H]1CCNC1</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.441487</td>\n",
+       "      <td>0.595603</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>CHEMBL17157</td>\n",
+       "      <td>CC(C)(C)c1ccc(C(O)CCCN2CCC(C(O)(c3ccccc3)c3ccc...</td>\n",
+       "      <td>6.692504</td>\n",
+       "      <td>7.362961</td>\n",
+       "      <td>1.255313</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>CHEMBL3663914</td>\n",
+       "      <td>CN1Cc2cc(CN3CCOCC3)ccc2C(c2cc3ccccc3s2)C1</td>\n",
+       "      <td>7.450997</td>\n",
+       "      <td>6.680147</td>\n",
+       "      <td>0.934940</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>CHEMBL3663910</td>\n",
+       "      <td>COc1ccc(C2CN(C)Cc3ccccc32)cn1</td>\n",
+       "      <td>5.554863</td>\n",
+       "      <td>6.002525</td>\n",
+       "      <td>0.945043</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CHEMBL3321789</td>\n",
+       "      <td>OC1(c2ccc(Cl)cc2)CC2CCC(C1)N2CCCOc1ccc(F)cc1</td>\n",
+       "      <td>6.207608</td>\n",
+       "      <td>6.304780</td>\n",
+       "      <td>1.087081</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     compound_id                                  base_rdkit_smiles  \\\n",
+       "0   CHEMBL561878         O=C(c1ccccc1Oc1ccccc1)N(C1CCC1)[C@H]1CCNC1   \n",
+       "1    CHEMBL17157  CC(C)(C)c1ccc(C(O)CCCN2CCC(C(O)(c3ccccc3)c3ccc...   \n",
+       "2  CHEMBL3663914          CN1Cc2cc(CN3CCOCC3)ccc2C(c2cc3ccccc3s2)C1   \n",
+       "3  CHEMBL3663910                      COc1ccc(C2CN(C)Cc3ccccc32)cn1   \n",
+       "4  CHEMBL3321789       OC1(c2ccc(Cl)cc2)CC2CCC(C1)N2CCCOc1ccc(F)cc1   \n",
+       "\n",
+       "   avg_pKi_actual  avg_pKi_pred  avg_pKi_std  \n",
+       "0        5.000000      5.441487     0.595603  \n",
+       "1        6.692504      7.362961     1.255313  \n",
+       "2        7.450997      6.680147     0.934940  \n",
+       "3        5.554863      6.002525     0.945043  \n",
+       "4        6.207608      6.304780     1.087081  "
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # use our provided best model\n",
+    "# model_dir = 'dataset/SLC6A3_models/SLC6A3_Ki_curated_model_02fec37e-67fa-407a-a59b-c7a7d1c4692c.tar.gz'\n",
+    "# # alternatively, use the model you just trained\n",
+    "model_dir = top_model.model_path\n",
+    "input_df = test_data\n",
+    "id_col = 'compound_id'\n",
+    "smiles_col = 'base_rdkit_smiles'\n",
+    "response_col = 'avg_pKi'\n",
+    "\n",
+    "# loads a pretrained model from a model tarball file and runs predictions on \n",
+    "# compounds in an input data frame\n",
+    "pred_df = pfm.predict_from_model_file(model_path = model_dir, \n",
+    "                                      input_df = test_data,\n",
+    "                                      id_col = id_col ,\n",
+    "                                      smiles_col = smiles_col, \n",
+    "                                      response_col = response_col,\n",
+    "                                      is_featurized=True)\n",
+    "                                      \n",
+    "\n",
+    "# show most useful columns\n",
+    "pred_df[['compound_id', 'base_rdkit_smiles', 'avg_pKi_actual','avg_pKi_pred', 'avg_pKi_std']].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c5a5fbc",
+   "metadata": {},
+   "source": [
+    "### Evaluating Prediction Performance\n",
+    "Calculate the $R^2$ score and compare it with the expected test $R^2$\n",
+    "score."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "1f306409",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-10-12T20:20:32.555253Z",
+     "start_time": "2026-10-12T20:20:32.532501Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Expected test R²: 0.597357\n",
+      "Calculated test R²: 0.597357\n"
+     ]
+    }
+   ],
+   "source": [
+    "actual_value = pred_df[\"avg_pKi_actual\"]\n",
+    "predicted_value = pred_df[\"avg_pKi_pred\"]\n",
+    "r2 = np.round(r2_score(actual_value, predicted_value), 6)\n",
+    "\n",
+    "expected_r2 = top_model.best_test_r2_score\n",
+    "\n",
+    "print(f\"Expected test R²: {expected_r2:.6f}\")\n",
+    "print(f\"Calculated test R²: {r2:.6f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c25c918c",
+   "metadata": {},
+   "source": [
+    "### Visualizing Prediction Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "ac7fdc21",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-10-12T20:20:36.381088Z",
+     "start_time": "2026-10-12T20:20:36.372449Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from atomsci.ddm.pipeline import perf_plots as pp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "9859bda7",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-10-12T20:20:37.026286Z",
+     "start_time": "2026-10-12T20:20:36.870443Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plots predicted vs actual values from a trained regression model for a given \n",
+    "# dataframe\n",
+    "pp.plot_pred_vs_actual_from_df(pred_df, \n",
+    "                               actual_col='avg_pKi_actual', \n",
+    "                               pred_col='avg_pKi_pred', \n",
+    "                               label='Prediction of Test Set');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f510576a",
+   "metadata": {},
+   "source": [
+    "If you have specific feedback about a tutorial, please complete the **[AMPL Tutorial Evaluation](https://forms.gle/pa9sHj4MHbS5zG7A6)**."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv-mchip (3.10.21)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.21"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/atomsci/ddm/examples/tutorials/dataset/SLC6A3_Ki_curated_5_fold_cv_scaffold_e5ae743e-485d-42a6-a1cd-482a88b4d0df.csv b/atomsci/ddm/examples/tutorials/dataset/SLC6A3_Ki_curated_5_fold_cv_scaffold_e5ae743e-485d-42a6-a1cd-482a88b4d0df.csv
new file mode 100644
index 00000000..76320cf8
--- /dev/null
+++ b/atomsci/ddm/examples/tutorials/dataset/SLC6A3_Ki_curated_5_fold_cv_scaffold_e5ae743e-485d-42a6-a1cd-482a88b4d0df.csv
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+CHEMBL1643898,test,0
+CHEMBL198842,test,0
+CHEMBL556714,test,0
+CHEMBL443979,test,0
+CHEMBL3633757,test,0
+CHEMBL372378,test,0
+CHEMBL577294,test,0
+CHEMBL450435,test,0
+CHEMBL566618,test,0
+CHEMBL1914694,test,0
+CHEMBL1829335,test,0
+CHEMBL1255834,test,0
+CHEMBL1818544,test,0
+CHEMBL4457754,test,0
+CHEMBL2152944,test,0
+CHEMBL426317,test,0
+CHEMBL4224830,test,0
+CHEMBL3310034,test,0
+CHEMBL1306,test,0
+CHEMBL561598,test,0
+CHEMBL2029054,test,0
+CHEMBL575289,test,0
diff --git a/atomsci/ddm/examples/tutorials/dataset/SLC6A3_models/SLC6A3_Ki_curated_model_02fec37e-67fa-407a-a59b-c7a7d1c4692c.tar.gz b/atomsci/ddm/examples/tutorials/dataset/SLC6A3_models/SLC6A3_Ki_curated_model_02fec37e-67fa-407a-a59b-c7a7d1c4692c.tar.gz
new file mode 100644
index 00000000..16780d20
Binary files /dev/null and b/atomsci/ddm/examples/tutorials/dataset/SLC6A3_models/SLC6A3_Ki_curated_model_02fec37e-67fa-407a-a59b-c7a7d1c4692c.tar.gz differ
diff --git a/atomsci/ddm/pipeline/model_wrapper.py b/atomsci/ddm/pipeline/model_wrapper.py
index 0def1ed4..c8d87e98 100644
--- a/atomsci/ddm/pipeline/model_wrapper.py
+++ b/atomsci/ddm/pipeline/model_wrapper.py
@@ -1735,6 +1735,10 @@ def train(self, pipeline):
             train_perfs (dict): A dictionary of predicted values and metrics on the training dataset
 
             valid_perfs (dict): A dictionary of predicted values and metrics on the training dataset
+
+            test_perf_data (PerfData): For k-fold cross-validation, contains predictions and performance 
+                from the single model retrained on the combined training+validation data (the model that
+                actually gets saved), not an average across the per-fold cross-validation models.
         """
 
         self.data = pipeline.data
@@ -1776,6 +1780,13 @@ def train(self, pipeline):
             fit_dataset = self.data.combined_training_data()
             fit_dataset = self.transform_dataset(fit_dataset, fold='final')
             self.model.fit(fit_dataset)
+
+            # Recompute test performance using a single prediction pass from the retrained model
+            test_dset_final = self.transform_dataset(self.data.test_dset, fold='final')
+            final_test_pred = self.model.predict(test_dset_final, self.transformers['final'])
+            self.test_perf_data = perf.create_perf_data(self.params.prediction_type, pipeline.data, 'test')
+            self.test_perf_data.accumulate_preds(final_test_pred, test_dset_final.ids)
+            self.test_perf, self.test_perf_std = self.test_perf_data.compute_perf_metrics()
         self.model_save()
         # The best model is just the single RF training run.
         self.best_epoch = 0
@@ -2174,6 +2185,10 @@ def train(self, pipeline):
             train_perfs (dict): A dictionary of predicted values and metrics on the training dataset
 
             valid_perfs (dict): A dictionary of predicted values and metrics on the training dataset
+
+            test_perf_data (PerfData): For k-fold cross validation, contains predictions and performance
+                from the single model retrained on the combined training+validation data (the model that
+                actually gets saved), not an average across the per-fold cross-validation models.
         """
         self.log.info("Fitting xgboost model")
 
@@ -2215,6 +2230,13 @@ def train(self, pipeline):
             fit_dataset = self.data.combined_training_data()
             fit_dataset = self.transform_dataset(fit_dataset, fold='final')
             self.model.fit(fit_dataset)
+
+            # Recompute test performance using a single prediction pass from the retrained model
+            test_dset_final = self.transform_dataset(self.data.test_dset, fold='final')
+            final_test_pred = self.model.predict(test_dset_final, self.transformers['final'])
+            self.test_perf_data = perf.create_perf_data(self.params.prediction_type, pipeline.data, 'test')
+            self.test_perf_data.accumulate_preds(final_test_pred, test_dset_final.ids)
+            self.test_perf, self.test_perf_std = self.test_perf_data.compute_perf_metrics()
         self.model_save()
         # The best model is just the single xgb training run.
         self.best_epoch = 0
diff --git a/atomsci/ddm/utils/compare_splits_plots.py b/atomsci/ddm/utils/compare_splits_plots.py
index bddfa24b..b80f9535 100644
--- a/atomsci/ddm/utils/compare_splits_plots.py
+++ b/atomsci/ddm/utils/compare_splits_plots.py
@@ -14,7 +14,9 @@
 
 
 class SplitStats:
-    """This object manages a dataset and a given split dataframe."""
+    """This object manages a dataset and a given split dataframe.
+    Supports both train_valid_test and kfold splits.
+    """
     def __init__(self, total_df, split_df, smiles_col, id_col, response_cols):
         """Calculates compound to compound Tanomoto distances between training and
         test subsets. Counts the number of samples for each subset, for each task
@@ -22,7 +24,7 @@ def __init__(self, total_df, split_df, smiles_col, id_col, response_cols):
 
         Args:
             total_df (DataFrame): Pandas DataFrame.
-            split_df (DataFrame): AMPL split data frame. Must contain
+            split_df (DataFrame): AMPL split data frame. Must contain at minimum
                 'cmpd_id' and 'subset' columns.
             smiles_col (str): SMILES column in total_df.
             id_col (str): ID column in total_df.
@@ -34,18 +36,112 @@ def __init__(self, total_df, split_df, smiles_col, id_col, response_cols):
         self.total_df = total_df
         self.split_df = split_df
 
+        self.split_mode = self._infer_split_mode(split_df)
+        self.is_kfold = self.split_mode == "kfold"
+
+        self.total_y, self.total_w = mss.make_y_w(self.total_df, self.response_cols)
+
+        if self.split_mode == "standard":
+            self._init_standard()
+        elif self.split_mode == "kfold":
+            self._init_kfold()
+        else:
+            raise ValueError(f"Unknown split mode: {self.split_mode}")
+
+    @staticmethod
+    def _infer_split_mode(split_df):
+        """Helper to infer the mode of splitting from the split DataFrame.
+        """
+        subsets = set(split_df["subset"].unique())
+
+        if {"train", "test", "valid"}.issubset(subsets):
+            return "standard"
+        if {"train_valid", "test"}.issubset(subsets) and "fold" in split_df.columns:
+            return "kfold"
+
+        raise ValueError(
+            f"Cannot infer split mode from subset values: {sorted(subsets)}"
+        )
+    
+    def _init_standard(self):
+        """Initialise attributes for a train_valid_test split.
+        """
         self.train_df, self.test_df, self.valid_df = split(self.total_df, self.split_df, self.id_col)
 
-        self.total_y, self.total_w = mss.make_y_w(self.total_df, response_cols)
-        self.train_y, self.train_w = mss.make_y_w(self.train_df, response_cols)
-        self.test_y, self.test_w = mss.make_y_w(self.test_df, response_cols)
-        self.valid_y, self.valid_w = mss.make_y_w(self.valid_df, response_cols)
+        self.train_y, self.train_w = mss.make_y_w(self.train_df, self.response_cols)
+        self.test_y, self.test_w = mss.make_y_w(self.test_df, self.response_cols)
+        self.valid_y, self.valid_w = mss.make_y_w(self.valid_df, self.response_cols)
 
         self.dists_tvt = self._get_dists(self.test_df, self.train_df)
         self.dists_tvv = self._get_dists(self.valid_df, self.train_df)
 
         self.train_fracs, self.valid_fracs, self.test_fracs = self._split_ratios()
 
+        self.n_folds = None
+        self.fold_dists = None
+
+    def _init_kfold(self):
+        """Initialise attributes for a k-fold cross-validation split. For each fold, the validation set is the subset of train_valid compounds assigned to
+        that fold, and the training set is the remaining compounds (all other folds).
+        
+        Results are stored in self.fold_dists, self.fold_trainvalid_dists, and self.fold_fracs
+        """
+        split_df = self.split_df
+
+        _, self.test_df, _ = split(self.total_df, self.split_df, self.id_col)
+        self.test_y, self.test_w = mss.make_y_w(self.test_df, self.response_cols)
+
+        tv_mask = split_df["subset"] == "train_valid"
+        fold_values = sorted(split_df.loc[tv_mask, "fold"].unique())
+        self.n_folds = len(fold_values)
+
+        fold_records = []
+        fold_trainvalid_records = []
+        frac_records = []
+
+        for fold_idx in fold_values:
+            train_ids = split_df.loc[
+                tv_mask & (split_df["fold"] != fold_idx), "cmpd_id"].values
+            valid_ids = split_df.loc[
+                tv_mask & (split_df["fold"] == fold_idx), "cmpd_id"].values
+
+            self.train_df = self.total_df[
+                self.total_df[self.id_col].isin(train_ids)].copy()
+            self.valid_df = self.total_df[
+                self.total_df[self.id_col].isin(valid_ids)].copy()
+
+            dists = self._get_dists(self.test_df, self.train_df)
+            fold_records.append(pd.DataFrame({"fold": fold_idx, "distance": dists}))
+
+            trainvalid_dists = self._get_dists(self.valid_df, self.train_df)
+            fold_trainvalid_records.append(pd.DataFrame({"fold": fold_idx, "distance": trainvalid_dists}))
+
+            self.train_y, self.train_w = mss.make_y_w(self.train_df, self.response_cols)
+            self.valid_y, self.valid_w = mss.make_y_w(self.valid_df, self.response_cols)
+            train_frac, valid_frac, test_frac = self._split_ratios()
+
+            for f in train_frac:
+                frac_records.append({"subset": "train", "fold": fold_idx, "frac": f})
+            for f in valid_frac:
+                frac_records.append({"subset": "valid", "fold": fold_idx, "frac": f})
+
+        for f in test_frac:
+            frac_records.append({"subset": "test", "fold": "all", "frac": f})
+        
+        self.fold_dists = pd.concat(fold_records, axis=0, ignore_index=True)
+        self.fold_trainvalid_dists = pd.concat(fold_trainvalid_records, axis=0, ignore_index=True)
+        self.fold_fracs = pd.DataFrame(frac_records)
+
+        self.train_df = None
+        self.valid_df = None
+        self.dists_tvt = None
+        self.dists_tvv = None
+        self.train_fracs = None
+        self.valid_fracs = None
+        self.test_fracs = None #self.fold_fracs for per-fold/test fractions
+        self.train_y = self.train_w = None
+        self.valid_y = self.valid_w = None
+
     def _get_dists(self, df_a, df_b):
         """Calculate Tanimoto distances between each compound in df_a and its nearest neighbor in df_b.
 
@@ -75,8 +171,21 @@ def _split_ratios(self):
     
         return train_fracs, valid_fracs, test_fracs
 
+    def _require_standard(self):
+        """Guard for standard splits.
+        """
+        if self.is_kfold:
+            raise ValueError("This method is not available for k-fold splits. Use dist_hist_kfold_plot() instead.")
+
+    def _require_kfold(self):
+        """Guard for k-fold splits.
+        """
+        if not self.is_kfold:
+            raise ValueError("This method is only available for k-fold splits.")
+
     def print_stats(self):
         """Prints useful statistics to stdout"""
+        self._require_standard()
         print(f"dist tvt mean: {np.mean(self.dists_tvt):0.2f}, median: {np.median(self.dists_tvt):0.2f}, std: {np.std(self.dists_tvt):0.2f}")
         print(f"dist tvv mean: {np.mean(self.dists_tvv):0.2f}, median: {np.median(self.dists_tvv):0.2f}, std: {np.std(self.dists_tvv):0.2f}")
         print(f"train frac mean: {np.mean(self.train_fracs):0.2f}, median: {np.median(self.train_fracs):0.2f}, std: {np.std(self.train_fracs):0.2f}")
@@ -92,6 +201,7 @@ def dist_hist_train_v_test_plot(self, ax=None):
         Returns:
             ax (matploblib Axes): Axes object for plot
         """
+        self._require_standard()
         return self._show_dist_hist_plot(self.dists_tvt, ax=ax)
 
     def dist_hist_train_v_valid_plot(self, ax=None):
@@ -103,8 +213,66 @@ def dist_hist_train_v_valid_plot(self, ax=None):
         Returns:
             ax (matploblib Axes): Axes object for plot
         """
+        self._require_standard()
         return self._show_dist_hist_plot(self.dists_tvv, ax=ax)
 
+    _kfold_bins = (10, 20)
+    def dist_hist_kfold_plot(self, n_bins = 10, ax = None):
+        """Creates a histogram of pairwise Tanimoto distances between training
+        and test sets
+
+        Args:
+            n_bins (int): Number of bins, defaults to 10.
+            ax (matploblib Axes): Axes object to draw plot in. If None, one will be created.
+
+        Returns:
+            ax (matploblib Axes): Axes object for plot
+        """
+        self._require_kfold()
+        if n_bins not in self._kfold_bins:
+            raise ValueError("n_bins must be 10 or 20.")
+        bin_edges = np.linspace(0, 1, n_bins + 1)
+
+        ax = sns.histplot(data = self.fold_dists, x = 'distance', hue = 'fold', palette='colorblind',
+                          bins = bin_edges, stat='probability', common_norm = False,
+                          multiple = 'layer', fill = True, alpha = 0.3,
+                          edgecolor = 'black', ax = ax)
+        ax.set_title('k-fold Train vs Test Tanimoto Distance', fontsize=14)
+        ax.set_xlabel('Tanimoto distance',fontsize=13)
+        ax.set_ylabel('Proportion of compounds',fontsize=13)
+        ax.legend_.set_title('Fold')
+        ax.set_xlim(0, 1)
+
+        return ax
+
+    def dist_hist_kfold_tvv_plot(self, n_bins=10, ax=None):
+        """Creates a histogram of pairwise Tanimoto distances between valid
+        and training set
+        
+        Args:
+            n_bins (int): Number of bins, defaults to 10.
+            ax (matploblib Axes): Axes object to draw plot in. If None, one will be created.
+
+        Returns:
+            ax (matploblib Axes): Axes object for plot
+        """
+        self._require_kfold()
+        if n_bins not in self._kfold_bins:
+            raise ValueError("n_bins must be 10 or 20.")
+        bin_edges = np.linspace(0, 1, n_bins + 1)
+
+        ax = sns.histplot(data = self.fold_trainvalid_dists, x = 'distance', hue = 'fold', palette='colorblind',
+                        bins = bin_edges, stat='probability', common_norm = False,
+                        multiple = 'layer', fill = True, alpha = 0.3,
+                        edgecolor = 'black', ax = ax)
+        ax.set_xlabel('Tanimoto distance',fontsize=13)
+        ax.set_ylabel('Proportion of compounds',fontsize=13)
+        ax.set_title('k-fold Train vs Valid Tanimoto Distance', fontsize=14)
+        ax.legend_.set_title('Fold')
+        ax.set_xlim(0, 1)
+
+        return ax
+
     def dist_hist_plot(self, dists, title, dist_path=''):
         """Creates a histogram of pairwise Tanimoto distances between training
         and test sets
@@ -114,6 +282,7 @@ def dist_hist_plot(self, dists, title, dist_path=''):
                 appended to this input
         """
         # plot compound distance histogram
+        self._require_standard()
         fig=pyplot.figure()
         _g = self._show_dist_hist_plot(dists)
         fig.suptitle(title)        
@@ -147,10 +316,12 @@ def umap_plot(self, dist_path=''):
             dist_path (str): Optional Where to save the plot. The string '_umap_scatter' will be
                 appended to this input
         """
+        hue_col = 'fold' if self.is_kfold else 'subset'
+        
         # umap of a subset
         sub_sample_df = self.split_df.loc[np.random.permutation(self.split_df.index)[:10000]]
-        # add subset column to total_df
-        sub_total_df = sub_sample_df[['cmpd_id', 'subset']].merge(
+        # add subset/fold column to total_df
+        sub_total_df = sub_sample_df[['cmpd_id', hue_col]].merge(
             self.total_df, left_on='cmpd_id', right_on=self.id_col, how='inner')
         fp = [AllChem.GetMorganFingerprintAsBitVect(Chem.MolFromSmiles(s), 2, 1024) for s in sub_total_df[self.smiles_col]]
         fp_array = np.array(fp)
@@ -159,11 +330,11 @@ def umap_plot(self, dist_path=''):
         sub_total_df['x'] = embedded[:,0]
         sub_total_df['y'] = embedded[:,1]
         pyplot.figure()
-        sns.scatterplot(x='x', y='y', hue='subset', data=sub_total_df)
+        ax = sns.scatterplot(x='x', y='y', hue=hue_col, data=sub_total_df)
+        ax.set_title('UMAP (Morgan Fingerprints)', fontsize=14)
         if len(dist_path) > 0:
             save_figure(dist_path+'_umap_scatter')
-
-        pyplot.close()
+            pyplot.close()
 
     def subset_frac_plot(self, dist_path=''):
         """Makes a box plot of the subset fractions
@@ -172,18 +343,30 @@ def subset_frac_plot(self, dist_path=''):
             dist_path (str): Optional Where to save the plot. The string '_frac_box' will be
                 appended to this input
         """
-        dicts = []
-        for f in self.train_fracs:
-            dicts.append({'frac':f, 'subset':'train'})
-        for f in self.test_fracs:
-            dicts.append({'frac':f, 'subset':'test'})
-        for f in self.valid_fracs:
-            dicts.append({'frac':f, 'subset':'valid'})
-
-        frac_df = pd.DataFrame.from_dict(dicts)
-
         pyplot.figure()
-        _g = sns.boxplot(x='subset', y='frac', data=frac_df)
+        if self.is_kfold:
+            g = sns.catplot(kind='bar', x='subset', y='frac', hue='fold',
+                             data=self.fold_fracs, palette='colorblind')
+            g.ax.set_title('Subset Fraction')
+        else:
+            dicts = []
+            resp_cols=self.response_cols.copy()
+            if isinstance(resp_cols, str):
+                resp_cols=resp_cols.split(',')
+            elif not isinstance(resp_cols, list):
+                raise ValueError("response_cols must be a string or a list of strings")
+            for t, f in zip(resp_cols, self.train_fracs):
+                dicts.append({'frac':f, 'subset':'train', 'task':t})
+            for t, f in zip(resp_cols, self.test_fracs):
+                dicts.append({'frac':f, 'subset':'test', 'task':t})
+            for t, f in zip(resp_cols, self.valid_fracs):
+                dicts.append({'frac':f, 'subset':'valid', 'task':t})
+
+            frac_df = pd.DataFrame.from_dict(dicts)
+            g = sns.catplot(kind='bar', x='subset', y='frac', col='task', data=frac_df, palette='colorblind')
+            g.set_titles("{col_name}")
+            g.figure.suptitle('Subset Fraction by Task', fontsize=14)
+
         if len(dist_path) > 0:
             save_figure(dist_path+'_frac_box')
 
@@ -194,17 +377,38 @@ def make_all_plots(self, dist_path=''):
             dist_path (str): Optional Where to save the plot. The string '_frac_box' will be
                 appended to this input
         """
-        # histogram of compound distances between training, valid, and test subsets
-        self.dist_hist_plot(self.dists_tvt, 'Train vs Test pairwise Tanimoto Distance',
-            dist_path=dist_path+'_tvt')
-        self.dist_hist_plot(self.dists_tvv, 'Train vs Valid pairwise Tanimoto Distance',
-            dist_path=dist_path+'_tvv')
-
-        # umap on ecfp fingerprints. visualizes clusters of training/valid/testing split
-        self.umap_plot(dist_path)
-
-        # box plot of fractions
-        self.subset_frac_plot(dist_path)
+        if self.is_kfold:
+            # histogram of nearest-neighbor distances between test and each fold's training set
+            pyplot.figure()
+            self.dist_hist_kfold_plot()
+            if len(dist_path) > 0:
+                save_figure(dist_path + '_kfold_tvt')
+                pyplot.close()
+
+            # histogram of nearest-neighbor distances between each fold's validation subset and that fold's training set
+            pyplot.figure()
+            self.dist_hist_kfold_tvv_plot()
+            if len(dist_path) > 0:
+                save_figure(dist_path + '_kfold_tvv')
+                pyplot.close()
+
+            # umap on ecfp fingerprints. visualizes clusters colored by fold
+            self.umap_plot(dist_path)
+
+            # box plot of fractions, grouped by fold
+            self.subset_frac_plot(dist_path)
+        else:
+            # histogram of compound distances between training, valid, and test subsets
+            self.dist_hist_plot(self.dists_tvt, 'Train vs Test pairwise Tanimoto Distance',
+                dist_path=dist_path+'_tvt')
+            self.dist_hist_plot(self.dists_tvv, 'Train vs Valid pairwise Tanimoto Distance',
+                dist_path=dist_path+'_tvv')
+
+            # umap on ecfp fingerprints. visualizes clusters of training/valid/testing split
+            self.umap_plot(dist_path)
+
+            # box plot of fractions
+            self.subset_frac_plot(dist_path)
 
 def split(total_df, split_df, id_col):
     """Splits a dataset into training, test and validation sets using a given split.
@@ -259,4 +463,4 @@ def parse_args():
 
     split_b = pd.read_csv(args.split_b, dtype={'cmpd_id':str})
     ss = SplitStats(df, split_b, smiles_col=args.smiles_col, id_col=args.id_col)
-    ss.make_all_plots(dist_path=os.path.join(args.output_dir, 'split_b'))
+    ss.make_all_plots(dist_path=os.path.join(args.output_dir, 'split_b'))
\ No newline at end of file