diff --git a/CrvCosmic/README.md b/CrvCosmic/README.md
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+# CrvCosmic
+
+XGBoost-based classifier for rejecting cosmic-ray-induced backgrounds at the Mu2e experiment. The model takes per-coincidence CRV and tracker features as input and outputs a probability that an event is cosmic-induced. It is trained on `CosmicCRYSignalAllOnSpillTriggered` (pure cosmics) versus `CeEndpointMix2BBTriggered` (beam+pileup) datasets.
+
+This code was ported from [`sam-grant/mu2e-cosmic`](https://github.com/sam-grant/mu2e-cosmic).
+
+## Layout
+
+```
+CrvCosmic/
+├── config/
+│   └── cuts.yaml              # Cutset definition
+├── src/
+│   ├── core/
+│   │   ├── analyse.py         # Cut flow + feature helpers
+│   │   └── postprocess.py     # combine_cut_flows / combine_hists / combine_arrays
+│   ├── utils/
+│   │   ├── io_manager.py      # pkl + cuts.yaml load, pkl/csv/h5/parquet write
+│   │   ├── hist_manager.py    # histogram booking/filling for the summary plots
+│   │   ├── draw.py            # plot_summary_ml
+│   │   └── mu2e.mplstyle
+│   └── ml/
+│       ├── process.py         # MLProcessor: extracts features from ROOT files
+│       ├── load.py            # LoadML: read processed data and trained models
+│       ├── assemble.py        # AssembleDataset: label, shuffle, fold-split
+│       ├── train.py           # Train: fit XGBoost, K-fold CV, save UBJ
+│       ├── validate.py        # Validate: ROC, threshold scan, money table
+│       └── optimise.py        # Optimise: grid search over hyperparameters
+├── run/
+│   └── run_ml_prep.py         # entrypoint: process ROOT files into parquet/pkl
+└── notebooks/
+    ├── assemble.ipynb         # Validate assemble module
+    ├── feature_engineering.ipynb # Feature evaluation
+    ├── optimise.ipynb         # Hyperparameter tuning
+    ├── train.ipynb            # Training
+    └── validate.ipynb         # Validation
+```
+
+The non-ML pieces of the upstream cosmic-background framework are not included here — only the cuts and helpers that the ML preselection actually exercises.
+
+## Pipeline
+
+1. **Process**: read ROOT EventNtuple files, apply the `MLPreprocess` cutset,
+   flatten per-coincidence features into awkward/parquet output.
+2. **Assemble**: load processed CRY and CE-mix outputs, label, combine, and
+   produce K-fold indices for nested cross-validation.
+3. **Train**: fit XGBoost on each fold, find the per-fold operating threshold at
+   a target veto efficiency, then retrain on the full set with the
+   CV-averaged threshold and export to `.ubj`.
+4. **Validate**: compute ROC AUC, score distributions, feature importance, and
+   a "money table" comparing the ML model against the simple `dT` cut baseline.
+5. **Optimise** (optional): grid search over XGBoost hyperparameters using
+   K-fold CV; minimises deadtime at the target veto efficiency.
+
+## Setup
+
+On a Mu2e VM or EAF:
+
+```bash
+mu2einit
+pyenv ana
+```
+
+The code depends on [`Mu2e/pyutils`](https://github.com/Mu2e/pyutils) (provided by `pyenv ana`) — `pyutils.pycut.CutManager` is used directly — plus `xgboost` `scikit-learn`, `awkward`, `pandas`, `hist`, `pyarrow`, `h5py`, `joblib`, and `pyyaml`.
+
+## Running the pipeline
+
+### 1. Preprocess ROOT files into ML-ready features
+
+```bash
+cd run
+python run_ml_prep.py            # production: full datasets via xrootd
+python run_ml_prep.py --test     # test: two local files, if they exist
+```
+
+This populates `output/ml/{run}/data/{tag}/` with `events.parquet`, `results.pkl`, `hists.h5`, `stats.csv`, and `cut_flow.csv`. The default production `run_str` is set inside `run_ml_prep.py` (currently `"k"`); change it when starting a new training round.
+
+### 2. Train and validate
+
+```bash
+cd notebooks
+jupyter notebook
+```
+
+
+The final model is saved to `output/ml/{run}/results/final/model_{version}.ubj`, suitable for inference with the standard XGBoost C API.
+
+## Configuring cuts
+
+Cutsets are defined in `config/cuts.yaml`. The ML preprocessing uses the `MLPreprocess` cutset by default (set in `src/ml/process.py`). To modify the preselection, edit thresholds or active flags under `cutsets.MLPreprocess`. Defining a new cutset is also possible by adding an entry under `cutsets:` and overriding `cutset_name` when constructing `MLProcessor`.
+
+## Output paths
+
+All outputs land under `CrvCosmic/output/`:
+
+- `output/ml/{run}/data/{tag}/` — processed per-coincidence features
+- `output/ml/{run}/results/{tag}/` — trained models, threshold scans,
+  money tables, CSV summaries
+- `output/images/ml/{run}/` — ROC curves, score distributions, feature
+  importance plots
+
+These directories are created on demand. Symlink them to another disk if space is a concern.
diff --git a/CrvCosmic/config/cuts.yaml b/CrvCosmic/config/cuts.yaml
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+++ b/CrvCosmic/config/cuts.yaml
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+# Samuel Grant 2025
+# Define cutsets for cosmic-ray-induced background analysis
+
+##########################################################
+# Define baseline defaults (from sam-grant/mu2e-cosmic)
+##########################################################
+defaults:
+    description: "Baseline defaults"
+    thresholds:
+        track_pdg: 11
+        lo_nactive: 20
+        lo_trkqual: 0.8
+        lo_t0_ns: 640
+        hi_t0_ns: 1650
+        lo_maxr_mm: 450
+        hi_maxr_mm: 680
+        hi_d0_mm: 100
+        hi_t0err: 0.9
+        lo_pitch_angle: 0.5
+        hi_pitch_angle: 1.0
+        lo_crv_start_ns: 420.0
+        hi_crv_end_ns: 1700.0
+        lo_veto_dt_ns: -150.0
+        hi_veto_dt_ns: 150.0
+        lo_wide_win_mevc: 85
+        hi_wide_win_mevc: 200
+        lo_ext_win_mevc: 100
+        hi_ext_win_mevc: 110
+        lo_sig_win_mevc: 103.6
+        hi_sig_win_mevc: 104.9
+    active:
+        one_reco_electron: true 
+        is_reco_electron: true
+        thru_trk: true
+        good_trkqual: true
+        within_t0: true
+        is_downstream: true 
+        has_hits: true
+        within_t0err: true
+        within_d0: true
+        within_pitch_angle_lo: true
+        within_pitch_angle_hi: true
+        within_lhr_max_lo: false 
+        within_lhr_max_hi: true
+        is_truth_electron: true
+        within_coinc_start_time: false
+        within_coinc_end_time: false
+        unvetoed: true
+        within_wide_win: true
+        within_ext_win: true
+        within_sig_win: true
+
+##########################################################
+# Define custom cutsets which are overridden from defaults
+# You can turn cuts off and edit thresholds here
+##########################################################
+cutsets:
+    ########################
+    # Analysis cut sets
+    ########################
+    MLPreprocess:
+        description:
+            "Preselection cuts for ML"
+        thresholds:
+            lo_trkqual: 0.2
+        active:
+            within_coinc_start_time: false 
+            within_coinc_end_time: false
+            unvetoed: false
+            within_wide_win: true # no real cosmics below 85 MeV/c 
+            within_ext_win: false
+            within_sig_win: false
diff --git a/CrvCosmic/notebooks/assemble.ipynb b/CrvCosmic/notebooks/assemble.ipynb
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+++ b/CrvCosmic/notebooks/assemble.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a2b0dd07",
+   "metadata": {},
+   "source": [
+    "# Run checks on assembled dataset\n",
+    "\n",
+    "Although we run assemble again in each notebook, the AssembleDataset class has some sanity check methods which I prefer to keep them. \n",
+    "\n",
+    "Use flattened data generated by `run/run_ml_prep.py`, where `run` is a string which indicates which round of processing was using (cuts/flattening technique)\n",
+    "\n",
+    "This is a good place to make misc plots as well.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d57e840",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0596ef1d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/exp/mu2e/app/users/sgrant/MLTrain/CrvCosmic/notebooks\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pwd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3809052b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append(\"../src/ml\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "628c3610",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[LoadML] ✅ Initialised\n",
+      "[Load] ✅ Initialised with in_path=/exp/mu2e/app/users/sgrant/MLTrain/CrvCosmic/output/ml/k/data/CRY_onspill-LH_aw\n",
+      "[Load] ✅ Loaded /exp/mu2e/app/users/sgrant/MLTrain/CrvCosmic/output/ml/k/data/CRY_onspill-LH_aw/results.pkl\n",
+      "[Load] ✅ Initialised with in_path=/exp/mu2e/app/users/sgrant/MLTrain/CrvCosmic/output/ml/k/data/CE_mix2BB_onspill-LH_aw\n",
+      "[Load] ✅ Loaded /exp/mu2e/app/users/sgrant/MLTrain/CrvCosmic/output/ml/k/data/CE_mix2BB_onspill-LH_aw/results.pkl\n",
+      "[LoadML] ✅ Got full results\n",
+      "[Assemble] ✅ Loaded data\n",
+      "[Assemble] ✅ Initialised\n",
+      "Help on AssembleDataset in module assemble object:\n",
+      "\n",
+      "class AssembleDataset(builtins.object)\n",
+      " |  AssembleDataset(run='k', cutset_name='MLPreprocess', verbosity=1)\n",
+      " |\n",
+      " |  Load, label, and split CRY/CE mix data for ML training.\n",
+      " |\n",
+      " |  Methods defined here:\n",
+      " |\n",
+      " |  __init__(self, run='k', cutset_name='MLPreprocess', verbosity=1)\n",
+      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
+      " |\n",
+      " |  assemble_dataset(self, n_outer_folds=5)\n",
+      " |      Combine, shuffle, and prepare data for nested CV.\n",
+      " |      Returns dict with full dataset and outer fold indices.\n",
+      " |\n",
+      " |  check_dT_window_results(self, dT_min=0, dT_max=150)\n",
+      " |\n",
+      " |  draw_cuts(self, ml=True)\n",
+      " |\n",
+      " |  draw_features(self, out_path=None, show=True)\n",
+      " |      Plot CRV feature distributions for CRY vs CE mix.\n",
+      " |\n",
+      " |  get_cut_flows(self)\n",
+      " |\n",
+      " |  load_data(self)\n",
+      " |      Load input data from processing\n",
+      " |\n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Static methods defined here:\n",
+      " |\n",
+      " |  get_fold_data(data, train_idx, test_idx)\n",
+      " |      Slice full dataset into train/test dict for a given fold.\n",
+      " |      Compatible with Train and Optimise interfaces.\n",
+      " |\n",
+      " |  get_full_train_data(data)\n",
+      " |      Return full dataset as train-only dict (for final model training).\n",
+      " |\n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Data descriptors defined here:\n",
+      " |\n",
+      " |  __dict__\n",
+      " |      dictionary for instance variables\n",
+      " |\n",
+      " |  __weakref__\n",
+      " |      list of weak references to the object\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "run = \"k\"\n",
+    "from assemble import AssembleDataset\n",
+    "asm = AssembleDataset(run=run)\n",
+    "help(asm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "3d280134",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Metric</th>\n",
+       "      <th>CRY</th>\n",
+       "      <th>CE Mix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Total</td>\n",
+       "      <td>20087</td>\n",
+       "      <td>578368</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Unvetoed</td>\n",
+       "      <td>10</td>\n",
+       "      <td>417011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Fraction [%]</td>\n",
+       "      <td>99.95</td>\n",
+       "      <td>27.90</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Metric    CRY  CE Mix\n",
+       "0         Total  20087  578368\n",
+       "1      Unvetoed     10  417011\n",
+       "2  Fraction [%]  99.95   27.90"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "asm.check_dT_window_results()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "29f7f01b-7621-4a70-b624-3c9ca367a76f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = asm.check_dT_window_results()\n",
+    "df.to_csv(\"baseline_dT_results.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7da2e203-7058-4c12-9eff-da27ad48197e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# print(data[\"y\"].value_counts())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "632a477b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Load] ✅ Initialised with in_path=test_out\n",
+      "[Draw] ⭐️ Initialised\n",
+      "[Draw] ✅ Wrote /exp/mu2e/app/users/sgrant/MLTrain/CrvCosmic/output/images/ml/k/process/h1o_3x3_cuts_CRY.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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LEY1YP5x23b59u7djd+7cud7b9fU6d+5sbteYCs5rbzvxbsaMGUG/Bu2d2MePfMfB5w0NPcbm/xHnW79+vYnNNGlHk3FqWrJkSbXrtKmzlZeXm0IF+r188cUXq92nMbrH4zHJVppEZaNNncPJx2O9e/c291199dXVPm/nnXeeuX38+PER2AOINP2uDxs2zHr88cetL7/80rr11ltJvEtCnH91H/rq3YV8AHda5OCcAxLvElRZWZl3tI0GhjV1797d3Ld8+fK4bJ/bUfHOmfTg2m4bRpvFn56E0BNb2iZ79uyJ9+a4TlFRkTlxpKO29SQUFe/ig8Q79yGGS26hfqfPPvtss/7kyZNr3acn7vU+HVSDxIr1w2nX++67z9yuo3Nr0s5eva9///4RegcIRTQS72jvxD5+5DsOPm9o6DE2/48431//+lfThlOmTAlqfdrU2b777jvTnl26dKmzQs5LL73kvY02dS6nHI8tXbrUO/tUSUlJtfs0YS8jI8PKz88358DhbPYxHRXvkgfnX6Hoq3cv8gHcp9wBOQep8Z3oFuH65JNPZOfOnWb+4h49etS6f9CgQeZy/vz57GTg/zv88MPNZWlpqfz+++/slzhLSUmRtLQ0SU1NlfT09Hhvjuvccccd8ssvv8ijjz4qGRkZ8d4cwDWI4WArKSmRhQsXVovdfRHPu6tdX3/99YCPOeecc8Tj8ci7775rnh+Jj/ZO3ONHvuPg84aGHmPz/4jz7dixQ+bMmSNNmjSRSy65pN71aVPny8rKCmq95s2bm0vaNHHFsu3sx/Tv37/WZ6ygoEBOOukk2b59u3z66acReW8Agsf5V8DdyAdwnxQH5ByQeJegvvnmG3N55JFH+r3fvt1eD4DIf//7X7Mb9ARofn4+uySOdLDJfffdJ3v37pXTTjst6BNgiIxvv/1WHnjgARk6dKj07t2b3eoATz31lPzzn/+UESNGyMMPPyxr166N9yYhSojh3CGY7/SPP/5okjlatGgh7dq1q3U/8XxiCrdd9bfZ935fmZmZcuihh5pOnp9++ilq246Gef/992XkyJFyxRVXyF133SXLly8PuC7tnbjHj3zHwecNDT3G5v8R59MkGY3nTjzxRNNpM2vWLLnmmmtk+PDhMnXqVNm6dWu19WlT5+vYsaNZtK20PX199tln8s4778gf/vAH0+aKNk1csWw7zu8AzsX3E3A38gHcxXJIzgElhhKU3Xnn7+DB9/Y1a9bEdLsAJ3vooYfMZd++fUn0ioMxY8bI+vXrpbCw0JzM+Pnnn6VLly7yxBNPxGNzXKuqqkouvfRSadq0qQlE4AzaQe/rhhtukNtuu80sSC7EcO4QzHe6vs9CTk6O+b9aK27ob2dubm6UtxqREE677t6921Qzr+txevuyZcvM8V337t1pLAd67rnnql3X7/v5558vzzzzjDRu3Nh7O+2d2MePfMfB5w0NPcbm/xHn++GHH7wVqzQRa8mSJdXuHzVqlDz77LPypz/9yVynTZ1Pq19oTKaVyQYPHiz/+te/zExCmzZtMlWRevbsKTNnzuT3PgnE8vvI+R3Aufh+Au5GPkDyG+PAnAMq3iWoPXv2mMtGjRoFPIDwXQ9wuzfffNNUn9FqBXfeeWe8N8eV5s2bZ05Mvvzyy+YH8LDDDpPZs2ebEaWInSlTpsjSpUvNSUZ7Cg3Ej1ZD0I56nZKoqKhI/vOf/8jdd99tRtXffvvt3gMEJA9iuOQWyne6vs+CIqZPPOG0q+8xG8d3iadz585y//33m056bct169bJCy+8IG3btpW5c+fK3//+92rr096JffzIdxx83tDQY2z+H3E+TchRmoj13XffyfTp02Xbtm2mcsa1115r2vDCCy809ynaNDHotJ+LFi0yle80geqll14y1zX+PvXUU6V169bedWnTxBXLtuP8DuBcfD8B9yIfwB3mOTDngMS7BC6ZaM9XXNf9AERWrlwpf/vb37ylRu253RFb33//vWmD3377zUzhoGX6tXz/888/T1PEiHYE6yiAk08+WYYMGcJ+d4Dx48eb/5/0xG92drYcdNBBMnr0aBM0qnHjxklxcXG8NxMRRAyX3EL5Ttf3WfBdB4kjnHb1vc7xXeLR7/z1118v3bp1Mx1yWg1DO+O/+OILk4Ch3//PP//cuz7tndjHj3zHwecNDT3G5v8R56usrDSXFRUVZgrhSy65RPbbbz/TiTN58mRT6U6nsrQrHNKmieHFF1+UY489Vvbff39TxVCTMnTaUB0kMWHCBDn99NOlvLzcrEubJq5Yth3ndwDn4vsJuBP5AO7xvQNzDki8S1D2VFM6V7E/WmFD+U5pA7iRlhnVqYF0tOrIkSPNyFTEl3ZAnnnmmbJw4UJp1aqVXHbZZaadEH3//Oc/paysTB599FF2t8Ppd+Too482U13UnNYGiY0Yzp38fafr+ywoYvrEE067+k4jzPFd8tCqKUOHDjV/v/32297bae/EPn7kOw4+b2joMTb/jzif3Uapqal+EyqHDRtmLj/88MNq6xP/OZdWwbjoooukRYsW8vrrr5sEPB0wceCBB8ojjzwiAwcONAMlZsyYYdanTRNXLNuO8zvOpf9311zsAZFwB76fgPuQD+BOzR2Uc0DiXYI64IADzGWgD86GDRvMZfv27WO6XYCTaJaz/me7du1a0+mlo1ThHE2aNJH+/fubyj8LFiyI9+a4gp5c1CkTrrzySjnllFO8y1/+8hdzv35X7NuYqjz+9ASw2rRpU7w3BRFEDOdeNb/T9X0W9IS/Juo1a9asWkcAnC2cds3LyzNxUV2P4/gueX7Lae/EPn7kOw4+b2joMTb/jzhfhw4dzKV23Hg8nlr329MXbdmyxVzSpolR7U6r2Wlyvb9CBYMGDaqWTEmbJq5Yth3nd5xLp56ruXz99dfx3izEEN9PwF3IB0ATB+QckHiXoOypTgIFi19++aW57N69e0y3C3CKwsJC6devnykrqydPpk+fXmeJecSHTtWhtm3bRhPEiJ5c+uijj6otdvUlDUjs23RKFcSXVlpROgobyYMYzr1qfqcPPvhgycrKMr+B9kl8X1999ZW5JJ5PLOG2a13/N2gnoZbP1+fV6YuR+L/ltHfiHj/yHQefNzT0GJv/R5yvR48e3t9xf9NL/v777+bSTuCiTRNjWmg7wcof+/bt27ebS9o0ccWy7Ti/41z6f3fNZdy4cfHeLMQQ30/APcgHgFNyDki8S1C9evUymZurVq2Sb775ptb9c+fONZea2Qm4TUlJiQwYMECWLVtmRjK+8MILkpaWFu/Ngh968ll16tSJ/ROnkw66rF692ntyyr6tadOmtEkcaWD48ccfm7+PPPJI2iKJEMO5k7/vdHZ2tpx22mnm7zlz5tR6DPF8Ygq3Xc855xxzOXv27FqPefPNN03H/RlnnOG36gqcSeOpV155xe9vOe2duMePfMfB5w0NPcbm/xHnO+yww0xVO42/li5dWut+uyqanaBHmzqfVi9U+lvvzxdffFGt2iFtmrhi2Xb2Y1577TUz7bivrVu3mnMAWllPzwMBiC3OvwLuQD4AHJVzYCFh3XrrrTrkzurVq5e1Z88e7+2TJ082t5988slx3T430/2flZUV781wpYqKCuvcc881bdC7d2+rqKgo3pvkagsWLLDmzZtnVVZWVrtd22XMmDGmnVq1amUVFhbGbRthWatXrzZtcfDBB7M7YujTTz+1XnnlFfP/Vs320N92bZMBAwbQJkmIGC45hfOdfvfdd83t++23n7Vq1Srv7Z999pmJJZs2bWpt3749Zu8BkYn1w2nX33//3crLy7NSUlLM58i2ZcsW68ADDzTP98EHH9BEDmvvrVu3WlOnTrV2795d7XaNbS+//HJvrLt3795q99PeiX38yHccfN7Q0GNs/h9xvscee8y04THHHGNt27bNe/uyZctMLKf3zZo1y3s7bepsy5cvN22myyOPPFLtPo3Rc3JyzH3ajjba1LmcdDxmH+tfe+213tvKy8ut888/39w+duzYCLxjRJu2k7bXxIkT2dlJhPOvoK8+uZEP4D4LHJ5zQOJdAisuLrZ69uxpPkRt2rSxLrjgAu/1Fi1aWL/88ku8N9E1Xn/9dbPv7UXbQA/SfG/TdRB9Dz74oPdEynnnnWddfPHFfhffk2aIHjsRWH/ozj77bOvCCy+0+vTpY0586O1NmjSxFi1aRBPEGYl38TFjxoxa3w89YefxeMzthxxyiDnBh+RDDJecwv1OX3PNNeb+Ro0aWQMHDrT69etnpaenW2lpadVO+COxYv1w2nXOnDlWamqqee5TTz3VGjRokNWsWTPzPNddd12M3i1CaW87hmrcuLFpM/3en3HGGVbz5s3N7dqp98knn9DeSXj8yHccfN7Q0GNs/h9xNu3M+fOf/2zaMT8/3/rjH/9onXLKKVZmZqa57dJLL631GNrU2W644Qbvb74em2n76vGaxt9622WXXVbrMbSpMzj5eOynn37yxv6HHXaYNXjwYKtTp07m+gknnGCVlJREZZ+g4XTwjf35adu2rWmz/fff33ub3o/ExvlX96Gv3l3IB3CfyQ7POSDxLsFpBudtt91mgnk98NcP2pAhQ6x169bFe9Nc2dFa16LrIHajk+pb9CQoou/HH3+0brzxRnOwWlBQYGVkZFi5ubnWEUccYd18883Wxo0baQYHIPEuPlasWGFdeeWV1pFHHmkS5vUEoAaGxx13nPXAAw9QsTPJEcMln4Z8pzVOPOqoo0yngCbq9O3b11TQQ2LH+uG0qyZp6Xq6vj7u6KOPtp555pkovjs0pL210p3GtFptXjtrtIKGtpt25l5//fXW+vXr69zBtHdiHz/yHQefNzT0GJv/R5yffDdt2jSrR48e5vddq6JpIs3MmTMDPoY2dbaXX37ZOvPMM02ilB6vaVKVJli98MILAR9Dm8af04/H1q5da/rktG9O++g6d+5s+uw06QfO1b59+zo/U3o/Eh/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+      "text/plain": [
+       "<Figure size 2560x960 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Draw] ✅ Wrote /exp/mu2e/app/users/sgrant/MLTrain/CrvCosmic/output/images/ml/k/process/h1o_3x3_cuts_CE_mix.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Zy/Xr1we8f926deaya9euMXk9BlAAAOxKY7fRr2+qMwXtwwPPFDfwzUuxNEnRGQdjNYDCUYl3mii3YMGCiB6jSXcDBw6U1atXS+vWrWXo0KGydetWM6XsokWLTAJfhw4dQj7HwYMH5dRTT23g1gP1C/RlZU23GqyaW7SVDlOhQmK4X97+U/UCAAAAAAAAAJDoJDszfWzZ8YC3A3bWr18/KSwslC1btsiGDRu8iXiWV1991VwOGTIkJq9HxTsAgB2ZmG3/ienVfZPuYlPvNTU4aZbBmVS8q6tv374m0OvVq5dZWrVqVe+OnDJlikm608e+9dZbUlBQ4J2q87777pORI0ea6WWDmTNnjrz33ntByw/i/2VkZMjZ/S6XXYcrRNLSHf0+r732Wu/1ZFd9S8XMYgAAgGhlpGfI6X0vk637j0laevxjMQBAePQ7uXHXAdKmSY6kZzi3TQCA89viACAWSXZaKUUr3PnKziRGgn1lZ2fLqFGjZPLkyaYyzOLFiyU/P9/cN336dFMJb8CAAdKzZ8+YvB4V79yDWA5AqtBYrayq2iyWymrPiaS7ZnnidIHyUlJ9xsE7qHhX1+jRoyPaiVVVVTJjxgxzXRPnrKQ7pclKmlT39ttvm/LIPXr0qPN4ra532223yfPPPy/du3eP4jC6LyjvPexHsnHnYdl1JPT0v6n+PiP9LDYEiXUAAAD/Lys7W8771k1y9PP9kp6Zxa4BAJvIyMySky/6nnRu00Qys7KTvTlwCSqluEOi2+IAuEughLpAIk2yK8jJkO5tC2O+vUjdSimJprN+6UxiviorK6VPnz7ev8eNGyeDBw/2/j127FhZunSprFq1Sjp16iT9+/eXkpISM3tYixYtZNasWQl9D3AGYjkAqSJQ7FZcckDcItTsjG7nqKlmI7Vy5UozTWzHjh0DJs4NGzZMNm7cKAsXLqyTePfnP/9ZRowYIc8995zceOONCdxqALGad5yKgAAAAAAAOBeVUgAADaWJdCUHysJPviPJznViVSkl0fbt22cS5nxpxRrf23QdX7m5ubJ8+XKZOnWqzJ07V+bPny/Nmzc3/aWaxKfvN1YYQAEAAOKNqWZjYMOGDeYyUDU739ut9SyabHfnnXeainjDhw+Pxaa4ggbs5aWHpOqolpvMcvT71IRO1bRpU0lLqz2SDcnnpHnHAQDxR0Nf6p3nK0oPS/WxI+JxQXl3AEil72f9bq48qtdbxPS5dVqryy+/XAoLU7tqTKpWSwGSjbY4APGsbqeJdCb57mBZnap1gVDJDqlCk+V0iVReXp5MnDjRLPHEAAr3xnIAACQKU83GwLZt28xlsBEY1u1aJtny5JNPygMPPGAaQgcMGCC7d+/2zj+vZZTjmeyTk5NjllRVXl4uL427Rcoqq6Xo9uniVPo+L7vsMnN9xYoV5kcI7DXveKrPNQ7AvSoqKswSLb77okNDX2qpqCiXRZNuk9KK49LhZ89qk3CyNwkAICLVVRWy7fcPyL7sDBk0Z0FM90lZWZl88cUXKZ94l6rVUoBkoy0OQKyS7EJNF6tJd72LmrGzASDOsRwAAKnG1VPNlpaWmstGjRoFvD8/P7/WeurXv/61VFdXy2233WYWS1FRkWzdujXk6+3cubNBjcDjx48302MCaNi848w1DiBV6TQOEyZMSPZmIIF0+g4AAAAAAJCYKWSDTRebnZnOIQAAAABQh6sT76yqL8GmAg1UFaa+5LpQ2rRpI5s3b4768alc7Q4AADTcmDFjvEnE0ejcubMZCIDUceDAAe/1mpqapG4LAABAqtEpert06RJxhUEAgLunkGW6WERCZ8jSJVgsgugQxwEAUonGlqNf31TrtsK8LHl44JniBkeOHKlTRExnI2xIn2YqxXGuTrxr3LixuTx69GjA+48dO2YuCwoKYvJ6e/fulT59+gS8j8Y+AAAQi2nnQwWJGosgtWhV5S1btpjrx48fT/bmAAAApJSWLVvKpk21G74BAM5OqAvU0Rmquh1TyKKhQvXvtWvXTnbs2MFOjgJxHAAgVWhMKftP5Bb5xqJF4h4ej0cOHz4sbo3jXJ141759e3O5ffv2gPdbO1E7PGOBIBGwb+Z1KmRcA0A4aOxzru7duyd7EwAAsI1TTjlFvv71r8t7772X7E0BAABJSrIz08KWnahc59/RWVhe5a06Eqq6HVPIAgAAIFoaS5ZVVZvFUlntOZF01yzP8TtWcywC5WAEml3UyVydeNetWzdzuX79+oD3r1u3zlx27do1odsFIDFSNfMaAOBOubm5yd4EAABsIysryyTfAQAAdwhVtU4T66yEOu3o1KS7pmVZsk3Kaq1HdTsAAADEUve2hXVuKy454JqdHKiw0aOPPuq6HAxXJ97169dPCgsLzfRdGzZs8CbiWV599VVzOWTIkJi8ns4B3KVLl4D3MdUskJzMazdmXANwtlBTzWosAgAAAAAAkGrV7UJVrSvIyfB2er6/41CdqiMWqtsBqYM+VQBAqtP41arA7FuZ+eGBZ4rbZh8MJZkzE8aqT9XViXfZ2dkyatQomTx5skl8W7x4seTn55v7pk+fbirhDRgwQHr27BmT13P7VLMZGRnSsdcA2XOkQiQtXZz8Pq+66irvddiP7xe3GzOuATgbU806R2lpqRkggshkpGdI+54XyBcHyiQtnVgMAOxCv5MLOveRlo1zJD3DuW0CABKPtjggdZLnwhVsCtlwqtYFqjoCIPW4vU/VTYjlADiRxq2y/1idpDszBa1LeFJg9sFY9ak6KvFu0aJFMmnSpFq3VVZWSp8+fbx/jxs3TgYPHuz9e+zYsbJ06VJZtWqVdOrUSfr37y8lJSVSXFwsLVq0kFmzZsVs+9w+OkMTHftff4ds3HlYdh0pFye/z3Ayd2Ev+qXvf9ySmV0NANGi4p1z1NTUJHsTUlJWdrZ8/bu3S8Xn+yU9MyvZmwMA+J+MzCxpcdkI+VqbJpKZlc1+ARAztMUBqTU1bLj8p5C1ULUOAJyFWA6A02i86l+BubLacyLprlmeuGn2wVCcNDOhoxLv9u3bZxLmfOmB8r1N1/GVm5sry5cvl6lTp8rcuXNl/vz50rx5cxkxYoRJ4tMsxlhhdAZgb3bPuAaAcFDxDgAAADjB7YNgAcCuU8OGy3cKWcCuGAQLAAB8BYpfi0sOuGYn3RtmYSMnzUzoqMQ7TZbTJVJ5eXkyceJEsyB+NAmyqqJcqivLHZO5Goi+t/Lycm9iZ1padI0KSA7reDn5MwoASD3nnHOO5OTkJHszUoKew49XlEtNVYV4PM4fPQYAqfT9rN/Nxx3eJgB7YRCsO9AWByQ/ya4hU8MCqY5BsEBsYzkAAFKNoxLv7M7to2w1aJr70A+krLJaim6fLk5+nxdccIG5vmLFCpPYidTRuHFjc+mU7GoA7sMoW7hdRUW5/O2Rm6W04rgU/exZHWaT7E0CAIhIdVWFlPz2p7I3O0Mun7OAfQIgZmiLA+KXUBdIqCQ7poYFECtu71N1cywHAECq9amSeJdAjLIF7D/HuP5N0h2AVMYo2+DeeecdeeKJJ2T9+vWybds2GT9+vCllDQAAAACAUzU0oS7ouuXH60why9SwAGKFPlUAAJAqfaok3gFwtUBzjJOEAQDOVFpaakbKXn/99XL33Xcne3MAAAAAAIhLQl1hXpY8PPBMc13XKTlQ1qCEukBIsgMAAAAAEu8AAABgA2vXrpUlS5bImjVrpLi4WHbu3Ck5OTlmqoFQ9P6pU6fKvHnzTBW75s2by6BBg2TSpEnStm3bWuteeeWVZlGjR4+O6/sBAAAAACAZCXW6TmF5lYx+fdOJ9cpPVLErOVhGQh0AAAAAWzly5EhYhZF05sJARZXsgIp3CaRzAGuVlUhLGAJIDp1y1v9L3s5f6ACgZs6caZZgsYhdaaLcggULInqMJt0NHDhQVq9eLa1bt5ahQ4fK1q1bZfbs2bJo0SKTwNehQ4e4bTMAAAAAAOEmyjV0ytdwEuoqqz1mnaZlWbJNymo9p67Tu6gZBwwAAACAbXg8HpOXkcpIvEugli1byqZNJ34UA0gNqf4lD8B9QiXzt2vXTnbs2CF21LdvX+nWrZv06tXLLK1atar3MVOmTDFJd/rYt956SwoKCszt06ZNk/vuu09Gjhwpy5YtS8DWAwAAAEimXUcqpLrGIz/7+2bJyskNmvgExEO4leeCJdSFM+VruAl17+84JGVV1Wbxl52Z3uD3CgAAAACx0KRJk7Ar4mlynp2ReAcA9UhLO9HAZfcvdABIZZFO/VpVVSUzZsww17XCn5V0p7Qy6Zw5c+Ttt9+WdevWSY8ePWK+vQAAALA/Zp9wD026qzxeI//ZdVgysiuDJj41pApZpMJ9DRIDYyfSYxmrz0Gkled8E+pCKcjJkO5tCyNKqLPWB5A4qTr7hN0RxwEA4Gz3hjnLoM5QGK9iSbGK40i8Q8JkZGRIUbfesu9IpUhauqPfp057Z11H6mvcuLG5pPodANjHypUr5eDBg9KxY0fp3r17nfuHDRsmGzdulIULF5J45zIZ6RnS9mvfkO2HyiUtnVgMAOxCv5Pzz+ghLQqyJT3DuW0CsBdmn3AH3zbHLw5XSk6OhEx8akgVsnDpaxSFWRHNd72GsFNyX7KSGSM5lrH+HERSec43oS5cJNQB9pWqs0/YHXGce9CvCgAIt/KdJuH5V80LN4EvnnEciXcJ5PbRGdnZ2XLRiPtk487DsutIuTj5ff7yl79M9mYAAFzKLaNsN2zYYC6DVbOzbrfWg3tkZWdL7+/fLcc/3y/pmVnJ3hwAwP9kZGbJKVfeKt3aNJHMrGz2C4C4tTmGSnyKRRWy+uhraDLdobwsb1JXsIpo/us1RLDEMf/kvlgkxdWXyBcs0bChrxlOolw4xzJenwMqzwEAEMX5k35VAC6ivzdCVWRHcDo7oV0LJZF4l0CMzgBScz5x/duuX+IA4NZRttu2bfO+p0Cs20tKSry3lZaWyqeffmquV1ZWyu7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+      "text/plain": [
+       "<Figure size 2560x960 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "asm.draw_cuts(ml=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ca3ed723",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Assemble] ✅ Saved feature distributions to /exp/mu2e/app/users/sgrant/MLTrain/CrvCosmic/output/images/ml/k/process/h1o_2x4_crv_features.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2560x960 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "asm.draw_features()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "506c20cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cry:\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>Cut</th>\n",
+       "      <th>Group</th>\n",
+       "      <th>Events Passing</th>\n",
+       "      <th>Absolute [%]</th>\n",
+       "      <th>Relative [%]</th>\n",
+       "      <th>Description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>No cuts</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>2610933</td>\n",
+       "      <td>100.000</td>\n",
+       "      <td>100.000</td>\n",
+       "      <td>No selection applied</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>thru_trk</td>\n",
+       "      <td>Preselect</td>\n",
+       "      <td>2528178</td>\n",
+       "      <td>96.830</td>\n",
+       "      <td>96.830</td>\n",
+       "      <td>Tracks intersect full tracker</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>is_reco_electron</td>\n",
+       "      <td>Preselect</td>\n",
+       "      <td>2447193</td>\n",
+       "      <td>93.729</td>\n",
+       "      <td>96.797</td>\n",
+       "      <td>Select electron track hypothesis</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>one_reco_electron</td>\n",
+       "      <td>Preselect</td>\n",
+       "      <td>2020330</td>\n",
+       "      <td>77.380</td>\n",
+       "      <td>82.557</td>\n",
+       "      <td>One reco electron / event</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>is_downstream</td>\n",
+       "      <td>Preselect</td>\n",
+       "      <td>1111321</td>\n",
+       "      <td>42.564</td>\n",
+       "      <td>55.007</td>\n",
+       "      <td>Downstream tracks (p_z &gt; 0 through tracker)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>is_truth_electron</td>\n",
+       "      <td>Preselect</td>\n",
+       "      <td>320059</td>\n",
+       "      <td>12.258</td>\n",
+       "      <td>28.800</td>\n",
+       "      <td>Track parents are electrons (truth PID)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>good_trkqual</td>\n",
+       "      <td>Tracker</td>\n",
+       "      <td>253908</td>\n",
+       "      <td>9.725</td>\n",
+       "      <td>79.332</td>\n",
+       "      <td>Track quality &gt; 0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>within_t0</td>\n",
+       "      <td>Tracker</td>\n",
+       "      <td>203298</td>\n",
+       "      <td>7.786</td>\n",
+       "      <td>80.068</td>\n",
+       "      <td>t0 at tracker mid (640 &lt; t_0 &lt; 1650 ns)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>within_t0err</td>\n",
+       "      <td>Tracker</td>\n",
+       "      <td>188693</td>\n",
+       "      <td>7.227</td>\n",
+       "      <td>92.816</td>\n",
+       "      <td>Track fit t0 uncertainty (t0err &lt; 0.9 ns)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>has_hits</td>\n",
+       "      <td>Tracker</td>\n",
+       "      <td>186108</td>\n",
+       "      <td>7.128</td>\n",
+       "      <td>98.630</td>\n",
+       "      <td>&gt;20 active tracker hits</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>within_d0</td>\n",
+       "      <td>Tracker</td>\n",
+       "      <td>53452</td>\n",
+       "      <td>2.047</td>\n",
+       "      <td>28.721</td>\n",
+       "      <td>Distance of closest approach (d_0 &lt; 100 mm)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>within_pitch_angle_lo</td>\n",
+       "      <td>Tracker</td>\n",
+       "      <td>48274</td>\n",
+       "      <td>1.849</td>\n",
+       "      <td>90.313</td>\n",
+       "      <td>Extrapolated pitch angle (pz/pt &gt; 0.5)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>within_pitch_angle_hi</td>\n",
+       "      <td>Tracker</td>\n",
+       "      <td>25564</td>\n",
+       "      <td>0.979</td>\n",
+       "      <td>52.956</td>\n",
+       "      <td>Extrapolated pitch angle (pz/pt &lt; 1.0)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>within_lhr_max_hi</td>\n",
+       "      <td>Tracker</td>\n",
+       "      <td>20678</td>\n",
+       "      <td>0.792</td>\n",
+       "      <td>80.887</td>\n",
+       "      <td>Loop helix maximum radius (R_max &lt; 680 mm)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>within_wide_win</td>\n",
+       "      <td>Momentum</td>\n",
+       "      <td>20087</td>\n",
+       "      <td>0.769</td>\n",
+       "      <td>97.142</td>\n",
+       "      <td>Wide window (85 &lt; p &lt; 200 MeV/c)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      Cut      Group  Events Passing  Absolute [%]  \\\n",
+       "0                 No cuts        N/A         2610933       100.000   \n",
+       "1                thru_trk  Preselect         2528178        96.830   \n",
+       "2        is_reco_electron  Preselect         2447193        93.729   \n",
+       "3       one_reco_electron  Preselect         2020330        77.380   \n",
+       "4           is_downstream  Preselect         1111321        42.564   \n",
+       "5       is_truth_electron  Preselect          320059        12.258   \n",
+       "6            good_trkqual    Tracker          253908         9.725   \n",
+       "7               within_t0    Tracker          203298         7.786   \n",
+       "8            within_t0err    Tracker          188693         7.227   \n",
+       "9                has_hits    Tracker          186108         7.128   \n",
+       "10              within_d0    Tracker           53452         2.047   \n",
+       "11  within_pitch_angle_lo    Tracker           48274         1.849   \n",
+       "12  within_pitch_angle_hi    Tracker           25564         0.979   \n",
+       "13      within_lhr_max_hi    Tracker           20678         0.792   \n",
+       "14        within_wide_win   Momentum           20087         0.769   \n",
+       "\n",
+       "    Relative [%]                                  Description  \n",
+       "0        100.000                         No selection applied  \n",
+       "1         96.830                Tracks intersect full tracker  \n",
+       "2         96.797             Select electron track hypothesis  \n",
+       "3         82.557                    One reco electron / event  \n",
+       "4         55.007  Downstream tracks (p_z > 0 through tracker)  \n",
+       "5         28.800      Track parents are electrons (truth PID)  \n",
+       "6         79.332                          Track quality > 0.2  \n",
+       "7         80.068      t0 at tracker mid (640 < t_0 < 1650 ns)  \n",
+       "8         92.816    Track fit t0 uncertainty (t0err < 0.9 ns)  \n",
+       "9         98.630                      >20 active tracker hits  \n",
+       "10        28.721  Distance of closest approach (d_0 < 100 mm)  \n",
+       "11        90.313       Extrapolated pitch angle (pz/pt > 0.5)  \n",
+       "12        52.956       Extrapolated pitch angle (pz/pt < 1.0)  \n",
+       "13        80.887   Loop helix maximum radius (R_max < 680 mm)  \n",
+       "14        97.142             Wide window (85 < p < 200 MeV/c)  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ce_mix:\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>Cut</th>\n",
+       "      <th>Group</th>\n",
+       "      <th>Events Passing</th>\n",
+       "      <th>Absolute [%]</th>\n",
+       "      <th>Relative [%]</th>\n",
+       "      <th>Description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>No cuts</td>\n",
+       "      <td>N/A</td>\n",
+       "      <td>1339362</td>\n",
+       "      <td>100.000</td>\n",
+       "      <td>100.000</td>\n",
+       "      <td>No selection applied</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>thru_trk</td>\n",
+       "      <td>Preselect</td>\n",
+       "      <td>1336977</td>\n",
+       "      <td>99.822</td>\n",
+       "      <td>99.822</td>\n",
+       "      <td>Tracks intersect full tracker</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>is_reco_electron</td>\n",
+       "      <td>Preselect</td>\n",
+       "      <td>1327509</td>\n",
+       "      <td>99.115</td>\n",
+       "      <td>99.292</td>\n",
+       "      <td>Select electron track hypothesis</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>one_reco_electron</td>\n",
+       "      <td>Preselect</td>\n",
+       "      <td>1097120</td>\n",
+       "      <td>81.914</td>\n",
+       "      <td>82.645</td>\n",
+       "      <td>One reco electron / event</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>is_downstream</td>\n",
+       "      <td>Preselect</td>\n",
+       "      <td>1096121</td>\n",
+       "      <td>81.839</td>\n",
+       "      <td>99.909</td>\n",
+       "      <td>Downstream tracks (p_z &gt; 0 through tracker)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>is_truth_electron</td>\n",
+       "      <td>Preselect</td>\n",
+       "      <td>1096052</td>\n",
+       "      <td>81.834</td>\n",
+       "      <td>99.994</td>\n",
+       "      <td>Track parents are electrons (truth PID)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>good_trkqual</td>\n",
+       "      <td>Tracker</td>\n",
+       "      <td>883129</td>\n",
+       "      <td>65.937</td>\n",
+       "      <td>80.574</td>\n",
+       "      <td>Track quality &gt; 0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>within_t0</td>\n",
+       "      <td>Tracker</td>\n",
+       "      <td>655442</td>\n",
+       "      <td>48.937</td>\n",
+       "      <td>74.218</td>\n",
+       "      <td>t0 at tracker mid (640 &lt; t_0 &lt; 1650 ns)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>within_t0err</td>\n",
+       "      <td>Tracker</td>\n",
+       "      <td>636325</td>\n",
+       "      <td>47.510</td>\n",
+       "      <td>97.083</td>\n",
+       "      <td>Track fit t0 uncertainty (t0err &lt; 0.9 ns)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>has_hits</td>\n",
+       "      <td>Tracker</td>\n",
+       "      <td>621990</td>\n",
+       "      <td>46.439</td>\n",
+       "      <td>97.747</td>\n",
+       "      <td>&gt;20 active tracker hits</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>within_d0</td>\n",
+       "      <td>Tracker</td>\n",
+       "      <td>617816</td>\n",
+       "      <td>46.128</td>\n",
+       "      <td>99.329</td>\n",
+       "      <td>Distance of closest approach (d_0 &lt; 100 mm)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>within_pitch_angle_lo</td>\n",
+       "      <td>Tracker</td>\n",
+       "      <td>617747</td>\n",
+       "      <td>46.122</td>\n",
+       "      <td>99.989</td>\n",
+       "      <td>Extrapolated pitch angle (pz/pt &gt; 0.5)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>within_pitch_angle_hi</td>\n",
+       "      <td>Tracker</td>\n",
+       "      <td>579353</td>\n",
+       "      <td>43.256</td>\n",
+       "      <td>93.785</td>\n",
+       "      <td>Extrapolated pitch angle (pz/pt &lt; 1.0)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>within_lhr_max_hi</td>\n",
+       "      <td>Tracker</td>\n",
+       "      <td>579342</td>\n",
+       "      <td>43.255</td>\n",
+       "      <td>99.998</td>\n",
+       "      <td>Loop helix maximum radius (R_max &lt; 680 mm)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>within_wide_win</td>\n",
+       "      <td>Momentum</td>\n",
+       "      <td>578368</td>\n",
+       "      <td>43.182</td>\n",
+       "      <td>99.832</td>\n",
+       "      <td>Wide window (85 &lt; p &lt; 200 MeV/c)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      Cut      Group  Events Passing  Absolute [%]  \\\n",
+       "0                 No cuts        N/A         1339362       100.000   \n",
+       "1                thru_trk  Preselect         1336977        99.822   \n",
+       "2        is_reco_electron  Preselect         1327509        99.115   \n",
+       "3       one_reco_electron  Preselect         1097120        81.914   \n",
+       "4           is_downstream  Preselect         1096121        81.839   \n",
+       "5       is_truth_electron  Preselect         1096052        81.834   \n",
+       "6            good_trkqual    Tracker          883129        65.937   \n",
+       "7               within_t0    Tracker          655442        48.937   \n",
+       "8            within_t0err    Tracker          636325        47.510   \n",
+       "9                has_hits    Tracker          621990        46.439   \n",
+       "10              within_d0    Tracker          617816        46.128   \n",
+       "11  within_pitch_angle_lo    Tracker          617747        46.122   \n",
+       "12  within_pitch_angle_hi    Tracker          579353        43.256   \n",
+       "13      within_lhr_max_hi    Tracker          579342        43.255   \n",
+       "14        within_wide_win   Momentum          578368        43.182   \n",
+       "\n",
+       "    Relative [%]                                  Description  \n",
+       "0        100.000                         No selection applied  \n",
+       "1         99.822                Tracks intersect full tracker  \n",
+       "2         99.292             Select electron track hypothesis  \n",
+       "3         82.645                    One reco electron / event  \n",
+       "4         99.909  Downstream tracks (p_z > 0 through tracker)  \n",
+       "5         99.994      Track parents are electrons (truth PID)  \n",
+       "6         80.574                          Track quality > 0.2  \n",
+       "7         74.218      t0 at tracker mid (640 < t_0 < 1650 ns)  \n",
+       "8         97.083    Track fit t0 uncertainty (t0err < 0.9 ns)  \n",
+       "9         97.747                      >20 active tracker hits  \n",
+       "10        99.329  Distance of closest approach (d_0 < 100 mm)  \n",
+       "11        99.989       Extrapolated pitch angle (pz/pt > 0.5)  \n",
+       "12        93.785       Extrapolated pitch angle (pz/pt < 1.0)  \n",
+       "13        99.998   Loop helix maximum radius (R_max < 680 mm)  \n",
+       "14        99.832             Wide window (85 < p < 200 MeV/c)  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cut_flows = asm.get_cut_flows()\n",
+    "for name, cut_flow in cut_flows.items():\n",
+    "    print(f\"{name}:\")\n",
+    "    display(cut_flow)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "20cbb8dd-5be5-44da-ae18-321e339bd64f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cut_flows[\"cry\"].to_csv(\"cut_flow.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "c7e02d10",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Assemble] ✅ Prepared 5-fold nested CV (event-level grouping)\n",
+      "  Total: 2163528 coincidences\n",
+      "  Fold test sizes: [432706, 432706, 432706, 432705, 432705]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = asm.assemble_dataset()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "3be90f1b-b4aa-4862-bdd2-d938f7a23dc2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "label\n",
+      "0    2139801\n",
+      "1      23727\n",
+      "Name: count, dtype: int64\n",
+      "90.1842205082817\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(data[\"y\"].value_counts())\n",
+    "print(data[\"y\"].value_counts()[0]/data[\"y\"].value_counts()[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "5bfc7b49",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>event</th>\n",
+       "      <th>subrun</th>\n",
+       "      <th>crv_x</th>\n",
+       "      <th>crv_y</th>\n",
+       "      <th>crv_z</th>\n",
+       "      <th>PEs</th>\n",
+       "      <th>dT</th>\n",
+       "      <th>nHits</th>\n",
+       "      <th>nLayers</th>\n",
+       "      <th>angle</th>\n",
+       "      <th>timeStart</th>\n",
+       "      <th>timeEnd</th>\n",
+       "      <th>sector</th>\n",
+       "      <th>PEs_per_hit</th>\n",
+       "      <th>t0</th>\n",
+       "      <th>d0</th>\n",
+       "      <th>tanDip</th>\n",
+       "      <th>maxr</th>\n",
+       "      <th>mom_mag</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2650.0</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>-4.547474e-13</td>\n",
+       "      <td>2684.661133</td>\n",
+       "      <td>-7921.097656</td>\n",
+       "      <td>90.724777</td>\n",
+       "      <td>527.722271</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.727466</td>\n",
+       "      <td>525.451538</td>\n",
+       "      <td>587.951538</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>22.681194</td>\n",
+       "      <td>1016.454419</td>\n",
+       "      <td>42.117722</td>\n",
+       "      <td>0.583941</td>\n",
+       "      <td>617.263550</td>\n",
+       "      <td>103.368034</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1859.0</td>\n",
+       "      <td>843.0</td>\n",
+       "      <td>4.071482e+03</td>\n",
+       "      <td>2862.049561</td>\n",
+       "      <td>-9957.799805</td>\n",
+       "      <td>285.257233</td>\n",
+       "      <td>-805.731684</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.353849</td>\n",
+       "      <td>1607.101807</td>\n",
+       "      <td>1782.101807</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>28.525723</td>\n",
+       "      <td>786.035162</td>\n",
+       "      <td>32.673702</td>\n",
+       "      <td>0.605967</td>\n",
+       "      <td>602.773804</td>\n",
+       "      <td>103.583145</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2192.0</td>\n",
+       "      <td>457.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1096.057591</td>\n",
+       "      <td>8.055742</td>\n",
+       "      <td>0.715509</td>\n",
+       "      <td>539.576172</td>\n",
+       "      <td>104.595360</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>556.0</td>\n",
+       "      <td>411.0</td>\n",
+       "      <td>6.362000e+02</td>\n",
+       "      <td>1513.145508</td>\n",
+       "      <td>-12567.080078</td>\n",
+       "      <td>34580.207031</td>\n",
+       "      <td>-397.368187</td>\n",
+       "      <td>1899.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>-1.526525</td>\n",
+       "      <td>412.966553</td>\n",
+       "      <td>1787.966553</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>18.209693</td>\n",
+       "      <td>661.050758</td>\n",
+       "      <td>24.049637</td>\n",
+       "      <td>0.879215</td>\n",
+       "      <td>526.783875</td>\n",
+       "      <td>104.189644</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2974.0</td>\n",
+       "      <td>707.0</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>2731.275635</td>\n",
+       "      <td>-12267.968750</td>\n",
+       "      <td>240.338318</td>\n",
+       "      <td>-22.465829</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>-3.002133</td>\n",
+       "      <td>983.740662</td>\n",
+       "      <td>1146.240723</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>30.042290</td>\n",
+       "      <td>1019.720328</td>\n",
+       "      <td>65.952957</td>\n",
+       "      <td>0.743986</td>\n",
+       "      <td>470.854187</td>\n",
+       "      <td>104.093399</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2163523</th>\n",
+       "      <td>3061.0</td>\n",
+       "      <td>925.0</td>\n",
+       "      <td>3.952777e+01</td>\n",
+       "      <td>2711.051514</td>\n",
+       "      <td>-7511.345703</td>\n",
+       "      <td>131.810699</td>\n",
+       "      <td>782.745075</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>-1.497381</td>\n",
+       "      <td>742.005493</td>\n",
+       "      <td>842.005493</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>10.984225</td>\n",
+       "      <td>1500.949542</td>\n",
+       "      <td>74.319374</td>\n",
+       "      <td>0.665570</td>\n",
+       "      <td>633.117065</td>\n",
+       "      <td>104.378433</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2163524</th>\n",
+       "      <td>3076.0</td>\n",
+       "      <td>722.0</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>2681.962402</td>\n",
+       "      <td>-2549.654053</td>\n",
+       "      <td>86.194878</td>\n",
+       "      <td>-311.175052</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.386727</td>\n",
+       "      <td>1285.348145</td>\n",
+       "      <td>1447.848145</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>17.238976</td>\n",
+       "      <td>983.938596</td>\n",
+       "      <td>30.310881</td>\n",
+       "      <td>0.856973</td>\n",
+       "      <td>541.797424</td>\n",
+       "      <td>104.623466</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2163525</th>\n",
+       "      <td>2061.0</td>\n",
+       "      <td>331.0</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>2701.172852</td>\n",
+       "      <td>-11403.797852</td>\n",
+       "      <td>260.580322</td>\n",
+       "      <td>678.858792</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>-0.721829</td>\n",
+       "      <td>716.635193</td>\n",
+       "      <td>891.635193</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>23.689120</td>\n",
+       "      <td>1384.573697</td>\n",
+       "      <td>65.948845</td>\n",
+       "      <td>0.930326</td>\n",
+       "      <td>555.570496</td>\n",
+       "      <td>104.027061</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2163526</th>\n",
+       "      <td>2004.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>-2.573767e+03</td>\n",
+       "      <td>-934.724060</td>\n",
+       "      <td>-4733.690430</td>\n",
+       "      <td>200.804611</td>\n",
+       "      <td>-146.186658</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>-1.477709</td>\n",
+       "      <td>1139.843506</td>\n",
+       "      <td>1252.343506</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>16.733718</td>\n",
+       "      <td>976.955921</td>\n",
+       "      <td>28.302200</td>\n",
+       "      <td>0.711944</td>\n",
+       "      <td>516.766479</td>\n",
+       "      <td>103.907005</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2163527</th>\n",
+       "      <td>2532.0</td>\n",
+       "      <td>780.0</td>\n",
+       "      <td>4.358051e+03</td>\n",
+       "      <td>2883.542725</td>\n",
+       "      <td>-9957.799805</td>\n",
+       "      <td>1386.245605</td>\n",
+       "      <td>508.213271</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.898777</td>\n",
+       "      <td>542.232422</td>\n",
+       "      <td>1729.732422</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>21.326855</td>\n",
+       "      <td>1506.241286</td>\n",
+       "      <td>53.792229</td>\n",
+       "      <td>0.684940</td>\n",
+       "      <td>468.728882</td>\n",
+       "      <td>98.656525</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2163528 rows × 20 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          event  subrun         crv_x        crv_y         crv_z  \\\n",
+       "0        2650.0    98.0 -4.547474e-13  2684.661133  -7921.097656   \n",
+       "1        1859.0   843.0  4.071482e+03  2862.049561  -9957.799805   \n",
+       "2        2192.0   457.0           NaN          NaN           NaN   \n",
+       "3         556.0   411.0  6.362000e+02  1513.145508 -12567.080078   \n",
+       "4        2974.0   707.0  0.000000e+00  2731.275635 -12267.968750   \n",
+       "...         ...     ...           ...          ...           ...   \n",
+       "2163523  3061.0   925.0  3.952777e+01  2711.051514  -7511.345703   \n",
+       "2163524  3076.0   722.0  0.000000e+00  2681.962402  -2549.654053   \n",
+       "2163525  2061.0   331.0  0.000000e+00  2701.172852 -11403.797852   \n",
+       "2163526  2004.0    40.0 -2.573767e+03  -934.724060  -4733.690430   \n",
+       "2163527  2532.0   780.0  4.358051e+03  2883.542725  -9957.799805   \n",
+       "\n",
+       "                  PEs          dT   nHits  nLayers     angle    timeStart  \\\n",
+       "0           90.724777  527.722271     4.0      3.0  0.727466   525.451538   \n",
+       "1          285.257233 -805.731684    10.0      4.0  1.353849  1607.101807   \n",
+       "2                 NaN         NaN     NaN      NaN       NaN          NaN   \n",
+       "3        34580.207031 -397.368187  1899.0      4.0 -1.526525   412.966553   \n",
+       "4          240.338318  -22.465829     8.0      4.0 -3.002133   983.740662   \n",
+       "...               ...         ...     ...      ...       ...          ...   \n",
+       "2163523    131.810699  782.745075    12.0      3.0 -1.497381   742.005493   \n",
+       "2163524     86.194878 -311.175052     5.0      3.0  3.386727  1285.348145   \n",
+       "2163525    260.580322  678.858792    11.0      4.0 -0.721829   716.635193   \n",
+       "2163526    200.804611 -146.186658    12.0      3.0 -1.477709  1139.843506   \n",
+       "2163527   1386.245605  508.213271    65.0      4.0  1.898777   542.232422   \n",
+       "\n",
+       "             timeEnd  sector  PEs_per_hit           t0         d0    tanDip  \\\n",
+       "0         587.951538     3.0    22.681194  1016.454419  42.117722  0.583941   \n",
+       "1        1782.101807     4.0    28.525723   786.035162  32.673702  0.605967   \n",
+       "2                NaN     NaN          NaN  1096.057591   8.055742  0.715509   \n",
+       "3        1787.966553     5.0    18.209693   661.050758  24.049637  0.879215   \n",
+       "4        1146.240723     3.0    30.042290  1019.720328  65.952957  0.743986   \n",
+       "...              ...     ...          ...          ...        ...       ...   \n",
+       "2163523   842.005493     3.0    10.984225  1500.949542  74.319374  0.665570   \n",
+       "2163524  1447.848145     3.0    17.238976   983.938596  30.310881  0.856973   \n",
+       "2163525   891.635193     3.0    23.689120  1384.573697  65.948845  0.930326   \n",
+       "2163526  1252.343506     1.0    16.733718   976.955921  28.302200  0.711944   \n",
+       "2163527  1729.732422     4.0    21.326855  1506.241286  53.792229  0.684940   \n",
+       "\n",
+       "               maxr     mom_mag  label  \n",
+       "0        617.263550  103.368034      0  \n",
+       "1        602.773804  103.583145      0  \n",
+       "2        539.576172  104.595360      0  \n",
+       "3        526.783875  104.189644      0  \n",
+       "4        470.854187  104.093399      0  \n",
+       "...             ...         ...    ...  \n",
+       "2163523  633.117065  104.378433      0  \n",
+       "2163524  541.797424  104.623466      0  \n",
+       "2163525  555.570496  104.027061      0  \n",
+       "2163526  516.766479  103.907005      0  \n",
+       "2163527  468.728882   98.656525      0  \n",
+       "\n",
+       "[2163528 rows x 20 columns]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[\"df_full\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "8d5d5e91-1f97-451a-a5f5-596a34e8bf45",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>event</th>\n",
+       "      <th>subrun</th>\n",
+       "      <th>PEs</th>\n",
+       "      <th>dT</th>\n",
+       "      <th>nHits</th>\n",
+       "      <th>crv_z</th>\n",
+       "      <th>angle</th>\n",
+       "      <th>crv_x</th>\n",
+       "      <th>crv_y</th>\n",
+       "      <th>nLayers</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>140</th>\n",
+       "      <td>87625.0</td>\n",
+       "      <td>41988.0</td>\n",
+       "      <td>878.959717</td>\n",
+       "      <td>43.955147</td>\n",
+       "      <td>16.0</td>\n",
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+       "      <td>-0.410212</td>\n",
+       "      <td>604.325256</td>\n",
+       "      <td>2705.346436</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1</td>\n",
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+       "    <tr>\n",
+       "      <th>210</th>\n",
+       "      <td>138516.0</td>\n",
+       "      <td>26117.0</td>\n",
+       "      <td>880.353516</td>\n",
+       "      <td>48.982808</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>-3588.013184</td>\n",
+       "      <td>-0.325533</td>\n",
+       "      <td>-2585.590576</td>\n",
+       "      <td>1575.125122</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1</td>\n",
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+       "    <tr>\n",
+       "      <th>222</th>\n",
+       "      <td>395786.0</td>\n",
+       "      <td>426.0</td>\n",
+       "      <td>598.232361</td>\n",
+       "      <td>56.394356</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>-2974.029785</td>\n",
+       "      <td>0.013093</td>\n",
+       "      <td>910.385193</td>\n",
+       "      <td>2707.904785</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>266</th>\n",
+       "      <td>151734.0</td>\n",
+       "      <td>2325.0</td>\n",
+       "      <td>825.234375</td>\n",
+       "      <td>36.065351</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>-5295.029297</td>\n",
+       "      <td>-0.674409</td>\n",
+       "      <td>-1614.841309</td>\n",
+       "      <td>2702.555176</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>374</th>\n",
+       "      <td>17688.0</td>\n",
+       "      <td>3895.0</td>\n",
+       "      <td>730.391541</td>\n",
+       "      <td>38.479597</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>-6355.976562</td>\n",
+       "      <td>-0.412615</td>\n",
+       "      <td>-468.313171</td>\n",
+       "      <td>2707.836426</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2163141</th>\n",
+       "      <td>123668.0</td>\n",
+       "      <td>10984.0</td>\n",
+       "      <td>830.904663</td>\n",
+       "      <td>51.340792</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>-6875.591309</td>\n",
+       "      <td>-0.433142</td>\n",
+       "      <td>1099.154419</td>\n",
+       "      <td>2707.906738</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2163187</th>\n",
+       "      <td>27030.0</td>\n",
+       "      <td>32367.0</td>\n",
+       "      <td>691.808167</td>\n",
+       "      <td>55.815986</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>-6071.390137</td>\n",
+       "      <td>0.325729</td>\n",
+       "      <td>721.309570</td>\n",
+       "      <td>2713.407959</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2163221</th>\n",
+       "      <td>439885.0</td>\n",
+       "      <td>10277.0</td>\n",
+       "      <td>633.933105</td>\n",
+       "      <td>60.822633</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>-3435.709961</td>\n",
+       "      <td>-0.328242</td>\n",
+       "      <td>-2578.015381</td>\n",
+       "      <td>2412.075928</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2163396</th>\n",
+       "      <td>336185.0</td>\n",
+       "      <td>76081.0</td>\n",
+       "      <td>1353.680908</td>\n",
+       "      <td>33.392916</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>-5278.848145</td>\n",
+       "      <td>-1.307007</td>\n",
+       "      <td>-1075.785767</td>\n",
+       "      <td>2718.874756</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2163467</th>\n",
+       "      <td>144839.0</td>\n",
+       "      <td>80532.0</td>\n",
+       "      <td>670.900940</td>\n",
+       "      <td>52.526624</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>-6458.053711</td>\n",
+       "      <td>-0.349880</td>\n",
+       "      <td>-1286.618286</td>\n",
+       "      <td>2705.157715</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>23727 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            event   subrun          PEs         dT  nHits        crv_z  \\\n",
+       "140       87625.0  41988.0   878.959717  43.955147   16.0 -6147.793457   \n",
+       "210      138516.0  26117.0   880.353516  48.982808   16.0 -3588.013184   \n",
+       "222      395786.0    426.0   598.232361  56.394356   22.0 -2974.029785   \n",
+       "266      151734.0   2325.0   825.234375  36.065351   16.0 -5295.029297   \n",
+       "374       17688.0   3895.0   730.391541  38.479597   17.0 -6355.976562   \n",
+       "...           ...      ...          ...        ...    ...          ...   \n",
+       "2163141  123668.0  10984.0   830.904663  51.340792   18.0 -6875.591309   \n",
+       "2163187   27030.0  32367.0   691.808167  55.815986   16.0 -6071.390137   \n",
+       "2163221  439885.0  10277.0   633.933105  60.822633   16.0 -3435.709961   \n",
+       "2163396  336185.0  76081.0  1353.680908  33.392916   34.0 -5278.848145   \n",
+       "2163467  144839.0  80532.0   670.900940  52.526624   16.0 -6458.053711   \n",
+       "\n",
+       "            angle        crv_x        crv_y  nLayers  label  \n",
+       "140     -0.410212   604.325256  2705.346436      4.0      1  \n",
+       "210     -0.325533 -2585.590576  1575.125122      4.0      1  \n",
+       "222      0.013093   910.385193  2707.904785      4.0      1  \n",
+       "266     -0.674409 -1614.841309  2702.555176      4.0      1  \n",
+       "374     -0.412615  -468.313171  2707.836426      4.0      1  \n",
+       "...           ...          ...          ...      ...    ...  \n",
+       "2163141 -0.433142  1099.154419  2707.906738      4.0      1  \n",
+       "2163187  0.325729   721.309570  2713.407959      4.0      1  \n",
+       "2163221 -0.328242 -2578.015381  2412.075928      4.0      1  \n",
+       "2163396 -1.307007 -1075.785767  2718.874756      4.0      1  \n",
+       "2163467 -0.349880 -1286.618286  2705.157715      4.0      1  \n",
+       "\n",
+       "[23727 rows x 11 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cols = [\"event\", \"subrun\", \"PEs\", \"dT\", \"nHits\", \"crv_z\", \"angle\", \"crv_x\", \"crv_y\", \"nLayers\", \"label\"]\n",
+    "df = data[\"df_full\"][cols][data[\"df_full\"][\"label\"]==1]\n",
+    "display(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "2073e4d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#from pyutils.pyplot imdataport Plot\n",
+    "#plotter = Plot()\n",
+    "#import matplotlib.pyplot as plt \n",
+    "\n",
+    "#fig, ax = plt.subplots(1,3,figsize=(3*6.4, 4.8))\n",
+    "\n",
+    "#plotter.plot_1D_overlay(\n",
+    "    #{\n",
+    "        #\"CE Mix\" : data[\"df_full\"][\"timeStart\"][data[\"df_full\"][\"label\"]==0],\n",
+    "        #\"CRY\" : data[\"df_full\"][\"timeStart\"][data[\"df_full\"][\"label\"]==1],\n",
+    "    #},\n",
+    "    #nbins = 210,\n",
+    "    #xmin = 0,\n",
+    "    #xmax = 2100,\n",
+    "    #show=False,\n",
+    "    #norm_by_area=True,\n",
+    "    #ylabel=\"Normalized coincidences\",\n",
+    "    #xlabel=\"Start time [ns]\",\n",
+    "    #ax=ax[0]\n",
+    "#)\n",
+    "\n",
+    "#plotter.plot_1D_overlay(\n",
+    "    #{\n",
+    "        #\"CE Mix\" : data[\"df_full\"][\"timeEnd\"][data[\"df_full\"][\"label\"]==0],\n",
+    "        #\"CRY\" : data[\"df_full\"][\"timeEnd\"][data[\"df_full\"][\"label\"]==1],\n",
+    "    #},\n",
+    "    #nbins = 210,\n",
+    "    #xmin = 0,\n",
+    "    #xmax = 2100,\n",
+    "    #show=False,\n",
+    "    #norm_by_area=True,\n",
+    "    #ylabel=\"Normalized coincidences\",\n",
+    "    #xlabel=\"End time [ns]\",\n",
+    "    #ax=ax[1]\n",
+    "#)\n",
+    "\n",
+    "#data[\"df_full\"][\"duration\"] = data[\"df_full\"][\"timeEnd\"] - data[\"df_full\"][\"timeStart\"] \n",
+    "\n",
+    "#plotter.plot_1D_overlay(\n",
+    "    #{\n",
+    "        #\"CE Mix\" : data[\"df_full\"][\"duration\"][data[\"df_full\"][\"label\"]==0],\n",
+    "        #\"CRY\" : data[\"df_full\"][\"duration\"][data[\"df_full\"][\"label\"]==1],\n",
+    "    #},\n",
+    "    #nbins = 150,\n",
+    "    #xmin = 0,\n",
+    "    #xmax = 1500,\n",
+    "    #show=False,\n",
+    "    #norm_by_area=True,\n",
+    "    #log_y=True,\n",
+    "    #ylabel=\"Normalized coincidences\",\n",
+    "    #xlabel=\"Duration [ns]\",\n",
+    "    #ax=ax[2]\n",
+    "#)\n",
+    "\n",
+    "#plt.tight_layout()\n",
+    "\n",
+    "#out_name = asm.img_out_path / \"h1o_1x3_coinc_time.png\" \n",
+    "#plt.savefig(out_name, dpi=300)\n",
+    "#plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c93ffe6f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "83019b30",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0912d745",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b87121c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e5885ff",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "current",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/CrvCosmic/notebooks/feature_engineering.ipynb b/CrvCosmic/notebooks/feature_engineering.ipynb
new file mode 100644
index 0000000..e235a5e
--- /dev/null
+++ b/CrvCosmic/notebooks/feature_engineering.ipynb
@@ -0,0 +1,1183 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "nkz2t8wdqn",
+   "metadata": {},
+   "source": [
+    "# Feature engineering/evaluation\n",
+    "\n",
+    "This is heavy going, best to run this on a GPU node. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "cqfw39bhcw8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append(\"../src/ml\")\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from pyutils.pyplot import Plot\n",
+    "Plot(verbosity=0)\n",
+    "\n",
+    "run = \"k\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "s762m241li",
+   "metadata": {},
+   "source": [
+    "## Load data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "y1nqoqec27f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[LoadML] [OK] Initialised\n",
+      "[Load] [OK] Initialised with out_path=/home/sgrant/mu2e-cosmic/output/ml/k/data/CRY_onspill-LH_aw\n",
+      "[Load] [OK] Successfully loaded results from /home/sgrant/mu2e-cosmic/output/ml/k/data/CRY_onspill-LH_aw/results.pkl\n",
+      "[Load] [OK] Initialised with out_path=/home/sgrant/mu2e-cosmic/output/ml/k/data/CE_mix2BB_onspill-LH_aw\n",
+      "[Load] [OK] Successfully loaded results from /home/sgrant/mu2e-cosmic/output/ml/k/data/CE_mix2BB_onspill-LH_aw/results.pkl\n",
+      "[LoadML] [OK] Got full results\n",
+      "[Assemble] [OK] Loaded data\n",
+      "[Assemble] [OK] Initialised\n",
+      "[Assemble] [OK] Prepared 5-fold nested CV (event-level grouping)\n",
+      "  Total: 2163528 coincidences\n",
+      "  Fold test sizes: [432706, 432706, 432706, 432705, 432705]\n",
+      "Features (9): ['crv_x', 'crv_y', 'crv_z', 'PEs', 'dT', 'nHits', 'nLayers', 'angle', 'sector']\n"
+     ]
+    }
+   ],
+   "source": [
+    "from assemble import AssembleDataset\n",
+    "asm = AssembleDataset(run=run)\n",
+    "data = asm.assemble_dataset(n_outer_folds=5)\n",
+    "\n",
+    "all_features = list(data[\"X\"].columns)\n",
+    "print(f\"Features ({len(all_features)}): {all_features}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ztrddo8nge",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Baseline hyperparameters from optimisation\n",
+    "best_hp = {\n",
+    "    \"n_estimators\": 1000,\n",
+    "    \"max_depth\": 3,\n",
+    "    \"learning_rate\": 0.1,\n",
+    "    \"device\": \"cuda\",\n",
+    "    \"tree_method\": \"hist\"\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8c5f8e57-149e-4a7f-b6c1-86490ddae1f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from train import Train\n",
+    "from validate import Validate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d8a04a6-1214-475c-8d81-748ae3c29c58",
+   "metadata": {},
+   "source": [
+    "## Feature importance "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9b18d15a-0243-449e-b88c-cb5c40ed44ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+DuxA7M033wxbm3n/m9LPXPv27c3+tvXr12c5ly+//NJzvEBJVwCIRsQgxCCRjkHsz6CdoOrfv7+1c+dOs23fvn3m5ped3NKbfb40uWMnj7p162ZiVTuO1XhYYzY7HtE40Xdf75jgb3/7W6b3acfN+RGPh5p4C2Z/1bZtWxNX6fnb7aQx10cffeS5Ad+lSxe/+0ouXiO7xJu2Ve3atc22pKQk69NPP7VOnTpltq1atcpq3ry5J/mm/x/o9fXvFU0Qzp8/3+x/+vRp6/vvv/ecv/69YB/Xn0cffdQ8T693sEi8AUCMB73t2rUzXyy+rrjiCs9zNFnkSwOoWrVqme0avARKvOny1VdfZdmuX8b16tUz2zVJ4ZsAsfd99dVX/b4/OzGniZ0jR474DSB0mTVrVoAWyn3iLSd2Tyu90xXoNXTp3bu33/3vv/9+s13vsvm65ZZbzLby5ct7Ena5cckllwRM5nnfadTndOrUyQrGxo0bPe/pxx9/DPi8UM5dPxflypUz+/3973/3+xxNxNqv7xuw653r7PbVu5l2r0XfHnN5aTPvf1Pagy27Hne2Xbt2efbRIA8AYgUxCDFIpGMQ78/grbfe6vc5jz32mCcBmJ6enmmbJtuy21fpqA59TsOGDQMmiTTGDHRzLT/i8fxKvGVn27ZtJumliU47sRjOxNvTTz/t6ZWmN3V9abxnt9H1118f8PUrVqyYpVee+s9//uN5zuLFiwOe4wcffOD5PB07dswKBnO8AUCMe/jhhzPNr2C75pprPD/r/Gy+dGL4tm3beuakCOTyyy/3lP/2pvMmPPjgg+bnuXPnmnkZbO+995551PmxdB4Qf5566inzuHv3bpk3b57f55x//vlmzgWnXX/99eZx2bJl2c4VonNG+NOpUyfzuGHDBjl8+HCmudHef/9987POOaZziuTG6tWr5YcffjBziz3wwAMBn3fbbbeZx6+++irHOU68bd++3fOzznfnT6jnrtdSq0Ip77lGvP3f//2fmdvEnufF26233moeZ86caebp8DVlyhTzqPOj6FwcTrSZfq7130dOdJ4ZnWfOt00BIF4Qg+QdMUjOnnjiiYDf1xqP6hxqM2bM8KzXOOSjjz7yxDA5xQQaQwSar7Z///5SokSJiP9biCSdq1fn4tMc19KlS8N+/PfPxJvdunUzc9750njvoYceMj/rvMzef3N403n4KlWqlGX9hRdeaObny6mNdS5ipZ+nXbt2BfUeSLwBQIxr1qyZ3/U6+budHNCJ5bN7TnaFAXQC+Zy26QTzOimr7ccff/RMsmonJnzphLN2OXH7+f6SfuGiAdWwYcPk0ksvNROtJiYmeiZjTU5ONs/RpFmgttB2rFu3rt9tOomuzXt/fV9aHEIFk0BcvHixp121oEWVKlX8Ltdee60nSfbnn3/m+vjewYS+L39CPXf7Wmqi7txzz/X7HA0y7c+O77Xv0qWLCbC0IIZ3EG1fQztJawfLTrRZbj93+tnW4g0q2AANAGIBMUjuEIMEF4N403giUPylE/BrkQXfeEJvpGo8oDTeCBQT6A1emxbqyktM4PS/Badpe2m1+44dO0qNGjVMQtO7UML3339vnrdt27awvu7x48c9yTA7AelPu3bt/P7N4U0LaARix+r2zWF/vD+PwcZ1iUE9GwAQdbx7/XjTxFJ2272fYydY/LGTYzlt27lzZ5afs9vX7hGXnp6eaV9v/u5ahUIDsPbt28vevXs96/TuZbFixUwwoT2ftOednZCx73h5y007+ralVte0aYWo3LJ7T+l5BboD68u7p11Ojh496vlZq3P5E+q5B3PtvZ9v02ty4403mmqy2rtNK2bZtHec3oXUYNkOwJxos2A+dxqYaqDs3aYAEC+IQXJGDBJ8DOItp3jC3u4dT3j3Qs+vmMDpfwtO0veulT8XLFjgWVeoUCGTiNKRBHbCSs9P4+Rw2rNnj2cEQnbX2o4bVaC/G/LaxhrT2YKN6+jxBgDIE3/d5nOzLTfbc3pebob75UQTNVraXZNujRo1ks8++0z2799vhjFqMKYJJu9y8oHKuecnOwDRUuhn5mvNcfEtC58d7fFnc+rual6uvT3cVMu5//7771mGmfbq1SvLZyOcbRbM586+c+rdpgCA8CAGyX9ui0FyG0/4ew+aSMnte2jdurVjsajb/etf/zJJN22vsWPHmt5/mnjSkQEaJ+ti9yZzMk5OyOXfHKF8JnLDuzdcsHEdiTcAQJ5k16Xce5v3HUH7Z++kSXb7B5pnLFx3mjWA0MBpzpw5ct1112W5I+bduyuc7HnMshvC4I/26FKbNm0K+51F3/YO1OU+1HMPx7XXIco6zEGHE7zzzjtmXWpqqmdogZ2Yy88280eDUvuOqJOfYQCIV8Qg8RmDeMtpaKOOnPCNQ+33oNNW6Py70cxO/GXXAyvQnGe5Zc/NrHPpDRw40MRgvsktp2LlcuXKed5jdrGj9zanYi7vz2Owr0HiDQCQJ97dzgNt07muGjdu7FnftGlTz3Z7jg1fv/76qydYuuSSS0I6N3v+uOzuvtlf1PoFGqgLu0607wRtB+2qr2bPnp3r/ez5RHTei48//jjs56Vzr9ld7jWwDue529deA+X169cHvBNtf3b8XXsN9rRXm3cvN/vxoosuMhP85neb+ePddjpnIQAgvIhB4jMG8Y3jNm7c6Hebjl5YsWJFpvhDXXbZZZ7EkZ1UilZly5bNNimlbbB27dqA+9vtkJtY2TuW95aWlpZtAjMhF68RiH5GNbZTX3/9dcDn2bG6xv4XX3yxOGHz5s2epHWZMmWC2pfEGwAgT3TSeh3y50vvvD333HOeik3eX1A9evQwj5pYmzhxYrYVqnQ+tewmU82OTqqrvOdu82VPfq/DSv3N86EJohdffFGcoPOV2W3x9NNP59gLzKbBox38DB06NMcJXnNzx9hb8eLFPUGLPVluuM5d516zu+cHqmr66quveuZf0WHA/tjFE7Snm06YbPd88y2qkF9t5s/y5cs9kyJrQQcAQHgRg8RnDOLrqaee8rte41Dt1aaJvK5du3rWa+83u+L86NGjA94IDGdM4BT7ZqNvwSnbmDFj5NixY2GJlbW6qz/ZVYbN7Wtkx/6cfvjhh/LLL79k2X7w4EF59tlnzc86Z7N9vuFmx3UtW7YMel8SbwCAPNEvN53sXr8Mdb40u7fa9ddfbx61e/iTTz6ZpbKT7qMGDBggL730kmfSWu2qfuedd8oHH3zgCaaKFCkS0rnZJcd1zrbp06f7fU6LFi1MkKd34W6++WZP8KW9rr744gszp4dTc0XY82ZoclHnydC7yHqeGiQqDZS0ktODDz7o6dGl9HxeeeUVM+nw1q1bzbwa2v7eE/9qUnPq1Kkm0aUl7INlz2ViBxnhOnedH8ROuGkxhH/84x+ehKee//jx480wBtW9e3dPNTJfOreMfff6nnvuMX8w6GetZ8+efp+fH23my267Vq1a5flYAICsiEHiNwbx/gxMnjxZ7rvvPk8hLO3lNXLkSE9CLiUlJcuoBk3K6Y1AjRE1FnzzzTczDcnUY3300UcmYRfoJqAb2OemMeuwYcPM+7HP/9FHH5V//vOf2fbOsmNlvYEZqICEXfFdj6VtYsf72gNM4y793Ng970J9jexonFe7dm1T+ECnhPn88889I2Z+/vlnc4Nfz0V7x+k5OiVPcZ0FAIhKw4YN0/7aZvG1YMGCgNtsb731ltles2bNHF+jVatWWbbpfrrt+eeft+rXr29+Lly4sFW6dGnPayckJFivvfaa32Pv3bvXHNd+bmJiolW2bFmzj71u8ODBfve199Pzy8lVV13lOV7JkiXNeesyduxYz3P+/e9/e56jS4kSJawiRYqYnytUqGDNmjXLs23z5s1Bt6PuE2h/tWLFCispKcnznIIFC2ZpC+/ztX355ZdW+fLlM+2n/1+sWLFM76dfv35WsH766Sezb9GiRa19+/YFfF6o5z5o0KBMnxPdRz8D9rorr7zS2r9/f7bn+OKLL2Z6n9dcc02O7yvUNsvNvylvp06dsqpXr26eP3PmzFztAwDRghiEGCTSMYh3jPrQQw+ZnwsUKGCVK1fOnIv9+m3btrWOHDni9xgrV660atWqlSUe0TjQ+z3oMYKJ6/IzHj958qSJmXzfgz7qMnr06Gzj5ilTpnj2Peuss0xMp+dy+eWXe56TlpZmVa5cOVPM7h3vjxw5Ms+vsTmHNv35558zxZsap5cqVcrz//o3yAcffOC3/ezn6PUIJKe/LdatW2e2FypUyNq5c6cVLHq8AQDyRO9w6VAA7Wauk63qHVKdCLVDhw6yZMkS03st0B1KnavhjTfeMHc2taCBdhXXCW+1N5zO26Ld//NK78IOGjTIzBmid8p0AmFdvLu7a6+rTz/91JxHiRIlzJ08vTOqvfG0W/2FF14oTtIhFTr/hg71aN68uWkLnbBYS6PrOT3//PN+e3LpnWSdU2PUqFHmbq22qb4vnd8iOTlZ7rjjDpk1a5bpRRYsrfCqPRP1zrfe3Qz3uev6+fPnm2utQzH12uu+WjhB7zrPmzcv27Lv9l1eu4x9dsNM86vNvC1atMgMU9bP0Q033JCnYwEA/CMGie8YxPbMM8+Yudq01572hNKeT3qMF154QebOnRtw5IQOmdUpK3TkhU5ror3/tLecHqNevXrmfetxc3MOkaK9/TWGHTFihBkNoO9deyVeffXVJpYaPHhwtvvfcsstpkejXkMdfrxjxw4TJ3sXrahZs6aZ1kOvabVq1cw6bVONb7Sn3ZAhQ/L8GjnRXnNr1qwxoyb02urwYf2bo06dOiaO123dunUTp9hTmnTp0iWk4g0JZzKAAAAERUvD65fmW2+9JX369KH1YtDbb78tvXv3NskwTZIh9/r27Wv+bWggbM9XCAAID2KQ2JdTDKIJGP2O1WF//uYaBsJFU2aaiNUiHnpjlTneAABA2GjlUL1rrb0PczvBMf5b/UvvjOodUXu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+      "text/plain": [
+       "<Figure size 1280x960 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.inspection import permutation_importance\n",
+    "\n",
+    "X = data[\"X\"].reset_index(drop=True)\n",
+    "y = data[\"y\"].reset_index(drop=True)\n",
+    "metadata = data[\"metadata\"].reset_index(drop=True)\n",
+    "\n",
+    "# Use outer folds from assemble for consistency with train/optimise notebooks\n",
+    "folds = data[\"outer_folds\"]\n",
+    "\n",
+    "fold_importances = {\"gain\": [], \"weight\": [], \"cover\": [], \"permutation\": []}\n",
+    "\n",
+    "for k, (train_idx, test_idx) in enumerate(folds):\n",
+    "    fold_data = {\n",
+    "        \"X_train\": X.iloc[train_idx], \"X_test\": X.iloc[test_idx],\n",
+    "        \"y_train\": y.iloc[train_idx], \"y_test\": y.iloc[test_idx],\n",
+    "        \"metadata_train\": metadata.iloc[train_idx], \"metadata_test\": metadata.iloc[test_idx],\n",
+    "    }\n",
+    "    trn = Train(fold_data, run=run, verbosity=0)\n",
+    "    results = trn.train(tag=\"cv_imp\", save_output=False, **best_hp)\n",
+    "\n",
+    "    booster = results[\"model\"].get_booster()\n",
+    "    booster.feature_names = all_features\n",
+    "    for imp_type in [\"gain\", \"weight\", \"cover\"]:\n",
+    "        scores = booster.get_score(importance_type=imp_type)\n",
+    "        fold_importances[imp_type].append({f: scores.get(f, 0.0) for f in all_features})\n",
+    "\n",
+    "    perm = permutation_importance(\n",
+    "        results[\"model\"], X.iloc[test_idx], y.iloc[test_idx],\n",
+    "        scoring=\"roc_auc\", n_repeats=10, random_state=42,\n",
+    "    )\n",
+    "    fold_importances[\"permutation\"].append(dict(zip(all_features, perm.importances_mean)))\n",
+    "\n",
+    "# Plot all four\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(2*6.4, 2*4.8))\n",
+    "\n",
+    "for ax, imp_type in zip(axes.flat, [\"gain\", \"weight\", \"cover\", \"permutation\"]):\n",
+    "    df = pd.DataFrame(fold_importances[imp_type])\n",
+    "    means = df.mean().sort_values()\n",
+    "    stds = df.std()[means.index]\n",
+    "    ax.barh(means.index, means.values, xerr=stds.values, capsize=3)\n",
+    "    ax.set_xlabel(f\"Importance ({imp_type})\")\n",
+    "    ax.set_title(f\"CV-averaged {imp_type}\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(f\"../output/images/ml/{run}/features/cv_importance_all.png\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c569c687-6076-4531-abe6-a15103544d53",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "o5fjjtdb0oe",
+   "metadata": {},
+   "source": [
+    "## Feature ablation\n",
+    "\n",
+    "Drop one feature at a time and compare performance with the baseline (all features)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3f5c78b0-59dc-491e-9dc1-5077a0c371d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CV baseline: test AUC=0.999889, deadtime=0.119%\n"
+     ]
+    }
+   ],
+   "source": [
+    "baseline_fold_aucs = []\n",
+    "baseline_fold_deadtimes = []\n",
+    "\n",
+    "for k, (train_idx, test_idx) in enumerate(folds):\n",
+    "    fold_data = {\n",
+    "        \"X_train\": X.iloc[train_idx],\n",
+    "        \"X_test\": X.iloc[test_idx],\n",
+    "        \"y_train\": y.iloc[train_idx],\n",
+    "        \"y_test\": y.iloc[test_idx],\n",
+    "        \"metadata_train\": metadata.iloc[train_idx],\n",
+    "        \"metadata_test\": metadata.iloc[test_idx],\n",
+    "    }\n",
+    "    trn = Train(fold_data, run=run, verbosity=0)\n",
+    "    results = trn.train(tag=f\"baseline_fold{k}\", save_output=False, **best_hp)\n",
+    "    val = Validate(results, run=run, verbosity=0)\n",
+    "    val.roc_auc()\n",
+    "    thr = val.find_threshold(min_eff=0.999, plot=False, show=False)\n",
+    "    baseline_fold_aucs.append(val._test_auc)\n",
+    "    baseline_fold_deadtimes.append(thr[\"deadtime\"] * 100)\n",
+    "\n",
+    "baseline_auc = np.mean(baseline_fold_aucs)\n",
+    "baseline_dt = np.mean(baseline_fold_deadtimes)\n",
+    "print(f\"CV baseline: test AUC={baseline_auc:.6f}, deadtime={baseline_dt:.3f}%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "87207858-bcba-4f59-8e85-97f588a1f223",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fold 0 done\n",
+      "Fold 1 done\n",
+      "Fold 2 done\n",
+      "Fold 3 done\n",
+      "Fold 4 done\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>dropped</th>\n",
+       "      <th>mean_test_auc</th>\n",
+       "      <th>std_test_auc</th>\n",
+       "      <th>mean_train_auc</th>\n",
+       "      <th>std_train_auc</th>\n",
+       "      <th>mean_deadtime</th>\n",
+       "      <th>std_deadtime</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>sector</td>\n",
+       "      <td>0.999893</td>\n",
+       "      <td>0.000040</td>\n",
+       "      <td>0.999984</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>0.113243</td>\n",
+       "      <td>0.063859</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>nLayers</td>\n",
+       "      <td>0.999885</td>\n",
+       "      <td>0.000050</td>\n",
+       "      <td>0.999983</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>0.146779</td>\n",
+       "      <td>0.100415</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>nHits</td>\n",
+       "      <td>0.999878</td>\n",
+       "      <td>0.000052</td>\n",
+       "      <td>0.999979</td>\n",
+       "      <td>0.000001</td>\n",
+       "      <td>0.167702</td>\n",
+       "      <td>0.078571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>crv_x</td>\n",
+       "      <td>0.999889</td>\n",
+       "      <td>0.000042</td>\n",
+       "      <td>0.999983</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>0.184124</td>\n",
+       "      <td>0.123810</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>crv_y</td>\n",
+       "      <td>0.999875</td>\n",
+       "      <td>0.000047</td>\n",
+       "      <td>0.999982</td>\n",
+       "      <td>0.000001</td>\n",
+       "      <td>0.215431</td>\n",
+       "      <td>0.191307</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>angle</td>\n",
+       "      <td>0.999883</td>\n",
+       "      <td>0.000064</td>\n",
+       "      <td>0.999980</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>0.265561</td>\n",
+       "      <td>0.131615</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>crv_z</td>\n",
+       "      <td>0.999869</td>\n",
+       "      <td>0.000046</td>\n",
+       "      <td>0.999976</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>0.366868</td>\n",
+       "      <td>0.141340</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>PEs</td>\n",
+       "      <td>0.999819</td>\n",
+       "      <td>0.000069</td>\n",
+       "      <td>0.999956</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>0.768309</td>\n",
+       "      <td>0.335294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>dT</td>\n",
+       "      <td>0.999659</td>\n",
+       "      <td>0.000104</td>\n",
+       "      <td>0.999903</td>\n",
+       "      <td>0.000004</td>\n",
+       "      <td>2.225684</td>\n",
+       "      <td>1.401065</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   dropped  mean_test_auc  std_test_auc  mean_train_auc  std_train_auc  \\\n",
+       "8   sector       0.999893      0.000040        0.999984       0.000002   \n",
+       "6  nLayers       0.999885      0.000050        0.999983       0.000002   \n",
+       "5    nHits       0.999878      0.000052        0.999979       0.000001   \n",
+       "0    crv_x       0.999889      0.000042        0.999983       0.000002   \n",
+       "1    crv_y       0.999875      0.000047        0.999982       0.000001   \n",
+       "7    angle       0.999883      0.000064        0.999980       0.000002   \n",
+       "2    crv_z       0.999869      0.000046        0.999976       0.000002   \n",
+       "3      PEs       0.999819      0.000069        0.999956       0.000002   \n",
+       "4       dT       0.999659      0.000104        0.999903       0.000004   \n",
+       "\n",
+       "   mean_deadtime  std_deadtime  \n",
+       "8       0.113243      0.063859  \n",
+       "6       0.146779      0.100415  \n",
+       "5       0.167702      0.078571  \n",
+       "0       0.184124      0.123810  \n",
+       "1       0.215431      0.191307  \n",
+       "7       0.265561      0.131615  \n",
+       "2       0.366868      0.141340  \n",
+       "3       0.768309      0.335294  \n",
+       "4       2.225684      1.401065  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ablation_fold_results = {feat: {\"test_auc\": [], \"train_auc\": [], \"deadtime\": []} for feat in all_features}\n",
+    "\n",
+    "for k, (train_idx, test_idx) in enumerate(folds):\n",
+    "    for feat in all_features:\n",
+    "        reduced = [f for f in all_features if f != feat]\n",
+    "        fold_data = {\n",
+    "            \"X_train\": X[reduced].iloc[train_idx],\n",
+    "            \"X_test\": X[reduced].iloc[test_idx],\n",
+    "            \"y_train\": y.iloc[train_idx],\n",
+    "            \"y_test\": y.iloc[test_idx],\n",
+    "            \"metadata_train\": metadata.iloc[train_idx],\n",
+    "            \"metadata_test\": metadata.iloc[test_idx],\n",
+    "        }\n",
+    "        trn = Train(fold_data, run=run, verbosity=0)\n",
+    "        results = trn.train(tag=f\"drop_{feat}_fold{k}\", save_output=False, **best_hp)\n",
+    "        val = Validate(results, run=run, verbosity=0)\n",
+    "        val.roc_auc()\n",
+    "        thr = val.find_threshold(min_eff=0.999, plot=False, show=False)\n",
+    "        ablation_fold_results[feat][\"test_auc\"].append(val._test_auc)\n",
+    "        ablation_fold_results[feat][\"train_auc\"].append(val._train_auc)\n",
+    "        ablation_fold_results[feat][\"deadtime\"].append(thr[\"deadtime\"] * 100)\n",
+    "    print(f\"Fold {k} done\")\n",
+    "\n",
+    "# Build summary\n",
+    "ablation_cv = []\n",
+    "for feat in all_features:\n",
+    "    ablation_cv.append({\n",
+    "        \"dropped\": feat,\n",
+    "        \"mean_test_auc\": np.mean(ablation_fold_results[feat][\"test_auc\"]),\n",
+    "        \"std_test_auc\": np.std(ablation_fold_results[feat][\"test_auc\"]),\n",
+    "        \"mean_train_auc\": np.mean(ablation_fold_results[feat][\"train_auc\"]),\n",
+    "        \"std_train_auc\": np.std(ablation_fold_results[feat][\"train_auc\"]),\n",
+    "        \"mean_deadtime\": np.mean(ablation_fold_results[feat][\"deadtime\"]),\n",
+    "        \"std_deadtime\": np.std(ablation_fold_results[feat][\"deadtime\"]),\n",
+    "    })\n",
+    "\n",
+    "ablation_cv_df = pd.DataFrame(ablation_cv).sort_values(\"mean_deadtime\", ascending=True)\n",
+    "display(ablation_cv_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1e4b869c-493c-4958-8566-f140f3d97e0e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1280x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "baseline_auc = np.mean(baseline_fold_aucs) \n",
+    "baseline_dt = np.mean(baseline_fold_deadtimes)\n",
+    "\n",
+    "features_sorted = ablation_cv_df[\"dropped\"].values\n",
+    "delta_auc = ablation_cv_df[\"mean_test_auc\"].values - baseline_auc\n",
+    "delta_deadtime = ablation_cv_df[\"mean_deadtime\"].values - baseline_dt\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(2*6.4, 4.8))\n",
+    "\n",
+    "ax = axes[0]\n",
+    "colours = [\"green\" if d >= 0 else \"red\" for d in delta_auc]\n",
+    "ax.barh(features_sorted, delta_auc, xerr=ablation_cv_df[\"std_test_auc\"].values,\n",
+    "        color=colours, capsize=3)\n",
+    "ax.axvline(0, color=\"grey\", linestyle=\"--\", alpha=0.5)\n",
+    "ax.set_xlabel(\"$\\Delta$ test AUC (vs baseline)\")\n",
+    "ax.set_title(\"$\\Delta$ AUC when feature dropped\")\n",
+    "\n",
+    "ax = axes[1]\n",
+    "colours = [\"green\" if d <= 0 else \"red\" for d in delta_deadtime]\n",
+    "ax.barh(features_sorted, delta_deadtime, xerr=ablation_cv_df[\"std_deadtime\"].values,\n",
+    "        color=colours, capsize=3)\n",
+    "ax.axvline(0, color=\"grey\", linestyle=\"--\", alpha=0.5)\n",
+    "ax.set_xlabel(\"$\\Delta$ deadtime [%] (vs baseline)\")\n",
+    "ax.set_title(\"$\\Delta$ deadtime when feature dropped\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(f\"../output/images/ml/{run}/features/ablation_cv.png\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07e09cab-303e-45d0-ae89-d4f985d2212d",
+   "metadata": {},
+   "source": [
+    "## Interesting plots\n",
+    "\n",
+    "Deadtime: \n",
+    "\n",
+    "- dT and PEs are essential\n",
+    "- crv_z and angle have moderate impact\n",
+    "- others have negligible deadtime impact, their error bars overlap with zero\n",
+    "  \n",
+    "AUC\n",
+    "\n",
+    "— sector actually improves things slightly when dropped "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "gelds1won5w",
+   "metadata": {},
+   "source": [
+    "## 2. L1 regularisation scan\n",
+    "\n",
+    "Sweep `reg_alpha` and track feature importances. Features whose importance drops to ~0 at moderate L1 are candidates for removal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "wcimmpeorxf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  reg_alpha=    0: test AUC=0.999889 ± 0.000044\n",
+      "  reg_alpha=  0.1: test AUC=0.999885 ± 0.000047\n",
+      "  reg_alpha=    1: test AUC=0.999895 ± 0.000043\n",
+      "  reg_alpha=    5: test AUC=0.999884 ± 0.000056\n",
+      "  reg_alpha=   10: test AUC=0.999874 ± 0.000042\n",
+      "  reg_alpha=   50: test AUC=0.999803 ± 0.000065\n",
+      "  reg_alpha=  100: test AUC=0.999647 ± 0.000133\n"
+     ]
+    }
+   ],
+   "source": [
+    "alphas = [0, 0.1, 1, 5, 10, 50, 100]\n",
+    "importance_records = []\n",
+    "auc_records = []\n",
+    "\n",
+    "for alpha in alphas:\n",
+    "    hp = {**best_hp, \"reg_alpha\": alpha}\n",
+    "    fold_importances = {feat: [] for feat in all_features}\n",
+    "    fold_aucs = []\n",
+    "\n",
+    "    for k, (train_idx, test_idx) in enumerate(folds):\n",
+    "        fold_data = {\n",
+    "            \"X_train\": X.iloc[train_idx],\n",
+    "            \"X_test\": X.iloc[test_idx],\n",
+    "            \"y_train\": y.iloc[train_idx],\n",
+    "            \"y_test\": y.iloc[test_idx],\n",
+    "            \"metadata_train\": metadata.iloc[train_idx],\n",
+    "            \"metadata_test\": metadata.iloc[test_idx],\n",
+    "        }\n",
+    "        trn = Train(fold_data, run=run, verbosity=0)\n",
+    "        results = trn.train(tag=f\"l1_alpha_{alpha}_fold{k}\", save_output=False, **hp)\n",
+    "\n",
+    "        booster = results[\"model\"].get_booster()\n",
+    "        booster.feature_names = all_features\n",
+    "        importances = booster.get_score(importance_type=\"gain\")\n",
+    "        for feat in all_features:\n",
+    "            fold_importances[feat].append(importances.get(feat, 0.0))\n",
+    "\n",
+    "        val = Validate(results, run=run, verbosity=0)\n",
+    "        val.roc_auc()\n",
+    "        fold_aucs.append(val._test_auc)\n",
+    "\n",
+    "    for feat in all_features:\n",
+    "        importance_records.append({\n",
+    "            \"reg_alpha\": alpha,\n",
+    "            \"feature\": feat,\n",
+    "            \"importance_mean\": np.mean(fold_importances[feat]),\n",
+    "            \"importance_std\": np.std(fold_importances[feat]),\n",
+    "        })\n",
+    "\n",
+    "    auc_records.append({\n",
+    "        \"reg_alpha\": alpha,\n",
+    "        \"test_auc_mean\": np.mean(fold_aucs),\n",
+    "        \"test_auc_std\": np.std(fold_aucs),\n",
+    "    })\n",
+    "    print(f\"  reg_alpha={alpha:>5}: test AUC={np.mean(fold_aucs):.6f} ± {np.std(fold_aucs):.6f}\")\n",
+    "\n",
+    "l1_df = pd.DataFrame(importance_records)\n",
+    "l1_auc_df = pd.DataFrame(auc_records)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "2441a63e-3398-4869-8ca8-38bb312e6cc3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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wKdJlDlYS5gA10BDXr18vso3EOX0e9c04eqjTce/evSXWqDEMUzsxusaLBBf9kiPLiFdeeUUYZNIXFLVQVwUUASGjzfzQCT//OtonP/Pnzxdt4CQOKIpCv2xJkJFfFLmaVxdIXHWt75HbvUg1XZRerOxIlx4SrCSQKFVLnw16nclYkk5Oly9fFkXxtJQlWkWiiz5jFPGi9A09B9kJlIcvvvhCiEB6z8n2Qg/Vk23ZskW411ORNvl51WRIXFnVd8ntXqSaLkov1vZIlx76HH/88cfCaJe+J/S2FtTFSJ51ZE9BEVLq4iaRRQ0a1PxB3ze0zhTpcIZhag5GqyQyT6VaDjqpTpkyBVUNmRzSUln3MzfyiywqpK8q0aWHOlzJ3JTEzfbt28XnhepgqNPxf//7H3x8fIxKN+prr0aNGlUuuwfy7aI0OQk4/aiYwmNimjdvLkQ4uaabkw9dRZBfZFEhPYuuPOhHA005mDt3Lnbs2JE77ur9998Xn2cS52T+S4a+J06cED8SqLTi3XffFZ/18nRKMQxT85BojXQ7pMJmmndHI3VK6kirqdAvW31HpCFbisqEzE/1PmCm8OJiahdkfKr3AKPOSCqSZxiGKQ+16bwUUAZNYFEe7yPqZKyNoothGIZhGMYYjBZe3bp1E7YLxflgMZVHbGo2YtPkyFaqc9ddiUyFTY4fl5ejNbycWBwzxaNOVUCdpoAm3+dHEZkOac7nR+ZoBVmhwnuGYRimCoQXDabduHGjKLJfuHBhOQ6BKQ8rjt/Dd7tvFlg3cmHeiJfpfUMxo3/RcSs1ERrSTl2qpUHfGVnbST8ehbTdBW0z4hdeyL3u2DcQzv2DquDIGIZhaiYW5Sk4pZMXeV0plUq89957ZuXTVVsY2zEQ/Zt4G9xOEa/aAnVSknllaWDhpcOhoy9sm7gbfJ0o4sUwDMOYgfDSiyyZTCZOYrRQ3VdJsw2pWywsLMzYp2SKgdKInErMM0NlygalETmVyDAMUw2EV3EnuYSEBLEYojaOEmEYhmGY2ohaoy0wW7gq/SVrhPAiZ3cGiImJQZMmurE9hSFTRVoYhmEYpjax7VIUZm26nHt74uKTYnbwrKFNKnWiir4BzRBlaUBbsGCBWAxpgQr38artmJuPF8MwDMOYi+iasvwMCosLfazr50ocZ/ftzhtFGtDyY6oGtErx8WIYhmEYhimcXvzo3ytFRBehzRFfH/17Bf2b+FRK2lHfgEaWS/qO/7WTOxewXKpsWHgxDMMwDGMSqJYrKiXb4HYtILa/+fc5Ib6a+DmhjqsdpBUkwvQNaOSir4eesypd9E32zJSxTEpKEgNiS8peBgYGmuopGYZhGIYxI2LTDIuu/Kw/GykWwsHaAo19HdHE1wmNfZ2EMGrg7ZgblapplFt4bd68Gd9//z2OHj2KzMzMEvelrkaVKk91MgxTtdCsVVoMQfYwJVnEMAzD5MfLsXSF6r0aeCA+Q4Eb0elIl6tw8k6SWPRQGjLE014nxHLEGF33cLCu9h2W5RJeb731FubOnVtihCs/XMdfAaRF6xZDOProFoYphlOnTmH//v0GX5uePXuid+/e/NoxDFMqOtR1E92LhtKNEgA+zjb4Y2IHIXyUag1ux2XgSlSKGHd3NSoNlyNTkJSpxI2YdLFsPKeLjOlrskiE6cUYXQa525coosylw7LcXY3btm3D4MGDYWlpiTlz5mDQoEFo2rQpPD09RfQrOjoaO3fuxA8//ACpVCrsJ5o1a4agoJoxfsRsuhr3zgH2f2F4e8+3gd7vVOYRMdUw4kWR6EWLFol1NI3CwsKiWka8goODxfSC8PBwcZ1hmMqHhM7k5WeKrC9tV6NWq0VMqlyIMRJiJMiuRKXiTgKVMhXd39ZShkY5qUp9ZKyRj6Oo46qsDstK6Wr85ZdfROrwgw8+wGuvvZa7npzsydWeli5duuC5554Tv5jp8ty5c8Y+HWOIdpOAhoMAVRawaKBu3bPbAAtb3XWOdjEloBdWCoUid52Pjw+srHhUEMMwxtGjgSdsLKTIVmkKrPcpZZRJIpGIfWnp0yhvJF6GXIVr0WlChOnF2PXoVGQp1Th7L1kseY8BBLvbicibuXRYllt4nThxQly+8MILBdYXDqCRCvzxxx9FROzLL7/Et99+a+xTMiWlEhUZeet8WgBW9vx6MQzDMJXO5vNRQnTVcbVFRFKWWPfnpPblrquyt7ZA2yBXseSv3QqPz8gVY1fpMioVcWlyhMeXXHeu77Ckmq/OIYZn1pqN8KLRQHZ2dvD29i4Q7SquwL5///6wsbHBf//9x8KLYRiGYWowK0/cE5ej29XB3J03cmu/KiKqJJNKUN/LQSzDWvrlrifhtehQOH7eH2ayTkxTITX2jk5OTkWiW87OzkhPTxeWEgWeRCoVNSOU/2QYxvzQaPJSAlQjlf92VUHzYCnl0KtXL2RlZeHtt98WNaLW1taoX7++iKCXp2EnOTlZ1KAOGDAg93Hd3d0xcOBAUZ9aGKpRpeO5cUN3IilMWFiY+K5r1KhRkW2HDh3C8OHD4eXlJZ6H6s+mTZuGuLi4IvtOnDhRPM++fftELS2Vari4uIh1dMz692jKlClo0KCB+AHs5uYmamxffPFFXL9+3ejXhGHKC0WdzkUkw1ImweOt84RQZePpaC1SnqbsxKxy4UUFZPRlSN5deuhLgDh8+HCBfW/evCkEmb5gl6kANOq863ePFLxdRdy7dw8vv/wyQkNDRcSTTmodOnTA559/Lj47BJ1U6YRCJ9kVK1agU6dOouaITjSnT58W22idIb766iuxz3vvvVemY/v777/F/caOHWtwn/Hjx4t9Vq9ejZrMlStXCswfo/dh/vz5Yr05QPVnjzzyCH799Vc0btxYCBH6EUdCjGpMjeXYsWNC/JCQatiwoRBG9B22Y8cOIcb0zQZ6SNQQv//+e7GPR+tJCBYuvyC7nR49euDff/8VgnHYsGGwtbUVoq9jx46Iiooq9vFWrlwpGpjohyyVarRv3158HiMiItCmTRssXLhQPM7QoUPRvXt30ej022+/ieYmhqkqVuVEux5p6gN3I60fTN1haSjORutpO+1XLYRXu3btxOXFixcLpBTpi+fdd98VXY0E/aKjLyL6wtDfhzExVzYBCzrk3V4xEpjfTLe+ijhw4ABatGghTugUPXnsscfQuXNnxMfHC5FUeKAodcZOmDBBFHU/+uijIrrQtm1bET04fvy4iCYYOjkRJQmo4nj88cdFEfm6deuQmJhYZDtFFmgbiUU6IddUSFytWbOmiJdXamqqWG8O4ouEhD7SRBEgWg4ePCh+yFHNaEk+ZCVBYosemzogSWyRwKbbVL9KEf0ZM2aIH4x66PNJ0aUlS5ZAqVQWeCzqCqX19Pml/fQcOXJEPA4ZR585c0bcJtFPr+vHH38snpvEX3GQiFq1apU4Hv0lZRVI4NFn9ptvvsH58+fx119/YePGjaJ5iR6PRBjDVAXkDr/hrC6zNaZD1Zuly6QSUcxPFBZf+tu0vbL9vIwWXvSrjUQWfSHomTp1qohUnD17VnzRUFTM19dXfEkSb775pmmOmsmDxNWaCUBaoV/NqVG69VUgvuikMHLkSKSkpIgT461bt8TJgcx2b9++LXyjXF3ziiOJpUuXYs+ePUKw0WeKUjPEmDFjCgis/NDJi048rVq1QpMmuv9cpYWiA2SbIJfLsWzZsiLbKepDUTk6iVJqqDpA/x8pOlTaJTs7G1u3bi3xMUnk0H5leVxT+/VR+o7EhoeHR+46+hFHUSCqKSUvMmOoW7dusdFUemz6LiPxuXfv3tz1JHqefPJJxMbGYtOmgv+vqH6VIlck6MlSRw+lQ+mHB0Xr6IeIHhKS77//vvjsrl+/XvwgKcyQIUPE8xVGn57s27dvkW2UMg0JCSnT68AwpmLzhSikyVUIcrdD53qVV6xeEtRBSZYRXk4Fv8epY7Iyh3Xnx+jcH0W3yJuLhJYeql+gL6Cnn35apJn0IXR7e3vx64xqJ2oaFLkxdNKnL29aSgWdrJQld2AUgdKJW9/K6c0o8oA6Tb9tJlCvFyAt5egFSztdH245oJMknRwocvXqq68W2U5pl8KQ3QiZdRaGIlkffvihEF6F00okjohx48YZdZwUif3iiy/E8U6fPr3Atj/++ENcPv/886guUBSG0rimhMQHvUZlgSLeprSjIDGhL2PIj36doVRdaVCr1di9e7eIRFGUnkSmvjwi/6WeyZMni+89ikY98cQTuev16cf8aUYSXPRjglLnxYkkEl9du3YVkSpKq1N6s/CP2+KgSDBBNV6ffvqpiHCx/QdjTmnGp9oHVtjsRWMgcdW1vgeaz95Rrg5LyuDkL8vIT+EsToUIL6rZeeaZZ4qsp3QSpYUoZE+1CPQrsVu3biJ0XxOhrk6TpGNIdH1u6kJELZAaCXxRp/R3eTey3FYUu3btKlATUxoMnWTID46iElSPQ5HU1q1b526jyBhFQ5566imjjpMKnOlkR1Efenx99INOgvRcdFIsaySNMT1kSVMcDg4O4jK/B1lZIJND+nFAUVNDFE5jUo0ifQap+J4K3EkURkZGis8QRdDyCyyKYulTlQ+rby0u4mVori0V31NqlFLB/fr1E3Ve9NmlH7aTJk0qEHFjmMqCbBzIR8tCKsHItsX/n61KZPlElrEdliUFU/QGqqWhQqrdyVaCxBZTOyHBTZQl5VHS8HSKepEwoqiXXnhRhILqWfr06SNS2sZC4pBOmhTB0Auv4qIX1QFKn1K0qbSQcNBHDUuCXv+yTJyg4zAlJK4rAopmkuiiyBWNP6OaL4pO0fNRapA+G8WlTWk9Rb6o+P6jjz4SlxQ5o8ejKJYefWcoPeaIESNKPJbiXl/6cWvo+5VS99RcQLVdlA6lhia6/Oyzz7B9+/YSG1IYpiJYnVtU7y06CpkKEF5UGEq/OPO71pcEdfZQwTKljRgDKT6KNpUF6l6kQvqHMXYtENSl9MdhIvKfhB6GoZMMQXUuVKBMxc/6LkZ9zZexaUY9FPGgXyp0IqNOPjqp0WNTpHbUqFGoTtDrUpaUEwljikRTOtEQtJ32qyjxU1VQpyBFrShiTe89ve/5oVrEkoQo1auS4KL0N11SRIsiUfmhmjT6XJMQ/fPPP03+N9CPEFpmz54t3kMSgfPmzRNpc2pIYZjKIkuhxvqcovqnzaCoPj+xqdmITZMjW6kuYHlhYynLnf3o5VRN7CToPzvVbZUWKrKmLwbGACRSKMVXliWkD+BE6ckSmmWd/HX7lfYxy1nflT96RUX1poBSJ1RTSKkhKr6nDjJKs9BJ7WGRhIdBJ1yKVNCJmFKX9Lh0EqOTK3Ww1WRITD2s7pK21zTRRVDjB0WkqPmnsOiiWrl//vnH4H3pByc1fdDnkQQYRV6pEN7Pr2CpAIkxskuhZhP63FYkJJCpvo/E96VLlyr0uRimMP9djEJatgp13GzRNSSvCcYcWHH8Hh794RBGLsyzWaHrtI4W2l7Z1Lxv1NoEFcwP/DLnhoFm2YFflL6w3kRQ3QlB6RpTobeLoGgURSr0xfsUmSovJLzo5EvpRn2asToV1ZcHqmEbPXp0kUHYdCKn9TW1xo0ageizQyIlv+8gpQxnzpxp0CRVD6UaCYqSlpSWfuedd4RwpXrYwv6GBNWHGSrWNQR14RYnrqgDlVKjhmriGKa2FdUTYzsGYvMr3QwutL2yqTRHU/rVV1I6iTGSJsOA0Ut13Y35LSUoEkaii7ZXMiRavv76a2EYSXM6qRgxf9qR7EWotb4soona9Kk7du3atbnu3eVNM+qhGjEScVQvo7cTyF/EX9MhcUVNDPruRRK51T29SN5rhmxAqBmDum2pros85aiblmoFyf2dUnTUnUSf2ZIEEdlAUKE9eWuR0DEUOaQO3u+++048H9W9tmzZUpioUvck1dhdvXpVRNBK3f0MCH85sjmh96h58+aiuJ4MiKkOkt4z6nRkmMrienQaTt9NEkX1o9qZn+j3crKp9FSiWQgvMgyk7iAqXmUqABJXZBmh716kmi5KL1ZypEsPeXSRQCJH7VdeeUVEBagFnjyXLl++LFIztJRFeJHoIhNWinhROpCeg3ycTAUVTOuFV3UrqjcF+UUWFXpXZ9FFkEWDIfSF59SIQKKJPp8UjSIBQ+KI6lfJ7PRhUAcjCS/ygyucrswPTW+gbm8qt6CUI3mAUYSRnpsiZ2WtJaS6WrovHTP9iKE0OaU5qRby9ddfZ6NqpkqiXf0ae1f66J3qikRbSrdD+tVGix76hUVfNnXqGLYqoIem6IS+eJcKpMtSF2bO6FtHKVpCtR5VjiIjz47CBJYQpoA+IxRFoS4rSqnoC7UpekURAIqAUg0MGaqSECN7h5LYsmWLqKUh/ve//+GXX34x2bGSKKTjo2MiX6jCqbeaDlky6D3ATO3DVROh7zaaqkB1jFSIX5auT4apKVDBeofPdiE1W4Ulz3ZAz0KzEcnJvsmH28X1Kx8PgJ1VzR0bWBZNUOpXgQQUnUjzQ/UQhdeV9OuQOxprFySkaJ5cSdAg4NJCc+tM7YquZ8OGDeLzTGmo2ia6mLJDUVeqA6OoLosuprby34UoIboCXG3Rvb55FdWbM6UWXhSl0Eck6ORH4XVKFemLS4uD0hUURaC5ezzGgjFXqItNX99EbuC1CSoBoIU6RfWQg7ve8JNEKAvRgvWL9COUxl9RxJ+6uxmmtqcZyULC3Irqa4TwoqJQWvSQ8KKaiOLc65lKJC1at6iy8tZFXwAsbHXXHX10C1MEqrWhSBfV6VDtGRVk68ex1BZoziGlevNDvlR6qPC8d+/eVXBk5gmNkiJRSuOKPvnkE7Rp06aqD4lhqoQbMWk4dTdJOMCPMkOnenPG6IQruSRTpxr9+ss/r5GpZE4tBvYXmqW3KF+HVc+3gd7v1Iq35Y033ih29EpxkKElFVDT3D0q1KdOvh9++AG1DergLKnphaNdBamoVDfDVN+iei+z6xqsMcX1xaURaSE/JTpx1TbMprheH/EyRC2KeFEqnFr0SwOfQBmGYYwvqu/4+W6kZCnFwOleDb2K3Y+L600c8aL6LqpxqI2iy6yoRcLqYZS20YNhGIYxnq2XooTo8nexRfdQHspeVow26yETQCrKlcvlxj4EwzAMwzDVjFXHI8TlU+3riBovpmwYHfGitvvTp0+Lturx48ejtkIu14bGqpAbdVkcqRmGYRjGnLkVm4YTdxJ1RfXtDPt41kQWLFhgcKIFaYEKF17Tp0/H+vXrhSuzu7u78FiqjXh7e+PKlStVfRgMwzAMU+GsOqGLdvVp5AUf59pVVD+1hGCKvu67QoUXuVzTHLKLFy8KE8GmTZuia9euYvhsSeMz2ESVYRiGYapnUf26M7pmsjEdKn+4dE3BaOFFxoFkJ6HvDrt06ZLwQnoYLLwYhmEYpvqx/XI0kjOV8HO2QY9C44HyE5uajdg0uRBqeq5EpsLGUheU8XK0rtUWFEYLL4p2kfBiGKb6IpfHQq6INbjd2soL1tbFt4ozDFO7WHlc5931ZPvAEovqVxy/h+923yywbuTCo7nXp/cNxYz+DVBbMVp4lWXGHsMw5smDB6sQfud7g9vrBk9DvXrTK/WYGIYxP8Li0nE8PBGkt0a3L9mpfmzHQPRv4m1wu5ejNWozNXdUeC0hLjMOcVlxBrd72nrC0459Vpji8fd/Gh6efaFRZ+P0mSfFurZt/oJUZpMb8WIYhlmVE+3q08gbvs45I+kMQGnE2pxKfBgsvKo5f9/4Gz+f/9ng9iktp+ClVi9V6jEx1QdKI9KiVmfmrnN0bAKZzK5Kj8ucoRKLoKAgNuxlamdRfcfaZSFhtsJLoVBg586dYuBubGys+GLy9PRE+/bt0a9fP1hZWcGcOXbsGL7++mscPnwYCQkJYj5d69atMWXKFIwcORLmzKgGo9CrTi9kq7LxzDbdwPIlA5fAxsImN+LFMAzDMOUpqk/KVMLX2QY9G3AUvMqF16+//ooPPvjA4HBiDw8PfPrpp3jhhRdgjqxduxZPPvkkNBqNGBjcq1cvREZGihq2PXv2YObMmfjii0JDqM0ISiPSkqnMi1g0cmsEO0uOWDAMwzCmG4j9JDvVV+3IIIJECUWFaFA22Ur4+fmhQ4cOYqHrtI62TZ48GW+//TbMDZVKJczQSHStXr0aJ0+eFJcHDhzAoUOHYGNjg6+++gphYWFVfagMU6FotXlt30nJJwvcZhim9nI7Lh3HbucU1dcyp3qzE1779+8X6TkSV0888YRwb4+IiMDRo0fFQtevXr0qUnW0D+178OBBmBPXrl0TqdFGjRqJqFd+OnfujAEDBohjp9FIDFNTiY3djmPHBuTePn/+WRw+0kOsr0r+++8/PPvss2jcuDGcnJxgb2+Pli1bCvPmwjNi//zzT1HiQP6C9+7dw5gxY0S5g62trYhk//vvv8U+B/3/phEgzZo1Ez+0yH361VdfFXNoKfpNj1mW4etkKD127Fj4+/vD2tpa/ACdNGkS14Mx1ZbVJ3VO9b0besHPpeSieqaChZd+XtFzzz2Hv//+W4iXwjRs2FDMcqR96Avuxx9/hDlBX4ylgUYimTtqTV6E4nTM6QK3GcYQJK4uXpoKuaLgnDG5PEasr0rxRd8b69atg5ubGwYNGoTu3buLH3TvvfeeGFGmVhf9jJNIotpSqtfs1q2bqNWkH06PP/44duzYUWT/V155RYw9o6h237590alTJyxfvlxcp9rVskDHSiJv5cqV8PX1xbBhw+Dj4yNEIa0vjcE0w5gTcpUaa0/riuqfZqd606E1En9/f61MJtPGxMQ8dN/o6GitVCoV9zEnVCqVtl69emS9r/3rr78KbDty5IjWxsZGW7duXW12dnaR+9LfQvczh79p552d2j5r+mib/dksd+m7pq9YX5XcvXtXO3XqVG39+vW11tbWWjc3N2379u21n332mTYzM1Ps07NnT/E6hoeHa5cvX67t2LGj1sHBQevs7Kw9deqU2EbrDPHll1+Kfd59990yHx/dr6QlKChIW53QaDRalSqj1ItSmao9eLCzdtfuegaWEO3BQ13EfmV5XDoOU7Bhw4bcz4melJQU7eDBg8X7s2TJktz1ixcvzn3fXnnlFa1SqczdNn/+fLG+e/fuBR5r//79Yr2np6f26tWrueuTkpLE51T/ePTZzE9xn42wsDCtnZ2d+NzS4+aHjpPuQ4/JMNWJTeceaINmbtZ2/GyXVqlSa6sjqhS5Vn4/zeBC201BWTSBhP4xRrBRWJ5C/9QFWNqoUUZGBrKzs2FOUPqTZk2mpKSIX6UhISGIiooSNV4dO3bE0qVLUb9+fYMDMSmlcP++7hdBVbDr7i68tu81aMU5Ig8JdK7C83rNQ7+gfpV+XFQnR7/46XWtV6+eeG3p/aeUdHh4uFiCg4NFOofS1v/73//w+++/i3mf9JpSZIPeA0ozUUr41q1b4r0pTKtWrXD+/HkRTWjSpEmZjnHixInFrqfnoogJHXd1qu8jS4h9+5tX9WGgV8+LFWpHcfPmTTRo0AAjRowQUSaCokqU0qtbty6uX78OS0vLArWcNEM2PT1dLPoua0oJUnTqyy+/xFtvvVXgOegzSZ9NQv9ZLclOYvr06fj+++/xyy+/iM9yYSjitnHjRhF9a9OmTQW8Kgxjesb8dgxHwhIwrW8oXqumTvMpO+8ibbeuOaA4HPsGwrl/ULmfpyyawOiuRrJcoJMqCSkSYSWRlZUlaiaoTsPcoPQFiYThw4cLOwxaCAcHB/Tu3VukDEqCdGtqamq50p200ONkqbLKdF9KJ845MaeI6BLHlbPuixNfoKNPR8ikhgeX58fWwrbco6ASExNFbR99Pr799ltxUsr/mPR6u7q6FrgPCVzqIu3Zs2eB9VSrQ/M96QRJ3bP5IRFHoovEV1lFl/5kXRj6IUHNIcScOXPK/JiM6UXWli1bhBgm4U6NMPrfirStMCSW8osuwsLCQohoEj3UfU11V8SRI0fEJdWoFoY+h9SRbahbuzBkp0M89thjxW6ntCcJL2rgYeHFVAfC4zOE6KKvbupmrK44dPSFbRN3aJRqxC+8INZ5TG4Bac7cSJmj7ocY1Y0Wrh0tC2WJYRktvFq0aCEsFxYtWoSXXirZoJP2oV+dVBhrblAXI0U+qLZj1apVaNq0qbCTIMFARbwkBkgoFP4y10P7Ojs7G/38s2bNEgXBJLo6ruwIUxOTGYMuq7uUev/jY46X24qCIlfUzfroo4+KQuXi5nwWV89TWHTpoxKGhNeKFSvE5bhx42AK6DM6atQo3L59WzzX6NGjUZ2QSm1FtKm0UPciFdI/jJYtF8HVpX2ZjqO80JfYG2+8If4fGvpCox9zxf3qLA76IUXkr9uiyDb9IDB0nzp16pRaeOmjX1TTVRKlfTyGqWpWn9RFiXo18IR/NS6qlzlZiUWjyKsJtfJzgNSqYDCCfmh/9NFHlXJMRgsvOiHu3bsXr7/+uojY0InT0EmY9qEvuPHjx8OcoF/REyZMgLe3NzZv3pz75RwaGoqffvpJiCr6lbp48eJi0wcE/Xqm7s2KLvCvTuzatUtcvvjii6W+D6Uli4MiFSSKyeT27NmzolhaDwllqVSKp556ygRHrSu0ps80RT8r6z+gKaH/Y2VJ8bm7dYO1tY8opNeVMxV5RLGd9pNIShcxNRV//fUX5s2bJ0TR/PnzRZcxdSnSDyAST/oocWHo82AqyvILliJx9PrT90lJ0A87hjF3FCoN1p6qXUX177zzDl577TWj709lMaQZKlR4UZRo2bJlufU5H3/8sUjNUX6TvoCoRodOYpTzpC8wSgE884zOWd2col1KpRIDBw7MFV35oXQZCS+K7BkSXvS3miKFSik+ijaVBepefGn3w8cB/dT3J7T1blvq4ygv9N4TxdVkGSIwMLBEkU/Ci6JeeuFFaSKqvenTp4/4zJUXEtoLFy4UkVz6XJc33VodIDHVIPRD0b1IIqug+NL9/Q1CP6h00UX8888/4pLekyFDhhTYRhFJU0BlBBSponqM4j6rZandJIFI9YBU52WOJRUMUxZ2XIlGQoYC3k7W6NOodjjVW+eU/RhLWc4ZRv88pF+WJEqowJWEFZ1s6YRFLu8UsqOWbPri0vt8bdiwwexOZnqBYOiLUr+eapYqGnptKMVXlqWLXxd423nnFtIXeUxI4GPnI/Yr7WOa8j0qy2OVVCdIHmtUp0NCWR+FIBFmqjQj/UCgOjSKqGzatEk0jdQWvLwGoHmzBUWGYVOki9bT9qogKSlJXBaXBqRomCno0kWXgtcX6OeHflCWJS3Yv39/cUnfcwxTY5zq29WBhcx0UWRGR7leURImNHLn+PHjmDFjhiggpW4jWug6raNt5PNljr8C9fUY+oL6wlAhLJG/o8mcoIL5tzsUPxFAL8ZmdphZ6sJ6U6GPXlEq1xSQIKITGwl5qrejWizyhyOxRsK/PFD0hOq66IfE+vXrRbdabYPEVadO2wvUdHXtsr/KRBdB3yH6kWT5U37UhfzNN9+Y5Dn0UWx6POqE1JOcnIw333yzTI9FKQoya6XvvOLMWunHG0VVqdGIYcyZO/EZOHxLV1Q/uhoX1ZszJpGyZFg4d+5c8SuR6p1ooeu0jraZK/oOJDqZ//zzzwW2UWqLCnsJcx6UTVYRZBnhZVcwYkGRsKqykqDB6PqTpqmgdKM+0kUdZPri/fI0NlBxNlmJUCcjnRTpx0JtJX86kQrpqyK9mJ9p06aJyCO9L+Qq//TTT4umDGrAoBFkpoAei0aG0WeJOmPp80QinOxjqGaLagsJvf1ESVBdKEX5SVhRvSIZSlOtINlIUHqc0pr0XOXpmmKYynSq79nAEwGuPPO3IqjVMURq66bOKYI6M+kLnjrZ6ARMflLUvk6/ivVCwlwhcbVh2IYCNV3bnthWJaKLeP7550WUin7507SCwkXKFLUgq4myQCcwOhFThFVvA1GeNCOdWMmqgiwpKM1oqDmEqbqIF0WcSRhTyo9SwOTBRT5ZNH7MVPzwww9iIf8vEvRUO0jNGtTNTM9L6fLC1ieGoOgr2ZtQUwnVjm7dulXUh5LYoh8O1MBTnh8KDFMpRfWnI2pVUX1VYLSBanHRgzNnzojZhwQZFtIvPXNMMRZXyEtFvOTzQ4KAPMroFzAJCDo5F4e5GKjqyVRm5tpRmMISorxQFJFOmuRxRoXLbdu2RWZmpjA6Lc5AtbBJZXHoDS8JOhlGR0eXKhpRHCT+KIIik8lENIUuC0M+TqZKa1Un89WKNkCtDtD/bfo8UvSrPF3LDFOd2HIxCi+tOAMvR2scfrsPLGtQfZdGoUbkhzrvPr+PuxSxk6gWBqp6zp07JzyPtm3bJqII+aG6GeoYpI7H/DYA5galBGhhTAeJGvr1T80W27dvF0XHJMJJhFEU8WF+Rw8TXpQSMlZ0Efo5f3RJKaLioHqv2iK8ais0FYHeZ6rP0kOpR3LBp1pCQz+8GKYmF9WPblenRokuQqvJizHJw1NgE+oKiVRS/SJe5G9F9Rb0BVXSw1BHGkWUnn324WaN1QWOeDE1Abk8FnJFLDTqbJw+86RY17bNX5DKdF2m1O1obV1z28np+0tvU0J1WDExMSJyT5FaKkWgsVX5RRnD1FTuJWSix9d7RVH9gTd7o45bzYl6Z12KR9KmMGhS8wyUZc5WcBkaAttmHpWuCYyWtCdOnMALL7wgahkoHE+F1NTFRsWlNEaIrtM6KjIlYUZRDroPwzDmw4MHq3Dy5GO5ooug67SOFtpek6G6LEp3kwcXRWXpO4oiYDRNglLgLLqY2uZU3z3Us8aJroTlVwuILkKdohDraXtlY3Sqkby6KLVIX1o0S62wDxM5jtNCbvWDBw8WXkl0H70xImMa4jLjEJcVh2xV3vDxa4nXYGOhez88bT3haefJLzdTLP7+T8PDs6/BV6ewv1dN45FHHhELw9RmlGoN1uQ41Y/pUKdGpReT/w0rcZ/kf2/Dpol7paYdjRZeFIKnjh+yYSjJ/JKcYBcsWCCGGNN9GNPy942/8fP5glYYz2zLmxAwpeUUvNTq4e72NQHqUC2t6WVxA7JrI5RGrMmpRIZhHs7uqzGIT5fDw8EafRt715iXTB6eIiJbJaFOketqvkJczF94kdUCFUs3bNjwoftSupHaqKmrjTEtoxqMQq86vQxup4hXbYGsJu7evVuqfVl4MQzD6Fh5QmchMbpdQI0qqtekKUy6X5ULL3Inp5McpRsfNpiWOseo7qs2uoJXNJRG5FSiDpq7xzAMw5SeiMRMHLwZJ64/1b5meXdJHa1Mup+pMFrakv2CQqEo1Wwy2odMBGlmI8MwDMMw5lNUT6YE3UM9EOhec4rqCeu6zpA8xK9L5mwt9qsWEa/33ntPFMpTt6Kbm5sosjdkpElOzpRufOedd1DToPZzql8rDhoRQgvDMAzDmHdRfc2KdhGKOynQKnSejYZwGVqv1IX1VK9OiyEtUOE+XkuXLhWDXz/66CPheUMjdvr06SM8LKjonnwsqJORCuqpvmvWrFkGR29MmDAB1Q1z8/FiGIZhmLKw7VI0Ji8/LYrqj75Tw5zqs1WImX8G6mQ5rEKcoYrLKuTjZS1EV1X4eBktvKiuiwQWQQ+hv16YkraJA5BIhM9XdYOFF1MTiJErEaNQGtzubWUJb2vLSj0mhmEqh2cWncD+G3GY0isEMwc2qlEve+Jf15F5NhYydxt4T2sjtEjU7KNim/ukpiZ3rq+UkUFUXF+SoGIYxvxZGhmPuXcMh8hfD/bGm3V9K/WYGIapnKL6A7lF9TXHu4vIPB8nRBckgNvohpBay8SsxgK1X1U0Lqhcwos7yBim+jPBzwMDPJyRpdbgsbO3xLqNrevDNiflQBEvhmFqHmtORYii+m71PRDkbo+agipFjqR/dN9ljn0CYR3kBHOj3EOymapFGRsLVZzuV0txWHh6wtKLDTKZ4qE0Ii0ZOUPDiWaOtrCXldwJxDBM9UWl1uCvkzrvrqdrUFG9VqNF0prr0GarYBngAKc+5hnJY+FVzUn+aw3iDXRZEB5Tp8LzlZcr9ZgYpqZAkf26deuiZ8+e2LdvX1UfDsOYhD3XYhGbJoe7vRX6N6k5TvXphyMhD0uBxFIKtycbQmKmzQIsvKo5Lk+OhkOf3tBmZ+Pu2HFiXdCK5ZDkjHGiiBfDMAzD6Fl1QjcQe2S7AFhZmKc4KSvK6AykbAsX150frQdLT/P1JCu38Lp16xbWrFmDCxcuICkpCUql4Q4pKsbfvXt3eZ+SyQelEWnR5BvHZNO4MaR2VfuhK9x4QbdpxFTz5s3xzDPP4Lnnniuwz+zZs4U1ycMIDw9HcHBwhRwzwzBMTedBchb23ahZTvVapQaJq68Bai1sGrnBvoMPzJlyCa93330XX3/9tRgbVBpXCu6CrH2QyNKPjQoLC8Phw4eFtxsJ8FWrVhXZv2XLlmjVqpXBx3NwcKjQ462tqPP9/z2WnI5ebk6QVXHX8qVLl/DFF1/g6NGjok3b0dFRtGqTWfPMmTPh65vXbXnx4kWxL6UDaVC6u7s7BgwYIPwDDQn1rVu3CjPEEydOICUlBd7e3mjTpg1eeOEFDBkypMCPgf379xf4/qLPdf55n1euXMFnn32GPXv2ICEhAZ6enujbt68wmi48z5aOsXfv3uIx5syZgw8++ADbtm1DdHQ0vvnmG7z66qsV8GoyjA6q7aL/7l1C3FHXo2YU1afsuANldCak9pZwfSLU7LWG0cLrp59+El90BH2x9evXT3xxWVhw9pIxPIyaTkwDBw7E6tWrMXbsWDz66KMFtj/++OPihMdUHv/FJeO9Gw9yb4+9EA5fa0t8GuqPIZ4uVfJWnD59Gt26dROjxjp06CCWtLQ03L59G9999534nOiF17p16zBmzBgxwqxt27bo0qWLEPn02fv333+FaGratGmBx3/ttdfw7bffQiaToXPnzsKDJzIyUpg+JycnC+FFPwBozBk9Pn230edWDx2bHvoRMXToUGRlZQnhRqLq6tWrWLZsmZjusWXLFnTv3r3I3xgXFyf+LvIxpMejebZ2VRypZmp+Uf2aGlZUn30rGemHdN9fJLpklTx30Si0RtKsWTOtVCrVPvvss1q1Wq2tbfj7+1OIQFyaA+qMDO2Vho3EQterGnptDH286DND255//vncdbNmzRLr6JKpPDbHJml99pzVehdafHIW2l4VTJgwQXwe1q5dW2TblStXtJGRkeJ6WFiY1s7OTuvs7Kzdv39/gf2WLFkiHqN9+/YF1i9btkysDwgI0J4/f77AtvT0dO3u3btzb4eHh4t9e/bsWexx0v7e3t5in59//rnAtrlz5+Y+T3Z2du76vXv35v7/GD58uDYrK6tMrw3DGMuOy9HaoJmbta0/3qHNVqqq/QupzlBoIz8/po2YeUCbuO5GsfuoUuRa+f00bVZ4stiPFrpO62ih7ZWtCaTlqe0i5s2bJ1zsmapFm88OIPPUqQK3zQ19KvHePV2Bp7GQvlu5cqWIJlBEwsbGBnXq1BHRV0PztGoy9HqQLURpl1SVSkS6iisS0K97/+YDsV9ZHtfIYRhFokEEpesK07hx49xoF0W/MjMz8dVXX6FHjx5FRpE99thjOHnyJM6cOZO7/vPPPxeX8+fPR4sWLQrcx97eXow+Ky1U30oz2ugzOHny5CJRNYrAkYs1Rb4KY21tjR9++EF8bhmmUovq2wbA2qL6W8YkbQyDOkUBCw9bUVBfHOnHoxD7w1nEL7yQu46u0zpaaHtlY3RekOYuUmic5jAyVUvqjh2I+fSz3NsR/3sRFj4+8H73HTg98gjMDUoZ6U885eGtt94SNTFU+0OpGhcXF0RFReH8+fPih0FtG1CeqdEg5MBFkz0eyacouRINDl4q0/3CejQvtw8YCRaqwRo3bhzef/99tG/fXqQFC7Nz505xSQKrOOhzsXHjRiG+KA1I6URKA1INGKURy8vBgwfFJaXNi4OOn9KmtN9TTz1VYBsdD9WsMUylFdVfj60xTvWZ52KRdT4OkEJYR0itiv/OcejoC9sm7gYfpypSk0YLL6pN2Lx5syhk9fAwzZBJxjjR9WD6qxTuKLBeFROjW//dfLMSXxQNoc8NQR2OxkKin6IFQUFBIprh5uaWu41qZo4cOWKS42WqhjfffFM0Yfz3339i0YvrwYMHi6J0up1/goaPT8ldTPQ9RURE6OpbQkJCTHKcJOQIQwX8+vX6/QqPXWOYyoJquzRaoHM9d9TzrN5NSqrkbCRt0GXdnPoEwqqO7vugOGROVmIxJyzK88VIX4jUyUNFqrUVSjM0adKk2G0UcSlt1IUEiTYrq0zPTelEEekqLrVD6yRAzGefw75zZ0hKGYGQ2NpWSEcIdTVSYTSleahLjaJdzz77bJH9qIvMkK1EfhPL1NRUUXhNacv8oougBo/CaafagJ1UKqJNpYW6F6mQ/mGsaFEXnVwcynQc5YWsR6gRg7pgqUCe3vcdO3aIKBh9hiiCROKJOqrp80ppxZIoXFxv6s/4wx6vuO2cYmQqtaj+VE5RfcfAGuBOfwPabDWsAh3h2Lvy/h4qYTFUxkJaoMKFV9euXfHzzz8LYUHdPG+//Xat9Fei2iJqJS8vJLqut2lrkmPKe1Bd5OtG+w6lvkvDM6chMWFnVXEnHIpWLFmypNioQ0l2Eo0aNcq97uXlJTrRSPx/+eWXoquN6rtqM/RalyXFR5YR1L0YLVcWW+dF7xxtryprCfp7KMql7yCkuq/p06cLGxKysvnrr7/EZ4A6GL///nsh1h6GPsqkr1EtL35+frn+csVx9+5dcZnf+oJhKpv9N+IQlZINVztLDGhavZ3q0w89gPx2CiRWUjEAWyKrvO+mkoIp9F1EtjeloVzeD88//7zwrHnnnXfw22+/iciDPgVg6IuUviSZ2ufjRQ0YegPVESNGiBrB4iiLnQSJN6qbIdFPC412oVZ+Wte/f3+T/h01ERJTZBnx/KU7QmTlF1/6r7JPQv2r3M9LD3lj0WeDhBd5fBH0PtN3yoYNGx4a9dILIIpQ04+l9evXi89iSVhZWeWmr4uDiuoXL16MFStW4MUXXyyyndbr92OYqqKmFNUrItORsl1XXuDyaIgoqq+OGC28KHVEX3Tkx6RPlZEIo8UQ5m5qVpVQio+iTWWBuhepkP5h1Pn1F9i1a1fq46hIHy9TQt1nFLmgmjEyoCS/pkWLFoll9OjRIiLClAz5dP3eLFh0N0Yr8qZOUKTrkyr08Vq4cKEwQCUxnR9KNep/Xeo7B0mAz5gxQ4h58tPKT2JioviOmjRpEmxzPtsULaOidzIqJXPT/GnIjIwMHD9+PLezkepXLS0thbij77zCBf70OaMfnpT6/PXXX/G///0vdxt1XFJRPx3r8OHDTf4aMUxpiErJErMZiacqybtLnaqAOk1RYkG7rIx1V8Kd/q/rOnf6Ju6wa199I3dGCy9qxdY7j9OvOfqSZANV4yFRWtYUn33XrqJ7kdKJxdZ5SSSw8PYW+5W2xqu6QVE0SjPSQhw7dgyjRo0Sbf4TJ07EoEGDqvoQzR4SV91dHXK7F6mmq6qd60l4TZkyRUSnyD6C6vauX7+Oc+fOidqoDz/8UOwXGhqK5cuXCyE1bNgwIaRof/ohSGk+imyRsSp9PvTCizoQSRCRMKLUNhmu6g1Uz549i9atW+cKL4p4kXEq1ZnRvtSJSOuo1ILEHNlPUFSLBB9FvEh8NWjQANeuXROPRdvJ8qS8HbwMYyxrTt4XRfUd67ohpJKK6smiIW23Ybsgx76BcO4fVKbHpDmMqphMSB0s4TqifrUO5BgtvP744w/xh9OvvU8//dS0R8WUChJTZBkhuheL5Ip0H0raXlNFV3F06tQJ48ePF6NYKB3Fwqt05BdZVEhf1enFTz75RKQPKfpEzvAknkgcUXnDG2+8UWAMD6ULyUJk7ty5wl6ComIkzqj+ikQW2UYUtr2hH44krmgCB4kweh7qjCTfMBoZlJ/ff/9dPCc9NokoinxR6pGEF0H3ocfQjwyiubUUKdNbYRQeGcQwlYVao8VfJ3UCaEwlFtXrLRw0SnWuf5bH5BaQWurORWW1cMi+mYT0w7rOYNeRDSBzMK8uxUoTXtTGrRdeTNUhrCK+my+6G1WxunAyQZEuc/XxMgVkvkonOUr15B+zQp2ONPYlfzqKqX5QBKlw2rAkKPJFUbKyQBEyWh4GNXIsXbq0xH0oXUmirDTQrElTmMwyzMM4cCMOkSnZcBFF9ZU3OFpv4aBR5Bl5W/k5GPTaKglNphKJf98Q1+07+cK2UcEu9upIuQxUqZuRQulM1ULiiiwj9N2LVNNVXdOLFOXQezMVBxXRU3cj1e5QxIE6TNq1aydEFtXnkH8Xdb/RuocVTjMMw9RkVuYU1T/RJgA2OdGm6oRWq0XSP7egSVXAwtMWzoML1nzWOuFFPklUR0Ptk+y+XPXkF1lUSF8dRRdBKSNaDEF1WyS8yIqCXOsp6kV1PCdOnBA/AqgYm4qnqciZ62oeToxciRiFEllqTe66S2lZsJXpvLi8rSzhbW1pkveWYZjKIzolO7eo/ukO1dNqJ/NsLLIuxgNSSYnu9NUNCQ1sNOaOVMdA9TTUraNvma5N6D07SHTSLLaqRpOZmesDRt2RUhN6cTE1l6/DozD3jmHjv9eDvfFmXfagYpjqxve7b2LezhvoEOyGNZM7V8kxUKox8kPdFBG/j7uUSTipErMR890ZaOVqOD0SJBzqa4omMDriRcNlyQeHuoWogJnm5tEYIU49Mkz1YYKfBwZ4GJ63ShEvhmGqY1F9RKUX1ZvSnT5xzXUhuqyCnODYs3pG7EwuvPL72dAoD1oeBhXjGzIiZIxDGRsLVVwctNnZueuyr16FxMZGXLfw9ISllxe/vEyxUBqRU4kMU7M4cDNODMV2trXEwGaVV1RvKtIO3IfiTiokVjK4jW5Qqe70Zi28uCvHPEj+aw3iC82Oujt2XO51j6lT4fnKy1VwZAzDMExVsOp49S2qVzxIR+pO3agtl2H1YOFePd3pK0R46Vv2marF5cnRcOjT2+B2ingxDMMwtYOY1GzsrqZF9VqlGol/XRPu9LZN3WHXtvq601eI8OrZs6dpj4QxCkojciqRYRiGIf4+FSFqvNoHuyLU2/DsZHMkZesdqGKzIHW0hMuI0GrtTl8Sup5xhmEYhmGqNRqNFqtO6Irqn66kuYymIvtGEtKP6Nzp3UY1hMy+5jb2GB3xYnTExMSIeXLFQeaetDAMwzBMRXPwVrwoqneyscDg5tXHBkadQe7018V1+86+sGngCnNkwYIFYjGkBUwqvGg8C2FpaQlfX98C68pKYGD1UuEPgwaDk4EnwzAMw5hDUf2IalRUr9Vqkbz+JjRpSlh42cJ5kPm605cUTNH7eJlMeJEbOEGO4ZcvXy6wriywnQTDMAzDmJ7Y1GzsuhpT7dKMmadjkXU5IcedvlGNcacvt/DSW0fkt5Awxk6CLSgYhmEYxvT8ffo+VBot2ga5oqFP9SiqVyVkIXlTmLhO7vRW/g6oDZRKeIWHh+emGguvYxiGYRimaovqV5+8V62iXVo1udPfgFahhlWwExx7BKC2UCrhFRQUVKp1TOWTnpSIjKREg9vtXd3g4OpWqcfEMAzDVB6HbsUjIjELjjYWGFJNiurT9kdAcTcVEmtyp28IibRmWkcUB3c1VnMu7NqKo2tXGdzeeeTT6DJqbKUeE8MwDFN5rDqRU1Tf2h+21aBGSnE/Dam7dMfsMiwEFm66EXe1BRZe1ZwW/QYhpG1HqBQKrJ71llj31EdfwcLKKjfiZW5MnDgRS5YsEdMP6Prdu7rxEKWFawUZhmF0xKZlY+eVnKL6ajAQW6Mgd/rrgEYL2+YesGtT+2YJs/DKISoqCl999RX+++8/REREwNbWVnRu9u3bV6w3VyiNSIsy35Bsr+B6sMwZkm3ujBw5EvHx8QXWnTt3DufPn0dISAi6detWZcfGMAxj7qzNKapvE+iCRj5OMHdStoRDFZcFqZMVXIfXr7Hu9CXBwgvA4cOHMWTIEKSkpAgz1GHDhiEtLU34c82bN8+shVd155tvvimybvbs2UJ4kej6888/q+S4GIZhqkVRfTVyqs+6loiMY1HiutuoBpDa1Vx3+pKo9cKLDM9IdCkUCqxfvx7Dhw8v8AKdOHGiyt4chmEYhjHEkbAE3EvMFEX1j7bwM7sXSqvJs53KupqQax3h0NUPNqHm6U5fGdT6WY0zZ84UkS6KahUWXUSHDh1QHdBo1LnX71+9VOB2VbFu3Trx+lHalhz+J0yYgMhI3SwuhmEYxjRF9cPNsKg+61I8Yuadzr2dtOo6tBkqSJ2t4DwwGLWZWh3xSkpKwtq1a+Hs7Iznn38e1ZWbx49gz+Jfcm+v/2I2HNw80Gfi/xDasUuVHNOPP/6IV155BTKZDD179oSHhwd27dqFTp06oWXLllVyTAzDMDWFuDQ5tl+OFtefah9odqIrYfnVYrdpUhTIvp4E22YeqK1Y1PbaLrlcjn79+sHCwgJr1qwR61QqFRo3bozRo0fDy8vL7EXXpnmfF1mfnhgv1g977d1KF1937tzBG2+8AWtra2zbtg29evUS6zMzM/H4449j8+bNlXo8DMMwNY11Z3RF9a3quKCJn5NZpReT/9WlFA2R/O9t2DRxrxTvLmVsLFRxcQa3W3h6wrKSz/O1Wnjp505SGowKuY8fP15g+zvvvCNsD0aMGFHhx0IWCSq5vEz3oXRi/khXcez58xcENm8JqbR0YWgLa+tyd5ksWrRICNoXXnghV3QRdnZ2+OGHH4SoZUsIhmGY8hTV69KMY8ysqF4engJ1iqLEfdQpcrGfTYhLhR9P8l9rEL9ggcHtHlOnwvOVl1EthRedSBMSEkRUIzDQvD4IJaUaiaVLl8LKygq//fabiMhQR+P333+P+fPnY8yYMTh58iSaN29u8O9OTU01+hgoKkQLia7vnxkJU5OemIAfJz1Z6v2nLVlbbiuKQ4cOiUuKGBamYcOGaN26Nc6cOVOu52AYhqmtHLudgDsJmXCwtsCjLc3LqV6TpjDpfuXF5cnRcOjTG9rsbNwdO06sC1qxHJKc8xxFvAgKFtBiLGUJJpRbeNEJ9NNPPxX1OxkZGSJaQqm6/OLm7bffFutJzJDAMRfUal0BOh0vRWL0dV5Uj/Ttt9/i3r17otORCu+XLVtW7GNQsTjViBnLrFmzhH1CTUJfQG9IgNN6Fl4MwzDGsTIn2vV4az/YWZlX4krqaGXS/coLpRFpUaQmI9wLkFsCGffPotXgCbCwzDuGOXPm4KOPPkJlUK53jMQIiRWlUmlwH1dXVzFQe/fu3Rg6dKiwbjAXHB11E9ylUqlwUC/Mc889J4TXvn37DD6Gn58frl4tvoiwNFC0S5/io2hTWaDuRSqkfxgj3p6NgMbNSvWYdBzlRa/8a6MxHsMwTEWSkJ5XVG+O3l3WdZ2FOaom1XBES+ZsLfarLA6t+AbS7xajrj45NXMuTnz2LTTTJ6Hb2DdyS4tee+01o5+DSmhK27VvtPAisUE1PCS6pk2bJqwCBg4cKNKNhaFtFBHbuHGjWQmv4GBdS6uPjw9sikmvkXM9EROjG8dQHCQunJzKX9hIj1PWFF9Qy9aie5EK6Q3h6O4h9ittjZcpIDF648YNMQooNDS0yHaKJDIMwzDGOdUr1Vq0DHBGU7/KEy+lhQrmres5I+uc4YJ2l6H1Km0o9o4/P0bAF0XnGTunaiD55A/sUGbikYkf5pb9GEtZAg1G+3iRozuZjk6dOlXUQrVp00ZYBxRHnz59xOXRo0dhTlCtkT4dWlx+Vi8iHRwcYI6QmCLLiJLo/cz/KlV0EfoxP2TVURgSZDQSiGEYhikbdJ7Se3eZY7SLUEZnIOuiLhggsZUViXS5j2tcaVYSKqUC9j+t1h1LMeKHzvp2P60W+1UmRguvPXv2CIVHBqSliYBQR5u5RTqoYJ6iWllZWcU61OtTjHqBZo6QVQRZRji4uheJdFWFlQTx7LPPil8O1BF68ODB3PX0Ok+fPh0ajabSj4lhGKa6czRfUf3QlmboVK/WIHHNdUCthU1jN3i/1T53m/ukpvCZ2b5S/bsu7FoNt1RtEdGVXwC5p2rFftVCeFEu097eHgEBAaXan9zL6cRrbuiFI5l95h/WfPr0acydO1dcnzx5MswZElcT5/1UoKbr+R//qDLzVBKzX3/9NbKzs9G7d2/hk/bUU0+hfv36uHTpEh599NEqOS6GYZjqzKqcuYyPtfKDvbV5FdUTaXsjoIzMgNTOAq4jQiGV5UkMqumqrPSinrSoeybdr8qFF0U0KNVYmhZKElzJycnl6v6rKKhObdSoUcIygqwOqAGAxEKXLl3EMeu3mzv504lUSF/Z6cXCkJClVCNFC8legporyNPr2LFjcHcvGJ1jGIZhSlFUf8l8i+oVkelI3aMThi7DQiCrpK7FknD0DTTpflUuvKgwnQrrb968+dB9t2zZIqwbmjRpAnODOhpXr16NBQsWICgoSKRQSYS1a9dO+Hv9+uuvVX2I1ZYnnnhCvJYU+YqLi8OKFSvg7++PP//8Uwj2/Oaq+SF7DdpO+zEMwzDA+jMPoFBr0CLAGc38zSuIoVVpkLTmBqDRwrapO2xb6ryxqpoW/Z5CVgn6j4peEpwkYr9qIbyog5FOjt99912J+1GB+ltvvSXqwcypo7Gw+HrppZeEtxR5kaWnp4vRQePHj6/qQ2MYhmFqOeZeVJ+6554oqpfaW8BleH2zsRI6s20pbHPq5rXFiC46ysyXnirg52XWwmvGjBmi22/hwoXCdIzc3gunF1euXCkiR+TjReklc6+VYhiGYRhz43h4Im7HZ8DeSmZ2RfWK+2lI25eTYnysPmQOVZ9iJGKjb0P1ybfi+p1GLkh2Kih3UpxlSPzgOWElUdkYXZ1H8w1JWI0cORIff/yxcH3Vd6s1bdoUt2/fzq0Bo3qwVatWmcTviilIelIiMpISoVLktcPG3rkNi5wJAfaubnBwdeOXjWEYppqij3YNa+UvOhrNKcWY+DelGAHbFh6wa2EeKUa1Ro2jr05Ag1QN4jys0HPpFlhAgl2PdhbO9XWmvY4OhZzrK5NyvYPUnXbgwAG8/PLLOHXqVO76/E7uVFxNUbH27fPaShnTcWHXVhxdW9AcbvWst3Kvdx75NLqMGssvOcMwTDUkKUOBrRejzXIgduque1DFZELqYCmiXebC5gVvoMG5BKikgNf770ISEQlVdjbqxuq2BwW0hvLGTShzZjXSSKHKpNzSuUOHDsID68KFC6J7jWwmqJCe3OC7du0qUo1MxdGi3yCEtO1ocDtFvBiGYZjqyboz90VRfTN/JzQPMJ+iekVEGtL261KMro/Xh8zeEubAuXPbUee3beJ60thH0PhmHO68OrLAPvph2YTH1KnwfOXlSj1Gk8UsW7RoIRamcqE0IqcSGYZhah7mWlSvVap1RqlawK6VZ6WaopZEckYCHrz1FuopgOj6bug5cy7UiYlw6NPb4H0o4lXZmE+ymGEYhmGYXE6EJyIsLgN2VjIMM6Oi+pSdd6GKy4LU0VJ4dpmLSN00eyLa31Mgy1qC1j8shtTCAlIvr0pPJT4MFl7lhAZoG/InozmWtDAMwzCM0UX1Lf3gaGMeqTz53VSkH3wgrrsOD4XUzjyOa9O/89Bm8y1x3fqtV+BSt4HJn4P8PmkxpAUqXHjpB1+XFhsbG7i4uIiOR/IAa9u2LWoC1N155cqVqj4MhmEYpoYV1W8xM6d6jUKNJOpipBRjGy/YNjGPKSTX7p+D05w/INMCCV0bo+uYirGuKimYQuMTHzzQCdIKE176AdKE3iyt8Pig4tbTug8//BB9+/YVQ5R9fX2NPQSGYRiGqZGsP/sACpUGTf2chFu9OZC6/Q5U8VmQOlnBZah5pBgzlZk49s6L6JikRZqLNTrNXWQ2Bq4mF16zZs0SI4N+/vlnJCUlITAwED179hQjYUhoUXcjWU3cvXsXbm5uwjyVHOHJduLIkSNidt+AAQNERyRFwxiGYRiGKVpUbw5CQn47BelHIsV11ydCIbU1j0qlpQunoufJVGgkQODX38DCxQXmjtGv3HvvvYd+/fqJOXw0U2/ChAnF7rds2TIhukhgbdu2TYzn2b9/Px5//HFcvnwZv/32mxiozDAMwzAMcOpuEm7FpsPWUobHWvmZRYoxcW1OirGdN2wbmodN0ZaTK9B60TFxXfXkYPh074fqgNEjg7799lscPHgQ33//vUHRRdC8Q9qHIlz6uY4UGfviiy+Eql+3bp2xh8AwDMMwNY5Vx82rqD5lazjUidmQOVvD5dF6MAfupNxB+uw5cMoC0oI90PzdOaguGC28VqxYAQsLi1INkh43bpzYd+nSpbnrnnrqKRE+pagXwzAMwzBAcqYCmy9GiZfi6Y5VX1SfHZaMjKO643EdGQqpTdWnGBVqBdZ9/iyah6mhtJSg6fe/Q5ozJq9GC6+wsDAxJNuqFH8szWqkfW/d0rV6Es7OzqLLMTU11dhDYBiGYZgaxT85RfWNfZ3QsoqL6jVyFZIoxUhTUDr6wCbUFebA7xs/RL/NOjHoNOMV2DdoiOqE0cKLIljJycmIitL98SVB+9C+dJ/8ZGZmCgHGMAzDMLWd/EX1YzrUqfKi+pQt4VAnySFzsYbz4LowB/be2oG68zfCSg3I2zdF0KSKsY4wS+Gl9+GaOXPmQ/d9++23xQeqTZs2BczG5HK58MFiGIZhmNrOmXtJuBGTDhtLKR5r7V+lx5J9MwkZx3U+Yq4jG0BqXfUpxuiMaJz/fCaCYwG5ozWazfu5ysVppQov6kQkMUW1XoMGDRKF9iqVKnc7XSc7icGDB2P58uXixZk2bVrudupw1A/ZZhiGYZjazsrjuqHTQ1v4wakKi+o12ZRivCmu23f2hU39slk0qFMVUDxIhyIyPXcdXRfrHqSL7WVFpVHhpz+moP+RbHE76PMvq2TOoikwWsI+9thjeO211zBv3jzs2LFDLJaWlnB3dxciKz4+Xvh8ESTQZsyYIe6j5+TJk2jZsiWGDx9umr+EYRiGYaopKZlKbL4QaRZF9Sn/hUOdIofMzQbOA8ueYkw/HoW03bqUqZ74hRdyrzv2DYRz/6AyPeYfR75D/2XXRLTI4vFBcO0/ANWVcsUOv/nmG7Rr106Yqd68eRMKhaJIzVf9+vXx0Ucf4emnny6w/scffyzPUzMMwzBMjeGfs/chV2nQyMcRretUnQlo9vVEZJyMBiSA2yhKMcrK/BgOHX1LHCckcyxbB+KJyOOwmPsHPNIAhZ8HGn74Kaoz5U7aki0ELefOncOZM2cQFxcnIlxeXl6ipqtVq1amOVKGYRiGqbFF9RFV7lSvyVIhaZ0uxejQxQ/WdY1rfstSpyNDnmhwu72dGxxQOhPWxOxErP/xVTxzVQuNVIIG8xdAamdX6mNJT0pERlIJx+LqBgfXyjWENVm1HAksFlkMwzAMUzbO3EvG9Zg0UVT/eBUW1Sf/Gybqryw8bOE0INjox7mwayuOrl1lcHvnkU+jy6ixD30cjVaDrza8jlGbk8Vt15cmw7ZFiyo5FlNS9W0KDMMwDFOL0VtIPNrCD862VVNUn3UlAZlnYkWK0ZVSjFZlTzHqadFvEELadoRKocDqWW+JdU999BUscnw/7UsZYVp2cQk6/n4MdlSL36IxfKdMrbJjMSUsvMoJ2WI0adKk2G1Tp04VC8MwDMMUR0pWvqL6DlVTVK/JVCLpn5wUY3d/WAc5levxKHVHizJb14FIeAXXg6WNTakf41L8JYQvmIcn7wNqWys0mPc9JDKZ0cciz8zIXafIyoRvg4aQSsv2eAsWLBCLIS1QacIrOjoaixYtwqFDh3D//n1kZGSIfHVxUN6aHO9rEuRDduXKlao+DIZhGKYasvHcA2QrNWjo7Yg2gVVTVJ+8KQyaNCUsPG3L3G1YEaQp0vD9iml45aDOoirgw49gFRBg9OPdPH4Eexb/knt7/Rez4eDmgT4T/4fQjl1K/TglBVMCAgLw4MGDihde//zzD5555pmHii39tupodMYwDMMwFQGdG1fmDMR+uoqc6rMuxSPzXFxuilFiaXyKsTAajTr3+v2rlxDUsvVDo0z0mny+70M8uToKFhrA5pG+cH48z4rKGNG1ad7nRdanJ8aL9cNee7dM4ssUGC28KMozZswY4T4/ZMgQsbz00ktiBNDcuXNFJGzXrl3Yt28fPDw8MHv2bNjb25v26BmGYRimmnIuIhnXotNgbSHF8NbGR3SMRZ2hRNIG3Qxlx54BsA4sX4pRj1wei2tH9+DIqk0Fokz2rs7o/NRQNOrcB9bWXsXe959b/8B30Xb4JQFaTzcEfvyp0YKUhN+eP38tcZ+9S35FSPuOZU47Vonw+vbbb4XoGjduHJYuXSrWkfCytbXFs88+K26/++672Lp1K0aNGoUlS5aIdCTDMAzDMHlF9UNa+MLZrvKL6pM33oImXQkLbzs49TNdivHE9vk4seJ8zq080ZSRlIxdPy9DauoFdB9WNAoVlhyGbcs+wYxzuixZ0FffQObikhsJU8nlUGRnidoxcSmXQ5n/du76bHE9KeqBiGyVRFpCPB5cvYw6TcvWLVklwosiWaRC33nnnRL3o3FCFAGbMmUK5s+fjzfffNPYp2QYhmGYGkFqthL/ntcZjo+pgqL6zAtxyLoQLwYHklGqxMLoCYJFokxXtpInWXFRKt268xtuwVayHkqFPEcsZUOelYFjt/ah4y1XHK0vATw8cGTVIigWL9AJLHk2qS9UBOnJSahMjBZeVERmYWGBxo0b564jIUZRsMKMHz8eL7/8MlavXs3Ci2EYhqn1bDwXiSylGqFeDmgb5Fqpr4c6XSGiXYRjrzqwCnA02WNT9Cg9sWQhI8/IxP7li4qsp9hWsn2OOWpWhm4pBktrG9EhaWVjKy4tbWxhRZc56/W3M1NTcHnfroces4OLa/UQXlZWVkXyrg4ODkhJSREDskmU6bGzs4Ojo2ON62hkGIZhmPIV1VeuUz09d/KGW9BkqGDpYw+nPoEVFz2SaOHgkwkLOxVUmRZIj7YDtLq/1a9BY7jXCRQC6V52JMKOb0fna2pIJRL4v/EG7IPrCgGVX2DRJflvSaTSUkff7l44V2K60dHdA/6Nm6JaCC8/Pz9cv34dWVlZoq6LCA4OxqVLl3D+/Hm0bds2d9+kpCQkJyfDpgweHgzDMAxTEzl/PwVXo1JhZSHFiDaV61SfRSnGSwmAVKLrYjRRirFw9Mi5bir8u8TAykFnCUEo0i3w4Ig3UsKd0O2p8aKu6n7affywaAQ+PJ4GKxXg/e67cHt8pEmOhQrmyTKiuK5GPb2f+V+lFtaL4zL2jvoUIw3H1tO1a1ehpml4dn7ef/99cdmwYUPjj5RhGIZhagCrcqJdQ5r7wsWubAOjy4M6jVKMusyTU586sPJ3MP1zqFRCdAX3fwBL+zzRRdDt4P4P4NMcIsqk1Cjx9p438Nx6neiy7doFruNMO76HrCLIMsLezRUOvhlwCUkRl47u7lViJVGuiBfZR5CP17p169AiZ3bS5MmT8euvv2LNmjW4ePEiWrZsKS4vX74sQqn6bkeGYRiGqY2kZSux6XzlO9VTUCTpn1vQZKpg6WcPx951TP4ct04ew+bv5qDhaJ2Le+EMKt3WagG/Lg+QmXkLi66uQfON51E3BpA4O8F/zpxSpxHLgnPdNDQdcwsKZWzuOmtrhVhfFRgtvIYOHSrMU/PXcpEAo87FGTNmCJ+v/I7uTz/9NF555ZXyHzHDMAzDVPOi+vpeDmgfXHlF3WSSmn0lAZBJ4Da6ISQy0wqcqwf3YutP38LeO61AerEwEgmgQSqOnxgMUVk1DogcDVhaKpEcPgEWEU6wtHCGhaUTLCycc69bWuhuW1jSOqecS2dIpTYl1sjFxm7HxUvkNl+wI1IujxHrmzdbAC+vAagWwsvT0xOLFy8usp66F/v164e1a9ciIiJCGKoOHDgQffr0Ke+xMgzDMEy1paqK6tWp8rwUY79AUVRvSs7v3IJdf/wswlnBbamkSPc3loRCA4gkK+k/K0CJFCgzUsr83BKJFSwsHGFp6awTavkEm8zCAfcjlhURXTp0665f/wienv0gkVQDA9WSaNSoUW5dF8MwDMMwwMUHKbiiL6pv7V95KcZ1N6HNVsEywAGOPUybYjyxcS0OrvxTXG81YAhaD26Fs+d3PvR+CRss0WY3IAvxR8CSn6C2UkKlJFeENChVKeK6UpUKlSoFSrE+Nd863XqtVg2tVgGlMkEsxqBQxiA5+SRcXTvB7IUXRbBIrVNNV0hIiGmPimEYhmFqqFP94GY+cLWvnKL6zNMxyL6epEsxUhejTGIyQXf4r2U4/s8acbvj8NHo+uR43L3320PuB2RlQ4guiVSGwM/mwda9oVHPr1ZnCBGmE2Y6caZU5og1VQpSU84jMelQqUYcVSZGCy8a/2Npacmii2EYhmEeQrpcJeq7KrOoXpUsR/K/t8V150eCYOltmhSjVqMRMxDPbd8sbncfMxEdHhuJ8DsLcPv2vJydcnbOp/P0xvMeqywg0UrgQWMGW7Y06hgo8GNh4SAWGxu/YvdJSjpWKuFlaG6k2Qkvb29vpKeno7YTExODJk2aFLtt6tSpYmEYhmFqN5vORSJToUY9T3t0qOtWSSnGG9DK1bAKdIRDd9MM4dao1di+8DtcObBHVMr3fXYKWvYfhLDb83DnzgKxjyy5G26dCC/i45WiksBykwz+p6SwbdUKHi++iIrExaU9rK19RCF98XVeErGd9isNCxYsEIshLVDhwqtHjx5iBBD5eIWGhqK2QgI0f/cmwzAMwxhKM46ppKL6jJPRkN9MBiykOqNUafmfU6VUYsv3X+PmiSPC9mHQSzPQqFsv3Ar7EvdyUoz1Q96Cu9MoxAf8h69u/QKXzEQ4ybRIVUvQbB8wZp8WsLWF39dfQZLPFaEioIL5BqEf5nQ1SgqJL93r0SD0g1IX1pcUTAkICBCjFEuD0f2kb7zxhrCSeP3114WyZhiGYRimKBfvp4jCeisZOdWbJvJUEqqkbKRsDhfXnQcEwdIzZ/5hOaBh1hu++liILpmFhTAfJdF14+YnuaKLRExQ0Is4mLAXH139CgnyZFjdkMD2pBTBRyUYvV+nFeJfHASrOqb3ESsOsoogywhrq4LpRIp0VYWVBGG03GzdujVWrVqFiRMnCsf6N998E126dIGXl1elzp1iGIZhGHNm1UldtGtgMx+4VXBRvVajRdLaG9Aq1LAKcoJD1/J3T8ozM7D+i48Qef0KLKyt8fgbHyCweQtcv/4BHkSuEvs0bPgJAvzHQK1R48uTX6PDdQ0m7tTAo5BH6XU/4DvnXdil+RiyShrVQ+LKza0L9h9oJW63bLkI7m7dKtVCwiTCSybLO+Djx49j5MiHz1YiQUYDtBmGYRimNpBBRfVnH1RaUX3GiSjIw1IgsTRNijEzNQXrPvsQsXfCYG1vjxFvz4ZvaANcvfo2oqLXiZRd48ZfwM9XpwHOxJ5ByMU0vL5eU+SxKN7VIBKodzEdZ3qfQXuf0tVWmYL8IsvVpX2Via5yCS9OLzIMwzBMydB4oAyFGnU97NGpXsUW1asSspCyRZdidBoYDEsP23I9XlpCPNZ++j4SI+/DztkFT7z7MTwCA3H5yuuIiflXiJcmjb+Bj8+w3PvEpcWISBdRWPLRbQ0gtsdNigF8UOGQVYRcEQuNOjvv70q7AqnMRlynFGS16Wrcu3evaY+EYRiGYWpoUf3THepUaBkOpRgT196EVqGBVV0nOHQu3mKhtCRHR+HvT99HalwMHNw9MOr9T+Hi44VLl6cjLm4bJBILNGv6Hby8Bha4n8+tRNimlVxY7pEG2N9KBCqhL+/Bg1UIv/N9gXWnzzyZe71u8DTUqzcd1UJ49ezZEzWNxMRE4bofFxeHhg0b4tq1a1V9SAzDMEw15dKDFFy4ryuqf6KCi+ozjkZCEZ4CiZUUbiPLl2KMj7iLtZ99gIykRLj4+GLU+5/B3s0ZFy9ORXzCHjGmp3nzH+Hp0bfIfRPu30Jp/tK6qsqZU+nv/zQ8PIsep57CRfeVQcX2clYzXnvtNcTHx1f1YTAMwzA1KNo1oJkP3B2sK+x5VPFZSNl2R1x3HlwXFu7Gpxijb93AujmzkJ2eBo/AYIx87xPYONrgwsUXkZh4EFKpNVo0/xnu7j2LlB/9euFX7I1ch1mleB4rL29UBpRGrOxU4sMw2XhyetFJtNy79/DhmObI7t27sWTJErzwwgtVfSgMwzBMTSiqz3Wqr1OxKca/b0Cr1MC6vgvsO/ga/Vj3r1zC35++J0SXT/0GGD1rDqwdrHD+/PM5ossWLVv8XkR0USfjZ8c/wy8nf8CA05qSjxeAhY8P7Nq1RW2l3MLrzJkzGDFiBJydnYWZaL169QpsT0pKwosvvojJkydDoVDAHMnKyhLHRw705E/GMAzDMOVh84VIMSYo2N0Oneu5V9iLmX44Eoq7qZBYyeD6RKjRKcbbZ09i3ecfQpGVhTpNW4iaLksb4Nz5SUhKPgaZzAGtW/0pbBnyk63Kxuv7X8fWM6sxe6UGnW4I24Pin0QiEXVu3u++A4mhfWoB5Uo1Llu2DM8//zyUSqXBfVxdXREeHi4iSkOHDsWQIUNgbnz00UcICwvDvn37xPxJhmEYhikPK09E5FpIVFRRvTIuEynbc1KMj9aFhauuU6+sXD96EFt++EaMA6rXpj0enfE2IMnG2XOTkJp6HhYWjmjV8k84O+t8sPSkyFPwyp5XEHvlDD5fo4FXihZSZ2fU+fEHqJKSEP3pp1DHxuXub+HtLUSX0yOPoDZjdMTr6tWrIi1HomvatGk4deoUPDw8it13woQJIhW5ceNGmBsXLlzA3LlzMWnSJDEGiWEYhmHKw+XIFJyPSIalTIIn2gZUnFHq3zcAlQbWoS6wb2+cN8PFvTvw33dfC9HVsEsPDHv9PUCSibNnx+eILhe0br28iOiKSo/ChK0ToDhxGp8t1Ykuy8BABK9ahYymwbjfNgDpiz/P3V/+5ZtQrvlerI/LzBNjtRGjI17z5s0TqUOaWzR//vwipqr56dOnj7g8evQozAmNRiPEo4uLC7766quqPhyGYRimBrA6J9r1SFMfeFRQUX36wftQ3EuDxJpSjA2Miqqd/m8j9i3Vjftp3ncA+j3/EpSqJJw7OwHpGddhaemGNq2Xw8GhYYH73Ui6gSm7pqDJsWi8uFUDmQawbd0aAT8tgIWrKzbu/RqbTiyBpVKLT3Pu88nFuVBe0x3jsA7P4Pneb6K2YrTw2rOHWkolmDlz5kP39fPzg52dndkV3v/www84ceIEFi9eDHd343LwFMlLTU01+hisra3FwjAMw1R/MhUqbMhxqqeB2BWBMiYDKTvuiusuQ+vBwsW6zOetY+tW48jfK8TtdkNHoMfYSVAoYnHm7HhkZobBysoLbVovg719/QL3PRl9EtN3T8Pg3Sl44ohu9qLT4EHwnTMH0pxzWb+zGnRdrC5wv0+X5xXd21pqgN4wK+RyuVgqw1TeaOEVGRkJe3t7MZG7NNja2iIlJQXmQkREBN5//33hR0bzJsvzOlBjgbHMmjULs2fPNvr+DMMwjPmw+UIU0uQqBFVQUb1WretihFoLm4ausGvrXWaBsH/ZHzj93wZxu+voceg44knI5VE4c3YssrLuwdraV0S67OyCC9x3+53t+HDv23j+Xzm6XdEJDffJL8Jz2jRIpHmVS/7jJkE14FGDx2Dh6QlzY86cOaLeuzIwWnhRlCY7O1u8iQ8LcVLXYHJyskjpmQsvvfSSSJX+/PPP5XociuZRvZuxcLSLYRim5nl3PdU+ENJyzkksjrQDEVDeT4fExgKuI0LLlGLUaNTY9dsCXNyzQ9zu/cwLaDP4MWRlReDM2XHIzr4PG5sAtGm9Ara2BYMqK66uwE/7vsDb61RodJ/UgwV8P/oILk+MKPI8ll5eYjEHYuRKxCgMNwB6W1nC29oS77zzjvDyNJbGjRuLQEyFCq/g4GBcvHgRN2/eRIMGDUrcd8uWLVCr1cKuwVzYvHmzEIJTpkwpsJ7EJEFp0V69euXu6+DgUOzj0IfeycmpEo6YYRiGMWeuRqXi7L1kWEglGFkBRfXK6Ayk7tIJO5dh9SBzLn2KUa1SYeuPc0UHo0QixSMvvoJmvfsjMzNciC65PBq2tkEi0mVjkzduiIIr88/Mx9b9f+CTv9XwTQKkjo4I+P472HfuDHNnaWQ85t6JMbj99WBvvFnXt9xlP2URwEYLr4EDB4qOwO+++w4LFiwwuF9CQgLeeustcVDmZiVBUbj9+/cbjNLpt6lUqko+MoZhGKa6RrseaeoNT0fT1u5q1RokrrmuSzE2doNd69JHlJQKOTZ/+wVunzkJqcwCQ6a9gQaduiE946boXlQo4mBnV1/UdOV3eVdqlJh1eBZu7t+ET9eq4ZgNWPr7o84vC2Fdv2Dtl7kywc8DAzyckaXW4LGzt8S6ja3rw1YmzY14VRs7iRkzZogo0MKFC0VeNC0trYhwWblyJdq1ayd8vKh4nUxKzQVS8cUtdKwEzWrUrzOnFCnDMAxjfmQp1PjnzINc7y5Tk7Y3AsrIDEjtypZiVGRlYv2cWUJ0WVhZ4/G3PhCiKy3tKs6cGSNEl4N9Q7Rts6KA6MpQZuDl3S8jadNGfLBKJ7psWrRA8F+rq43oIiiN2MLRDs0c88Yo0XVaRwttrzbCi1zqSViR4ejHH38MT09PEd0imjZtCjc3N4wfPx53794V4btVq1ZxSo5hGIapsU71VFQf6GaHriHFe1oaiyIyHal7dBYVLsNCIHO0KtX9stJS8fcn74lRQFa2tnji3Y9Qt1VbpKZeEIX0SmUiHB2bok2bFbCyyjvm+Kx4PLttEnzXHMK0fzWwVAOOjzyCoCV/wsKAXydTSSODHn30URw4cABt27YVheqUkqMIERWbU1smXW/durXYp29fw9PBGYZhGKZGFNV3qGPSonqtSoOkNTcAjRa2Td1h27J0HYHpSYlY89E7iA67CRtHJ4z64HMENG6GlJSzwjJCpUqBk1MrtG61HJaWrrn3u5d6DxM3jUPvJZfw5EGdBYTbc8/Cf/63kNoaP3ybMdHIIKJDhw7CC4vqvQ4dOiSq+qmQ3sfHB127dhWpRoZhGIapqVyLTsWZCiqqT91zTxTVS+0t4DK8fqlSjCmxMVj76ftIjomCvaubmLvoHhCIpOSTOH/+OajVGXBxbo+WLX+HhUVe49il+Et4c/MUPLsyAc3uaQGZFD4ffAjXp5406d9U2ym38NLTokULsVR3qFuzLEZoDMMwTO1G71Tfv4k3vByNm5dYHIr7aUjbl5NifKw+ZA4PTzEmRt7H35++j/SEeDh7eWPk+5/BxdsHiYmHcf7Ci9BosuDq2hktW/wKmcwu934H7x/EFxtm4PWVGfBPBCT2dgiY/x0cuncz2d/DlFN43b9/v9TmqQzDMAxTU4vq15+5b/KiekoxCqNUGsfTwgN2LR6eYoy9cxtrP/sAWakpcPOvg5HvfwJHNw/EJ+zDxYtToNEo4O7WA82b/wyZLE8gbri1ASvWfIgP/1bCKQuQeXsj8NdfYNOw4KggpoprvCgy1K9fPyxduhQZGRkmOhyGYRiGqT5suRiF1GwVAlxt0a2+6QrPya9LFZMJqYOliHY9jAfXr4qaLhJdXnVD8OTsL4ToiovbiQsXdKLLw6MfWrRYmCu6KLvz24XfsOX39/D+Cp3osm7SGHXXrGHRZY7CiwZM7927F5MmTRIdjtTBuGPHDk7TMQzDMLWuqJ6iXaYqqldEpCFtvy7F6Pp4fcjsS7Y8uHPhLNZ+9j7kmRnwb9QEoz/8HHZOzoiJ3YKLl16GVquAl+cgNG/2I6RSnb+YWqPGZ8c+xd2f5mPGBg2s1IB9714IXr4clt7m4TpfUzFaeO3atQsTJkwQXl6ZmZnCWmLQoEEi/UiDs8nVnmEYhmFqKjdi0nDqbhJkUglGmaioXqvMMUrVAnatPGHbrOQo2s2TR7Hhy4+gkssR3LINnnj3Y1jb2SMqegMuXZoOrVYFb+9haNp0PqRSnYCTq+V4c89rcJm/CmP36ToXXSeMR50ff4TULq/uizEz4dWnTx8sXrwYMTExQnQNGDAAMpkMUVFR+Oabb9CqVSthJTF//nyxD8MwDMPUxGhXv8Ze8HIyTVF9ys67UMVlQepoKTy7SuLKwb34d94cMQ4otGMXPPbmB7C0tkFk5N+4cuUNyk3B13ckmjb5BlKprqQ7RZ6CVzY+i47f7ETf81popRJ4v/cefN59FxKZzCR/A1OBPl6EjY0NnnrqKTGPkQru582bJwQX5Y7Pnz+P119/HXXq1MHgwYPx119/lffpGIZhGKbKyVaqse60aYvq5XdTkX5Q95iuw0MhtTOcYjy3/T8xe1Gr0aBpz354dPpMWFha4v79Fbh67W2KncHffwwaN5oDiUQnqKIzovHqqrF4Yt4ZtLijhdbGGnUWLIDb+HEmOX6mkoRXfry8vPDqq6/i1KlTuHz5skg5UuqRjFW3bduGMWPGmPLpGIZhGKZKi+r9XWzRPbR0pqYloVGokURdjJRibOMF2ybuBvc9vuFv7F70s7jeeuBQDJg8DVKZDPfuLcL1Gx+K9XUCJqJhg4/FQGziZtJNvPPrKPzvhzAExgPwcEXdFSvg2Lt3uY+dqSIfr8I0btwYc+bMwciRIzFlyhQhxmoilEZt0qRJsdumTp0qFoZhGKaGOtW3ryNqvMpL6vY7UMVnQepkBZehxacYKZN0aNUSnNi4VtzuNOJJdBk9Tpiq3rmzEGG3vxbrgwJfREjIm7lmq6eiT2HxT5MxfX0GrFWANLQe6v36Oyx9fct93LWJBQsWiKU4ylJSVSHC68GDB1ixYgWWLVuGK1eu5K6nuY41DerozP83MgzDMDWbmzFpOHknp6i+XZ1yP578dgrSj0SK665PhEJqW/TUTCnF3Yt/wfkd/4nbPcZOQvthTwgxdjv8e4SHfyfW1w2ehrp1p+WKrp13dmD/N2/gpV1KkeKy7tYZQfN/gMzBvtzHXduYWkIwhbJ7pH0qVXiRl9e6deuE2Nq3b5+wm9A7wNPYIOqAfPrpp031dAzDMAxTJazKcarv08gLPs425U4xJq7NSTG284ZtQ7ei+6jV2P7zfFFMD4kE/Z57CS37DxLn2LDbc3H3ri7tGFLvDQQHT8m938pLyxH32RyMPavrXHR8chT8P/gQEosKS3YxpaBcrz696Tt37hQmqhs2bEBWVlau2AoMDMTYsWOF4GrI7rcMwzBMTSmqz3GqH9Ox/EX1KVvDoU7MhszZGi6P1iuyXaVU4r/vvsStk8cgkUox6OXX0bhrT3GuvXnrc0RELBL7hdZ/D4GBz4rrtG3BkW/g+tliPHJbC60E8HrzTbhPmlSqWY81ibS0NLFkafJGAUZHRcM2Jz3s6OgolmohvN544w2sWrUK0dHRuW80HfwTTzwhzFR7c8EewzAMU8PYdikaKVlKUVTfo5xF9dlhycg4GiWuu44MhdSm4ClZmZ2NjXM/w90LZyGztMTQGW8jpG1HaLUaXL8xGw8erBD7NWzwEQICdJ2JSo0SX21+C22+2YbgWEBtZYHAb+bB6ZH+qI2cOnUK+/fvh1IqA7oPFesWLVoES41aXO/Zs2el6xWjhRfZRhDk3UWjg0hsDR8+HLa2tqY8PoZhGIYxG1bmFNU/Wc6ieo1chSRKMQKw7+gDm1DXAtuzM9LxzxcfIfLGVeHN9fhbHyCwWUtotWpcu/Y+IqPWAJCgUaPP4O/3pLhPpjITc5a/gIELzsAtHVC5OKD+b4tg27w5aivt2rUTWbc0hRJ/hCeJdZSJc7TS1ZxXdrSrXMKrRYsWQmxROtHHx8e0R8UwDMMwZsat2HScCE8E6a3R5SyqT9kSDnWSHDJXazgPrltgW2ZKMtZ+/iHi7tyGtb09Rrz9EfwaNIJGo8LVqzMRHbNBuEE1afwVfH2Hi/skZCVg/g/jMWJZOGyUgCrIFw3/WAarAH/UZhxzUonJWdlAjvCipjgXW9MY3laq8Dp37pxpj4RhGIZhzJjVOdGuPo28y1VUn30zCRnHdWU6riMbQGqddypOS4jH35++j6TI+7BzdsHI9z6BZ1BdaDRKXL7yGmJjtwhD1KZNvoW39xBxn4jUCCz95Gk8tTkBUi2gadccTX76HTInp3L/zYzp4dYGhmEYhslHbGo2YtPkBV4ThUqDNad03YyDmxuf5dFkU4rxprhu39kXNiEuuduSoiOx9tP3kRoXC0d3T4x8/1O4+flDo5Hj4qVpiI/fBYnEEs2bfQ9Pz0fEfS7HXMDemRMx4liWuC0bNgCNPvsakhpo31RTYOHFMAzDMPlYcfwevtutE0fFER6fYfTrlfJfONQpcsjcbOA8KC/FGH/vDtZ+9gEykpPg6usnRJeThxfU6mxcvPQSEhL2Qyq1QvNmP8HDQ1cMfvjWHtx5bTr63lCJ2/avvIg6L02vdZ2LpYEsrvyS42CnkCPini2cQkMhlZp0eE/lCS+ax0hOrocOHRKzGsnPyxD0YaDxQQzDMAxjroztGIj+TbyFdcTIhUfFuqZ+TrgcmYoxHQIxvlOQUY+bfT0RGSejqSYebqMaQGqlm6EYdes61s+Zjez0NHgGBuOJ9z6BvYsr1OpMXLgwGYlJhyGV2qBli1/h5tZV3Oe/k8shefNztInWQmUhgfecz+A1VFfvxRSETM63bN2KYWlp4va6q6ew08kJAwcONDh5xmyF148//ojXXnsNarU617+LYRiGYaozXk42YknLVuauI9FFcaSX+9QX28qKJkuFpHW6KJpDFz9Y13UW1yMuX8A/X30CZXYWfEMbikJ6GwcHqFTpOH/hBSQnn4BMZo+WLX6Hq2sHca5d9d8XqPPxUnikAtkOVghZ+Duc2rU34StQs0TXmjXUAVqQ1NRUsX706NGVLr6MjrMdP34c06dPF6LrpZdewpYtW8R6Nzc37Nq1C8uXL8fEiRNhZWUFDw8PrFy5Env27DHlsTMMwzBMhbDtUhT6zdtfYJ2VhRQX7icb9XjJm29DnaqAhYctnAYEi3W3z5wUkS4SXYHNWoj0IokupTIVZ89NzBFdDmjd6k8hutQaNf744xU0fFcnutJ9ndF43UYWXSWkF7dt24aSoO20X7UQXt9//71Q3iS+fvjhBxGyI0ho9enTB2PGjBEmZceOHRMpxg8++ABt2rQx5bEzDMMwTIWIrinLzyAmtWCBvVylEetpe1nIupqAzNMxIsXompNivHbkADZ+8ylUSgVC2nXE8JmzYWVjC6UyGWfPjUdq6llYWDijTetlcHZuA7lajj8+exKd5u6GnQJIaxqINv9sg02QTsQxRbl7966IbJUEbaf9qoXwOnz4sBBUJLzyUzjl2KpVKyHMwsLC8PXXusnpDMMwDGOOqDVafPTvFRqdaBDaTvuVBk2mEknrc1KM3f1hHeSEC7u347/vvxYzGBt17YmhM96BhZUVFIoEnDk7Fmlpl2Bp6YY2rZfDyakFUrKTseqVQei+4jJkWiC9bzu0X/UvZC55HZFMUeLi4lAa0tPTUS2EV0xMDKytrREUlFdkSB0C2dnZRfYlR3tLS0usX7/e+CNlGIZhmAqGDFKjUoqex/SQ3KLttF9pSN4UBk2aEhaetnDuH4TT/23Azl9/oCgFWvQbiMEvvw6ZhQXk8lghutLTr8HKygNtWq+Ao2MTRCXcxY5xj6DjHl2UTT5xONr9uBQSKyuT/c01jZSUFFH+9LA0ox4HBwdUi+J6Ozu7Ih2K5A5LYTu5XC5EmR4SXbR/ZYfzKgMSoIYK86ZOnSoWhmEYpnoQm5Ztsv2yLsUj81ycLsU4sgGObliNo2tXiW3thz2B7mMmisxRdnYUzpwdh6ysO7C29hGRLju7urgZdhI3X3wOze4roZQBVu+9ilZjXiz331hTSUpKEg4LZ8+eza3borGGVItuCCcnpwIBpJIgBwdaDGmBChde/v7+uHz5shBadOBESEiI+INPnjyJbt265e4bGRkpFCiJr5oGjR6grgmGYRim+uPlaGOS/dQZSiRtuCWuO/QIwJF9f+HMlo3idrenJqDD46OE6MrKui9EV3Z2BGxs/IXosrUNxNnjm5A2/R3UTdYgw1YKr+/mIrCHrpaaKUhCQgIOHjwo7K305U7BwcHo0aOHyMIV19Woh+rTS+vnVVIwJSAgAA8ePKj4WY0kvK5fv4727XVtrL169cKZM2fw8ccfY9OmTbCxsYFCocC0adPE9ua1eFAnwzAMY/4Eu9uJ4deGarjIUoLGBXWo61bi4yRvvAVNuhIWXrY4cWczLu7bLtb3mfQiWg8cKq5nZt4RoksujxJiq3UrEl3+OLBxAew+/BGeciDRwxoN/1gKj4YtKuCvrf41XAcOHMClS5dyBVe9evXQs2fPAlEssowgH6/0HB8vwqk6+ng9+uijwiLir7/+yhVepAQpDLd7926h/mgi+I0bN5CYmCiU/csvv2zKY2cYhmEYkxGdko2xvx8vUXQRs4Y2EeLMEJkX4pB1IV5UUV9SHxOiSyKRYsCU6Wjas6/YJyMjTIguhSIWdnb10Lr1MthY+2D7gpnwW7AJFhogKsQZHf5cDwdPP36X8xEdHS0EV/5sU2hoqIhw1alTdHg5iSufoGD023FYONf/3CoUTaujc/3gwYMxa9Ys+PjkzayqW7euEGOTJk0SYuvoUZ3jL/1xb775JsaOHWuao2YYhmEYExKVkoWnfz2GOwmZ8HexxeSeIfhx780ClhIU6SLRNbCZr8HHUacrRLSLeGB5G+dPbYNUZoEh099Eg4461/n09Os4c3Y8lMoE2Ns3EKLLUuaK/94ei3obzoh97nUMRO+F62Fla8/vc76yJRJc165d069Co0aNhODy8ytZnJIOiXTxFNfrBAZVmegql/CiMB0Jr+I6GCnMRx0FERERcHZ2xiOPPIL69euX91gZhmEYxuQ8SNaJrnuJmQhwtcWqFzqhjpsdHm/th+azd4h9/pzUHt1DPUuMdFG6K3nDLWgyVMiQpOLItXWwsLLGY6+/i+BWbcU+aWmXcfbcM1Aqk+Dg0AStWy2BVm2F7c8PQr0TuiHcd4e3xyOfLoZUphspVNuJiIgQguvmzbz5mU2bNkX37t0LBH9q9ZBscq8fN25cRTw0wzAMw5iMiMRMPP3bMdxPykKgmx1W/a8TLKUSXHqQImY16nGwtsDVKJ0Zp5ejdbFjg7IoxXgpARpocOj+OljY2mD427MQ0Kip2J6Seh7nzk2ESpUKJ8cWaNXqT2QnpePExEEIvp0KlRSIeXk4Br70Ob/DAO7cuSME1+3bt8XrQSVLVCtOgsvTUxe9qo5UiPBiGIZhmOogup769ZiIeFFRPYkuX2dbfLvzBr7bnRddIfTDsonpfUMxo3+DAtvVaQok/aNLMV5OOgy5dTZGv/s5vOvpsj3Jyadw7vxzUKvThRN9q5aLkHDrFm49NxF+CQpkWAPyT6aj37DJqM1otVqEh4dj//79uRZUlBZs2bKlcEtwd3cv0+OlpaXpFoWygPVDppVlrg0WLZUJCy+GYRim1nE3IUOkFyNTslHPwx4rX+gkariIsR0D0b+Jt8H7UsSrsFiIW30J2mw1kuTRiJBcx5Ozv4R7gK7QOzHpKC5c+B/U6ky4uHREyxa/4f6hfYif8RbcsjSId5HC6fuv0K7DENQE9GLHEI7FiB16DW/duiUE1/3793MFV+vWrYXgcnV1NepYTp06JR5TKZUB3XXdpEuXLoWlRhfNpNKo3r17ozJh4cUwDMOYBTFyJWLyRSYK421lCW9rXaSiPITH60RXdGo2QjztRU1X/tQhXS8ulWiI2D3XoArLgFqrxmXNcTz58Zdw9tLVHiUkHMCFi5Oh0cjh5toNLVosxLW//oT6s+9hrwbuBlihwa+LUa9ezZllrBc7huiZT+yQ4CJbKkopUvG83vS0bdu26Nq1q6gTLw/t2rUTDgtZGi3+uKkbIfTss8/CNqdWr7KjXQQLL4ZhGMYsWBoZj7l3DDuAvx7sjTfrGu4oLA1hcekY89sx0a0Y6uUgIl2ehSJYZSH64nVkbn8AK6k1wjWX8Oisd+DgpkuHxcfvwYWLU6HVKuDu3hvNmv6Ic1/Nht2SjWJe3+XmTuixcC283ItaIFRn9GKHfDz//PNPsW7QoEHCeF0qlQqxQ87yV69eFYJL7/pOU27ovl26dDGZINJH1zLIvT5HePn4+sC+ChsXWHgxDMMwZsEEPw8M8HBGllqDx87q6qU2tq4PW5k0N+JVHm7FpuHp344jLk2Oht6OWPFCR3g4FBVd6UmJyEgyPIvR3tUNDq5uuH/1MqL/OA0fm7pIRSLavzcO9jmDq2Njt+HS5enQalXw9HwETUO/xqnpz8Np92mx/WRffwyfux6ONrrJLzUJEjrUibh169bcdXSd3BAGDBiA+Ph44TSvH2JtZWWFDh06oHPnzrC3r/n2GSy8GIZhGLOA0oi0iOhEDs0cbU0SnbgRkyYiXfHpCjT2dcKK5zvCzb74QdMXdm3NnalYHJ1HPg2/Bo1x7qd/0M71EWigRtCUbrmiKzp6E65cfQNarRreXo+igd/7ODl2BJwvR0AtAY6PaY4J766Apaz8aVNz5MqZI1izSWfDkR8aMfj333/n3qaZzp06dULHjh1r5EhBQ7DwYhiGYWo016JTMfa340jIUKCpnxOWP9cRrgZEF9Gi3yCEtO0IlUKB1bPeEuue+ugrWFjp7hN75za2z52H/j4TxW2n/kGwD9KlF6Oi1uHK1ZlUvQRfnxEItn0R5554FM5Rqci0Ai5OH4BJz86DVFJ1Bp4VCaUQt20j0UXu/4Y9z3r16iVEF40WrG2w8GIYhmFqLFciUzH292NIylSiub8zlj3XAS52hkUXQWlEWuSZGbnrFFmZ8G3QEFcP7sPO335Ed8+Roq7Lso4DnHsHi30ePFiFa9c/EKLDz+9J+KYNxbUJI+CYrkS8ExA1+1lMHPwmahpKpRJJSUliYg11JqYqUKLoImiWYm0UXQQLL4ZhGKZGQiao4/44juRMJVoGOGPpcx3hbFu69N7N40ewZ/EvubfXfzEb1nb2QozVc2wJX7u6gIUUbqMbQiKVICJiCW7c/FjsGxAwAU7XmyHinedgq9Ii3EcC2dcfYHT7p1FdoUJ5vbgqvKSkpJT58dLT01FbYeFVTqgbw9B0cxoaTgvDMAxTuVy4n4xxvx9HarYKrQNdsOTZDnCyKb3o2jSvqHs8iS47Cye08egnbjsPCIKlpx3u3vsNt259IdYF1nkeFtutkPzj++IEe7aBBerN/xGd6vWEuSOXy3PFVUJCQgFxVZIvl75AnsxNqW6LHOcfhoODA6obCxYsEEtx6DszTSa8+vTpA1NAdv+7d+9GTcLb27vAhHSGYRimajkXkYzxfxxHWrYKbYNcxZxFx1KKLo1Gjd2LFxrc3sFjEGSwgFWQIxy6+iM8/EfcDv9WbAuqMxmqXyOg+nenuL2nkx36fL0UjT11I4PMgezs7GKjVrQ8LApFqUEaCZh/IbFFl1QcT+d4qvGaP3++KKQ3hJOTk0g1VjdKCqYEBATgwYMHphNe+/btK3E7vdh6I7Ti1uu35b/NMAzDMKbm9N0kTFx0AmlyFdoHu2LxpA5izmJpeXD1skEriRDHVvC2DYZKo4SmpQa3w+fhzt2fxLZg/6nImn0UstOXoJEAmx71wIRZq+Hv4I/KJisrq4io0kewMjMzS7yvra1trpgqvJSm85B8ugYOHIg1a9YUU2Cvu03bab/aSqk+jbNmzTKY8/3555+RnJwsjNGoS4FUH0HKjwQbWf+T1f/kyZNFKJJhGIZhKoJTdxIxcfFJpMtV6FjXDYsmtod9GUQXkZ6clHtdAgk8bAJgK9OlxVq66dzWLyTthZcyERl3t4nbdT2nIP2Vf2F5LxrZlsCGCSF49ZVlcLUxbszNw6BABgkoQ5ErEl4lQV5ZxQkrWkh4lZcmuIXR+Bfb0AupyDNCdUI6BmIfmoBc+osv0akNGC28VCoV+vXrJ97gX375Bc8//3yRiBZ9OP744w+88sorOHLkCHbt2mW6I2cYhmGYHE6Ek+g6gUyFGl1C3PH7M+1gZ1U20UXnrKQoXbrI364B2rj3FTVd+UmWxyCz+VZkqHUCra7D80h/YTUskzOQ6ADsfLkD3h63ELYW5RMwdCwZGRkGxRWlDB9WQ1U4HUgLBUIqtJtQo4Zmy1tojCg0Qhjuwh/psIcDMhCEByL+pdk6E9JGQwCan1gLMbq4/ttvvxXOs1Ro9sILLxS7DwkxEmQk0igvSvd54403ynO8DMMwDFOAo2EJePbPk8hSqtGtvgd+m9AOtlZlO6mTN9feJb/i/pVLQnR19Xocaqhxz/0k5FYJsFa4wze+JbJbbYFnHRJdEgSpxyDz+ZWwVKhwxwu48NZQvD/4c1hILUotrqiuqrhidlooq1QSVCtVXNSKxBUVuVcJd49Amh4lrkqgRV3oBl7nR5IWKfZD3e6ojRgtvFasWAELCwtMmjTpofvSPtOmTcPy5ctZeDEMwzAm4/CteDy35CSylRr0aOCJX8e3hY1l6UVXZmoKDv+1DBd374BWq4GFpTU6+A7Cbc89yGy4HlY2Oi8vSt7dVFtAJlMBWgn844dAMftvyLTAmXoSpH/wP7zVaXqRzA8Vm1NHoKHIFXlglQQNiTaUFqTZhmaBRg1EnQPC9gAX8pzpSyQpnIVXWQkLCxOhzNKoatqHZjfRfcwJypHv2LED//77L06ePClaYNVqNUJDQ/HEE0/gtddeqxVzoxiGYaojB2/G4fklpyBXadC7oSd+Hld60aVWKXFu+39iNJDeKLVh5+7o0uNJXNz/J5QtlqGwrCHRRT1ksntdIf3jjri+o40Uvu+9jyf9ByE8PLxIQTvZM1DWxxAk1PTiqnBRu4uLi/mIq8Ik3wPC9urEVvh+ICuvNq5UbH4NCD8AtBoD1O1Zq9KORke8KNpFRfVURE+F9SVB+9CHjz5c5sTKlStz06RNmzYVnRbUAkv1aB9++CFWr14tGgQ8PT2r+lAZhmGYfOy7Hov/LTsNhUqDvo288NO4NrC2KN3JO/zsKexd+juSInVpMK/gEPSe+AICGjdD2pkHItJFcsdQI36W91mcatwb1zx84OAVguvrb+GKen6J4orSf8VFrUhc0fnU7MlOBe4c0gmt23uBBN0Q81ysnYC6PXTLwW+AdBqAXdDpIBepBaBRAhf/1i1O/kCLJ3UizCO0Qg5fnc914VhyOnq5OUFWRU4LRr/b7dq1w549e0TqcNUqw8NECX1dF93HnKAuyylTpmDGjBkiyqUnKioKQ4YMwdmzZ/Hqq6+KtCrDMAxjHuy9FosXSXSpNejfxBsLxrSBlcXD7QkSHkRg/9LfEX7utLht5+yCbk9NQNNefSGVykTN1Y3wbbByzRsVVBg6V1vbZCCmiRzSFF9kpujsGcgeoThxRVEsCjrITDDou1JRq4DIs3lCK+IEoM0bXg6JDAhoB9TrDYT0AfzbArIcSeHoC6yZAK3oC80vvnKEzshFgHMAcG4lcHEtkPoAODRPtwR0AFo9DTQdAdjqho6Xl//ikvHejTyPrbEXwuFrbYlPQ/0xxNM0z1EWJNrC5lulZOvWrUKckJInG4n3338f3bp1yw2LUt760KFD+Oyzz7B3716xbvPmzRg0aBCqA0ePHkWXLl1EmpSiYIWtMPRmaRTtI8sMhmEYxjRkqNUIOXBRXA/r0Rz2+UTLrisxmLLiNJRqLQY29cEPY1rDUlay6MrOSBcpxXPbN0OjVkMqs0CbwcPQacSTYgwQoYzLRPI/t3AxazW0zf966DHGxA1FaL2xuQKLxFW196ZKDM8TWrcPAPJCo4Dc6uUJLSqMtykhi3VlEzRb34I0TVdoL6DI1sAvgCbD8tap5MD1rToRdmtXnriTWQONH9VFweg5jUxFkuh6/tKdIrE3fazr92bBJhFfZdEERgsvYvbs2fj4449ziwkpXOrh4SFux8XFiby2/uFJmNG+1QWq/9LXd0VGRsLX17fAdhZeDMMwpiU2NRuxaXJkaTQYeuuuWPdv/SDY5ggaGnj93oaLQnQNae6L+U+1KlF0kQs9Fc1T8XxWms5JvV7bDug1/jm4+upKZLQqDdL2RSBl7z2ku1xARMOlkDnEP/RY3eSvoPWgV1GtyUrW1VmR0CLBlVRo1A8JK6q/IqEV0htw1Q0DLy0ZGckYv+MfeCkS8G2r5rAN7VeygEqLBi6s0YmwuKt56ymCpk9FejYsU3qx3dEriJIX38BAyoUiXyc7Nyl32rEsmqBciWUSXm3atME777yDq1eviigXpeny07hxYxH1evzxx1GduH37trikCB79mmEYhmEqlhXH7+G73TehlUmAfn5i3ciFRyFR637ASyWARgsMbemHb0e3hEUJouvepQvYt+RXxN3TiQn3gED0mvA8gluSeacOeXgKEtffQIrmJCLbroXW5S5IFujDEcWdi2mbItsWmiSjYxZVh1oJ3D+VJ7QenAa0moK1V5TqI5FFYsuvdfmK3qUyHHFpLa7ODQx5+GM5+gBdpwFdXtF1SYpU5N8ARc0Oz9ctlNIkAdbsCcC2ZINaquUyJLoIegcj5UqxX1fXPKPXiqbcFX3Dhg0Ty8WLF3Hq1CnExsaK9V5eXqKmq3nz5qiOfPfdd+KSCu6rzA+FYRimFjG2Y6Co2UrMUuCp87cBuRrvDWoElVKDb3ZcF6Lr8VZ++GaUYdGVEhuN/csXiUHXhLW9PbqMGoeW/QdBllPErslUInlbOOJu7UZU/bVQu+jEmVotQ+z9EFhFqOHWNUyIrPziSy/ILLc4IqV/NajZogNOvK0TWaL78CCgKDTs2j00T2gFdwOsK0+AGIRedBJ9tDzyKXBju06E3dyhE4u0bHsHIBPWlmN0x66vL8tHrMJwN6kx+5kKk7VSkMCqriKrMFu2bBGO+xTt+uSTT0rcl1KpJQ0DfRgk6ljYMQzDAF5ONjhzLwkfbroMq1S5eEk+v5BnUzCijT++HtkSMgp9FUKRnYUTG/7Gqc3/QK1UQiKRouUjg9Bl1FjYOjrljdo5H4cHBzciyn8NVO1u5wqumAf1YbPHCs3P3oZMpURkjBfUg9NgZZs3fociXRZbHGF7Ihk273Y0z7csM1Fn7yCsHvYCKfcKbqcoUb1eOrFCtVMudUx/DJQypEVBr12Og3/MJcAq5zpFtmgpDRbWupowWtJjdREwEmH0eJf/0S0O3nmpSK/G4m5KjRaHkwqJTAN4WVlALpeLxVjKUrVVrhqvmgilTLt27SrsL8hpn7oayzuJ/GHjmChlyzAMU9vZdikKU5afMWRCgJ/GtMHgFgXrbbUaDa4c3IuDq5bkDrcObNYSvZ55AZ6BeTVJyoQs3N+xDvetlkLhGpYruKIjQyE7aI86l27iTF05DjWVwiVdi9c2aKCWAvHtnKBwlcEqSQ2PU6mQaYDfHrfBN5+fgswcvKdUCuD+iTxPLepEzP8KSi2BwE66qBYJLd+WFe+ZtXcOsP8LZEhtENJ9u1gVdnAA7DU5Y456vg30fqd8zxF1IScVuQbITMhb79cap5r/D29KWuJqlj7NWHhYtw7quPS1lOFk1+b45KOP8NFHH5XvmGjUVEUX1+vZtGkTtm/fjrt374rZjbt3787dRrOmzp8/LwruO3fuDHOGXiwSXffu3RMWE/PmzTO4r154+fn5CbFmLBzxYhiGAdQaLbp9uQdRKcXPIKTTpo+zDQ7N7JMb8Yq8cU2M+Ym+dUPcdvb2Qa/xzyOkXcfcpi+NSoP7O//Cvcw/IXfVeU9pNFJERTaA6oQrsuNv4FhoBi7UlaCBZxOMbDASl278jgdHH2DiTg088gVN4h2BP/tLEdylDj54VDcgu9KhU3b8jTyhRd5aykL2F56N8iJaQV0Aa92Q70ojJ+KVoQFCbupWhYUC9vrssGMZIl6lEZ63dgoRlhJ2EJ8FP4dlvkOhlUjhpsnCY2ln8acTaQ+tWKdHImrbJPg9dTOGPP5BuSNeVM9OjXgVXlwfERGBESNG4MyZM+I2abjC4xJIWDz99NPiQM6dO2e26cj4+Hg88sgjQnTRiKO5c+eW6n7099K8LIZhGKZ8Q671oouiARpXK8BaJuq8pEm6mYW0nfZr5qrFwZVLcPWgzqrI0sZWWEO0GfwYLHIsjVQpKbi/9Vc8kG1Htns4YJ0juKIaIP6KN8K1N3C05V3I7OwxuN5ozGwwEk3dm4r7tnEIwRbnjXi38U74hafCNR1IcgCi6znhicD+GBzyWOW+1RnxwO19OrFFhfHke5UfO4+8iBZdOukaE6oMvbBSq4GbOlsQ+DYn63+YHAsraBsOxkbXLvggIAJxSl2zwJPRW/Fh2M9wV6Wgm0d3vB8yDVE2Xrl385XH4ZOwHzBEfhPQvFvuIEhh7VPiIZfHboGEyvXr10X0h7oWFy9eLNYXeAILCzEom1JqGzduNEvhRXO0yF+MIlcjR47Eb7/9VqYXkWEYhjGOTIUK2y9H4+d9Oek/LxsoGzkDtvlOT1kqWF5LgVV0Oq5tW4cTx7dDlROdaNqrnzBBdXB1g0YuR8r27Yg+tgxxIWGQ++mavXSCKxR3wwNxwukm7jQ8iSYeTTCzwXQMrjsY9pYFR8NF3EhF4n4puksHYln7BpCqkzHw8hU0uuWGxFsSRKhTUb/kgS3lg7yt7h3L89SKOl9wO3lcifQh2Tz0AbybkYMraiN3suR4+/p97Mup56pvZ40vGwSgq/xRQHoNuLoBQ+IPYmD8YRxzboFYK3dhb9Ep5QJkyOnorOSB3UYLrwULFgjRRXYS+/fvF55Xf//9dxHhRTz22GNCeNFcRPLzMieys7NFVyZ1ZFIHI7nUVzuHYYZhmGqERqPFiTuJWHf6PrZcjEKGQp0nuloVY99jIxPre+0+gMQDOtd5vwaN0Xvi/+AdXA+Zx48j8t/NiAvbgtRBWii6pxcQXLfvh+CEUxgS6p3F4JDB+CJfdKs4qCO/YcOGyNJosegmjb4B3ho7DLY5KU6aPWzy9GHs1TybhzuHAVVeUb/Aq2lO92FvILALYGWH2oxCo8HCiDjMuxONbI0W1lIJpgV64+UgL1gLEdocaDJUCC+CRFbXlHPFP1h6TKUeu9HCa+3atSIqRHVQDxsk3axZMxH5unFDl4c3F2ggNqVBaR5jjx49sH79+iIO9QzDMIxpuJeQiXVn7mP92fuISMwTFoFudnistR++UybrVhTOONBtrRbHOvZGl6R76DXmGQQ5uSFtzVrc3LIFmS6xSBsqgaK/MldwRUeF4s79Rjhtfxu2TePxbKNnMajuoCLRreJwRAYcEYv8lVM+iEPePenEXk7xRR16In1IVg97gfTogtupU0+fOqQuRFPVRNUAjien460b93E9Q5ea7ubigC8bBiDEzqboa1gaSrtfVQsvinZRZIiK0R8GjVGgcQrUKWhO/Pjjj9iwQaeGaZ4WzW0sjm+++UY48jMMwzBlIy1bia0Xo7H29H0R5dLjYG0h3OefaBuA9sGuOJyUCsX5QlGe/EgkSHN0gW/rHrD65AvRzCUP1SBtrBqKBtoCgisiogmu2ccipJszPm/yOZq4NynbQZ9aLLryILUBcrrysGgAUJ6uPGUWcO9ojtDaB8Tk1D7psbABgrrmeWp5NTE8pbuWkqRU4bOwKCyP0nUxulnK8FF9f4z0di2+PIgaCxx8iora/JCgpf2qg/Ci6n9bW9tSp+Wou9Hc/KryC8F//vnH4H5k98DCi2EYpvQdikfC4kUqcdvlaGTnFDzTubFbfQ+MbBuAR5r4wNZKlps2Wn0jvFSPfe3YcQRbhiNthhaKUHWe4Iquj/v3miHVTooWQ1rh1SaPwc7SyHRcu0lAw0E6Hyr9YU3YUNCH6mFoNEDs5byIFokuVaGOTZ8WeUKrTifqEjDueGs4Wq0W62OS8OGtSCQodWanY33d8H6IH1wtS5AxZJsR2Bm48o8BQwkAdTpXvL2GqYQXOdNTp2JycjJcXEoeMEl2ElRL1ahRI5gTJKjYQ4thGMY0hMWlC7H1z9kHBWwhQjztRWRreGt/+DrniBcAcVly/HTpOlanyJEk03UjPoy9T3REHZtL8EFUruCKiGgGW0tP9BnVEx1DO5quKy8jGV3O6mYNSv2aA4EdSj5Jp0bl1GnldB9mxBV6XL88oUUzEB08y3+sNZzbmXK8fSMCB5J0dXuhdtb4umEddHIppUXGoC8A/zbQHvkekvzvh72XbjRRi9GobIwWXl26dMGaNWvE8r///a/EfWlWI4UBe/bsaezTMQzDMGZISqYSmy5ECsF1LiKnRos8tWwtMbSlL55oE4BWdVxyU0FZ6WnYc/Yc/oxNxRFnb6hp1IvMEnYZaVCTgzg5lefzW8olZ37PedvWeEP7PZpnXUKTsAcITbPB6OGDUb9J6Ycnl4orm2C79S2spzmBBNk1kk3DwC91LuqEIlPXEacfyZN/sDNBETcaw6P31KIBz5w+LBVyjQY/3YvF/LsxkGu0sJFKMCPIB1MCPWFVlg7OnPmPWR0mY/zmFbqB3e3aw7Zut0qPdJVbeE2ePBl//fWXiBh169YNTZoUzaFTh+Obb76ZW4hP92EYhmGqNyq1BgduxmHd6QfYeSUGCrUulUjGpr0aeIroVt/GXrC2kIk0UULEXdw6ewqb7z7ADrc6uOdfD3DTFaf7xD5Av2O70e/kYZx6pwF+tHhNN7g5v/jKMbscpVmBS/I2uGrbGBfsWuBC8xbo6eqIlr5eCCnGR9JormwC1kwQzuZFIlprxgPNR+vqhsjyQa3zGNNBMwZb5QmtOh10I2+YMnGUiuevR+Bmps4ypJerI75oGIBgW2vTDOwObl5loqtcwouiV88995yYadixY0cMGTJE1HERX3/9tRia/d9//4lUJEGjd1q2bGm6I2cYhmEqlWvRqTmpxEjEp+e5fDfycRR1W4+18oenozWUCjnuXzyLsDMnce3iORxxr4MzzTshuXl9sb9Eo0Hrq+fwxN6t6Hj5EugUmNXBEZ1tj8ACaizFs0hEXkOTGxIxAYvQXnIcza7JhUP9gRbB2JiQgv1JaWJp7WiHV4K8MNDDGdLyCDCNGtg2U9i4Fn2UHCFGY2r0ONfJN/uwF2BXjB0GUyoSlSp8fCsSq6N1TRieVhb4uL4/HvfKi5jWBMrlXL9w4UJhJfHDDz+IlCNBL87bb79dwMn+tddeE2KMYRiGqV4kpMux8VyksIG4HJmau97d3grDWvkJwdXUzxlpCfG4fWwPDp09iXsXzyHe2h5nmnXCxYGToLDWFY3bZWVg0OF9GLl3GwKsLGDfuRMSRrfFZe9byFYcA01hbI/jaIuTuKZtjGS4wgVJaISrkOaYXdZv446+vXrjKQBvZ8nxc0QcVkUl4GxaJp69dEfUAE0N9MIIb9eypaT03NoNpEY+fL8Ok4EOzwPu9Tl9WE60Wi3+jknC7FsPkKjUNUxM8HPHu/V84VJS8Xw1pVx/EXU0zp8/Hy+88AJ+//13HD58WMwqIn8sHx8fYTVB2zjSxTAMU31QqDTYcy1WiK2912Kh0ugiPZYyCfo28hapxB6hbkgIv4XbBzZi6ZmTiLsbLuJB932Dcar3SIQFNYI2R/jUiY7EyKN7MUKqglXburjRvzOO4izslOvhKlPBVVPwbEQiqwkuF3tsKpu8Zq5AW2vMaRCA14K98cf9eCx6ECfSU69ei8BX4dGYXMcTY33dYW8hK7n7MOpcjkv8Pl3NVmmo0x7wCC3dvoxBbmVmY+b1+zicrCueb2RvI4rn2zs/3G+tumISKdm0aVN8++23pngohmEYpoqiDpcepAqxtfHcAyRl6sxIiRYBzqJIfkCoM5JvXsTtPcvxx9xTolCeUMkscK1BK5xr1hlRXnmzdDo9uINx2nQEBNzDvdF3cSb7KtwkmZBqALGXDNBoJUhL80Byog98/W7A0lJebP051dbL5XbwUrcrss3TyhJv1/MVka6lkQn4JSIWkXKlsB/49k4MngvwxLMBHnDTR09S7ufZPJDYysrzFys1lWy6WdPIVmvww70Y/HA3FgqtVkwFeC3YB5PreMEyZ0JATaXmxfAYhmGYUhObmi3sH0hw3YjRRR0IL0drDG/lh0cCpNDcvYzb2//Fyu+vCIGmJ9vKFtdD2+BYq65IdXQS62w0agyQxKGdzT44+e2FozYeGVrAnWrQczJ/6ZnOSEn0RVKyL1JTvKFWW0KqlSAjww2Nm+zXNzDmon/K22HtEdLIsDeXo4VMiK/n/D2wNiYJC+7FIDxLgW/uRGPB3UiMV97Ai2GL4B99vOAdrRyBuj10Vg9k87DsMV0hfeHieoFE191Yyaab1RF1vs/KseR09HJzgkwiwaGkNBHlCsvS1Qn2dnPEFw0CEFSe4vlqRIUJr7i4OBw6dEikI2kcz8O8vqorMTExxXZ0ElOnThULwzCMOZGtVItuRBJbB27EISeTCCsLKQY2ckdfl3TYx1xH+Kal2JdcMBrkkK1Atp0bjrbpjkOtO4poF+EuyUQ37EUfrIUHUgG9jZcEyJLbIjXJD0nJPkhO8oVSqfPyctU4oInGDf4WnvDz9MFfcbtw9QoQUv8krK3z5v5SpItEV3Z8KAKDgx7699lItBinvYOn5XvwX2w8frBrh4sOofjVohEWh36GJ5x3YariIkIDm+mK4v3bCkuLXMgyYs0EUV5fsLMxRw0OJFd7nulbEv/FJeO9Gw9yb4+9EA5vKwvUs7PG0WRdI56XlQU+CfXHMM/qUTxPM6ppMaQFSotEm//nSxmgodI//fSTSDO+/vrrBbatXr1adDySaSpBBfhLlizB8OHDUVMICAjAgwcP4O/vL4xkGYZhzBn6qj9zL1mIrX/PRyItW+cATnTysUA/+wQ43b+IqJtXoVLnbZNqNHBPz4Z7WibCm7XDf/2G4ay7V+72EO0NDMa/aIfjoiORUKoskJrii6REXyQn+yAri6JhEthqreCvcUOAhSfq1gmGWwMfWNdzhqWPrp7n8Kf/YJf6AhVewdklFlZWWVAobJGSTM8nRT9ZC3R9fzgkxaWikiNy6rT06cO8ySR0ktsfMAQ/BI3HYQvfXAk1yMNZDFVu41RMPdGVTdBsfQtSvY8X4eSvE116Hy/GoOh6/tKdYuOFeib6e4jieaeS6u9MSIZajZADujFNYT2aw76UU3cqQhMYHfFauXKlEFM0xzA/VFxPoisrK2/mVnp6OsaMGYNLly4hJCTE2KdkGIZhysiD5Cz8Q4OpzzzA7ficsc9aLZpZpaCvxX04PriClPAEpFDpU859rJUqeKVmwluugmvLttgx5hHMc/VCtFaXCpJpVeiIIxiI/xCCW1BrJEhP9UJSki59mJ5GlgpSyLRS+GpcdELLPwh+jQJhHeIKS2+7YsVT2xE9oF2pwTHLm0hJyRvLYw9rdFKGou2oHnn3k6cBdw7p6rRIcCXcLPhg1k656UNJSB/0cquHXgDOpGTgx3ux2BKfkrvQkOVXgrzRw9UhL/LSZBjSA7tj+N5dsFVn4/V6/ujRtAdkORE+Jg9qvsjUaJCl1iBNpcbb1++XKLo8LC3wWai/SDvWRoz+BB04cEBcDhtWUPn/+uuvQnS1aNEC69atg42NDcaNGyf2//777/Hdd9+V/6gZpoYRI1ciRpFXzFwYbytLeFuXbqQKw2QqVNh2KVpEt46EJYgaKSuNHC2z7qKH4hYsUx9AqVEJgwYhtrRauGTK4ZWRjTp+AfDt3hl32vnjFy2wO7sO5LAWYSMHbSr6Yif6YRus0rVISfLFxeS+SE3xgkaTk3LUOCJE5o66fsEIblQP9qHusPAqXmgVxlZ6BO1kf6Cu/HlESi2RBTlsYQ1/jRJulr/DNikCOKDSia2I44AmLzIHiQwIaKczLs1NHxY9xbVxtsei5nVxIyMbC+7FYl1MIg4lp4ulhYMtXg7yxhCbLGyLisF7MRpEO+g6F59OAHyPXMKnXsAQv5yRQtWo1ipTrRFLlkZ3WdztAtfpUpPves7t/Nf126g4vizEK1Wi5qurq85Et7ZhtPCKiooSvwyCggrm28k0ldZ/+umnudEtElutWrXCnj17yn/EDFMDWRoZj7l3DNcIvB7sjTfr6lIkDFMcGo0WJ+4kCoPTLRejkCFXwV2RiAEJ59FQeR9adQa0OdqHJL6FWg2PtCz42zsjuHVb2PUIRbxXDLYm3MUOhQPOZ+nMTokA7T30Ve5E4/hwpCd74EZyXyiVOd5cWmvUl3qgrk8d1GsUCrfGPjqhVdZohkYN7ZaZsJFFwl96DB6aptDAFVIkwdryMrmuArsLWT241s2bfRjcHbAtfS1xA3sbfNc4EG/V9cEvEXFYFpmAC+lZ+H97ZwIeVXW+8XdmspONLCQkQMIqiywioCyyKu6igoq2KihuxWoVWlekFa3VVrEq/6d1w6VWW8VSpVZRQFYXENmx7PuSkED2bWbu/3nPzJ1MJjOZzGSSTGa+H89l7nLuvSf3zL33ne/7znfu3H4AGajESWXdM9QZWflEjRUzjhrw+rHPcPm42xBoYdQYseOYd7PsSUT5Koz8hZcqymBAVSPOl1ftJJrDDL+FV0FBgQqYj4ioPQQtXZs2bUJ0dDQmTpzoWE/rV1RUFA4cOND0GgtCCHJLVhouTktSD9FJP+5R6/59Tg/EmowOi5cguONgQRkWbTyKjzcewbGCEgwu3I4pxf9De2shLCbbC1ClHjUA7SqrkWk1ILdXH3QcNxiVvSw4WLISW4r/je8sQ/DF8ctxxDDWITbOrtqOwXlb0f5YDaoqU3AAqYjQTMgypiAnvRN6nNUTHfvnINIfoeUMX9Q7PoGh1J641GBFjMkWj1OPLsOB/tfZBFdKtyZ/KbJjovBkz2z8SuUCy8cbh/Nx0hJTR3A5qmkfxujhiCFIP1OqBI2z9ciTSPJkSdK3cSzCloB/UpzJqKZYo+1TTUajeta42+Zx3r6f83y00YB1Z0oxedNer3XpEBW+Llu//3IKruLi2izGZP369Sp56vDhw5XQciY+Pt4xpJDgHnE3hS90I3JiAKjO2QmxAQ8AFUKDksoaZdXiWInHt27DeQWbMLnyKBBZDSuTlhoAi4mB8RpSqmrQOSMbucOHIHZ4Co5ZNuJk4RoUmD9F4ZEUfIlLsBy3odRgSwcRZa1Gv1O7cdaBo0iuKAe0GCQaM5CTko3uPbqj6+BeiM5wioXyFUsNcGo3cGIrcGKL/XNr43NpDZ0B9J+CQMMcX7QqD06Iw8+27m+wbL7ZiqvsP5BaQhj5JYRcBZVdGDV378Hzk+PRMToSJ6pqPCXjALezXLjit/DKzc3Fzp07ldgaOnSoWvfJJ5+oRmXGemcoxoqKipCVldX0Gocw4m4SBMETFquGdXtP4ZPVO1H69VJ0L96FsVoxKmJsj3Grins3ItpsQVZsPHL69UPmhB4oaX8Mx/KXYV/lfBiO216FB9ATn+MmfI8RsDA2ijmwKstw9tF96H38EFItJuQkZaFb727oMbgvEju19++FXVkMnNxWK6445e0ELLXjPNZCa5JtWKDWTFxabB/w2xupkSaVuNWbFcijEHIjomJaQBg1NwyYf6pnturVyL/ETTIOzAvjwPomCa+LLroIO3bsUHmqOFYjY74YWE+uvPLKOmU5YDbFF7tbCp4Rd5MgCK7sPnwKKz/8HGfWrUBixTF0idZQzQzs0UCF/RHOSKgunXPQeWwfRPUx4vjpr1FY/AHOlNYA9pyoFpjwTdU4fG64HAeiuziO3/HMKZxzZD8uqAJ65HRFr7Hjkd6tI4y+jHNIV2HxUSeBZbdknfYQXsKEpZn9gcyz7Z/9gdRewIIhrZ64tLEusFf75YZtcLg3Lk9Pxutn56o8XiecOg3R0jWvZ7baHs74Lbxmz56t0kn88MMPGDFihCNPzPjx4x3LrgH3dEEKnhF3kyAImsWCUxu34LsP/43CHT/CopWiOC4GBpMBJfE265RJ05CVlIrcob2QOjIdxdYtKChcjSOWz4CDtmtI2WSpjsXJM73wuWEsvks6F6UxtnxVRqsF/QtOYbKm4aK+3dH5yrF14nW9ugrz/2cTVsqaZRdZTnmz6pDYqVZc6VNyDuBO2AVB4lJxlQUGiqsL2sej1+ptavm9AV0dmevDHb+FF5OErVixQiVP/eabb1Sg/RVXXIHnnnuuTjmKsYULF6rPcePGBaLOghCyVFusMBRXs5sT3tpxDHf0zUaUPcBeaFviqXzDDzDn5yMiPR1xQ86FwUO8Hp+N1QcO4Mzq1dj25TIUnDiIwrgolMVEMYEVkyyocrHGSHTtnYtOYzrBlFGIvIJVKKleixJ7Z1i+zqyWCFSd6YT8M+n4qbIXvk85B7szOjuyyydYzLgmNgL3nt0LXZLO9f6HVBYBJ5xdhVuA/J+o6OqXNUYAaWfVF1lxzOnVSJiY9Pp3oP33NzDUSVya1WKJS8VVFthr6SxoRXQ1MXN9Y6GLUc/iSrHW6F9VYZi5vqSkRE15hafw/upFMEdHILXGgmsvmIzE5FQkJCSoSQg92O5PbzyId8srYbHH7BBTpRk3x8XgscE50vZthOKlS3Hi6d8jr7QIVZERtmSk8UnIfOxRJNp7e9fk5aH8229xavVq7Nu8EXkGC04lxMHsLLI1oF1iMnqNzUGHs00orvoR5WU761iCNM0Ac3FHFJzOxMmiNBQXp+Fg+yxsze6OIym12eV7x0TintyOuDojGdHuLE18DRQdtosrJyvWGbv5zBUmJ3UVWOm9gYgmjrVXckJNZZWluHnzbnSoLsD8LvGI7TzUZulKaLn8WfqQN86usixxlQVVtviwy1zfWDhWo2uuL8E961csx9ITG/Hv7qNxunft8ErvHs/DpDUfYWLmYIy/apJcvhBk1lcb8UlSIhBd92FgiTbiLUsNCr/aiFevGdNq9RMaL7o2zHkUO7NTEXFOMiLizDCXR2DzoSj0mfMoun+0CAUnjuJIUSHyEuJQFBcNtLdZtIgVRiT0SkPPUe2RmHoCxSUboGnf4KS9wx/tB5ayNDXu4fEzqThT1AEWSxRqjCbsyszBjj49URBtOx7l1SVpSbijczrOT2pXG7RtrgZO2V2FzpYsWrfckdTZvauwOVxGGxYCK+lOjMG6C75Qq57/8mLAah/4cczDwLhH0BKIq0xoLkLD/BQiLDuxEW/1uLre+tOG9mq9ac9ijIcIr0BDoy/7MTGVjgZNfVrt62lbsG2zz6ttmjIQ2LbZymsuZZy31Slf55i2c1RZrVjWzp6ny/VlxrxBmhUrY43KDSlux9o2UxfValWT1ph5dcGtNMO7X9/gvNVxPjVv32abt01Wsxnrn56H/WPj0G3kAUTF1yaIrC6NwL61Gdi65xDMsSYgttb9VpMUiYSBiehxVg2iInfCatmu1uvZerSqBJSf7qSEVsGZDFRXxzn2NaZkYH+vflgTk4gyuyEswWTETVmpuC07DTmoAE5uAn5yElh5PwHWGveuwvQ+dQVWRj/fXIVNZch04KxLbTeIPgLQbV/YVKT641o2W7y4yoRWE16HDh1Sn5GRkejYsWOddb7SpUttbxqhljOnC/Bx99ENvHw1vN/jIpz+23MwGIw2YaCyKtPxYIDV7oDQuGxfpyY1byvr2F5nmz5vL2OwHYtYuZ0b7NvUOVTdDLZt9n3VsvOxHdvcnY/HMtbWyfk4Bj5v3dVb/ztRpw6u9a3/Nzkdo059nesaJPFTkbUv03oYjCiKTsCIT5cgozwfiVWliK8sRUJliUoBkFhVhoSKUkSaLTCwvawGGKyq0dQLjPNqvaV22fapGhkGqwYj19liD+wtqqn52nU25ehoWXs59anK1S3vWNYjGTytcyzbj2kXv3XWq+SSFK929aqvr3uRbId0e/3sp2tEM+jfD9fkmQ3uay/L7+Hp84zInXi0Xu7NqHZmtf6AIRslx+JQlRuLlJ4aumSdRKQhz1HOyjYyR6PqdGcVp0X3YXl5kuMkMZHR6NOrG6q698BXUQn46ky5LQGDBnSNMmJGdAFuKFmP+PWbgE+3AkUentPRSW5chWc13VXYVHRXIvPZ7bYnUO3Yn66T1q2XILS08Oratav67N27N7Zv315nnS/Q1G02h+8wAQ3xl48W4HSPBgJHDQaUIx4fZ9eOCCC0HgZNl4h0DtXKSWdpqcthfdLXEUpnfT3HwSs2eO9efSSpi5o8EaeVIhlnkIzTSLJ/6su1608jHqXuknLbtI6VIt8AzWqbbPNcTxHOoHGbaLOVNShjj2Obvo9jnfO+TuudtlH41dvfab52f6PLcT2cx3V/+7zreWxKKsCuMoOG/hfYchfVOzR/O2hA7oRjMNizyTuwGmEu7oTTpzNx7Ex7lJSm2f5efk8MRuR07oQevXqic9eu+N4Ui9eP5GNLaSVQXq7KjK7YgxkH/44LTy63fxddSO4CZA5wsmKdbVsnvcsEIXiFlx5/7xyH709MfjPH8bcKJ0+eRN++fd1uY44zTo3hTETjLC/dzHuQVlNsf6mjrv1Ic375o/bl72zFcGyzv/hdLBzOQsFhz3K73mXfOvYvDUbdwuEiSPjyabC+jnO5HNelDkTfX827/p2ObRoMBvt57N8/1s1xDP36uf0baSmqL6C4vh66EcdrC7ruq2FPQhrmJ97vdc8LK79AlMWCElM8io2JtsmQiCJjEsyGSJQb4pU4P4aG8+WZtBr3As1wGkmmM2jvWH8GEWpUv9DEqsSYEVat9lOt04ywOISaEVa9jH07l9V6TZ+3fSZFFMPUzh6L5A6KL7vo0koyUHwmG8fOpKCwKA1Wa+2QUGmpaejRswe6deuGnA6JKD61G+8eL8TM/xmRZ7Slg4ixVGFy3peYcXQR+pTts+1ojAQ69HYRWf2A2PbNfCUFITxYsGCBmjxpgYAKr/379ztcja7rwp2MjAyVSLapJJsbly35qgM78PAdTyAosZkToFktsHLAW81im9fMKhbGCn2Zn4y/qS3n+OQ2/Rj6vMWq5q0qNkc/hlXtw/NZeWx7DI7t2Pbjq3NyX4qu2nI8Jt1XPIZjXsVjWWxWFC5ZNdvEeXVcWzn1T3OabIFa6hzq0y7i9Piw2m21844fIPb4r9z/lSFlyCkUIsXmVq53Xa1IQSGu3nwAZSbbi54vfpt1issmVJhiUBwVj5LIOJRExqI0KhYlkTEoiYpBaWQ0SqKiURoVjYrIKFgMkShAupq8EWOuQnxNOdpxMpcjnlNNKeItZYg3lyHBUooEcylitEoYDRS5Vvuk1f00Os1TvDovO8p53t9Iv6hj3i6mnfZzzBuVGa123r7eHUYjfwUwrWjLsnvXMJw4cZZjOS42Tg3F0y0zEd1jipFYzMD3t7F9WREeShqFf2VMQJWRea+AzKp8TD+2GD8vXInUtFyg/6W1iUiZyiGi7lBtgiAEjoaMKXqvxoAJL3e9EqWnYmC5e8pMvL1xkwqk9/jy1U6rckELXRcGEwxGU4u/zNoy5duWYteR97Ag6j5bMLdz+yt/nAE3Vv8dl10xFXFnu7iaLWbAXAHUcCoHqstr5x2fRY7lqupKNc5cntmAPAtwwhqBk1oUTiAa+cY4nDS2Q74pAacikmA2RqAyIlpNp7xYTSIt1UisKUFCTRkSq0ttYq2qDPE1lWhXVYF2NVVoV1WJmJpqGDUDLJoJVpjs9kZaj2iD1e2fdnssLUlO8Yn+4yIC63162KbEYd11rJnJAJeJwk9DZFwhUrO9/wirrEhCt+x0dEu0oLvxKDKKv4Nx18vA1mJYYMTnqcPxaqfrsK73OY59BtUcx52R+bgiJw1R5z8MJL0Skq5CfbxaDiSts62kos5g8Uw0LQhtGenVGCQkt0/FNfvW4M3uV3l8+V69bw2SJ0xozWoKzUBc3wno/5+/4P4hf8S7httQiDTHNlq6btbeRJ/NlYib6KbtmRjTlABENy6/G0On6Yj0NngXLXNnzBb1IsyvqsbJ8jLkVVYir6oKedV8OVqQb9Zw0gwUa0bUmKJQYEpFQUxqg8el5THFUoYO5iJkVBcivboAGZV56MCpugAdqguRYf+Mt5Q7HLMUXmaYYEaE02ftfE2d9W62a5xMqlw1lw1RajIbo1CDSOWqNSNSCUK1D92NajIol2LjyMGwtAOIiip3q4lo6KyqikO34tO4quhRwOnHcYkpDu93vgFvdLoeB6Ns7U9ZekVqO9yRk41zEwe2+TH8/B2vdpLTYNSzcjPUQNaC0JYR4RVEPDL1QRj+OR8f547EaUPtC4yWrmsOrMXDUx9s1foJzYTRhO6ZfWHYsB1/7P8g9sXkgpFWjL3qVnkQ5q3d0C2zX7MPlVKnSgYDUiIj1NQnPhZIZc8699A6QTGWX21W1oq8ajPyqmrsAs2sPvOqzMivqVEWnYKIBDXtjGlY/sUaNHQwacgwWpBuqEEGqtBBq0AHazk6WEuQYS5Ch5oiZNWchklZ9NxZ/DiV1a7T0TtGNsLD70308ZOC7oiWie17hqJP35VKZDnrJN27vG/vUFxq/R6Ia69chPszh+ONhPPxQU0KSu11SY4w4edZqZienYZsZq8Pw/FqPUGLlyCEVTqJQCDpJDzDrPRP3/4EfnEqDy99uEBlrk+3aMq9KJau0Cb32oeQvWURIlY+A4PVgqroYkRXWTDKWAXzmEmIHDAZwQrdQDmx0WryZkUrqDHbBJoSZnZRpgs0p3WlFisqNAMOmg04qJI48YXLlBtOLk+ujgaM0UBqVIR6KafbPznQcYdofkYig/NqnQnttBoXt6w712zddYbqckTWcGqoXBl6VGzCdwXnYueOMejaYwP2R9cK6NyqAziwdwjKCjLR9ZJ7seasm/Da0VNYeqoYWpXtz+kZF407OqVjcmb7Vs3wHQzj1QpCKONTOommIukkGu92fNueub61h1kQWgY1FNTIaSgbdhNeX/KebaiUIUMR23VUi1q6mtuKlq7EUST60ormZXgPZ4FGS1rtcq1AO1VtVkYrbuPkjXYmo5Mwi0KHqDibYGtnF2xKrEUgNTJC1denv2//akx6+1f4g+FOvIgZKDPU5mZrZyjH+YZtGJy5HRdahmLnZntPRADjUxJwZ+d0jGmfEBbuREEId3xKJ9FUQjGdRCCRwFKBImtdsi2o+vnc/iEjunyFPzbaxZqQ68WKZrZqKKypdXEqgValL9cKtJNVZlRYrSizWLGvokpNDcGg+fRIZ8uZk0XNbklT26IiHYHfyBmBXendsLTPsHrHK4uOxbK+Q7HMMAzM0BFrNOL6zPaY0SkdPdvFNO1iCYLQpvApnYTQvEhgqSD4RoTRYBNGXtxT/NFH0eUQaFXOMWm2GDR9G92hFg1qcGQ1QHJpRYPHTowwKgGWHhmBH/s+bAsKqzf6hG2Z/TYf6ZaFm7NSkRwpIbaCEI74nU5CCDwSWBq+iLWzeaELLz7CpKbuDYzORGqsGk7V2Kxkda1mLh0IqmtQZdVQbLai2FyFPaAVzdRgQnymyRicGCeiSxDCGPnJFURIYGn4ItbO4CHSaEDH6Cg1ebOiFTPlhl2E/Se/CAuPnvJ6fIo2QRDCFxFeghAEiLWzbVrRkiIj1NSrXYwydDVGeDE2TBCE8CVgTwCOU8R0+WVlZQ0G0Y8ePTpQpxSEkEGsnW2f85Pj0TE6Eieqatxm2qcw43aWEwQhfGmy8HrllVfw0ksvYe/evV7LSjoJQRBCFZPBgKd6ZmPGtgNKZDmLLz3sa17PbFVOEITwpUnCa+rUqfjwww8bnSZC0kkIghDKDElshzndO2LBoTwU1Fgc69MiI3BPl3S1XRCE8MbNaMyN44MPPsA///lPJCYm4qOPPlIuRpKZmQmz2YwjR45g4cKF6NGjB9LS0rBs2TJYrY0Yn6ONQRdr37593U4LFixo7eoJgtDCnSSe3Hu8jugi+TVmtZ7bBUFom/Cd7ul9Ty3Q7Bavt956S7kO582bh2uvvbbONqPRiKysLNx6662YPHkyxowZg0mTJmHjxo1KiIUSGRkZ2LFjR2tXQxCEIEA6SQhC6DJz5kw1uaNTp04qzr1ZhdePP/6oPn/+85/XWe9q1YqPj1dxYCNHjsSzzz6L1157zd9TCoIgBDXSSUIQhGZzNZ45c0aJquTkZMe6yMhIh8vRmeHDhyMuLg5fffWVv6cTBEEQBEEIX+GVmpqKmpqaOusowsrLy5Uoc8eJEyf8PZ0gCIIgCEL4Cq/s7GxUVVUhPz/fsa5Pnz7qc8WKFXXKMraLgoxWL0EQBEEIZjg81JaScmwrqR2nk/Ncx4nbBaHFhRfdh7qo0rn88stVyojZs2dj/fr1yiK2YcMGFWTPQHzGeQmCIAhCMMPepxM37MKkH/c41nGe6zhJ71ShKfgdXH/VVVepoPm//e1vuPjii9W6e+65RyVT3b9/P84//3xHWYoxxn899thjTaqsIAiCIDQ30ju1adAiyMHkKyzWOhbDWJPN1pMRFak6ooQrfguvsWPHKpdibGysYx2D7ZcvX45p06bhm2++cazv0qWLyn9x3nnnIRiprKzEM888g/fffx+HDh1CSkoKLrnkEpUqgy5VQRAEIXyQ3qlNgxbB5w/UzWvlbD2clZuBX3ftiHDFoDVTOnkmUD18+DCSkpJUcrFghaJrwoQJWLduHTp27IgLLrgABw4cwPfff48OHTrgu+++Q25ursecHRRm/FsDDePnKAYfeeQRREdHB/z4QvAibR8aSDuGBtKO/lu8PJHRghYvZ+ubLv4+OjsHiTHRAa2LL5qg2YRXW+GJJ55Qli3GrC1dulRZ7cgLL7yAWbNmYfz48SrrfksLr+LiYiVai4qK1OgAQvggbR8aSDuGBtKObZs/7j9ez/rmTKCsb75ogiYPkt2WYfD/yy+/rObpCtVFF3nwwQfx9ttvK9cpOxAMHjy4FWsqCIIgCEJT4vWYZ3T06NFYtWoV2rVr57B4tTQBE14VFRUqf5drbi9XGO8VLKxZs0bVuXv37jjnnHPqbZ8yZQq2bNmCTz/9VISXIAiCILTheL1izQzz7p/QLy4aiQmtl96qScKrtLQUzz33nBowe+/evV7LM6UEB9AOFjZv3qw+PVmz9PV6OUEQBEEQhFYRXnl5ecpkt3v3bpUuojEEWzgZezDqvll36OsPHjzY4HEsFotbkcnBwhsq44zJZKpTlhPX6fMNlfXluK1dlmN5NvQ98KUsry+vc6iV5fXUyzVnHVjOdWxVT99hKev7dSCe7mF3ZYPh/gyGssH2jHD3LG7tZ0RLlQ21+97qtC3QdWgR4cWcXLt27VLZ6BmEzlxeGRkZiIhoO2FjtNgRTxn1dR+wXs5Tjxd3Y1By+CQ9kz9h70h3DccbOi0tDYMGDXKs+/bbb9U52cOSvSudezUmJCTg3HPPdSwzUS17ZrqDf9ewYcMcyz/88IMaQcAdMTExdXKvbdq0CSUlJW7LMiebczLcrVu3ehwmil9cCnSdbdu2obCwEA2lKdHZuXNnnZERXOH10R/C/C42NCTViBEjEBUVpeb37NmDY8eOeSzL68DrQZiTjr1zPTF06FDH94RCnj1iPUELqt5RgsGX+/bt8/idYscKnePHj6sfOJ7o37+/GsJL/0H0008/eSzLHsbsrUt4bXfs2OGxbO/evZGZmanm2WZsZ0/07NnTkXqFHUL4/fFEt27dHCEH/I45J2F2hT2K9V7F/O7y++6Jzp07q7AB/RryPvJEVlYWevXqpeYZHsFezZ7gNeC1ILyHV69e7bFseno6+vXr51h2dw/rMG3NgAEDHMtr1671+HDn88T1GeEprEOeEYF9RvC75NqOrf2MIPw+6GMlyzMit1HPCP056e4ZQW+c8w8E6hk+q4ieDL6h+94Xw5LfKmnJkiVK5b311lsqFqotol8oT2q1MReSPV7+8Ic/1FtfUFBQ50Xl/ABwZsyYMbj66qt9rLkgCIIgCIGM+V65cqVjmT0U9R+8rsYGVyjoG/oxH7B0ErSmcFdaZtwJirYAey7Onz8fDzzwgEof4Qpju/irgr9CaC1y13WUub+2b9/utxuBv6A4uZrlKeioommVcE0nEWyugbbsRgjGsmx7/jKjFZFtL26EtlmW7Uirlrt72LVssNyfwVA22J4R7p7Frf2MaKmywXAfaQEsSwt7+/btlVWelmHnsrSAcfK3DrR0U3w1azoJmvV5krYquoju7vB0kSisSE5OjsdjsGHYkIGE11SPKdDnGyrry3Fbu6zzi0bKer6ezg/G5rpmfFg0tu2krO/XgTTmHg6m+zMYygbbM8LbszjY6hvIsqF23xud/nbXsjQmeQo7auy5m32QbLrHvPlTg52BAweqT0/xKHrsiXMchiAIgiAIgr/4Lbx+85vfKEvQ3Xff7TGwOtihz5ZBzPTjuksZsWjRIvV55ZVXtkLtBEEQBEEINfx2NTIGhb35brrpJtVT6q677sKQIUOU37QhnHu4tTbswXLvvffi6aefxsyZM/HFF184ep+8+OKLyhLG4HfnXoSCIAiCIAj+0qTcD0wdwa7e7Gb75JNPtrkEquTxxx9XApJdudm9fNSoUSpvF9M/MKDyzTffbO0qCoIgCIIQ7q5G5iJh3pMPP/xQLTMY2NvUUK+A1oK5WFasWIE5c+YgNjYWixcvVsJr2rRpKsZLz+MhCIIgCILQasLriSeeUL0a6XJ84403VM9AJhmjuGpoCkYouGixY9I8didlMrqFCxd6zGgvCIIgCILgD37n8WKuCiYNW7p0KSZMmIBwg/FhFJrsnspcXoGGzUJhywzbvg5HILRtpO1DA2nH0EDaMXTQmvG9SoMNjUtMtlpdXd08wotB6Kw4E5KFozBg/o9gteAJgiAIgtDy0BjjLWGw38H1TCXBWKhwFF16bBjHSKQA08e+EwRBEAQh/MjLy1OCSx/Ds1mE1/XXX4958+Zh+fLlGD9+PMKNsrKy1q6CIAiCIAhtDL9djRUVFWqEdo7VyHQMXbt2DXztBEEQBEEQQgi/hdc777yjMtb/9re/VYFkkydPxrBhw7wmUL3lllv8rasgCIIgCEJ4Ci/XEcwbE+sVjAlUBUEQBEEQWgq/Y7y6dOkStoH1giAIgiAILWrxEgRBEARBEFrI4rVq1Sr1OWDAACQnJ/t7GEEQBEEQhLDB7yGDxo4dq9JIiMEssDA32Ny5c9WA3cwHwgy7t912G44ePRrgMwmhCn8UTZo0SeXaYzgAO8AIwc9nn32GQYMGITo6Grm5ufjTn/7U2lUSfETuvdBg4cKFGDduHNLT01WHwXPPPRfvvfde6wuvpKQkZelq3759wCoT7lB0cfgljhvJNB18eXbu3Fl9CQYPHqwGJhcEb/C707dvXzz33HPIzMyUC9YG2LBhg7rfJ06ciE2bNimx/Pjjj+OVV15p7aoJPiD3XmiwbNkyXHXVVerH0I8//oipU6fi5ptvxj/+8Y/AnEDzkyFDhmhRUVFaZWWlv4cQXJgzZw7j7bThw4drJSUljvXPP/+8Wj9+/Hi5ZkHGhg0btGeeeUa75pprtKysLNVO0dHRXverqKjQnnjiCa1nz56qfMeOHbXp06drR44cCWj9cnJytLlz5wb0mOFGS7TxjTfeqA0dOrTOutmzZ2udOnXSrFZrQP+ecKWl71W590LrmXvppZdq1157bQD+Ak3zW3j96U9/0gwGg/bOO+8EpCLhTnV1tZacnKy+RBs3bqy3fcCAAWrbDz/80Cr1E9wzadIk1S7Ok7eHAB8AI0aMUGV5819//fXasGHD1HKHDh20/fv3B+xyy8O/bbRxly5d1A8vZ7766itVPpDfh3Cmpe9VufdC65k7fPhw7c477wzI3+B3cP3999+Pjz/+GPfeey9SU1Nx2WWXBcYEF6asWbNGJaTt3r07zjnnnHrbp0yZgi1btuDTTz9VbkchOBg+fDgGDhyIoUOHqqkxrr3f//73WLdundp36dKliI+PV+tfeOEFzJo1C7fffrsydeucPn0aBQUFDR4zMTFRxgxtw218/PjxesfVl7mNMV9C8LejEJrt+Pbbb6twgAULFgTmj/BXsf3ud7/THn74YS0hIUEzGo1a//79tbvvvluZ8rjN0yS4Z/78+Up9X3fddW63L1myRG2neVUIXrz9+vLHsjlv3rx6v/Bcp1tvvdXt+eRXd9to48jISG3BggV1ym3btk2V++abbwL8FwjN1Y7OyL0XGu24ePFiLSYmJqDePb8tXgz+ZI8pvVfjtm3bsH37dq/7PfHEE/6eMqQ5dOiQ+uzUqZPb7fr6gwcPtmi9hNa3bD722GN4+OGHvY4kIbTdNu7YsSNOnDhRp5y+LB0kWgfxQoQGa5rgTfrggw8wbdo0vPbaayq4PlD4LbxGjx4tmesD3BuGxMXFud3erl27OuWEtsnmzZvVpyd3sb5eL0f4Ayciwu9bVWgDbTxy5Eh8/vnnqkezDpf5g4tpQYS20Y5C6LTja6+9hl/+8pfKzXjDDTcEtE5+P82//vrrgFYk3NEth56GYZJ8aaFBS1g2Kc737Nmj5jmAPS0nTFEQFRWl0kwIwdfGDzzwAEaMGIGHHnoI06dPx3fffYeXX35Z5fKSodnaTjvKvRca7Th//nz8+te/VjFdY8aMcVifTSaTyu3VVMQ/ESQwSRspKytzu728vFx96kGBQtukJSybDAKlSZ0TA7P/+te/qnnpABO8bcwg4cWLFysrFwOHGZIxb9481XlJaDvtKPdeaLTjn//8Z1gsFtx9990qDECfeJ8GAvFfBAkcdJwcOXLE7XY9c724Hdo2LWHZ5KgSYiFte218+eWXq0lou+0o915otOOBZk5WHhDhRXfGl19+qdR+Xl6e+gNpjqM6vPDCC5WLQ2gY/soldAm5Y+PGjY6xMYW2i1g2Qx9p49BA2jE0SAhCb1KThderr76KOXPm4NSpU263p6Wl4amnnsIdd9zR1FOFNAyu5TBMu3fvVkF+uhDTWbRokfq88sorW6mGQiAQy2boI20cGkg7hgZdgtCb1KQYLwaC3nPPPcjPz1fmOg7oPGzYMDVxnuu4jX5Sb93hwx1aBfV4jpkzZ9ZR5y+++KKyhDHIj4N1Cm0XsWyGPtLGoYG0Y2gwMBi9Sf4mAPv666/VkEGcpkyZou3cubNemZ9++kklBGUZJlldtWpVU/OOhTQc1uC8885Tydw4BhWHNdCX09PTtb1797Z2FYUmJvOrqqrSkpKSVLlNmzbV2z5o0CC1jeORCcGJtHFoIO0YGqANPnP9Fl66oJoxY4bXsizDshQSQsOUl5erMdu6d++uBiHPzMzUpk2bph0+fFguXRugMeOGPfbYY6rcyJEjtdLS0nqjF4wZM6YFair4i7RxaCDtGBqgDT5zDfzPH0sZc18wt8WxY8e8jhF38uRJ5Xpkd0xPflZBaIv85z//Ud3+dZh/iZ1L6G7XYQykc2+1yspK1fuJZXlfjBo1SuWQ4TI7pXz77bfo1q1bi/8tgnukjUMDacfQ4D+h8Mz1V7FRYaakpDS6PMt6U6WC0NZYuHCh13EUWcYVsWy2HaSNQwNpx9BgYQg8c/22eFElFhUVobi4GDExMQ2WraioUD32EhMTPfZ+FARBEARBCHX87tXIHgDM7Prmm296LcsyZrO5XooEQRAEQRCEcMJv4fWzn/1MpYuYNWsW3njjDY/lXn/9dVWGPthAju4tCIIgCILQ1vDb1Wi1WjFhwgSsXLlSiSoG248bNw7Z2dlq+fDhw1ixYoVKTsZTMLBt2bJlMuCrIAiCIAhhi9/CizC+67bbbsPHH3/sdiwk/dCTJ09WVjHGeAmCIAiCIIQrTRJeOuvXr8cHH3zgGKuRMMXEkCFDMHXq1ICN6C0IgiAIgoBwF16CIAiCIAhCM4/VKAiCIAiCIDQeEV6CIAiCIAgtRERjC65atSogJxw9enRAjiMIgiAIghCyMV5Go7HJqSC4PxOpCoIgCIIghCONtngRicMXBEEQBEFoAeG1f/9+nw/O1BJPPfUUlixZIqJNEARBEISwp9HCKycnp9EXq7y8HM8//7yaSkpKlOjq3bs3fv/734f9BRcEQRAEIXzxydXoDQ6a/Ze//EVZuWjtouDiUEK//e1vMW3aNBUnJgiCIAiCEK4ETHgxc/2cOXOwb98+Jbjat2+Phx9+GPfddx+io6MDdRpBEARBEITwFV5Lly7FI488gk2bNinBFRsbi1/+8pdqXVJSUmBqKQiCIAiCEM7Ci+My0qK1YsUKJbhMJhOmT5+u3IpZWVmBraUgCIIgCEI4Cq89e/bg0UcfxaJFixw9Fa+55hoVOH/WWWc1Rx0FQRAEQRDCS3idOHECv/vd7/Dmm2+ipqZGrRszZgyeffZZDBs2rDnrKAiCIAiCEF6Z6+Pj41FRUaGsXAMHDsQzzzyDSy65pPlrKAiCIAiCECIYfcnNpQ/7c/r0afziF79At27dfJq6d+/enH+LIAhC2PL444+r5/Nzzz3X2lURnGAqJbYLP8Px/KFW50suuUTVbfny5S07ZNChQ4f8OllTx3oUBEEQ6nPkyBG88MILSE9Px8yZM+ttZ6cnhor4OvQbf3CvXLkSP/zwAzZu3Kg+9ef/3Llz1XEFwRfeeustHDhwAGPHjlVTW4Pf+S+++AKzZ89WnQz9yU/aaOHFm0wQBEEIPh577DEVCvLkk0+iXbt2ATvu999/j8suuyxgxxNano4dO6qOb/wMFuG1cuVKNe9JeAVbnZ05//zzcfHFFyvx9be//Q233HKLz8cQ4SUIgtCGOXr0KN577z1ERUXhtttuC/jxmQx78ODBjumBBx5Qna2EtgHjsTm1JZ4J8jrffffdSnjRrd+swksQBEEIPl577TU1XNuVV16JlJSUgB77ggsuQGFhYZ11zN8oCOHMZZddpu617du3Y+3atRg5cqRP+8vgiYIgCM3M+++/r9LvcDSP5ORkDB06FK+//roSTIRj3DIG9s477/TpuIzXeuONN9T8TTfdFPB6MzF2c0E3E/9mxswwRdHzzz+PIUOGqOvD9V9//XW9fWhpo/Bjz3pey5iYGNVxa8aMGdixY0eD12nhwoUYPnw4EhIS1L7nnXceXn31VbWtoWDu3NxctY0uskAHgxcVFanh9n72s5+hf//+6mXOvyknJ0e157ffftvk6+etbv/85z9x6aWXIiMjA5GRkWr/nj174qqrrsKCBQtQWVkZkDrz+rEeK+1uRsYcctl5YuxXY68n/77rrrsO2dnZaljCtLQ0TJgwQbWzfl95u25se/5w4XchMTFRfTf4HaELsSFoXZ48ebKa53fIV8TiJQiC0ExUV1fj5ptvVi83whcUH/YMyuW0efNmvPzyy47t+sO8sWzbtk0F1uvWqbYIX+x8Ga5btw4RERHq5eeOJUuW4MYbb0RpaalapkjgC3D//v1KfL777rvqJerq+uFLmCLhH//4h1rmS5figtefMWx8gfM4rcH8+fMdnR70tE2EHRg4UeC8+OKLaszjpl4/d9x+++0qN6fz+SnimCid06efforLL79cic+m1pnDCVLcFRYWqnMwFlHf11eh/+CDD6p66O1JIX3mzBnV05AThdPixYsbvBb8XjD5+7///W913eLi4lBSUqKEI6fdu3fX+TtdGT16tPq+ff755/AVsXgJgiA0Y9A7RRUf7G+//bYSDWVlZfjrX/+qXhivvPIKvvrqK6xatUqJgfHjx/t0fO5HOnfujMzMTLRFaFXZsmWLslQUFxerF/OpU6cwYMAARxkKJIpSXr+77roLO3fuVJ0JuHzw4EGV3ogil0KCgsqZP/7xjw7RxRd2fn6+OgfTInHEFQqFTz75BK0B24wxc3zRsz588fPv2rdvH+6//35HnX/88ccmXT93rFmzRoku9spjIvSCggJ1fn4/uT9jmG699dZ6otTfOt9www3KYjlixAi1zF6BXHae+D32Bu8ZXXTRQnzs2DFVD1riuJ73GsXXHXfc0eBxeN0oummJ43Xj/ocPH1Yue/LUU08p8eUJWslIXl4efvrpJ/gEE6gKgiAIgSU/P1+Ljo5m7gbt3nvvrbf9lltuUds6d+6sPm+++Wafz8F9uO8VV1zRYLm5c+eqcoF45Ofk5Kjj8JhNYcyYMY46ffLJJw2WHTp0qCo3Z84cj2Xuu+8+VWbSpEmOdWVlZVpiYqJaf/vtt3u9NrfeeqvHv3fhwoUez839PO3f0DZvzJw502PdG3v9PJ3/2WefVesnTpzoc738rbNzvec28P3xVOfy8nItJSVFbbvxxhvd7vvSSy85rsv69es9np/T8uXL622vrKzUsrKy1Pannnqqwb81Pj5elXvzzTc1XxCLlyAIQjOwdOlSVFVVOXpBuTJ16lT1yV/Z/rgZCX/tE+bvaqv069fPYWVwB92x69evV67FWbNmeSynuxhpQdRjfGi1oTVDtz66g8ekmykYoZtPt075e/08QQsroQWwoZio5qizv3z55ZeOzh6ecsjR+qmnoWBspScYED9u3Lh66xkvxnQRhJbEhkhNTa1zHzYWifESBEFoBrZu3ep4OPPl6AqDeHUY66I/7H2BL00S6N6MLYm3HmH6C9xqtarcTp7QxQNdZXSbdejQQSV9JV26dEHXrl3d7sc4oHPPPRerV69Ga0AX3f/93/9hxYoV2Lt3r3Ld8W91Ro/jc4evPep0LrzwQhVzSJcg4wPppqWr29N1CmSd/UV3I9Ml2atXL7dlGCfGv4MpVlzdzu5che7IyspSn649el3hfUdXt34fNhYRXoIgCM2A/tDmS9+TxYHDqPHFxe7pfAn6it7jjL/S2yoUSA2hWxMorE6ePOnTEHf6C1F/kXqCPeNag3/961+qw4BuGSXsXcfvAmMAGbfG+CWKSX+vnyfYG5Q9a2mN/eabb9SkW09pCWIPRfZsdB1xJhB19hfGUzWmvTp16lSnvDsaCrxnnBhhJ4CGYIcB4trz0xviahQEQWhG9IezO/RgYr7g/EF3dfBF11bx1pNNt2T17t1b9QhtzKT3wtOHR/I2XJ0vwygFClrlmC6BAoYWGgZ6UzAyyJsCk8HmH374YbOm/GBvT1psmM6Ewe/8PlKsskPI1VdfrVKg6K7aQNa5qTR2+MHmHqZQ/3Gl34eNRYSXIAhCM6C7/zy5IfjQZgoAoqdI8BU9tsubS6Qto/fWpHvLVyuKbg3yFoPT0Hbd+tGQVYPCw1c+++wzJWo4MgDTNlDkuIr0lhghgN9T9hRl706mg2AaCeZKo2ih+9U5lqq169zB3p56XKQndDdnc8c+6vedr+cR4SUIgtAMnH322Q7BQEuBK7Qq0C3TmCBeT/Tt29dxjlBFj2HitaKbyxc4xBGhVUdPzukKRS8H//YERUZDL3vGNjUUS+QJ/XiMW/MU3M+OAi0N3d8crkdPyMuA9kDW2WgfVNofKyMTxOrCateuXR4tpIw9I0xU3Fwwro1pN0ifPn182leElyAIQjMwceJEZS3hi4B5u5zhOiZO1WGvPX9gEke9559zzE0owZftOeec4+iZ6C2Q2dn6xzZg/BFhzi53MPeTHhPmDmbJJxR97sQC87P5E0jOpJ+EAsKdNW3Tpk34+9//jubC2/dFt2Q5uzIDUedEe3sw4amvXHTRRQ63nqdejbzXdAsmY9GaC4ptim7e4zJkkCAIQhBA9wMTUOovCWa5NpvN6oU3c+ZMNcQN3Wh8kVB46dnrfYEPfD74aQ3iS68x8Fd6Q5PrC5HxY87b9d5rFCvO6/11l3qDLi/GILEDAV1h7I320Ucf1RFLHCic2cr5Yn7ooYcc65kdXV/m9f/Nb37jEGa0WDBxKNtGt2q5Q395M2krE3bq1ku63CjaGJzuT69SikJaf1gfxlrxbyBsS34XuN2XLPS+cu+99+L666/HokWL6gShsx15vd955x21zI4fgazz2XZLMN2W+v6NhWJQF1xMFcFrr3e44PeBP2Z+9atfqWXGrLG3anPx3XffOayqrhn4veJT1i9BEASh0Zw+fVobNGiQI2FjTEyMFhUVpeaNRqO2aNEibfbs2Y7tHTp00NauXevTFWbCUO776KOPeizjnCTU2zRw4EC3CUS9Tb4mCG1MIk1nli5dqqWmpjrOZzKZ1HJcXFydesyYMaPOfjU1NdqUKVMc23nd27dvr/bXE9fqyWzvuusut+fWt+tTcnKyOo6eHNffBKoPPfRQneMmJSVpkZGRar5r167ae++95zHxbWOvn6fz6+v1iclA+Xc5rxs1apRWWloasDqTXbt2qftAb4uMjAz1HeN0+PBhr9eMPPDAA45zGAwG1Z4RERGOdePGjdOKi4vd7tuY66bfLyzrieHDh6syL774ouYr4moUBEFoJpgyYu3atWr4EQ7hQrcNLUbsyk+LwrXXXqtcYEziSTcOLQ963FdjYWA0oYunNXrntRS0ZjHwm/FHo0aNcozPRwsMY92Yh4pD/zi7cAktgrTGMHXCsGHDlNWElke6MLmO7aBb+fSkoq5waJ0///nPGDRokNqfbUhrI4cicj2fL/zhD39Q59frxfQFPXr0wKOPPqrya3lLg9EU5syZg5deekmNV8geo7xOtHYxgJ3Xmn8zey3SahjIOvfs2VPFYLEnL63CtCAyBo8T26UxvPDCC2pYICYd5viPrDctbUyDwXozLq05rYUcH5TpN/j3u44N2hgMVF/NUjNBEASh2aEIYDJJ5gNbuXKlI+5LaBx8BTLXGuO0KCg4qLkgNMSTTz6JuXPnYvr06XUGGW8sYvESBEFow9DiM2/ePIc1QvCNd999V4kuWnwmTJggl09oEKY0oZWTMYcUX/4gwksQBKGNw3Ef6fr573//6wj6FeoGyDMgX+/+TxiUTaF6xx13qGW6jJrTtSeEBq+88or6Ht13333Iycnx6xjiahQEQQgB2Ktx8eLFKneRPlCxAEfslp7klPmnOOC2c9JTjlW4ZMkSR6oDQWhIeDEujb0n9fQaviLCSxAEQQhpGLtFayCDv9mBgcHYFGMMlqe1kHFdFGOC0BKI8BIEQRAEQWghJMZLEARBEAShhRDhJQiCIAiC0EKI8BIEQRAEQWghRHgJgiAIgiC0ECK8BEEQBEEQWggRXoIgCIIgCC2ECC9BEARBEIQWQoSXIAiCIAhCCyHCSxAEQRAEoYUQ4SUIgiAIgtBCiPASBEEQBEFAy/D/aufMJK+mfUoAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.cm as cm\n",
+    "\n",
+    "colours = cm.tab10(np.linspace(0, 1, len(all_features)))\n",
+    "\n",
+    "fig, ax = plt.subplots() # figsize=(, 5))\n",
+    "\n",
+    "for i, feat in enumerate(all_features):\n",
+    "    feat_data = l1_df[l1_df[\"feature\"] == feat]\n",
+    "    base_imp = feat_data[feat_data[\"reg_alpha\"] == 0][\"importance_mean\"].values[0]\n",
+    "    if base_imp > 0:\n",
+    "        norm_mean = feat_data[\"importance_mean\"].values / base_imp\n",
+    "        norm_std = feat_data[\"importance_std\"].values / base_imp\n",
+    "    else:\n",
+    "        norm_mean = feat_data[\"importance_mean\"].values\n",
+    "        norm_std = feat_data[\"importance_std\"].values\n",
+    "    ax.errorbar(feat_data[\"reg_alpha\"], norm_mean, yerr=norm_std,\n",
+    "                marker=\"o\", label=feat, capsize=3, color=colours[i])\n",
+    "\n",
+    "ax.set_xlabel(r\"$\\alpha$ (L1 regularisation)\")\n",
+    "ax.set_xscale(\"symlog\", linthresh=0.1)\n",
+    "ax.set_ylabel(\"Normalised average importance\")\n",
+    "ax.legend(loc=\"upper left\", ncols=2) # bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n",
+    "ax.axhline(0, color=\"grey\", linestyle=\"--\", alpha=0.5)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(f\"../output/images/ml/{run}/features/l1_importance_cv.png\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "505c4961-01a4-4069-aa65-ad0076cc2c2b",
+   "metadata": {},
+   "source": [
+    "## Very interesting plot\n",
+    "\n",
+    "L1 penalises the absolute value of leaf weights, which pushes small weights exactly to zero rather than just reducing them (L2). Features that contribute little have their weights zeroed out and the model stops splitting on them.\n",
+    "\n",
+    "- sector is the only feature that gets suppressed by L1, drops to near zero at alpha=100; L1 is doing actual feature selection, confirming sector is the least useful feature.\n",
+    "- crv_y stays flat around 1, L1 doesn't penalise it but it doesn't grow either. Genuinely low importance.\n",
+    "  \n",
+    "Everything else increases with alph. As the model gets more constrained, the remaining splits need to extract more gain per feature, so the per-split gain goes up. It's an artefact of normalisation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f07388b-b788-40cf-81ee-643298d34c90",
+   "metadata": {},
+   "source": [
+    "## Forward selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5ca4bac-d879-4019-86db-e94280c3468b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fold 0 done\n",
+      "Fold 1 done\n",
+      "Fold 2 done\n",
+      "Fold 3 done\n",
+      "Fold 4 done\n"
+     ]
+    }
+   ],
+   "source": [
+    "# From permuation importance\n",
+    "\n",
+    "# feature_order = [\"PEs\", \"dT\", \"nHits\", \"crv_z\", \"angle\", \"crv_y\", \"nLayers\", \"crv_x\", \"sector\"]\n",
+    "feature_order = [\"dT\", \"PEs\",  \"crv_z\", \"angle\", \"crv_x\", \"crv_y\", \"nHits\", \"nLayers\", \"sector\"]\n",
+    "\n",
+    "fold_deadtimes = []\n",
+    "fold_aucs = []\n",
+    "fold_train_aucs = []\n",
+    "\n",
+    "for k, (train_idx, test_idx) in enumerate(folds):\n",
+    "    deadtimes_k = []\n",
+    "    aucs_k = []\n",
+    "    train_aucs_k = []\n",
+    "    for n in range(1, len(feature_order) + 1):\n",
+    "        features = feature_order[:n]\n",
+    "        fold_data = {\n",
+    "            \"X_train\": X[features].iloc[train_idx],\n",
+    "            \"X_test\": X[features].iloc[test_idx],\n",
+    "            \"y_train\": y.iloc[train_idx],\n",
+    "            \"y_test\": y.iloc[test_idx],\n",
+    "            \"metadata_train\": metadata.iloc[train_idx],\n",
+    "            \"metadata_test\": metadata.iloc[test_idx],\n",
+    "        }\n",
+    "        trn = Train(fold_data, run=run, verbosity=0)\n",
+    "        results = trn.train(tag=f\"fwd_fold{k}_{n}\", save_output=False, **best_hp)\n",
+    "        val = Validate(results, run=run, verbosity=0)\n",
+    "        val.roc_auc()\n",
+    "        thr = val.find_threshold(min_eff=0.999, plot=False, show=False)\n",
+    "        deadtimes_k.append(thr[\"deadtime\"] * 100)\n",
+    "        aucs_k.append(val._test_auc)\n",
+    "        train_aucs_k.append(val._train_auc)\n",
+    "    fold_deadtimes.append(deadtimes_k)\n",
+    "    fold_aucs.append(aucs_k)\n",
+    "    fold_train_aucs.append(train_aucs_k)\n",
+    "    print(f\"Fold {k} done\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "dc8565a9-0321-4463-9be0-c7b9c6a8ee1e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot\n",
+    "xlabels = [feature_order[0]] + [f\"+ {f}\" for f in feature_order[1:]]\n",
+    "x = np.arange(1, len(feature_order) + 1)\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 4.8))\n",
+    "\n",
+    "# AUC panel — train and test\n",
+    "ax = axes[0]\n",
+    "for k in range(len(folds)):\n",
+    "    train_label=\"\"\n",
+    "    test_label=\"\"\n",
+    "    if k == 0:\n",
+    "        train_label=\"Train folds\"\n",
+    "        test_label=\"Test folds\"\n",
+    "    ax.plot(x, fold_aucs[k], \"o-\", alpha=0.2, color=\"blue\", label=test_label)\n",
+    "    ax.plot(x, fold_train_aucs[k], \"s-\", alpha=0.2, color=\"red\", label=train_label)\n",
+    "mean_test = np.mean(fold_aucs, axis=0)\n",
+    "std_test = np.std(fold_aucs, axis=0)\n",
+    "mean_train = np.mean(fold_train_aucs, axis=0)\n",
+    "std_train = np.std(fold_train_aucs, axis=0)\n",
+    "ax.errorbar(x, mean_test, yerr=std_test, fmt=\"o-\", color=\"blue\", capsize=3, label=\"Test mean $\\pm$ std\")\n",
+    "ax.errorbar(x, mean_train, yerr=std_train, fmt=\"s-\", color=\"red\", capsize=3, label=\"Train mean $\\pm$ std\")\n",
+    "\n",
+    "ax.set_xticks(x)\n",
+    "ax.set_xticklabels(xlabels, rotation=45, ha=\"right\")\n",
+    "ax.set_ylabel(\"AUC\")\n",
+    "ax.set_xlabel(\"Features (cumulative)\")\n",
+    "ax.legend(loc=\"best\")\n",
+    "\n",
+    "# Deadtime panel\n",
+    "ax = axes[1]\n",
+    "for k in range(len(folds)):\n",
+    "    label=\"\"\n",
+    "    if k == 0:\n",
+    "        label=\"Test folds\"\n",
+    "    ax.plot(x, fold_deadtimes[k], \"o-\", alpha=0.2, color=\"grey\", label=label)\n",
+    "mean_dt = np.mean(fold_deadtimes, axis=0)\n",
+    "std_dt = np.std(fold_deadtimes, axis=0)\n",
+    "ax.errorbar(x, mean_dt, yerr=std_dt, fmt=\"o-\", color=\"green\", capsize=3, label=\"Test mean $\\pm$ std\")\n",
+    "ax.set_xticks(x)\n",
+    "ax.set_xticklabels(xlabels, rotation=45, ha=\"right\")\n",
+    "ax.set_ylabel(\"Deadtime [%]\")\n",
+    "ax.set_xlabel(\"Features (cumulative)\")\n",
+    "ax.legend(loc=\"best\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(f\"../output/images/ml/{run}/features/forward_selection_cv.png\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b32aab9b-b70e-45f3-b111-39ea9e1020c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xlabels = [feature_order[0]] + [f\"+ {f}\" for f in feature_order[1:]]\n",
+    "x = np.arange(1, len(feature_order) + 1)\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
+    "\n",
+    "# AUC panel — train and test\n",
+    "ax = axes[0]\n",
+    "for k in range(len(folds)):\n",
+    "    train_label=\"\"\n",
+    "    test_label=\"\"\n",
+    "    if k == 0:\n",
+    "        train_label=\"Train folds\"\n",
+    "        test_label=\"Test folds\"\n",
+    "    ax.plot(x, fold_aucs[k], \"o-\", alpha=0.2, color=\"blue\", label=test_label)\n",
+    "    ax.plot(x, fold_train_aucs[k], \"s-\", alpha=0.2, color=\"red\", label=train_label)\n",
+    "mean_test = np.mean(fold_aucs, axis=0)\n",
+    "std_test = np.std(fold_aucs, axis=0)\n",
+    "mean_train = np.mean(fold_train_aucs, axis=0)\n",
+    "std_train = np.std(fold_train_aucs, axis=0)\n",
+    "ax.errorbar(x, mean_test, yerr=std_test, fmt=\"o-\", color=\"blue\", capsize=3, label=\"Test mean $\\pm$ std\")\n",
+    "ax.errorbar(x, mean_train, yerr=std_train, fmt=\"s-\", color=\"red\", capsize=3, label=\"Train mean $\\pm$ std\")\n",
+    "ax.set_xticks(x)\n",
+    "ax.set_xticklabels(xlabels, rotation=45, ha=\"right\")\n",
+    "ax.set_ylabel(\"AUC\")\n",
+    "ax.set_xlabel(\"Features (cumulative)\")\n",
+    "ax.legend(loc=\"best\")\n",
+    "\n",
+    "# Overfit gap panel\n",
+    "ax = axes[1]\n",
+    "fold_gaps = np.array(fold_train_aucs) - np.array(fold_aucs)\n",
+    "for k in range(len(folds)):\n",
+    "    label=\"\"\n",
+    "    if k == 0:\n",
+    "        label=\"Folds\"\n",
+    "    ax.plot(x, fold_gaps[k], \"o-\", alpha=0.2, color=\"grey\", label=label)\n",
+    "mean_gap = np.mean(fold_gaps, axis=0)\n",
+    "std_gap = np.std(fold_gaps, axis=0)\n",
+    "ax.errorbar(x, mean_gap, yerr=std_gap, fmt=\"o-\", color=\"purple\", capsize=3, label=\"Mean $\\pm$ std\")\n",
+    "ax.axhline(0, color=\"grey\", linestyle=\"--\", alpha=0.5)\n",
+    "ax.ticklabel_format(axis=\"y\", style=\"scientific\", scilimits=(0, 0), useMathText=True)\n",
+    "ax.set_xticks(x)\n",
+    "ax.set_xticklabels(xlabels, rotation=45, ha=\"right\")\n",
+    "ax.set_ylabel(\"Train AUC $-$ test AUC\")\n",
+    "ax.set_xlabel(\"Features (cumulative)\")\n",
+    "ax.legend(loc=\"best\")\n",
+    "\n",
+    "# Deadtime panel\n",
+    "ax = axes[2]\n",
+    "for k in range(len(folds)):\n",
+    "    label=\"\"\n",
+    "    if k == 0:\n",
+    "        label=\"Test folds\"\n",
+    "    ax.plot(x, fold_deadtimes[k], \"o-\", alpha=0.2, color=\"grey\", label=label)\n",
+    "mean_dt = np.mean(fold_deadtimes, axis=0)\n",
+    "std_dt = np.std(fold_deadtimes, axis=0)\n",
+    "ax.errorbar(x, mean_dt, yerr=std_dt, fmt=\"o-\", color=\"green\", capsize=3, label=\"Test mean $\\pm$ std\")\n",
+    "ax.set_xticks(x)\n",
+    "ax.set_xticklabels(xlabels, rotation=45, ha=\"right\")\n",
+    "ax.set_ylabel(\"Deadtime [%]\")\n",
+    "ax.set_xlabel(\"Features (cumulative)\")\n",
+    "ax.legend(loc=\"best\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(f\"../output/images/ml/{run}/features/forward_selection_cv.png\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "02fdc85b-1c18-4edb-a68f-86be2af770d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>features</th>\n",
+       "      <th>n_features</th>\n",
+       "      <th>mean_train_auc</th>\n",
+       "      <th>std_train_auc</th>\n",
+       "      <th>mean_test_auc</th>\n",
+       "      <th>std_test_auc</th>\n",
+       "      <th>mean_deadtime</th>\n",
+       "      <th>std_deadtime</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PEs</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.976098</td>\n",
+       "      <td>0.000077</td>\n",
+       "      <td>0.975535</td>\n",
+       "      <td>0.000348</td>\n",
+       "      <td>35.157984</td>\n",
+       "      <td>8.269605</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>+ dT</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.998693</td>\n",
+       "      <td>0.000007</td>\n",
+       "      <td>0.998462</td>\n",
+       "      <td>0.000072</td>\n",
+       "      <td>8.971119</td>\n",
+       "      <td>4.079052</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>+ nHits</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.999871</td>\n",
+       "      <td>0.000003</td>\n",
+       "      <td>0.999749</td>\n",
+       "      <td>0.000051</td>\n",
+       "      <td>2.829368</td>\n",
+       "      <td>0.812798</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>+ crv_z</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.999964</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>0.999856</td>\n",
+       "      <td>0.000043</td>\n",
+       "      <td>0.931047</td>\n",
+       "      <td>0.415382</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>+ angle</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.999977</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>0.999861</td>\n",
+       "      <td>0.000052</td>\n",
+       "      <td>0.264191</td>\n",
+       "      <td>0.110024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>+ crv_x</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.999980</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>0.999878</td>\n",
+       "      <td>0.000046</td>\n",
+       "      <td>0.199696</td>\n",
+       "      <td>0.100939</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>+ crv_y</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.999983</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>0.999879</td>\n",
+       "      <td>0.000055</td>\n",
+       "      <td>0.139000</td>\n",
+       "      <td>0.095477</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>+ nLayers</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.999984</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>0.999893</td>\n",
+       "      <td>0.000040</td>\n",
+       "      <td>0.113243</td>\n",
+       "      <td>0.063859</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>+ sector</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.999984</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>0.999889</td>\n",
+       "      <td>0.000044</td>\n",
+       "      <td>0.119294</td>\n",
+       "      <td>0.066426</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    features  n_features  mean_train_auc  std_train_auc  mean_test_auc  \\\n",
+       "0        PEs           1        0.976098       0.000077       0.975535   \n",
+       "1       + dT           2        0.998693       0.000007       0.998462   \n",
+       "2    + nHits           3        0.999871       0.000003       0.999749   \n",
+       "3    + crv_z           4        0.999964       0.000002       0.999856   \n",
+       "4    + angle           5        0.999977       0.000002       0.999861   \n",
+       "5    + crv_x           6        0.999980       0.000002       0.999878   \n",
+       "6    + crv_y           7        0.999983       0.000002       0.999879   \n",
+       "7  + nLayers           8        0.999984       0.000002       0.999893   \n",
+       "8   + sector           9        0.999984       0.000002       0.999889   \n",
+       "\n",
+       "   std_test_auc  mean_deadtime  std_deadtime  \n",
+       "0      0.000348      35.157984      8.269605  \n",
+       "1      0.000072       8.971119      4.079052  \n",
+       "2      0.000051       2.829368      0.812798  \n",
+       "3      0.000043       0.931047      0.415382  \n",
+       "4      0.000052       0.264191      0.110024  \n",
+       "5      0.000046       0.199696      0.100939  \n",
+       "6      0.000055       0.139000      0.095477  \n",
+       "7      0.000040       0.113243      0.063859  \n",
+       "8      0.000044       0.119294      0.066426  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame({\n",
+    "    \"features\": [feature_order[0]] + [f\"+ {f}\" for f in feature_order[1:]],\n",
+    "    \"n_features\": range(1, len(feature_order) + 1),\n",
+    "    \"mean_train_auc\": mean_train,\n",
+    "    \"std_train_auc\": std_train,\n",
+    "    \"mean_test_auc\": mean_test,\n",
+    "    \"std_test_auc\": std_test,\n",
+    "    \"mean_deadtime\": mean_dt,\n",
+    "    \"std_deadtime\": std_dt,\n",
+    "})\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "45a19537-f206-4e7e-ae26-9dbe0781fc7a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fold 0: 432706 samples, class ratio = 4692/432706 (1.08%)\n",
+      "Fold 1: 432706 samples, class ratio = 4797/432706 (1.11%)\n",
+      "Fold 2: 432706 samples, class ratio = 4806/432706 (1.11%)\n",
+      "Fold 3: 432705 samples, class ratio = 4684/432705 (1.08%)\n",
+      "Fold 4: 432705 samples, class ratio = 4748/432705 (1.10%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Class imbalance across folds\n",
+    "for k, (train_idx, test_idx) in enumerate(folds):\n",
+    "    y_test_fold = y.iloc[test_idx]\n",
+    "    print(f\"Fold {k}: {len(test_idx)} samples, \"\n",
+    "          f\"class ratio = {y_test_fold.sum()}/{len(y_test_fold)} \"\n",
+    "          f\"({y_test_fold.mean()*100:.2f}%)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "735cb213-9c2e-444b-8375-2cc1f749e4de",
+   "metadata": {},
+   "source": [
+    "## CRV-XY out of bounds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "08a3b601-dfc9-404d-8b5b-88fd6345d2be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CRY x range: [-3270, 4359]\n",
+      "CRY y range: [-2282, 2898]\n",
+      "\n",
+      "CE Mix out of CRY bounds:\n",
+      "  crv_x: 58505/2139801 (2.73%)\n",
+      "  crv_y: 19116/2139801 (0.89%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# CRY bounds\n",
+    "cry_x_min, cry_x_max = data[\"X\"][data[\"y\"]==1][\"crv_x\"].min(), data[\"X\"][data[\"y\"]==1][\"crv_x\"].max()\n",
+    "cry_y_min, cry_y_max = data[\"X\"][data[\"y\"]==1][\"crv_y\"].min(), data[\"X\"][data[\"y\"]==1][\"crv_y\"].max()\n",
+    "print(f\"CRY x range: [{cry_x_min:.0f}, {cry_x_max:.0f}]\")\n",
+    "print(f\"CRY y range: [{cry_y_min:.0f}, {cry_y_max:.0f}]\")\n",
+    "\n",
+    "# CE Mix out of bounds\n",
+    "ce_x = data[\"X\"][data[\"y\"]==0][\"crv_x\"]\n",
+    "ce_y = data[\"X\"][data[\"y\"]==0][\"crv_y\"]\n",
+    "oob_x = ((ce_x < cry_x_min) | (ce_x > cry_x_max)).sum()\n",
+    "oob_y = ((ce_y < cry_y_min) | (ce_y > cry_y_max)).sum()\n",
+    "print(f\"\\nCE Mix out of CRY bounds:\")\n",
+    "print(f\"  crv_x: {oob_x}/{len(ce_x)} ({oob_x/len(ce_x)*100:.2f}%)\")\n",
+    "print(f\"  crv_y: {oob_y}/{len(ce_y)} ({oob_y/len(ce_y)*100:.2f}%)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "2c363462-b03a-42ba-a332-f64c2695843d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Median PEs (in bounds):  220\n",
+      "Median PEs (out of bounds): 369\n",
+      "Median nHits (in bounds):  10\n",
+      "Median nHits (out of bounds): 17\n"
+     ]
+    }
+   ],
+   "source": [
+    "ce = data[\"X\"][data[\"y\"]==0]\n",
+    "oob_mask = (ce[\"crv_x\"] < cry_x_min) | (ce[\"crv_x\"] > cry_x_max)\n",
+    "\n",
+    "print(f\"Median PEs (in bounds):  {ce[~oob_mask]['PEs'].median():.0f}\")\n",
+    "print(f\"Median PEs (out of bounds): {ce[oob_mask]['PEs'].median():.0f}\")\n",
+    "print(f\"Median nHits (in bounds):  {ce[~oob_mask]['nHits'].median():.0f}\")\n",
+    "print(f\"Median nHits (out of bounds): {ce[oob_mask]['nHits'].median():.0f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a229f4b-2d0d-4b93-8277-a87ea0ba87cc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "mu2e_env [conda env:.conda-ana_v2.6.1]",
+   "language": "python",
+   "name": "conda-env-.conda-ana_v2.6.1-ana_v2.6.1"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/CrvCosmic/notebooks/optimise.ipynb b/CrvCosmic/notebooks/optimise.ipynb
new file mode 100644
index 0000000..de4edb5
--- /dev/null
+++ b/CrvCosmic/notebooks/optimise.ipynb
@@ -0,0 +1,913 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4fa3ed40",
+   "metadata": {},
+   "source": [
+    "# Optimise model (nested CV)\n",
+    "\n",
+    "Run grid search with cross-validation for hyperparameter optimisation.\n",
+    "For each outer fold, run inner CV grid search on the outer training set.\n",
+    "\n",
+    "This is heavy going, best to run this on a GPU node! "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6bb564a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run = \"k\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ac435b93",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append(\"../src/ml\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6eda72df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/sgrant/mu2e-cosmic/notebooks/ml/xgboost\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pwd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3cf17833",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[LoadML] [OK] Initialised\n",
+      "[Load] [OK] Initialised with out_path=/home/sgrant/mu2e-cosmic/output/ml/k/data/CRY_onspill-LH_aw\n",
+      "[Load] [OK] Successfully loaded results from /home/sgrant/mu2e-cosmic/output/ml/k/data/CRY_onspill-LH_aw/results.pkl\n",
+      "[Load] [OK] Initialised with out_path=/home/sgrant/mu2e-cosmic/output/ml/k/data/CE_mix2BB_onspill-LH_aw\n",
+      "[Load] [OK] Successfully loaded results from /home/sgrant/mu2e-cosmic/output/ml/k/data/CE_mix2BB_onspill-LH_aw/results.pkl\n",
+      "[LoadML] [OK] Got full results\n",
+      "[Assemble] [OK] Loaded data\n",
+      "[Assemble] [OK] Initialised\n",
+      "[Assemble] [DEBUG] Got sorted and labelled DataFrames\n",
+      "[Assemble] [DEBUG] Got combined dataset\n",
+      "[Assemble] [DEBUG] Columns: Index(['event', 'subrun', 'crv_x', 'crv_y', 'crv_z', 'PEs', 'dT', 'nHits',\n",
+      "       'nLayers', 'angle', 'timeStart', 'timeEnd', 'sector', 'PEs_per_hit',\n",
+      "       't0', 'd0', 'tanDip', 'maxr', 'mom_mag', 'label'],\n",
+      "      dtype='str')\n",
+      "[Assemble] [OK] Prepared 5-fold nested CV (event-level grouping)\n",
+      "  Total: 2163528 coincidences\n",
+      "  Fold test sizes: [432706, 432706, 432706, 432705, 432705]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from assemble import AssembleDataset\n",
+    "assembler = AssembleDataset(run=run, cutset_name=\"MLPreprocess\", verbosity=2)\n",
+    "data = assembler.assemble_dataset(n_outer_folds=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f69e1cfd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "============================================================\n",
+      "Outer fold 0\n",
+      "============================================================\n",
+      "[Optimise] [OK] Initialised\n",
+      "[Optimise] [INFO] CV grid search: 48 combinations x 5 folds = 240 fits over ['n_estimators', 'max_depth', 'learning_rate']\n",
+      "[Optimise] [INFO] [1/48] scan_cv_000: deadtime=7.487±3.384%, veto_eff=99.907%, overfit_gap=0.00003\n",
+      "[Optimise] [INFO] [2/48] scan_cv_001: deadtime=0.805±0.329%, veto_eff=99.907%, overfit_gap=0.00004\n",
+      "[Optimise] [INFO] [3/48] scan_cv_002: deadtime=0.195±0.093%, veto_eff=99.907%, overfit_gap=0.00008\n",
+      "[Optimise] [INFO] [4/48] scan_cv_003: deadtime=4.465±5.710%, veto_eff=99.907%, overfit_gap=0.00032\n",
+      "[Optimise] [INFO] [5/48] scan_cv_004: deadtime=0.439±0.235%, veto_eff=99.907%, overfit_gap=0.00005\n",
+      "[Optimise] [INFO] [6/48] scan_cv_005: deadtime=0.124±0.059%, veto_eff=99.907%, overfit_gap=0.00007\n",
+      "[Optimise] [INFO] [7/48] scan_cv_006: deadtime=2.243±2.504%, veto_eff=99.907%, overfit_gap=0.00032\n",
+      "[Optimise] [INFO] [8/48] scan_cv_007: deadtime=0.391±0.385%, veto_eff=99.907%, overfit_gap=0.00007\n",
+      "[Optimise] [INFO] [9/48] scan_cv_008: deadtime=0.111±0.034%, veto_eff=99.907%, overfit_gap=0.00009\n",
+      "[Optimise] [INFO] [10/48] scan_cv_009: deadtime=2.829±4.435%, veto_eff=99.907%, overfit_gap=0.00037\n",
+      "[Optimise] [INFO] [11/48] scan_cv_010: deadtime=0.362±0.368%, veto_eff=99.907%, overfit_gap=0.00008\n",
+      "[Optimise] [INFO] [12/48] scan_cv_011: deadtime=0.129±0.049%, veto_eff=99.907%, overfit_gap=0.00009\n",
+      "[Optimise] [INFO] [13/48] scan_cv_012: deadtime=0.954±0.469%, veto_eff=99.907%, overfit_gap=0.00003\n",
+      "[Optimise] [INFO] [14/48] scan_cv_013: deadtime=0.319±0.158%, veto_eff=99.907%, overfit_gap=0.00005\n",
+      "[Optimise] [INFO] [15/48] scan_cv_014: deadtime=0.116±0.058%, veto_eff=99.907%, overfit_gap=0.00008\n",
+      "[Optimise] [INFO] [16/48] scan_cv_015: deadtime=0.480±0.304%, veto_eff=99.907%, overfit_gap=0.00005\n",
+      "[Optimise] [INFO] [17/48] scan_cv_016: deadtime=0.181±0.098%, veto_eff=99.907%, overfit_gap=0.00006\n",
+      "[Optimise] [INFO] [18/48] scan_cv_017: deadtime=0.117±0.061%, veto_eff=99.907%, overfit_gap=0.00011\n",
+      "[Optimise] [INFO] [19/48] scan_cv_018: deadtime=0.359±0.344%, veto_eff=99.907%, overfit_gap=0.00006\n",
+      "[Optimise] [INFO] [20/48] scan_cv_019: deadtime=0.160±0.118%, veto_eff=99.907%, overfit_gap=0.00007\n",
+      "[Optimise] [INFO] [21/48] scan_cv_020: deadtime=0.106±0.045%, veto_eff=99.907%, overfit_gap=0.00011\n",
+      "[Optimise] [INFO] [22/48] scan_cv_021: deadtime=0.322±0.263%, veto_eff=99.907%, overfit_gap=0.00007\n",
+      "[Optimise] [INFO] [23/48] scan_cv_022: deadtime=0.186±0.145%, veto_eff=99.907%, overfit_gap=0.00009\n",
+      "[Optimise] [INFO] [24/48] scan_cv_023: deadtime=0.137±0.072%, veto_eff=99.907%, overfit_gap=0.00012\n",
+      "[Optimise] [INFO] [25/48] scan_cv_024: deadtime=0.236±0.113%, veto_eff=99.907%, overfit_gap=0.00005\n",
+      "[Optimise] [INFO] [26/48] scan_cv_025: deadtime=0.106±0.036%, veto_eff=99.907%, overfit_gap=0.00007\n",
+      "[Optimise] [INFO] [27/48] scan_cv_026: deadtime=0.101±0.051%, veto_eff=99.907%, overfit_gap=0.00012\n",
+      "[Optimise] [INFO] [28/48] scan_cv_027: deadtime=0.158±0.069%, veto_eff=99.907%, overfit_gap=0.00007\n",
+      "[Optimise] [INFO] [29/48] scan_cv_028: deadtime=0.118±0.070%, veto_eff=99.907%, overfit_gap=0.00010\n",
+      "[Optimise] [INFO] [30/48] scan_cv_029: deadtime=0.109±0.052%, veto_eff=99.907%, overfit_gap=0.00013\n",
+      "[Optimise] [INFO] [31/48] scan_cv_030: deadtime=0.125±0.088%, veto_eff=99.907%, overfit_gap=0.00007\n",
+      "[Optimise] [INFO] [32/48] scan_cv_031: deadtime=0.114±0.067%, veto_eff=99.907%, overfit_gap=0.00012\n",
+      "[Optimise] [INFO] [33/48] scan_cv_032: deadtime=0.103±0.042%, veto_eff=99.907%, overfit_gap=0.00015\n",
+      "[Optimise] [INFO] [34/48] scan_cv_033: deadtime=0.183±0.158%, veto_eff=99.907%, overfit_gap=0.00010\n",
+      "[Optimise] [INFO] [35/48] scan_cv_034: deadtime=0.157±0.148%, veto_eff=99.907%, overfit_gap=0.00013\n",
+      "[Optimise] [INFO] [36/48] scan_cv_035: deadtime=0.136±0.073%, veto_eff=99.907%, overfit_gap=0.00015\n",
+      "[Optimise] [INFO] [37/48] scan_cv_036: deadtime=0.114±0.040%, veto_eff=99.907%, overfit_gap=0.00007\n",
+      "[Optimise] [INFO] [38/48] scan_cv_037: deadtime=0.094±0.042%, veto_eff=99.907%, overfit_gap=0.00009\n",
+      "[Optimise] [INFO] [39/48] scan_cv_038: deadtime=0.106±0.047%, veto_eff=99.907%, overfit_gap=0.00014\n",
+      "[Optimise] [INFO] [40/48] scan_cv_039: deadtime=0.104±0.049%, veto_eff=99.907%, overfit_gap=0.00008\n",
+      "[Optimise] [INFO] [41/48] scan_cv_040: deadtime=0.098±0.059%, veto_eff=99.907%, overfit_gap=0.00012\n",
+      "[Optimise] [INFO] [42/48] scan_cv_041: deadtime=0.125±0.063%, veto_eff=99.907%, overfit_gap=0.00016\n",
+      "[Optimise] [INFO] [43/48] scan_cv_042: deadtime=0.104±0.059%, veto_eff=99.907%, overfit_gap=0.00011\n",
+      "[Optimise] [INFO] [44/48] scan_cv_043: deadtime=0.131±0.109%, veto_eff=99.907%, overfit_gap=0.00015\n",
+      "[Optimise] [INFO] [45/48] scan_cv_044: deadtime=0.117±0.054%, veto_eff=99.907%, overfit_gap=0.00018\n",
+      "[Optimise] [INFO] [46/48] scan_cv_045: deadtime=0.161±0.158%, veto_eff=99.907%, overfit_gap=0.00012\n",
+      "[Optimise] [INFO] [47/48] scan_cv_046: deadtime=0.147±0.127%, veto_eff=99.907%, overfit_gap=0.00014\n",
+      "[Optimise] [INFO] [48/48] scan_cv_047: deadtime=0.147±0.076%, veto_eff=99.907%, overfit_gap=0.00016\n",
+      "[Optimise] [OK] CV grid search complete. Best: scan_cv_037 (deadtime: 0.094%, veto eff: 99.907%)\n",
+      "Fold 0 best: {'n_estimators': 1000, 'max_depth': 3, 'learning_rate': 0.1}, deadtime: 0.094%\n",
+      "\n",
+      "============================================================\n",
+      "Outer fold 1\n",
+      "============================================================\n",
+      "[Optimise] [OK] Initialised\n",
+      "[Optimise] [INFO] CV grid search: 48 combinations x 5 folds = 240 fits over ['n_estimators', 'max_depth', 'learning_rate']\n",
+      "[Optimise] [INFO] [1/48] scan_cv_000: deadtime=8.566±3.022%, veto_eff=99.913%, overfit_gap=0.00002\n",
+      "[Optimise] [INFO] [2/48] scan_cv_001: deadtime=0.731±0.282%, veto_eff=99.907%, overfit_gap=0.00003\n",
+      "[Optimise] [INFO] [3/48] scan_cv_002: deadtime=0.175±0.039%, veto_eff=99.907%, overfit_gap=0.00006\n",
+      "[Optimise] [INFO] [4/48] scan_cv_003: deadtime=1.951±1.548%, veto_eff=99.907%, overfit_gap=0.00030\n",
+      "[Optimise] [INFO] [5/48] scan_cv_004: deadtime=0.476±0.241%, veto_eff=99.907%, overfit_gap=0.00006\n",
+      "[Optimise] [INFO] [6/48] scan_cv_005: deadtime=0.210±0.063%, veto_eff=99.907%, overfit_gap=0.00009\n",
+      "[Optimise] [INFO] [7/48] scan_cv_006: deadtime=0.833±0.226%, veto_eff=99.907%, overfit_gap=0.00036\n",
+      "[Optimise] [INFO] [8/48] scan_cv_007: deadtime=0.413±0.228%, veto_eff=99.907%, overfit_gap=0.00006\n",
+      "[Optimise] [INFO] [9/48] scan_cv_008: deadtime=0.189±0.091%, veto_eff=99.907%, overfit_gap=0.00012\n",
+      "[Optimise] [INFO] [10/48] scan_cv_009: deadtime=0.966±0.584%, veto_eff=99.907%, overfit_gap=0.00036\n",
+      "[Optimise] [INFO] [11/48] scan_cv_010: deadtime=0.316±0.139%, veto_eff=99.907%, overfit_gap=0.00009\n",
+      "[Optimise] [INFO] [12/48] scan_cv_011: deadtime=0.160±0.065%, veto_eff=99.907%, overfit_gap=0.00014\n",
+      "[Optimise] [INFO] [13/48] scan_cv_012: deadtime=0.784±0.294%, veto_eff=99.907%, overfit_gap=0.00004\n",
+      "[Optimise] [INFO] [14/48] scan_cv_013: deadtime=0.290±0.127%, veto_eff=99.907%, overfit_gap=0.00005\n",
+      "[Optimise] [INFO] [15/48] scan_cv_014: deadtime=0.118±0.024%, veto_eff=99.907%, overfit_gap=0.00008\n",
+      "[Optimise] [INFO] [16/48] scan_cv_015: deadtime=0.498±0.250%, veto_eff=99.907%, overfit_gap=0.00006\n",
+      "[Optimise] [INFO] [17/48] scan_cv_016: deadtime=0.219±0.083%, veto_eff=99.907%, overfit_gap=0.00007\n",
+      "[Optimise] [INFO] [18/48] scan_cv_017: deadtime=0.153±0.055%, veto_eff=99.907%, overfit_gap=0.00014\n",
+      "[Optimise] [INFO] [19/48] scan_cv_018: deadtime=0.435±0.227%, veto_eff=99.907%, overfit_gap=0.00007\n",
+      "[Optimise] [INFO] [20/48] scan_cv_019: deadtime=0.210±0.122%, veto_eff=99.907%, overfit_gap=0.00008\n",
+      "[Optimise] [INFO] [21/48] scan_cv_020: deadtime=0.139±0.037%, veto_eff=99.907%, overfit_gap=0.00016\n",
+      "[Optimise] [INFO] [22/48] scan_cv_021: deadtime=0.375±0.159%, veto_eff=99.907%, overfit_gap=0.00010\n",
+      "[Optimise] [INFO] [23/48] scan_cv_022: deadtime=0.191±0.063%, veto_eff=99.907%, overfit_gap=0.00008\n",
+      "[Optimise] [INFO] [24/48] scan_cv_023: deadtime=0.187±0.065%, veto_eff=99.907%, overfit_gap=0.00016\n",
+      "[Optimise] [INFO] [25/48] scan_cv_024: deadtime=0.213±0.075%, veto_eff=99.907%, overfit_gap=0.00005\n",
+      "[Optimise] [INFO] [26/48] scan_cv_025: deadtime=0.122±0.015%, veto_eff=99.907%, overfit_gap=0.00008\n",
+      "[Optimise] [INFO] [27/48] scan_cv_026: deadtime=0.127±0.056%, veto_eff=99.907%, overfit_gap=0.00012\n",
+      "[Optimise] [INFO] [28/48] scan_cv_027: deadtime=0.212±0.087%, veto_eff=99.907%, overfit_gap=0.00008\n",
+      "[Optimise] [INFO] [29/48] scan_cv_028: deadtime=0.141±0.037%, veto_eff=99.907%, overfit_gap=0.00010\n",
+      "[Optimise] [INFO] [30/48] scan_cv_029: deadtime=0.152±0.068%, veto_eff=99.907%, overfit_gap=0.00017\n",
+      "[Optimise] [INFO] [31/48] scan_cv_030: deadtime=0.183±0.072%, veto_eff=99.907%, overfit_gap=0.00010\n",
+      "[Optimise] [INFO] [32/48] scan_cv_031: deadtime=0.176±0.088%, veto_eff=99.907%, overfit_gap=0.00013\n",
+      "[Optimise] [INFO] [33/48] scan_cv_032: deadtime=0.150±0.048%, veto_eff=99.907%, overfit_gap=0.00019\n",
+      "[Optimise] [INFO] [34/48] scan_cv_033: deadtime=0.227±0.107%, veto_eff=99.907%, overfit_gap=0.00011\n",
+      "[Optimise] [INFO] [35/48] scan_cv_034: deadtime=0.187±0.082%, veto_eff=99.907%, overfit_gap=0.00012\n",
+      "[Optimise] [INFO] [36/48] scan_cv_035: deadtime=0.178±0.053%, veto_eff=99.907%, overfit_gap=0.00019\n",
+      "[Optimise] [INFO] [37/48] scan_cv_036: deadtime=0.115±0.014%, veto_eff=99.907%, overfit_gap=0.00008\n",
+      "[Optimise] [INFO] [38/48] scan_cv_037: deadtime=0.105±0.009%, veto_eff=99.907%, overfit_gap=0.00010\n",
+      "[Optimise] [INFO] [39/48] scan_cv_038: deadtime=0.114±0.042%, veto_eff=99.907%, overfit_gap=0.00015\n",
+      "[Optimise] [INFO] [40/48] scan_cv_039: deadtime=0.161±0.055%, veto_eff=99.907%, overfit_gap=0.00011\n",
+      "[Optimise] [INFO] [41/48] scan_cv_040: deadtime=0.144±0.066%, veto_eff=99.907%, overfit_gap=0.00014\n",
+      "[Optimise] [INFO] [42/48] scan_cv_041: deadtime=0.172±0.087%, veto_eff=99.907%, overfit_gap=0.00019\n",
+      "[Optimise] [INFO] [43/48] scan_cv_042: deadtime=0.165±0.058%, veto_eff=99.907%, overfit_gap=0.00014\n",
+      "[Optimise] [INFO] [44/48] scan_cv_043: deadtime=0.183±0.092%, veto_eff=99.907%, overfit_gap=0.00016\n",
+      "[Optimise] [INFO] [45/48] scan_cv_044: deadtime=0.160±0.049%, veto_eff=99.907%, overfit_gap=0.00021\n",
+      "[Optimise] [INFO] [46/48] scan_cv_045: deadtime=0.208±0.116%, veto_eff=99.907%, overfit_gap=0.00014\n",
+      "[Optimise] [INFO] [47/48] scan_cv_046: deadtime=0.208±0.116%, veto_eff=99.907%, overfit_gap=0.00017\n",
+      "[Optimise] [INFO] [48/48] scan_cv_047: deadtime=0.188±0.061%, veto_eff=99.907%, overfit_gap=0.00020\n",
+      "[Optimise] [OK] CV grid search complete. Best: scan_cv_037 (deadtime: 0.105%, veto eff: 99.907%)\n",
+      "Fold 1 best: {'n_estimators': 1000, 'max_depth': 3, 'learning_rate': 0.1}, deadtime: 0.105%\n",
+      "\n",
+      "============================================================\n",
+      "Outer fold 2\n",
+      "============================================================\n",
+      "[Optimise] [OK] Initialised\n",
+      "[Optimise] [INFO] CV grid search: 48 combinations x 5 folds = 240 fits over ['n_estimators', 'max_depth', 'learning_rate']\n",
+      "[Optimise] [INFO] [1/48] scan_cv_000: deadtime=6.890±3.016%, veto_eff=99.906%, overfit_gap=0.00004\n",
+      "[Optimise] [INFO] [2/48] scan_cv_001: deadtime=0.971±0.288%, veto_eff=99.906%, overfit_gap=0.00004\n",
+      "[Optimise] [INFO] [3/48] scan_cv_002: deadtime=0.224±0.044%, veto_eff=99.906%, overfit_gap=0.00007\n",
+      "[Optimise] [INFO] [4/48] scan_cv_003: deadtime=3.228±3.584%, veto_eff=99.913%, overfit_gap=0.00026\n",
+      "[Optimise] [INFO] [5/48] scan_cv_004: deadtime=0.522±0.372%, veto_eff=99.906%, overfit_gap=0.00006\n",
+      "[Optimise] [INFO] [6/48] scan_cv_005: deadtime=0.314±0.251%, veto_eff=99.906%, overfit_gap=0.00011\n",
+      "[Optimise] [INFO] [7/48] scan_cv_006: deadtime=2.685±3.744%, veto_eff=99.913%, overfit_gap=0.00022\n",
+      "[Optimise] [INFO] [8/48] scan_cv_007: deadtime=0.600±0.451%, veto_eff=99.906%, overfit_gap=0.00009\n",
+      "[Optimise] [INFO] [9/48] scan_cv_008: deadtime=0.344±0.326%, veto_eff=99.906%, overfit_gap=0.00010\n",
+      "[Optimise] [INFO] [10/48] scan_cv_009: deadtime=1.284±1.333%, veto_eff=99.906%, overfit_gap=0.00030\n",
+      "[Optimise] [INFO] [11/48] scan_cv_010: deadtime=1.054±1.098%, veto_eff=99.906%, overfit_gap=0.00011\n",
+      "[Optimise] [INFO] [12/48] scan_cv_011: deadtime=0.254±0.276%, veto_eff=99.906%, overfit_gap=0.00014\n",
+      "[Optimise] [INFO] [13/48] scan_cv_012: deadtime=1.015±0.331%, veto_eff=99.906%, overfit_gap=0.00005\n",
+      "[Optimise] [INFO] [14/48] scan_cv_013: deadtime=0.415±0.221%, veto_eff=99.906%, overfit_gap=0.00006\n",
+      "[Optimise] [INFO] [15/48] scan_cv_014: deadtime=0.199±0.101%, veto_eff=99.906%, overfit_gap=0.00010\n",
+      "[Optimise] [INFO] [16/48] scan_cv_015: deadtime=0.524±0.414%, veto_eff=99.906%, overfit_gap=0.00007\n",
+      "[Optimise] [INFO] [17/48] scan_cv_016: deadtime=0.376±0.316%, veto_eff=99.906%, overfit_gap=0.00009\n",
+      "[Optimise] [INFO] [18/48] scan_cv_017: deadtime=0.350±0.360%, veto_eff=99.906%, overfit_gap=0.00014\n",
+      "[Optimise] [INFO] [19/48] scan_cv_018: deadtime=0.570±0.444%, veto_eff=99.906%, overfit_gap=0.00010\n",
+      "[Optimise] [INFO] [20/48] scan_cv_019: deadtime=0.547±0.681%, veto_eff=99.913%, overfit_gap=0.00010\n",
+      "[Optimise] [INFO] [21/48] scan_cv_020: deadtime=0.446±0.608%, veto_eff=99.906%, overfit_gap=0.00013\n",
+      "[Optimise] [INFO] [22/48] scan_cv_021: deadtime=0.944±0.965%, veto_eff=99.906%, overfit_gap=0.00010\n",
+      "[Optimise] [INFO] [23/48] scan_cv_022: deadtime=0.423±0.512%, veto_eff=99.906%, overfit_gap=0.00012\n",
+      "[Optimise] [INFO] [24/48] scan_cv_023: deadtime=0.264±0.300%, veto_eff=99.906%, overfit_gap=0.00016\n",
+      "[Optimise] [INFO] [25/48] scan_cv_024: deadtime=0.315±0.168%, veto_eff=99.906%, overfit_gap=0.00006\n",
+      "[Optimise] [INFO] [26/48] scan_cv_025: deadtime=0.259±0.206%, veto_eff=99.906%, overfit_gap=0.00008\n",
+      "[Optimise] [INFO] [27/48] scan_cv_026: deadtime=0.307±0.305%, veto_eff=99.906%, overfit_gap=0.00011\n",
+      "[Optimise] [INFO] [28/48] scan_cv_027: deadtime=0.379±0.319%, veto_eff=99.906%, overfit_gap=0.00008\n",
+      "[Optimise] [INFO] [29/48] scan_cv_028: deadtime=0.434±0.392%, veto_eff=99.906%, overfit_gap=0.00011\n",
+      "[Optimise] [INFO] [30/48] scan_cv_029: deadtime=0.478±0.482%, veto_eff=99.906%, overfit_gap=0.00016\n",
+      "[Optimise] [INFO] [31/48] scan_cv_030: deadtime=0.372±0.318%, veto_eff=99.906%, overfit_gap=0.00010\n",
+      "[Optimise] [INFO] [32/48] scan_cv_031: deadtime=0.412±0.493%, veto_eff=99.906%, overfit_gap=0.00014\n",
+      "[Optimise] [INFO] [33/48] scan_cv_032: deadtime=20.107±39.946%, veto_eff=99.925%, overfit_gap=0.00016\n",
+      "[Optimise] [INFO] [34/48] scan_cv_033: deadtime=0.362±0.382%, veto_eff=99.906%, overfit_gap=0.00013\n",
+      "[Optimise] [INFO] [35/48] scan_cv_034: deadtime=0.403±0.577%, veto_eff=99.913%, overfit_gap=0.00014\n",
+      "[Optimise] [INFO] [36/48] scan_cv_035: deadtime=20.091±39.955%, veto_eff=99.925%, overfit_gap=0.00016\n",
+      "[Optimise] [INFO] [37/48] scan_cv_036: deadtime=0.267±0.278%, veto_eff=99.906%, overfit_gap=0.00008\n",
+      "[Optimise] [INFO] [38/48] scan_cv_037: deadtime=0.333±0.366%, veto_eff=99.906%, overfit_gap=0.00011\n",
+      "[Optimise] [INFO] [39/48] scan_cv_038: deadtime=0.408±0.540%, veto_eff=99.906%, overfit_gap=0.00015\n",
+      "[Optimise] [INFO] [40/48] scan_cv_039: deadtime=0.374±0.327%, veto_eff=99.906%, overfit_gap=0.00009\n",
+      "[Optimise] [INFO] [41/48] scan_cv_040: deadtime=0.470±0.464%, veto_eff=99.906%, overfit_gap=0.00012\n",
+      "[Optimise] [INFO] [42/48] scan_cv_041: deadtime=20.197±39.902%, veto_eff=99.925%, overfit_gap=0.00019\n",
+      "[Optimise] [INFO] [43/48] scan_cv_042: deadtime=0.303±0.272%, veto_eff=99.906%, overfit_gap=0.00013\n",
+      "[Optimise] [INFO] [44/48] scan_cv_043: deadtime=20.122±39.939%, veto_eff=99.925%, overfit_gap=0.00015\n",
+      "[Optimise] [INFO] [45/48] scan_cv_044: deadtime=20.096±39.952%, veto_eff=99.925%, overfit_gap=0.00018\n",
+      "[Optimise] [INFO] [46/48] scan_cv_045: deadtime=0.312±0.378%, veto_eff=99.906%, overfit_gap=0.00015\n",
+      "[Optimise] [INFO] [47/48] scan_cv_046: deadtime=20.087±39.956%, veto_eff=99.925%, overfit_gap=0.00016\n",
+      "[Optimise] [INFO] [48/48] scan_cv_047: deadtime=20.092±39.954%, veto_eff=99.925%, overfit_gap=0.00017\n",
+      "[Optimise] [OK] CV grid search complete. Best: scan_cv_014 (deadtime: 0.199%, veto eff: 99.906%)\n",
+      "Fold 2 best: {'n_estimators': 200, 'max_depth': 3, 'learning_rate': 0.3}, deadtime: 0.199%\n",
+      "\n",
+      "============================================================\n",
+      "Outer fold 3\n",
+      "============================================================\n",
+      "[Optimise] [OK] Initialised\n",
+      "[Optimise] [INFO] CV grid search: 48 combinations x 5 folds = 240 fits over ['n_estimators', 'max_depth', 'learning_rate']\n",
+      "[Optimise] [INFO] [1/48] scan_cv_000: deadtime=7.811±2.269%, veto_eff=99.907%, overfit_gap=0.00004\n",
+      "[Optimise] [INFO] [2/48] scan_cv_001: deadtime=0.723±0.142%, veto_eff=99.907%, overfit_gap=0.00005\n",
+      "[Optimise] [INFO] [3/48] scan_cv_002: deadtime=0.206±0.151%, veto_eff=99.907%, overfit_gap=0.00008\n",
+      "[Optimise] [INFO] [4/48] scan_cv_003: deadtime=1.608±0.796%, veto_eff=99.907%, overfit_gap=0.00011\n",
+      "[Optimise] [INFO] [5/48] scan_cv_004: deadtime=0.390±0.262%, veto_eff=99.907%, overfit_gap=0.00009\n",
+      "[Optimise] [INFO] [6/48] scan_cv_005: deadtime=0.210±0.211%, veto_eff=99.907%, overfit_gap=0.00010\n",
+      "[Optimise] [INFO] [7/48] scan_cv_006: deadtime=0.726±0.389%, veto_eff=99.907%, overfit_gap=0.00020\n",
+      "[Optimise] [INFO] [8/48] scan_cv_007: deadtime=0.493±0.611%, veto_eff=99.907%, overfit_gap=0.00011\n",
+      "[Optimise] [INFO] [9/48] scan_cv_008: deadtime=0.168±0.146%, veto_eff=99.907%, overfit_gap=0.00011\n",
+      "[Optimise] [INFO] [10/48] scan_cv_009: deadtime=0.618±0.467%, veto_eff=99.907%, overfit_gap=0.00024\n",
+      "[Optimise] [INFO] [11/48] scan_cv_010: deadtime=0.471±0.602%, veto_eff=99.907%, overfit_gap=0.00015\n",
+      "[Optimise] [INFO] [12/48] scan_cv_011: deadtime=0.205±0.210%, veto_eff=99.907%, overfit_gap=0.00014\n",
+      "[Optimise] [INFO] [13/48] scan_cv_012: deadtime=0.696±0.123%, veto_eff=99.907%, overfit_gap=0.00006\n",
+      "[Optimise] [INFO] [14/48] scan_cv_013: deadtime=0.264±0.053%, veto_eff=99.907%, overfit_gap=0.00004\n",
+      "[Optimise] [INFO] [15/48] scan_cv_014: deadtime=0.160±0.134%, veto_eff=99.907%, overfit_gap=0.00009\n",
+      "[Optimise] [INFO] [16/48] scan_cv_015: deadtime=0.427±0.329%, veto_eff=99.907%, overfit_gap=0.00007\n",
+      "[Optimise] [INFO] [17/48] scan_cv_016: deadtime=0.207±0.180%, veto_eff=99.907%, overfit_gap=0.00008\n",
+      "[Optimise] [INFO] [18/48] scan_cv_017: deadtime=0.170±0.191%, veto_eff=99.907%, overfit_gap=0.00011\n",
+      "[Optimise] [INFO] [19/48] scan_cv_018: deadtime=0.401±0.439%, veto_eff=99.913%, overfit_gap=0.00012\n",
+      "[Optimise] [INFO] [20/48] scan_cv_019: deadtime=0.323±0.440%, veto_eff=99.907%, overfit_gap=0.00009\n",
+      "[Optimise] [INFO] [21/48] scan_cv_020: deadtime=0.161±0.164%, veto_eff=99.907%, overfit_gap=0.00015\n",
+      "[Optimise] [INFO] [22/48] scan_cv_021: deadtime=0.787±1.184%, veto_eff=99.913%, overfit_gap=0.00013\n",
+      "[Optimise] [INFO] [23/48] scan_cv_022: deadtime=0.292±0.387%, veto_eff=99.907%, overfit_gap=0.00013\n",
+      "[Optimise] [INFO] [24/48] scan_cv_023: deadtime=0.188±0.197%, veto_eff=99.907%, overfit_gap=0.00016\n",
+      "[Optimise] [INFO] [25/48] scan_cv_024: deadtime=0.180±0.049%, veto_eff=99.907%, overfit_gap=0.00005\n",
+      "[Optimise] [INFO] [26/48] scan_cv_025: deadtime=0.113±0.042%, veto_eff=99.907%, overfit_gap=0.00007\n",
+      "[Optimise] [INFO] [27/48] scan_cv_026: deadtime=0.180±0.191%, veto_eff=99.907%, overfit_gap=0.00011\n",
+      "[Optimise] [INFO] [28/48] scan_cv_027: deadtime=0.245±0.285%, veto_eff=99.907%, overfit_gap=0.00008\n",
+      "[Optimise] [INFO] [29/48] scan_cv_028: deadtime=0.161±0.177%, veto_eff=99.907%, overfit_gap=0.00010\n",
+      "[Optimise] [INFO] [30/48] scan_cv_029: deadtime=0.156±0.166%, veto_eff=99.907%, overfit_gap=0.00015\n",
+      "[Optimise] [INFO] [31/48] scan_cv_030: deadtime=0.195±0.208%, veto_eff=99.907%, overfit_gap=0.00011\n",
+      "[Optimise] [INFO] [32/48] scan_cv_031: deadtime=0.179±0.194%, veto_eff=99.907%, overfit_gap=0.00013\n",
+      "[Optimise] [INFO] [33/48] scan_cv_032: deadtime=0.201±0.246%, veto_eff=99.907%, overfit_gap=0.00017\n",
+      "[Optimise] [INFO] [34/48] scan_cv_033: deadtime=0.218±0.269%, veto_eff=99.907%, overfit_gap=0.00014\n",
+      "[Optimise] [INFO] [35/48] scan_cv_034: deadtime=0.186±0.219%, veto_eff=99.907%, overfit_gap=0.00016\n",
+      "[Optimise] [INFO] [36/48] scan_cv_035: deadtime=0.204±0.221%, veto_eff=99.907%, overfit_gap=0.00019\n",
+      "[Optimise] [INFO] [37/48] scan_cv_036: deadtime=0.120±0.051%, veto_eff=99.907%, overfit_gap=0.00008\n",
+      "[Optimise] [INFO] [38/48] scan_cv_037: deadtime=0.112±0.065%, veto_eff=99.907%, overfit_gap=0.00008\n",
+      "[Optimise] [INFO] [39/48] scan_cv_038: deadtime=0.219±0.242%, veto_eff=99.907%, overfit_gap=0.00014\n",
+      "[Optimise] [INFO] [40/48] scan_cv_039: deadtime=0.226±0.309%, veto_eff=99.907%, overfit_gap=0.00009\n",
+      "[Optimise] [INFO] [41/48] scan_cv_040: deadtime=0.148±0.157%, veto_eff=99.907%, overfit_gap=0.00012\n",
+      "[Optimise] [INFO] [42/48] scan_cv_041: deadtime=0.183±0.206%, veto_eff=99.907%, overfit_gap=0.00016\n",
+      "[Optimise] [INFO] [43/48] scan_cv_042: deadtime=0.159±0.162%, veto_eff=99.907%, overfit_gap=0.00013\n",
+      "[Optimise] [INFO] [44/48] scan_cv_043: deadtime=0.201±0.254%, veto_eff=99.907%, overfit_gap=0.00015\n",
+      "[Optimise] [INFO] [45/48] scan_cv_044: deadtime=0.222±0.275%, veto_eff=99.907%, overfit_gap=0.00020\n",
+      "[Optimise] [INFO] [46/48] scan_cv_045: deadtime=0.172±0.197%, veto_eff=99.907%, overfit_gap=0.00016\n",
+      "[Optimise] [INFO] [47/48] scan_cv_046: deadtime=0.169±0.197%, veto_eff=99.907%, overfit_gap=0.00018\n",
+      "[Optimise] [INFO] [48/48] scan_cv_047: deadtime=0.230±0.261%, veto_eff=99.907%, overfit_gap=0.00021\n",
+      "[Optimise] [OK] CV grid search complete. Best: scan_cv_037 (deadtime: 0.112%, veto eff: 99.907%)\n",
+      "Fold 3 best: {'n_estimators': 1000, 'max_depth': 3, 'learning_rate': 0.1}, deadtime: 0.112%\n",
+      "\n",
+      "============================================================\n",
+      "Outer fold 4\n",
+      "============================================================\n",
+      "[Optimise] [OK] Initialised\n",
+      "[Optimise] [INFO] CV grid search: 48 combinations x 5 folds = 240 fits over ['n_estimators', 'max_depth', 'learning_rate']\n",
+      "[Optimise] [INFO] [1/48] scan_cv_000: deadtime=6.323±3.537%, veto_eff=99.907%, overfit_gap=0.00003\n",
+      "[Optimise] [INFO] [2/48] scan_cv_001: deadtime=0.621±0.096%, veto_eff=99.907%, overfit_gap=0.00006\n",
+      "[Optimise] [INFO] [3/48] scan_cv_002: deadtime=0.187±0.082%, veto_eff=99.907%, overfit_gap=0.00007\n",
+      "[Optimise] [INFO] [4/48] scan_cv_003: deadtime=3.463±4.514%, veto_eff=99.907%, overfit_gap=0.00040\n",
+      "[Optimise] [INFO] [5/48] scan_cv_004: deadtime=0.339±0.093%, veto_eff=99.907%, overfit_gap=0.00009\n",
+      "[Optimise] [INFO] [6/48] scan_cv_005: deadtime=0.121±0.058%, veto_eff=99.907%, overfit_gap=0.00009\n",
+      "[Optimise] [INFO] [7/48] scan_cv_006: deadtime=1.617±2.251%, veto_eff=99.907%, overfit_gap=0.00033\n",
+      "[Optimise] [INFO] [8/48] scan_cv_007: deadtime=0.240±0.078%, veto_eff=99.907%, overfit_gap=0.00009\n",
+      "[Optimise] [INFO] [9/48] scan_cv_008: deadtime=0.198±0.090%, veto_eff=99.907%, overfit_gap=0.00016\n",
+      "[Optimise] [INFO] [10/48] scan_cv_009: deadtime=0.818±1.035%, veto_eff=99.907%, overfit_gap=0.00041\n",
+      "[Optimise] [INFO] [11/48] scan_cv_010: deadtime=0.197±0.080%, veto_eff=99.907%, overfit_gap=0.00013\n",
+      "[Optimise] [INFO] [12/48] scan_cv_011: deadtime=0.159±0.062%, veto_eff=99.907%, overfit_gap=0.00012\n",
+      "[Optimise] [INFO] [13/48] scan_cv_012: deadtime=0.689±0.134%, veto_eff=99.907%, overfit_gap=0.00007\n",
+      "[Optimise] [INFO] [14/48] scan_cv_013: deadtime=0.241±0.068%, veto_eff=99.907%, overfit_gap=0.00006\n",
+      "[Optimise] [INFO] [15/48] scan_cv_014: deadtime=0.117±0.060%, veto_eff=99.907%, overfit_gap=0.00010\n",
+      "[Optimise] [INFO] [16/48] scan_cv_015: deadtime=0.327±0.100%, veto_eff=99.907%, overfit_gap=0.00007\n",
+      "[Optimise] [INFO] [17/48] scan_cv_016: deadtime=0.164±0.071%, veto_eff=99.907%, overfit_gap=0.00009\n",
+      "[Optimise] [INFO] [18/48] scan_cv_017: deadtime=0.104±0.053%, veto_eff=99.907%, overfit_gap=0.00010\n",
+      "[Optimise] [INFO] [19/48] scan_cv_018: deadtime=0.232±0.060%, veto_eff=99.907%, overfit_gap=0.00010\n",
+      "[Optimise] [INFO] [20/48] scan_cv_019: deadtime=0.167±0.076%, veto_eff=99.907%, overfit_gap=0.00010\n",
+      "[Optimise] [INFO] [21/48] scan_cv_020: deadtime=0.144±0.089%, veto_eff=99.913%, overfit_gap=0.00020\n",
+      "[Optimise] [INFO] [22/48] scan_cv_021: deadtime=0.180±0.069%, veto_eff=99.907%, overfit_gap=0.00013\n",
+      "[Optimise] [INFO] [23/48] scan_cv_022: deadtime=0.137±0.041%, veto_eff=99.907%, overfit_gap=0.00012\n",
+      "[Optimise] [INFO] [24/48] scan_cv_023: deadtime=0.123±0.050%, veto_eff=99.907%, overfit_gap=0.00015\n",
+      "[Optimise] [INFO] [25/48] scan_cv_024: deadtime=0.169±0.036%, veto_eff=99.907%, overfit_gap=0.00007\n",
+      "[Optimise] [INFO] [26/48] scan_cv_025: deadtime=0.102±0.029%, veto_eff=99.907%, overfit_gap=0.00008\n",
+      "[Optimise] [INFO] [27/48] scan_cv_026: deadtime=0.126±0.080%, veto_eff=99.907%, overfit_gap=0.00012\n",
+      "[Optimise] [INFO] [28/48] scan_cv_027: deadtime=0.132±0.058%, veto_eff=99.907%, overfit_gap=0.00009\n",
+      "[Optimise] [INFO] [29/48] scan_cv_028: deadtime=0.104±0.045%, veto_eff=99.907%, overfit_gap=0.00012\n",
+      "[Optimise] [INFO] [30/48] scan_cv_029: deadtime=0.098±0.047%, veto_eff=99.907%, overfit_gap=0.00015\n",
+      "[Optimise] [INFO] [31/48] scan_cv_030: deadtime=0.144±0.055%, veto_eff=99.907%, overfit_gap=0.00010\n",
+      "[Optimise] [INFO] [32/48] scan_cv_031: deadtime=0.109±0.038%, veto_eff=99.907%, overfit_gap=0.00015\n",
+      "[Optimise] [INFO] [33/48] scan_cv_032: deadtime=0.120±0.069%, veto_eff=99.907%, overfit_gap=0.00023\n",
+      "[Optimise] [INFO] [34/48] scan_cv_033: deadtime=0.142±0.049%, veto_eff=99.907%, overfit_gap=0.00012\n",
+      "[Optimise] [INFO] [35/48] scan_cv_034: deadtime=0.123±0.061%, veto_eff=99.907%, overfit_gap=0.00016\n",
+      "[Optimise] [INFO] [36/48] scan_cv_035: deadtime=0.107±0.048%, veto_eff=99.907%, overfit_gap=0.00017\n",
+      "[Optimise] [INFO] [37/48] scan_cv_036: deadtime=0.109±0.044%, veto_eff=99.907%, overfit_gap=0.00008\n",
+      "[Optimise] [INFO] [38/48] scan_cv_037: deadtime=0.091±0.037%, veto_eff=99.907%, overfit_gap=0.00011\n",
+      "[Optimise] [INFO] [39/48] scan_cv_038: deadtime=0.108±0.056%, veto_eff=99.907%, overfit_gap=0.00016\n",
+      "[Optimise] [INFO] [40/48] scan_cv_039: deadtime=0.104±0.049%, veto_eff=99.907%, overfit_gap=0.00010\n",
+      "[Optimise] [INFO] [41/48] scan_cv_040: deadtime=0.097±0.051%, veto_eff=99.907%, overfit_gap=0.00013\n",
+      "[Optimise] [INFO] [42/48] scan_cv_041: deadtime=0.108±0.070%, veto_eff=99.907%, overfit_gap=0.00020\n",
+      "[Optimise] [INFO] [43/48] scan_cv_042: deadtime=0.127±0.050%, veto_eff=99.907%, overfit_gap=0.00014\n",
+      "[Optimise] [INFO] [44/48] scan_cv_043: deadtime=0.096±0.028%, veto_eff=99.907%, overfit_gap=0.00017\n",
+      "[Optimise] [INFO] [45/48] scan_cv_044: deadtime=0.134±0.093%, veto_eff=99.907%, overfit_gap=0.00024\n",
+      "[Optimise] [INFO] [46/48] scan_cv_045: deadtime=0.110±0.047%, veto_eff=99.907%, overfit_gap=0.00015\n",
+      "[Optimise] [INFO] [47/48] scan_cv_046: deadtime=0.109±0.063%, veto_eff=99.907%, overfit_gap=0.00019\n",
+      "[Optimise] [INFO] [48/48] scan_cv_047: deadtime=0.115±0.058%, veto_eff=99.907%, overfit_gap=0.00018\n",
+      "[Optimise] [OK] CV grid search complete. Best: scan_cv_037 (deadtime: 0.091%, veto eff: 99.907%)\n",
+      "Fold 4 best: {'n_estimators': 1000, 'max_depth': 3, 'learning_rate': 0.1}, deadtime: 0.091%\n"
+     ]
+    }
+   ],
+   "source": [
+    "from optimise import Optimise\n",
+    "\n",
+    "param_grid = {\n",
+    "    \"n_estimators\": [100, 200, 500, 1000],\n",
+    "    \"max_depth\": [3, 5, 7, 9],\n",
+    "    \"learning_rate\": [0.05, 0.1, 0.3],\n",
+    "}\n",
+    "fixed_params = {\"device\": \"cuda\", \"tree_method\": \"hist\"}\n",
+    "\n",
+    "# Nested CV: run inner CV grid search on each outer fold's training set\n",
+    "outer_best_hps = []\n",
+    "for k, (train_idx, test_idx) in enumerate(data[\"outer_folds\"]):\n",
+    "    print(f\"\\n{'='*60}\")\n",
+    "    print(f\"Outer fold {k}\")\n",
+    "    print(f\"{'='*60}\")\n",
+    "    fold_data = AssembleDataset.get_fold_data(data, train_idx, test_idx)\n",
+    "    opt = Optimise(fold_data, run=run, min_efficiency=0.999, scale_features=False)\n",
+    "    best = opt.grid_search_cv(param_grid, n_folds=5, fixed_params=fixed_params)\n",
+    "    outer_best_hps.append(best[\"hyperparams\"])\n",
+    "    print(f\"Fold {k} best: {best['hyperparams']}, deadtime: {best['mean_deadtime']*100:.3f}%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e91082c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best hyperparameters per outer fold:\n",
+      "  Fold 0: {'n_estimators': 1000, 'max_depth': 3, 'learning_rate': 0.1}\n",
+      "  Fold 1: {'n_estimators': 1000, 'max_depth': 3, 'learning_rate': 0.1}\n",
+      "  Fold 2: {'n_estimators': 200, 'max_depth': 3, 'learning_rate': 0.3}\n",
+      "  Fold 3: {'n_estimators': 1000, 'max_depth': 3, 'learning_rate': 0.1}\n",
+      "  Fold 4: {'n_estimators': 1000, 'max_depth': 3, 'learning_rate': 0.1}\n",
+      "\n",
+      "Most common: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 1000} (4/5 folds)\n",
+      "\n",
+      "Consensus best HP: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 1000}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from collections import Counter\n",
+    "\n",
+    "# Summarise best HPs across outer folds\n",
+    "print(\"Best hyperparameters per outer fold:\")\n",
+    "for k, hp in enumerate(outer_best_hps):\n",
+    "    print(f\"  Fold {k}: {hp}\")\n",
+    "\n",
+    "# Check consistency\n",
+    "hp_tuples = [tuple(sorted(hp.items())) for hp in outer_best_hps]\n",
+    "hp_counts = Counter(hp_tuples)\n",
+    "print(f\"\\nMost common: {dict(hp_counts.most_common(1)[0][0])} ({hp_counts.most_common(1)[0][1]}/{len(outer_best_hps)} folds)\")\n",
+    "\n",
+    "# Use the most common HP set as the consensus\n",
+    "consensus_hp = dict(hp_counts.most_common(1)[0][0])\n",
+    "print(f\"\\nConsensus best HP: {consensus_hp}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "c4f0bb9f-c344-4dc6-945f-6ecde45bf470",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Optimise] [OK] Saved to /home/sgrant/mu2e-cosmic/output/images/ml/k/optimise/overfit_max_depth.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1590x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Optimise] [OK] Saved to /home/sgrant/mu2e-cosmic/output/images/ml/k/optimise/overfit_n_estimators.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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kiDZfW0+/sVQLChibtIdN1F/AtQtpYrQJw86RZxbvW7Ro8cTfwxNof99N+Tuvv46WSgqv+J3Bw37qIJSxzgD4uaOWDlIipoaG+Oeee04FogzdkyCFgTNA2gUw1GhhLICmQUPKlClTVKMEilXiPuz9999XaRyN3Ydhf4bOl/bfq6E81URERLag3Q/op5XUnp3RGG7Ks7M+XMOQ2hDXQqQownURqYNA/7qIBvb79+9L1apVpVChQmn2Y417WEOQuig1pFMGdIjQT5cE6OSH+QgioJ6Dpe0LRM6ENRKI3JShC6oGDS54AE+viM/du3dN/qywsLDknvD6tNy+phZcRqMQGrdQuGnBggXSs2dPNV+rm4CbCf0RFmg8Qk5i9N5r2rSpevBGfmT0ZkBPRXOKJKZHO08Z5U7UvzlAAWV8l08++URN6E2B3gbIg2hu/Yn04GYLeRiRS1ILrGjwWaiFgbyVKIipf+5MySFu6MYQIyzwbyxDD2AETIjIdeCBC4FX1IdB/nj07kLvMARskcsWPabQ2Itc99aoNYCaNWDsb4U2Xyt0awnUokk9Ag0BXQROrDUyzROgxxx60+kH8o3B33/A74h+zzv87qAuDwLcffv2Tb4HQE5k9KDXf2DWfuYo8JzR7xqur6kbz9O7z7EnrbMACogbguNEzYfUUBcC9ZQwmsEcGPVhiHbPY+vOC0RERKmff/VH4WvPzrjnNPXZGSNiW7ZsqUYwmtI+oV3rjN0L2OoewVBHTW2EoLFOnNpy/TYRS9oXiJwFAwlEbspQSiNAAzAK/uDihR6bmBBFR6M/HuTRux8FDM3peW7NwpZohEAgAb0XtUCCsQKN2oN4t27d1IgFFOtE0WQUOsIwQQQjjPWEMMfDhw/Vd8Qww/ToF0NCuiUUVUaDHYo+olgSgguYcP5xbNZMa4TUEvoFrjRoSEOqJQRj8Lka9EgGFJY0Bg2LoJ8+AcGasmXLyr///qt6gjCQQOR6ENRcvny5CiijYRdpjjB66q+//lJD0PF3ylg6FnMhCAxZs2Y1uFz7+6KtR46DdHy4hqLRO/UIt9S0hnFc9/QLM2tp9XB9QGFEBLkxOg8NBEippf97gGur1hkhowA7Uh6Yep/jKMbuhQzdT6GQMopMo1DlDz/8oEZkojFBS82A0TSmjuYkIiJyFIz8BDwfpr6+Y6Sesfu/1IEGdH5DEAH3qEhPifuJ7Nmzq86Kv//+u0qNqH89NdThzR7S+zxzjsWS9gUiZ8FAApGHwYM9JqQLMJRHF73bHQlD9pHmAA/ZOBb0dFy3bp1qaKhXr57BbdBLUev5j/RG6H2PnIwIMFgjkIDGctSNQP5FpFcyFdbFqABMgO+EGyqkFsEwTaQQeRLo1aAN9UQP0vR6kSLgoB9I0HpOpvfz1palDhag1zICCQjwOEtqCSIyDxpu8fcDoxLwcAZIHYf/rg2NMLNURg96tkqXNmzYMDWR6XBN+v7779UoNgSW9AMEqWkBfoxsSQ1B/0OHDql10BhgrDOAdm1BA/pPP/3ksj8q3J9gJAdG35QoUSLNctQqSk2rUYL0DaitoA+pGpBb2hhjo3e0+QhCEBER2Ro6CaDDHOg/p+P6juvioEGDDNZlNATXRdx/ovMbguz6DD2vatc6Y9dEbUSss7K0fYHIGbBGApGH0QohGepJjmVavj5H0hob0PiAhi4M+0eeQ1N6yKKHInr54cE+o8Z1/V72kJCQYHA50n4AevA+iWrVqiX38tRqUZjy+cZgtAN+ZiiyiMY4QxNGI+CcIBijPzRSK+qEnqKGilgCbuT019X06NFD9ZpEQGTjxo3pHiOCVvrFt4jIeaARGEFl/d5g6TUeW5p2KL3RT9rIJy0VHjkOiv8iMI+8/V988YXR9TByBcEGXLtQ4Dc1BM8ROMI1HNcgXGeQakq7lmpwH4LPQ9DhzJkz4miWXotfeOEF9YpzkhoaP/bv359mvnZdNJQOCfc96QXY0PvzxIkTaeajYDXUqFHDrOMnIiKyBJ65cX+HZ9Hq1as/0bMzrou4Z0wdRAA8c6aGlL54xt21a5fBgH3qWk1Peq23Nmu1LxA5AgMJRB4GBYDQII+hg/oFi9CTH2mOkCLH0bQe/MixnF5aI1x40cs/NTy0R0REqJsR/dzNxmg9GtBzwthNElIBoR4D8omnhnOGXpzoRaj1gEDvSq2BTH8Egdbwrp+3MaPPzyitUXrFpHAO0DiEAk/6N1TohYw8jvhMpLPShqBq8H1Q9BrbowCkPjR8oC4FGjowIgHHkbrRA+cC62DIKnqrEJFzwX/z+LuKmggoeIxeYPjvWRuibi3a37qLFy8aXI5G6/TyvpP94N4A1y4Ek4YMGaKCCamvDWvWrFEj/fA3H7UQDNUiQoAAAWj0tENwCvcXr732miqQnRp6K+IzcE36559/0izHPmbMmCH2YOm1GPWbcM5w7rTCy4DvjSLKhoL1JUuWVK/Tp09X12fNkSNH1DlLD/bXq1evFPcYSDWF0Zj4GWI0JhERka0gSI7rOq5hSFGJV324DuXOnVtGjx4tM2fOTPOciODD7NmzU9wb4rp4+/btNOl/v/nmG4Md19ABBfexCAj07t07Rf0BpEIyFHx4kmu9tZnbvkDkVJKIyCW9/fbbuCInDR06NMX8jRs3qvlYbsy7776r1vH3909q0qRJUuvWrZNCQ0OTcuXKldSxY0e1bObMmSm2qVOnjpp/5syZFPMxr3DhwgY/B/swdIymeOGFF9S2mEqXLm1wnd69e6vl+fPnT3rllVeS3njjjaS6desmZc6cWc0fP368SZ91//79pDx58qht8D3feeedpM6dOydt27YteZ0lS5ao84V1SpUqldS8efOkZs2aJVWoUCHJ19dXzb9165Zad//+/erfWbNmTapdu7Y6LqybO3duNb9KlSpJDx48SN73nj17kjJlypSUJUsWtR4+G9ONGzeMHnNkZKT6XC8vr6SLFy+m+/0WLlyoPrdq1aop5uP7Zc+eXS0rWLCg+j147bXX1PfDPBzPsmXLjO530qRJah2si98f7Wfw4osvqu+O+fny5Uu6fv26ST8HIrKPhw8fJrVv3179N1qvXr2ke/fuJc2dO1f9PcF14J9//jF5X9rfCmP+/PNPtU6JEiUMLh85cqTF1wmyjTVr1iQFBwernwuujbgutW3bNqlMmTJqHn5P8HNLzw8//JB8Dce0c+dOo+t+8sknah1vb++k5557LqlNmzZJjRo1Utd+zC9fvrxJ9yOmrqPdP+F+Sd/XX3+dfD17/fXX1XW4f//+Sab46quvkr9DgwYN1LW0QIECSYUKFUpq2rSpWqZ/T4Hre968edX8okWLqvOL7Xx8fNT3x31V6sc07f4O11rsF9tjO5wrbIdlgwYNMul4iYiI0qNdv3HNxPTWW2+p+wHcC+C5Vbu32717t8Htt27dmhQSEpLcVoA2h5YtW6rrfEBAgJqPZ2bNzz//nPyZtWrVSmrXrl1S2bJl1T1Hnz59DLZv4BmzePHiyc+yuHbjvhbbvP/++8nP9vouXbqU5Ofnp67X4eHhSZ06dVLX+2PHjqW41qb+LNynGmojAdxrGPqsjO5JzGlfIHImDCQQeWAgISEhQT0w4+KMCykemt98882ks2fPGr1I2juQgEZq7WZixIgRBtfBzUe/fv2Snn/+edXYgcYsHMurr76apoEgI7gJeumll1TjiXZzlPocnDhxIqlbt25JTz31lPosrIubKQQefvvtt6TExES1XlRUVNLYsWOTGjdunFSkSBF1jtE4h+P89ttvVaNdamjEq1SpUvLNREaNJNr5MXbDog+fFxQUpNY/fvx4imXnz59XARk02OCz8b3w/XBDdeTIkQz3fe7cuaSPP/5YNfTgfCCIg59Fw4YNkyZPnpwUHR2d4T6IyH7wd6pLly7q70GNGjVS/DeKv3n4+4drgvZA9aSBBARNtUbpAwcOpFmOhyUsQ0CVnMe1a9eSPv30U/W3HdcPXMfQ4I3fnYMHD2a4PR58tUBzsWLFMlx/w4YNSS1atFCN42gUx3UE10RcX/bu3WuXQEJ8fLxqiMfxag3zxu5vDJk/f746ZnxvdBxAo8vly5dVgAD7Sv3f1IULF1TwHZ0hcH5xPzFmzBh1j5ZeIAHHj4YQBAPxOfg8/JwMNW4QERFZQr8zACY84yEw8Mwzz6jrEBrBcd1MD65VeFbXnjMDAwOTSpYsqYLtCxYsSNGxDlatWpVUrVo1dd+Bzm64fm7atCnd9o2IiAh1b4L7BlxLy5UrlzR9+vR0G/fXrVun7oFxPNr30+4J7BlIMKd9gciZZML/OXpUBBERERHZHmqcIAUK8tkifVnqAm9Tp05Vae6Q+mzz5s1SrFixdPeHXPiomYI0LsYgfc1nn32mcrejXguGwQPSn2FId506dWTTpk1W+oZEzgPpG5D+CakJkObvSQqZ478RFLNEcWZXLk5NRERERK4rbbJSIiIiInI7yCOLnPMVKlRQDfqpgwhaXlvkmUXtFNSaSR1IQOHckSNHppgXFxenislrBg8eLE2aNEkRSEDQYtu2bSoHbs2aNeXcuXOqQB5y6NorBz6RLfNF58yZM0WRyOjoaHn//fflxo0bqtbQkwQRiIiIiIicAQMJRERERB4AxW6XLl2qirSmV4gehVxbtGihCqundv36dRUA0IfBrfrzsI4+Pz8/VShvzJgxMm/ePFm+fLmEhIRIx44dVVACxXmJXBmKOg4bNkwqV66sfp9v3bol+/fvV0EEjEhAwUkiIiIiIlfH1EZEREREREQW2r17t3z99deyc+dOuXbtmgquFSpUSJo2bSr9+/dXI2+eFFMbEREREZGjMZBARERERERERERERERGeRlfREREREREREREREREno6BBCIiIiIiIiIiIiIiMorFlj1QQECAxMbGire3t+TJk8fRh0NERE4GOb4fPnyoiuTGxMQ4+nDIQrzeExFReni9dw+83hMRkb2u96yR4IEQQEhMTHT0YRARkZPz8vJSNxzkmni9JyIiU/B679p4vSciIntd7zkiwYNvNPALFBYW9kT7ioiIkNDQUKsclzX3lZSUJJcvX5Z8+fJJpkyZnO74nH1/PH88f476/ePvnnOcvytXrqjrBK4X5Lp4vXe/67O198e/uTx//N1zzf3xek+2uN57wu+8q3xfa++P54/nj797rrm/JGd8vk8ij5M/f/4k/Ojx+qTKlCljlWOy9r7u3LmjviNenfH4nH1/PH88f476/ePvnnOcP2teJ6wtPj5efT9zvqO2fkJCQpIn4fXe/a7P1t4f/+by/PF3zzX35wnXe7L/z9ETfudd5ftae388fzx//N1zzf3dccLrvVkjEnbv3i07duxQOZW6du1qSpBCpk2bJvfv35datWpJxYoVnyTmQURERB7u9ddfl2XLlkmzZs1k6dKlJm3TqVMntc0bb7whc+bMsfkxEhER0ZN58cUXbXIKc+bMKYsWLbLJvomIiNydyYGEhIQE9QD+33//yffff2/SNtqwiw8//FCefvppOXTokNWGoREREZFn+ffff1XwIDg4WGbMmGHydj/++KNs2LBB5s+fL0OGDJESJUrY9DiJiIjoyWzatEm1HaBzojXlzZtX3BXSaZQtW9bgsu7du6uJiIjc06RJk9Rk7Ppg90DC6tWr5fTp01K+fHnp1q2byR/w7rvvypQpU+TAgQOydu1aefnlly09ViIiIvJgc+fOVa8ffPCBZM+e3eTtcuTIIT179pRRo0bJzz//LMOHD7fhURIREZE1BAQESL9+/ax2Mt39+o+c3EeOHHH0YRARkQOkFzAuUKCAXLp0yb6BBPQARI+AXr16mf0h2Oadd95RQwgZSCAiIiJLbNmyRd2LtGrVyuxtW7ZsqQIJ6OHoadhDkYjIc9mrh6ItBAYGytChQ622P3cPJBAREdlaZnPqI4AlgYBGjRqp17///tvsbYmIiIjgxIkT4uXlZVHNpXLlyqltjx075nEnkz0UiYg8l716KBIREZH7MzmQgBuMLFmyWJRTENugQPPFixfN3pacmzXzLDp7zkZrH5+z78/anP378vy57/lz9t8VZz53zub27dsqpZEl9ZYQRMC2d+7cEVvCqAd0nEA9h2vXrklcXJwULFhQXnrpJenfv78UKVLErP3FxsbKmDFjVH2H8+fPS0hIiISHh8vIkSMlf/78Yi+83rvv/qzN2b8vzx/PHX/3XMM333yjUhs5+z7dCf/e8vy50+8Lr/c8d/zds41MSSZWL0IQAUMLIyMjLfqgnDlzSnR0tDx48MCi7cl6tJ4naIBw1+BOVFSUKsaJBqNs2bI5+nBcDs8fzx9/9zz7v11nvU6g1sH9+/dV47ol0KnB399fbt26JbaSOXNm9TkYAYHzh0AC6kQhCICfyZ9//imVK1c2aV/4nvXr15ft27dLWFiY1KpVS86ePasCFXny5JFdu3alG5hw1p+jNfF6xfPH3z/XxP92neP8ecJ1whN4ws+RfzN4/vj755r43677Xe+9zHl4x4EnJCSY/SHYBr0IsQ8iIiIiS6DxPD4+Xk6fPm32ttgGjfrYhy1t3LhRbt68qRr/URtqxYoV8t9//8mAAQPUjSAKRZtq9OjRaj/Vq1dXaZ0WLFigggdff/21Gu3QuXNnm34XIiIiMmzv3r3y+eefqxpMaJjBaEl0JDAX2knmzZsnb7zxhpQtW1aNmAgKCpKqVavKt99+a1H7CxERka2YHEgoXLiwYPDCzp07zf4QbINtsQ8iIiIiS1SrVk29Ll261OxtlyxZol7xYG5LGDXg6+ubYp63t7eMGDFCNTBgNEFMTEyG+0HA5LvvvlPvUSQTo0I1ffv2VSMeMLph3759NvgWRERElB6kGEQngWXLlsnly5ctPlljx46VN998U3UWyJo1qzRt2lSqVKkiBw8elN69e6uRiffu3eMPg4iIXCuQUK9ePRUMmDJlitkfgm0Qoa9bt67Z2xIRERHBK6+8ou5FvvrqK7ly5YrJJwUP+HhQx70I9uEI+GwEFFCrAemPMrJ161bVS7FYsWIGi0u3bt1ava5cudImx0tEROQqNmzYID179pQmTZqo6zzer1+/3qafidGCQ4YMUdfhq1evWrwfdBQYOHCgSoG4Z88e+eWXX9T3OXz4sBQqVEj++usv+eyzz6x67ERERDYvtowoOR7ccWHD8D1MpkCvQRQIxAN0+/btLT5Qsr6IiAg1fNJYYRpnL05DRESWQy93TMauD86oVatWUqJECTl16pQ0atRI9QJEQ3t6sC7uWW7cuKG2bdOmjdgbgh9ffvmlGonQoEEDVXcqI+iJCJUqVTK4XJuvrUdERORpkPYHbQxIJZi69OP333+v7huQNsiUAL65+vfvb5X9/O9//zM4H/csSJ2ElEdoT2EwgYiInIHJV9Rnn31WXn/9dXURa9eunQwdOlT69OmjihYagmKI48aNU0P5AQ/u2Ac5j9DQUDly5IijD4OIiBwgvYCxVozJ2aA3/6xZs9QoyX///Vel90EDQvPmzVWv/ZCQELUeahTs379fBRrQgICUAGi8/+mnn1THBnsYNGiQKmR19+5dOXTokApolC5dWn744QeTtkfPRO1nYYg2/9y5c1Y8aiIiItcxatQoWbhwobq+vvfee6rxHe0QO3bsUNdbpDXEOsOGDRNXVL58efXqjPdkRETkmcwKzU+dOlX1fEPj8+DBg1WagJdeein54R0P55GRkerh/Y8//lDFmdEzAL3ef/zxR9t9C7IfpJJIL51EWJhuIiIislGdBDQavPXWW6p48bRp09RkDO5DkDZgzpw5Kg2BvSxfvlwFOzToTIGgRtGiRU3aPjo6Wr0iX7IhKMaov156cA5wrswRfTVaYq7qajmg5kPqug+BYYESFBZk1j6JiMi6Hjx4YPbfd32pe/G7mtmzZ6vgwe7du5Ovi4CRiK+++qrUqVNHreOqgYT//vtPvebNm9cm+79y94pciTb+bB8WGCZhQXy2JyIiCwMJeBBHzl4Mr1u7dq3K3bt48WI1GbspadiwoXpw1i8SSC5s6lSR4cONLx86VMRFb9SIiMg1oBAh8gh/+umnqrdhYmKi0REMqCWA3ohoaLCnf/75R72ig8XevXtV/mOkI5oxY4ZJqR61+yhjIyjMafxBjYjg4GAxR91H/zOmztA6UncYa18RETkSMgAg/Y27+vDDD1VKH/0gQerrG1IwG1peq1YtlT3hSQohO9qECRPUa7NmzWyy/6l7p8rwzcaf7YfWGSrD6vLZnoiIHjM7WWD27Nll9erVsmbNGpVbGcV/UveGQ9Cgdu3aKmXCyy+/bO5HkDPr1k3k1VeRu0qkZk3dvK1bRbQUV04yGgEpLJB+y5Q81MTzx98/58H/dnn+TFW8eHFZsGCBXLt2TTZu3Kh6/6PRHg3suXLlkqefflqlQMqTJ484Us6cOVWniqpVq6pRCV27dpW6desaTVmkCQrS9fZHXQVDkK4JTOmokS9fPjl69KhFIxIS7ifIwvCFat47W98RH38f3eeGOUcHEf7N4Pnj759r4n+71jl/ffv2lQEDBli8nzJlyjh1Q/u3336r0hROnDhRdSJILX/+/LJt2zaVzih1ymV0gMR8U0cCOpspU6aogtFofzFWR+FJRyC+UfINyZyYWSbsmSA37t9Inp8nII98/MLH8uazb4qz4N8Mnj/+/rkm/rdrnfMHzjICMVPSE+7t4cOHKj+v/sN74cKFxdvb22oHSdal5b7GjRfyN1sEDRta4wUCSUZ6iRARkYdeJygNdLBA8cfp06dLp06d0j1D48ePV7WoUGMKqZxSQ6eOJk2aSIsWLWTp0qU2+znGxcTJmMAx6v2A6AHiG5AyxREREbkuZ7/ez5w5Uz755BNV+wj1kL777jsVHNcMHz5cTaiZhEB9sWLFVPDg77//Vg3xSLWMmkVYx9YwghANPrGxsU+8L3TWRArp+Ph4lf0BqZrSY3FtqzIi0vbRe70BkJke/WNx28XSskz6n01ERLaHFH3WuJZZ43pv9oiE1BAweOqpp9RERERERIahswVcv37d5AKLBw4cMLh837596hWNJ0RERO7onXfeUbUOEFj/+eefZcOGDTJ69Gj54IMP1HKkOMSIRDS29+zZM8W26OSIbRFIcCWHDh1SqYzi4uJUaqOMggiWjkB8mPhQnpn+jFyOTjsiJUmSVDDhw7UfSrNSzcTbi51EiYgcacCAAWoUojOMQHziQAIRERGRPZw/f97kdZHiICQkxKlGSG7evFm9osdkRmrUqKHqGpw8eVIOHjyYHFjQoDYEGEr1QERE5C6QIhAFk99++2157733VMBgzpw58uOPP8ozzzyjRu398ccfsnz58uTixEhnhMb4Ro0aiSs5ffq0OmbUokTv0169epk9KiJbtmwmrbvp7CaDQQT9YMKFqAuy5fwWqVuENZGIiBwpS5YsT5S63VjdPZsGEjC8zpyH99y5c0uRIkUsPS4iIiKiFMzNc4xiy+ix/9prr6nei6bUE3gSaMhA7QI07uOzNUizgB6UCCTkzZtXwsPDU2zXoUMHlYZhzJgxKlUR+Pr6So8ePVSRSaREWrduXXIxSaQ9wkiFOnXqSOXKlW36nYiIiJxB/fr15Z9//pERI0bI2LFj1fUPvTOROxppgDC5MvQUbdCggVy9elWNwNByYtvKlbtXrLoeERF5BpMDCSgMaG4EAz0Bkdt34MCBGRYVJCIiIkqPuWWdUMdp//79qtF92rRpqq4AijTbCtIr4OEfwYJKlSqpAokRERFqRMGNGzfUCAP0nEwd0MBIi+PHj6tczvqQjgGFFlFIsmTJklKzZk1Vl2rXrl2qw8aMGTNs9l2IiIicDXpjIsD+xhtvqJoIX3zxhUprhPpDrhxIQA2Ihg0bytmzZ6Vz587y9ddf2/wzw4LCrLoeERF5hsfd5Ux8gDdnQgHmqVOnqodp9LSzpb1798rnn3+ucgiieASCHn5+fhbvD8MJP/zwQ1U4GjcseMW/Md+YxMRE1Uvw2WefTR6V0bZt2wzzFP7222+qVyEaGDAUEUGbVatWpbsN9okgDT4Dn4Uel8ihiGMgIiJyRyi6aOo0adIk1ZGhdu3aattTp06pXMnIOWwrL7/8snz88cfqngH3JYsWLVL3P+hM0b9/f3XtrlWrlsn7w33Mxo0bZfDgwepaj7QNCCR07NhR1UhgfSoiIvJETz/9tAqyI4CAQD1G+r311lsm1SBylIkTJ0rp0qVVnmt9MTEx0rhxY9UZASMof/jhB6umoDCmVqFaUiBbgeTCyqlhfsFsBdV6REREZo9IwIOsqTCs/9KlS7J161bVQwAXdzTw4wE6KChIbGHkyJGyYsUKq+wLAZDq1aurvMR4SG/evLm6sKOhfu3atbJjxw7JkSNHim0QOMGFH98XPRCbNGmivjf+jaDApk2b5Pnnn0/zWd9++6307t1bMmfOrIYyImjx+++/yyuvvKIaQbRCUvp27typhnbiPFepUkWlkELqKQQ6tm/fLr/88otdbj6IiIjsCfmRLYHGBgQR0OsfeZa7dOkitlCqVCn58ssvzd4O9wjGIICANA6YiIiIPBU6BCBQgI502uhC1ExASkDUEpg7d66sWbNGvvrqK1Wk2dbwjI82CH3orFCtWrXkf6MjANoFAG0DuA+5cuVKmtGHGGmImk6YOnXqZPDzfvrpJ6sePwooTwifIK0XtlZBA9RE0GjBhfHh41lomYiILAskoMe8ufCgjovniy++KBcvXlRpBTDk3xbQ8I9ChGisx4S0ApbCMSKIgODHggULVCM/4Ablu+++k48++kimT5+eYhv0fkTQoESJErJlyxYJDQ1NLobYunVrefPNN1UgRb/o44kTJ6Rfv34qeIBADb6DNv+FF15QgQH0rtDvcZiQkCDt27dXQYRx48Yln8/o6Gg1HBIpExCEQI8MIiIi0hUuRgP/u+++K0uXLrVZIIGIiIisC6P30fEOaX806Ej3zTffqE4CeO7GMzs6G6ATHq7x6DSAzAhIC2grCGogAJC6c6H+PFNGSNy6dSs5HeO8efOMrmftQAJUL1BdvnzpS/ly25dy/d7jY83hn0MG1ByglhMREenLlGRuwmELoIEdKX7q1asnGzZsEHtAj3w00MfGxpq1HYobITUSGvwvXLiQHBCABw8eSMGCBVUOQxRDypMnT4rhlUeOHJFly5apEQz6mjVrJr/++qsKKiA4oUHxRAzHxI0RUiLpw40Rikf17NlTjVrQIE0CziWCJsj5rA95oJFGCqmVDh06ZPQ7IsUCRozgeyLAY5GYGBEtx3N0tMijApBEROT6rHKdcDJRUVFqNCE6GuC7edLPER0i0NHBENyLYDImLiZOxgSOUe8HRA8Q3wBfmx0vERFZH0a5YzIEnefQUc1Zr/fobIfOcmhkx7M3AggIKFy7dk09r69bt051WtSgs92QIUNUJgFc+5BGCJOPj4+4M0vv24ZtGibDNw83unxonaEyrO4wKx0lERG5w/O9WTUSLIXhfF5eXio9kLPDcEjUGUBOZf0gAiAw0bRpU3Ujg/U0Z86cUUEEpB/Qhi7qw4gEWLlyZZreFfrL9aH+gbnbVKxYUY1eOHz4sMqh7HEwTHTfPuNTqmGkRETkOVCDCKkH0RnA0+B+Bvcphqb0gghEROT68Hfe2DUg9fOus0FaPzybo4MdGkCQ4hevGNWPZ/LUqYWyZs0qY8eOld27d8szzzwjw4YNUx3wyLBulbvJ3q57k6dWZVqp+Z0rdlb/xnIiIiK7BxLQwI6Hd23YnjM7ePCgekXPfkO0+dp6+u9xs2Kot4OhbVC0+fz588kBAEPRoly5cqkeF3fu3Hmi4/MYU6eKVK5sfMJyIiLyWPHx8cnpComIiMi57dmzRz1ff/7558kpgvE6ZswY8fX1VcsNqVChgvz9999qlL8zjrRwFmFBYVIprFLyVKNgDTU/6kGU+jeWExER2T2QAEgx5OfnJ85Oa9xHQ74h2nz9Hv9Psg3SLAQYSQukbaeta+lneYxu3UT27hXZuvXxPLzHPExYTkREHgkNCagn5Oy9L4mIiEhHy8KMtMXmwjZIIYw6hWSakjl1NSVO3jzJU0ZERAbZpVseci8iX2GpUqXE2aGRQRsWaYjW6K+tZ6ttrPlZ6d2YIWe0RWJiJJs4mbAw3YT6DZoKFVi/gYg8Dmr6YLKUHcon2d3kyZPVa9WqVR19KERERGQCjLbftm2bqnswatQoFRzAPcrgwYMlLi5OqlSpkuE+kA+azAsknIg8oc6zJQEcIiJyb3YJJHz11VfqIvTCCy+Iq/d6MNS4YklPCVO2Sa8hx5zjMwYFo4ODg8USCGPoNdcTEZGjPXwosmWLqgczf9066TxrliQ6+picwP379+Xbb7+VL774Ql0733rrLUcfEhEREZkAAYOXX35ZpTaaOXNmcrHliIgIVYNx0KBBPI9WVCR7EcnslVnuxd+Ty3cvS/5sDMIQEZEdAwlIw4P8hdOmTVMP7126dBFnFxQUpF5j9Hu168HICggMDDR5G22+OdsY+yy8R60Jc47PmHz58lk+1BOfny+fZdsSWQLFsjGhsXT/fpEbN0Ry5UKRESRLfTwihcjT4L+LuXMRtRe5dk3N6igiHXLnlge9eklC27aSlDevWbssU6aMCjY7m06dOpkVQEBBxv3796trIwLtr7zyioSHh9v0GImIiMg6XnrpJVm0aJH06dNHtS1cvXpVzS9YsKCMGzdOGjZsyFOtBwGWsmXLGi26jSk9Pt4+8lSOp9SIBEwMJBARuY5Jkyapydj1we6BhBdffNHsh3dMms6dO0v16tXF2RUqVEi9GivKpH2nwoULW2UbBAXQwGEoVZG2nbau9h7b4LPKlStn0mcZg+BOtmwWJih6VOyKyG5QLHv4cOPLhw4VGTaMPxDyPB9+KLJwYZrZXtevi//gwSKHD4ssWGDWLp11KPtPP/1k0eg/aNWqlerNSERERK6jRYsWajpx4oTcuHFDcubM6RIpkx0BdaCOHDnyxOmNtEBCvaL1rHZsRERkW+kFjFFPV7+N3i6BhE2bNiXnJDTrAzJnVkWOMBzRFZQvX169HjhwwODyffv2qVf9Rnxtm3///Vfi4+PFx8cnw22yZ8+uggLoWYHPSp32CT/g69evq4CAfvohfNbBgwfVNo0bNzbp+IjcQsGCT7acyBhc1xITdRNGvKR+b2iesyxPSBBZuzb9ny2KzmNdNwgA47ppaiDB399fcuXKJZUrV5bXXntNqlWrZvPjIyIiItsoWbKkmsi2SoY8rpNARERkcSChdu3aFj28t2zZUg09dBVIeYB8i5s3b1YN+blz505ehoJOK1euFG9vb5WrUVO0aFGVBgJpglavXi3NmjVLsc8lS5ao16ZNm6aY36RJE1X8EcM1UwcSFi9ebHSb2bNnq+UDBw5MsQwBhtOnT8szzzyj8kcSuQ00gqY32gB/mzBaoWNH52gsRcM0Jls0IDtzo7arLnfDwsIpIEURaifUrSuuDnmRiYiIiMjGBZdvMpBARERPOCLBnUycOFFNGCaJOg6asLAwadeuncydO1c++OADmT9/vhpVAf3795dr167J22+/rYYN6uvbt6+8++678sknn6gUTnny5FHzly1bJitWrJDixYunCTBgpMYPP/ygggmvv/66VK1aVc0/deqUjBo1Knk0hz4cLwIXyPk8YcKE5OWomaANYenXr59NzhmR3aBx9/ZtkchIXS0E/P0xkjpMQUPwhQsideqI5Mzp+EZrTOR+ELDy8tIFq/Rfjb3PaLk19oXf+x07TKujQB7rSXMmExGR67JXzmRys0ACRyQQEZG9iy1rEhMTZdWqVTJ9+nRZvny5TT4D+x85cmSKeRhBoJ/KYPDgwapHPyC/4vHjx+WKgcaV8ePHy86dO1WvfzTYP/fccyrX4OHDh6VEiRKqsJOhApAYjYDAQenSpaV+/foSGRmpAjB+fn7y888/JwckNMjt+NVXX6kgRK1ataRBgwbi6+srf/zxh6qb8O2336oAhD6kTcK+sO6HH36oAh1If7Rlyxb1XZADGoEOW9e7lfteIlJRN/OAl4i/7i3r3VIaaGC/dUsXENACAxm93rxpWWP8tm2u3TBt7UZmezZou/uxYnLGugEIstUzIX8tC5Hb5V7EnXMmExGRa7JXzmRry5Ejh+rkZ83rly326a6BhP9u/SfxD+NVAWYiIiK7BBJQEGnGjBkqFY+tezsgDdGuXbtSzEM9B/15WMcUSMu0e/duGTp0qGpsQHAAD+G9evWS4cOHq/oGqSEdElIUYZQAvvNvv/0mAQEBqmEf2xjrCdinTx8VLEBAAcEAQEoojGx45ZVXDG6DNEja8SFQgZRGxYoVk48//lgdoy2LZD6ud4vIga4eg9R8vJz1bt1cfLyukd/UgABeEUSwNHVMUBD+g0QEDX9QMl6/Tx8R/LfmKg3aztgwTa6lVi2RbNlEoqKMr4PlWM9D2fNehIiIiKzjzp07KmWys+/T3eQLyidZfbLKvfh7cvb2WSmRs4SjD4mIiJxIpiRzqydnAD3pFy5cqB7atz3qHax9BOoIoCAxOZbW8yR//vxyMb10MemMSLh/877UfEl3E7b1j/viH+LvPCMSYmJEAgN176OjRQICHHxATurBA11Dv6HGf2OBgTt3LP88BOCQdgiBAVNeMfn6Ph7VgLof6DFl6E8WGuQLFBA5c8Y5aiQQ2dP06SJduhhehv82fvxRpHNnu10nnAHvRaz3c4yLiZMxgboUkAOiB4hvwKO/y0RE5PKc+XqPjnp58+aVy6j15MT7dMefY8WpFeXA1QPyW7vfpElJXUYHIiJyXda8TlhtRAJSASF4sGDBAolG4+2jAALS/LRp00ZNKAJMrk0LFMRce5x2pkK5RAnQlYQgR7l/37xRAnh99N+p2dAwmSOH6QEBvIaEiKRK7WUWBAcmTBBp3Vr3+frBBK1X//jxDCKQZ0KQAP9NomaO/k1BwYK6/y5athRPwXsRIiIi94GRhN7sJOSQ9EYIJKBOQhNhIIGIiKwUSECqoDlz5qh8w8eOHUsx+gDpdZB+B2l6iMhE+O/n3j3zAgJ4xTaWQHodrfe/qYEBNFg64oa+enWRL7/UTfppylDY/KOPdMuJPBWCBc2aiSBFHoaNIeKLdEYe8PDNexEiIiL3ZOXkCWSikiEsuExERFYKJOBivmbNGhU8QB2AhIQENQ+5Bps3b64K/YaHhyenMiKyO6TB0fz1l0jDho5pTMON79275o8UQMohS6DHvzkBAbwGB+uCCa7gcYGOlJDz/OOPdSMshg1zxJEROQf8natbVzwB70WIiIjc28aNG22yX18tdSplWHD5xE0TatQREZFHMTmQcPr0aZW6aNasWXLlyhX1EI9RBzVr1pQOHTpI27ZtJQiFUYkcaelSkV69Hv+7cWNd7nykxXmS9B4ICqA+gLkjBVCc2BK4wTUnIIBXFFR15+K93bqJvPqq8eUOL85BRLbGexEiIiLPUKdOHUcfgsdKDiREMpBAREQWBhJKlCihAgcIIDz11FPy1ltvqQBC0aJFTd0Fke2DCMihn3oILAr0Yv7ixbpgQmKiyK1b5gUEMOmPdDCHn5+usd+cwAAKRLtzUMASTlHJm4gcifciRERERDa+38pZQr1ejLooMXExEuAbwFNORESWpTbq1auXfPnllxwSSM4FjfwoNGooj6Y277XXdL32b9/WBRMsgQZ+c0cKZM36ZN+NiIhS4L0IERERkW2E+IdIrqy55Ma9G3Lq5ikpn7c8TzUREZkXSEAuwbi4OPnuu+/k559/ltdee02NSqhWrZqpuyCyHRQYvXgx/XUSEkRu3nz8bwQVzAkIYMLoAiIicgjei1gmIiJCypYta3BZ9+7d1URERO5p0qRJajJ2fSD3Yc3rPdIbIZCA9EYMJBAROb9JdrremxxIuHr1qgogoMjywYMHZfLkyTJlyhQpXry4KrDcvn17KVSokNUOjMgsV66Ytt4XX4h06CASEqKrQ0BERC6D9yKWCQ0NlSNHjlj5p0FERK4gvQbkAgUKyCWkgSW3YM3rPQIJ2y9sZ50EIiIX0d1O13svU1fMnj279OjRQ/bv3y979+6V999/X4KDg+XkyZMyePBgVTfhxRdflJkzZ1rlwIjMYmru/CpVRPLmZRCBiMgF8V6EiIiIyPZKhjwquHyTBZeJiMiCQIK+ihUrquESV65ckTlz5kidOnVUEeZNmzZJly5dktf7/fffJQHpZMiphz4amowNh3FatWohxGZ8OQoXFyyoW4+IiNTfeWPXAFdIdcB7ESIiIiLbwIgEQGojIiIii4st68uSJYu8+eabajpz5oxKezR79my5ePGiCiy0atVKjVpo1qyZtGnTRho2bCiZMz/RR5IVuVWqA29vkdGjdWmLDAURYPx43XpEROQ2qQ54L0JERERkXQwkEBGR1UYkGFK0aFEZNWqUnDt3TlavXi0tW7ZUQYPbt2+r4ELTpk1VwzWRzRw9qntNHazCSIXFi0VatuTJJyJyY7wXISIiInpyxUOKq9eb929K5L1InlIiIrJuIEGTKVMmCQ8Pl8WLF6vejGPHjlVpEjBCAUEFIpu4eFHkm2907+fMeTx/9WqRM2cYRCAi8iC8FyEiIiKynL+PvxQKLqTeM70RERHZLJCgL1euXNK3b185fPiwbN++XTp37mzLjyNPNmSISGysrgbCK688nl+7NtMZERF5MN6LEBERuafr16/Lnj175K+//nL0obglpjciIqLU7FawoFq1amoisrrDh0V++kn3/quvHtdEICIi4r0IERGRW/n1119l2LBhcvDgweSRiAkJCcnLb926Je3atVPvlyxZIgEBAQ47VldWMqSkrP9vPUckEBGRfUYkENnFJ5+IJCWJtG0rUrUqTzoREREREZEb+vzzz6VFixZy4MABlT5Zm/TlyJFDsmbNKn/88Yeq30hPOCLh5gmeQiIiUhhIINe2fr3I2rUiPj4io0c7+miIiIiIiIjIBnbt2iWffvqpZM6cWb755hu5ceOGhIaGGly3ffv2KsCA0QtkGaY2IiIih6U2IrK6xETdaAR4/32RYsV4komIiFKJiIiQsmXLGjwv3bt3VxMREbmnSZMmqcnY9cGVTJgwQb0OGDBAevfune66derUUa+7d++2y7G5cyDhZORJSUxKFK9M7IdKROTpGEgg1zV/vsj+/SLZsokMGuTooyEiInJK6K155MgRRx8GERE5QHoB4wIFCsilS5fEVWzdulW99ujRI8N1c+bMKYGBgS71/ZxN4eyFxcfLR+4n3JdLUZekYHBBRx8SERE5GEPK5JpiY0UGDtS9/9//RHLndvQRERGRh7t3754sX75cOnfuLOXKlZNs2bKpAo8VKlSQkSNHSkxMjFn7q1u3riogaWxai9R+REREHuLatWsSFBQkuXLlMml9Hx8fiYuLs/lxuavMXpmlWIhu1P+JSNZJICIijkggVzVxosj58+hGI/Lhh44+GiIiIpk3b568++676kw8/fTTEh4eLlFRUbJ9+3YZMmSI/PLLL7Jp0ybJbWbwu1WrVqpXZWr58+fnWSciIo+BAsrR0dGSmJgoXl7p94nE9ff27dtmX3NdmS1SGSK90bEbx1Qgof5T9a1wlERE5MqpDJnaiFzPzZsin32mez9ypIi/v6OPiIiISHx9feX999+XPn36SIkSJZLPyJUrV6RJkyayf/9++fDDD2Xu3Llmna2xY8dKkSJFeIaJiMijlSxZUtU8OHTokBrtl54lS5aoYsvly5cXT2GLVIYlQ3R1EjgigYjIudkrlSFTG5HrGT1a5PZtkWefFXnrLUcfDREROcj58+fNuiG6fPmy2sZWOnToIN9//32KIAKEhYUl9w5BwwbTLBAREZmvadOmKjjw+eefp7veqVOn5H//+59KA9i8eXOeaisUXD5xk6mNiIiIgQRyNWfOiHz3ne79l1+KeHs7+oiIiMhB0Eu/SpUqJq9fo0YNeeqpp8QRtB6RDx48kMjISIccAxERkSvr2bOn5MmTRxYtWiTvvPOOHDt2LMXy//77T0aPHi3PP/+8XL9+Xd0ndOrUyWHH61aBBNZIICIipjYilzNokAgKZjVoINKoUcplV67opvv3H887cOBx6qOwMN1ERERuAz0Tbbm+taBxQyv8GBISYta206dPV8EH5INGWgf0rixUqJCNjpSIiMg5ZcuWTVasWKFqEM2ePVtNGtQSuv/oORDX+pw5c8rSpUslS5YsDjxi9wkknLl1RuIexomvt6+jD4mIiByIqY3Idezdi0qWj0cjZMqUcvnUqSKVK4vUrPl4Ht5jHiYsJyIijxUbGyuZMzumPNSECRPUKxo/zG3UGDVqlEyePFmlR+rdu7cUL15cRqJGEBERkYepWrWqHDhwQFq2bKlSFyFogOnevXvJnQUQcP/7779tWh9h7969KsUSjiN//vzqWPz8/CzeHwpDo45S4cKF1X0CXvFvzHekvIF5JdA3UB4mPVTBBCIi8mwstkyuATeFH3+se9++vUjFimnX6dZN5NVXje+DoxGIiDwW6iMgzQFSItjb6tWr1agCjEYwJwBQu3Zt6dKli7zwwguqzsKFCxdk8eLFKrAwZMgQ1TMTgYWMoGElKirKomOPj4m3aDsiIrI9pMvDZClHjdJ7UmhkR3qjW7duyY4dO9Q1/uHDh5I3b151zcydO7fNjwHXc4yOsAaMOqxevbqcPHlSpWBEIOTff/9VnRDWrl2rvmOOHDnEERAgKRFSQvZf3a/SG5XKVcohx0FERM6BgQQPFhERIWXLljW72rdDrF0rsnGjCHpxjhpleB2mLiIiMhl6t2sFgA1dH5zRX3/9JZs2bUoxLzo6WkaMGJFuIwl686ExH+/Rk9Gejh49Ku3bt1ef/eWXX5rVOzL190Jao4EDB8pzzz0njRo1kmHDhknXrl3FX0vhZwQaWIKDgy06fh/xkU/lU4u2JSIi2xozZowMHz7cY08zGtcbN27skM9Gwz+u6ajHgAlBDEv16dNHBREwumHBggXJoyd79eol3333nXz00UeqQ4Ij0xtpgQQiIvJsmZJctRsCWaxAgQJy6dIlNQTz4sWLFu0j5lqMBIYGqPfRETESkEf33iYePhSpUEHkn390oxKQ1oiIiJz6OmELaCzBhN5xgFsY7X1GsC5SDiAQYU6B5ieBc4cCz+fPn1eNBOPGjbPavtFosWfPHtm4caPUrVs33Z9jvnz5VEDD0hEJE/NNVO8HRA8Q3wDmRiYicpcRCWXKlFHBZme73rsi3I8gJRHSKJrj6tWr6vx7e3urkYehoaHJy/CzLViwoNy8eVP9nIyNqrT1fduQjUNk5F8jpWulrjK1KdMFExG5GmteJzgigZzfrFm6IAKGcw4YYHQ1rdayMRywQETk2ooUKSJ16tRJ/vfmzZtVuiD0CjQGBYqRAuiZZ56Rt99+W9UXsIcbN25Iw4YNVRDhnXfeka+//tqq+y9RooQKJFxJ78Kn17iBc2CJOO84i7YjIiLbQ8P1kxQTNjUYT7azZs0aSUxMlHr16qUIIgB+tk2bNpUZM2ao9XAf48iCyyduckQCEZGnYyCBnNu9eyKDB+veDxqkCyYYgVrK6Y3sHTpUZNgwGxwjERHZBR6g9R+iESQICQlRvfKdyd27d+Xll19WowBat24tP/74o9Uba5AXGgICbDgikIiIyMlghOHMmTPll19+kUOHDqnrYUJCgtH1cf1Nb7mjHTx4UL1WqlTJ4HLMRyBBW8+hgQSmNiIi8ngMJJBzGz8eyZ3RDRWFG9JdVau1fP++SM2aunlbt4poqaNZa9m1ccQJEaWGhoSM6gPYG1IavPrqq2q0QHh4uMydO1elK7AmFI3esmVLug0PRERE7gZ1kVATYdu2bS5bKDo1jFzU0k4Yos0/d+6cOAqKLcPlu5clOi5aAn0DHXYsRETkWAwkkPO6dk3k88917z/7TFdoWTJOXRQT83geSiuws6Z74IgTIkrNUUP8jXn48KG0a9dO1WGoXbu2LF26VHx9M64p0KFDB/n7779V0cwWLVqoedu3b5dr166plAb6gYizZ8+q4s0xMTEqYGGs4YGIiMjdDBs2TLZu3aqui2+88YY0atRIpQPSihO7anAEsmbNanC5NvJQWy89CK5ERUVZPVVWDv8ckjtrbrl+77qcjDwpFcMqWvwZRERk/5pI1gy+u+4Vl9zfyJHIDyFSubLI6687+mjIwTjihIhSi4uLU0UK0VifN2/eFMvwwI0Ghz/++EM1ODRp0kQGDhxo0xEMEydOlOXLl6v3OXPmlPfff9/gemPHjpVcuXKl6I14/PhxuXPnTvK8EydOqNoK+F4YdZA9e3bVG3Hv3r1q1MPTTz+tUiYRERF5ikWLFqlURRMmTJAPPvhA3IHWuGMsBaI5jT8oyBwcHGzxsQwdOlTdOxlLb4RAAtIbMZBARGRf6HA2PL1c7nbEQAI5p5MnRaZM0b3/6iskwnb0EZGDccQJEaU2bdo06dmzpxqZgPzB+hA4QK9F7QEcuYWRDgj1FGxVXFKrWwDLli0zuh4e0vUDCYZUrVpVBSJ27dolu3fvVvtGr8QKFSpImzZt1DJnS+tERERkSxiph9EHXbp0cZsTHRQUpF4x0tCQe6gZKCKBgRmnE8qXL5+qz2Sp9Ap3I5Cw7cI21kkgInKAAQMGSN++fS3evkyZMirYbA0MJJBzGjhQBEWxGjcWqVfP0UdDROS0PLl+yLp169Qr0hvo+/XXX1XQAMWYsQwN7rNnz1bz5syZo1IJ2QICBMZ68qUHqZAM3ex9//33VjoyIiIi1xcWFqYC66akDXQVhQoVUq8XL140uPzSpUvqtXDhwhnuCx0lsmXLJjYtuHzzhE32T0RE5qeeM5U1O9Kxmzc5nx07RBYv1o1C+OILRx8NEZHT1w9BBjhjE5a7K63XXWV8UT3z5s1TN0v9+/dXgYMffvhBxo8fr0YnYBkRERG5HtREQA2AY8eOibsoX768ej1w4IDB5fv27VOv5cqVE0dKDiREMpBAROTJOCKBnAtSUHz8se79O++IPPOMo4+IiMipeXL9kOvXr6vihDly5EgxH+mLQD/1wVtvvaXyKSPFkaeJiIiQsmXLGlzWvXt3NRERkXuaNGmSmoxdH1wttcOSJUukV69esmrVKvHx8RFXFx4erkZQbt68Wd3X5M6dO0UtqJUrV6paTy+//LLTBBLQMcNWaSKJiMi5MZBAzmXFCpFt23QtYE5SSISIyJl5cv0Q5BNOXSfg7Nmz6kEcqQKKFi2aPB/1BVCw+ObNm+JpQkND5ciRI44+DCIicoD0AsYFChRITp3jCnBtRwChbdu2ajRiv3795LnnnkuuM5Dedo42ceJENbVo0UIVzdRP19SuXTuZO3eu6vAwf/58VQcCMLISdSFQCwrXckcqlqOYZJJMcjv2tty4d0NyBzwOeBARkedgIIGcR3w87pZ071FEJH9+Rx8RERE5sZCQEBU0uH37tgoSwJ9//qleX3jhhTTrJyQkmFSskIiIiJxTqVKlpGnTpqpRvlOnThmuj57zuP5bGwIaI0eOTDEPIwiqVauW/O/BgwdLkyZN1PsbN27I8ePH5YqBwlZIv7hz505ZvHix7N+/XwVH0AHg8OHDUqJECRk3bpw4mr+PvxQKLiTn7pxToxIYSCAi8kyskUDOY/p0kRMnRHLlEvnkE0cfDTmZ8+eRIxT5Qx/Pw3vMw4TlRORZKlWqpF6n4/ohIomJieo9Gg3q1auXYl0EHKKjoyVv3rwOOVYiIiJ6MmiMR0cBLVUTUuxkNOHewBZwX7Fr167kSTse/XlYxxS5cuWS3bt3S8+ePVUwYtmyZaqTBFI4/f3336rjhDNgnQQiIuKIBHIOd++KDB2qe4/XbNnM3gUakm/c0OUJ129o1rJeID7hBKNayQL42ZYqJRIbm3K+lg8e/PxEjh/nz5jIk3Ts2FHWrl0r//vf/2T9+vXqgR1FCZHioE2bNinW3bJli3otU6aMg46WiIiInsTw4cNVT33UR0JaIxRfRsofLRWQve9BMJlq2LBhajIG9Z6+/fZbNTkrBBL++O8PFlwmIvJgDCSQcxg7VuTaNZESJXSVQ83Ehmb3hgBR6iBCaliO9RgsIvIcyJG8bt06mTlzpnoFPz8/mTJlSnKqI82CBQsMjlQgIiIi14DCw7iWz5gxQ90DkINGJNw8wVNPROShGEggx0OeSAQSAIWnfHzM3gUbmnWSkpDa4/H08GHKV1PeP+lyW+zL1Bpwq1aJnD6tG52gTRiRov9v/ckBnZeIyMqQygg5krdv366CBw0aNEhRZBmQJiA4OFg6dOggjRs35s+AiIjIBaHwsK+vr7Rq1crRh+KRmNqIiIjYjEaOhyGe9+6JoDBVy5Y2/ahp00SQHtuajeHO1AiPQIInGzLEvPURSDAUYEgv+GBsmbnb+Pqi+JutzgSRZ6lRo4aajEGjww8//GDXYyIiIiLrypcvnwomeHt789Q6MJBwMvKkJCYlilcmltwkIvI0DCR4sIiICClbtqzBZd27d1eTzR05omvdB4xKsHHL6uTJNt29y8Bp9vISwT04Xk15b811zd1XZKTI4sUZfy/EotBAjzRHqJWB19RTfPzj9RMSRKKjdZMjfgbGAg/WDFgYm49zS54NhQq1YoWGrg+uCEUOIyMj5d69e1KIec6IiIjcyquvvioTJkyQPXv2yHPPPefow/E4hYMLi4+Xjzx4+EAu3LkghbMXdvQhERGRnTGQ4MFQmArFqhzqf//TdaVv0QJdSm3+cfiYPHmcp4HcUeu6Wk/4fftMCySgTbRSpfTXwegNQwGG9IIPli4zNF+D0SNYrl8c3J6QQcweAQtDyzASxNV+B52ZpYXm0wsYFyhQQC6ZmlPMCaDA8qhRo1TB5ZiYGJU/OQGRwkdu3bqlCjJjPooYYoQCERERuZZBgwbJokWL5L333lPX/NT1kMi2vL28pXhIcTl646gquMxAAhGR52EggRxn82ZUzNK1cKM2gh0MGpRxQzO5N/y6BQToJntD8CAuzrqBCVOXYULMToORGZju3rX/eUAwy55ppPTnZ8niXkEMFpoXmTNnjnTp0kXi9YcbpZIjRw45c+aMbNiwQZo2bSpNmjQRT+IUIxCJiMgh3GkE4j///COjR4+W3r17q+vau+++K1WqVJGgoKB0t6tdu7Z4Antc75HeSAskvFTspSfeHxERudb1noEEcgy0qH78se59164ipUrxJ0FuDw3YaMjGFBxs/89HB21bjLIwZdmDB4+PAwENlEXB5Ag4//ZMI6W/zNopfT290PzRo0dVIwKCCL169VLFlMPDw1V6o9SwDL0XV6xY4XGBBKcYgUhERA7hTiMQ69atq0YXwp07d9RoxIykHqXozuxxvWfBZSIiz77eM5BAjrFwocju3SKBgSJDh/KnQOlCahY0xKbXYIrlWI+MQ0oh/CeHyd4QPEAwwRHppDBPvxA5jgPTnTv2Pw+GCnw/ScDiyhXxaOPGjZO4uDh1wzR+/Hg1z1gBxhdffFG97tixw67HSERERNath2TL9cnEQMLNEzxVREQeiIEEsj+04A0YoHv/ySfoOvHEu2RDs3tDT+rjxx/nga9ZUzd/69aM88CTc0A6I/ystJ+XPeH5ER3R7J1OytkKfLujP//8U/U07N+/f4br5suXT7JmzSrnkQ+KiIiIXE6ifp5OcgiOSCAi8mwMJJD9TZkicuaMSN68In37WmWXaECeMUPkjTdS9lxnQ7P7wM8YU0zM43kVKjim1gG5FoyAR3FpTNmy2f/zUeBbG41h7REY166J7NkjHuvy5csSEBCghmqawt/fX6VCICIiIiLLAwlnb5+VBwkPJEvmLDyNREQehIEEsq/bt0VGjtS9HzHCqq3ACxboXt96S+THH3Xv2dBMRI6GTDtZs+oma9u3T6RyZfFYWbJkkdjYWJW2QMuZbMz9+/fl9u3bkj17drsdHxEREZE7CQ0IlSDfILkbd1f+u/WflMldxtGHREREduRlzw8jks8/F0ERzDJlRN55x2on5OxZkZUrde+7deN5JiLyBEWKFFGFlk+ePJnhuqtXr5aHDx9K2bJl7XJsRERERO4GHTeY3oiIyHNxRALZD/JSPyqGKV98oas6aiXff68r5vrSSyKlS1ttt0RE5MTCw8Pl0KFDMmHCBJk0aZLR9SIjI+WTTz5RD79NmjSx6zESERGR+WbPnq1eg4ODpVmzZinmmatDhw78EVgRAgl7r+yVE5EsuExE5GncKpCA9AZjxoyR+fPnq2KKISEhqpFh5MiRkj9/frP29euvv8r48eNl3759EhcXJyVLlpROnTpJjx49xAtVQw04fPiwfPbZZ7Jp0ya5deuWKuzYokULGTJkiNFUCtevX5fRo0erz7t48aJar169ejJs2DApbaRFHD0qp06dKrNmzZIjR47IgwcPJCwsTOrXry8DBgyQEiVKiFMaMkSXKLxOHZFXXrHabu/dE5k2Tfe+Z0+r7ZaIyOl5eqH5Pn36yPfffy9TpkyRPHnySN9UdXeQzmjZsmXy6aefyrlz5yRXrlzy3nvvOex4iYiIyDQdO3ZUHQBKlSqVHEjQ5pkD6zOQYF0ckUBE5Lkyu1MQAQ3p27dvV43quNk4e/aszJw5U1atWiW7du1SKRBMMWLECBk6dKgKGFStWlVy584tO3fulN69e8vmzZtl0aJFaYIJ69atk+bNm6vjKFOmjNSoUUMFFr755htZuXKl7NixQzVg6Ltw4YLUrFlTBT1wzI0bN1aFIxcuXKgCCxs2bJDq1aun2AZ5oFu1aiUrVqxQBSZr1aolgYGBsn//fvVdcWwbN26U5557TpzKwYPoQqJ7/+WXuuqnVjJ3rsitWyJPPSXSuHH6DWrkuq5c0U0oOqs5cADFU3Xvw8J0E5EnQQHy48dFbtzQ/bdRs6ZnFZoPDQ2VefPmSevWrdW1G50JEjE8TUSefvpp+e+//1RnAFw7UU8BHQ2yOaLiNhEREZmlUKFCKgiAznmp55GTBBJuckQCEZGncZtAAnr1I4iAhvfff/9dNa7DuHHjpF+/ftK5c2fVMJ8RBAwwGsDHx0cFABo1aqTm37lzR5o2bSpLly6VH3/8UbrpJeKPjo6W9u3bqyACRh8MHz5czUdjBj4bIxt69uypGjD0de3aVQURXn75ZRUAQGAAlixZIm3atJF27drJiRMnxNfXN3mb3377TQURihYtqr5v3rx5kz/ro48+UoEL9Mj866+/xKn0748oiMhrr4lUqWK13WKX336re9+9u66oKbmnqVNFHv2nlUxrNIWhQ0WGDbP7YRE5HIIEmGJiHs/zpELzr7zyirrmYcTgnj17kucfPXo0+X3FihXVqIXnn39ePFFERITR2hDdu3dXExERuSek/jOW/g/XB2eFToGmzCP744gEIiLPlSkJ3fRcHAotIqXB7du3VSoiNBjoK1++vMqhvHfvXqlUqVK6+0L6IvTs79KliwoY6Pvnn3/k2WeflaeeekpOnz6dPB+5Gt9++22V/ggNF/qjFZB2qFixYmqkwZkzZ6Rw4cJqPgIIeO/t7a32pc3XoHclAgpIX6Q/FBPBgq+//lo+//xz6Y/GeT1Ip4R0Tv7+/nIP+X6MKFCggFy6dEmle0I6JUvEXIuRwFBdK1V0RIwE5EmnxeqPP0QaNhTx8RE5dkw3dMBKNm0SqVdPJGtWkUuXRJBBCo1pj+JIEh3tOY1pnjIiwRiOSCBPZ82/fda4TjgCrvVbt25V11ykAUSwHSMEnW6Unp1Y4+cYFxMnYwLHqPcDogeIb8Djzg1EROTaXPV6T479Od6JvSPZv9Clbr7zvzuSLQtHexIRecp1wi1GJKDRAEEENNinDiJojfJoXMAIg4wCCUgRBHXr1k2z7JlnnpGcOXOqVAlIW4Sggv42tWvXTpPyCKkUqlWrpoICSFeEkQn622BkQeoggvb52AajD/QDCdifMdowTxyj00CKiY8/1r1Hj0crBhFAG42AU2SkDAW5CQYKiCgj5cqVUxMRERG5H3TGQ0c8U+sfomNBQkKCSolE1hPsFyyhAaESERMhJyNPSuV8lXl6iYg8hOGqwS7mIPLvixgNEmjztfXSE/MoNwR69huiNdLr78te20CDBg3U6w8//CBXr15Nno/URlpKJacqJoUCBvgOwcEigwZZddfnzomsWKF736OHVXdNREQu0qCAnhWmQoMCtiEiIiLXg5qHVcxIk4tRicgmQNbH9EZERJ7JLUYkaI0CGKphiDb/HFqeM4DCyidPnjS4LhrrUSA5dX5GbJPe/rX5T7oN1KtXT9VAQO2H4sWLq2LLQUFBKqUTGlM+/PDD5ICCw6Hq8aef6t4PGIDoiFV3P3mybsBD/fooqslivEREntigEBYWZnIwAQ0KuI6jdyIRERG5HnMzM7tBJmenrImEQMKW81vkRCQLLhMReVJNJLcIJKDYMWRFonwDtCLG2nrpQXoiFDFGbYL33nsvxbKFCxfK/fv31fu7d++m2AbFnletWiU3btyQXLlypQhybNy4Mc026EmBNEX4Ya5bty65qLMWsJgzZ456jxzP+EzUPdCgRgKGZ6Jewtq1a5PnI9USAguZM2c2+aYqKipKLBFzFzUYAjLOO4TAS8GCIr16iTXhx6CVsHiULYrFeImI9OrzPHz4wOLz4UoP3WxQICIiIkNiY2NNfjZ2B6GhoXLkyBH7jki4yUACEZEzSC9grNVIsAa3SG2kNSJoNQKMLTcFTnq2bNlk586dqoDyqVOnVP2FBQsWyAcffJB8I6JfC6Fhw4ZSuXJlFah4+eWX5e+//1bvt23bJo0bN1aBgdTbBAcHq/1pqYiWL18ud+7ckePHj0vbtm3l2LFjyd8ndfHm1157Tfr16ycDBw5UBZwRDECwAuu3atVKvvvuO5NTPOA4LJmKFS+W/s4jI0VGj9a9HzVKRC8QYg3z5oncvIneqCKvvKKb162byN69xicsJyLyBGPHjrX47zsmXB/ckac1KBAREXkq3Mtcv37dueoHuhGmNiIi8kxu8TSN1D76dQdSu3cPvedFAgMDM9wXojQocozG/NmzZ6tJU6FCBXnuuedk2rRpkl2vsi8a8JcuXSpNmjSRPXv2SNWqVZOXYXTCoEGDZNiwYSm2gTFjxqgUC4sXL5YWLVokz0cjB0Yd9OnTR/z8/FIUWP7888/VyIjUKYxQnBkjIkqXLq0CDG+++abR+guafPnyydGjR8USMdfvSb7i6azw2Wcid+6IlC8v8uabYk2IC2lFlhFs8/bWvWcxXiLyRFeu6KZHA+aUl176WMLDe6v3efMmqckcZcqUcbtggtagkCdPHkcfChEREZngr7/+kk2bNqWYhw57I0aMMLoNOhGiI+Dq1avVe/1nc7JNIAHn2VinTiIici9uEUhAmh+4ePGiweXa8I3ChQubtD8UND59+rQahXD48GE1IqBatWrSpk0badeunVonde5BHMP+/fvVyAKkRkLwAo367du3l/nz5xvcBgGCRYsWydatW2XNmjWqgQON+6+//rrEx8cb3EZLedS6dWuDQRAc54YNG1RAAyMl0oOLPUZfWMI79lHrvSFnzohMnKh7/+WXj1v6rWTLFpFDh3SDHDp1suquiYhcztSpIqlL47z4oq+IYBIZOlRk2DDz9umsD4NsUCAiIvIcGHWPznP69yXoPGhKTUA0bqNT3gDU6iOrK5ajmGSSTBL1IEquxVyT0MBQnmUiIg/gFoGE8uj1LiIHDhwwuByFiKFcuXIm7zNHjhxpaiSgcR+NGD4+PvLCCy+k2QYjCdDAn7qRHw372qgBQ2rWrKkmfePHjze4jVbs2VgAQJt/E3l/HGXgQJwsdIlF3ier717L3PTWWyIZDLogInJ7SNv26qvGl2O0lrtggwIREZHnKFKkiNSpUyf535s3b1bP4tWrVze6DToB4pn4mWeeUamKixdPbxg9WSpL5ixSJHsROXP7jBqVwEACEZFncItAQo0aNVRO55MnT8rBgweTAwsapCqCpk2bPtHnYDQAiikj7ZGpuRZR8wDDKjHSAPUTTBEXF6cqbaPnRadUXe7z5s2rCjhjxAGKK+tDYWaMitBuuhxi926RX35Bd1bdaAQrQxxl2TLd+x49rL57IiKX40lp3digYJmIiIg0IxxNKcpFRESuD8+VmIxdH5wZAgGY9IMESN+LjgXkHOmNtEBCrcK1HH04RERkB24RSPD19ZUePXrIZ599ph6G161bJwEBAck9+zFSAT0ZUBBZM3HiRDWhNgFqFejbu3evVKpUKcUQSuyzd+/ear+oU5Dav//+K8WKFVPDJzUo1Iz9YyTDN998k6bAIwIC/v7+kjt37uR5KJyM4AG2RTHmp59+OsU2zZo1U8WUhwwZogIoJUuWTA4ioDbC2bNnVQon1HKwOxQv+Pjjx8MFKlSw+kdMnozvKlKvnkiqOAoREbk5NihYJjQ0VI4cOWLlnwYREbmC9ALGSI2rpQF2BTNnzlTPz+Q8gYR1p9epQAIREXkGtwgkAAoar1+/XrZt26Ya15Eq6Ny5c7Jr1y7VUD9jxowU62NkAUYLXEGFylRatWqlGubR4x8jHY4dO6aCEQgiLFu2TIoWLZpmm6+++krVR0AAAqMGUNQRtRISEhJUgAOjGFL7888/5d1331WN/mj8R+GoLVu2qGACRk8g+JDa0KFD5ffff1fHjlRNSLGEXhkYifDff/+pGyt819RBC7tYvRrjTVH8QWTkSKvvHoVEf/hB975nT6vvnoiIXIyzNSigPhKu0StXrpTdu3er4D7uJ0qUKKHuLfr27Zvc0cFUsbGxqsMD6i2hAwKu+eHh4TJy5EjJnz+/zb4LERGRs9EfnUBOVHD5JgMJRESewkvcBEYCYIjj4MGDVaMCGvURSOjYsaOqkfDUU0+ZvC/URsDDOYIQS5culdu3b6t5GHXwEvL+G9C8eXOpWrWqHD16VBYvXqxeX3nlFVVTASMFDMEICTQsXL16VVasWKGCIBUqVJCffvpJ/RsjLVJDSiU0TiCgUKpUKfn777/l119/VQEL3FhhNMWLL74odpeQIPLJJ7r3vXuj+rTVPwIZkyIjUTQbaaqsvnsiInIxuO4ZCtQ7yrx589RIRAT0ExMTVYN/rVq1VKAfIwmrVKki169fNyuIUL9+fRkxYoTqbIBRiQULFlQBFHRcQKCCiIiIyKGBBI5IICLyGJmSkpCPhjyJNoQVwZKLFy9atI+YazESGKrrVRkdESMBv84XefddXfXj06dFsme36jHjt7RSJRTUFvnii8cxCyIics7rhCeaPXu27Ny5U/r06aNGIWgw+rFJkyZq9OAbb7whc+fONWl/CD5g5AGKSmKkQ2BgoJo/btw46devn+o4sGHDBpv+HONi4mRMoC4F5IDoAeIbkLaTAxERuSZnvt5bq3Mc0hWnd610B476OZ69fVaKTigqvt6+cm/gPfH28rbbZxMRkWOuE26T2ogcKCYGrR2694MHWz2IANu26YIIKEHRubPVd09ERPTEOnTooKbUwsLCVKFLpCNcsmSJGlFgaNShPtRXQk0kwLZaEAGQImnWrFkqRSJGXWJ0AhERkTvZtGlTusu1eoap+0Xq1znEMv1/k3UVzFZQsnhnkQcPH8j5O+elaI60KaCJiMi9MJBAT8xn6kR0txRB7Yj337fJGX3UliLt2yO9k00+goiIyGbKly+vXh88eCCRkZEquJCerVu3qtSKxYoVk4oVK6ZZ3rp1azl06JCqx8BAAhERuRuk8jUkLi5OJk+erK6R6FlZt25d1dMS0NsSAQj0tsyRI4dKT5xR4J4shxEIxUOKy7/X/1XpjRhIICJyfwwkkGUePpQ6sklKyTHxmfCVbt7o0bpCy1aGUTdLlujes8gyERG5ItRJAB8fH1UwOSMHDx5Ur8aCBNp8bT0iIiJ3DySgLmCDBg3k/v37MnXqVOnSpUuaEQcYhTB9+nTp2bOnbN++XdavX2/Ho/bMOglaIKFR8UaOPhwiIrIxBhLIfEuXin+PXrJJLun+HataRkQy2+bXacoUFbeQOnVEypWzyUcQERHZ1IQJE9QrCjBnMSHofv78efWq9bJMTZt/7tw5qx4nERGRs/rmm29ky5YtKuXfu6jPZwACCwgwIOjQvXt3tc1HH30kniAiIkLKli1rcBnOBSZrY8FlIiLngGsjJmPXB2thIIHMs3Qp8imgSnfK+fHxIm3biixeLNKypdXOamysyNSpuvccjUBERK5o9erVqnckRiOgeLIpoqOj1WvWrFkNLg8ICEixXnrQOzMqKkosER8Tb9F2RERke0iXh8lSqesLOLu5c+dK5syZ5Z133slwXazTq1cv+fnnnz0mkBAaGipHjhyx62cmBxJunrDr5xIRkekBY63YsjUwkECmw7CA3r1xxylGS1Z9+KFIs2Yi3t5WObMLFojcuCFSsKBut0RERK7k6NGj0r59e9VY8+WXXybXSjC1ccdYkUhzGn8uX74swcHBYgkf8ZFP5VOLtiUiItsaM2aMDB8+3GNO8+nTpyUwMNCkkX1YJygoSG1DtsMRCUREnoWBBDLdli26ggXGoFHjwgXdenXrPvGZxe60IssffGCzzElEREQ2gWKPSGV069Yt6dOnj3yIYLuJ0PgBMTExBpffu3dPvaJBJSP58uVTAQ1LRyRMzDfRom2JiMh67l65K9FXUo5Ceyf8HXnzxTfV+4C8ARKYN+Nrgr4yZcqoYLOrwGgEFFlGr0oUWk4P1sH119JAOpkXSDh3+5zEJsSKX2Y/njoiIjfGplky3ZUr1l0vAzt2iOzdq6vf3KWLVXZJRERkFzdu3JCGDRuqWgdIr/D111+btX2hQoWSgxGGaENTCxcunOG+MKohW7ZsYok47ziLtiMiIuvaO3WvbB6+2ejyOkPrSN1h5nXmMjbqzVk999xz8ueff6pURfPnz093XS2dEbYh28mdNbcEZwmWOw/uyOmbp+XpPE/zdBMRuTEvRx8AuZCwMOuulwFtNMKbb4rkymWVXRIRkZv7/fff1fQQ6fgc5O7du/Lyyy+rUQCtW7eWH3/80ezGGi0F0oEDBwwu37dvn3otV66cFY6YiIicXeVulaXr3q7yztbH9QHwHvMwYbm769u3r0rtt3DhQqlfv75s3LhR4lGr7xG8x7wGDRqodXDtxTZkOzjHTG9EROQ5OCLBg6Fqd9myZU0v0lGrFip0oBukLu9QamgkwXKs94QwwhZ1m4FFlomIrG/SpElqMnZ9cFVIJeTl5SXXrl2TkJAQu39+bGysvPrqq7Jnzx51LCgM6W1B3aAaNWqodAwnT56UgwcPpqmtsGTJEvXatGlTqx07ERE5r6CwIDXFxTweKZa3Ql7xDfAVT4Eg/ZAhQ2TEiBGyadMmNSHdUa5cuVSD9vXr1yUhISG5jtCgQYPUNra85qNOBUZHYAQi7jtw7R85cmSGqZdSW7t2rUyYMEF2794td+7ckRw5ckiVKlVUakQETZwZAgm7L++WE5EsuExE5O44IsGDhYaGypEjRwxOBit9oyFkwgT1Nil1z0rt3+PHW6XQ8pQpIgkJuphEhQpPvDsiIkoFf+eNXQNwfXBl5hQitiaMgmjXrp1q2Khdu7YsXbpUfH0zbuDp0KGDlC5dWpYtW5Y8D9v16NEj+WelXyth/PjxaqRCnTp1pHJl9++BSkREpBk2bJgsX75cXTdxvccohCtXrqhaD3iPeaj9gGswAg62DCKggR+fER0dLc2aNZOCBQvKzJkzpVKlSnL27FmT9/XNN9+ogMe6devUsbdq1UqKFCkiq1atUqMrpuDh2IlxRAIRkefgiAQyT8uWaqhAUo9ekumKLj+zgpEICCJg+RN68EBk6lTde45GICIiVzFx4kTVuAE5c+aU999/3+B6Y8eOVb0nNejFePz4cdUDUR96Uq5fv162bdsmJUuWlJo1a8q5c+dk165dkjt3bpkxY4aNvxEREZHzwcg/TIcPH1YjADEKEfLkyaNqIjz77LM2P4bRo0fL9u3bpXr16iqlYmCgrtD1uHHjpF+/ftK5c2fZsGGDSTWVBgwYoDoQYH1c6/VHH7Zp00btr3379smf4bSBhJsckUBE5O4YSCDztWwp96s3kCb59kmYXJEZS7OL/6sNrTISARYuFMG9IEaDNm/OHxAREbmGW7duJb/XH11gqDelfiDBGD8/P5XrGWkT5s2bp4IUSJvQsWNHlTahAIL4REREHgoBA3sEDVLDyIfvHhX0Q5pI/QZ+1GSYNWuWKgqNekYYnZCenTt3yoMHD1RKJP0gAmBkAmohIcUhRowi1ZEz4ogEIiLPwUACWcbbWzZLXfV2Wo0YqwURkI3i22917z/4QMTHhz8gIiJyDQgQYDIXUiEZ4+/vr9Im2DI9AxEREZlu69atcvv2bSlWrJhUrFgxzfLWrVvLoUOHZOXKlRkGErJkyWLSZ2Kko7MqEVJCvV6LuSa3Y29Ldr/sjj4kIiKyEdZIIKeya5fInj24oRJ5911HHw0RERERERHRYxghAMaCBNp8bb30PP/88xIcHKxGMCBAoQ91HhCQeOGFF1TQwlkFZQmSsMAw9f5k5ElHHw4REdkQRySQU3k0QlTatRPJndvRR0NEROT6IiIipGzZsgaXoZAzJiIick9IvYPJ2PXBFV29elXVCULD+8WLFyUmJkYVWTYkU6ZMcvr0aat+PmobgbEUg9p81DXKSPbs2WXatGmqBkLt2rWlRo0akj9/fjlz5ozs3r1bFWFGAWdnh/RGV6KvyInIE/J8/ucdfThERGQjDCSQ07hyRVcfAVhkmYiIyDpCQ0NVbmUiIvI86QWM0eB96dIlcSWoQfT2229nGDzQluG9tUVHR6vXrFmzGlweEBCQYr2MIBUSUhe99tprKUYloHh0nTp1VH0kU+A7R0VFiaWQZsnUVEuGAgmbz21WgQQiIrIu1NLBZClj10tLMLUROY2pU0USEkRq1MBwUEcfDRERERERETkLBMXfeOMN1UDfuHFj+f7779V8pAZCr/5Ro0ZJ3bp1VYNJrly5ZOLEiWrkgrVlFKQwt8Hm66+/lgYNGkitWrVUKiN8v8OHD6t/9+/fX15//XWT9nP58mV1LiydxowZI09ccPkmAwlERNaGv89P8vcd1wdr4YgEcgpxcSJTpujeczQCERERERER6fvmm29Uj0ykAZo9e7aa98EHH4i/v7906tRJ/XvgwIGyZs0aadOmjcyaNStN3QFrCAoKUq8YFWHIvXv31GtgYGCG+9q8ebN89NFHqq7CokWLxMtL19fzmWeekYULF6oaCkuWLJHff/9dGjZsmO6+8uXLJ0ePHhVLWToaIUUggSMSiIisbsCAAdK3b1+Lty9TpozVggkMJJBTWLQIOTpx8yPSsqWjj4aIiIiIiIicyaZNm9QoADSopAd1BdDL//3335fx48fLxx9/bNXjKFSokHpFfQZDtHRRhQsXznBfc+bMUa8tW7ZMDiJo8O8WLVrIvn371HfPKJCAc5MtWzZxBP1AAkZk2CKlFBGRp8ryBKnnwJp/k5naiJyqyPL774v4+Dj6aIiIiIiIiMiZoIE+c+bMqmelfuOIobzRb731lnh7e8svv/xi9eMoX768ej1w4IDB5Wj4h3LlymW4rwsXLqhXYwEAbf7NmzfFmT2V4ynxyuQl0XHRcjX6qqMPh4iIbISBBHK4v/8W2bVLxNdXpGtXRx8NERERERERORtfX980BY6RPujOnTuSgGJ7erAeUhCdPn3a6sdRo0YNlXP65MmTcvDgwTTLkYoImjZtmuG+8ubNq1737NljcPnu3bvVa5EiRcSZ+Xr7StHsRdV7pjciInJfDCSQ04xGQA2pPHkcfTRERERERETkbFADICoqSu7fv588Dw3sSKWTukH/1q1bcvv2bYlDMT4bBDR69Oih3nfv3j1FrQSkUsJIhTp16kjlypWT56Pwc+nSpdOkZWrWrJl6nTt3rvz2228plq1YsULmzZuXnOLI2bFOAhGR+2MggRzq6lWRBQt071lkmYiIiIiIiAzRUhphJID+6AAEEsaOHZti3UGDBqnXUqVK2eRkYv9Vq1aVbdu2ScmSJeW1116TatWqSZ8+fSR37twyY8aMFOvfuHFDjh8/LleuXEkxHwECFIZ++PChGsGA4spt27ZVr82bN5fExEQZOXKkzb6HNTGQQETk/hhIIIf64QeR+HiRatVEnnuOPwwiInoyM2fOVA/vSGdARERE7qNJkyYqaKClDoL33ntP9dhfuHChPPPMM/Lmm2+q2gRTpkxR9RM6depkk2Px8/OTjRs3yuDBg8Xf31+WL18u586dk44dO6oaCU899ZRJ+8ExLliwQKZPny61a9eWU6dOybJly+Ts2bPSuHFjWbNmjQwcOFBcQXIg4eYJRx8KERHZSGZb7ZgoIxhlOmWK7n2vXjxfRET05N5++22exlQiIiKkbNmyBs8LUjJgIiIi9zRp0iQ1Gbs+uBL02Md1HgWXNQgaIJ0QRgIcOXJETZp27dpJTxsOe0cAYcSIEWrKyLBhw9RkiBbwsFXQw144IoGIyP0xkEAOg44kGNmJ+lKtWvEHQUREZAuhoaEpGlaIiMhzpBcwLlCggFy6dElcBVIGYeRhaqhX0KBBA1m8eLFcuHBBFUIODw+XF1980SHH6am0QMLpm6clITFBMnuxuYmIyN3wL7sHc3QPRa3I8vvvo2CVTT+KiIjcuIciEREReTYUMtbqIpBjFMhWQPwy+0lsQqycu31OioUU44+CiMjNMJDgwRzZQ3HPHpEdO0R8fES6dnXIIRAReTR36qFIRERERI7llclLSoSUkMPXDsuJyBMMJBARuSEWWyaH0EYjvPaaLrURERERERERkSlu3LghixYtkrFjx5pUo4Dsg3USiIjcG0ckkN1duybyyy+69zasfUVERERELubulbsSfSXa6PLAsEAJCguy6zERkfNISEiQ/v37y/fffy9xcXHJ84cMGZL8/tatW1KsWDG5d++enDlzRsLCwsQTODp1MTCQQETk3qmLGUggu/vhBxHc81WpopuIiIiIiGDv1L2yefhmoyejztA6UndYXZ4sIg/Vpk0b+fXXX9X7p59+Wo4fP66CC/py5Mghb7zxhgo2rFixQt577z3xBI5MXZwmkHDzhEOPg4jI03S3U+pipjYiu4qPF5k8Wfe+Vy+efCIiIiJ6rHK3ytJ1b1d5Z+s7yfPwHvMwYTkReaYFCxaowECePHlkz549cujQIQkJCTEacIDffvvNzkfp2TgigYjIvXFEAtnV0qUily+jtwRu7njyiYiIiOgxpC3CFBfzOGVJ3gp5xTfAl6eJyMPNnDlTMmXKJF999ZVUrFgx3XWrVKmi1j18+LDdjo8eBxLO3zkv9+Pvi7+PP08LEZEb4YgEckiRZYwu9eXzIBEREREREZlg37596rVVq1YZruvv7y/BwcFy/fp1nls7yumfU3L45VDvT908xXNPRORmGEggu8F937ZtIpkzi3TrxhNPRETmQx7kqKgoNZlKW//hw4c85WZKfJiY/P7cX+dS/JuIiMie7ty5o4IDCBKYIjGR1yx7wygQpjciInJfDCSQ3UcjtG0rEhbGE09EROZ7/fXXVRHFjh07mrxNp06dzN7GnUREREjZsmUNTpMmTTK63dGlR+X7st8n/3te43kyocgENZ+IiFwD/s4buwbg+uBKcC1HMCE2NjbDdS9evKg6EaCeAtkXAwlERO6LNRLILjCidP583fuePXnSiYjIfP/++68sXbpU9UacMWOGydv9+OOPsmHDBpk/f74MGTJESpQo4VGnPzQ0VI4cOWLWNggWLGy9UCQp5fyoS1FqftvFbaVMyzLWPVAiIrK67t27q8mQAgUKyKVLl1zmrJcvX17Wr18vmzdvlkaNGqW77tSpU9Vr1apV7XR0lCaQcPMETwoRkZvhiASyix9/FHnwQOS553Azx5NORETmmzt3rnr94IMPJHv27Gb1YOzZs6dKcfDzzz/z1GcA6YvW9l6bJoigPJq39sO1THNERER21a5dO0lKSpLBgwfLvXv3jK63cOFC+eKLL1Sanbfeesuux0gckUBE5M4YSCCbi48XmTxZ975XL+RN5EknIiLzbdmyRTUKmFJkMbWWLVuq102bNvHUZ+D8lvMSdTGdGhRJIlEXotR6RERE9tKhQwepUqWK7N27V6pXry5TpkyReDxsisiePXvUaMWGDRuqgANqKr344ovyyiuv8AdkZ0xtRETkvpjaiGxu+XLkqBRBekrURyAiIrLEiRMnxMvLSypWrGj2tuXKlVPbHjt2jCc/A3ev3LXqekRERNaA6/ivv/6qggMIHOinbNJPYYRRC/j3ggULeOIdoHhIcfV6494NuXn/poT4h/DnQETkJjgigexWZLlbN5EsWXjCiYjIMrdv31YpjTAqwZLGB2yLIo2UvqCwIKuuR0REZC0onrxt2zb57rvvVCcB3BMgcKBNZcqUkfHjx6s6CiEhbMB2hEDfQMkflF+9Pxl50iHHQEREtsERCWRTBw4gFYVI5swi773Hk01ERJbLmjWr3L1reS/46Oho8ff3548gA4VqFZJsBbKpwsoG6yRkErUc6xEREdmbj49PchFpXNuvXr0qDx8+lNDQULNqKJFt0xtduntJTkSekKoFWCSRiMhdcEQC2WU0AtJZ58vHk01ERE/WCxG5kE+fPm32ttgmLi5O7YPS5+XtJeETwnX/MDT4I0kkfHy4Wo+IiMiRAgMDpXjx4lKqVCkGEZywTsLJmxyRQETkTjgigWzmxg2RefMeF1kmIiJ6EtWqVZNTp07J0qVL5eOPPzZr2yVLlqTJoUzGlWlZRtoubitreq2Ru5dSjgLxC/GTIvWK8PQRERE5kYiICClbtqzBZdoIDnthwWUiIvuaNGmSmoxdH6yFgQQPZusbjWnTRGJjRSpVEqle/Yl2RURELnqjYU0orjhnzhz56quvpH379hIWFmbSdpcvX5axY8eqPMrYB5keTCjaoKh8EfyF+nfbZW1l/Sfr5ebJm7K6+2ppNa8VTyUREdm1VtLy5ctV/QOMNLx586aanzNnTilWrJjUrVtXmjdvLtmyZfPInwpSOx05ckScAQMJRET2lV47boECBeTSpUtW+RwGEjyYLW80EhJEvv/+8WgEC+piEhGRG9xoWFOrVq2kRIkSalRCo0aNZNmyZarhID1Yt2XLlnLjxg21bZs2bcTTPEnHAf30RcVeKibZfs4m01+YLv/M/0dKNSslz7z2jE2OmYiIPLfjgCFffPGFfP755xIVFZU8D8WVAR0Ftm7dKrNmzZIPP/xQBg4cKB999JEDj5b0Awn4OeFnREREro+BBLKJFStELlwQyZVL5LXXeJKJiOjJeXl5qUaCevXqyb///ivlypVTIxPQ+7BixYoSEhKi1kMPxf3796tAw7x58+TevXuSJUsW+emnnzzyQdaaHQfyV8kvtT6tJX+N+EtWvb9KCtUsJNnye2bPTyIiR0h8mJj8/txf56RYw2Lp1qxxxY4Dqb311lvqeq4FDry9veWpp55S133Mu3Xrlvz333+q4DJGLfTv31/dJ8ycOdPRh+6ximYvKt6ZvCUmPkauRF+RfEEsmEhE5A5YJY9sWmS5WzcRPz+eZCIisl6dhIULF0pQUJDcv39fpk2bptIV5c+fX/z9/dWE95g3ffp0FURAIcZffvlFqtshz97evXtVj0mMgsBxIHDhZ+GFECkasL2xae3ateIItQfVlnzP5ZPYW7Hya+dfkxt2iIjIto4uPSrfl3007FtE5jWeJxOKTFDz3dWUKVNk7ty56lqDTgOLFi1SwYLjx4/Ljh07ZOfOneo95uH+AOtg3dmzZ6t7BHIMH28feSrHU8mjEoiIyD0wkEBWd+iQyObN6Cki8t57PMFERGRdTZs2lT179qg0RWhQR4OBoQnLsA4a95s1a2aXH8PIkSNlwIABajQEajNYK6XT22+/nWZCoMIRvH28pcWcFpLZL7OcXnda9kze45DjICLyJAgWLGy9UO5euptiftSlKDXfHYMJ8fHxMnjwYHU9b9eunQoa4JoYEBCQZl3Ma926tVrn9ddfV/cBn376qSQg5y45BOskEBG5H6Y2IpuNRmjZEsNleYKJiMj6ihcvLgsWLJBr167Jxo0bVQqDyMhI1XCQK1cuefrpp1UKpDx58tj19GPUQ/ny5eX5559XU968eZ94nygUXaRIEXEmuUrnkgZfNJC1vdfK7x/9Lk81eEpylszp6MMiInLbdEb4eyuGBoBhXiaRtR+uVbVr0ktz5Gp+/fVXdW1HGiOMMvTx8clwG6wzY8YM2bVrl5w9e1ZWrlwpLVq0sMvxUtpAwqqTqzgigYjIjTCQQFYVGSkyd+7jIstERES2hEDBa05UjAd5mT1FlR5V5Pivx+XMhjOy7K1l0mlbJ/HK7D4NWEREjpCUmCQx12Pkzvk7EnUhSr2e33Zeoi5GpbORqHXPbzkvReo6V+D5SaCjAEYj9OjRw6w0gVgXdSFQcHnDhg0MJDgIRyQQEbkfBhLIqqZPF7l/X6RCBZEaNXhyiYiI3FUmr0zSbGYzmfzsZLn09yXZMmaL1Blcx9GHRUTk1B7cfZAcILhz4Y4uYHA+6vH7i1Hy8MFDi/Z990rKtEeubv/+/er1pZdeMnvbRo0aqUCCtg+yPwYSiIjcDwMJZDUPH4p8//3j0QiZMvHkEhERPSmkc0BqBy8vLylZsqQ0b95cChUq5BQnNrhgsDSe1FiWtV8mf434S0o0LiH5Kudz9GERETnEw/iHcvfyXV2QQG9EQXLg4Pwdib0dm/GOMokEhQVJcKFgNSVJkhxZeCTDzbCNOzl//rwakVC2bFmzt8U2uG5iH+TYQMLpW6clITFBMnux+YmIyNXxLzlZzcqVIufOieTMKfL66zyxRERkXeY0Bvj7+0tISIh4e3u7/I9h1KhRKf6NHpYoPonJGTz7xrNyfMVxObLoiEpx1HVvV/HxzziPNRGRK0ENnns37qUICmAUgf5ogugr0So1UUb8svtJtoLZkgMFye8L6v4dlC9IvH29U9RIuLj9oiqsbLBOQiaRbAWySaFazhFktpaoqCgJCgpSwQRzYZts2bKpfZBj5AvKJ1l9ssq9+Hty9vZZKR5SnD8KIiIX51aBhNjYWBkzZozMnz9fNTagASE8PFxGjhwp+fPnN7uw0/jx42Xfvn0SFxenegB26tRJ5WdEzwZDDh8+LJ999pls2rRJbt26Jfny5VP5GIcMGSLZs2c3uM3169dl9OjR6vMuXryo1kNxyGHDhknp0qXTPcb169fLxIkTZceOHXL79m1VXBIFHt977z159dVXxd6+/Vb32rUrGnDs/vFEROTmihYtatb6uF6XK1dO1VD44IMPJDAwUFxJ7dq1pUuXLvLCCy9IWFiYXLhwQRYvXqwCC7i3QANJ7969TWr8srQhJT4m3qTGmiaTm8j5reflxtEbsmHgBgn/JtyizyMicpS4mDhdkOBRUMDQiIKE2IQM94MAgAoMPAoKZCuk9/7R/CzZsph1bCigHD4hXBa2XqiCBimCCY/a2GuPri3RMdFm7RfXB2cWHR0toaGhFm+fJUsW9bxNjuGVyUtKhJSQgxEHVcFlBhKIiFxfpiRnv3swI4hQv3592b59u3rYrlWrlpw9e1b+/vtvVYhx165dUqSIaYWnRowYIUOHDlUNEFWrVpXcuXPLzp075dq1a9KyZUtZtGhRmmDCunXrVKoBHEeZMmXUhMDCyZMnpXjx4qqxHw39+tAgULNmTRX0wDHjsy5fviy7d+9WBaJQGKp69eoGj/Hjjz+WsWPHiq+vr9SoUUPdYF26dEkFPl5//XWZNm2a0e9XoEABtS6CKwheWCLmWowEhgao99ERMXLmWoA8+6wIOn6eOSNSsKBFuyUiIidgjeuELRgL5JvS0F2sWDFZvXq1uibbEz4bDRm4P7CW33//XeV+RucD3Ddg9EV6P8cn4SM+8ql8qt4PiB4gvgG+Rtc9ueakzGs8T73vsKGDFH3RvMAPUepG3TGBY0z63SPKSGJCoqofkDrNkH6tgvuR9006kYF5A1OOIkg1oiAgT4CqIWMLR5celTW91sjdS49rIdyRO7JW1spROWrxfp3teq9/3c+bN6+61lkCz9h4hn+IHLxuzFnv26Dtoray6Mgi+abRN/JhtQ8dfThERB6pgBWvE24zIgG9+hFEQMM7HrC1Xofjxo2Tfv36SefOnVXDfEYQMMBoAB8fH1m5cqV6UIc7d+5I06ZNZenSpfLjjz9Kt27dUvSUaN++vWokQA/B4cOHq/mJiYnqszGyoWfPnmqkhL6uXbuqIMLLL7+sghMBAbqG+SVLlkibNm2kXbt2cuLECRUs0IdRCAgiPP/882rdgnqt9vfu3ZP//vtP7O2773SvzZsziEBERLYxc+ZMk9fF9RA3S1u3bpUtW7bIqVOn1Gi9AwcOpLmuupqGDRvKc889J3v27FEdJerWrZvu+hghefToUYtHJEzMN9GkdUu8XEIqv1dZ9k7ZK8s7Lpf3D72v0ndYGxoDkT7EmMCwQLfLE05ExqFfXOyt2BTphlIXMEbdgqSHGfef8w3yTRsc0E85lD9IMmdx3CN0mZZlpGiDovJF8Bfq321WtJF8tfLJMO9hlu2vTBmLG+mJTMGCy0RE7sUtAgnx8fHy3aOW7EmTJqVIXdC3b1+ZNWuW/Pnnn6q3fqVKldLd1w8//KBuRt9+++3kIAIEBwfL999/L88++6x8+eWXKQIJCC7cuHFDpT/CSAb9HhSff/65ChIsWLBAvS9cuLBahgDC2rVrVe7myZMnJwcRoFWrVmrkA4IEv/zyi3To0CF5GVImDRgwQOWKXLFiheploS9r1qzyzDPPiD3dui0yZ87jIstERES2gGuzJbZt26aCCMePH5fZs2erdEGurkSJEiqQcOXKFZPzRFsizjvOrPUbjm0o//3xn9w6fUv1mm0xu4VY296pe2Xz8M1Gl9cZWkfqDks/uEJEriP+frxEXTQwikDvffy9jNOweWX2UnUEDAUKtPd+wdYPflob0hxpitcv/kSjdSypPWBvERERFtc7wnO9K3xHa54rY4Wpu3fvriZ7YyCBiMg+0B6Oydj1wVrcIpCA3oaoEYC0BRUrVkyzvHXr1nLo0CE1wiCjQML+/fvVq6HefWigz5kzp+rxj7RFCCrob4NcxqnTLiCdQbVq1VRQAHUQMDJBfxvke9aCC/rw+dgGwQL9QAJGNWAEBEYzpA4iOMrseZnl/n2RcuVEatVy9NEQERGlhBSA6ATw7rvvquC/OwQS0LEA9DsiOAM0aLWY00Jm1pwph+YcklLNSknZVoYbNSxVuVtlKfVqKdW4iM+Bd7a+k1zgGSMSiMg1oIhw9NXodAsY37t+z6R9IaWQwXRDWsqh0IAUjfDkGtwkE7NdIN3xkSNHxJkwkEBEZB/pBYytkfLWrQIJBw8eVK/GggTafG299MTExKhXFGo2BIGEyMhItS8tkGDKNqk/35JtACMr4KWXXlK9EOfNm6fqMGCEAuotIP2SpTmkLfXDDJ/k0Qge1OGDiIhcCFIGIghvyr2As0PhSKRrgow6SDhCweoFpcb/asjW0Vvlt26/ScEXClo11RD2hQk59DV5K+RlDn0iJ2wAfnDnQYp0Q6kLGGOkAeoXZMQnwCc5IKCKF+ulG1JBgwLZJLOfWzzakh790f7kmrRAwoWoC3Iv/p5k9cnq6EMiIqIn4BZ3W0gTpEVYDNHmnzt3LsN9obAyGuYNrYuaByiQDCjkrL9NevvX5j/pNvDvv/+q1zNnzqi6D1FRUcnLUDehcuXKauSFPUcrnLvgJYiHvPGG3T6SiIjILEjtg+LEN2/edLozh5GHf//9t4wZM0ZatNClAkLdJxSIRAcB/ZQOuC9AXSZ0SEC6JmP3Po5Wd2hdObXmlFzdf1VWdlkp7X5r51HpJYg8QcKDhOSUQ8YKGMfdzTg9WibvTJItvy44YGxEAeqt8G+I52EgwfWF+IdITv+cEnk/Uk7dPCXlQss5+pCIiMjTAwlI9aPVBzBEG/avrZcepCfCwzvqKrz33nspli1cuFDuI4cPCv3dvZtiGxR7XrVqlaqVkCtXrhRBjo0bN6bZpkqVKirtEfJUrVu3LkU9BgQs5jwqOvDw4UP1mf7+/ilSGQwcOFCNiEDdhqefflr++ecfef/992Xv3r0qlRPyQZvSS0g/EGGKCxcySWRkJrl/KxZnNnn+K6+IoI4jvnqhQmbtkoiIrOzBgwdqspS7phFATaXMmW1764N7gZEjR6aYFxcXp9IcagYPHixNmjRJca+A+g137txJnnfixAl55513JG/evGrUAYIg6GSA63xsbKy69v/444/irLx9vVWKox8q/yAnV5+UfT/uk8pdKzv6sIjIREmJSRJzLSbdAsYxEboR1hnJmiur4QDBoxEFSEfGlENE7j0qYcfFHXIi8gQDCURELs4tAglag4exXirmNIggnxQa53fu3KmKOuJhH4EBNPZ/8MEHqgEiISEhRfqghg0bqpEAeLh/+eWXVXELFDlC+gQUZUZgAPS3QfFm7O+bb75RPRGnTp0q9erVk6tXr8qnn34qx44dU98Hx66/HQIL4Ofnp45JG9lQvXp1VbwZdSIQCNmwYYPUr18/3e96+fJldRymKygix0UEQY2U+Ydnz9ZNfn4ix48zmEBE5Ejo2T58+HD+EPRcvHhRdSh46qmnbJ52aNeuXSnm4VquPw/rZKRq1aqqgwC22717t+pIgI4RFSpUUGmasEzrZOCs8jydR+qPri+/9/td1vVdJ0XrF5WQYoZTOhKRfT2IemAw3VDy+wt3JDE+45RDmf0zG005pAIGBYPFJ6suDSoReSb9QAIREbk2twgkoD6Aft2B1O7d0xXoCgzMuPgeUgSgyHHbtm1l9uzZatLg4f25556TadOmqZ6BGjT4o3gjehfu2bNHPfxrEIQYNGiQDBs2LMU2WkMPUiUtXrw4OZUBIFjx9ddfS58+fVTAACMX9L8rRj0gnYEWRNCg1yKOYdGiRbJp06YMAwn58uWToxhGYKIDB7ykTp30Gy1iY0Vu3GAggYjIkQYMGCB9+/a1ePsyZcqoYLM7mTx5snrVv0bbQseOHdVkDlyzDf0M0LHB1VX7sJqcWHlCzm46K8s7LJeOf3Vkz2MiG3sY91CiLkUZLmD86D1qF2Qkk1cmCcoXZDzlUMFg8c/pz5RD5LEwQhDP9PPnz1ejC1H/MDw8XI1MzJ8/v9n7Q4rlL7/8Uv744w/VwRDP/iVKlFBtBR9//LG4KhZcJiJyH24RSCj0KJcOehsaolWmLly4sEn7a9CggZw+fVoWLFgghw8fViMCkJIAPQDbtWun1sGIg9THsH//flm+fLkaEYDgRenSpVUeY9xYGNoGAQI0+m/dulXWrFmjeiiicf/1119X6RcMbVOkSBFVHwGvhhQtWlS9ImVSRhAAQc5oU5kQhyEiIieA64t+ENpc7pSHGukBv/32W/niiy/U93rrrbccfUgeBQ2RzX5qJlPKTZEL2y/Iti+3Sa0BtRx9WEQuCyOc7l2/l24B47tX7oqYMCDbL4efwXoE2ogCBBG8Mj8eGU1EKYMI6LiHZ3/UJ2zWrJmqYzRz5kyV5hAjCo09sxuCzoxvvvmmSodYsWJFlXEgMjJStUcgewEDCURE5AzcIpBQvnx59XrgwAGDy/ft26dey5UzvbBPjhw50tRIQOP+X3/9JT4+PvLCCy+k2QYjCVCfAJM+pBmCunXrGvysmjVrqknf+PHjDW6DmwrUXDBWLBI3G6aOviAiInIlnTp1MiuAgI4ECPIjuI/Gt1deeUX1FPQ06FyQumOCfkpHTLaUvXB2Cf82XFZ0XCGbhm6SEi+XkLwV8tr0M4lcVVx03ON6BEYKGD98oEt1mh7vLN7JAQGDIwoKBotvoK9dvhM5FtLuYjLElM5nZBhqJCKIgAb/33//Pfn5e9y4cdKvXz/p3LlzcjtARnCvgg6L6OSHjon6bQNIk6y1Z7gqjkggInIfbhFIqFGjhsr1j6GAqEugBRb0o/vQtGnTJ/ocFEBGWiGkPcqZM6dJ26B44urVq9VIA9RPMAV6IeBmDz0nUzeaIKURbk4Q0MBNRer6CVu2bEkOOBAREbmTn376yazREvo1klq1aqV6CXqi0NBQOXLkiEOPoXyH8nJ8xXE5tuyYLG2/VLru6SqZ/dziNpTIZIkJiXL38t10CxjH3orNeEeZRILCggynG3o0oiBr7qxuNbqMLJdewBhpfbXR+2Q6dDD87rvv1Hs8t+t34kNqyVmzZsmff/6pAgCVKlXKcH89e/ZU+8R9TuoOhnjeR3plV1Y8pLh6jbwfKZH3IiVnVtPaUoiIyPm4xROcr6+v9OjRQz777DN1k4QixChIqPXsx0iFOnXqqILImokTJ6oJ+QaR11Afiibjgq9/84199u7dW+33888/T3MM//77ryp0jJoGmlOnTqn946YARZUxYkEf8iiiUKJ+rYOoqCgVPMC2KMb89NNPp9gG3wO9Hnbs2CGjRo2SIUOGJC9DYc0TJ05Injx5pGXLlhaeTSIiIueENIKmNozh+oo6Rbj2v/baaypFITkOfm6vTH1FpTe6/u91+XPQn9JwbEP+SMhtIHB5P/J+ugWMEURISsw451CW4CxGCxhrKYe8fb3t8r2IKC2kJr59+7Z6/jfUgQ8ZCg4dOiQrV67MMJCAQP+2bdukZMmSauSkO8rqk1UKZisoF6IuyMmbJxlIICJyYW4RSAAUNF6/fn3yRRiR/HPnzqnchGionzFjRor1MbIAowWuXLmSZl/otYje/c8++6wa6XDs2DEVjEAQYdmyZcl1CPR99dVXahgibhRQ9BhFKjHUMSEhQQU4MIohNfRSePfdd1UPA9RviI6OViMKEEzA6AkEH4yNjEBqpaFDh8ovv/yi0hUgkIHjRMPJ3LlzkwMpRERE7gK5h8l1BeQOkFenvSrzm86XHeN2SMmmJaVIHdPzRxM5Uvy9eF3B4nQKGCfcT8hwPwgAZCuQLd0CxlmyWV7jhohsD1kQwFiQQJuvrZcetAnASy+9pFIxok4jOjZiJAIyLaBOozl1DZ05vRECCSciT0i1AuzcQUTkqtwmkICRAKgdgNEF8+bNU436ISEh0rFjRxk5cqQatmkq1EbA9ghCoHEfaYkw73//+5/Rgs3NmzdXQQn0PEAPBdRYQI8CDG1MPTxRg16SCFrgcxCowHeoUKGCGpHQoUMHo70u0fMBNyXDhg1ThZx+/fVX9V2RV/HTTz9NM4qBiIiIyBmUfKWkVOxSUfZP2y/L314u7x96n42mZFDiw8Tk9+f+OifFGhYTL28vm31W9JVoowWMMWG0gSkC8wYmBwUMFTAOyBOgipATketCZgEw1sagzUfHxoygQyBobQFI16xvwIABqjMj0jm7eiBhw5kNKpBARESuy20CCYDe+CNGjFBTRtAIj8kQBAwwmQOBBEzmwIgHjCiwBEY9TJkyRewpVy7c4IjEppO6FcuxHhERkTNBXSEE36dPn646C5DjNBrXSM5sOCO3z9yWtR+ulWYzmvHHQSkcXXpU1vRak/zveY3nqV784RPCpUzLMmanHIq9HWs03ZB6vRQlSQ8zTjmE4sTBhfVSDqUqZoxjzJzFrR6viMgAdDaErFmzGjw/WnYAbb303Lp1S71OmDBBsmfPLosXL5b69evLtWvXkjMQoE4iAg5oA8jo7x2yG1gqS5YsarIFFlwmIrLcgwcP1GQp/dqBT4p3umSyQoVQPBppoUTu37wvNV/yV/O3/nFf/EN07xFEwHpERETOALWDkN5w9uzZEhER4ejDITRUBGWRFrNbyMzaM+XAzANSqlkpKd2sNM8NJQcRFrZeKJLqeQeN/ZjfdnHbFMGEhNgEiboYlW4B4/iY+AzPrldmrzQph1KPKEDtAhYwJiKtQcbY3wNzGmyQUln9LUtIUCmMw8PD1b8RVECmBYxQQKojFHVGpoX0IL0yUjNbCoELY50tnxQDCURElkP2HdTFdQYMJJBZECTAFHPt8XDzCuUSJSAPTyQRETkH5BheuHChCiCgdpL+Q32ZMub1ZibbKFSzkLzw8Quy/cvtsvLdlVKwekGV8oU8G1IMre29Nk0QQXk0DymxDs45qEYTYIq5FmPSvrPmzmqwHoE2LyA0wGapk4jIvQQFBanXmJgYo/chEBgYaPK+kE5ZCyJoEKhA2mMEEjZt2pThvrCPo0ePiqVsNRpBP5CAYsuJSYnilYl/b4mITIU0d0idbyk8AyPYbA0MJBAREZFb2LlzpwoeoFChlk4AAYTSpUurYoWYnnnmGUcfJj1Sb0Q9Ob32tEQcilDBhNeWv8be3h5ezPjATwfU6IL0xEXHyfHlx1PM88nq8zhIYCTlkI+/j42/ARF5ikKPhuBfvHjR4PJLly6pV2P1FfUVKVIkxWtqRYsWVa+mjKpE4MFZCzMXyV5EMntllnvx9+Ty3ctSIJvpNSyJiDxdlidMPWfNEbUMJBAREZHLun79ukoFgNoHx44dS5NyYPfu3VK5cmUHHyUZglzyLea0kB+f/1GO/3pcpTmq2KkiT5YHwCiCqweuPp72X5XIE5GSlGhaOpDyHcur9EbaqAK/HH4MQhGR3ZQvX169HjhwwODyffv2qddy5cpluK+KFXXXvZs3bxpcHhkZafLoBmeGIEKxHMXkeORxVXCZgQQiItfEQAIRERG5FAQK1qxZo4IHv/32m8orjHn+/v7SvHlzefvtt5PTAzCVka4XY9myZQ2ey+7du6vJUULLhUq9kfVkff/1KqVNkXpFJEfRHA47HrIuBAZunr6ZHDCIOBAhV/ZfkegrhguQIiAQeys2w/1WeLuCFKlruPcuEaWE3PqYDGHtIMvUqFFD1SJA/YKDBw8mBxY0S5YsUa9NmzbNcF8orIzizKdPn5YLFy5IwYIFUyzXUhppAQdXhvRGWiDhxaIvOvpwiIjIAgwkEBERkUvAQzZSF82aNUuuXLmiggcYdVCzZk3p0KGDtG3bNjnXMD0WGhoqR44ccdpTUr1fdTnx2wk5v+W8yn//9sa3maveBaHo8bV/rqUYaRBxMEKlIkojk0jOkjklb4W8KSbUMZhQZIIqrGywTkImUWmKCtXSpRUhooylFzAuUKBAchoeMp2vr6/06NFDPvvsM3Vu161bp4IBMH78eDVSoU6dOilGRE6cOFFNLVq0UEUzNVmzZpWePXvK559/Lu+//75Kz6jta+3ateqeB/c63bp1c/kfEQsuExG5PgYSiIiIyCWUKFFCPUwjgPDUU0/JW2+9pQIIWv5gck0ocNt8VnOZUm6KCibsGLdDanxcw9GHRem4F3kvxSgDvF4/el2SHqZt/c/sl1mNPAmtEJocMMC/fQN8De47fEK4LGy9UAUNUgQTHqV2DR8fzkATETncoEGDZP369bJt2zYpWbKk6tRw7tw52bVrl+TOnVt1fNB348YNOX78uOoIkdrQoUNly5YtsmrVKnWvU7VqVbl27Zqq/ZSYmKgCFlWqVBFXx0ACEZHrYyCBiIiIXEqvXr3kyy+/VD0CyT0gnVGj8Y1kZZeVsnHQRineqLhqbCbHQtDu9tnbyXUMtOBB1AXDBZGz5soqeSumHGWAkQdemb1M/kzUPmi7uK2s6bVG7l66mzwfIxEQRMByIiJH8/Pzk40bN6rRBfPmzZPly5dLSEiIdOzYUUaOHKlGe5izrz///FPGjh0rP//8s0rfiHn16tWTPn36SJMmTcQdMJBAROT6GEggIiIil4DAQVxcnHz33XfqQfu1115ToxKqVavm6EMjK0Ch5eMrjsuJlSdk2VvLpMvfXVRBZrKPh3EP5fqR6ypQgDoGaqTBwavy4M4Dg+vnKJZDFyzQCxwE5QuyStFjBAuKNigqXwR/of79xuo3pFjDYhyJQEROBbWZRowYoaaMDBs2TE3p3eMMHDhQTe5KCyT8d+s/iX8YLz7ePo4+JCIiMhOfzjyYMxdfJCIi23LF4otXr15VAQQUWUZxw8mTJ8uUKVOkePHiqsBy+/btpVAh5k53VWiAbvpjU5n87GSJOBQhm4ZukgafN3D0Ybml2NuxKkigP9IAQYTE+MQ063r7ekueZ/Ko1ERhFcOSUxNlyZbF5imvNIVrF2YQgYjIxYUFhkmAT4DExMfImdtnkgMLRETkOhhI8GDOXnyRiIhsxxWLL2bPnl0VN8S0f/9+mTZtmsyfP19OnjwpgwcPliFDhkjt2rXVKAVyTYGhgdL0h6ayoMUC2fblNin5SkkpVJPBoSdJTYQ0RPoFkBE4QLoiQ/xy+KUsgFwxr+QqnUu8fbyf4KdKRESk6zCA4MH+q/vlROQJBhKIiFwQAwlERETkcipWrKhGVIwbN04WL16sRils3rxZNm3apF41v//+u7zyyiuSOTNveVxF6ealpULHCnLgpwOyrMMyee/ge5IlyLa9393Bw/iHcuPYjTRFkO/fvG9w/exFsutGF+gVQQ4uFGyV1ERERESG6AcSiIjI9fCpmoiIiFxWlixZ5M0331TTmTNnVEBh9uzZcvHiRdUbu1WrVhIcHCzNmjWTNm3aSMOGDRlUcAHhE8LlzMYzcvvMbVnXd528+uOrjj4kp/Lg7gOJOKgLFGjTtX+uycMHD9Osi0LHucvmTlHLILR8qPjn8HfIsRMRkediwWUiItfGQAIRERG5haJFi8qoUaNk5MiRsm7dOpX6aOXKlXL79m0VXMCE9EiRkZGOPlTKAPLvN5/VXGbVmyX7p+2XUq+WklJNS3nceUMwLPpKtCp+rD/K4OapmwbX9w3yTVMAGUEEFq0mIiJnwEACEZFrYyCBiIiI3ApSs4SHh6vpxo0bKoAwc+ZM+ffff1VQwdOgeHbZsmXNrpXhaEXqFJHqfavLjq93yMouK6XAPwUkIHeAuKvEh4kSeSIyufixNt27fs/g+tkKZEtOTaQVQUa6okxeTE1ERI8hDSAmY9cHch+ucL1nIIGIyLWv9wwkEBERkdvKlSuX9O3bV007d+6UGTNmiKcJDQ2VI0eOiCt6cdSLcmrtKbn+73X5rdtv0nZJW7fI4R8XEyfXDl9TgQKMNsBIg4jDEZJwPyHNupm8M6mCx/oFkPOWzytZc2V1yLETkWtJrwG5QIECcunSJbsfE3nu9b5ESAn1eunuJYmOi5ZA30BHHxIRkVvobqfrPQMJRERE5BGqVaumJnIdmf0yS4s5LWRa1WlybNkxOTTnkJTvUF5cSXRE9OMRBo9GG2DkgSSlXdcnwEcFCbQCyBhpkPvp3OLj7+OIQyciIrKqHP45JFfWXHLj3g05dfOUVMhbgWeYiMiFMJBARERERE4Ljel1h9WVPz/9U9b0XCNF6haR4ELB4mySEpNU7QL9tEQIHERfjTa4fmBY4ONRBo9GGoQUC2FqIiIicmtIb4RAwonIEwwkEBG5GAYSiIiIiMip1fikhpz47YRc3HFRlndcLh3Wd3Do8cTfj5dr/+hSEyUXQT54VeJj4tOunEkkVyldaiJtpAGmwFCmcyAiIs8MJGy/sF0FEoiIyLUwkEBERERETs0rs5e0mN1CplSYImc3npWdE3ZK5a6V7fLZ927cSznK4MBVuXHshiQ9TDKYiim0XKiujsGjgEGeZ/OIb4CvXY6ViIjI2ZUMKaleGUggInI9DCQQERERkdMLKR4iDb9uKKveWyUbBmyQQjULWXX/SUlJcvvMbVX8OHmUwYGrEnUxyuD6KHacHDB49JqzRE4V9CAiIiLjIxKAgQQiItfDQAIRERERuQSMQjjx6wk5ufqk/Nrl1+T55/46J8UaFhMvb9Ma8RMeJMj1I9dTFECOOBghD6IeGA1iaKmJULNBpSYKC5RMmTJZ7bsRERF5UiDheORxFcTntZSIyHUwkEBERERELgGNDU2nNZWJJSfKtUPXkufPazxPshXIJuETwqVMyzIptrl/674KEugHDRBESExITLN/b19vlYpIvwhyaPlQyRKUxS7fj4iIyN0VDymuXm/H3pbI+5GSK2suRx8SERGZiIEEIiIiInIZKLgcFx2XZn7UpShZ2HqhKsyc2T9zctDgzrk7Bvfjl8NPjS7QL4Ccq3Qu8fbxtsO3ICIi8kz+Pv5SKLiQnL9zXqU3YiCBiMh1MJBARERE5MYiIiKkbNmyBpd1795dTa4i8WGirO291vDCR7WPt32xLc2i7EWypyiAjClbwWxMp0BELuPulbsSfSVa4u/HJ89DsNTH30e9R7q1oLCgNNtNmjRJTcauD0SOSm+kBRJeKPgCfwhERC6CgQQiIiIiNxYaGipHjhwRd3B+y3mjxY/1FWtUTIq/XFwXNCifV/yy+9nl+IiIbGXv1L2yefjmFPNm1pyZ/L7O0DpSd1hdswLGBQoUkEuXLtngaInSVzKkpKz/bz0LLhMRuRgGEoiIiMilnT59WhYuXCiHDh2SmzdvSnz8496ahnLsb9iwwWbHsnfvXvnjjz/k77//ll27dsnly5clS5YsEhsba9H+sN2YMWNk/vz5cv78eQkJCZHw8HAZOXKk5M+fXzyxR64pyr9dXp5t96zNj4eIyF4qd6sspV4tZXQ5RiQQuVrBZYxIICIi18FAggdzp1QHRERkHndJdTB8+HAZNWqUJCYmSlLSo9w26UAgwZbQwL9ixQqr7AtBhPr168v27dslLCxMmjVrJmfPnpWZM2fKqlWrVKCiSJEi4kkMpe14kvWIiFwF/q7xbxu5y/M9AwlERK75fM9Aggdzp1QHRERkHndIdTB37lwVSIB8+fJJo0aN1GvmzI67valevbqUL19enn/+eTXlzZvX4n2NHj1aBRGwz99//10CA3W9TceNGyf9+vWTzp0723R0hTMqVKuQZCuQTRVW1moipJBJ1HKsR0RE5Elc6fleCyScvHlSEpMSxSuTl6MPiYjIpXW30/M9AwlERETkkrQeF6+++qpKbeTr6+voQ5L+/ftbZT9Iz/Tdd98lf08tiAB9+/aVWbNmyZ9//in79u2TSpUqiafw8vaS8AnhsrD1QhU0SBFMeDTYJHx8uFqPiIiInFPh7IXFx8tHYhNi5WLURSkUzA4ARESugE9ZRERE5JL++ecflaro+++/d4oggjVt3bpVbt++LcWKFZOKFSumWd66dWv1unLlSvE0ZVqWkbaL20pQvpTpizASAfOxnIiIiJxXZq/MUiykmHrPOglERK6DgQQiIiJySQgiZMuWTaUzcjcHDx5Ur8ZGG2jztfU8DYIFHxz5IPnfb6x+Q3qf6c0gAhERkYtgnQQiItfDQAIRERG5pNKlS8u9e/fkwYMH4m7Onz+fnM/SEG3+uXPnxFPppy8qXLsw0xkRERG5kJIhujoJHJFAROQ6WCOBiIiIXFKXLl2kW7dusmjRImnfvr24k+joaPWaNWtWg8sDAgJSrJeepKQkiYqKsug44mPiLdqOiIhsD4H0Jwmm4/pA5CgckUBE5HoYSCAiIiKX9O6778qqVaukV69eUqhQIaldu7a4C61xB+mb0ltuisuXL0twcLBFx+EjPvKpfGrRtkREZFtjxoyR4cOH8zSTS2IggYjI9TCQQERERC5pxIgRUr58edmyZYvUq1dPatSoIVWrVpWgoJRFeFMbMmSIODvtO8TExBhcjpROEBgYmOG+UEPi6NGjFo9ImJhvokXbEhGRbQ0YMED69u1r8fZlypRRwWYiRwYSztw+I3EP48TX25c/CCIiJ8dAAhEREbmkYcOGJffYRw/9rVu3yrZt2zLczhUCCRhhARcvXjS4/NKlS+q1cOHCJheltkScd5xF2xERke1lyZJFTZYyNuqNyB7yBuaVQN9AiY6Llv9u/Selc5XmiScicnIMJBAREZFLQiojd20EwUgLOHDggMHl+/btU6/lypWz63ERERERWQPu4TAqYd+VfargMgMJRETOj4EEMs+VK7rpJop6VdfNO3RIJORRT5iwMN1ERERkY5s2bXLbc4w0TahrcPLkSTl48GByYEGzZMkS9dq0aVMHHSERERHRk9EPJBARkfPzcvQBkIuZOlWkcmWRlxo8nof3mIcJy4mIiMhkHTp0kNKlS8uyZcuS5/n6+kqPHj3U++7du6eolTB+/Hg1UqFOnTpSGddeIiIiIhdUMkRXJ4GBBCIi18ARCWSebt1EXn1V5L6XSM1H87ZuE/FP1L3naAQiIvJgq1atkpEjR6aYFxcXJ9WqVUv+9+DBg6VJkybJ/z5//rwcP35c7ty5k2K7QYMGyfr161Xdh5IlS0rNmjXl3LlzsmvXLsmdO7fMmDHDDt+IiIiIyLYFlxlIICJyDQwkkHm01EWPO0aKVKggEsATSUREdP36ddXQrw+FoPXnYR1T+Pn5ycaNG2XMmDEyb948Wb58uYSEhEjHjh1VsKJAgQI84URERKRERERI2bJlDZ4NjG7E5GwYSCAiso5Jkyapydj1wVoYSCAiIiKn16lTJ/UaFhYmn332WYp55hb2mz59utgKGvkxWavWg7+/v4wYMUJNRERERMaEhobKkSNHXOoElchZQr1eib4idx/claAsQY4+JCIil9Q9nYAxOqBdunTJKp/DQAIRERE5vZ9++kkFAUqVKpUcSNDmocd/RrT1bB1IICIiIiLTZPfLLnkC8si1mGty8uZJqRRWiaeOiMiJMZBARERELlGQGEEAjEhIPY+IiIiIXBPSGyGQgDoJDCQQETk3BhI8mCvmUCQiItfKoWgtGH1gyjxKi9d7IiLP5WrXe/I8JUNKytbzW1lwmYjIBTCQ4MFcMYciERG5Vg5Fcjxe78mV3L1yV6KvREv8/fjkeVcPXBUffx/1PjAsUILCmEObyFS83pOzY8FlIiLX4eXoAyAiIiIiIoK9U/fKD5V/kJk1ZyafELzHPExYTkTkDGJjY2Xo0KFSsmRJ8fPzk3z58kmnTp2euDPGyZMnxd/fX6VvDA8PF3fHQAIRkevgiAQiIiIiInIKlbtVllKvljK6HCMSiIicIYhQv3592b59u6rf1KxZMzl79qzMnDlTVq1aJbt27ZIiRYpYtO9u3brJgwcPxFPoBxKSkpJY/4qIyIkxkEBEREQu7eDBgyr/89atW+XixYsSExNjdF307ktISLDr8RGR6ZC2iKmLiMjZjR49WgURqlevLr///rsEBuqCnOPGjZN+/fpJ586dZcOGDWbvd/r06bJx40bp2rWr/PDDD+IJioUUk0ySSe48uCPX712XPAF5HH1IRERkBFMbERERkcuaOHGiPP/88+rB+9ixYxIdHa16s6U3EREREVkqPj5evvvuO/UeHRm0IAL07dtXypUrJ3/++afs27fPrP1eu3ZNPv74Y2nQoIG0a9fOY35Afpn9pHD2wsmjEoiIyHkxkEBEREQuCWkDevfuLQ8fPpQPPvhAVq9ereaHhITI+vXr5eeff5aOHTuKr6+v5MqVS+bNm6ce7ImIiIgshRGQt2/flmLFiknFihXTLG/durV6XblypVn77dWrl9y/f18mT57scT8c1kkgInINDCQQERGRS/r222/VCAMEE9AzUCtIiMDBiy++KG+88YbMmDFDdu7cqVIaDR48WCpVquTowyYiIiIXT6kIxu4ptPnaeqZAZ4gFCxbIwIEDpXjx4uJpSoY8rpNARETOi4EEIiIicknbtm1TAQIEEvSlTl9UoUIFFWg4ffq0fPXVV3Y+SiIiInIn58+fV68FChQwuFybf+7cOZP2h9pOGFlZqlQp6d+/v3gijkggInINLLZMRERELikiIkKyZMkihQvr8uqCl5eXxMbGplm3RYsW4uPjI0uXLpURI0bY+UiJiIjIXaAeE2TNmtXg8oCAgBTrZWTQoEEq6ID0ixhVaSl0pIiKirJ4e9xTYXIEBhKIiIx78OCBmixlzTqBbjUiAQ0HQ4cOlZIlS4qfn5/ky5dPOnXqJJcuXTJ7X7/++qtKi5A9e3Z1g4DejEihkJiYaHSbw4cPy+uvvy558+ZVF+CiRYuqYkvIn2jM9evXpU+fPiq/IrYJDQ1V+0DBSFPNnj1b9cjE9Pnnn5v9XYmIiFwRrs8IDugLCgpSD9Gpb7SwHtY3tXeguwVcypYta3BCkUgiInJf+Dtv7BqA6wNZ3iCD5+/0lptiz549atRkhw4dpF69ek/047h8+bIEBwdbPI0ZM0YcRQsknLp5Sh4mPnTYcRAROaMxY8Y80d93XB+sJbM7BRHq168v27dvl7CwMGnWrJmcPXtWZs6cKatWrVIFGYsUKWLSvtBTEQEJ9GqsWrWq5M6dW+VXRuqEzZs3y6JFi9QyfevWrZPmzZur4yhTpozUqFFDBRa++eYbVWRpx44dqtCjvgsXLkjNmjXV0Egcc+PGjdUPd+HChSqQsWHDBqlevXq6x3rjxg3p16+fuomxZoSJiP7f3n3AN1V2Dxw/bWlpoZSyoewte4MKCAoOVAQZoqiAICqKMvR9X33/IkNFXxcg4EIFcYAyFEVFBZnKRpaggOyN7LJb8v+cp96QtEmbpmkz+vt+PpeEu3Jzm+RJ7nmecwAEutKlS8vvv/9uAgdxcXFmngbmf/vtN1m5cqVpYy3avp48edJt78FQpp0UNm3a5O/DAAD4wWOPPWYmdyl4vOl0l9tppwUrJZErZ8+eNbexsbHp7icpKUn69u1rLvK89tprWT4u7Ui5efNmr7f312gEVa5gOYmKiJILyRdkz6k9UiHes2s3AJAbPPPMM6ajurf0OrWvggkhE0gYOXKkCSLohfcff/zR3mi/8cYb5kJ7nz59zIX5jGjAYNiwYabnogYAbr75ZjNfLz60b9/epESYMGGCPPzww/ZtdMjifffdZ4IIzz33nAwfPtzM19EL+tijR4+Wxx9/XKZMmeL0WA899JAJIrRr184EJ6whkDNmzJCuXbvKPffcI1u2bEl3eKOOZtDHv/fee+WTTz7x8uwBABB86tatawIJf/75pzRp0sTMa926taxZs8Z0CtCgvI5QvHjxojzxxBNmeZ06dfx81AAAIJiVK1fO3O7du9flcis445h60RXdfu3atSajgf7+d2RlNVixYoX5bqPXN2bPnp3u/rRzodWxIthEhEdIlcJVZNORTabgMoEEAPBd6jl3I+hybWqjS5cumeGA1tBNx8i/Rmz0QoPmG9QLCxl57733TM/+nj172oMISnsJvPXWW+b+K6+84rSNBhd0ZICmVNKRDBYdtaCphrTH5Oeff+6UTkEDCHPmzJGIiAh5++237UEE1blzZ+nUqZNZf+rUqW6P9aeffjLBA82pqD0wAQDITW6//XbTZmsba9Fel/olSzsPaE9LHSGo7fCXX35pvkD179/fr8cMAACCW7169cytBgFcsa476HUITxw8eNBkPnCc1q1bZ5YdP37c/H/JkiUS6qiTAACBLyQCCdqoasReL6Y3aNAgzfIuXbqYWx1hkBFNh6A06p9a7dq1pUiRIrJ9+3aTtij1Ntddd12alEd6MePqq682Fzq0Z2TqbbSOgqueCtbjz5o1y+Vxnjt3Th555BEzPOVf//pXhs8LAIBQoykBNYBftWpV+zxtVz/77DOTduDYsWMmteDRo0dNEOHf//63GcEHAADgLe2koB0Nt27dar/g70gzDCjNaJAeTb2s1wlcTfPnzzfraOdG/X96dRdDRbXCKXUSdEQCACAwhUQgwWq8GzZs6HK5Nd9VI5+aleewcOHCLpdrICH1vnJqG0d64UQDGjqaIb3URwAAhCodvq/toWO6QXXnnXeaNnLy5Mny4osvyrhx4+SPP/7waxFBAAAQGvT3tzXCUUdCOtZK0LTGOlKhVatW0qhRI/t8/S5y1VVXmTzXcI0RCQAQ+EKiRoKmCVKawsAVa75jaiF3tLCy9ixwta7WPNACyUoLOTtuk97+rflZ3caiX0y0iPMDDzxgvqAAAJAbLVq0yJ46ID4+3mmZBuq1fhEAAICvaXrhuXPnyi+//GJSHLdo0cL8hl++fLn5rf/hhx86ra+pkLWm04EDB/hjuEEgAQACX0iMSNBiwypfvnwul1v1B6z10qPpidRHH32UZtkXX3xhUgqp06dPp9nm22+/NV8QUgc5rGGJjts0bdrUpD06dOiQ/PDDD2kCFh9//LG5n5ycbH9M6/99+/Y1QylfffVVyQodInnq1CmvJwBAYLpw4UKWPt+1fQgGmgbwhhtuCJrjBQAAoSE6Otr8zh8yZIjExMTIV199ZQIJvXr1MjUSKlWq5O9DDNpAws4TO+VC0gV/Hw4AIFQDCdYFBHdVqDNzgUGHJmqqhGXLlpmCy9u2bTP5CLWQ46OPPip58qQM4nCshXDTTTeZYYsaqGjXrp2sWLHC3NfeCZq/WQMDqbfRQIDuT/Xo0cN88Th58qTppXDXXXeZFAzW83HcbsyYMbJq1SoTRLDSH3lr//795ji8mRISSmXpsQEA2UdT+Hj7+a6Ttg/BQI9VRyIUKlTI34cCAAByGQ0gjBgxwlwz0E4cOtpg4sSJLjMlDBs2zFyXmDRpksedJXT9OXPmSG5RPH9xicsbJzaxyV/H//L34QAAQjW1kRZUVI65CR2dPXvW3MbGxma4L230tTiSXszX3Mo6WerXry+NGzeW999/3ymFgl7wnzlzptx2223mIn+zZs3sy4oWLWqGPeoXh9RpF/RCj6ZKmj59usnnbNFgxeuvvy6DBg0yPR105ILSHg7PPfecGQGhPR2yKiEhQTZv3uzVtnqqExKyfAgAgGyg+XcHDx7s9fY1atQIimBClSpVZP369ebHu9VWIvSdPnBaEg8kyqVzl+zzDq49KJExkeZ+bKlYKVAq5bshAAAIDnpdRUclrNq/yhRcrlmspr8PCQAQioGEcuXKmdu9e/e6XL5v3z5zW758eY/217ZtW/nrr7/MKIQNGzaYEQFXX321dO3aVe655x6zTs2aNdMcw2+//WZGFvz6668meKHFlDQ/85QpU1xuoxc9pk2bJkuWLJHvv/9ejhw5Yi7u33333XLp0qU02+jQSQ2WHD58WK6//nqnfVm1FN577z3Ta0FzNL7wwgsZNtQ6+sIbERFebQYAyAHavmTlwrq7EX6BRtvL1atXm9SD999/v78PJ2BpGsXU30EcR2LqFExWv7taFg5f6DRvYouJ9vuthraS1sNa++HIACDwjB8/3kzu2gcgkDgGEgAAgSckAgn16tWzFyF2RXMUWsUYPaVpEh555BGneXpxXws7RkZGyrXXXptmGx1J0KVLFzM5mjdvnn14oit60V8nR6NHj3a7jaY90smVHTt2mCn16AcAAELNgAEDzIjA/v37m3R/mk4QaZUoUUI2bdoUMqem0cONpPod1d0u1xEJAICMA8Y6Gt/qdAcEgmqFU+okEEgAgMAUEoGE5s2bmzzJW7dulXXr1tkDCxZNVaTat2+fpcfRAshaTFnTHnlan0BrHnz33XdmpIHWT/DExYsXTa8R7RHau3dv+3xNZ+QupZGmTho+fLhJl/T00097+IwAAAheI0eONOn+dPSgtvG1atUy3wmKFy8uEekMndM0gQhemraI1EUAAIRuwWUCCQAQmEIikBAVFWV6I7744oumt8UPP/wg+fPnt/fs15EKrVq1MgWRLePGjTOT1ibQi++ONE1Cw4YNnVI76D6156Pu9+WXX05zDL///rtUrlzZ1DSwaNEl3b+OZBg1apS9ULNl9+7dpkBTsWLF7PNOnTplgge6rRZj1osiAADAdRBd22otRqg2btxo2uOMEEgAAAAIPAQSACCwhUQgQWlB47lz58ovv/wi1apVM6mCtDjx8uXLzYX6Dz/80Gl9HVmgowUOHDiQZl+dO3eW5ORkqVOnjhnpoGmENBihQYQvv/xSKlasmGabV1991dRH0ABEyZIlTZFKrZWQlJRkAhw6iiG1n3/+Wfr27WsKOGv9hsTERFm8eLEJJmjPSg0+AAAA13Q0QrDUcwAAAMhOoVATqWqRqub20JlDcvL8SSkYXdDfhwQAQWF8DtVECplAgo4E0GLEOrrgs88+Mxf1CxcubFIBPf/88yb/o6e0NoJur0EIvbivaYl0nqYMclewuWPHjiYosX79elM8WWss3H777TJ48OA09Q8sOkJCgxb6OBqo0OdQv359MyKhR48eXBwBACAdCxYs4PwAAACESE2kuLxxUjK2pBxMPChbj22VxgmN/X1IABAUHsuhmkhhNisfAHIN6wVUunRp2bt3r1f7OHNGJPafWoaJiSL/ZJICAIQAX7QT2WHy5MkmJWDXrl39fSi55u948cxFeSk2JQXkM4nPSFT+KB8fJQDAXwK1vUfu/ju2mtRKFu1aJJ92+lS61+nu78MBgKBXxoftRLjPjgoAACAb6SjDgQMHco4BAABCVLXCFFwGgEBFIAEAAAQNBlICAACELgouA0DgIpAAAAAAAAAAvyOQAACBK2SKLQMAACCtQ4cOSc2aNTNdlAsAEPzGjx9vJnftAxDIgQQdiRoWFubvQwIA/INAAgAAQAgrUaKEbNq0yd+HAQDwg/QCxlbxRSCQVCpUScLDwuX0xdNy6MwhKRlb0t+HBAD4B6mNAAA558ABkTVr3E+6HAhy58+fl6FDh0q1atUkOjpaEhISpHfv3pm+WNO6dWvTC8/dNGfOnGx7DgAAAP6QN09eqRBfwT4qAQAQOBiRAADIOe++KzJ8uPvlQ4eKDBvGXwRuaRqGiIgIr8+QXoBPSkrK1iBCmzZt5Ndff5VSpUpJhw4dZOfOnTJx4kT59ttvZfny5VKhQsqPY0917txZYmNj08wvXbq0D48cAAAgcNIbbT++3QQSrit/nb8PBwDwDwIJAICc8/DDInfcIXLunEiLFinzliwRiYlJuV+qFH8NZEjz5QaqkSNHmiDCNddcIz/++KM9APDGG2/Ik08+KX369JF58+Zlap+vvfZapoMPAAAAwapa4WoyR+YwIgEAAgyBBABAztFAgU5nzlyZV7++SP78fvkrZLZ4W/ny5U3vcl/S9DULFy6UHTt2cLHYA/nz5zcX5APRpUuXZOzYsea+FrZ0HEUwePBg+eijj+Tnn3+WNWvWSMOGDf14pAAAAMFRcBkAEDgIJOTy9BA1a9bMdFEuAAgVPXv2TDNvyZIl8tdff0m9evWkvgY5HBQtWlRChV7o1sld+xCo9OK81h8IRPraOXHihFSuXFkaNGiQZnmXLl1k/fr18s033xBIAAAAcINAAgAEJgIJuViJEiVk06ZN/j4MAPCbSZMmpZnXq1cvE0jo2LGjDMuBeg2TJ0+Ws2fP5ni++/QCxmXKlMl0YWCIrFu3zpwGd6MNrPnWep764IMP5OjRoxIeHm4KOOtrs1y5cpxyAAAQ0oGEbce2SfLlZIkI974+FgDAdwgkAAByXnLylfuLFoncdJNIFgroBjMuCIeO3bt32wMxrljzd+3alan9vvDCC07/f+qpp2TIkCFmAgAACLWMA2ULlpW8EXnlQvIF2XVyl1QqVMnfhwQAAS2nMg6E+2xPAAB4YuZMEccfObfeKqKFZHV+AFuwYIGpqaAjFg4cOCAPPviguTCcJ08eGT16tFlH57/yyivSqlUrM8IgKipKSpYsKZ06dZKVK1e6rZGg+01de0HnaYHd5ORks0/tiZ43b14pW7as/Oc//5ELFy7kyPOG5xITE81tvnz53NZ3cFwvI9ddd518/PHHZoSMjlr5888/5cUXXzSvueeee07GjBnjcXHqU6dOeTWdPnXa4+cPAMhZ+l3A2893nbR9QOhlHHA1BVMQQYWHhUvVIlXNfeokAEDG9HPeXRug7YOvMCIBAJBzNFjQpYte2XSer2l0dP706SKdOgX0X+TIkSPStGlTSUpKkhYtWsj58+ftF45nzZplLvJXr15d6tatK3FxcbJ161b58ssvZfbs2Wa6SUdfZMK9995rttPH1P0uXrzYBBY09dAnn3ySTc8S3rAuyLgr4p3ZCzYjRoxw+r8Gk/773/9K48aN5eabbzaptx566CGJiYlJdz/79++XggULijciJVL+T/7Pq20BANnrpZdekuHDh3OaEbLpjTYe3mgCCbdUucXfhwMAIJAAAPCKXhA9ezbz6YyeeCJtEMHan158HTBApG1bz9Mc6QV8Nxdts8t3330nd955p3z22WcSHR3ttKx58+ayceNGqVWrVpptNK/9o48+agIL7i40p6YpcDRIofvU0Qlqx44d0qhRI/n000/NxQMt7IvAUKBAAXN75swZl8t1VIFVMDorNBilwYRVq1bJ8uXLzaiW9CQkJMjmzZu9eqxLZy7JuIRxXh4pACA7PfPMMzJ48GCvt69Ro4YJNgOBqFrhlDoJjEgAgMDBiAQAQObpBdEsXgx1GUzYu1ckMz2nNUXMP+licoqmFxo7dmyaIIKqU6eOy21uvfVW6dq1qwk+aFDA3Xqu6GNZQQRVsWJFue+++8z8RYsW5apAwuXLlyUY6l3s1dexC1YB6/Lly2f5sapWrWoCCZpOKyMauNLRMd64GHHRq+0AADnznUQnb3nasQHwZ8FlAgkAEDgIJAAAkAkNGzY09Q/Sy1c8Z84cWbFihUmDdPFiyoVYDSAoHZHgaSAhMjLSZW9zTXGjPLmIjJxTr149c7t27VqXy9esWWNuNe1VVh0/ftyp7gIAAEAoIZAAAIGHQAIAIPM0pZCHBWPtFi1KKaycke++0yqznh+Hn3qdu7Jhwwa544470hROdnT6tOfFa7VQc4SLNE9WahwrSIHAoKmttBaBBovWrVtnDyxYZsyYYW7bt2+fpcfRAJXWyrACWwAAAKEaSNh9crecu3ROYiLTrwkFAMh+4TnwGACAUKND4bUndGYmLTJcpoz7mgY6v2zZlPU83acfhuS7SmlkFdK96667TBDhkUceMb3ST506ZdLx6DLNY2yt56nwcJrpYBIVFSX9+/c39x977DGnWgmjR482r4lWrVqZGheOevToIVdddZUpym359ddf5auvvpJkrS3iQF9fWqND961BqzL6ngIAAAgxRfMVlfjoeLGJTf46/pe/DwcAwIgEAECO0Z71Y8aIdOmSEgBwvKBuBQRGj/a80HKA+eOPP8ykRXDffvvtNMu3b9/ul+NCznr22Wdl7ty58ssvv5gUVC1atDBFs7UocrFixeTDDz9Ms83u3bvlzz//lJMnT9rnbdmyRR544AEzKkVHHcTHx5v9rF69Ws6fP28Kek+YMCGHnx0AAEDO0BoeOiphxb4Vpk5C7eK1OfUA4Gd0dQQA5JxOnUSmTxdJSHCer72qdb4uD1JWznpXPcR12U8//eSHo4I/RqzMnz9fhgwZIjExMWZUgQYAevXqZWokVKpUyaP9NGvWTPr16ycJCQmycuVK+eKLL0ydjfr168vrr79u5hUvXjzbnw8AAIC/UCcBAAILNRIAADlLgwVt24oULHilJoKmMwrSkQiWKlWqmFREP//8s8mRX7VqVTNfe49rqqNjx475+xCRQzSAMGLECDN5YsGCBWnm1ahRQ956661sODoAAIDgUK1wSp0EHZEAAPA/RiQAAHKeY9BACysHeRBBae/wPn36mLoIWmT39ttvl65du0qFChVMcEF7pAMAAADwDCMSACCwMCIBAAAf0doIWjT3gw8+kHnz5knBggWlbdu28uKLL8rEiRM5z/CLQ4cOSc2aNV0u06LQOgEAQtP48ePN5K59AAIZgQQACCxhNptjtUvkBpq/e9++fVK6dGnZu3evV/s4c0YkNjblfmKiSP78vj1GACGOD5GQbycQGn/Hi2cuykuxL5n7zyQ+I1H5o3x8lAAAf6G9Dw2h/HdMvJgoBV4qYO4f/89xiY+O9/chAUCubicYkQAAAAAAABDEQnEEYmxUrCQUSJD9p/fL1qNbpUnpJv4+JADI1SMQCSQAAHLOgQMp07lzV+atXavVaVPulyqVMgEAAADwWIkSJWTTpk0hmd5IAwlacJlAAgBIpgPG1ogEXyCQkIuFYo8FAAHu3XdFhg93nteixZX7Q4eKDBuW44eVG5EzGQAAAIGuWuFqsmDnAhNIAAD4F4GEXCxUeywACGAPPyxyxx3ulzMaIeR6LAAAAADeqlK4irn9aftPcn3F66VluZYSER7BCQUAPyCQAADIOaQuAgAAAOCBmZtnyv9++Z+5v3TvUrn+o+ulTFwZGXPLGOlUoxPnEAByWHhOPyAAAAAAAEAwO3/+vAwdOlSqVasm0dHRkpCQIL17987UqM4TJ07IZ599Jt27dzdph/Pnzy8FChSQZs2ayZtvvilJSUmSm4MIXb7oIkfPHXWav+/UPjNflwMAchaBBAAAADg5feC0HFhzQA6uPWifp/d1nk66HACA3BxEaNOmjYwYMUISExOlQ4cOUrZsWZk4caI0bNhQdu7c6dF+XnvtNbn33nvl888/l3z58kn79u2ladOmsm7dOhkwYIB5jLNnz0puk3w5WQbMGSA2saVZZs0bOGegWQ8AkHMIJAAAAMDJ6ndXy3uN3pOJLSba5+l9naeTLgcAILcaOXKk/Prrr3LNNdfIli1bTCBg+fLl8vrrr8vhw4elT58+Hu0nNjZW/vvf/8ru3btl1apVMnXqVJk3b55s2LBBypUrJ4sWLZIXX3xRcpvFuxfL3lN73S7XYMKeU3vMegCAnBNms9nShngR0qwimqVLl5a9e903zuk5c0a/9KTcT0wUyZ/ft8cIAAjudgKB83fMkyePVK1aNVNFt3XEQeKBRLf7ji0VKwVKFfDp8QIAfG/8+PFmcmXr1q0mdQ7tfeZcunRJihcvbtISrVmzRho0aOC0vF69erJ+/XpZvXq1GZ3grSlTppiURxUrVpTt27fnqu9tUzZMke4zu2e4Xt0SdaVzjc5ydZmrpVnpZlIwumCOHB8ABBNfthMUWwYAAAhhJUqUkE2bNmVqGw0SECgAgODnLmDseGEBmbNkyRITRKhcuXKaIILq0qWLCSR88803WQokaEBC5ca/UakCpTxab/2h9WZSYRImNYrVkGvKXGMmDS7o/8PDSMQBAL5CIAEAkGMOHEiZ3ClVKmUCAAAAApHWL1DuggTWfGs9b1mjEEqWLCm5TctyLaVUbCk5kOj+h0OxfMXk2euelRX7VsjSvUtl+/HtsunIJjN98NsHZp24vHFmpIIJLpS9xtwvFFMoB58JAIQWAgkAgBzz7rsiw4e7Xz50qMiwYfxBAAAAEJi0noE1osMVa/6uXbuy9Dhjxowxt1rIObeJCI+QcbeOky5fdDH/dyy6rCMP1Du3vyOdanSyzz985rAs27tMlu5ZKsv2LTMBhlMXTslP238yk+WqoleZ0QrWyIWaxWqaxwMAZIxAAgAgxzz8sMgdd4icOyfSokXKvCVLRGJiUu4zGgEAAACBLFGLBIpIvnz5XC7P/08BQWs9b7zzzjsyd+5ciY+Pl6efftqjbbT85alTp7x+zLx585opUGiQYPpd02XAnAFOhZfLxJWR0beMdgoiqOL5i8sd1e8wk0q6nCQbD2+0Bxb0duuxrfLH33+YadLaSWa9AlEFpGnppvbgQrMyzaRovqI5/GwBwL0LFy6YyVu+LI9MIAEAkGOs1EVasN1Sv77/CraHhaX0aPJU+fLlZefOndl2PAAAAAhs1gUZd98js3rBZtGiRTJgwACz/w8++EASEhI82m7//v1SsKD3xYaHDh0qwwJsaLAGCzpU7yCLdy+WA6cPmNoJmvbIkxEEecLzSP2S9c3Ur0k/M+/vs3/L8r3LTSokHb2wfN9yOX3xtMzbMc9MlqqFq14ZtVD2GqldvLbZHwD4w0svvSTD00vtkIP4JAQA5Fo9e/Z0WUDvr7/+MgXu6muUw0HRotnTO0mDExUrVpRWrVrJggULsuUxAAAAkHUFChQwt2cce8Y4OHv2rLmNjY3N9L61SLOmMrp48aJJbdSpk3Ov+/RowGHz5s3irUAajeBIgwatK7T2yb50pMFt1W4zk0q+nCy/H/k9JSXSP8EFHa2gIxd0+nj9x2a9/JH5pUnpJnJ16atNYEGDDDoCAgBywjPPPCODBw/2evsaNWqYYLMvEEgAAORakyalDGl21KtXLxNI6NixY8D1ygIAAIB/lStXztzu3Xsl3Y6jffv22UeyZoZ+/7z55pvlxIkT5jvoE088kantdQRDXFxcprbJ7TRIUbdEXTM91OghM+/YuWNm1IIVXNBRC1prYcHOBWayVCpUyYxYsEYu6D4iIyL9+GwAhKq8WUw9l9lMDOkhkAAAAAAAAOABHbWq1q5d63L5mjVrzG3dunU9Pp/aU7Rt27Zy8OBBGTRokEkzBP8oHFNY2lVtZyZ12XZZNh/Z7DRqYdORTbL9+HYzfbrhU7NeTJ4YaZzQ+Epwoew1UjK2JH9GACGFQAIAIMfs3i3y998pxZYt+hvMKrasmYP+6eQVkHSY+dtvvy2ffPKJ/PHHH3L58mWpVauWPPzww9K7d+80kf5du3bJyy+/LPPmzTO91qKjo6VUqVLSokULMzSxevXqpseZle9w4cKFTvvQ1EuuRk0AAADAP5o3b25qEWzdulXWrVtnDyxYZsyYYW7bt2/v0f6OHTsmN910k0l12adPH3n99dez5bjhnfCwcKlVvJaZ+jTsY+adOH9CVuxb4RRc0Hlay0EnS4X4CvYRC3qr9RqiIqL4UwAIWgQSAAA5FkSoXl3k/Hnn+S1aXLkfHS3y55+BGUzQPLjt2rWTxYsXm1oJGgwIDw+XpUuXyoMPPigrV66Ud955x77+nj17pGHDhubHofZI0x+T58+fN8GFCRMmyDXXXGMCCVqHoXPnzuZHZ4kSJeSWW26x70MfA8iqQ4cOSc2aNV0ue+yxx8wEAAhN48ePN5O79gGZFxUVJf3795cXX3zRtKE//PCD5M+f3ywbPXq0Gamgda8aNWpk32bcuHFmuvPOO03RTMfvl7feeqv8/vvv0q1bN3nvvfd8moIC2SM+Ol5uqnyTmaxRC1uObpGle5bagwsbD2+UnSd2mmnqxqlmvbwReaVRQqOUIs7/BBdKx5XmzwQgaBBIAADkCB2JkDqIkJou1/UCMZDw1FNPmSDC/fffL2+99Za9gN6RI0dMkODdd981t7fdllK87f333zdBhNdee02efPJJp31pMCEpKcnc11oMGkzQQMJVV13FCAT4nAaoNm3axJkFgFwovYBxmTJl7Pn8kTnPPvuszJ07V3755RepVq2a6fyh3++WL18uxYoVkw8//NBp/b///lv+/PNPOXDgQJr96DYRERFm0hGurjBCNfBHLVxV9CozPdDgATNP6yqs3LfSadTC0XNH5dc9v5rJUjaurNOohYalGkrePIFZ+BoACCTkYvRQBOAtm03k7NnMbeOYziij9c6c8WzdfPm0cJDkyOflBx98IBUrVjSjCRwLHemPRR2J0KBBAxNMsAIJGmBQbdq0SbO/zBbfyw70UAQAAPCOpqucP3++GV3w2WefyVdffSWFCxeWXr16yfPPP2+CNJ44fvy4uU1OTjb7cYdAQvCJyxsnbSq1MZOy2Wyy7dg2e1BBb9cfWi97Tu2RPZv2yLRN08x6mvpIgwlXl06ps6DBBQ02MFIFQCAgkJCL0UMRgLc0iPBPh3yfy0w2n8REkX9GkmcrrV1w6dIlk3bIMYhg0REFBQoUMOmNLNZw9n79+skLL7wgLVu2NEPhAwU9FAEAALwXExMjI0aMMFNGtCaWTq4CBAQJcgcNBFQtUtVMPer1MPMSLybKqv2rUlIi7Vtmbo+cPWICDTqNXj7arJdQIOFKEecy15hAQ0zkP0XmACAHEUgAACADWvxOaaFlndw55zDsQnuk/fjjj/LFF19I27ZtzY/Nq6++2gQjHnjgATOSAQAAAEDuFBsVK60rtDaTNWph+/HtTumQ1h5cK/tP75cZm2eYSUWGR5rCzVZgQUculC9YnlELALIdgQQAQKZpSiEdDZAZa9d6NtpgyRLt4e/5ceSEy5cvm1tNX6SFkz2heW4///xzefrpp2XWrFlm+Lvm0dVbLc6nhfk0sAAAAAAAOmqhcuHKZrq37r3mhJy9dFZW71/tlBLpYOJBWbl/pZnGrhhr1iuRv0RKKqR/UiI1Tmgs+SJz6McSgFyDQAIAINO0LkFmUwrFxHi+Xk6kK8oMK89t69at5Y033sjUthp80EmHs586dUqGDx9u9jFgwABTXA8AAAAAXNFgQMvyLc1kjVrYdXJXSlDhn5RIvx34TQ6dOSRf/fGVmVREWITUK1nPKSVSpUKVGLUAIEsIJAAAkIHrr7/ejDCYPXu2vPrqq+a+N+Li4mTkyJEyatQo2bhxo32+VTshKSmJvwUAAAAAt6MWKsRXMNPdte82885dOidrDqyxj1jQSdMh6Tydxq8cb9Yrlq+YPaigt01KNzHplQDAUwQSAAA5omhRkehokfPn3a+jy3W9QFO6dGlT8+CDDz6Q+++/X958800pmupAf/31Vzlx4oTceuut5v8ff/yxGYlQu3Ztp/XmzJljehJZoxyU7isyMlL++usvSU5O9jpQAQAAACB30cLLzcs1N5Nlz8k9TumQNKCghZy/2fKNmVR4WLjUKV7HXmdBgwtVC1dl1AIAtwgkAAByRLlyIn/+KfL331qU+Eq9BK2JYKU90mvzul4g0uDB9u3bZcqUKWZkggYJSpUqJQcPHpRt27bJvn37TLoiK5AwY8YM6dGjh1SuXFnq1Kljii1r0eZly5ZJeHi4vPDCC04jErQI8zfffCP16tWThg0bmnnNmzc3hZkBAACA9Bw6dEhq1qzpctljjz1mJuQeZQuWNdNdte4y/7+QdEF+O/jblVELe5bKnlN7ZN2hdWZ6Z/U7Zr3CMYWdRi00Ld1U4vLG+fnZAMjI+PHjzeSuffAVAgkAgByjQQKdzpy5Mk8LKwdaTQRX8uXLJz/++KN89NFHZrTB+vXrTVCgePHiJligQYR77rnHvv7gwYPNqAMtsLx48WI5c+aMJCQkSLdu3eTJJ5+Uxo0bO+3//fffl6eeekp++ukn+eyzz8zIBE11RCABWcWFBQDIvXLqwgL8r0SJErJp0yZ/HwYCVN48eU1gQKeBMtDM23dqnwksWMGFVftXybFzx+S7rd+ZSYVJmNQuXtspuFC9aHUzmgFA4EgvYKzXJbTjoy+E2TS/AnIV6wWkqTr27t3r1T70ImDsP6n0EhOD4yIggMDBZ0jotxPwP/6OAADaidBHew9fuZh8UdYdXOeUEmnniZ1p1ouPjk8JSpS+2qRE0lELOg9A6LcTIRVCPH/+vAwdOlSqVasm0dHRpudn7969vYq6fP3113LDDTdIfHy86YVav359k9bi8uXLbrfZsGGD3H333VKyZEnJmzevVKxY0fRI1ZzZ7hw5ckQGDRpkerPqNtqLQPfxxx9/uFx/165dMnbsWJM6o1KlSmYbza3drl07k2oDAAAAAAAAyIyoiChTgPmJZk/IZ50/kx0DdsiBJw/Il92+lH9f+2+5rvx1EpMnRk6cPyFzts2RYQuHyc2f3CyF/1dYar1VSx78+kF5f8378vvh3+Wyzf21M0fJl5Nlwc4FMmXDFHOr/wcQuEJmRIIGEdq0aWOKXWrO6pYtW5pc1CtWrDBpJ5YvXy4VKlTwaF8jRowwAQnNYd2sWTMpVqyYSV9x+PBh6dSpk0ybNs0sc/TDDz9Ix44dzXHUqFHDTBpY2Lp1q1SpUkWWLl2apjDnnj17pEWLFrJ7925zzPpY+/fvl5UrV5pAyLx58+Saa65x2kbX1zQZmmtb19eghRbn1G2UpsV49dVX031+jEgA4C8HDqRM7moklCqVMsG/6NmWNfpd4KWXXjL1NLSNL1y4sKmB8fzzz5teIDm1L/6OAADaidBHe4+cdCn5kmw4vMHUWFi2b5m5/ev4X2nW07oKzUo3s6dEalammam/4Gjm5pkyYM4A2XvqSg/pMnFlZMwtY6RTjU458nyA3KCMD0ckhEwg4bnnnjM/qvXCu+awjv0n784bb7xhclHr6AK9MJ8RDRhce+21kidPHlP08uabbzbzT548Ke3btzd5rt955x15+OGH7dskJiaa0Qd///23OY7hw4eb+Tp6QR979OjRZpSBXgRwpKMI5syZY241OJH/n/xAWqCza9euUq5cOdmyZYspuGnp3r27tGrVSu677z77+urbb781gQzNp63P/8Ybb3T7HAkkAPCXYcNE/vmIdGno0JR14F/8IA2Mjg1Z3Rd/RwBAemgnQgN/R/jb4TOHZfne5faUSCv2rZAzlxyK4v2jepHqJhWSpkTS5U/9+JTYxPmSpNZkUNPvmk4wAfARAgmpXLp0yfyg1hRCa9askQYNGjgtr1evnimKuXr1amnYsGG6J1dTIU2cOFEefPBBmTBhgtOyjRs3Sp06dUxKIR0FYJk8ebL07NnTpFTavHmz02iFCxcumLRFOtJgx44dUr58eTNfexXq/YiICLMva76lS5cuJqCgRT179Ojh0QtDgxvvvfeeKcz54Ycful2PQAIAf49IcIcRCYGBH6T+79jgi33xdwQApId2IjTwd0SgSbqcJBsPb7TXWdDbLUe3ZGofRWKKyHu3vyfRkdESGR4pkRGRmb6NCIuQsLCUwASQm5Xx4YiEPBIClixZYoIIesE+dRDBuiivgQQdYZBRIOG3334zt61bt06zrHbt2lKkSBHZvn27SVukQQXHba677ro0KY+0hsHVV19tggJad+Hxxx932kZHMqQOIliPr9vMmjXL40CCBkyUrypxA4CvEShAKNOODVrHSI0fP95+4V9pzSTtHPDzzz+bTg8ZfR/x5b4AAACAnJInPI/UL1nfTI80fsTMO3r2qAko6PTd1u9kzcE16e7j6Lmj0nla5ywfixVU0PoP3gYknG693Darjx8RHpHlc4Hgknw5WRbvXiwHTh+QUgVKSctyLQPidRASgYR169aZW3c/pK351nrpOXMmZfiV5iB2RQMJR48eNfuyAgmebJP68b3ZJiMa4FBaNwEAAARvxwZf7gsAAADwpyL5isht1W4zU81iNaX7zO4ZblO5UGUpGF3Q1GW4dPlShreumOWXL8nZS2clmGnKp6wEQdIEMnwRVPHy8cPDnDtgI61Arh8SEoEETRNkDdVwxZq/a9euDPelhZW1QLKrdbXmgRZIVpqj2HGb9PZvzc/qNunRiw2aYkl16NDBo20AAEBgdmzw5b4AAACAQKG9qz3x/h3vS+sKabOFuKLlX5NtyR4HHRxvLyZfzPQ2Lm999LiaGirN8xObWV8ncR0zCRoaSPBpoMKDdUwgIxsCJOHZEBTRIEKXL7qkqR+y79Q+M9/f9UNCIpCgxY5Vvnz5XC63ihJb66VH0xNpUUNNGfDIIylDsCxffPGFnDt3ztw/ffq00zYjR440BY+14HLRokWdghzz589Ps03Tpk1N2qNDhw7JDz/8YC/qbAUsPv74Y3M/OTnZPGZMTEy6x92vXz85cuSISaN05513iqcftKdOnRJvpAyoiJNApbUpXnrpJXnmmWfMeQbnj9dfcOC965vzp6lvskLbB/i3Y4Mv92V9n0hNc8Y6pmR0tY4jresUaOvqa/6VV16Rf//732nae2/3q9/D0nsPZGZdPb9Wbt5AXDf1+Qu049X1dH13HF/D/ljXOn/W981AP97U62b03sjOz4ikpCT7d/U8efIE1WdPIHxG6O/D//3vfy4/+zK7XyAY8BshtM6fpmjR3tV6YTT1xVKr970u1/U8pZ95ecLymLRKMZL+9bNAP3/6ma3BBJ8EN7wJnPjw8VwFRS7bLsuF5AtmCnbhHgZFPApk/JPCasqGKS7fFzpP3xsD5wyUDtU7+C3NUUgEEqwvRu6KqGTmi9Njjz0mb731lixbtswUUB4yZIgJDOjF/kcffdR80dUvvo5fqm+66SZp1KiRKebcrl07k8u4Zs2appegFkC2vsw7blOwYEGzv1GjRpkaCO+++65cf/31cvDgQfm///s/+eOPP8zz0WNPXXc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+      "text/plain": [
+       "<Figure size 1590x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Optimise] [OK] Saved to /home/sgrant/mu2e-cosmic/output/images/ml/k/optimise/overfit_learning_rate.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1590x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Optimise] [OK] Summary saved to /home/sgrant/mu2e-cosmic/output/ml/k/results/optimisation_summary_nested_cv.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The last opt instance still has its summary from the final outer fold\n",
+    "# For a full picture, run the diagnostic plots on the last fold as a representative example\n",
+    "opt.plot_overfit_diagnostic(\"max_depth\")\n",
+    "opt.plot_overfit_diagnostic(\"n_estimators\")\n",
+    "opt.plot_overfit_diagnostic(\"learning_rate\")\n",
+    "opt.save_summary(tag=\"nested_cv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "35f08dc7-3419-4fad-94ca-c594588e98fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Optimise] [OK] Initialised\n",
+      "[Optimise] [INFO] CV grid search: 4 combinations x 5 folds = 20 fits over ['n_estimators', 'max_depth', 'learning_rate']\n",
+      "[Optimise] [INFO] [1/4] scan_cv_000: deadtime=0.106±0.036%, veto_eff=99.907%, overfit_gap=0.00007\n",
+      "[Optimise] [INFO] [2/4] scan_cv_001: deadtime=0.094±0.042%, veto_eff=99.907%, overfit_gap=0.00009\n",
+      "[Optimise] [INFO] [3/4] scan_cv_002: deadtime=0.091±0.045%, veto_eff=99.907%, overfit_gap=0.00011\n",
+      "[Optimise] [INFO] [4/4] scan_cv_003: deadtime=0.097±0.057%, veto_eff=99.907%, overfit_gap=0.00012\n",
+      "[Optimise] [OK] CV grid search complete. Best: scan_cv_002 (deadtime: 0.091%, veto eff: 99.907%)\n",
+      "[Optimise] [OK] Saved to /home/sgrant/mu2e-cosmic/output/images/ml/k/optimise/overfit_n_estimators.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1590x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Extend n_estimators to check we've found the plateau\n",
+    "param_grid_ext = {\n",
+    "    \"n_estimators\": [500, 1000, 1500, 2000],\n",
+    "    \"max_depth\": [3],\n",
+    "    \"learning_rate\": [0.1],\n",
+    "}\n",
+    "\n",
+    "train_idx, test_idx = data[\"outer_folds\"][0]\n",
+    "fold_data = AssembleDataset.get_fold_data(data, train_idx, test_idx)\n",
+    "opt_ext = Optimise(fold_data, run=run, min_efficiency=0.999, scale_features=False)\n",
+    "best_ext = opt_ext.grid_search_cv(param_grid_ext, n_folds=5, fixed_params=fixed_params)\n",
+    "opt_ext.plot_overfit_diagnostic(\"n_estimators\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a4be0c6-b956-4bd7-a57d-6602e4536fc8",
+   "metadata": {},
+   "source": [
+    "## Analysis\n",
+    "\n",
+    "Check whether the consensus best HP is consistent across outer folds. If all folds agree, the optimisation is robust. If they disagree, the landscape is flat and the choice doesn't matter much. Best way is to pick the simplest model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f8c3a071-da16-452c-8de5-656c216aeaf4",
+   "metadata": {},
+   "source": [
+    "## Class imbalance "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b99023dc-1084-4f79-a7dc-cc1453e3a49f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "label\n",
+       "0    2139801\n",
+       "1      23727\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[\"y\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4a74bb5e-c443-4f54-920e-566b45e34d11",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Optimise] [OK] Initialised\n",
+      "[Optimise] [INFO] CV grid search: 4 combinations x 5 folds = 20 fits over ['scale_pos_weight']\n",
+      "[Optimise] [INFO] [1/4] scan_cv_000: deadtime=0.094±0.042%, veto_eff=99.907%, overfit_gap=0.00009\n",
+      "[Optimise] [INFO] [2/4] scan_cv_001: deadtime=0.107±0.071%, veto_eff=99.907%, overfit_gap=0.00009\n",
+      "[Optimise] [INFO] [3/4] scan_cv_002: deadtime=0.094±0.035%, veto_eff=99.907%, overfit_gap=0.00013\n",
+      "[Optimise] [INFO] [4/4] scan_cv_003: deadtime=0.108±0.042%, veto_eff=99.907%, overfit_gap=0.00012\n",
+      "[Optimise] [OK] CV grid search complete. Best: scan_cv_000 (deadtime: 0.094%, veto eff: 99.907%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "param_grid_spw = {\n",
+    "    \"scale_pos_weight\": [1, 10, 50, 90],\n",
+    "}\n",
+    "fixed_params_spw = {**consensus_hp, \"device\": \"cuda\", \"tree_method\": \"hist\"}\n",
+    "\n",
+    "# Use a single outer fold for the SPW scan (it doesn't affect ranking)\n",
+    "train_idx, test_idx = data[\"outer_folds\"][0]\n",
+    "fold_data = AssembleDataset.get_fold_data(data, train_idx, test_idx)\n",
+    "opt_spw = Optimise(fold_data, run=run, min_efficiency=0.999, scale_features=False)\n",
+    "best_spw = opt_spw.grid_search_cv(param_grid_spw, n_folds=5, fixed_params=fixed_params_spw)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ee05011b-5c4c-4249-ad0b-67f48b3b413e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Optimise] [OK] Saved to /home/sgrant/mu2e-cosmic/output/images/ml/k/optimise/overfit_scale_pos_weight.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1590x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "opt_spw.plot_overfit_diagnostic(\"scale_pos_weight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "22bcbbc1-161f-4fbf-8490-22885da89fcf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>tag</th>\n",
+       "      <th>scale_pos_weight</th>\n",
+       "      <th>train_auc</th>\n",
+       "      <th>test_auc</th>\n",
+       "      <th>overfit_gap</th>\n",
+       "      <th>threshold</th>\n",
+       "      <th>veto_efficiency</th>\n",
+       "      <th>veto_efficiency_std</th>\n",
+       "      <th>deadtime</th>\n",
+       "      <th>deadtime_std</th>\n",
+       "      <th>signal_efficiency</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>scan_cv_000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.999984</td>\n",
+       "      <td>0.999897</td>\n",
+       "      <td>0.000088</td>\n",
+       "      <td>0.199340</td>\n",
+       "      <td>0.999068</td>\n",
+       "      <td>0.000009</td>\n",
+       "      <td>0.000936</td>\n",
+       "      <td>0.000425</td>\n",
+       "      <td>0.999064</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>scan_cv_002</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.999988</td>\n",
+       "      <td>0.999862</td>\n",
+       "      <td>0.000126</td>\n",
+       "      <td>0.650185</td>\n",
+       "      <td>0.999068</td>\n",
+       "      <td>0.000009</td>\n",
+       "      <td>0.000940</td>\n",
+       "      <td>0.000346</td>\n",
+       "      <td>0.999060</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>scan_cv_001</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.999988</td>\n",
+       "      <td>0.999894</td>\n",
+       "      <td>0.000094</td>\n",
+       "      <td>0.437644</td>\n",
+       "      <td>0.999068</td>\n",
+       "      <td>0.000009</td>\n",
+       "      <td>0.001068</td>\n",
+       "      <td>0.000709</td>\n",
+       "      <td>0.998932</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>scan_cv_003</td>\n",
+       "      <td>90</td>\n",
+       "      <td>0.999988</td>\n",
+       "      <td>0.999863</td>\n",
+       "      <td>0.000124</td>\n",
+       "      <td>0.683228</td>\n",
+       "      <td>0.999068</td>\n",
+       "      <td>0.000009</td>\n",
+       "      <td>0.001081</td>\n",
+       "      <td>0.000420</td>\n",
+       "      <td>0.998919</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           tag  scale_pos_weight  train_auc  test_auc  overfit_gap  threshold  \\\n",
+       "0  scan_cv_000                 1   0.999984  0.999897     0.000088   0.199340   \n",
+       "1  scan_cv_002                50   0.999988  0.999862     0.000126   0.650185   \n",
+       "2  scan_cv_001                10   0.999988  0.999894     0.000094   0.437644   \n",
+       "3  scan_cv_003                90   0.999988  0.999863     0.000124   0.683228   \n",
+       "\n",
+       "   veto_efficiency  veto_efficiency_std  deadtime  deadtime_std  \\\n",
+       "0         0.999068             0.000009  0.000936      0.000425   \n",
+       "1         0.999068             0.000009  0.000940      0.000346   \n",
+       "2         0.999068             0.000009  0.001068      0.000709   \n",
+       "3         0.999068             0.000009  0.001081      0.000420   \n",
+       "\n",
+       "   signal_efficiency  \n",
+       "0           0.999064  \n",
+       "1           0.999060  \n",
+       "2           0.998932  \n",
+       "3           0.998919  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "opt_spw.get_summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "7d678952-06bf-4ef3-a356-1bc8f7a9a668",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'threshold': 0.06090609060906091,\n",
+       " 'veto_efficiency': 0.9992477432296891,\n",
+       " 'deadtime': 0.0017631349230357034,\n",
+       " 'signal_efficiency': 0.9982368650769643,\n",
+       " 'precision': 0.9513010264979709,\n",
+       " 'recall': 0.9992477432296891,\n",
+       " 'f1': 0.9746850923321512,\n",
+       " 'thresholds': array([0.00000000e+00, 1.00010001e-04, 2.00020002e-04, ...,\n",
+       "        9.99799980e-01, 9.99899990e-01, 1.00000000e+00]),\n",
+       " 'veto_efficiencies': array([1.        , 0.99974925, 0.99974925, ..., 0.39117352, 0.25952859,\n",
+       "        0.        ]),\n",
+       " 'signal_efficiencies': array([0.        , 0.95352757, 0.96665601, ..., 1.        , 1.        ,\n",
+       "        1.        ])}"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from train import Train\n",
+    "from validate import Validate\n",
+    "\n",
+    "scale_pos_weight = 1 \n",
+    "\n",
+    "hp = {**consensus_hp, \"scale_pos_weight\": scale_pos_weight}\n",
+    "\n",
+    "# Train on first outer fold to check score distribution\n",
+    "train_idx, test_idx = data[\"outer_folds\"][0]\n",
+    "fold_data = AssembleDataset.get_fold_data(data, train_idx, test_idx)\n",
+    "\n",
+    "trn = Train(fold_data, run=run, verbosity=0)\n",
+    "results = trn.train(tag=\"spw_check\", save_output=False, **hp)\n",
+    "\n",
+    "val = Validate(results, run=run, verbosity=0)\n",
+    "val.roc_auc()\n",
+    "val.find_threshold(min_eff=0.999, plot=True, show=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "58597bc7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "thr = val.find_threshold(min_eff=0.999, plot=False, show=False)\n",
+    "val.plot_score_distribution(threshold=thr[\"threshold\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e607e24-44cb-4cc8-89e1-a4fcb6b5d8fd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "mu2e_env [conda env:.conda-ana_v2.6.1]",
+   "language": "python",
+   "name": "conda-env-.conda-ana_v2.6.1-ana_v2.6.1"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/CrvCosmic/notebooks/train.ipynb b/CrvCosmic/notebooks/train.ipynb
new file mode 100644
index 0000000..0dbe987
--- /dev/null
+++ b/CrvCosmic/notebooks/train.ipynb
@@ -0,0 +1,1241 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e576f452",
+   "metadata": {},
+   "source": [
+    "# Train and evaluate XGBoost model (nested CV)\n",
+    "\n",
+    "Use flattened data generated by `run/run_ml_prep.py`, where `run` is a string which indicates which round of processing was using (cuts/flattening technique), and hyperparameters found via the `optimise.ipynb` notebook.\n",
+    "\n",
+    "**Flow:**\n",
+    "1. Assemble full dataset with outer fold indices\n",
+    "2. For each outer fold: train with fixed HP, find threshold, evaluate on held-out test\n",
+    "3. Report CV-averaged metrics with uncertainties\n",
+    "4. Train final model on all data with mean threshold, export to UBJ"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "80a198db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "version = \"1.1.0\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "80e7706a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append(\"../src/ml\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "eb460438",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/exp/mu2e/app/users/sgrant/MLTrain/CrvCosmic/notebooks\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pwd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70b480ea",
+   "metadata": {},
+   "source": [
+    "## Assemble dataset\n",
+    "\n",
+    "run = \"k\" \n",
+    "\n",
+    "- 2026 MLPreprocess cuts with coinc start/end time\n",
+    "- All coincidences and all combinations of dt, each row is a coincidence\n",
+    "- Events with no coincidence have NaN CRV parameters \n",
+    "- No start/end time cuts\n",
+    "- Duration as a feature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6af77f44",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[LoadML] ✅ Initialised\n",
+      "[Load] ✅ Initialised with in_path=/exp/mu2e/app/users/sgrant/MLTrain/CrvCosmic/output/ml/k/data/CRY_onspill-LH_aw\n",
+      "[Load] ✅ Loaded /exp/mu2e/app/users/sgrant/MLTrain/CrvCosmic/output/ml/k/data/CRY_onspill-LH_aw/results.pkl\n",
+      "[Load] ✅ Initialised with in_path=/exp/mu2e/app/users/sgrant/MLTrain/CrvCosmic/output/ml/k/data/CE_mix2BB_onspill-LH_aw\n",
+      "[Load] ✅ Loaded /exp/mu2e/app/users/sgrant/MLTrain/CrvCosmic/output/ml/k/data/CE_mix2BB_onspill-LH_aw/results.pkl\n",
+      "[LoadML] ✅ Got full results\n",
+      "[Assemble] ✅ Loaded data\n",
+      "[Assemble] ✅ Initialised\n",
+      "[Assemble] ✅ Prepared 5-fold nested CV (event-level grouping)\n",
+      "  Total: 2163528 coincidences\n",
+      "  Fold test sizes: [432706, 432706, 432706, 432705, 432705]\n"
+     ]
+    }
+   ],
+   "source": [
+    "run = \"k\"\n",
+    "from assemble import AssembleDataset\n",
+    "asm = AssembleDataset(run=run)\n",
+    "data = asm.assemble_dataset(n_outer_folds=5)\n",
+    "\n",
+    "# drop sector\n",
+    "data[\"X\"] = data[\"X\"].drop(columns=[\"sector\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "b2650b12",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Metric</th>\n",
+       "      <th>CRY</th>\n",
+       "      <th>CE Mix</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Total</td>\n",
+       "      <td>20087</td>\n",
+       "      <td>578368</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Unvetoed</td>\n",
+       "      <td>10</td>\n",
+       "      <td>417011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Fraction [%]</td>\n",
+       "      <td>99.95</td>\n",
+       "      <td>27.90</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Metric    CRY  CE Mix\n",
+       "0         Total  20087  578368\n",
+       "1      Unvetoed     10  417011\n",
+       "2  Fraction [%]  99.95   27.90"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "asm.check_dT_window_results()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6b9d405a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Load] ✅ Initialised with in_path=test_out\n",
+      "[Draw] ⭐️ Initialised\n",
+      "[Draw] ✅ Wrote /exp/mu2e/app/users/sgrant/MLTrain/CrvCosmic/output/images/ml/k/process/h1o_3x3_cuts_CRY.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Q5zds2GCuuuoqs88++9hZOdTe1Gxf//d//2deeeWVuNptqpvTTz/drl+vH51kIh4d25tvvtmWRfusdqvK/pe//CXia3S+RToX3exfOpbh9Rvexv3pT39qZyjTdtXOHDp0qH1PnHHGGeaZZ55JamcAAIhq2rRpAX1ljBo1KvDvf//b/j1mzJhAR0dHt2VfeOEF+/x2220XePbZZ+3fui1cuNB33Y2NjYFf/epXgf322y/Qr1+/QFVVVWCbbbYJnHjiiYE33ngjYplUFq33tttuC9TX19sy7rTTToGamprAsGHDAt/5zncCH3/8cXD5NWvWBH784x8Hdthhh0B1dXVgyJAhgVNPPTWwcuXKqPuu8t14442Bgw8+ODBw4MBARUWFfe0xxxwT+Ne//hXxdW6/n3vuucDGjRsDP/nJTwI77rij3faAAQMCkydPDrzyyivdXjdx4sTga/1uej68XryPhdP23WvDhb/+4YcfDhx22GG2fL1797Z18ve//z3kNXfeeWdg//33t3VVW1sbOOiggwJPP/101GOYTNm8dO645XRO6dzS388//7zv8qpjPf+f//wneJ5oXyNZsWJFYOrUqYHdd9890KdPH3sObrvttvb8eOedd3xfc+6559r1avmGhoaI625vb7fns5b91re+FXG5X//611GXWbJkSeCiiy4KjB8/3m5T55GOw1e+8pXAHXfcYc/TeN4rAABkgmtjRPqO97aTVq1aFTjvvPPs97facd7X6DtW7ZPvf//79jtw0KBBgcrKStvWU1vsn//8Z8yyNDc3B26++ebAl770pcBWW21lXz906NDAvvvuG7jyyitD2oty0kkn2TLo3s8TTzwRqKurCy7T2toaswyx2nfuO9rb5vG2nd13eaRbpLJ6edcdqxyxjoPbH9VzW1tb4Prrrw987nOfs23DwYMH27qZN29ecHm11WfMmBHYZZddAr169bJtzW9+85uBDz/8MGqZtW6V6Ytf/KKtO7XDdQ7o/3fffbfvb5F4eM+/eI6X/tZvhnPOOScwevRo21ZUeY4//vjAu+++G3Edn3zyiW0zjhs3zu63O3f32GMP+/irr77a43ICAIDMtV3VplSfoP5Wv4z6zryvUbvF27ZS21ZtAO9jF1xwQcRyqF266667BpctKSmx/W9lZWXBx370ox+FvCZam+3Pf/6zbT/pefWJJkptIG87VG0Z9Um5v9VOj9ReccdCr4+2/kj9xuFt1/Ly8uC23U19kpH65KK1o/ye+9rXvmbbme45/a26djc97+3LLC0tDSxevDjivqmfUcuNHTs2yhEGACA+Gv9Rm0DfLbNnz7aPffTRR7atoO/IaOOc6pM57rjjQr5D1YZR34b7v/rcHH3vue9cfd95vw91++UvfxlzjPKss86yj3/+85+Pul+HHHKIXe7kk0/u9twf//jHYDtGN5XX9Ru6sbknn3wyoVMokX1zbazvfve7gWOPPTb4mv79+9t7bx+a99jqOVdX7qZ+IfV/Rur7Un+Td3n1r3nbkH379o17bHXRokV2rNzV6/Lly0Oe17i0a8/qpnama9O69ufVV1/dbb3ettlf//rXYN+oyqnyjorS5ovUr3jZZZfZPsRIbb2f/vSnUV/vHfdV3Wm/dPzd+RFev97+Ojdu661Xb5s72rh7JATeAQASCrzTAJeC7lxgU7jvfe979rnp06eHfPn7Bd4tXbo0pENJjSg1ILxfdgpI8uM6fm644QYbMOU6vbwNLw1sabtqgCqQyjUC1DnkltEg74YNG3y38cEHHwSDuFyDw1s+3c4880z/L9iu5++6667A9ttvHyyft7Gk/X388cdDXhdvR0+qA+/UgHHHPHwff/e739l6dw1NNYC8DTE1Rh599NGIZUimbJEadHqNBsn19ymnnNJtWQXjeQNDYwXePfLII8EGoqsTNRK9nYoKbAv39ttvB5dRR2Ykavi75Z566qmIy02aNMku87e//a3bcwp21LnjLVN4HcUbpAoAQCYweBlK7Te149z3ubsIxN3c93+kwUd1VKoDyD0X3nGkDrpYFMSvZV07x11M4leOeAPvLr300sAXvvCFYPvE24ZS++q///1v4NNPPw1MmDDBt62u4LVIA5bqNN5nn31C2jvh7R9dgKDAykR525WReOtC7VyV1f2W8HZOqyPPG2To6DFvnam9rP/r90S8AZPxlBMAAGSu7ar2jS6qfeaZZ+yFlvL+++8Hl9PFq6effrr97lYbyNGAp/qy3OCxAtbCqW/S9UGqzaDB5vXr19vnWlpa7Hauu+46e8GDV6Q2mwYf1e7QTX2nidIgsNqgrjz33ntv8IITXaSqoDfvoHKqA+80IKqB2HvuuSewbNmy4PHetGmTXffw4cPt63QBT6LtqEjPRSuP18477xx1MFj15Qa0r7322ojrAQAgXn/5y1/s94rGGr30fazHvQFj4XThoxv7U2C4vmNF42cao1UQ1RlnnJHw93i0Mco5c+YEv1MjXbCo/iDXR6KEG15qU7m+q4svvtgGk6m8ur333nvBQEL1yUQLhPcT7765NpbafxoT1Xe6G0tWe8Qb0Ka+sVtvvdWWxbWXdHHAgw8+aNuOWo93bNdLF0e4Y6XxdW/bUsF6Dz30kL3wM56x1bfeeivYRjr00EO7jX2rfePabwomfP3114Pl1bYuv/xyu696Pjwpi7edpGOiPjv1+znve8odi+tXVBtTfX2333578GIKnZ9HH3108JzVOH2k1/uN+8YzHqqkK1pGF9cqsYzavaJ7nWsaE9d7JVEE3gEAEgq8E2Ws8OvU2bx5s/3C1ZehBveiBd7pC8wNpumLVQ1HN3CmQLmjjjoq+Fq/bCbuy1ONBH05KsBJnTBar/52wWtqVO699942C8fLL78c7ABRx40LglM2unDr1q2z69XzygKngK6mpib7nDq+1NHlArb8OrBc2dVw0NUMajiqfGoYKruFa2xpP1znUaIdPakKvNPx12Dgz372s2Cnnhrcygyj5xVkp84kDZb+/ve/t1fIiBo8rhNOVweE70e6Au/UeNU5puOvBq6XrozRctoXiRZ4p8a/C8L8wQ9+YH8AuAaWtvHDH/7QPqeGprcB6ei8cg3YSJTBztVzpKwsOtf0A0K38IbwY489FvzxccABB9iMku44a1mdl6eddlrEzHzeY0DgHQAgUxi89Bcrm160NmC87aZ46ybWlZvxBN6pHa6M0Pfdd59tX7t2rstOrCzJ6lhUm1qZAtWG0U2dWsqMp2W+/e1vd1u/fhPstdde9nllh1N7yLU/9XtDF0W4QDhljkt34J3a82qHufagOiV1QYUu8tHz6uiOdFGFyq/fIK4dqH1TG1odttdcc02PywkAADLXdtXgrhusToYGxbUetRPCKeOHnlOA/9y5c+NeZ3ibTW0OBaPpMfV5+V3gGQ/1m7r99pvpQm0zd2F2OgLvYlG7TK/ThR9+s0CkM/BO/cBaZuTIkcF+RK/7778/ePw1AwsAAD2lMSiX8MRL2Xj1uDKc+dF3uPtu++1vfxv39noaeCduDPSSSy7xfa2yqun5rbfeOmTsTP0mI0aMsM/dcsstEbetizH9sgGnat9cG0u3SAli4qGxVrXvNM4XHiSoYDWXoS2R7MR+fYSarc5dsKrARDee7eUy92nGuUg09h2eBTG8naRjFz42mwjvzCDhQZeisrsAQjfWm8rAO3cRhRLnpBKBdwCAhAPvFFSnxoA6N7xfroro13KHH364/X+0wDt1/LjnwrO+uQEtF5inrHiRvjyVNWPBggXdnleDzK1fVxl6rzR1FL2v59VRFO7CCy8MBt1FmkJMVytoGQX5hS/jtq1BRb8UwrrywC3z4osvZjXwLlLjRYFd3swlCo4MpynC3PMKCstE4J247Co65xydiyqvC/yMFXjnBnR1HkTiUjzrKttwf/jDH+xzajCHT1PnAupcZpsrrrgi4jZ0RZGW0bRpXjqnXKbGAw88MKmMLkLgHQAg0xi8LI7Au0jtP2WBcc/HaqvreQXtef3mN7+xz2lq2o0bN/qW77XXXrNtMA1oRpquI1WBd+rA9pvG7B//+EdwmfBBeJfZ76WXXkqobImWEwAAZK7tqmnTemL+/Pl2PboQODxgyw0uJroNb5tNbar/+7//C15E6xcwFy9dPKH16OKDSFy/WDYC78RdiOEutM5U4J36+9zF3GoPhnMXMqsuAADoKTelrG7h308aw3P9D24KWq8TTjgh2L+SiFQE3mnM0SXt8EtK4YKfwgPzlOHNjetGSmbhDXSPFHTY031zbSxdjJns2Fx4Eo+777475HHNJKHHdVGr34UEkYT3EepYuNkZpkyZ4psk5bPPPgsG+UVL5KGxdLdu7xTG3nZStAyL8XD9itHamZrxzCXXSXXg3X777WeXUTbpVCo1AAAkaOuttzZf+MIXTH19vbn33nuDj9922232/nvf+17Mddxzzz32fr/99jNf+tKXuj1fXl5upk2bZv/+3//+Z95++23f9XzjG98w22+/fbfHves8/fTTzcCBAyMu89FHH9l9cdQHc+utt9q/L7jgAlsWP1/96ldNnz59zKeffmpef/1132W07a222qrb47vttpvZdttt7d9vvfWWyabq6mpz7rnndntc+6b6kW222caccMIJ3ZYZM2ZM8Phncj/cOebOOdG5qHo8/PDD7TkazZtvvmn++9//moqKClvHkXz3u9+1908//bRpb28Pee5b3/qW6dWrlz1fbr/99m6vveuuu0xTU5MpLS01J598csRtPPzww/b+mGOOCXn8ueeeMwsXLrR//+pXvzKVlZVR9wkAgHxz4oknmpEjRyb9+smTJ9v7l19+udv3tGvLff/73zcTJkxIav2tra3m29/+tv0e7t27t/nnP/9pjj/++KTLW2gOPPBAews3ceJEU1VVZf8+9thjo7bVGxsbzYIFC0Ke+9Of/mTvf/jDH9rj7mfPPfc0u+yyi2lpabFtpnRSW7Gmpqbb41/+8peD7bPw3yr9+vWz9ytWrEhr2QAAQOYccMABMZdZtWqV7c9Uf5r6ItWnWFJSYm/jxo2zyzQ0NJh169YFX7N48WKzfPly+/fRRx+dVNk2bdpkjjzySHP33XebIUOGmP/85z9m0qRJJlmvvfaavT/ssMMiLhPtuVRQO+/3v/+9+eIXv2iGDx9u+y/dsdRt9erVdrmlS5eaTFI7z/0muPnmm0OeU10+9dRTwT5hAAB6Sv1bGoM66KCDzOjRo7uN4Wmc0i0X7qWXXupR+6KnfX76vl6yZIltl3hpPPXdd98NGYNzXnzxRXuvttKwYcPM0KFDfW+nnXZa8Ls3nfbaa6+YY3MdHR12PPArX/mKHUtVH5K3zfLqq6/6tllc/WhMU+2cZMyaNct885vfNM3Nzeaqq64yN910kx2TDKe+U5XTteEiHVf1tTmRjm08beJ47LPPPhGfU9tP1q5da1LtqKOOsvcXX3yxba89/vjjZuPGjT1eL4F3AICknHLKKSGNuQ8//NC88MILtvPBNfTi6cBRAF8khx56qCkrKwtZPtzee+/t+7g6mbwNo1jLrF+/Pvj3/Pnzg1/mCpiK1ABRo2/z5s1RGyDZajgkQh1/tbW1UY/R5z//edtAjLaMt9Mw3b72ta/Zc03nnBusdeeiOzejcY13NTR33HHHiHV8xBFH2OUU0PfZZ591+1GjwWRR4J1rtDquPOroHDVqVMROxH/961/2bzXK/RrdKoeOPwAAhaaYBi8LUaR2uNrvgwYNirsd7q07HXd3Mcfll18esY2m2/vvv5+RTtZI7Xmdi4MHD/Ztz7tOvJNOOskG7un80XkKAADyl9+FteEDmjvttJOZPn26eeWVV2z7QAOvep3aPq59JN4LgFeuXBn8O1L/USwPPvigvWhUHnnkkaQvPHFcUNuIESMiLtOTC2ji2b76ws4880wbyKaLGdT+1zHUsdTNDSp7j2WmnHHGGfZeF+YsW7Ys+LguIHF9jYccckjGywUAKCz6Trnjjjt8A9Qc9Tu4ZCduvDK8jZFs+6InFICmCzPlz3/+c8hz7v/qM1Lbycv152nsTH2CkW6uL0kXdGaz/ae+Ho1z68JdtcE++eQTW28DBgwItlmUAMSvzZKK+pkyZYrd3llnnWUuvfTSiMu54yrRjqtu3n1L5pjEK9LFtuIS4uii6FS76KKLbLCi1q2LKHRhrcablTBHz33wwQdJrZfAOwBA0oFP/fv3N7Nnz7ZfQi7zmLKixROZH08HjtbjOqXc8vF+MXuz1MWzjPfL29sAWbNmTdQGiAu2itQAyVbDIRHxlDHX9kPnxv/93/8Fg94UfKdzUedkPIGfro6VHSda/SqboeNXx6eeeqq911U7zzzzTPBxZT1xWRDdMn6effZZO8CszsTwDsts/igCACATimnwshD1pA0ZqR2uunPta9V3tHaae126A9qSaQdfc8019iIidXpff/31duBVF22ozadAUu8ALQAAyA/u4mA/bW1ttp9KF/Z+7nOfswFZypyhPh+1W9TGUXvW6ZzxtLtIF73GooFtN/uDMj6rPzMVopUn2bLG47zzzrN9a7rwRhe2KvBOA+vaLx1L3dwFzZGOZbovQNljjz1sv+Itt9xiH9Pfrn/cZeEBAKAnnnjiiWCWNH2/e7OouZtLHqH+B+8MZd7v6nR+Z8fKeif3339/MEBObSZd5BopmNDNaKH90nd8PLdstf9EWeY0E4P6KzVjhi4O1UxYSuTh2izugs5Ut//kO9/5jr1XAJn6LyNxx1XljPe4RrqIINYxyXUKhFSg6rx588xPf/pTmwFQs5tp9r1rr73WXuh93XXXJbxeAu8AAEnR9FEu8EkdDHfeeWfc2caSaVBksmHonapMjaJ4GiDRphJFerhzTeeem5JMgZ9uarN46liD+fE2MsPTeMvBBx9sdthhh27T3rpsd7qqJVogYKRpZr2y9aMIAIB0K8bBS8TfDlf9xtNGu+KKK3LusOpKWV1goezMP/7xj212RwXp6cIMBZKq/eg6mgEAQP7TBSMaaFX79tFHH7WZM8KD970Xh3hpRg1n0aJFSW1ffVb//ve/7UUnyh6sCwAiXcScyAUy0aZxjfacuzhBA8+RbNiwwfdxXdCgi2DkN7/5je3/U7bj8Daj92LZbHBZ79QvrgtHXPY79Uu67EMAAPSEC+6OV/h0s+77M9n2RU8dd9xxNtBL/XluLOzJJ5+0bRQFP33rW9/q9hpXZgXg54O//e1v9l4BXOeee67N9BfeFxmrDdiT+lFGRLU7lCHwG9/4hnnooYd8l3PHVQGQmsEOxowfP95ceeWVNqmK+p91AbbGfNXOVOa7N998M6HDROAdAKDHgU833HCD7WzZdddd454S03XgKO1uJO6qAHHTOGWCtzMnlxt3PenEKgRKQ61zTueezsFEAj9dHX/88cc9npLie9/7nr3/+9//bhtn6iD861//ah9TeulIgYAaKP7HP/4RMfDONboXLlzYo/IBAJCPCm3wEvHxTkGby+3weB144IHmF7/4hXnxxRdtO1EdzZq6Qh2dakN6p/AAAAD5y/Vvqv8y0uweLptyOA3QulkQomUqiWW77baz09tvu+225p133rFZSiK1l2Nx/bvK4BKJLjKIRDNSiNrPzc3NvsvMmTPH93Fd8OL6OiNlnVbbKlp/aDLc1LUST/YcXfyrjMaaBUMZiZRpRr7+9a+HZOYGACAZ+j5040fKGKcLUSPdXn31VbucZoV67733guvYf//9k2pfuO/EnmaTUz+eS0zhppd19+rn8/u+1IWLomB2fd+nWqr2LbwNGKnNon7JSIFurn6eeuqppNs12h8FXGrmLY1NagrVBx54wHdbLiDQBQsWotIk61fj7ZMmTTKPPfaYHdPV6yO13SNuO6GlAQAI64TRwJEi6b0BSIl04Hin5wynwU5lO3FBVpmiYC51nGSrARJvR4/rxIoWvBipE6tQuEA7nYO777672XPPPeN6nWu863UKmOsJXU2ioAA1jO+66y77Y8hlxYk2zex///tfO+WtOkb1PorU6NaA7GuvvdajMgIAkG8KbfAy0wOBsdaTjSm54qH2raZ0KMSOwOrqavOVr3wlmMFFbcd0dCIDAIDM69u3b7APxy+wXheN/vrXv474etenqhkd3njjjaTLoYtG1J86ZswY8+6779r2q/qeEnX88cfbe7VVtL5wuojgl7/8ZdQMIq7N6dfvptdrOjY/6pN1A8N+mUbUV/yTn/zEpJrrCxZdMBFLbW1tcAq9n/3sZzbjnZx++ukpLxsAoPgoQE2BVGpjHH300aauri7iTeOnmt0pPOudG59Sn9bvfve7hL8T4/k+jMVNJ6tMdwsWLAhmvvObZla0r+6C2h/96EemoaEh6vrXrl2bUHlSuW/eNmCk7GgXX3xxxNdqJjWNLSoBzbRp03rU16cLANQG0TmjTILh0w4rGY5LAKI23AcffJDS45or4qnfSBeFiILu3AwtiU6pS+AdAKBHlMHhggsusDc3l3w8XAphZTNRg8uvE0XTMLlAON0yRZHtrsNLaXpjDYilugESb0eP68RSB5p3qjVHV5W6qy0LlTq43Pn385//PKHAT3cFijrrYk0fF62O9SPgyCOPDE4366acVRCgq6NkpplVZh0N+Mt5550XDHAFAKAYFNrgZaYHAjPVwZgObrBSF+jECr7LxY5A/Y7RdGORaJoVJ9FOPAAAkLtZbhWIpUAzZRpxg5maqkrZ0NSGDJ92zOvCCy+0U9FrIFDZNtSfp2nZRAOoWp/6Sa+99tqYZdFFKGq/an3vv/++3bayxiRCU5Xtsccewb+VOUX7ImoTK0tNtGzQughGx0TOP/98e8GMe/3rr79uvvCFL0R8vQII3AWzeq0y67m21f/+9z/bB6cLVHW8U6lfv37BC37Ut+cuBo9nutmXXnrJ7t+OO+5ojzcAAD3lAug0flRZWRnXtK5y5513Br/DNMbkxmKnTJliLrnkkuBU8WqzqH9L/WbhCSTceKzaIuEBXIk6/PDD7QxUKpOyxSr4XhddHnXUUREvWvztb39r201z5861bQK1pbzjY5ol6g9/+IPZe++97bKJSOW+yRFHHBEMwteFlu7Yq4zaX23DJVEJt/3229spTeWaa64x3//+921woqNxy3vuucd87Wtfi1kOHa/f//735oc//GHwWN99990hy1x33XVm4MCBdt/VTtM55p017dNPP7X7oOy9//d//2fy0a5d9asskevWrYvY16v3gsbVvUF4ykyoWcwU7Klgxi996UsJbZvAOwBAj6ijRZ0+uiUyHaw6bfbZZx/7tzqklClMHUmuQaLnFZTnGhyZdvnll9sBVjVQ1HC6/vrrQ4Kz1Bh5/PHHbbazgw46KCsdPcqIpgaCuzJCnU5qLKszSh1s6uiJNuhXCHTOufNP52K8XCNUVy9oSgidi2qIea+eUafkX/7yF/vDYOrUqVHX536YqA7+9a9/xZUBMlbgnQZif/Ob39iyKvhTHa+6d3WqxrHqWQGv8+fPj3vfAQDIB4U2eNlTY8eODXa0qlM02Yx1rgNKVztrgDAXaQDT/U7QRRaXXXZZSIZntddUH+o0Vns916gTW+eKOl0V9Olty2vaYnexks7vgw8+OIslBQAAqbxoxLUrn3/+eRuApenVFESmfkX1I7oLNf1oWfUzKvOvBgl1IYIGaQcMGGCD9rU+ZUJxg+WxKPBN7SW9TgO4EydOjDpjht9Fyffdd5/Zeuut7YUOxx57rG27qM9SZVSfrQb2o7npppvsfq1YscL2rbmsPLoY9qOPPgpONefnhhtusNtTm1tt+V69etkLSDRjhKa/Vds+HdO5ukA6lV1l1e+A0aNHB4MW/NrWLsBQTjvttJSXCQBQfBQQpH4bb0BdLG45Xbyq6TKdW265xQZSaVxJySv03a52i9oXGofUd5eC4sMDwvT967Lg6jtY34e66Ts6ERrnUhCYuJmd1M+nsblIND2t2gn6/p8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+      "text/plain": [
+       "<Figure size 2560x960 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Draw] ✅ Wrote /exp/mu2e/app/users/sgrant/MLTrain/CrvCosmic/output/images/ml/k/process/h1o_3x3_cuts_CE_mix.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2560x960 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "asm.draw_cuts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "0b3bedaa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Assemble] ✅ Saved feature distributions to /exp/mu2e/app/users/sgrant/MLTrain/CrvCosmic/output/images/ml/k/process/h1o_2x4_crv_features.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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DzNEwBAAAAAAAgOlZOSDOTPeMYDyg5r768on8AAD/EIwVdqVRLjkR97/PsEbHCsRVVhrZz1BK3QMwS+Ddvn37arXOalJSUvTj9u3bPW7Pzs7Wjx07dpRIoEyZxXn6Z9isH+6UD/HaMAx7oxCAI1GmDAAAAABCgyA56702BOTB0ar01QMAAkQwVtiosdUls//3GTZ4fFJ4E554+gyl1D0AswTe2VWPHj0kLi5O9uzZIzt27CgPxHNbtmyZfhwwYEBEro8yZRbnqZQsH+6mLimrguyqNgzD3igE4EiUKQMAAAAA4AKy4wEAgKAhGAsAEGKODryrV6+ejBo1SmbOnKkHvFevXi3R0dF629y5c3UmvF69ekmXLl0ifakAghhkZySQTgXlqSC8ik7GJsiHkzwEWAIAHI2sxQAAOANZiwEg8opLRJZ8nzlvcIlITKQvCAAAAADgKIYD786cOSP/+te/dPDa1Vdf7XNftZ/av0OHDnLRRRdJqKxatUrS0tKqXWe3bt3Kv58yZYr079+//PvJkyfLunXrZPPmzdK+fXvp2bOn7N+/XzIzM6VZs2ayYMGCkF0vYBdmCUozEmRX3KRltXXq2qtS5WbJfAcAqA2yFgMA4AxkLQbMV46UsrL2VfW1JcgOAAAAwWxf+vo/AwBCEnj3zjvvyLBhw+Thhx+WP/7xjz73VRnllixZIm+++abce++9EiqHDx/WAXMVlZWVVVqn9qmoQYMGsnHjRklPT9fXuHz5cmnatKn+2VQQX5IqDRohZEuBVZglKE0F3fm6jqpBdrUJDvQUhKcCDdXPXBVlMAAYRbYUAAAAAPAPQXYAAAAAAMCygXfLli3Tj0OGDKlx3+HDh8tbb70l77//fkgD71SwnFr81bBhQ5kxY4ZezIRsKTA7f4LSwqk0yiUn4hKDkoHP0zEqu58ZAg0B2AfZUgAAAIDgW79+vdxyyy1y2WWXyb59+7jFAAAAABzFDFndPFVOi2QFNQAwTeDdV199pR9TUlJq3LdLly768csvvzT6dIC9FBSIVM2mmJAgkmWthoVZg9JU0N2S2bkRvQYAAAAAABA5+fn5MnToUB14t2vXLl4KwEGKS0SWeBlkdqOEGAAAgLMqpwGA6QLvVOdVXFycxMTE1Lhv48aNpUmTJlKggo0AiJSWiuTRwLA6TzM0ajMzwwyzSwAAoVNYWCjJycl+ZzcEABiUmipy8KD37Rac5ARryMjI0Iu39oATbNu2TdauXStbt26VzMxM3V9Yv359OXXqlM/j1Pb09HRZunSpHDhwQJo2bSr9+vWTtLQ0admyZcDXdf78eV114/HHH5eSkhIC7wAAAGyMvjjAOpXTzFRBDYD1ZJi0L85w4F29evXk5MmTtdq3rKxM73vRRRcZfTpHoqFoQ2rApyoVkKoC8eCXO2amSsPjB6s10sKJGRoA7N5QhDHx8fGSk5PD7QOAcFFBd0xsQgT4CqhPSkqSPAf8XqpAuRUrVvh1jAq669Onj2zZskUSExNl4MCBugzswoULZdWqVTqAr02bNgFd1zPPPCPR0dEybtw4mT59ekDnQnh5m6wIhOt3jYmxAGA99MUB5uQrUYkZKqgBsJ6RJu2LMxx4d/nll8vOnTvl//7v/6R79+4+91UdaadPn5YrrrjC6NM5Eg1FG/KUZUGVnHVAZ3ywqaC7SDXIPM3QYGYGADs2FAEAsBSXSyQx8X/fM8kJCDnVJ5iSkiJdu3bVS4KnCYdVzJo1S/cVqmPXrFlTXk1jzpw5MnbsWBk+fLisX7++fP+jR4/KkSNHfJ4zNjZWmjdvrr9WwXtvvfWWfPHFFxIVFRXwz4jQIcgOAAAAiExyE5nho3KASasHeL1uK1ZdrKl6g0lfAwBBDrz78Y9/LDt27JAJEybozrC6dT2f6ty5czJx4kTd0XXLLbcYfToADinD6q/SKJeciEusderiYPD0M3ibmUEnMgAAABAmKuguN/d/3zPJCQi58ePH+7X/2bNnZd68efprlfHZHXSnjBkzRhYvXiwbNmyQ7Oxs6dy5c/l+U6ZM8XneoUOHyqJFiyQ3N1ceeOABeffdd6VZs2aGfiaEBv0jACCeAwVUm9X9NQAAYaAritWU3EQlA3B/Rn1vcEloxnqDet2hDEo08lntLcCuNskWPLwGGgF5gH0C70aPHi2/+93vZNOmTdK3b1956aWXpFOnTpX2UZ1kTz75pN6nQYMG8vjjjwfjmgHUNiI+gh+84SrDqoLulsyuMLgGAAAAAABMSfURFhUVSbt27ar1IyqDBg3SFTZWrlxZHng3adIkPfHXF5fKeKkLDWTJ4cOHdV+lW2lpqZSVlelJw/Pnz5cHH3ww6D8XAACGlJZSDQcAYJ7KAVUDwqoEh/1v2pQJr1upRQb2sAf3qTH8moLsWras9WvgNzLrAeYOvFNlz1599VUZNmyY/P3vf5fU1FRdTqJ169Y6u90333wjhYWFumNLfa86tlq1ahXcqwdQuw/sMKIMKwAAAAAA8ERVz1DcQXVVude791NUv6K3ShtV9enTR7788stK637/+9/LihUrZPXq1dKy6oAGANQiO+OIEdwmBJmvwIAgBA0AAGCocoCvQC2V7U0FjJv1usMU3Ff8fda/GjPZVlzn43wek+gE8zUwWRwBYFeGA++UIUOGyCWXXCKjRo2Sffv2SUFBgV4qatu2rc6M169fv0Cv1XFU4GJycrLHbSNHjtQLUK7iB3YEGz++yrAaLT+r0vrqGQYVqHMBgF2o8llq8dYeAAAAAOzgwIED5RN6PXGv379/v6HzN27cWH7wgx9UWte8eXO56KKLqq33RE0gPn78uBhVv359vQCwPwL0EJBQVqnxNOjvoJJ0p0+f1otRqi0AAHbhaXy1xjFWb58X6rMlz/NYry3HbL0E9y3xMEmjVpls/QkWrOE18PhZX9NnvqfAP3c8gbfzOaj9AEQ08E659dZbZc+ePbJx40bZsmWLHPw++jYxMVFuuOEG6d27d3m5B/gnPj5ecnJyuG1OEeg/xBU/sN0fvCZjtPysahSGo2wtAESKr4B6NfiYZ8L3dHjH5AkAAJyByRP+Ky4u1o+NGjXyuD06OrrSfuGWn58vcXFxho+fOnWqTJs2LajXBMCcQXaAaTm8fG16erpMnz490pcBAMHno2To4BLPiU5CMb5qdKzXG0+BfLVN3GIW6lpjLvwrG56MtkY+6z0F/rnjCRzedgBMEXin1KlTR/r27asXAAbZ+EMtWOVnS6NcciKucjS+13S+FsRMWQCwByZPAADgDEyeMJ5FRpWP9bU9mFQgXG2D4Vq0aCG7du0y/FxkuwOcjb49RJSnAX0zlQUMk4kTJ8qYMWMMH9+hQwcdiA8ApuOjZGiMWoryvGai8zS+qgPF/AkGS0jQZVZ9MTJmG+xAvlpLTZXBe/3MBuiFChAcMUJCz9fr5S17XZVqlbU6nwPbD4ApAu8AmPQfYl+pZt3bI1x+1p+ysqpRuGR2LdPwRojRcroAAAAAANidKgWrlJR4HrE5ceKEfoyJUUNH4acCAmNjYyPy3HZCdjAAiABPlXNMWhknlAIt++5tcgAAmEbVkqEV3ue9BbB5Gl/1O1AsK8t3mVU/eQvSM5K4xZCDvrMBqiDDYP68QeGrSp6R7HU1lbQFUGsE3gF2/oe4lh+uZmo8WL2sbMRmZgAAAAAAYHKtWrXSj7lVS9x8L+/7PozWrVtLJBQWFkpycrLfGQ4BwF9kxwMiJyMjQy/e2gIAYGpVS4ampkqxh8xtZq8e5i1hibfELaHiKRugWe+ZTzVlLzRS6tZXgh91Pl+BgIDDBBx498knn8jSpUtl586d8t///lfOnj3rc6bI3r17A31KADXx48NTBd1FsvFQNUOcr7THZm7kBKucLgAAAAAAdpWSkqIft2/f7nF7dna2fuzYsaNEQnx8vOTk5ETkuQEAQHj4CqZPSkoqnwgAAJYQ5Ex0TmOFamu1EoogOF8JftR6T0F5wQrIS029UF7ZGwL/YJfAu7KyMnnwwQfljTfeKP++JqRo9g+zbGGYHx9okW6MecsQZ7WGjj/ldP3B7FfAGZhpCwAAACfo0aOHxMXFyZ49e2THjh3lgXhuy5Yt048DBgyI0BUCQHBRehkAAMAEqmZvU9/D/wQ/FQPxQhmoroLuCISHEwLv5s2bJ4sXL9Zfd+nSRW677TZp0aKF1K1L9dpgYZYt7Kym7HVmzm4HAMHGTFsAAAA4Qb169WTUqFEyc+ZM3QZevXq1REdH621z587VmfB69eql+xojgUmwAADYHxNgAcD8qlZMc68LSfY21D7Bj7dMdCqQUd3jYHO5LpRXDvXzAAEyHCW3cOFCncHuoYcekldffTXQ6wDgMJ4yxAEAAAAAAOtYtWqVpKWlVVp35swZ6datW/n3U6ZMkf79+5d/P3nyZFm3bp1s3rxZ2rdvLz179pT9+/dLZmamNGvWTBYsWCCRwiRYAADsjwmwAGD+jL3eKqYFM3tbcQmJYIIWlKeyCarAxqqZBQMtQ6uC7nJzqz8PYJfAu3//+9/68fnnnw/m9QAAAAAAAACwgMOHD+uAuYrKysoqrVP7VNSgQQPZuHGjpKeny5IlS2T58uXStGlTGTZsmA7iS/LWSQ8AAAAAduYtm1gISqOaIbjOSEW0mOgaSqFW5SPQa4lJ70E4efs9GDHC4AnJLAiHMhx4pzrJ1HLxxRcH94oAAAAAC6NEGQAAzkCZMtHBcmrxV8OGDWXGjBl6AQD4NxhqeCAUAGyKvjjYhgq6c3g2r5oqpjm9HeQrYDKi98ZXMCTlYeGAvjjDgXfXXnutLglRXFwsMTExwb0qALWf6RDkGQ6wJjrhAMA8KFEGAIAzUKbMfhi0BRBJtc08Qz8gYM8BWxhHXxxsx+W6UGLTE3+yvQHh4quELOVh4YC+OMOBd6NGjZLPPvtMFixYIKNHjw7uVQG4oGoddIfPcoC50z8DAAAAAGBlDNoaU1xCmSYAgHWYdcAWgIN4Kyn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",
+      "text/plain": [
+       "<Figure size 2560x960 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "asm.draw_features()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "66f8d446",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>event</th>\n",
+       "      <th>subrun</th>\n",
+       "      <th>crv_x</th>\n",
+       "      <th>crv_y</th>\n",
+       "      <th>crv_z</th>\n",
+       "      <th>PEs</th>\n",
+       "      <th>dT</th>\n",
+       "      <th>nHits</th>\n",
+       "      <th>nLayers</th>\n",
+       "      <th>angle</th>\n",
+       "      <th>timeStart</th>\n",
+       "      <th>timeEnd</th>\n",
+       "      <th>sector</th>\n",
+       "      <th>PEs_per_hit</th>\n",
+       "      <th>t0</th>\n",
+       "      <th>d0</th>\n",
+       "      <th>tanDip</th>\n",
+       "      <th>maxr</th>\n",
+       "      <th>mom_mag</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2650.0</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>-4.547474e-13</td>\n",
+       "      <td>2684.661133</td>\n",
+       "      <td>-7921.097656</td>\n",
+       "      <td>90.724777</td>\n",
+       "      <td>527.722271</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.727466</td>\n",
+       "      <td>525.451538</td>\n",
+       "      <td>587.951538</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>22.681194</td>\n",
+       "      <td>1016.454419</td>\n",
+       "      <td>42.117722</td>\n",
+       "      <td>0.583941</td>\n",
+       "      <td>617.263550</td>\n",
+       "      <td>103.368034</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1859.0</td>\n",
+       "      <td>843.0</td>\n",
+       "      <td>4.071482e+03</td>\n",
+       "      <td>2862.049561</td>\n",
+       "      <td>-9957.799805</td>\n",
+       "      <td>285.257233</td>\n",
+       "      <td>-805.731684</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.353849</td>\n",
+       "      <td>1607.101807</td>\n",
+       "      <td>1782.101807</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>28.525723</td>\n",
+       "      <td>786.035162</td>\n",
+       "      <td>32.673702</td>\n",
+       "      <td>0.605967</td>\n",
+       "      <td>602.773804</td>\n",
+       "      <td>103.583145</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2192.0</td>\n",
+       "      <td>457.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1096.057591</td>\n",
+       "      <td>8.055742</td>\n",
+       "      <td>0.715509</td>\n",
+       "      <td>539.576172</td>\n",
+       "      <td>104.595360</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>556.0</td>\n",
+       "      <td>411.0</td>\n",
+       "      <td>6.362000e+02</td>\n",
+       "      <td>1513.145508</td>\n",
+       "      <td>-12567.080078</td>\n",
+       "      <td>34580.207031</td>\n",
+       "      <td>-397.368187</td>\n",
+       "      <td>1899.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>-1.526525</td>\n",
+       "      <td>412.966553</td>\n",
+       "      <td>1787.966553</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>18.209693</td>\n",
+       "      <td>661.050758</td>\n",
+       "      <td>24.049637</td>\n",
+       "      <td>0.879215</td>\n",
+       "      <td>526.783875</td>\n",
+       "      <td>104.189644</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2974.0</td>\n",
+       "      <td>707.0</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>2731.275635</td>\n",
+       "      <td>-12267.968750</td>\n",
+       "      <td>240.338318</td>\n",
+       "      <td>-22.465829</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>-3.002133</td>\n",
+       "      <td>983.740662</td>\n",
+       "      <td>1146.240723</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>30.042290</td>\n",
+       "      <td>1019.720328</td>\n",
+       "      <td>65.952957</td>\n",
+       "      <td>0.743986</td>\n",
+       "      <td>470.854187</td>\n",
+       "      <td>104.093399</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2163523</th>\n",
+       "      <td>3061.0</td>\n",
+       "      <td>925.0</td>\n",
+       "      <td>3.952777e+01</td>\n",
+       "      <td>2711.051514</td>\n",
+       "      <td>-7511.345703</td>\n",
+       "      <td>131.810699</td>\n",
+       "      <td>782.745075</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>-1.497381</td>\n",
+       "      <td>742.005493</td>\n",
+       "      <td>842.005493</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>10.984225</td>\n",
+       "      <td>1500.949542</td>\n",
+       "      <td>74.319374</td>\n",
+       "      <td>0.665570</td>\n",
+       "      <td>633.117065</td>\n",
+       "      <td>104.378433</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2163524</th>\n",
+       "      <td>3076.0</td>\n",
+       "      <td>722.0</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>2681.962402</td>\n",
+       "      <td>-2549.654053</td>\n",
+       "      <td>86.194878</td>\n",
+       "      <td>-311.175052</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.386727</td>\n",
+       "      <td>1285.348145</td>\n",
+       "      <td>1447.848145</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>17.238976</td>\n",
+       "      <td>983.938596</td>\n",
+       "      <td>30.310881</td>\n",
+       "      <td>0.856973</td>\n",
+       "      <td>541.797424</td>\n",
+       "      <td>104.623466</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2163525</th>\n",
+       "      <td>2061.0</td>\n",
+       "      <td>331.0</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>2701.172852</td>\n",
+       "      <td>-11403.797852</td>\n",
+       "      <td>260.580322</td>\n",
+       "      <td>678.858792</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>-0.721829</td>\n",
+       "      <td>716.635193</td>\n",
+       "      <td>891.635193</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>23.689120</td>\n",
+       "      <td>1384.573697</td>\n",
+       "      <td>65.948845</td>\n",
+       "      <td>0.930326</td>\n",
+       "      <td>555.570496</td>\n",
+       "      <td>104.027061</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2163526</th>\n",
+       "      <td>2004.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>-2.573767e+03</td>\n",
+       "      <td>-934.724060</td>\n",
+       "      <td>-4733.690430</td>\n",
+       "      <td>200.804611</td>\n",
+       "      <td>-146.186658</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>-1.477709</td>\n",
+       "      <td>1139.843506</td>\n",
+       "      <td>1252.343506</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>16.733718</td>\n",
+       "      <td>976.955921</td>\n",
+       "      <td>28.302200</td>\n",
+       "      <td>0.711944</td>\n",
+       "      <td>516.766479</td>\n",
+       "      <td>103.907005</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2163527</th>\n",
+       "      <td>2532.0</td>\n",
+       "      <td>780.0</td>\n",
+       "      <td>4.358051e+03</td>\n",
+       "      <td>2883.542725</td>\n",
+       "      <td>-9957.799805</td>\n",
+       "      <td>1386.245605</td>\n",
+       "      <td>508.213271</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.898777</td>\n",
+       "      <td>542.232422</td>\n",
+       "      <td>1729.732422</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>21.326855</td>\n",
+       "      <td>1506.241286</td>\n",
+       "      <td>53.792229</td>\n",
+       "      <td>0.684940</td>\n",
+       "      <td>468.728882</td>\n",
+       "      <td>98.656525</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2163528 rows × 20 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          event  subrun         crv_x        crv_y         crv_z  \\\n",
+       "0        2650.0    98.0 -4.547474e-13  2684.661133  -7921.097656   \n",
+       "1        1859.0   843.0  4.071482e+03  2862.049561  -9957.799805   \n",
+       "2        2192.0   457.0           NaN          NaN           NaN   \n",
+       "3         556.0   411.0  6.362000e+02  1513.145508 -12567.080078   \n",
+       "4        2974.0   707.0  0.000000e+00  2731.275635 -12267.968750   \n",
+       "...         ...     ...           ...          ...           ...   \n",
+       "2163523  3061.0   925.0  3.952777e+01  2711.051514  -7511.345703   \n",
+       "2163524  3076.0   722.0  0.000000e+00  2681.962402  -2549.654053   \n",
+       "2163525  2061.0   331.0  0.000000e+00  2701.172852 -11403.797852   \n",
+       "2163526  2004.0    40.0 -2.573767e+03  -934.724060  -4733.690430   \n",
+       "2163527  2532.0   780.0  4.358051e+03  2883.542725  -9957.799805   \n",
+       "\n",
+       "                  PEs          dT   nHits  nLayers     angle    timeStart  \\\n",
+       "0           90.724777  527.722271     4.0      3.0  0.727466   525.451538   \n",
+       "1          285.257233 -805.731684    10.0      4.0  1.353849  1607.101807   \n",
+       "2                 NaN         NaN     NaN      NaN       NaN          NaN   \n",
+       "3        34580.207031 -397.368187  1899.0      4.0 -1.526525   412.966553   \n",
+       "4          240.338318  -22.465829     8.0      4.0 -3.002133   983.740662   \n",
+       "...               ...         ...     ...      ...       ...          ...   \n",
+       "2163523    131.810699  782.745075    12.0      3.0 -1.497381   742.005493   \n",
+       "2163524     86.194878 -311.175052     5.0      3.0  3.386727  1285.348145   \n",
+       "2163525    260.580322  678.858792    11.0      4.0 -0.721829   716.635193   \n",
+       "2163526    200.804611 -146.186658    12.0      3.0 -1.477709  1139.843506   \n",
+       "2163527   1386.245605  508.213271    65.0      4.0  1.898777   542.232422   \n",
+       "\n",
+       "             timeEnd  sector  PEs_per_hit           t0         d0    tanDip  \\\n",
+       "0         587.951538     3.0    22.681194  1016.454419  42.117722  0.583941   \n",
+       "1        1782.101807     4.0    28.525723   786.035162  32.673702  0.605967   \n",
+       "2                NaN     NaN          NaN  1096.057591   8.055742  0.715509   \n",
+       "3        1787.966553     5.0    18.209693   661.050758  24.049637  0.879215   \n",
+       "4        1146.240723     3.0    30.042290  1019.720328  65.952957  0.743986   \n",
+       "...              ...     ...          ...          ...        ...       ...   \n",
+       "2163523   842.005493     3.0    10.984225  1500.949542  74.319374  0.665570   \n",
+       "2163524  1447.848145     3.0    17.238976   983.938596  30.310881  0.856973   \n",
+       "2163525   891.635193     3.0    23.689120  1384.573697  65.948845  0.930326   \n",
+       "2163526  1252.343506     1.0    16.733718   976.955921  28.302200  0.711944   \n",
+       "2163527  1729.732422     4.0    21.326855  1506.241286  53.792229  0.684940   \n",
+       "\n",
+       "               maxr     mom_mag  label  \n",
+       "0        617.263550  103.368034      0  \n",
+       "1        602.773804  103.583145      0  \n",
+       "2        539.576172  104.595360      0  \n",
+       "3        526.783875  104.189644      0  \n",
+       "4        470.854187  104.093399      0  \n",
+       "...             ...         ...    ...  \n",
+       "2163523  633.117065  104.378433      0  \n",
+       "2163524  541.797424  104.623466      0  \n",
+       "2163525  555.570496  104.027061      0  \n",
+       "2163526  516.766479  103.907005      0  \n",
+       "2163527  468.728882   98.656525      0  \n",
+       "\n",
+       "[2163528 rows x 20 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[\"df_full\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "0a204790",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "crv_x: inf=0, nan=149937\n",
+      "crv_y: inf=0, nan=149937\n",
+      "crv_z: inf=0, nan=149937\n",
+      "PEs: inf=0, nan=149937\n",
+      "dT: inf=0, nan=149937\n",
+      "nHits: inf=0, nan=149937\n",
+      "nLayers: inf=0, nan=149937\n",
+      "angle: inf=0, nan=150001\n"
+     ]
+    }
+   ],
+   "source": [
+    "# import numpy as np\n",
+    "# df_full = data[\"df_full\"]\n",
+    "# dur = df_full[\"timeEnd\"] - df_full[\"timeStart\"]\n",
+    "# print(dur.describe())\n",
+    "# print(f\"inf count: {np.isinf(dur).sum()}\")\n",
+    "# print(f\"nan count: {dur.isna().sum()}\")\n",
+    "\n",
+    "import numpy as np\n",
+    "for col in data[\"X\"].columns:\n",
+    "    n_inf = np.isinf(data[\"X\"][col]).sum()\n",
+    "    n_nan = data[\"X\"][col].isna().sum()\n",
+    "    if n_inf > 0 or n_nan > 0:\n",
+    "        print(f\"{col}: inf={n_inf}, nan={n_nan}\")\n",
+    "\n",
+    "# import numpy as np\n",
+    "# for col in data[\"X_train\"].columns:\n",
+    "#     mx = data[\"X_train\"][col].abs().max()\n",
+    "#     if mx > 1e300 or np.isinf(mx):\n",
+    "#         print(f\"{col}: max abs = {mx}\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c360498a",
+   "metadata": {},
+   "source": [
+    "## Train and evaluate (nested CV)\n",
+    "\n",
+    "- Use hyperparameters found via grid search optimisation in optimise.ipynb\n",
+    "- 5-fold nested CV: outer folds for evaluation, inner folds used during optimisation\n",
+    "- Threshold found per outer fold, reported as mean ± std"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "91ba7d1f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-04-29 15:53:27.394012: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\n",
+      "2026-04-29 15:53:27.428039: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2026-04-29 15:53:29.679421: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fold 0: test AUC=0.999838, threshold=0.0356, deadtime=0.222%\n",
+      "Fold 1: test AUC=0.999911, threshold=0.3086, deadtime=0.058%\n",
+      "Fold 2: test AUC=0.999878, threshold=0.1930, deadtime=0.086%\n",
+      "Fold 3: test AUC=0.999855, threshold=0.2367, deadtime=0.056%\n",
+      "Fold 4: test AUC=0.999963, threshold=0.0549, deadtime=0.173%\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>fold</th>\n",
+       "      <th>train_auc</th>\n",
+       "      <th>test_auc</th>\n",
+       "      <th>threshold</th>\n",
+       "      <th>veto_efficiency</th>\n",
+       "      <th>deadtime</th>\n",
+       "      <th>veto_purity</th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>f1</th>\n",
+       "      <th>figure_of_merit</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.999984</td>\n",
+       "      <td>0.999838</td>\n",
+       "      <td>0.035604</td>\n",
+       "      <td>0.999248</td>\n",
+       "      <td>0.002221</td>\n",
+       "      <td>0.939415</td>\n",
+       "      <td>0.997828</td>\n",
+       "      <td>0.968408</td>\n",
+       "      <td>0.997028</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.999981</td>\n",
+       "      <td>0.999911</td>\n",
+       "      <td>0.308631</td>\n",
+       "      <td>0.999009</td>\n",
+       "      <td>0.000579</td>\n",
+       "      <td>0.983655</td>\n",
+       "      <td>0.999407</td>\n",
+       "      <td>0.991272</td>\n",
+       "      <td>0.998430</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.999983</td>\n",
+       "      <td>0.999878</td>\n",
+       "      <td>0.193019</td>\n",
+       "      <td>0.999015</td>\n",
+       "      <td>0.000865</td>\n",
+       "      <td>0.975938</td>\n",
+       "      <td>0.999131</td>\n",
+       "      <td>0.987342</td>\n",
+       "      <td>0.998151</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.999986</td>\n",
+       "      <td>0.999855</td>\n",
+       "      <td>0.236724</td>\n",
+       "      <td>0.999248</td>\n",
+       "      <td>0.000562</td>\n",
+       "      <td>0.983955</td>\n",
+       "      <td>0.999432</td>\n",
+       "      <td>0.991542</td>\n",
+       "      <td>0.998687</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0.999984</td>\n",
+       "      <td>0.999963</td>\n",
+       "      <td>0.054905</td>\n",
+       "      <td>0.999003</td>\n",
+       "      <td>0.001729</td>\n",
+       "      <td>0.952483</td>\n",
+       "      <td>0.998296</td>\n",
+       "      <td>0.975189</td>\n",
+       "      <td>0.997276</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   fold  train_auc  test_auc  threshold  veto_efficiency  deadtime  \\\n",
+       "0     0   0.999984  0.999838   0.035604         0.999248  0.002221   \n",
+       "1     1   0.999981  0.999911   0.308631         0.999009  0.000579   \n",
+       "2     2   0.999983  0.999878   0.193019         0.999015  0.000865   \n",
+       "3     3   0.999986  0.999855   0.236724         0.999248  0.000562   \n",
+       "4     4   0.999984  0.999963   0.054905         0.999003  0.001729   \n",
+       "\n",
+       "   veto_purity  accuracy        f1  figure_of_merit  \n",
+       "0     0.939415  0.997828  0.968408         0.997028  \n",
+       "1     0.983655  0.999407  0.991272         0.998430  \n",
+       "2     0.975938  0.999131  0.987342         0.998151  \n",
+       "3     0.983955  0.999432  0.991542         0.998687  \n",
+       "4     0.952483  0.998296  0.975189         0.997276  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.metrics import roc_curve\n",
+    "from train import Train\n",
+    "from validate import Validate\n",
+    "\n",
+    "best_hp = {\n",
+    "    \"n_estimators\": 1000,\n",
+    "    \"max_depth\": 3,\n",
+    "    \"learning_rate\": 0.1,\n",
+    "}\n",
+    "\n",
+    "fold_metrics = []\n",
+    "fold_scans = []\n",
+    "fold_rocs = []\n",
+    "fold_scores = []\n",
+    "fold_raws = []\n",
+    "\n",
+    "for k, (train_idx, test_idx) in enumerate(data[\"outer_folds\"]):\n",
+    "    fold_data = AssembleDataset.get_fold_data(data, train_idx, test_idx)\n",
+    "\n",
+    "    trn = Train(fold_data, run=run, verbosity=0)\n",
+    "    results = trn.train(tag=f\"nested_fold_{k}\", save_output=False, **best_hp)\n",
+    "\n",
+    "    val = Validate(results, run=run, verbosity=0)\n",
+    "    val.roc_auc()\n",
+    "    thr = val.find_threshold(min_eff=0.999, plot=False, show=False)\n",
+    "    fold_scans.append(thr)\n",
+    "\n",
+    "    # ROC curve data\n",
+    "    fpr, tpr, _ = roc_curve(val.y_test, val.y_proba)\n",
+    "    fold_rocs.append({\"fpr\": fpr, \"tpr\": tpr, \"auc\": val._test_auc})\n",
+    "\n",
+    "    # Event-level score distribution\n",
+    "    df_scores = fold_data[\"metadata_test\"][[\"subrun\", \"event\"]].copy()\n",
+    "    df_scores[\"label\"] = np.asarray(fold_data[\"y_test\"])\n",
+    "    df_scores[\"proba\"] = np.asarray(val.y_proba)\n",
+    "    event_df = df_scores.groupby([\"subrun\", \"event\"]).agg(\n",
+    "        max_proba=(\"proba\", \"max\"), label=(\"label\", \"first\")\n",
+    "    )\n",
+    "    fold_scores.append({\n",
+    "        \"signal\": event_df.loc[event_df[\"label\"] == 1, \"max_proba\"].values,\n",
+    "        \"background\": event_df.loc[event_df[\"label\"] == 0, \"max_proba\"].values,\n",
+    "    })\n",
+    "\n",
+    "    money = val.money_table(\n",
+    "        X=fold_data[\"X_test\"],\n",
+    "        y=fold_data[\"y_test\"],\n",
+    "        metadata=fold_data[\"metadata_test\"],\n",
+    "        threshold=thr[\"threshold\"],\n",
+    "        save_csv=False,\n",
+    "    )\n",
+    "    fold_raws.append(money[\"raw\"])\n",
+    "\n",
+    "    fold_metrics.append({\n",
+    "        \"fold\": k,\n",
+    "        \"train_auc\": val._train_auc,\n",
+    "        \"test_auc\": val._test_auc,\n",
+    "        \"threshold\": thr[\"threshold\"],\n",
+    "        \"veto_efficiency\": thr[\"veto_efficiency\"],\n",
+    "        \"deadtime\": thr[\"deadtime\"],\n",
+    "        \"veto_purity\": money[\"raw\"][\"veto_purity\"],\n",
+    "        \"accuracy\": money[\"raw\"][\"accuracy\"],\n",
+    "        \"f1\": money[\"raw\"][\"f1\"],\n",
+    "        \"figure_of_merit\": money[\"raw\"][\"figure_of_merit\"],\n",
+    "    })\n",
+    "    print(f\"Fold {k}: test AUC={val._test_auc:.6f}, \"\n",
+    "          f\"threshold={thr['threshold']:.4f}, \"\n",
+    "          f\"deadtime={thr['deadtime']*100:.3f}%\")\n",
+    "\n",
+    "import pandas as pd\n",
+    "metrics_df = pd.DataFrame(fold_metrics)\n",
+    "display(metrics_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "k6832ozkpo",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Validate] ✅ Initialised analyser for model: final\n",
+      "[Validate] ✅ Saved to /exp/mu2e/app/users/sgrant/MLTrain/CrvCosmic/output/images/ml/k/final/threshold_overlay_cv.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# CV threshold overlay plot\n",
+    "cv_threshold = metrics_df[\"threshold\"].mean()\n",
+    "val_plotter = Validate({**results, \"tag\": \"final\"}, run=run, verbosity=1)\n",
+    "val_plotter.plot_threshold_cv(fold_scans, cv_threshold, min_eff=0.999, show=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "v61915smtyj",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Validate] ✅ Saved to /exp/mu2e/app/users/sgrant/MLTrain/CrvCosmic/output/images/ml/k/final/roc_curve_cv.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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9C4kM9fCKYVjoqlWr+Ps33ngjPFvtaDS6c+dBV6PDzllDQDs/f/684U9JTkI0F73tjlPbeLFixbjG4pw5c7j2IuK/naZ50LdteLemTJlC9evXd03LmjUr/fzzz1zAEu68EydOcGA9Au4//fTTWPMK4UG6GgVBEASzgt6vZ5991pUJAXTp0oWaNGlCTiXoDlWMakSxSk8qVapE+/fv5xJC6L9FwH3VqlU5GF8IB1GUKJGUC9ITdJMjEbAgmtsVaeOieSRA4vROnTrR2bNnOYE68npGG9hNY5Z2HpFINoxKgLElhB9Ni/mvVuP9OC/paow88NrCPY0T1ujYAKcgmovedsfObRyZDV5//XVOtA4wevHbb781fD9jTKJ50L+sUkQgUE7Q12LXtAc1pqSrUR/N0dalQLZ+iOb6Inrrj10137hxI5UpU8ZldKHQNboZixcvbvSmkVk0D9rjhZT+SJiKoaCCnkhXo97gJMWgEUE0tyvSxkXzcBldCDdCjDcG102bNo1q165NZsEs7Tw6lCytd+7cCe/WCH52NT7Q3WYPS6YE7mmUvMK7IJrbEWnjonk4gKerTp061KJFC66zaCajy0ztPGjDq0GDBnTz5k3OPivox30X6QNry2ahAabVHAXejXZPOwnRXPS2O3Zo40jL8MMPP7hKByIECWkiZs6caYogdrNqHvRtu3///pQlSxbq1q0bp40Q9CKKoqNlVKOe4CTF6FyjT1YnIZqL3nbH6m0cQerPP/88tWzZknr16uWajvRRZt2nKJNoHnSM1z///EMffPAB9e7dmxOitW3blqpUqcK5vOKrtYQimEKoXY0xrkNn0vZtK+CWRgUGPGgYPSrHKYjmorfdsXIbRyHrdu3acf1lBKvny5ePvV9GGzRW0Txow6tmzZqxRJ4wYQK/4gPzuxd4FgIHGrrrbrHz1ZJAbzM/xdkR0Vz0tjtWbOO3bt2it956i0aNGsWGVsGCBWnGjBlUvnx5sgJRJtE8pDxegabdNzpNv96cOnWKvYHeQHklvAIHhtcDj6KFzlnLgpM0TZo0Rm+GoxDNRW+7Y7U2jtRRzZo1o23btrmyz48ePZpSpUpFTtF8QjwOJtzvI254GT0qwApky5aNdu3aFdZ1SgJV/UFbx0mF42m1LgGrIpqL3nbHam0cBsvx48e5yPTkyZO5XrPTNO8ej8MEmR7Q9WpY5nohsha7e4OxwPlqC81R8spo97STEM1Fb7tjhTZ+6dIlDkYHMFbmzZvH8VzZs2cnKxJlEs3ltm050GAedDWK4aWD4lFR7E43+mR1EqK56G13zN7GYWTlz5+f00UoKleubFmjy0yai+FlMSSBqjHuabiQpXtdNLcr0sZFc8XVq1epc+fO1KRJEzp37hx98cUXtonPjjHJtVwML8uBrkbxeBlR0d7opyQnIZqL3nbHjG18/fr1VLp0aZo0aRJvV9++fWnRokWm2kY7aC4xXpZD0knornhUFNcmFURzuyJt3NmaI83T0KFDaciQIXTv3j16+OGHuc4i0kbZiSiTaC4eL4t3NUqMV+SBW/ro0aOGu6edhGguetsdM7XxtWvX0uDBg9noQjZ6pIywm9FlJs3F42XJBKqSx0tvzZHp2Gj3tJMQzUVvu2OmNl6tWjXq168fPfroo/TCCy+QXYkyiebi8bIcMLwknYSuikdFUfLkyQ0/WZ2EaC562x0j2ziC5jt27EiHDx92TRs2bJitjS4zXVfE8LJgV+OdOw+6GsUWiDxwS+MCZbR72kmI5qK33TGqjf/+++9UsmRJmjp1Kv3f//0fOYkYk1zLxfCyGLDU3YuQS4yXPprnyJHD8KckJyGai952R+82fvPmTXrttdfoiSee4Az0hQsXpuHDh5OTiDLJtTwsMV6wHjdt2sSW5PXr17lquRAp0GCkSLae4CRNmjSprr/pdERz0dvu6NnGd+zYwd2IeAfdunWjkSNHcsFoJxFlkmt5yB6v8ePHswX5+OOPU8uWLbnf2J0LFy5wwF6RIkXo/Pnzof6c47k/qvGuSwfxeEUePFgcOnTIcPe0kxDNRW+7o1cbX7FiBZUvX56NLgSWL1iwgD799FPHGV1muq6EZHj16NGDevXqRWfOnOECmt7cdxkyZKCyZcvS3r17aeHChaH8nCC1Gg0B7RoFUI12TzsJ0Vz0tjt6tfGKFStSgQIFqEGDBmx8NWzYkJxKlEmu5UEbXr/++itbzalTp6a5c+fSxYsX2Zr2RuvWrbnkwPz580PZVoGD66Wr0ajC5EafrE5CNBe97U4k2zgC6JGTC2AU3/Lly9nThULXTibKJNfyoA2vzz77jDcemW6fffbZeOetVKkSv2/dupWcxKlTp6hYsWJeXxMmTAihq/H+CQXEFog8cEsfOXLEcPe0kxDNRW+7E4k2fuXKFerUqRMH0H/44Yeu6ZkzZzbc2LCD5hMmTPB5T8f9PuLB9evWreN3HOSESJs2Lb9OnDhBTgJPF7t27QrrOpHDK1EiCa7XEzwh5c6dm98F0dyOSBu3vubIPt+mTRs6cOAAG1kYxSiEV/Pu3bvzyxvowkQB7ogaXgiUT5cuHcd2+QN2VLk+heBBl+39SvH3jS+xBSIP9MYT0v2qAfLUqAeiub6I3tbVHIOt3n//fX7hHpsnTx6aPn06Z6MXzNnOgza14cG6fPlyrGSevjh79izHgMHdKYSKRvfuPXCTiuGlz8l67Nix/wxeQQ9Ec30Rva2p+f79+6lq1ar07rvvstEFjxfqLIrRZe52HrThVbx4cd74DRs2JDgvrG+A0Y1C6F2N0dEPHJVieEUeeGsfeeQR6WrUEdFcX0Rva2qO7kTETqP36bvvvuN7LT4L5m7nQf9606ZN2fB655134g1U+/PPP+ntt99mt16LFi2C/TkhVlejeLz01vz27duGPyU5CdFc9LY7wbZxLOPuAIHBtX37dnr++ecjsJX2QjPJtTxow6tr166cG+SPP/6g+vXr0y+//OIywNC1uHTpUurSpQvVqlWLrl27RqVKlaJWrVqFc9sdSuyuRgk50kFxTeOBIUafrE5CNBe97U4wbfy3337j+64a3AaaNGnCAeOCda4rQQfXJ0uWjH7++Wd68sknOWcIDDCFe64Q7GD+/Pk515fR7j07cH9U4wMdRdLIg3aLgFVBP0RzfRG9za35jRs3qF+/fvTxxx/z/x988AHn5RKs2c5DsoQKFizI/cuvv/46Z6hXI+7UCyMekdn+r7/+MsXO2mlUhkIML300RyyF0U9JTkI0F73tjr9tHMHyKPmjjC5UjPn+++912kp7oZnkWh6yCwqBfCNGjODuxb///ptdoYsXL6YtW7bQuXPnaPTo0WyU6QEEHTx4MBUqVIiz9ebMmZPzjPmbW8MddJ0+9dRTPBIzSZIklDVrVi614O7ZMwYYXg8ajRheOiiuaVwWy+iT1UmI5qK309s4HrBRyLpChQq0c+dO7klatGgR10d2Yp1FO11XojSjtyCMRledOnU4mB9FuzGcFsUw4W2D0bR+/XoezeAPY8aModdee40HBFSpUoVy5crFSenUCM6JEyfSSy+95HN5lUgNy2Hoajhp145o0SKic+fu///kkyjfFNafEARBEAxmzpw59Nxzz/HnZ555hiZNmuSzLJ9gPIHc94P2eH377bfc72wWhg4dykYXyhPt2bOHXbEwtkaNGkWnT5+mF1980a/1wHPXv39/Spo0Ka1cuZJWrVpFM2fOZANu9uzZbIyha/Xq1atkhq5GCa7XR3O0dZs8o1gC0Vz0dnobR9B88+bN6YsvvqB58+aJ0WWj60rQhhcStWXPnp278pYtW0ZGgiSucL+qWkoo3K2A56pkyZI8ynLz5s0JrgujRW7dukW1a9fmxHTu4OkD67p+/XrYSwH5j3Q16q64pnGlBqNPVichmoveTmvjSEjep08ffgd4yP/hhx+oc+fOUjHDZteVoA2vFClScEHOr7/+murWrcvB8wMHDqTdu3eT3qxevZoz42P0ZOnSpeN836xZM373ZxQIRmv6Q6ZMmci4BKqJXP8nevBRiOBIGLiPZVSufojm+iJ6G6v5mjVrOOUSemgwIE2wdzsP+tfRfTd16lTO0wXL/OjRozR8+HBO6IZgwE8++YS77fQAoz5AmTJlvH6vpqv54gOjRzBgAB4yGHSefe5IVFe5cmU28syQQFW6GvXRHLnojH5KchKiuejthDYOh8Fbb71F1atX55hkxCH7GxYjWPe6ErThlSpVKmrXrh3n8Dpy5IjL6MIObdy4kV599VW2LBs3bsyxUe7ZdsMNfl8Ft3lDTT98+HCC60qfPj0HMcKYxMmAIH1kBK5YsSJ7zjDSETnJjANdjQ/+E4+XDoprGrv/jT5ZnYRoLnrbnf/9739Us2ZNzsmFuF3cT+EcwIAuwd7XlaATqLqDtA1vvvkmv9Bwpk2bxgHpyBA7f/587uKDF6lly5Y8IjDcqEB3X0NsYSS6z5cQMLDQlYjtdfd6YXRkjRo1KGPGjEEdZBhz7pXRE/qMlBhIZeE+nSh2VXV4TD2XhRtV5VKL5Odg9inYz0buE0A8o532yQrHCZqr7bfLPpn1OGE60hXYaZ/MfJyQcgn3GcQLI90S7ou432Ae/IYV98nq1/Lbt2/zK5R98pewd3Sqfmp0PSIX1gsvvMAGEVyqGJ0RCdxFju97f8H2I24N3i50LcJg27FjB//ft29fv2tiHT9+nA1OeNHwwmecZP5+HjZsGBuvSJUBMFT13r27cboa4fFDZXo0AHzGO/5XnkAMPsDxABg4oPKaYb1YP8AF4NSpU/wZ+4tcJwCGo+oyvnTpEgcmggsXLvALYBq+A5hXBYdiHcrYxbrxG8Bzn7BNANuIbQVm26eTJ09yO7LTPpn5OEFraG6nfTLzccINZ+/evay7XfbJzMcJKY8wCAweL+TmwghGq++T1a/lQ4YMCfgerT7jHfd7U+TxQhzYN998Q5MnT6Z//vmHDSMIHm4wchG5t3r37s0JWz2BF+6xxx7jWK9NmzbFu64VK1bwyYB5kbfLPQgPDQYxYBgd+euvv3K5pPjyecATiP0Op8erXTuN5s+PoitX7m9X8+ZEM2ea54nCjk9Jd+/e5RMWHk/1vdX3yezHSV0/kMA4ceLEttgnMx8nXJehN7xemMcO+2S244T7BgZ/qen79u3j6i7IzZUoUSJL7pOdruW3Q/R4FS1alI0vf/J4haWr0R1YqYiBmj59Osd/4YRWIsD4iQSqQKivnVUWtD9li7DdoGnTprGMLoD/8WSCE2j58uU+DS8FDkratGkpXOD3sUnujj38776d6rNqFJH87Pmbkf5s1D7hxg/3tCdW3icrHCd3ze2yT2Y9TrjxwwujsMM+meU4IW8UwnAw4Az5L1u1asXTUWHFE6vskx2v5cmTJ+dXsLj/ZkKEzfBCLi8YLT/++CO775SxBa9P69atqX379hx8HwnQvQlQN9IbKn8XcnAlhHJ5+jKY1HTl0tQb6Hq/ZND9xiQlg/TRHC5tHPtATi5BNLcK0sYjA0rnIdwGPR/APf+jaK4/ZtE8JMMLjQnG1owZM1zeJuwYYrrgGcIoDcRKRXoHMQoEfayIUUC3ojLEFDAGQaNGjRJcl7KGMTLTG6pskL/lh8IP3KkP/hPDSwfF/4t7wbsYXvogmuuL6B1e0NODOouDBg3iGCbcV5B+qV69eqK5gZimnWtBUrZsWS06OppfUVFRWqJEibQ6depoX3/9tXb16lVNbwYOHAhzRKtSpUqs3x8zZgxPr1GjRqz5x48frxUuXFjr169frOk//vgjz4/9WbBgQazv5s2b59rn3bt3+9yWXLly8TrwHm7attW01Klhet1/tWsX9p8QBEEQguTw4cN8v+GnZCKtSZMm2pkzZ0RPm5MrgPt+0B4v1X1XrFgxatu2LZcQQlCZUSAJHWLKkAEYfeco94O8XajXiODFr776Ktb8CLBDHhU1asKzPtasWbPYQ1auXDnKmzcvHTx40OUFQ96VwoULk3Fdjfik+rIN2QxHAc0xKgZeVfF4ieZ2RNp4+ED1FgzSQhqjjz/+mDp27Oj1uiGa649ZNA/a8HrllVe4K7Fs2bJkBhAUhzgzpGBAACOKiiLfVocOHei9997zmVzVExwMFNiuX78+l0NCOgnEjmHo6NNPP837je+MQ/Uz3m800tWog+KaxqNhDHdPOwjRXPS2Eu7XBgy6Qu1g3CcKFCgQ7zJyXdEXs2ge0XQSTkWlk/BnWGmgtGuHmDXkNbn/P6pLTJoU1p8QBEEQ/GTlypX8QP7TTz8ZGPsrWOm+b2ylSCFg3PMcAfF4RR6zVLR3EqK56G12kPNpwIABnPcRPSMIdwkEaeP6YxbN/epqVBlmkcxT5XpR04LNuSUEj4xqFARBMDaOC3HNKiE34rjGjRsnh0QIn+GF4HJQpEgR2rlzZ6xpgYA+VfSvCsHj2S8tRbL10dzf+pyCaG5FpI37Bzwln3/+OVdLQWJUXBdQCu+5554TzS1AlEmu5dGBdG+5u+fcp/n7Qmp9ITRQp9H9OEisd+RBu8UoWGm/+iGa64vo7R8YHd+tWzc2up544gmu4RuM0SWaO7ud++XxQioF1dXoOU3Qm9geL4nx0kHxqCguNSEjGvVDNNcX0ds/0L0IDxeqsSCg3rOsnGhubqJMci33y/DyVuPQn7qHQvjxbDBieOmjOdKJCPohmuuL6O2d69ev04QJE6h37958w06WLBn9+eefXNtSNLceUSa5lsuoRgt2Nd6v1XgfMbwiD9zSp0+fNtw97SREc9HbaBA4X6ZMGS5wjfyQinAYXUDauP6YRfOgE6jmy5ePsmbNSuvWrfNr/mrVqtHx48dp//795BROnTrFmf290b17d34FjgTXG/GUhCddo93TTkI0F72NrLP40Ucf0dtvv82DwXLmzEmVKlUK++9IG9efUDWH9xMvX/f7iBtehw4dops3b/o9PxKKBZuCwqpky5YtVjX6cODZYMQWiDzQHCUmBP0QzfVF9L4PysyhBN6qVav4/2bNmvEoxkiMhBPN9SdUzeNzmKgEqqbqasSTQyiBiIL3UY0iaeSBWxpPM0a7p52EaC56682iRYuoZMmSbHSlTp2apk6dSj/88EPE0g9IG9cfs2getMcrEDD0Fv2qadKk0ePnbE6UJFDVW/GoKEqZMqV0NYrmtkXa+P3clMhGX7lyZZo+fTqH04jm9iLKJNdyvw0vdBOie9EdNFI8HfhKv4/pFy9epBkzZtCdO3eoRIkSoW+xw5FRjcZoLg8NormdcWobx31NVVMpWrQo110sXbo0j2CMNE7V3EjMornfrWvKlCk0ZMiQWNMuXLjAdaoSQlUC79q1a3BbKXh0NcJav2+xS1ejfu5pxOxJd7k+iOb64jS94TQYNGgQjR49mlasWMFeLlC+fHndtsFpmpuBGJNoHtAvu2ehhyGVUKZ6kDZtWqpSpQpNmzaNk84JoSJ5vPQGbR3t2Gj3tJMQzUXvSPHPP/9QxYoVeeQiYo9//fVXMgJp487V3G+P1+DBg/mlgLWYPXt2ThEh6Md9g9f9f1FfD81TpUolQuuIaK4vTtAbzoBPP/2U+vTpwyPyM2XKRJMmTaLGjRsbsj1O0NxsRJlE86B9be3ataMWLVqEd2uEBJFRjca4pzFM2OiRME5CNBe9w8nJkyepQYMG1KNHDza66tWrx3UWjTK6gLRx52oedAQhhtoKRiBdjUZVtDfaPe0kRHPRO5wsXLiQFi9ezMkzR4wYwbmYjI6rkjbuXM11SSchhBMxvPQGJ2mKFCl0/10nI5qL3uHkxRdfpN27d1PHjh2pePHiZAakjTtXc78MLwTGA2R8ffbZZ2NNC6aLUgitq9G9h1gGw+jnns6VK5fhT8lOQTQXvUNh48aNNHDgQE6AivsWbrgjR44kMyFt3Lma+2V4dejQgRtu4cKFXYaXmhYImF8Mr9C4n0riAWIHRB602yxZshjunnYSornoHWydxeHDh9M777zDIxZRb3HcuHFkRqSNO1dzvwwvJJjDhqJYqOc0QW+kVqPuikdFUfLkyXX/XScjmovegXLw4EGus7hmzRr+H4O/YICZFWnjztXcL8PLM2O9r2lC5JGuRmPc00ePHqWHH35YuhpFc1ti5TaONBEo8YMRi1euXOHM5BMmTKA2bdqY2jlgZc2tSoxJNJfgeoshXY36g4t3jhw5TH0RtxuiuejtL2PHjqXXXnuNP1etWpWNsEceeYTMjrRx52ouhlcEQWmCYsWKef0Ow5nxChyJ8dIbnKRJkybV/XedjGguevsLuhfHjBlDL730EvXt25cSJUpEVkDauPU0hycVL1/3e8MNLySn+/333/kkQLI6BOY7DdSD2rVrV1jXeT/xm4xq1BNororpSpeAaG5HrNTGb926RbNnz6YXXniB/8+cOTOnikiZMiVZCStpbhdiQtQ8PofJQw89xCMm/SHoo7106VKqXbs2DRgwIM53KDyKCu8ozdC7d2969NFHafz48cH+lBAL8XgZ8ZSEk8po97STEM1Fb2/s3LmTKlSowPFb3377rWu61YwuIG3cuZoHbXjNmjWLq7p79qXv3buX3b2wLOHSQ7IyDPGFAbZly5ZwbLPDkVGNuiseFcVPR0afrE5CNBe93cH95OOPP6ayZcvS9u3b2cuF/FxWRtq4czUP2vD6888/+f2pp56KNf3LL79kQ6tGjRp09uxZunDhAjVr1oxPHBQoFULDs8aUeKj1c08bXd/LSYjmorfixIkT9PTTT9Orr77K3Yy45yCUBbUXrYy0cedqHrThdfr0aY7fgtvOnV9++YWtSSSuQxXwJEmS0LBhw/i7lStXhr7FDicqKvYhE8Mr8uAJSeIw9EU0F73BokWLqESJEvTrr79y/iUENv/888+UPXt2sjrSxp2redDB9efPn6e0adPGctkhhwr64GFwweOlyJ8/P580x44dC32LHQ5y1rh3N4rhpY/meEJCWzfaRe0URHPRGyBc5dy5cxwzPGPGDCpatCjZBWnjztU8aLMPhtSlS5f+MwQedD/i/4oVK8axKM1QmNIOxMQ80BuI4RV50Kbx0ODe1gXR3E6YqY3jvqKoW7cuLViwgNatW2cro8tsmjsFzSSaB214FShQgC1HBNgr5syZw1Ykkti5c/v2bT6ZkF5BCBXpatQbPERgEInR7mknIZo7T2/UVhwyZAjlzZuXDhw44JresGFDW+bRM4PmTiPaJJoH/esIbITV+OKLL9L333/P2YOnTp3K3zVt2jTWvBjNCCMNfatCeD1e0vMVedDO8fBg9FOSkxDNnaX3/v37qXr16jR48GAekPXdd9+R3TFacyeimUTzoA0vlGhAvSMUJm3dujW9/vrrdOfOHS5MimBId3766SevnjAhcDwbjDws6aM5RlYZfbI6CdHcGXrj96ZMmUKPPfYYrV27luOGEcs1cOBAsjvSxp2redDB9enTp+eYLjyh4ITB/3AJv/HGG7Hmg3X51Vdf8Y7WqlUrHNvscKSrUXfFo6MpT548uv+ukxHN7a83gua7du1KP/74I/8Pj9e0adMcc65JG3eu5iGVDMqVKxdNmjQp3nnQN3/y5MlQfkZwQ0Y1GqM58gclS5ZMRjWK5rbEiDaO1BAwuhInTkzvvfceP7Rbpc5iOJDrinM1l6g+iyFdjcZofubMGcPd005CNLe/3qhwgtCU9evXU79+/RxldAFp487VPKxFsg8fPsyJVWFJZsmSxRQuPbuhabFtZQmu18c9jXhGQT9Ec/vpjWzzqOP7xRdfcGJteB0wMMupSBt3ruYhe7wQqNazZ0/KmjUr5cuXjx5//HHO44XPmNarVy+eRwgXElyvN3g6unHjhuFPSU5CNLeP3hjRPmbMGCpXrhyPfB85cmTYf8OKSBt3ruYhGV5r1qyhkiVLcl896jJiZ9xfmDZ+/HgqVaqUq7ajEBrS1WiM5qjUYPTJ6iREc3vo/e+//1K9evV4FDwGWmEAFlIQCdLGnXxdCbqrEV2KzzzzDOdcwRDgl156iZ544glX7UZkh/3999/p888/ZwMM8+7atYu9YE7h1KlTVKxYMa/fde/enV+BI6MajXBPYyCJIJrblUi0cQTOd+nShW90qFwCrxf+l7JbkdNciKzmcDLh5et+7zdakLz55ptaVFSUVrRoUe3YsWM+5/v33395nujoaK1v376aE8iVKxfMaX4PN08/jQyqmus1f37Yf0LwICYmRrt69Sq/C/ogmltb76FDh/I1EK+yZctqu3fvDst67YS0cXtpHsh9P+iuRlSIx5PLl19+Ga8FmTNnTp4Hrr2FCxcG+3PCf0hXozGaX7582XD3tJMQza2td5MmTSh16tQ0YMAADjMpXLhwWNZrJ6SNO1fzoLsaDx06RKlSpaIqVaokOC/mwbwY9SiEioxqNMI9nSNHDt1/18mI5tbSG1VLVq5cSXXq1OH/ixQpwvUWMbpdiIzmgnU1D9rjBW9XoFaj0VamHRCPlzGaX7lyRdqvaG5bQmnj+/bto2rVqtGTTz5Jq1evdk0XoytymgvW1jxowws5uq5fv07r1q1LcF6UFLp27RpXBRdCRdJJ6A1OUrR1o09WJyGam19vzIvKJaiziCSoGGSFQHohcpoL9tA8aMPrqaee4o3HKBVkgo1v9KMayfL0008H+3PCf3iG5UmRbH3c09myZeN3QR9Ec3PrjZHqTZs2pc6dO/NDdc2aNWn79u08el2IjOaCfTQP+tf79OnDhbF37txJRYsWpUGDBtHy5ctp79697HpetmwZvfXWW1S8eHGeJ126dLyMEBrS1WiM5pcuXTL8KclJiObm1fu3336jEiVK0Lx58zgD/UcffUR//PGHKTKCWwlp487VPGjDC1bj3Llz2aCCe3no0KEcWImgSoxgqVu3Lg0bNowr0MNAw0ka6RxeN2/epMGDB1OhQoUoefLkPKKyU6dOnMQvGGBE4okOXaRYH2IWKleuTCNGjCCjEMPLuMKqRp+sTkI0N6/eGCR18uRJfuD+66+/uLi10R4EKyJt3Lmah3S21KhRg93LXbt2pQwZMsTJXI9p3bp14xpd1atXp0gbXTD8hgwZQlevXqVnn32Wn8CmTJlCZcqU4VGYgSb/w1Pd5MmTKVOmTDw8unTp0nTw4EFOCmscMqpRd8Wjo/mhQW4uorlT2ziyziv+7//+j+stbtq0ieO7hMhoLthX85CLZCNT/cSJE/kFowQxXQA7lzdvXtILeNyQL6ZSpUrsCkcOGYCirK+//jqXqYA73B+2bNlCrVq14mBReOqqVq0aq+7Y5s2bySjuW+pRrv/lnNXPPQ3vrmTd1gfR3Bx643qHaygeNjds2MC9F/gePQFCZDQX7K95WM0+GFookI2XnkYXcsigJiRAOn9ldAHUCEM9yaVLl/ptML3yyiu8ThR0dTe6ACxlFHs1DhnVqLvimkZ379413D3tJERz4/U+evQoh4ygKxFxu+g9ECKrueAMzW3h40TumIsXL1L+/Pm5O9CTZs2a8fuCBQsSXBfqSaL4N+LEUNDVbMioRv2BsZ05c2bD3dNOQjQ3Vu8ffviBH1gxSCplypTctdirVy+dt8reSBt3ruYhdzXCcpwzZw7NnDmTNm7cGKurEZ6h559/nuOjIrmj27Zt43fEcnlDTVfzxQc8YwAFv5Hv4/vvv+dYBmx/qVKlqHnz5twFaRTS1WiM5igGj5hF6RIQze3cxnGd69mzJ02fPp2nly9fnmbMmEEFCxY0ehNth1xXnKt5SIbXkSNHqEWLFtz3D9zddxj5gu9hlJUtW5ZmzZrFSVcjAX5HxZt5Q033p2QRUl8AjGJE4ChGNrrTv39/Hs3pT6kkPRAnjCAI4WLgwIFsdMEAw2ekCULKCEEQTGB4IUANoxph9MDgQpqF2rVruwpmI4UD3NTotoMnrFatWhy0jqC2cINRjAAucW+gTqT7fPEBaxiMGzeOA0lnz57NoyXhyUOqCnj2kCQQBlr27NkDKsgJCxvBqnj35zOMP1z03KdrWmwrHUa757K4aKqRpZH8HMw+BfvZyH1SI3TttE9WOE5qpLQ6BnbYJ7MeJ4DrHUaFY6Q6BiuhDBC+D0UDOU7xH4+MGTPyZ186OaHtaSa5lmPkLl6h7JO/BN3/98EHH7AHCTuxZMkSjrPCSYvUEnjh86pVq3gkIRoX5sXJHAncRY7ve3+4d+8evyMAD09+zz33HF+QEPP17bffsvcOecsQxJ8Qx48fZ0MTy+OFz9DL38/Ig3bixAlOlaGMWbV9ClwzYfxiOhoAPuMd/ytPIAYKIFAWIIeJymuG9WL9AN2qp06dchmoqhoBDEdkqVbGtioJAgNVGamYhu8A5sUyAOtQxi7Wjd8AnvuEbQLYRmwrMNM+YTm1DXbZJ7MfJ7U+tX922CczHickvcYDJZb/559/XNdzVd7NivtkleOEz1gO22uXfbLytXzIkCEB36PVZ7zjfu83WpAUKFBAi46O1mbNmpXgvJgnKipKy58/vxYJevfuDcuK372xdetW/r5MmTIJrqtjx448b86cOb1+P2HCBP6+atWqPteRK1cu1zouXbqkXbx4kV/4fOHCBb8/37x5U7t3754WExPD68XnmjXvaThq6rVlixZnHoD/I/3Z/Tcj/dnIfbp79652/vx517x22CezHye8Q3Nob5d9MtNxwufPPvtMS5kyJV+rvv76a+3cuXOu7bDiPlntOOGFaz3auF32ycrX8hs3bgR8j1af8Y77Pc4l3P8TIuiuxmPHjlHSpEm5XldCILg+WbJkQWeQT4jcuXO7tskb6nf9iTFTT3q+CnqrNBnK+o4PeODCGYh/353pOe3+dPd51G8rD2CkPnv+ZqQ/G7VPiRIl4qeaSOy3HCffGijNndz2IvEZHgIkQVWjvBFKgTAR9EworLZPVj1O8Jq4Y4d9suq1PHny5PwKFl89bmHtasTGYyPdd8oX2FnM634hDScYbQi2bt3q9XuVvwvDoxNCpaNQLktPUAIJuOcK0xNNux8PoJDg+sgDtzRi/FQshiCaW5VFixZxRQ4YXXhwHjVqFCecRnk1aeP6ItcV52oetOGFYHr0r+7ZsyfBeTEP+lg9k5GGC4wwRB8rRiB6SxmB8j+gUaNGCa4LT38Ixt+/f7+r39kdxEQAb/nC9CG2VS2Glw6KR0WxxzaQJxpBNDcbiGFp0KAB33iKFy/OdRaRYBoPz9LG9Uc0d67mQRte/fr14xF3L7/8siugzhsYJYB5MC+WiQR4cuvRowd/7t69O127ds313dixY9kThhGYCIxXfPLJJ1zQG+kh3MHISJW5HnUm3df1yy+/0Ndff80HDQMIjMDbqEYhsuB4G11iwmmI5uEHoxRhZCERKkaaq54C0dsYpI07WHMtBH766Sctffr0WpEiRbSvvvpKO3jwoHb79m1+4TOmFStWTMuQIYM2f/58LZIgMK5ixYquoPYWLVq4/s+SJYu2f//+WPMPHjyYv2vfvr3XdVWpUoW/z5Ejh9a4cWOtcuXKPJgA0z744IN4t0UF1/sTZBco1arFDq7fvTvsPyF4gMDLkydPugI3hcgjmocOAom3bdsWa9revXtFb5Mgbdxemgdy3w86uB5xWwp0OSJYMz4aN27sdTosT6RuCBXEkCFvGFIwIO0DilsjWLRDhw703nvv+Uyu6mtdyGA/cuRI+uabb2jx4sU8DbnIevfuze56o/D0eElXY+RBG4Un1PCnJAchmocGhsy3a9eOPVsIv0A5NVCgQAHR2yRIG3eu5lGwvoJZ0J+ger82ICoqTm4qqwMjDyMpkUzW10jLYEGY3Jo1D/7ft4/ov2uqIAgCfffddxwmgbhaxKvif3/iWwVB0Oe+H7THC94lwahRje7Dc+UoRBqMgEH6kGzZsoXtgUMQzcPNxYsXOdYVtRXB448/zh575e2KD2nj+iOaO1fzoA0vBKsLRiDB9bor/l8+NqPd005CNA+MlStXUtu2bbmLEWEgqLGIWouJE/t3iRe99Uc0d67mIRXJFvRHYrz0ByepqvcpiOZmBHGoMLrg3UKps0qVKgW0vLRx/RHNnau59JtYDkmgqrviMTHcd2900j0nIZonjHt47rvvvsuvLVu2BGx0id7GIG3cuZqL4WUxxONlzFMSRsga7Z52EqJ5/AbXxIkTeZS1KkSMXIZvv/02pUmTRvS2CNLGnau5GF4WQwwv/cFJmiJFCsNPVichmnsHgcEYoYik1CtWrOBuRdHbmkgbd67mYnhZDulq1F3xmBguH2W0e9pJiOZxWbhwIddZ/Pnnn7nsCapyIE+h6G1NpI07V3MJrrcYUjJIf/B0lCVLFsOfkpyEaP6A69ev0+uvv06fffYZ/w/jCykj8C56Wxdp487VXAyvCHcLFCtWzOt3qCmJV6BIV6P+4CRF5QJBNDcCVAVBElSAotYffPBB2NujtHH9Ec2tp/mECRP45et+H/HM9YIxmesrVIihDRse9BCfP0+UIYMcDT3c0w8//LAkUNUJ0fwBe/bsoYYNG9Knn35KdevWFb1tgrRxe2keyH1fYrwshni8jHlKypEjh+HuaSfhZM0PHz5MkydPdv1fqFAh+ueffyJmdDldb6MQzZ2redgMLzjOzp49y0n8hMghhpf+4CTFcH2jT1Yn4VTNEbtVsmRJ6ty5M2ejVyAbfSRxqt5GIpo7V/OQDa/NmzdT06ZNKV26dFz/KF++fLG+v3DhAnXt2pVeeuklun37dqg/J8ioRkPc04cOHTJ8JIyTcJrmuE62bt2a2rRpQ5cvX+YkqOi60Aun6W0GRHPnah6S4aVKU8ybN4+uXr3KXi/PkLEMGTLQwYMH6csvv6QlS5aEur2OR0Y16g+ejnATNPopyUk4SfPly5dTqVKlOIAenq0hQ4Zwji7Ph9hI4iS9zYJo7lzNgza8EHMAdzgyJ/fs2ZM2btxImTNn9jpvu3bt2CD76aefQtlWgYndYAwssO4YcJIiENPok9VJOEVzlPmpXbs2B/wWKFCA/vzzTy5w7W9x63DhFL3NhGjuXM2Dvm2PHj2auw6REgGJ/MqUKeMzDgEXFrB27drgt1RgNE0SqOoN3NKIXTTaPe0knKI5RlfhoRQPsaizWKFCBUO2wyl6mwnR3LmaB/1YtXTpUrYa+/btm+C8OXPmpJQpU0rgfRjQtNi2sni8Ig+ekHLnzi2pJHTErprDyMKQcxW/1bFjRypatGhQha3DiV31NjOiuXM1D/rXjx8/TqlSpfI7ABT1kW7cuBHszwkuYsfQyXVSn5slnpAk5Z1+2FHzkydPUoMGDdjIQjA9wMOr0UaXXfU2O6K5czUP2vBCrTB0NfqzAzC4Ll68yCMfhdDw1FtCMvTRHAnxjD5ZnYTdNEd8K0r8LF68mNPurF+/nsyE3fS2AqK5czUP2vB65JFHOLB+7969Cc67aNEiunfvns/yOULwXY1ieEUeuKXR3o12TzsJu2h+7do16tKlCzVu3JgNLoxexECk+vXrk5mwi95WQjR3ruZB/zouHLAax40bF+98586dozfffJNd6nCzC6HywFKXa6Q+oJ37690VRHPFX3/9RaVLl+ZUOrj+vfHGG+zpKl68uOmaibRx0dwJaCa5lgdtePXu3ZtSp05Nn332GQ+JvnLlSpzuxW+//ZbKlSvHebwyZcrESVSF0HBvMGJ46af5iRMnDD9ZnYQdNMdob/QIIA72jz/+oI8++ohDNMyIHfS2GqK5czUPqUj2woULqVmzZtzlmCRJEg5aQ5dikSJF6MCBAy7LEhcbzFunTh1yApEskl2iBNHff9//nDQp0a1bYV29IAhh4vz585yT6/333+dE0oIg2BfdimQ3bNiQ64mVLVuWjay7d++yoYXkqrdu3eLPcLVjHqcYXZHngZ0s8V06Ka5pdPPmTcOfkpyE1TTHdk6bNo06derk2uaMGTPShAkTLGF0WU1vOyCaO1fzkNMjI+EfYhm2b99Oq1ev5jQT8Hplz56dqlSpwl2NTuXUqVM+BxQg8SxegeLeXqSrUR9wkp45c4afZIzOeOwUrKQ5PFvdunWjH374gf9/9tln+WUlrKS3XRDNrac5HqTw8nW/16WrUdC/qxFxubt23f+cMiVGTclREASjQOxW+/bt+XxHmR/EuyKptK8qHoIg2BPduhoF/ZHgemM0x2AReUYRzRUIpejTpw/VrVuXL7aFChXiOosDBgywpNElbVw0dwKaSa7lYnhZDPf2Ij0CemmucXeS0SerkzC75hhUNGrUKP6M0dqbN2+m8uXLk1Uxu952RDR3ruZBx3ipwteBgD5VuOaFUHjQLy0xXvqAZHtwHwv6YXbNX3/9ddqwYQPn6GrUqBFZHbPrbUdEc+dqHrThtXz5cr/mUwFssDAlaDN07lvq9zUVw0s/za9fv86F3qUNO1NzDBratm0bPfXUU/x/zZo1OWUOts8OmE1vJyCaO1fzoA2vwYMHx/v9pUuXOEvz2rVrOXkqRv1YMfbBbEhXoxGaa3T58mUu9C43JedpPnfuXOrcuTPHhmzdupUKFizI0+1idJlNb6cgmjtX84gZXoqlS5dS06ZNadeuXTR79uxgf05wIV2NRrinc+TIIW3QYZpfvXqVXn31Vfrqq6/4f+QkNDo2xM56Ow3R3LmaRzy4HrFgqOeIp8ZJkyZF+udsj4xqNEZzlMSy603XjBit+bp16+ixxx5jowtPxv369eNpGL1oR4zW24mI5s7VXJdRjS1btuRuRjG8Qke6Go2LCzD6ZHUSRmqOEj9Vq1al/fv3U+7cuWnZsmU0bNgwSooaXTZF2rho7gQ0k1zLdTG8kidPTqlSpeJSQkKoSFejEe7pbNmy8btgf81RUgTVN1q3bs0B9TVq1CC7I21cNHcC0Sa5luvy60gwiGB7o61MOyBdjcZoLu3Xvpqr33KPX50/fz7NmDGD0qdPT05A2rho7gQ0k1zLI254YSTQyy+/zJ9LlCgR6Z+zPdLVaITmmqvou2Avzc+dO8fJUBGLevv2bZ6WJEkSW+TmCgRp46K5E9BMci0PelTjkCFDEnTXHz16lH799Ve+uCFANZii0IIn0tWoN3BLZ82aVZqizTRfsmQJ11k8ceIE11lE6hsndCt6Q9q4aO4Eok1yLQ/a8HrnnXf8yoMByxI7O3DgQI6ZEEJDEqga555Oly6d5DiygeZ4KOzfvz+NHTuW/y9SpAh3K5YpU4acirRx0dwJaCa5lgdteFWvXj3eDccTZIYMGahUqVLUokULV9JBITTcPaQS663fyXr37l2pvmADzbdv304vvPAC/f333/w/wiBGjBhhq2SowSBtXDR3AppJruVRmtGdnTbkoYce4gEFMD59GZzodg2m6zVvXqJDh+5/zp+faN++ULdWEJwDSv2sWLGCuxumTJlCTz/9tNGbJAiCRZgwYQK/vLF371426lAL8tixY/GuRwyvCBpe/hyAQHnkEY0OH75vqcOm27MnrKsXvIBnkwsXLrAHV8qpWFvzffv20VtvvUUff/yxKWI9zIK0cdHcCWgRvJYHct8PelQj4rbg0cGFTDAG6WoUhPhBmTIkRFUUKFCAZs6cKUaXIAjWi/FCkUkMu8aFTNCTB1a61LLVSfGoKMqYMaNOvyaEQ3MUwkWdxalTp/K66tSpQ5UqVRJxfSBtXH9Ec+dqHh2KW+3OnTvh3RohQWJiHoTkicdLH2JiYujs2bP8Lphf8z///JPrLMLogmd+wIABVK5cuYhsp12QNi6aO4EYk1zLgza8GjRowMOyEagqGIMYXvo9JaFbXeK7zK05HgTffvttqlatGh08eJDy5MlDy5cv565GeOeF8OothIZo7lzNgza8kAcnS5Ys1K1bN05AKOiFJFDVG5ykKB1j9MnqJALVHEGzGKH43nvv8dNs27Ztuc4ijDAh/HoLoSOaO1fzoGO8UPD6gw8+oN69e1OxYsX4QlelShUOWk2UKFG8+b+EULsa7zca8Xjp657OnDmz4cVVnUKgmuNC2qZNG9q4cSN99tln1LJlS1220y5IGxfNnUCMSa7lfhte06ZN44D65s2bu/LhuFuN8eW3UGB+5LkQwoM8nOoD2m2yZMkMf0pyEv5ojgvo4cOHqWzZsvx/u3bt2OsFT7wQfr2F8CKaO1dzv02+Dh06UK9eveK49wN5RTqgDTFngwcPpkKFClHy5MkpZ86c1KlTJ86tEQpIjAajEwerfv36ZCzS1ai74lFRhpeYcBoJaf7LL79QiRIl6JlnnqHz58+7lhGjKzJ6C+FHNHeu5gH52tyT3MOICuYVSaMLQ8ZRvPvq1av07LPP0sMPP8zZqVGD7ZBK9x4EXbt25YrmZsD9GEivlz6g3Z46dcrwkTBOwpfmN27coJ49e9JTTz1FJ0+e5HgNeL6EyOgtRA7R3Lma2yZgZejQoTyMHLl69uzZQ99//z2tX7+eRo0aRadPn6YXX3wxqPVOnjyZli1bRp07dyYzILUa9QdPR6jlZ/RTktM137p1K6eFGD9+PP//yiuvcEwXPNxC+PUWIoto7lzNbWF4YRi5uhgjzix16tSu71577TUqWbIkLV26lDZv3hzQemGwvfHGG1S3bl1q1aoVmQPpatRd8agoSpMmjeEnq1M1h5d35MiRVKFCBdq1axdlz56dFi9ezGV/EAIghFdvQR9Ec+dqbgvDa/Xq1XTx4kXKnz8/lS5dOs73zZo14/cFCxYEtF50aaBrY+LEiWQW3Lsa5RqpD3BLI2WK0e5pJ2u+adMmfsBCCMH27dtNEGtpL6SNi+ZOIMYk13JbGF7I1wMQy+UNNV3N5w+LFi3i7kpkvTZTWSTpatQfPB2lTZvW8KckJwGtMUAG73jh4efrr7+muXPnSgB9hPSWNq4vorlzNQ8ojxeC0uLL0ZUQkUonceTIEVcZI2+o6Rh67g/Xrl2jl19+mQoXLkx9+/YlcyFdjborHhVFqVKl0v13ncqlS5c4fuvKlSs0Z84cnoYgeqSLECKDtHH9Ec2dq3niULq6zAJGMQIEzXlDCa3mS4i33nqLjTTEhSVNmjTo7YJWKNarNMNBh4tTPcUn9BlP/Ch14j5d06LcEqjeT6bquSwSw6kUHpH8HMw+BfvZyH3CwwJG0CE9ifre6vtk1uO0atUqTsaM8w//b9iwgWO7rLxPVjhO9+7do+PHj1OuXLlccXVW3yezHyesC9eVbNmysUPDDvtk5Wv57du3+RXKPkWkqxEGDPJkBftCHbVI4C5yfN/7A0ZJIVAfT9e1atUKabtwIUPOEDyt44XPGTJk8PvzsGHDuD8aqTIA8pG570tUlOby+OHCiQaAz3jH/8oTiNiYo0eP8mekxVB5zbBeVe7p+vXr7NFUBuqZM2f4MwxHNVwfngiVM+nChQv8ApiG7wDmxTIA61DGLtaN3wCe+6RSdWAbVeF1M+0TPsMAR/uyyz6Z7TgdO3aMBg4cyImZYXTlzZuX5s+fT4888ohl98lKxwk3JCyHNm6XfTL7ccJNPmPGjPzZLvtk5Wv5kCFDAr5Hq894x/3eX6I0P60SWHQYTRTIyvUCIxfHjBnD5YtGjx4d53vEdj322GMc64UgXV/g4lO+fHluELt3744VS4JiuzDE6tWrx8kb4wNdm2g8sKpRWimcHq/s2dFg7huYdetqtGSJuZ4o7PiUJPsU2eP0v//9z1XuB7Rv355HLGL0kRwnOZ/kGiHXPU2Ha3moHq+iRYu6vMZ4kIxIrUYzkTt3bn73tbPKgs6TJ0+868HyyBUEA1OVRlJg1CT466+/+KkcKSsWLlwY7/pUIF+48HRnKg+fe80p9Vk1ikh+9vzNSH82ap9U28AJZZd9MstxwkWradOmnCYCT45ffPEFj0LGdKW51fYp2M9G7hOuK7hOqjZuh30y+3Fyb+NyLY/S5TjFdy2HowOvYHH/zYSwheFVqlQpfofR5A2Vvwv5vPwBfcB4eQPuyxUrVrBr0QhkVKP+4ISC9zOQE0vwD1zwUNT6/fff52TFaiCMaK4vorf+iObO1dwW6SSqVKnChhBqKnpLGfHjjz/ye6NGjeJdD+JJlLvS84Xs9QBdjfhfecD0R0Y1GpnaQAgdpGqZPn266/9q1arRr7/+GmtUsmiuL6K3/ojmztXcFoYXguV69OjBn7t3787pIBRjx45lT1iNGjWobNmyrumffPIJFSlShPr3709Wwr2rMYTMHkIAoEsAAd94F4IHAbI4Pxs0aMD1T/GgJJqbA2njorkTiDHJtdwWXY0qBcTvv/9Oa9as4dptVatWZYFRrxGuxa+++irW/BgJgaBeNWrCiogDRi+doyhHjhyGPyVZGXT3v/DCCzxoBXTp0oWL2PtCNNcX0Vt/RHPnau63xwsWohlHNCrgPkR34KBBg7h+27x589jw6tChA1/08+XLR3bgfh6v+4jHSx9wkqohyEJgYNj4hx9+SI8//jgbXbjooVsRnuj4AllFc30RvfVHNHeu5n6nkxD8R6WT8GdYaaBkyID4svuNpkkTov8SewsRROWfwehZzxEyQvxGF2Ii//jjD/6/SZMmPGoxc+bMornJkDYumjuBmAheywO578tdxMKIDaAPeDrCSWX0U5LVQDbuypUrc+JljFjEIBd/jC4gmuuL6K0/orlzNRfDy8JdjWJ46QNOUvfcRoJvMNr34MGDrv/R9b9jxw7q1KlTQPqJ5voieuuPaO5czcXwshjuPcNieOmDe6kLwTfIb4ecekiIigzQAJUXUP5HNDc30sZFcycQY5JruRhelkM8XnqDJySJ7/INjCykZUFJLVzUrly5EnJso2iuL6K3/ojmztVcDC+LIR4vYzTHE5KMQ4kLRipWqlSJhg8fzvqgS3HLli0hjyIWzfVF9NYf0dy5movhZTHc24uEHOmlucYeHKNPVjMBLT799FMuPI90LRkzZuTgeQTRo7h1ONYvmuuH6K0/orlzNbdNAlXnIHm89AZuaZSTEmKnivjmm2/oxo0b9MQTT9DUqVMpZ86corlFkTYumjuBaJNcy8XwiiCnTp2iYsWKef0OpVPwCpT7lvp940uC6/UBmt+5c4cDxY0eDWMGLaBB4sSJ2fBauHAhl+sKd8yEaK4vorf+iObW03zChAn88nW/9xdJoGqxBKqpU2t07dr9BvPii0STJoV19YIXEBNw9OhRLnFjdFCmkXUWX3/9dc7JNXLkyIj/nmiuL6K3/ojm9tI8kPu+eLwshuTx0h+coHny5CGnsmnTJq6ziNqm0KJbt26UP3/+iP6m0zXXG9FbNHcC0Sa5rjjz8d3CyKhGYzS/efOm4QGZRsRxDRs2jOsswujCk9xvv/0WcaPLyZobhegtmjsBzSTXFTG8LIxDe710ByfpmTNnDD9Z9eTQoUNUs2ZNGjBgAN29e5eaN29O27dvpzp16ujy+07U3EhEb9HcCWgmua5IV6PFkK5GY9zTiAlwUkLU6tWrcyxE6tSp6ZNPPqF27drpOrDAaZobjegtmjuBaJNcV8RnYjmkZJDuimsap00w+ilJL5ImTUpDhw7lAtfbtm2j9u3b6z6a02maG43oLZo7Ac0k1xUxvCyGe3uRrka9NNfo/Pnzhp+skWT58uW0bNky1/8Ipl+5cmXIGeiDxQmamwnRWzR3AppJritieFkM6Wo0xj2NwHI7ppK4desWvfnmm1S7dm02ts6ePcvT4eFKlCiRYdtlZ83NiOgtmjuBaJNcV+SqZjkeWOoOz+WpG3g6unbtmuFPSeFm165dPGJxxIgRvG8NGzak5MmTkxmwq+ZmRfQWzZ2AZpLrihheFsO9vRjokHAUOEkvX75s+MkaLrAf48ePp7Jly9LWrVspU6ZMNHfuXPriiy84mN4M2E1zsyN6i+ZOQDPJdUVGNVqOB24u6YXRB7ilc+TIQXYAOWyaNGlCv/zyC/9fv359+uqrr0y3f3bS3AqI3qK5E4g2yXVFPF4WQxKoGqP5lStXDH9KCgfoSoSHC+/wei1atMgUFyI7a24FRG/R3AloJrmuiOFlMWRUoxGaa1yr0OiTNVgQ04CRPAoUeUUZIBS3NmvRb6trbjVEb9HcCWgmua6I4WU5pKvRCPd0tmzZDB8JEwx//fUXlS5dmjp27Oi62KRLl46KFStGZsbKmlsR0Vs0dwLRJrmuyFXNYrhb6iZ1VthS80uXLhn+lBQIKPPz/vvvcxLUvXv30ubNm+nEiRNkFayouZURvUVzJ6CZ5LoiwfUR5NSpUz49C927d+dXoEhXo/7gJEW+K7ybtWvOnQMHDlDbtm3pzz//5P9btmxJEydOpAwZMpBVsJrmVkf0Fs2dgBbidQVhGnj5ut/7S5RmtOlnQx566CH6999/OVHbsWPHwrpueEjVERsyhGjQoLCuXrAwOJWnTZtGr7zyCgeQpk2bli8SSIwqxosgCII57vvS1WgxZFSjMZpfvHjRcPd0QqAG2bvvvstGV9WqVbnOYps2bSxpdFlFc7sgeovmTkAzyXVFuhotxP22IsH1+uuuccyU2bu9UqZMSd988w3XXezbt6+hJX+corldEL1FcyegmeS6IoaXhfA00mXAlz5gBEzmzJnJbCBWYeDAgZQ/f37q1q0bT0MwPV5Wx6ya2xXRWzR3AtEmua5IV6OFiImJ/b84ApxV0d6dv//+mypUqECjRo2i119/PaDATitgRs3tjOgtmjsBzSTXFTG8LIR4vISYmBgaN24clStXjrZv305ZsmSh77//nnPTCIIgCOZHuhot7PGSrkZ9QCxAxowZyWiOHz9OHTp0oCVLlvD/Tz/9NNdZtKPRZRbNnYLoLZo7gSiTXFfE42UhxPAySvcYOnv2LL8bBUYqIgM9jC7UWUSaiIULF9rS6DKL5k5C9BbNnUCMSa4rYnhZCDG8jHtKSpw4saGjYNKkSUMvv/wylSlThrPQ47OdR/uZQXMnIXqL5k4gyiTXFTG8LIRZDC802vheNWvWDHn9jzzySEDL4Dex3KFDhwJa7p9//qHmzZtzrFSKFCmoZMmSHEPl/kSE9aZPn173k3XdunW0a9cu1/8Ywbh27VoqWrQo2R2jNHcqordo7gSiTHJdkRgvC2G2UY3t27f3Or1IkSJkBWDY1KlTh6vVY4QgjL2VK1dSr169uNzOzJkz+QRV7mkMQ9ajuKqqs4jXo48+SuvXr6dkyZLxk5pT0FtzpyN6i+ZOIMYk1xXnXMltgNlGNU6dOpWsCowbZHWH0TV69Gjq3bs3T7969So9+eST9MMPP1DDhg255iGMLxg+ejwl7du3j7cLxhYoXrw43b59m3/fSeipuSB6G4G0cedqLo+SFsIsXY12YO7cubR//34qVaqUy+gCqVOndhVBHTFiBL/jJE2XLl1ET1bklcEIxccee4yNLvzejBkz+IX4Lqehh+aC6G0k0sadq7ncui2EVQ2vo0ePUteuXSlPnjz8tJE1a1Zq2rQpbdiwIeB1ffHFF1SiRAke2YdipCgIfenSpYDXgxGBoFmzZnG+w+jBfPny0Y4dO+jw4cPsnkaC0kiNhMGIRWzHiy++SNeuXaPq1atzncXWrVuTU4m05oLobTTSxp2ruUVu3YJVDS8YLxiJB4MJtQRhcBUoUIA9TihtM2vWLL/X1adPHzbg9u7dS7Vr16ZKlSqxR6hWrVpcPicQYNgAbJs31HTMh6cjbHuknpKw7tOnT1OSJElo+PDhtHTpUjZSnUykNRdEb6ORNu5czSXGy0JYzfBC99kLL7zAwYz9+/enDz74wNXgZ8+eTS1btmQvDzw8CeWjQrA7yuMg+R0C4BH7BM6dO8dGGALlA+HIkSP8/tBDD3n9Xk2HxwvbHO7uvps3b/I7PHcoZj19+nQuZeHLEHQakdBcEL3NhLRx52pu8lu3tYFLs1ixYq5X0aLFXa/x4yewIRXI6+7duMH2ga4jnPhKJ3Hx4kX+fvny5ezxyps3Lw0ZMiTWUwa61ho3bszdbFOmTEnwtz777DN+R11CZXSBTJkyuWKxAgFB9ABPP95IlSqVaz64pU+cOBE29zRK/aDkD4xRBUZUitH1gHBrLsSP6K0/orn1NEf8r/s93f0VSL1c8XhFEHhxVB4mHOctW2J/7/l/Qpw+Hfv/o0cDX0fp0uHzlPlKJ5E0aVJ+X7VqFb/Ds+UtFQJGDM6ZM4fn69evX7y/tXr1an5v0aJFnO8wChGeMHiMAsWXy9m9iCrmSZs2bcjuaZzsY8eOZYMLIxXhrRs8eDDnlRHiHpdwaC74h+itP6K59TTv3r07v3z1kvz7779+rUcMLwthtq7GhNJJoLYg8JUMVU1X8yW0LpwsDz/8sNfvc+fOHZDhhdGLFy5c4GB2byDNhJoPv6s8YMFy7NgxrrP4xx9/8P+NGjWiSZMmidHlg3BoLviP6K0/orlzNRfDSydgJMHbFAqHD8f+P2/ewNdphLGW0NOFEV4NGGowvGAQIVu9J+rJBUHuyj2dI0eOoJLuYQABBgXg95Adf8yYMdSlSxfx5sRDqJoLgSF6649o7lzN5Yqmp9jRob08Qe9dqOuIJDlz5uT3gwcPev0egesAJ0FCYB50/yE1RXzB8v6C/F1g69atXr9HPUQAo0xVtA/GQER3YufOndnoQlzXli1b2AiTLrT4CUVzIXBEb/0RzZ2ruRheFsJsJYMSolq1avz+/fff07179+J8/80338SaLz6qVq3qGg3pye+//x5wfFeDBg18rg8pJJBcFeV60B2KkxSeqmBOVgT/Y2DAgAEDeGRm4cKFA16HEwlFc0H0tgLSxp2ruRheFsJsMV7+FK5GslN4vN5+++1YAevI44XAesRQIfYpIV566SV+R0oJFLZWwOB64403At62Jk2a8GhLeKBQFFuBmC8VPIkRlMo9DU+bPyNh7ty5w/u6ePFi17Tnn3+eU2kgT5fgH4FoLoSO6K0/orlzNTf5rVswc63GhMBTBRKcwuszdOhQTgOBbOzwXiGRKvrYUSYne/bsCa6rSpUq9Nprr3FOMGSWRx3F5s2bU8GCBfn7xx9/PKBtgxEEjxueflAUG8tj9CXWt2bNGnruuedcozaxH1myZEnwKQmJXbFv7733HnXs2JEuX74c0DYJD/BXcyE8iN76I5o7V3OT37oDT0qJ4fmFChXixJSIMerUqZPfQzwBclB9++23bCAgNwdGQCDhWsWKFenjjz/m4spGYTWPF4DHC/FSiHNCTix07f3vf//jHF4wcGA8+cvIkSO52w6Z75csWcLLI73EsmXLgioijcz5KFsEIwvFqefPn8/9/yiaje5RdXLiHe0pvtQTGKEIg/Cvv/7ikYrwomHYshAcCWkuhBfRW39EcwdrrtmEGzduaJUrV4ZPSMuRI4fWokULrUKFCvx/1qxZtYMHD/q1noEDB/Iy0dHRWtmyZbWWLVtqtWvX1pIlS8bTq1evrl27di3edeTKlYvnxXs42bkTPq8Hr3nzwrp6wQf37t3TDh06xO+enDlzRmvcuDEfb7xq1aqlHTlyRLSMoOZC+BG99Uc0t5fmgdz3LeAz8Q90ZSF4GfX79uzZwx6L9evXc0wQ6uChNI0/IOYIgdAYJbdx40aaOXMm515CBnakIEC5GsTrGIEVPV52AE9HGFXp+ZSEdgWP3rx587jrEhn0EejvK9eYELrmQmQQvfVHNHeu5lGwvsjiIKA5a9as3E2Ibi10+XimDkCZlk2bNoVUluW7777jLkgEZR84cMDnfCqDba5cuThPVLjYvh378uD/BQuIGjYM2+qFIEAcGAx0xLI99thjoqEgCIIDeSiA+74tfCYoJwOjK3/+/HGMLlUXECyApRICKvdTIDFj4UQ8XsaAETCHDh3id+T9cq/JhdpdMLzE6Iqc5kLkEb31RzR3rua2MLyQdwn48map6Wq+YFFeLn9G4UUCq41qtAtwS2OgBoL7K1SowN3WylGMrmmMjBTCrzmeII3uEnAKordo7gSiTHJdsUXJIJW1HIJ6Q01XmdKDReV7evbZZ8kIxONlDHAbt2vXjpYvX87/I57rxo0blDJlSoO2yP7gwoh0I0ZfIJ2C6C2aO4Eok1xXbGF4IU0B8HUjVEUx1XzBgDQGCJxGqoB+/fr5tQy8IsjlpLwjONhwceLdn88Y9oqbvJp+9y7cow/cXFFRWG/cZdGw8Jt4RfJzMPsU7Gej9umHH37g5K3oykY7Qp1FeLwwv1X3yQrHST0oYaBC4sSJbbFPZj5OqCwBvVWlBjvsk9mPkyqBBsdAokSJbLFPZj9Od+/eZc1Rg1d9r+a5ffs2v0LZJ3+xRWeVu8jxfR8sGMn46quv8vonT57sqkGYEMePH6d06dKxsYYXPmfIkMHvz8OGDeOCnshPBk6dOhNr/TExd10eP1w40QDwGe/4X3kCMfhA1Ti8deuWK0YN68X6wfXr112xSzBQz5y5/1swHJG0FFy6dMlVmge1B/ECmIbvAOZViUOxDmXsYt34DeC+T9gWbBPANmJbzbBPuAm1atWKs87D6CpbtixnuX/mmWc4u70V98lKxwkXMpwHaj/ssE9mPk74DM3xsss+mf04YfswUl59tsM+mf04nT9/nq8raOee+zRkyJCA79HqM95xv3fUqEZkNIcnonfv3pz80hPEdiH4GbFeGNkYCBgNWaNGDb75oquxZ8+efo9ugIGG8jbh8nitWRNDVas+sJX/+EOj2rXN9URhl6cknKgwtlDuqH///pxiBLFcmMeq+2Sl46SeTjEN3gA77JOZjxOmQW9V1soO+2T24wTUNKzbDvtk9uN07z8DUHnRw+nxKlq0KBtf/oxqtEVXI54agK+dVRa0ci/6Cwol16tXj42ud955xy+jyx0clHBmL4+Kiu2gTJTovocPB12hPqtGEcnPnr8Z6c+R3ifceJTLH08wSB+CpzGUE8LTGdqZ1fbJqscJFzOct+rctsM+JfTZyH0CuGmoNm6HfTL7cUIbxz0Lmqv1Wn2f/P0cZdA+Ybq6rnhuFxwdeAWL+286oqtRpXnAUH9vILcXKFmypN/rxEWobt26dPLkSfakoRSR0UhwfeRAGSMk30V6CEX58uW5rBBOSsS+uJ+oQmQRzfVF9NYf0dy5mtviToICyvBQoEixt5QRP/74I783atTIr/WhH/jJJ5/kfB8IpEb2ezMghlf4gbsZAyeQ/w3d0MOHD3fFLLjPAxe0cl8LkUc01xfRW39Ec+dqbgvDK2nSpNSjRw/+3L17d1fwMxg7dix7whCnhZgdxSeffEJFihTh+B13sOzTTz9NO3fupJYtW9IXX3wRkAsxkojhFV5Q8gfB8t26deP0EHXq1OEi157uZpykCPI0+mR1EqK56G13pI07V3NbxHiBt956i9M9rFmzhgoVKkRVq1blkWmo15glSxb66quvYs2PkRDoXlKjJtzXg2UQ64NXp06dvP7e1KlTSW/E8AofixYtoo4dO7LxBcMdni6MXPXmgsa0QOMDhdAQzfVF9NYf0dy5mtvG8IKXYtmyZZyC4dtvv+XCxRkzZqQOHTrQe++95zO5qidqCCpGP2A9vjCD4WUSR5zlQKA8kuAimP7RRx/lOovxxf/h6QjDnJMlS2Ya76fdEc1Fb7sjbdy5mtsinYTZiFSR7CVLiJ588sH/GzYQlSsXttU7ig8++IDzuMDTldBIFjXCDsfT6KBMpyCai952R9q4vTQP5L5vG4+XE5CuxuCA9xIDJJ566ikqUaIET0NeLn+feHCCIoO6oB+iub6I3vojmjtXc3l8txBmMrxUzhS81q5d63M+lNxR82EYrxHdigia79u3L7Vu3ZpHtIBA3MxwCiP43j0pn/sLJzNG1SLfFxL5qozNvkB8IWIJMZIS2Y9R6qpAgQLUpUsX2rFjh1/bhEEg+K1atWpRtmzZOE4NXesY4YsMzMF4Wt99912Oa9y1a5fPeRBDqfZ7xIgRPudDXUt/jnnNmjV5Ps+ue6U5MkuHez8jDUbFIv0MYk3hTUUiZcSKqnyCgbB69Wpq2LAhZcqUideFAUE4Tp4jb0NdDnr/8ccfAf/WunXr6LnnnqPs2bNz8lUcG5xvs2fPDusyoWhh1nbgeV0JZV3hXE6vZTwzCWTNmpWvBTiuCV0vfL1++eWXWPMjhhuJrzHoLlDNIwq6GoXwkitXLhxVfg8nCxeitTx4bdumGQb2T726d+/uc75GjRq55suTJ4+u2zhjxgwtXbp0/NupU6fWvvrqKy0mJibg9dy7d087duwYvwO1P+3bt+dXmzZttCpVqmjR0dE8vU6dOtqdO3e8ruuXX35xbVOWLFm0Z555RmvevLlWpEgRnpYoUSLt/fffj3d71q5dq+XIkYPnT5kypVa7dm2tVatW2tNPP83rxPTkyZNrS5Ys8XsfT548yRq1bNky3vleeukl1/6XKFHC53zLli3z65jXqFGD55syZUqs6dD6p59+Cvt+RpobN25olStX5m3Dtrdo0UKrUKEC/581a1bt4MGDfq8Lmqg2VaZMGa1Jkybaww8/zP9XqlSJfytcy02ePDngZWbNmuVaply5ctx2qlWr5prWt2/fsCwTihZmbgee15VQ1hWu5fRaxhNcR6PuFx/WChcurCV0vXjuuedc11/31/bt2+Ms07NnTy1x4sTa//73P781j/R9XwwvCxle8+fHNrx27NAMA/uXLFkyrVixYlrmzJm9Ghpnz57VkiRJwhdKPQ2vCxcuaK1bt3YZCI8//ri2b9++sK1frdeTNWvWsCb4bvr06XG+X7duHeuBC8ywYcPiaPbzzz9rGTJk4OVHjx7t9be3bdumpUiRwnWTunr1aqzvcUGZM2eOVqBAgTjGTHy8+uqrvM6tW7f6nOfWrVtaxowZ+QaYLVs2nh/bEwnDK1L7GWkGDRrkMgauXLnimj5q1CieDuPRHw4fPuxqSzCKFDdv3mRDHdP79esXluWCWQZtFzdVfDdz5sxY3/35559sEKOdu593wSwTihZ2aAehrCuY5fRaxp3ff/+d5+vSpYvfhlcgDzBHjx7la9bzzz+vRRIxvGxqeM2bF9vw+vtvzXDD64MPPuDPC+GO82DChAkuI0IvwwsnZO7cuV3eo3feecen98lf4CXDjV95y3wZXqBr1678HbxgnoYCLij47r333vP5WytWrOCbD7T1vLjg9+FlwjqwX/Fx8eJFbYeflvn169e19OnTayVLlox3Phg66kI6YMAA/tynT5+wG17u+zl48OCw7WekuX37NuuI7d68eXOc76Evvtu0aVOC63r33Xd53rp168b57vTp02yUpkqVio9dqMupZWrVqhXHI+xrGWiOZeCp9cazzz7L33///fchLROKFoGC8015svVoBxs3box1XQlnmwpmOb2WcQfHDA9PeIDfs2dPRAwvgGsWHnrh2U9Icz3u+xLjZSE8u6XNMMDuhRde4L71b775Js53mJY6dWpO3eALVAfo2rUrxwJhiC9yrjVr1oyLk3vj559/5tgBFCRFHcxUqVJxyaihQ4fyMGEETmJd+fLl45iQ9u3bcxwJYgPQt9+vXz/O44LfQlzVhx9+mGB/P76/fPmyX3EBSE8BkB/MM28Y8sZhxAvizXxRvXp1at68Oe+Le/ki8Ouvv3IMGEbPDBw4MN7tQMyZ2paEmDVrFtcjRQxcfEyfPp3f27Rpwy+AlCuq4G+4UPuZI0cOHgQRrv2MNGhv0DF//vwcv+cJ2jVYsGBBguvasmULv6PdeoJzpFixYhznh9yFoS6nlqlQoUKcNu5rGZw//oB4rFCWCXafrNIOErquBNumgllOr2XcQYweaiJPnDjRVaA9EuDahthbxJL6ey2PJCa4dQtWDK5XwIhBoPP8+fM5EFpx8OBBDrpv2rQpB497AyctjCZUB4CBhizyBQsWpDlz5nCgOvKyeYISTigBhYBcjFKsVq0aJ8qFIYKKA2DmzJlcrQDrUCCoHmWg8Fsw2hCsjcBPGGKDBg2Kdx8RPA8jwJ/hx1euXOF3BIq6s3jxYn6HUZXQBaZVq1b87hkoCqNTrSNx4vANSF64cKHPG5t7fjsYjwicRWA0NCxTpgzXNF26dCmFE7Wfzz//PAfTWwVVrgy6eENN91bWzBNVfQPtPD7jxHNdwSynlsG57K2Ne1sGDzZ47d69mwfQuIPzHsZz3rx5OZF1KMuEooXZ2wEeLhO6rgTbpoJZTq9lFNh/jDZHIms8cAbC5MmT6eWXX+aKNR9//DEPoooPdW3DNczfa3kkkXQSEeTUqVP8NAbu3UtNt24Vcn0HgwSvQNi5M/b/qP195kzg24WMCunSUdiA9wNGFAymdu3a8TTlAYNHzBuXLl1iAwJeKHhc1JMRwJNrgwYNqG3btnTgwIFYN9/PP/+cDSiMVMFTC56UVqxYwTcNGABIhqq2wfPCDiNtz549lDlzZp62ceNGLoyNUXPwQqVJk8brtuJ3YFTCOExoNORvv/3G7/Xr1481XV143MtW+ULNg7JVeEpThpp68vd1kQuWVatW8W889thjPufBjRJeOBwzeBrVcUcBehxrFJQPF2o/YdxB+3AmOsQFGO0lEPAAEJ9RqlAXf1/JmtV0PCgkBDw58c2rpsNjHOpyahnUuvWmt7dlMPoV3gPUv0VpNYxwhdcDo8hwLahYsSJNmzYtlpcrmGVC0cIoAmkHeFCL77oSbJsKZjm9lgHwknfu3JlHdX/00UcUKO+//36s//v06cMP0L4eotHOcN1HSTiMKoexHsx1Bb0Qnj0R7vd7vwl7R6fgta931arY8VlGvrAt4YrxAufPn9eSJk2qPfnkk67v0U+fPXt27e7du9qJEyfixPuouK/+/fvHG+z9448/ev0effUY3abirapWrcrvTZs29Rq3geBKjGrxNepy6dKlPvcV8Vn4Pc9Rje7fIyAYozsxHSMVPePK1KhFjGpMCIwSUr+B3w1mHf5y6tQpXifiLOIDozYxH0YaKnBcEUeH0ZDXrl0LW4yX2s/vvvsu7KOPMKjB22io+F7//POPX+vu3Lkzb/fAgQO9fr93717+vlChQgmu67PPPuN5Ea+IQQ2eo1pV+8AIslCXU8vgeuU5OjC+31KDIPLly+eaBy+0B8QAeg6ICHaZYLXwp90F8kKbDnc7cL+uhLquUJfTaxkwduzYOOe9ulbHF+OFQH4MXNq/fz/Hh+GajjhjNRAH603oGoZrvdGjGsXjJYRMhgwZuJsP/fgnT56ko0ePcjxT7969+SnXG0uQhp+IGjdu7PV7dDeMGzeONmzYEMczCK/X66+/zl0QcBmXK1eOuyjw1Iyndm/AI4YcM56oaZ41O93BbyCHlCfenpjgNp80aVIcV7Z7DrCEcJ/H/XMg6/AXFYvmqxtHdRsjf5fq3lUgDxM8Xegi+umnn1xdpKGi9hO/F+4uAXQtR4qEjk8gcSXwJqK6AjwK6IIfOXIkt2EcB3gK0NWMklee+gSznFoG5y3OR39/C136KMmGLv3vvvuOihcvzl3P8CAj5hIe6JUrV8bqWg9mmWC1iA94pD1zzMGrjTAGeEc8uztVew93O/B2XQl2XaEup9cyR48e5TyGNWrU4LYQCMjd53n9Rhwo7gH16tWjd955h/MhokfEE3WNg7dNuhoFW4CLI+pj4sKKG7Wa5gvVLYDuhfiAW9j9woh4MvfAe5xEcB/j5R5j5YkvVzjc/EAlVo0vuB5dbO4XGATuq+SBiCmDsTllyhS+qeDkdwdubnzvGXTvDTUPfgtGrec6UOooXKDLF/jqZnXvNm7RokWc+DQcYxheCLx3N7wCNQ7d51f7ie6JcHc1RhKloYpJ8uT69eux2lx8YNAIHmSQMBT64qWAwdCrVy82QNBVE+pyWAYxmoEss2/fPu7Sh+GAGEG1T4jR/PTTT9mYgjGO80GdC8EsE4oWgRrguCbB8ILRFUot3kDaAc4/z+tKsOsKdTm9lnn55Zf5eoswkXCB8BMYXwgfWb9+vdfQABUigXZm9HVFPF46gbiqVatCWwfCh95778H/SPScwAOTz20JN7go4sKHGA00bBV87Qs1Eg4xQ76C7z0Ns6+//tpldCFOBB4xGFQwBnAiIzbE1xNgKE84qrCq58nqeXFGrAJixXAjwIXA/YkagwjwhL5p06Z4DVKAuCmA+EB3QwcxWFgHvvcVOxco6mbly2B1N7yQ2dzTE6CyhsODCYNRDSpQx9TXBVmhvsfN1dt+hvsCidqcCO4OBNyk48umrcidOze/+8qmr7J4w1vjD2gzMEARA4l2A68OtMEILWQJByqGNNTlUCQeHmPEsyHGLqFl8ICF+EN4jrwZkojZhBGFCgbKiApmmVC1MAJ/2wHm83ZdCUebCmY5vZZZuHAhX3e6devm9VoCz6YynNwN9ISAAQ/Dy1fvhXrIxLVJDC+HgGB2L97rgPjPkeQCNokPR47uwOjBhRPdbKBnz57xzg+DCRdSuJxx0feFuyGFLggVWOmZTgFB+JECRpvnKEVvvPnmm2ycIMAeLvGvvvrK9R1uNniqR1kUBBXHNyoRXTBqGXcw4ACBnbj5wMjz1Y0bCCpwGSU7vIGnRwxIAOjG9dWVixshtvvVV1+N5WGEx1J5C72hvKPuHkm1nxjd+Mknn1A4wUjRQIPr0R3ij+EF4wDA+xmfQR1fe/cENwl4VpV3VYF2BnwF/Qe6HNo4HhTQVY5XQsuguwj4Oq5qunu7CmaZUPbJKPxtB5gvoetKsG0qmOX0WgYgBYWv8xADrtR3uK74C0Zeez7Eefse3ZNGdzVKcL2FEqhOnRo7SP7ffzXDcA+uV6xcuVLLlCkTZ7I/dOiQa7q34Prhw4fzNCRH9AWCLVFORCVnfeKJJ3xmV0dCUW/B3CpgE8G03kCCTs8gT0+QbA/Z8P1JoLplyxZOgIoSFe4aIJgTAaZYbujQoT5/a/Xq1TwQAIMVDhw4EGc7ihcvnmASVnDp0iXtbz8z7KLMB5ILeiu7ogYMxJcZfPHixa4SMO4gYB/TUbrJG8j0j+/TpEnDWci97eeQIUPCtp+RBoHfqhyUtzb62GOPuRJnhsIff/zB60FZlnAt59nGE1rm7bff5unVq1f3+ltvvfUWf4+EwqEsE8o+GZVA1d92sGHDBq+ah6NNBbOcXsv4wp/gel8gkS6S6GJ5ZKr3Bu5NqI6AAQ1GJ1AVw8tChtdXX8U2vE6c0ExlePnCm+GFkZCot4d1wOhxPxHw+csvv+STRI24w+hIZQS8/PLLseaHwYdRUZEyvGA0nTlzxueoRk8aN27M3/fo0SOOoQGDDIbVRx99xPvkacCgJA+WHTFihE/DTunibRQYdMHIw4IFC/pdSkeVXUFJI3eQlRpGtK+LqgIjOFXtRPcRgOPHj+dpDz30UJyRgcePH3dltfaW/R6ZrsO9n3qA0V3YZoygct/mMWPG+GyHbdu25ZsNKgN4HmvP0bG4gcFQRhvCiD5vBLMc9MZ56j7aK75lML86Dz799NNY32FedRN0r6MZzDKhamHmduB5XfHVDoJpU8Eup9cywRheuH7OnTs3znUTy6kRixhR7g2MOldGv7vm4UQML5saXpMmxTa83DINWM7wUt4dZWg88sgjWoMGDbSGDRu66hXiVapUKZfnB0OH1cUZJSZQewseMXiYcPOOlOHlbd/jM7xgpGCbMMQZ6Ro86zGmTZuWl0e9Q5RJwTD4okWL8jTcROLzAirdVK1E6IGi3KhNCf3UdBgtqIHmD1OnTuVl4IV0Z/78+fGWeHGnW7ducYaVwzjCMcJ0eNRq1qypvfDCC5x2RA3/xnHxVeA43PupB9iXihUr8rblzJmTj636H8YphsH7W68S07EMPL0oDo6ao2gf0PLrr7/2uQ3BLBfMMuqcwwseShjw7sXiUXsvHMuEooURaUXC3Q6CWVewy+m1TDCGF3TB90hThFRCuBag7agHNLQnz+utYtKkSV6vceFEDC+bGl5ffhnb8Dp9WrO04QX+/fdf7fXXX+ebO9anKtTjHSewZ36oXbt2ce4tFNtNmTKlVrp0ae2LL75wbVOkuhrPnTvnV1ejAvnEfHXR4eIALw48Puhmw4Ujb9682osvvhivZ8kdFKMdOXKk64YETxpqpuGCh33y5W73BvLhoLvAs1aj8oQlVBdSeR2V/p7eS+TjgrEF7xm2E4Y1DOaJEyeyVy0+zS9fvhy2/dQL6Il8Q/nz5+cuY9woOnTo4HNbfd1w4fXFEzr2GwYGrieoAbp9+/Z4fz+Y5XAOVa5cOeDfgncGxxbdOOrYouajr+7lYJcJVgsj8nj52w48ryu+2oE/6wp2G4xcJlDDC9d+POCVKVPGdS3AdQtGOApyx1erU9VqxL3IXXOj7vtR+GNslJn9QKAwRnOgLp+v0R7B8MUXRF27PvgfmRY8yppZlr///psDMNEcMToFI+lQO84MYJsQmInUDlZJbRAoyLk2duxYDpJVAbNG4gTNzYToLZrblWPHjvGoSgz+wsjaSF1XArnvm6Dan2DlWo3hAoWOMXoMCRExEsYsRhfACYrke3Y2APr378/DtocNG0ZmwAmamwnRWzS3KyNGjOBRjBhpbpZ2bqNbt/2xk+GFJ+zPPvuMM90rvvzyS1fBbDOBnGNIi6Byj9kRDGt/4403OFXFrl27jN4cR2huJkRv0dyOnDhxgu8peKAvXLiwadq5hW/dzsMuhhdOBpQYQgI95AxSvd3hyEsVCfB0hLxbRj8lRZq3336b7t27Z4pElE7R3CyI3qK5HcmRIwfnBUMORTO1c8lcb2HDy4r3JJQVwtMHnjqSJ0/OyTLNDk7SQMqRCKK51ZA2Lpo7gSiTXMst6jNxJp7DIKzk8UKdRRhcTZo0YaML5T5Q+qNHjx6GP30kBNzSKIdjtHvaSYjmorfdkTbuXM3F42UhrNrViJIz8GyhSC6MLMQSIdARZYasALYZ22p2A9FOiOait92RNu5czcXwiiCnTp3yGS/TvXt3fjnB8MqZM6druC2KaNeqVYusBE7SdCi2KYjmNkXauGjuBKJCvJajhixevu73fm+H5PGyTh6vESNQiPnB/3fuEMVTa9lQsN8wuFQx0n/++YeyZ8/O+VOsBtzSZ86c4YLShhdXdQiiuehtd6SN20tzyeNlU6zg8cIIxa+//po9fePHj3dNL1q0qCWNLvWUlDJlSsPd005CNBe97Y60cedqbsJbt2DVUY3nzp2jFi1acCLUK1eu0M8//+xKFWFlcJKmSZPG8JPVSYjmorfdkTbuXM3F8LIQnjaMmeyA33//nUv+zJ49m/OkDB06lBYvXmx4Aw+Xexq5x4weCeMkRHPR2+5IG3eu5iaNEBK84d5WoqNhhRlv1Ny8eZMGDBhAY8aM4f8LFSpEM2bMoHLlypFdgPGYNm1aWxiRVkE0F73tjrRx52ouHi+LGl5GNxzF//73P1cs10svvcR1Fu1kdCmtU6VKZRrNnYBoLnrbHWnjztVcDC9Le7yMp1SpUjR27FiaP38+TZw4kRu13YBbGqNUjXZPOwnRXPS2O9LGnau5dDVa1uNlzDYcP36c02R4C1jEyMWWLVtyNvokSZKQGXnkkUfo8OHDAQX9m6WivZMQzUVvuyNt3Lmai+FlIYzuapwzZw6X/VG0b9+e31FY+eDBg7R27Vpav349j2b85ZdfOMjeDkDrFClSGL0ZjkI0F73tjrRx52ouXY0Wwt1Jo2dXI1JDdOrUiZ577jk6f/68a/rUqVP5NX36dFq9ejWtWrWKyzH88ccfNHPmTLILcEsfPXrUcPe0kxDNRW+7I23cuZqL4WUhjOhqhBcLBa2nTJnCTwv9+/f3OW/lypU5hxf49ddfyS5gv5Hp2Gj3tJMQzUVvuyNt3Lmai+Fl2eD6yDecNWvWULVq1ejAgQOUO3duWr58Oefnio9HH32U31EB3h3EVH333Xf0/PPPc8oJBOEjLqxChQr06aefen0Ceeedd/gEgVdtx44d9Mwzz3D2eyxbo0YN+vPPP71uw927d2nYsGFUsGBBSp48OeXLl48GDRpEt2/fjnfbFy1aRE888QT/BpYrXLgw9evXjy5dusT/u5+s7tu2adMmeuqppyh9+vQcP4AksqpU1LVr17goOGLLsA7og1xnQvxAW0/NhcgheuuPaO5czcXwsqzHK/JdjY8//jgbXi+88AJt27aNqlev7le3JMiaNWus6bdu3aLWrVtzolXUbGzUqBFVrFiRdu7cycXC0ZXpi40bN/K2IHVFnTp12KBauXIlf/7777/jzN+qVSvOLXby5EmqX78+lShRgkaPHk1Nmzb1GVQPQ61BgwZsXJYtW5YaN25M169fpw8//JC3E9vgzThETFuVKlW4/le9evUoU6ZMNGvWLN42GGwoCA5vIbahUqVKtGvXLjbM7OQRjATQGoMgjO4ScAqit2juBGLMcl1BkWwhvOTKlQt3d34PJ6+9Bqvh/itt2hgt3MTExGjfffeddu3aNdc0988K7JuvplOzZk3+7ptvvok1/c6dO9rcuXO127dvx5p+8uRJrUyZMrzMihUrYn03ePBg1299+OGHsb7r1asXT2/btm2s6d9++y1Pz5cvn3bs2DHX9AMHDmgPPfSQ123/66+/tOjoaC1NmjT8WXHz5k2tWbNmPP9zzz3H+njbtokTJ7qmY//q1q3L04sVK8Z6nD9/3vX9pEmT+Ltq1ap51U+4D7S+detWLM2FyCF6649obi/NA7nvi+EVAeI7AFevXvX5unHjRrzzdu9+VSO6/0qX7rrf671+Pfa83jhz5ozWpEkT3u7u3bvHO6+n8XLv3j1t3759vBymP/PMM2xo+ctvv/3Gy70Gy9INZdxUrVo1zjJnz57l73Lnzh1rOgwaTJ8xY0acZT7//HOvhle7du142qBBg+Isc+rUKS1FihRsmLkbcvFt2/z58/k7LLN3795Y3929e1fLnDmzliRJkjhGqCAIgmBNAjG87DHe36ScOnWKihUrFmvaP//843P+p59+mlMxKNBdh+4ub1y7VoOIlrv+RwzR2bNnvc6LTPIbNmzw+bu//fYbB8WjhhXybyGeC/ZVQv3g3r7v2LEjTZo0iaKjvfdib926lX8P7l7sG35HdU/u3bvX6zKIu/IEXXp4oTtRcefOHe76w283a9bMaxdk165d40zHaEyALlVPcAzw+0gQi5GbyFPmzpNPPhlnGcSUqWNSoECBWN8lSpSIp6PrEscrR44cXvfZ6aAr4MiRI9wWfbUlQfS2MtLGraf5hAkT+OXrfu8vYnhFkGzZsnFMjztGB/W5c+PGDQ4e//jjj/n/IkWKcJ3FMmXK+LW8yuOFeo0wqBCDhXgmxGN16dIl1rwIbIdxhwB7XygDzJOHHnrI6/TUqVPTuXPnXP/jM34HxkzSpEnjzI9gfgTAX7x4MU5SWByXPHnyeP0dGEoAhqknnslkgcre7+079+8R9yZ4B8cDx91M54udEb1FcycQFeJ1BfHIeHkD60VWfH8Qw0tnrl696vM7eEPc8RwZ+NprRF98cf9z+vSxrfVDhw75XK83y3737t3sFUJwO0Bj+uijjyhlypT+7ch/ebzcwfJ9+/alXr16sSdIGSwAwe0wuhBkjvlg3GH0IDxse/bs4RGEvgLf/X0yUcuH+2at1udtO+LbNvHUhKY59BPDSx9Eb/0RzZ2rufjwdQbeDl8vDHONb95EieApuf+Kjo5/XveXt0y98P7AgwOvHLo3P/nkk4CMLm+8+eabbHDBkzZkyJBY382dO5ffYXxhpCG68FRZIaSrCAeZM2dmTxe6H72ljoBHzdPbBXLmzMlGG7o/vaGMWmgl6NslYPjoI4cgeovmTiDGJNcVMbwcVDII6Q0U6Ab76aefaPv27RxbFi6QfgHbhmz27obMhQsXfHYbfv/992H5bRhyyAuGkwrljfz9HaTMAOhm9QRpIpYsWcJPSVWrVg3LdgoJA70lvks/RG/9Ec2dq7kYXhbCvScu0DxeyC2VN29eDhJXwJDwzLcVKshy/+yzz3IS05EjR7qmI2kq+Oyzz2LNj2SiMNLCRbdu3fgdCVPdY7JgBHp64RToZsWJOG7cOA56V8Br9sorr/AggCZNmrBnTNAHeCBhQAdSzFwQva2EtHHnai6Gl2Uz1/u3zOXLlzmoHUk74XX6/PPPKdKorO6TJ092xamhGxIxbAjmxyhLJFMtX748NW/enHr37h2238Z6UVNy3759HDcGgwnJUIsXL87xZXja8QResvfee4+1QpJTdJciwz5GJMJLhoStiF0z+mR1EtAa2f9Fc9Hbrkgbd67mYnjZuGQQSv7AA/X111+zR2fgwIE0b968yG4kEZUqVYoNHsR6jRkzhqch6z3SMdSuXZtjuhYuXMjxWD/++KPPUSLBgjgylDaCNw9lgDDiEp4rdD/66qJFpntsE0oR/fXXXxyThoLfMBiRogJGotHuaScBrTE4QzQXve2KtHHnah6FZF5kE5DWAKVfcONFAB3q5iGQG94MX0P7fYEgbHhucANGsDbK3MCYwDSkJIgPNawUv6lq9oWDjh0xkvD+59y5EQzu3YhAPit0q8H4gFsVDQ3deRKjFBw4RaApYsiMHg3jFERz0dvuSBu3l+aB3Pej7WR0oT4eDA6kbECc0cMPP8x5pZC6IL50C54gHxS6nxDzkzhxYu6qwihA/I8cVSpQ3Njget/zrVixgt5//302utq1a8ceHzG6QjtZES9mo2cU0yOai952R9q4czW3jeEF786ff/7JMTrIC4XYHHQRjRo1iuOMXnzxRb/XhZgjZFFHUWUkBcW6UIwZ3VX4v0+fPmTmrsa6devyNmK70c2YLl06fTbQpsAtjeSqRrunnYRoLnrbHWnjztXcFl2NcB0ingfdg5s3b6bSpUvHiTlC2oRNmzYlmJUd3YpwFSIQ/OjRo7FyNyHTOLxo58+f52znvkYERqqrsU0bpDy4/zlfPo32749ypTyAoYVuVhl5F35wiuDYI+ZLuhr1QTTXF9Fbf0Rze2nuuK5GBG3D6MqfP38cowuoun0LFixIcF2LFy/mLjoEg3smzMTBatSoEd27d4/nM8OoRmwHRutNmzbNax1CITwnK4xbGzyjWAbRXPS2O9LGnau5LQyvbdu28bsvb5aarubTa13hJnay3Zvc9Ynkp6oYN+K6hPADtzQ8nUa7p52EaC562x1p487V3BZ3EoxgjK+YspruqyRMpNYVOcNrKx05UpbL/ICePXty4k90qQrhB09HSI1h9FOSkxDNRW+7I23cuZontlPhaV+1BlGv0H0+vdaFg4uknO7Fm9GNiXd/PqN2oxr2ite9e1jP70TUgG7fvsMpLlCo+oknnnD1V2NZWPP4Tbwi+TmYfQr2s5H7hK5ljHRVNR3tsE9mP04AmqONY2SxHfbJzMcJ086ePetKu2OHfTL7ccK6EC+MkBbEFNthn6x8Lb99+za/QtknR3m83EWO73u914UAfIwoRN4vvPA5Q4YMfn9GsDyGviJVBrh+/QYRVSaivJQqVUP2ctWrV4+9dGhQ7gVA8b/y3mHwAQYKAAQWIgAQYL2qrA7K4qDLUhmV6AcHMBxxQVa1HnGhAEipodJqYJqqA4l5sQzAOpSBinXjN4D7PmFbsE0A24htBWbaJ6zjm2++4d+0yz6Z/ThhOWiuptthn8x8nOAFQM5CzG+XfTL7cbp27Rp9+eWX3Htil32y8rV8yJAhAd+j1We8437vN5oN6N27N6whfvfG1q1b+fsyZcokuK4mTZrwvOPGjfP6/bx58/j7pk2b+lxHrly5eJ6cOXNqly5d0i5evMgvfL5w4YLfn2/evKndu3dPi4mJ4fU++2yMdr9i42mtRInbrunu8+AzwP+R/uzr9yPx2ch9On/+vBYdHc3HxC77ZPbjBK2hObS3yz6Z+TjhmpM4cWLW3S77ZPbjBM1xn0Abt8s+WflafuPGjYDv0eoz3nG/x/HE/T8hbNHVqOrv+RrCqSxo5O/Qc13wmqVNm5bCxYQJUTRgwGWqWLE6TZu2nqKi7q/bPVBQfVZu0Eh+9vzNSH82ap/cuwbssk9mP07QWrnz7bJPCX02cp8ACtu7d79YfZ+scpzc12+XfbLitTx58uT8Chb330wIWxheKqgcGdq9gdxeoGTJkrquK9wg/CJNGnzaTfny6f7zjkWdoO4nqiCa2wlp46K5E4g2ybXcFneSKlWqcB8rss17S/OAQswAObgSArUdcVBQdkf1IysQeIdcYAiEfOqpp8gIVIxZILFmgmhuNaSdi952R9q4czW3heGVNGlS6tGjB3/u3r07By0qxo4dy96rGjVqUNmyZV3TkYqhSJEi1L9//1jrypEjB7Vq1YqNrJdffpnd74q+ffty+aE2bdrESa7qtIbjJERz0dzuSBsXzZ2AZpL7py26GsFbb71Fv//+O61Zs4YKFSrERaExWgT1GrNkyUJfffVVrPkxEgJ1F9WoCXdgrK1bt45mz55NW7ZsoXLlytGuXbtox44dVLBgQRo9ejQ53VXqJERz0dzuSBsXzZ1AtEnun7a5eyMobtmyZTRo0CBKkSIFzZs3jw2vDh06cFxWvgCCojJnzkwbNmzgzPDwfGGYNUoSIVHpX3/9RRkzZiSnW+xOQjQXze2OtHHR3AloJrl/2qJIttmIVJFsAAMQeUOQowR5RITII5rrj2guetsdaeP20txxRbKdRKRdpRMmTIjIeiO97kiuXzTXV2/R3H5t3MrXFqtqbkVNrK6539th6K8LpnOVyskqmpuhrUg711dzPbpgrHptsarmVtTE6pr7ixheFsMsDcdJiOaiud2RNi6aOwHNJPdPMbwshllcpU5CNBfN7Y60cdHcCUSb5P4pd2+LYRaL3UmI5qK53ZE2Lpo7Ac0k908xvARBEARBEHRCDC+L4a0IqCCa2w1p56K33ZE27lzNJY9XhEoY3blzh/uRUYIonMBFevz4ccqZM2dEGs+pU6ciVg4pkuuO5PpFc331Fs3t18atfG2xquZW1MTKmqMKTkxMDCVJkoQTr8eHGF4RAEW0cQAEQRAEQXAO0dHRdO/ePWfUajRb+aKbN2+yAZY1a1ajN0cQBEEQhAhy+vRpNrhw/08I8XgJgiAIgiDohATXC4IgCIIg6IQYXoIgCIIgCDohhpcgCIIgCIJOiOElCIIgCIKgE2J4CYIgCIIg6IQYXgaDtBODBw+mQoUK8TBUJHbr1KkT/fvvvwGv6+LFi9SrVy/KkycPJUuWjN/xP6YL4dUcmn777bfUunVrKlasGKVKlYrSpElDFStWpI8//pju3r0rkkegjbuzd+9eSpEiBSdCrF+/vugd5jbuqXXnzp3pkUce4fVlyZKFKleuTCNGjBDdI6D5L7/8Qk899RRlzpyZE3IiLVHDhg3pjz/+EL3/Y9OmTTR8+HBq2rQp5cqVi68D/qRyMMX9UxMM48aNG1rlypVRrVPLkSOH1qJFC61ChQr8f9asWbWDBw/6va6zZ89qBQsW5GXz5cvH6ypevDj/X7hwYe38+fMR3RenaT5w4EBeJjo6WitbtqzWsmVLrXbt2lqyZMl4evXq1bVr165pTiecbdyTWrVqaVFRUbyuevXqhXW7rUy4NZ89eza3a2hdpkwZ7fnnn9eeeOIJLXv27Fr+/Pkjth9O1Xz06NG8HPSuWrUqX1vKly/P0/CaOHFiRPfFKjz77LMuTdQL7TQY9L5/iuFlIIMGDeIDW6lSJe3KlSuu6aNGjeLpuJH7S9u2bXmZpk2banfu3HFNf+WVV3h6p06dwr79TtZ82LBh2oABA7Rjx47Fmr5nzx4td+7cvC5873TC2cbdmTRpEi/fpUsXMbwiqPnmzZu1JEmSaJkyZdJWrVoV67t79+5pGzZsCOr42Y1waX7mzBk2HpImTRpHbxjAMMZSpkwZ6zecyvDhw7W3335bW7BggXby5MmQDC+9759ieBnE7du3tfTp0/NBxcXNk5IlS/J3mzZtSnBdJ06cYM8LLpBogO7cvHlTy5Ili5YoUSLt1KlTmpMJp+bx8e233/J68ubNqzmZSOmNdpwhQwatbt262rJly8TwiqDmVapU4flxcxMirzl0xrz169f3+n2pUqX4+/Xr18vh8CBYw8uI+6fEeBnE6tWrue84f/78VLp06TjfN2vWjN8XLFiQ4LoWL17MtSGrV68ep7Ao+qobNWrEpQwwn5MJp+bxUapUKX4PNYbJ6kRK7549e9KNGzdo4sSJYdtWuxBOzXft2kVr1qzhmCXEFwmR1xzXa3/IlCmTHI4wYcT9Uwwvg9i2bRu/lylTxuv3arqaT6912Rm9dDpw4AC/Z8+enZxMJPRetGgRff/99zRgwAAqUKBAmLbUPoRT86VLl/L7E088QdevX6cpU6ZQjx492PCdPHkyXb58OazbblXCqXn58uUpXbp0rD0MOnfmzJlD27dv50ENMPKE8GDE/VOKZBvEkSNH+P2hhx7y+r2afvjwYV3XZWf00mncuHH8/uyzz5KTCbfe165do5dffpkKFy5Mffv2DeOW2odwar5z505+x0ixxx57jEc2utO/f3+aO3cuValShZxMODVPnz49TZo0idq0acMeGGiLEXsHDx6kDRs28EhHGMBC+DDi/ikeL4O4evUqv6dMmdLr90hP4D6fXuuyM3ro9Nlnn9Hvv//OF9B+/fqRkwm33m+99RZf/NDFmDRp0jBuqX0Ip+YXLlxwPUjg8+zZs/n9f//7Hz3//PN05swZeuaZZ+jkyZPkZMLdztE1iW4tpJKA1wse3r/++otTeNSoUYMyZswYxq0Xrhpw/xTDyyDuxwIS5x6J73u912VnIq3TypUr6dVXX+X1oysGeXycTDj13rhxI40fP57atWtHtWrVCts22o1wao64FoCcdNOnT6fnnnuOHygQ84UcdmXLlqXz58/ThAkTyMmE+7oyatQoqlu3LlWrVo27FnHD37FjB/8PTy+MXiF8GHH/FMPLIJBsU3WfeAMxFSB16tS6rsvORFInXCDRtXj79m0aO3YsJ/VzOuHSGzd+JO9E7MvIkSMjsKX2IRLXFTxAeCaoxU0KyUHB8uXLycmEU/MVK1ZQnz59uGt31qxZVKJECfa4PProo/TDDz9wvNGPP/5Iv/32W5j3wrmkMeD+KTFeBpE7d25+P3bsmNfv1Yg4ZM/Vc112JlI67d+/n+rVq8cjm9555x0OPhbCpzeW37p1Kw9WaN68eazvVFZpdMXUrFmTL44LFy50rPzhbOPIUu/+7knevHn5/dSpU+Rkwqk5PIsAD27R0bH9Ivi/SZMmtHnzZjZ2n3zyyTBsvZDbgPunGF4GoVIO4IbiDZxcoGTJkrquy85EQqfjx49ztwDiXHr37s0lQ4TI6A2NfcUTIfYI3gJ4xZxMODVXqRHQneiNc+fO8bvTPenh1Pzo0aP8njZtWq/fq+m+jokQOIbcP8OWEUwIiFu3bmnp0qXjpG9bt26N8/1jjz3G323cuDHBdR0/fpwTwCHb8enTp+P8DkpWIAGcZ3I4pxFOzcG5c+dcZSVefPFFLSYmJgJbbV3Crbc3JIFq5DRHyatUqVJxYskjR47E+R5tXqpihFfzdu3a8bx490abNm34e1TOEMKTQNWI+6cYXgai6v0hO/TVq1dd08eMGcPTa9SoEWv+8ePHc92ofv36xVnXCy+8wMs0a9YsVsmDXr168fT27dtHeG+cpTmWrVixIi+DWmoonyJETm9fiOEVWc0xDcs0aNAg1roWL16sJU6cmEvYSBb18Gn+448/8vy40XtWC5g3bx4bCHjt3r3br/PDSVAChpeZ7p9ieBlcWFXdvHPmzMmFOdX/KFOwf//+WPMPHjzYZyNAjS8UrMX3eIcxUKJECf4fxT/hnRHCp7k6IXGBbN26NX/v7eV0wtnGvSGGV2Q1x7pU2SAUf27cuDEXg8bNH9M++OCDAFuEPQmX5vCaN2/e3FX0uVy5cvw/3tU00fw+CxcuZI3VSxUWd5+Gecx4/xTDy2CuX7/OBVZxsOHqzJ49u9ahQwft6NGjceZN6KaECuoo6vnwww/zuvDes2dP7cKFCzrsibM0x//qQhjfSwhvG/dEDK/ItXH37hbc7IsWLcoeBXSr1alTJ9ZNTQif5jC+Jk+erFWvXp1rQMKzmDlzZu3pp59mT6NwnylTpiR4/cU8Zrx/RuFP+CLGBEEQBEEQBF9IHi9BEARBEASdEMNLEARBEARBJ8TwEgRBEARB0AkxvARBEARBEHRCDC9BEARBEASdEMNLEARBEARBJ8TwEgRBEARB0AkxvARBEARBEHRCDC9BEARBEASdEMNLEARBEARBJ8TwEgRBEARB0AkxvARBEARBEHRCDC9BEARBEASdEMNLEARBEARBJ8TwEgRBEARB0AkxvARBEARBEHRCDC9BEARBEASdEMNLEARB8JtDhw5RVFQUv/BZCC/Lly936Rss77zzDi9fs2bNsG6bEB7E8BIEQbA46kbrz0uwLlu3buVjPXbsWKM3xbJMnTqVNYSBaxSJDftlQRAEIexky5ZNVLUwKVOmpMKFC/s0vN59913KkycP9erVy+c6MmfOzOvInTt3BLfUuobXihUr+LNRHkExvARBEGzEyZMnjd4EIQQqVKhAu3fvDknDHj168EswJ9LVKAiCIAiCoBNieAmCIDiMO3fu0JIlS6hnz55Urlw5ypEjByVNmpSyZs1K9erVo++++440TQtq3ceOHaPevXtT8eLFKVWqVJQsWTLKmTMnlS1blqdv2LDB57KIu2nVqhV3kSVPnpzSpUvHHqCPPvqIrl27FtT2dOjQgWPb8I59+uyzz3idWHfatGmpatWqNGPGjATXg21r3rw55cqVi/cJ3Xl16tShKVOm0L1793wut379enrhhRcob968vE/QBF2FNWrUoPfee4/18vwdb/F4+L9jx478+fDhw3Fi9xC3FF9wPY55lixZePrHH38c775OnjyZ54M+169fj/P9/v376ZVXXqGiRYtS6tSpuXsUn9H9eeTIEQq2CzAqKooeeeQR/n/ZsmXUuHFjbpuJEiXi46fAb0yYMIEaNGhAhQoVYk2xHcWKFfO5DWr9qpsRXbaeGnobLLJlyxbq1KkT5c+fn/cTv1OqVCl666236OzZs0HtKxqiIAiCYGEGDx4MK4lf/rBs2TLX/HglS5ZMS506daxpzZs31+7duxdn2YMHD7rmwWd3tm7dqmXIkMH1faJEifj/qKgo17T27dvHWeedO3e0//u//4v1+9geLK/+L1y4sHbo0KGAtcHvqd9t2bIlf46Ojo6zXR07dtRiYmK8rqN3796u+bBM+vTpY21b7dq1tcuXL8dZburUqbF+AzqnTZs21n5OmTLF57FxJ1u2bK5lsf343/01YsSIOO2hRo0asdbRvXt3nl6uXLl4NatZsybP16FDhzjfffHFF1qSJEli7VOKFClc/2Mbf/vtNy1QpkyZwsvnyZNHGzdunEu3dOnS8e+5txvsl7uGmAeauP+/atWqWOufOXMm66S2PVWqVHE0PHLkSKxl3n777VjHL2XKlFrSpEld/+fIkUPbvHlzwPsqhpcgCILDDK9169ZprVu31n7++Wft5MmTLoPj3LlzfNNTN3h8DsTwqlOnDk8vU6aMtnbtWtd6b926pe3Zs0cbOXKk9tFHH8VZ56uvvsrL4eb36aef8naA27dvsyFSunRp13q9GYP+GF64GeMm+t5772mXLl3i706fPq316NHDtT/e9nf8+PGu77t06aKdOHGCp1+9elUbM2aMljhxYv4ORp07165d09KkScPftWnTRtu3b5/rOyy7ceNG7Y033uBj4I/h5WmcxIcvw2v9+vWudf/zzz9elz18+LDL2Fi6dGms7+bOncvTYbz069ePDWEcY7x2797NxroyvrCeQJjy374lT56cjVoYfcoQunv3biz9YEAOHz5c27Vrl3b9+nWX8Y79q1+/Pq8nZ86cru/cUUYbNIoPHFvMh2M4bNgw13HHtuDYwdjG9w899JB25cqVgPZVDC9BEAQbGV6eT/Hur7///tuv9c2aNYvXlT9//oAML+X5+PPPP/3e9h07dvCNHt6E7du3e50H3iTc4LBu3PyDMbzwGjRokNd5YBjh+4wZM2o3btxwTceNG9PwXatWrbwu+/HHH7vWv2HDhjhGDjwrMAr8JZKGF4DnEN/179/f67JDhw7l7x9++OFYHkAYz7ly5eLvJk+e7PO3n3nmGZ4HxnQgTPlv3/Bq2rSpFiwwjEqWLMnrmT59elCG15kzZ7g9ol3+/vvvXufBMS1btiyvC0ZaIEiMlyAIgo04deqUzxfifPwBsTMqlufEiRN+/3b69On5PZBlEE8EJwB+s0SJEl7nSZMmDcf7gF9//ZWCIUWKFNSnTx+v37399tv8fv78eY59U+AzpgH3GCp3Xn75ZY5DAoiN89Ti9u3bdO7cOTILbdu25XfEtXmL45s+fTq/t2nTJlac2eLFi+nff//ldCUq1swb7dq1C+k4gf79+1OwIB6sfv36/Hn16tVBrQPaILYN8Y+I4/NG4sSJOR4xmH2VdBKCIAg2wt+g+CtXrnCg+cKFC+mff/6hixcvejXMcLNVhkVCNGzYkL788ktq3749rVmzhp555hkqX748ByX7Qt0ccWPPnj27z/muXr3qCiwPBtxEESzujYIFC9JDDz3Ege4bN26kRo0a8XR8Bg8//DAHcfu60deuXZtv1mp+gGDsIkWKcGqIihUrUrdu3XjgAoxLLGOk4TVo0CAOQEeguXsA/qZNm7gtuBtQnsfpwoUL8bYHGJqhHKcUKVJQmTJlEpxv1apVbLSvW7eOj5u3wReeAxf8Re3r33//HW+bvHHjRlD7KoaXIAiCw9izZw8/ybvfmGAcwUsTHX2/IwQeMhDIaEKMPty3bx+PSBs9ejS/YGQ89thj7NHq0qULjwp05/jx4y7DShlX8eFtlJ0/eP6ut++hx+nTp13T1OeEloXR5j4/wH7PnDmTmjRpQgcPHqR+/frxCzpXrlyZmjZtygZqfEZpJMCIUYyoxOhJeLfcDS/l7YKxDKPR23GCYaXaRnwooyRQMmXK5GqDvujbty+3NXetM2TIwCNzAdoR2m2wI2HVvmIf/NmPQNukdDUKgiA4DHQVwcjA0P1Zs2ZxVxhuUjAckIAVXi5FIGklYLgtXbqUvRFvvvkmValShbtk4EkZMmQIe5bcu+OASsUwfPhw/q2EXsGWegmlXJK/y3rOh7QD8Hj9+OOPbHQ++uijfCP//fffuYsSxs2OHTvIqO7G2bNnuwyLu3fvuo6Np7fL/TihG8+f4xRsOpJECXgD0f2rjC5oCP1u3brFXcJou3ghbQkIdhvUvr700kt+7WegNUvF8BIEQXAQR48epT///JM/40bbrFkzypgxY1iz3yM31ocffshdNujC/Omnn7iLDTd55ERy95iorpxIGyAJdTspYxO5zBTqMzTzZ93Ik+UJvDDwbn3++ee8j2fOnOEuXmiO9cLrpTfIR4YuvcuXL/OxAb/99hsb3kmSJKHnn38+zjJ6HaeEgBcRoNsWubxgzHoaa6G230jvqxhegiAIDsLdiChdurTXeeCRCRdIGopYrzlz5vD/N2/ejBX0DK8Y+Pnnn/3qagwWxF8hrs0b6B5VxhNiwRTqM75D96wv7wi6VlUXnT9daV27dmXDVCXo9Df4XnXBBevJ8TZYQXUvqvennnqKk8N6oo4TDNRgg9bD2X5L+2i70AZe11A0VPuK+LFgY9XiQwwvQRAEB4GM7Ypt27bF+R7Gyfvvvx/wetFVFRMT4/N7eFgU7h6Kzp07cxcdPGNvvPFGvL+B4P9gjTN420aNGuX1O7W/8EI98cQTrun4DEMpvlGN8GSpmCA1yg2g+ys+fOkRH2pwALQKFdWdCE/X3r17XZ4vb92MAAMOVFD9q6++mmBckxoNGqn2u81L2wXwJh44cCAkDdEVi+MDo7p79+7xViZAmw/0eIjhJQiC4CBQVgUB1gDdfoi/Uqxdu5aDrTFyLVDgFUIMF4wYeHFgiCm2b9/O6QkAyrtUr17d9R0C71HmRd000Q22detWl0cCNz3cZFFeByMF8V2wN2ysY9iwYS7PF0q+wIj4+uuv+X+M9oOHToGbrzK40C2LmB/VTQrDY/z48a5tb9myJZdFcu8Sg+cEhpm7IYD9QfoBBNqDSpUquVJPJAS61QC6CH/44QcKBRiV6FLDcWrdujUbpghQx8hUb0CXTz/9lI3kzZs3875hP9QoRoBBBNhflGTCvJGg/n+pIjAKFsdTBdDD+Bk6dCiXMlLGcnwaLlq0KFYsozvQBTGHyhMLrTBKVxlgaJuI3cPgEawPI4MDIqCsX4IgCILlM9cvWLDAlXFdlULBS31G0kj1HRJ6+pNA1X26KheE5KPuJVbwGclZvSW97NWrV6zlkcE8U6ZMsbYTr9WrV4dcMshbKaN27dr5zIrvWTIIy7pvV61ateKUDHJPCKpK62B/3EvbILu6Zwb5+BKoulcHUFnVkUwVL/cknvElUHXntddei7WNXbt2TVDPb775xtVW8IIO2C/sn/u63n//fS0QpviZHBbVDKpVqxbneChdGzRooL311ls+9x8VFNC23EsvKQ2PHj0aa15UWXAvDYX2i311L5mEFzQJBPF4CYIgOAx4NVauXMkpHuBtgdcDcT0Y7Qhvhq+kkfGBlAvz58/nEWWPP/44d0uhWxCjGuFlQ5cN8iIhmN8TdLWNGTOGfxuj/woXLszTLl26xF4YeFfgeYK3S8XfBAO8VhMnTuT4IOwzvG/wOE2bNo29Xr7SGMCzgbih5557jhOIYr8QJ1WrVi366quveKQd/ncHcW1YLzTF6EZ43LA/mA8eIXhrdu7cGSdtQ0JgJCI0Rl4xdL0iBgmvYLofPbsVfXUzuoOC34iJQ5FoxMChaDR+Gx4xeC979OjBMYJI+RAJkiRJwt2jgwcPZg3wPzxQ0BTHFm0wvq5beGURk4fjg8EQiK9TGrp7aQG6vuHZgt4lS5bkfcS+Yp8Rz4eRuxioAo9hIETB+gpaAUEQBEEwMR06dGCjCqMHp06davTmCILEeAmCIAiCIOiFdDUKgiAIgiDohBhegiAIgiAIOiGGlyAIgiAIgk5IcL0gCIIgCIJOiMdLEARBEARBJ8TwEgRBEARB0AkxvARBEARBEHRCDC9BEARBEASdEMNLEARBEARBJ8TwEgRBEARB0AkxvARBEARBEHRCDC9BEARBEASdEMNLEARBEASB9OH/AdsShvx9APXcAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# ROC curves — 5 folds + mean with uncertainty band\n",
+    "val_plotter.plot_roc_cv(fold_rocs, show=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "39pn2z8u5rj",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Validate] ✅ Saved to /exp/mu2e/app/users/sgrant/MLTrain/CrvCosmic/output/images/ml/k/final/score_distribution_cv.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Score distribution — 5 folds overlaid with threshold\n",
+    "val_plotter.plot_score_distribution_cv(fold_scores, cv_threshold, show=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3fad1d83",
+   "metadata": {},
+   "source": [
+    "## CV summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "e1a1c8fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Validate] ✅ Initialised analyser for model: final\n",
+      "[Validate] ✅ Saved CV money table to /exp/mu2e/app/users/sgrant/MLTrain/CrvCosmic/output/ml/k/results/final/cv_money_table.csv\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>Metric</th>\n",
+       "      <th>dT cut</th>\n",
+       "      <th>ML model</th>\n",
+       "      <th>Improvement</th>\n",
+       "      <th>Description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Veto efficiency</td>\n",
+       "      <td>99.950 +/- 0.016%</td>\n",
+       "      <td>99.910 +/- 0.012%</td>\n",
+       "      <td>-0.040 +/- 0.020%</td>\n",
+       "      <td>Fraction of cosmics vetoed (TP/(TP+FN))</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Deadtime</td>\n",
+       "      <td>27.899 +/- 0.101%</td>\n",
+       "      <td>0.119 +/- 0.067%</td>\n",
+       "      <td>-27.780 +/- 0.116%</td>\n",
+       "      <td>Fraction of CE mix vetoed (FP/(FP+TN))</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Veto purity</td>\n",
+       "      <td>11.065 +/- 0.062%</td>\n",
+       "      <td>96.709 +/- 1.798%</td>\n",
+       "      <td>+85.644 +/- 1.765%</td>\n",
+       "      <td>Of vetoed events, fraction that are cosmics (T...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>F1 score</td>\n",
+       "      <td>19.925 +/- 0.100%</td>\n",
+       "      <td>98.275 +/- 0.932%</td>\n",
+       "      <td>+78.350 +/- 0.881%</td>\n",
+       "      <td>Harmonic mean of precision and recall</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Overall accuracy</td>\n",
+       "      <td>73.036 +/- 0.094%</td>\n",
+       "      <td>99.882 +/- 0.064%</td>\n",
+       "      <td>+26.846 +/- 0.107%</td>\n",
+       "      <td>Overall correct classification rate</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Figure of merit</td>\n",
+       "      <td>72.065 +/- 0.090%</td>\n",
+       "      <td>99.791 +/- 0.065%</td>\n",
+       "      <td>+27.726 +/- 0.104%</td>\n",
+       "      <td>eff_veto * (1 - deadtime)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Metric             dT cut           ML model         Improvement  \\\n",
+       "0   Veto efficiency  99.950 +/- 0.016%  99.910 +/- 0.012%   -0.040 +/- 0.020%   \n",
+       "1          Deadtime  27.899 +/- 0.101%   0.119 +/- 0.067%  -27.780 +/- 0.116%   \n",
+       "2       Veto purity  11.065 +/- 0.062%  96.709 +/- 1.798%  +85.644 +/- 1.765%   \n",
+       "3          F1 score  19.925 +/- 0.100%  98.275 +/- 0.932%  +78.350 +/- 0.881%   \n",
+       "4  Overall accuracy  73.036 +/- 0.094%  99.882 +/- 0.064%  +26.846 +/- 0.107%   \n",
+       "5   Figure of merit  72.065 +/- 0.090%  99.791 +/- 0.065%  +27.726 +/- 0.104%   \n",
+       "\n",
+       "                                         Description  \n",
+       "0            Fraction of cosmics vetoed (TP/(TP+FN))  \n",
+       "1             Fraction of CE mix vetoed (FP/(FP+TN))  \n",
+       "2  Of vetoed events, fraction that are cosmics (T...  \n",
+       "3              Harmonic mean of precision and recall  \n",
+       "4                Overall correct classification rate  \n",
+       "5                          eff_veto * (1 - deadtime)  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# CV money table — ML model vs dT cut baseline\n",
+    "cv_threshold = metrics_df[\"threshold\"].mean()\n",
+    "val_table = Validate({**results, \"tag\": \"final\"}, run=run, verbosity=1)\n",
+    "df_money = val_table.cv_money_table(fold_raws)\n",
+    "display(df_money)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9acf5db5",
+   "metadata": {},
+   "source": [
+    "## Final model\n",
+    "\n",
+    "Train on all data with fixed hyperparameters and CV-averaged threshold. Export to UBJ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "3d38d75d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/exp/mu2e/app/users/sgrant/MLTrain/CrvCosmic/notebooks\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pwd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a21f4838",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Train] ✅ Initialised\n",
+      "[Train] ⭐️ Training model: XGBClassifier\n",
+      "  Tag: final\n",
+      "  Random state: 42\n",
+      "  Scale features: False\n",
+      "\n",
+      "[Train] ⭐️   Hyperparams: {'n_estimators': 1000, 'max_depth': 3, 'learning_rate': 0.1}\n",
+      "\n",
+      "[Train] ✅ Training complete!\n",
+      "CV threshold: 0.1658 ± 0.1177\n",
+      "[Train] ✅ XGBoost model saved to /exp/mu2e/app/users/sgrant/MLTrain/CrvCosmic/output/ml/k/results/final/model.ubj\n",
+      "[Train] ✅ Results saved to /exp/mu2e/app/users/sgrant/MLTrain/CrvCosmic/output/ml/k/results/final/results.pkl\n",
+      "Model saved to ../output/ml/k/results/final/model_1.1.0.ubj\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "# Train on full dataset\n",
+    "full_data = AssembleDataset.get_full_train_data(data)\n",
+    "trn = Train(full_data, run=run)\n",
+    "results = trn.train(tag=\"final\", save_output=False, **best_hp)\n",
+    "\n",
+    "# CV-averaged threshold\n",
+    "cv_threshold = metrics_df[\"threshold\"].mean()\n",
+    "cv_threshold_std = metrics_df[\"threshold\"].std()\n",
+    "results[\"cv_threshold\"] = cv_threshold\n",
+    "results[\"cv_threshold_std\"] = cv_threshold_std\n",
+    "print(f\"CV threshold: {cv_threshold:.4f} ± {cv_threshold_std:.4f}\")\n",
+    "\n",
+    "\n",
+    "\n",
+    "trn._save_results(results, \"final\")\n",
+    "\n",
+    "# Export UBJ\n",
+    "out_dir = Path(f\"../output/ml/{run}/results/final\")\n",
+    "out_dir.mkdir(parents=True, exist_ok=True)\n",
+    "model_name = f\"model_{version}.ubj\"\n",
+    "results[\"model\"].get_booster().save_model(str(out_dir / model_name))\n",
+    "print(f\"Model saved to {out_dir / model_name}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "043fb4d3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "current",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/CrvCosmic/notebooks/validate.ipynb b/CrvCosmic/notebooks/validate.ipynb
new file mode 100644
index 0000000..a29a6bc
--- /dev/null
+++ b/CrvCosmic/notebooks/validate.ipynb
@@ -0,0 +1,449 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a3f8f516",
+   "metadata": {},
+   "source": [
+    "# Validate model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "673449c7",
+   "metadata": {},
+   "source": [
+    "Mostly redundant honestly, we do the important stuff in train.ipynb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "fdf091e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run = \"k\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "b5d3b282",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append(\"../src/ml\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "77f86841",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[LoadML] ✅ Initialised\n",
+      "[LoadML] ✅ Loaded training results for 'final' from /exp/mu2e/app/users/sgrant/MLTrain/CrvCosmic/output/ml/k/results/final\n",
+      "dict_keys(['tag', 'model', 'feature_names', 'y_train', 'y_val', 'y_test', 'y_pred', 'y_proba', 'y_pred_val', 'y_proba_val', 'y_proba_train', 'X_val', 'X_test', 'scaler', 'metadata_val', 'metadata_test', 'cv_threshold', 'cv_threshold_std'])\n",
+      "[Validate] ✅ Initialised analyser for model: final\n"
+     ]
+    }
+   ],
+   "source": [
+    "from load import LoadML\n",
+    "loader = LoadML(run=run)\n",
+    "results = loader.load_training_results(\"final\")\n",
+    "print(results.keys())\n",
+    "\n",
+    "from validate import Validate\n",
+    "val = Validate(results, run=run)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "82db5346",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on Validate in module validate object:\n",
+      "\n",
+      "class Validate(builtins.object)\n",
+      " |  Validate(results, run='j', img_out_path=None, verbosity=1)\n",
+      " |\n",
+      " |  Validate and analyse trained model. Works with results dict from Train.train().\n",
+      " |\n",
+      " |  Methods defined here:\n",
+      " |\n",
+      " |  __init__(self, results, run='j', img_out_path=None, verbosity=1)\n",
+      " |      Initialise with Train.train() results dict.\n",
+      " |\n",
+      " |  cv_money_table(self, fold_raws, save_csv=True)\n",
+      " |      Display CV-averaged money table from list of fold raw dicts.\n",
+      " |\n",
+      " |      Args:\n",
+      " |          fold_raws: list of raw dicts from money_table()[\"raw\"], one per fold.\n",
+      " |          save_csv: whether to save the table to CSV.\n",
+      " |\n",
+      " |  find_threshold(self, min_eff=0.999, n_thresholds=10000, plot=True, out_path=None, show=True)\n",
+      " |      Find highest threshold meeting min_eff event-level veto efficiency.\n",
+      " |\n",
+      " |  get_events_by_threshold(self, threshold, above=False)\n",
+      " |      Select test events by event-level max score. Returns DataFrame with all coincidences.\n",
+      " |\n",
+      " |  money_table(self, X, y, metadata, threshold=None, dT_min=0, dT_max=150, save_csv=True)\n",
+      " |      Event-level comparison of ML model vs dT window cut.\n",
+      " |\n",
+      " |  plot_feature_importance(self, importance_type='gain', feature_names=None, n_repeats=10, dataset='val', out_path=None, show=True)\n",
+      " |      Plot feature importance.\n",
+      " |\n",
+      " |      Args:\n",
+      " |          importance_type: \"gain\", \"weight\", \"cover\" (XGBoost built-in),\n",
+      " |                           \"perm\" (permutation importance).\n",
+      " |          feature_names: List of feature names. Auto-detected if None.\n",
+      " |          n_repeats: Number of repeats for permutation importance.\n",
+      " |          dataset: \"val\" or \"test\" — which set to use for permutation importance.\n",
+      " |\n",
+      " |  plot_physics_by_score(self, threshold, above=False, variables=None, nbins=50, out_path=None, show=True)\n",
+      " |      Plot feature distributions for events above or below threshold, split by CRY/CE mix.\n",
+      " |\n",
+      " |  plot_roc(self, out_path=None, show=True)\n",
+      " |      Plot ROC curve for test set.\n",
+      " |\n",
+      " |  plot_roc_cv(self, fold_curves, out_path=None, show=True)\n",
+      " |      Overlay ROC curves from CV folds with transparent lines + bold mean.\n",
+      " |\n",
+      " |      Args:\n",
+      " |          fold_curves: list of dicts with keys \"fpr\", \"tpr\", \"auc\".\n",
+      " |          out_path: optional save path.\n",
+      " |          show: whether to display.\n",
+      " |\n",
+      " |  plot_score_distribution(self, threshold, out_path=None, show=True, nbins=100)\n",
+      " |      Event-level max score distribution over full range [0, 1].\n",
+      " |\n",
+      " |  plot_score_distribution_cv(self, fold_scores, threshold, out_path=None, show=True, nbins=100)\n",
+      " |      Overlay event-level score distributions from CV folds.\n",
+      " |\n",
+      " |      Args:\n",
+      " |          fold_scores: list of dicts with keys \"signal\" and \"background\"\n",
+      " |                       (arrays of max-probability-per-event).\n",
+      " |          threshold: CV-averaged threshold to draw.\n",
+      " |          out_path: optional save path.\n",
+      " |          show: whether to display.\n",
+      " |\n",
+      " |  plot_threshold_cv(self, fold_scans, cv_threshold, min_eff=0.999, out_path=None, show=True)\n",
+      " |      Overlay threshold scans from K-fold CV on a single plot.\n",
+      " |\n",
+      " |  roc_auc(self)\n",
+      " |      Compute ROC AUC for train, val, and test sets.\n",
+      " |\n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Static methods defined here:\n",
+      " |\n",
+      " |  event_level_confusion(y_pred, y_true, metadata)\n",
+      " |      Event-level confusion matrix. Vetoes event if ANY coincidence is flagged.\n",
+      " |\n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Data descriptors defined here:\n",
+      " |\n",
+      " |  __dict__\n",
+      " |      dictionary for instance variables\n",
+      " |\n",
+      " |  __weakref__\n",
+      " |      list of weak references to the object\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "dd97e828",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Feature importance (gain):\n",
+      "  crv_z          : 489.9995\n",
+      "  nLayers        : 366.1406\n",
+      "  dT             : 288.1821\n",
+      "  PEs            : 185.9877\n",
+      "  nHits          : 153.1056\n",
+      "  angle          : 20.5342\n",
+      "  crv_x          : 15.5406\n",
+      "  crv_y          : 8.3965\n",
+      "[Validate] ✅ Saved to /exp/mu2e/app/users/sgrant/MLTrain/CrvCosmic/output/images/ml/k/final/bar_feature_importance_gain.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'crv_x': 15.540645599365234,\n",
+       " 'crv_y': 8.396490097045898,\n",
+       " 'crv_z': 489.99945068359375,\n",
+       " 'PEs': 185.9877471923828,\n",
+       " 'dT': 288.1820983886719,\n",
+       " 'nHits': 153.10556030273438,\n",
+       " 'nLayers': 366.1405944824219,\n",
+       " 'angle': 20.534231185913086}"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "val.plot_feature_importance()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "25755a23",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Validate] ✅ Saved to /exp/mu2e/app/users/sgrant/MLTrain/CrvCosmic/output/images/ml/k/final/roc_curve.png\n"
+     ]
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "val.plot_roc(show=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "89883afd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# should be passed from training results\n",
+    "threshold = results[\"cv_threshold\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "894413b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.16577657765776582"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "2fd5cdf7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Validate] ✅ Saved to /exp/mu2e/app/users/sgrant/MLTrain/CrvCosmic/output/images/ml/k/final/h1_score_distribution.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "val.plot_score_distribution(threshold=threshold)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "6cfaa16f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Validate] ⭐️ Events below threshold 0.1658:\n",
+      "  CRY:    10\n",
+      "  CE mix: 578004\n",
+      "[Validate] ✅ Saved to /exp/mu2e/app/users/sgrant/MLTrain/CrvCosmic/output/images/ml/k/final/h1_low_score_physics.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1920x1440 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "val.plot_physics_by_score(\n",
+    "    threshold=threshold,\n",
+    "    above=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "c4b7856f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['tag', 'model', 'feature_names', 'y_train', 'y_val', 'y_test', 'y_pred', 'y_proba', 'y_pred_val', 'y_proba_val', 'y_proba_train', 'X_val', 'X_test', 'scaler', 'metadata_val', 'metadata_test', 'cv_threshold', 'cv_threshold_std'])"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "9f7100b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df_money_cv = val.cv_money_table(results)\n",
+    "# display(df_money_cv)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "85e52648",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fb2fee5b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35a5efac",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "998df38d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5549dac4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d8750ecd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "current",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/CrvCosmic/run/run_ml_prep.py b/CrvCosmic/run/run_ml_prep.py
new file mode 100644
index 0000000..c109a3b
--- /dev/null
+++ b/CrvCosmic/run/run_ml_prep.py
@@ -0,0 +1,97 @@
+"""
+Prepare cosmic data for ML work
+Sam Grant 2025
+"""
+
+# preamble
+import sys
+import argparse
+from pathlib import Path
+
+sys.path.extend(["../src/utils", "../src/ml"])
+
+from process import MLProcessor
+from io_manager import Write
+
+def run(defname=None, file_name=None, feature_set="crv",
+    tag="test", run_str="test", max_workers=None, cuts_to_toggle=None):
+
+    # Output directory
+    out_path = Path(f"../output/ml/{run_str}/data/{tag}")
+    out_path.mkdir(parents=True, exist_ok=True)
+
+    # Processing 
+    process = MLProcessor(
+        defname=defname,
+        file_name=file_name,
+        feature_set=feature_set,
+        use_remote=True, # if defname else False,  # Use remote only if using defname
+        max_workers=max_workers,
+    ) 
+    results = process.execute()
+
+    # Save results
+    Write(out_path=out_path).write_all(results)
+    
+def main(): 
+    
+    # Add argument parser for test mode
+    parser = argparse.ArgumentParser(description='Prepare cosmic data for ML work')
+    parser.add_argument('--test', action='store_true', help='Run in test mode with local file')
+    parser.add_argument('--file', type=str, help='Local file path for test mode')
+    args = parser.parse_args()
+    
+    if args.test:
+        # Test configurations with specific local files
+        test_configs = [
+            {
+                #"file_name": args.file or "les/nts.mu2e.CosmicCRYSignalAllOnSpillTriggered.MDC2020aw_perfect_v1_3_v06_06_00.001202_00010066.root",
+                "file_name": args.file or "nts.mu2e.CosmicCRYSignalAllOnSpillTriggered.MDC2020aw_perfect_v1_3_v06_06_00.001202_00010066.root",
+                "tag": "test_CRY",
+                "feature_set": "crv",
+                "run": "test"
+                
+            },
+            {
+                # "file_name": "/exp/mu2e/data/users/sgrant/mu2e-cosmic/files/nts.mu2e.CeEndpointMix2BBTriggered.MDC2020aw_best_v1_3_v06_06_00.001210_00000245.root",
+                "file_name": "nts.mu2e.CeEndpointMix2BBTriggered.MDC2020aw_best_v1_3_v06_06_00.001210_00000245.root",
+                "tag": "test_CE_mix2BB", 
+                "feature_set": "crv",
+                "run": "test"
+            }
+        ]
+        
+        print("Running in test mode with two configurations:")
+        for config in test_configs:
+            print(f"  Processing: {config['file_name']} -> {config['tag']}")
+            run(file_name=config["file_name"], tag=config["tag"], feature_set=config["feature_set"], run_str=config["run"])
+        return
+
+    run_str = "k"
+
+    configs = [
+        {
+            "defname": "nts.mu2e.CeEndpointMix2BBTriggered.MDC2020aw_best_v1_3_v06_06_00.root",
+            "tag": "CE_mix2BB_onspill-LH_aw",
+            "feature_set": "crv",
+            "run": run_str
+        },
+        {
+            "defname": "nts.mu2e.CosmicCRYSignalAllOnSpillTriggered.MDC2020aw_perfect_v1_3_v06_06_00.root",
+            "tag": "CRY_onspill-LH_aw",
+            "feature_set": "crv",
+            "run": run_str
+        },
+        {
+            "defname": "nts.mu2e.CosmicCRYSignalAllMix2BBTriggered.MDC2020aw_best_v1_3_v06_06_00.root",
+            "tag": "CRY_mix2BB_onspill-LH_aw",
+            "feature_set": "crv",
+            "run": run_str
+        },
+    ]
+
+    for config in configs:
+        run(defname=config["defname"], tag=config["tag"], feature_set=config["feature_set"], run_str=config["run"]) 
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/CrvCosmic/src/__init__.py b/CrvCosmic/src/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/CrvCosmic/src/core/__init__.py b/CrvCosmic/src/core/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/CrvCosmic/src/core/analyse.py b/CrvCosmic/src/core/analyse.py
new file mode 100644
index 0000000..7e597c0
--- /dev/null
+++ b/CrvCosmic/src/core/analyse.py
@@ -0,0 +1,518 @@
+import awkward as ak
+import numpy as np
+import sys
+import os
+
+from pyutils.pyselect import Select
+from pyutils.pyvector import Vector
+from pyutils.pylogger import Logger
+
+# Add relative path to utils
+script_dir = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(os.path.join(script_dir, "..", "utils"))
+from io_manager import Load
+from hist_manager import HistManager
+
+
+class Analyse:
+    """Apply cuts driven by config/cuts.yaml and compute ML feature helpers.
+
+    Each cut registered with the cut manager has a counterpart ``active`` flag
+    in cuts.yaml — flipping the flag toggles the cut without code changes. The
+    set of cuts defined here therefore intentionally exceeds what the
+    MLPreprocess cutset enables, so other cutsets can selectively re-enable
+    them.
+    """
+
+    def __init__(self, on_spill=False,
+                 cut_config_path="../../config/cuts.yaml",
+                 cutset_name="MLPreprocess", verbosity=1):
+        """Initialise the cut/feature handler.
+
+        Args:
+            on_spill (bool): apply on-spill timing cuts.
+            cut_config_path (str): path to the cuts YAML, relative to src/utils/.
+            cutset_name (str): cutset key inside cuts.yaml.
+            verbosity (int): logging verbosity (0=critical, 1=info, 2=debug).
+        """
+        self.on_spill = on_spill
+        self.cut_config_path = cut_config_path
+        self.cutset_name = cutset_name
+        self.verbosity = verbosity
+
+        self.logger = Logger(print_prefix="[Analyse]", verbosity=self.verbosity)
+        self.selector = Select(verbosity=self.verbosity)
+        self.vector = Vector(verbosity=self.verbosity)
+
+        # Load thresholds and active flags from cuts.yaml
+        self.cut_config = {"description": "FAILED TO LOAD"}
+        Load().load_cuts_yaml(self)
+
+        # Histogram manager (consumes self.thresholds, self.selector, self.vector)
+        self.hist_manager = HistManager(self)
+
+        self.logger.log(
+            f"Initialised with:\n"
+            f"  on_spill        = {self.on_spill}\n"
+            f"  cut_config_path = {self.cut_config_path}\n"
+            f"  cutset_name     = {self.cutset_name} ({self.cut_config['description']})",
+            "info",
+        )
+
+    def get_pitch_angle(self, trkfit):
+        """pz / pT for each track segment in trkfit."""
+        pvec = self.vector.get_vector(trkfit["trksegs"], "mom")
+        pt = np.sqrt(pvec["x"] ** 2 + pvec["y"] ** 2)
+        pz = trkfit["trksegs"]["mom"]["fCoordinates"]["fZ"]
+        return ak.where(pt != 0, pz / pt, 0)
+
+    def get_trk_crv_dt(self, trkfit, crv):
+        """Per-(track, segment, coincidence) time differences.
+
+        Output shape: [event, track, segment, coincidence]. Used to define the
+        veto cut and to construct the dT field consumed as an ML feature.
+        """
+        trk_times = trkfit["trksegs"]["time"]              # [E, T, S]
+        coinc_times = crv["crvcoincs.time"]                # [E, C]
+        coinc_broadcast = coinc_times[:, None, None, :]    # [E, 1, 1, C]
+        trk_broadcast = trk_times[:, :, :, None]           # [E, T, S, 1]
+        return {
+            "dT": trk_broadcast - coinc_broadcast,
+            "trk_times": trk_times,
+            "coinc_times": coinc_times,
+        }
+
+    def _append_array(self, data, arr, name):
+        """Append a per-track or per-segment array under data["dev"][name]."""
+        try:
+            if "dev" not in ak.fields(data):
+                data = ak.with_field(data, ak.zip({name: arr}, depth_limit=1), "dev")
+            else:
+                new_dev = ak.with_field(data.dev, arr, name)
+                data = ak.with_field(data, new_dev, "dev")
+            return data
+        except Exception as e:
+            self.logger.log(f"Error appending '{name}' to data array: {e}", "error")
+            raise
+
+    def define_cuts(self, data, cut_manager):
+        """Register all cuts on the cut manager.
+
+        All masks are at track level (event-level masks are broadcast as needed).
+        Track-level masks for trkseg-level conditions use
+        ``ak.all(~at_<surface> | <condition>, axis=-1)`` so reflections are
+        handled implicitly.
+        """
+        # Tracker-surface masks
+        try:
+            at_trk_front = self.selector.select_surface(data["trkfit"], surface_name="TT_Front")
+            at_trk_mid = self.selector.select_surface(data["trkfit"], surface_name="TT_Mid")
+            at_trk_back = self.selector.select_surface(data["trkfit"], surface_name="TT_Back")
+            in_trk = at_trk_front | at_trk_mid | at_trk_back
+        except Exception as e:
+            self.logger.log(f"Error defining tracker surface masks: {e}", "error")
+            raise
+
+        # Tracks intersect full tracker
+        try:
+            thru_trk = (
+                ak.any(at_trk_front, axis=-1)
+                & ak.any(at_trk_mid, axis=-1)
+                & ak.any(at_trk_back, axis=-1)
+            )
+            cut_manager.add_cut(
+                name="thru_trk",
+                description="Tracks intersect full tracker",
+                mask=thru_trk,
+                active=self.active_cuts["thru_trk"],
+                group="Preselect",
+            )
+            data = self._append_array(data, thru_trk, "thru_trk")
+        except Exception as e:
+            self.logger.log(f"Error defining 'thru_trk' cut: {e}", "error")
+            raise
+
+        # Electron track-fit hypothesis
+        try:
+            is_reco_electron = self.selector.is_electron(data["trk"])
+            cut_manager.add_cut(
+                name="is_reco_electron",
+                description="Select electron track hypothesis",
+                mask=is_reco_electron,
+                active=self.active_cuts["is_reco_electron"],
+                group="Preselect",
+            )
+            data = self._append_array(data, is_reco_electron, "is_reco_electron")
+        except Exception as e:
+            self.logger.log(f"Error defining 'is_reco_electron' cut: {e}", "error")
+            raise
+
+        # One reco electron per event (handles split reflected tracks)
+        try:
+            one_reco_electron = ak.sum(is_reco_electron, axis=-1) == 1
+            cut_manager.add_cut(
+                name="one_reco_electron",
+                description="One reco electron / event",
+                mask=one_reco_electron,
+                active=self.active_cuts["one_reco_electron"],
+                group="Preselect",
+            )
+            data = self._append_array(data, one_reco_electron, "one_reco_electron")
+        except Exception as e:
+            self.logger.log(f"Error defining 'one_reco_electron' cut: {e}", "error")
+            raise
+
+        # Downstream tracks (pz > 0 through the tracker)
+        try:
+            is_downstream_seg = self.selector.is_downstream(data["trkfit"])
+            is_downstream = ak.all(~in_trk | is_downstream_seg, axis=-1)
+            cut_manager.add_cut(
+                name="is_downstream",
+                description="Downstream tracks (p_z > 0 through tracker)",
+                mask=is_downstream,
+                active=self.active_cuts["is_downstream"],
+                group="Preselect",
+            )
+            data = self._append_array(data, is_downstream_seg, "is_downstream_seg")
+            data = self._append_array(data, is_downstream, "is_downstream")
+        except Exception as e:
+            self.logger.log(f"Error defining 'is_downstream' cut: {e}", "error")
+            raise
+
+        # Truth PID: track parents are electrons
+        try:
+            is_truth_electron = data["trkmc"]["trkmcsim"]["pdg"] == self.thresholds["track_pdg"]
+            is_trk_parent = data["trkmc"]["trkmcsim"]["nhits"] == ak.max(
+                data["trkmc"]["trkmcsim"]["nhits"], axis=-1
+            )
+            has_trk_parent_electron = ak.any(is_truth_electron & is_trk_parent, axis=-1)
+            cut_manager.add_cut(
+                name="is_truth_electron",
+                description="Track parents are electrons (truth PID)",
+                mask=has_trk_parent_electron,
+                active=self.active_cuts["is_truth_electron"],
+                group="Preselect",
+            )
+            data = self._append_array(data, has_trk_parent_electron, "has_trk_parent_electron")
+        except Exception as e:
+            self.logger.log(f"Error defining 'is_truth_electron' cut: {e}", "error")
+            raise
+
+        # Track quality
+        try:
+            good_trkqual = self.selector.select_trkqual(
+                data["trk"], quality=self.thresholds["lo_trkqual"]
+            )
+            cut_manager.add_cut(
+                name="good_trkqual",
+                description=f"Track quality > {self.thresholds['lo_trkqual']}",
+                mask=good_trkqual,
+                active=self.active_cuts["good_trkqual"],
+                group="Tracker",
+            )
+            data = self._append_array(data, good_trkqual, "good_trkqual")
+        except Exception as e:
+            self.logger.log(f"Error defining 'good_trkqual' cut: {e}", "error")
+            raise
+
+        # t0 at tracker mid (only meaningful on-spill)
+        try:
+            if self.on_spill:
+                within_t0_seg = (
+                    (data["trkfit"]["trksegs"]["time"] > self.thresholds["lo_t0_ns"])
+                    & (data["trkfit"]["trksegs"]["time"] < self.thresholds["hi_t0_ns"])
+                )
+                within_t0 = ak.all(~at_trk_mid | within_t0_seg, axis=-1)
+                cut_manager.add_cut(
+                    name="within_t0",
+                    description=(
+                        f"t0 at tracker mid ({self.thresholds['lo_t0_ns']}"
+                        f" < t_0 < {self.thresholds['hi_t0_ns']} ns)"
+                    ),
+                    mask=within_t0,
+                    active=self.active_cuts["within_t0"],
+                    group="Tracker",
+                )
+                data = self._append_array(data, within_t0, "within_t0")
+        except Exception as e:
+            self.logger.log(f"Error defining 'within_t0' cut: {e}", "error")
+            raise
+
+        # t0 uncertainty
+        try:
+            within_t0err_seg = data["trkfit"]["trksegpars_lh"]["t0err"] < self.thresholds["hi_t0err"]
+            within_t0err = ak.all(~at_trk_mid | within_t0err_seg, axis=-1)
+            cut_manager.add_cut(
+                name="within_t0err",
+                description=f"Track fit t0 uncertainty (t0err < {self.thresholds['hi_t0err']} ns)",
+                mask=within_t0err,
+                active=self.active_cuts["within_t0err"],
+                group="Tracker",
+            )
+        except Exception as e:
+            self.logger.log(f"Error defining 'within_t0err' cut: {e}", "error")
+            raise
+
+        # Minimum active hits
+        try:
+            has_hits = data["trk"]["trk.nactive"] > self.thresholds["lo_nactive"]
+            cut_manager.add_cut(
+                name="has_hits",
+                description=f">{self.thresholds['lo_nactive']} active tracker hits",
+                mask=has_hits,
+                active=self.active_cuts["has_hits"],
+                group="Tracker",
+            )
+            data = self._append_array(data, has_hits, "has_hits")
+        except Exception as e:
+            self.logger.log(f"Error defining 'has_hits' cut: {e}", "error")
+            raise
+
+        # Distance of closest approach
+        try:
+            within_d0_seg = data["trkfit"]["trksegpars_lh"]["d0"] < self.thresholds["hi_d0_mm"]
+            within_d0 = ak.all(~at_trk_front | within_d0_seg, axis=-1)
+            cut_manager.add_cut(
+                name="within_d0",
+                description=f"Distance of closest approach (d_0 < {self.thresholds['hi_d0_mm']} mm)",
+                mask=within_d0,
+                active=self.active_cuts["within_d0"],
+                group="Tracker",
+            )
+            data = self._append_array(data, within_d0, "within_d0")
+        except Exception as e:
+            self.logger.log(f"Error defining 'within_d0' cut: {e}", "error")
+            raise
+
+        # Pitch angle bounds
+        try:
+            pitch_angle = self.get_pitch_angle(data["trkfit"])
+            within_pitch_lo_seg = pitch_angle > self.thresholds["lo_pitch_angle"]
+            within_pitch_lo = ak.all(~at_trk_front | within_pitch_lo_seg, axis=-1)
+            cut_manager.add_cut(
+                name="within_pitch_angle_lo",
+                description=f"Extrapolated pitch angle (pz/pt > {self.thresholds['lo_pitch_angle']})",
+                mask=within_pitch_lo,
+                active=self.active_cuts["within_pitch_angle_lo"],
+                group="Tracker",
+            )
+            data = self._append_array(data, pitch_angle, "pitch_angle")
+            data = self._append_array(data, within_pitch_lo, "within_pitch_angle_lo")
+
+            within_pitch_hi_seg = pitch_angle < self.thresholds["hi_pitch_angle"]
+            within_pitch_hi = ak.all(~at_trk_front | within_pitch_hi_seg, axis=-1)
+            cut_manager.add_cut(
+                name="within_pitch_angle_hi",
+                description=f"Extrapolated pitch angle (pz/pt < {self.thresholds['hi_pitch_angle']})",
+                mask=within_pitch_hi,
+                active=self.active_cuts["within_pitch_angle_hi"],
+                group="Tracker",
+            )
+            data = self._append_array(data, within_pitch_hi, "within_pitch_angle_hi")
+        except Exception as e:
+            self.logger.log(f"Error defining pitch-angle cuts: {e}", "error")
+            raise
+
+        # Loop helix max radius (lower and upper bounds)
+        try:
+            within_lhr_max_lo_seg = (
+                data["trkfit"]["trksegpars_lh"]["maxr"] > self.thresholds["lo_maxr_mm"]
+            )
+            within_lhr_max_lo = ak.all(~at_trk_front | within_lhr_max_lo_seg, axis=-1)
+            cut_manager.add_cut(
+                name="within_lhr_max_lo",
+                description=f"Loop helix maximum radius (R_max > {self.thresholds['lo_maxr_mm']} mm)",
+                mask=within_lhr_max_lo,
+                active=self.active_cuts["within_lhr_max_lo"],
+                group="Tracker",
+            )
+            data = self._append_array(data, within_lhr_max_lo, "within_lhr_max_lo")
+
+            within_lhr_max_hi_seg = (
+                data["trkfit"]["trksegpars_lh"]["maxr"] < self.thresholds["hi_maxr_mm"]
+            )
+            within_lhr_max_hi = ak.all(~at_trk_front | within_lhr_max_hi_seg, axis=-1)
+            cut_manager.add_cut(
+                name="within_lhr_max_hi",
+                description=f"Loop helix maximum radius (R_max < {self.thresholds['hi_maxr_mm']} mm)",
+                mask=within_lhr_max_hi,
+                active=self.active_cuts["within_lhr_max_hi"],
+                group="Tracker",
+            )
+            data = self._append_array(data, within_lhr_max_hi, "within_lhr_max_hi")
+        except Exception as e:
+            self.logger.log(f"Error defining 'within_lhr_max_*' cuts: {e}", "error")
+            raise
+
+        # Coincidence start time
+        try:
+            within_coinc_start_time = data["crv"]["crvcoincs.timeStart"] > self.thresholds["lo_crv_start_ns"]
+            within_coinc_start_time = ak.all(within_coinc_start_time, axis=-1)
+            cut_manager.add_cut(
+                name="within_coinc_start_time",
+                description=f"Coincidence start time (t_start > {self.thresholds['lo_crv_start_ns']} ns)",
+                mask=within_coinc_start_time,
+                active=self.active_cuts["within_coinc_start_time"],
+                group="CRV",
+            )
+            data = self._append_array(data, within_coinc_start_time, "within_coinc_start_time")
+        except Exception as e:
+            self.logger.log(f"Error defining 'within_coinc_start_time' cut: {e}", "error")
+            raise
+
+        # Coincidence end time
+        try:
+            within_coinc_end_time = data["crv"]["crvcoincs.timeEnd"] < self.thresholds["hi_crv_end_ns"]
+            within_coinc_end_time = ak.all(within_coinc_end_time, axis=-1)
+            cut_manager.add_cut(
+                name="within_coinc_end_time",
+                description=f"Coincidence end time (t_end < {self.thresholds['hi_crv_end_ns']} ns)",
+                mask=within_coinc_end_time,
+                active=self.active_cuts["within_coinc_end_time"],
+                group="CRV",
+            )
+            data = self._append_array(data, within_coinc_end_time, "within_coinc_end_time")
+        except Exception as e:
+            self.logger.log(f"Error defining 'within_coinc_end_time' cut: {e}", "error")
+            raise
+
+        # CRV veto window — also produces the dT field used as an ML feature.
+        try:
+            dt_trk_condition = at_trk_mid & is_reco_electron
+            trk_crv_dt = self.get_trk_crv_dt(data["trkfit"][dt_trk_condition], data["crv"])
+            dT = trk_crv_dt["dT"]
+            dT = ak.flatten(ak.flatten(dT, axis=3), axis=2)  # -> [E, T]
+
+            veto_condition = (
+                (dT > self.thresholds["lo_veto_dt_ns"])
+                & (dT < self.thresholds["hi_veto_dt_ns"])
+            )
+            unvetoed = ~ak.any(veto_condition, axis=-1)
+            cut_manager.add_cut(
+                name="unvetoed",
+                description=(
+                    f"Veto: {self.thresholds['lo_veto_dt_ns']} < "
+                    f"dT < {self.thresholds['hi_veto_dt_ns']} ns"
+                ),
+                mask=unvetoed,
+                active=self.active_cuts["unvetoed"],
+                group="Veto",
+            )
+
+            # Useful dT representations
+            dT_idx = ak.local_index(dT, axis=-1)
+            min_dT_idx = ak.argmin(abs(dT), axis=-1, keepdims=True)
+            min_dT = dT[min_dT_idx]
+            mid_value = (
+                self.thresholds["lo_veto_dt_ns"] + self.thresholds["hi_veto_dt_ns"]
+            ) / 2
+            cent_dT_idx = ak.argmin(abs(dT - mid_value), axis=-1, keepdims=True)
+            cent_dT = dT[cent_dT_idx]
+
+            data = self._append_array(data, veto_condition, "veto_condition")
+            data = self._append_array(data, unvetoed, "unvetoed")
+            data = self._append_array(data, trk_crv_dt["trk_times"], "trk_times")
+            data = self._append_array(data, trk_crv_dt["coinc_times"], "coinc_times")
+            data = self._append_array(data, dT_idx, "dT_idx")
+            data = self._append_array(data, dT, "dT")
+            data = self._append_array(data, min_dT, "min_dT")
+            data = self._append_array(data, min_dT_idx, "min_dT_idx")
+            data = self._append_array(data, cent_dT, "cent_dT")
+            data = self._append_array(data, cent_dT_idx, "cent_dT_idx")
+        except Exception as e:
+            self.logger.log(f"Error defining 'unvetoed' cut: {e}", "error")
+            raise
+
+        # Wide momentum window
+        try:
+            mom = self.vector.get_mag(data["trkfit"]["trksegs"], "mom")
+            within_wide_seg = (
+                (mom > self.thresholds["lo_wide_win_mevc"])
+                & (mom < self.thresholds["hi_wide_win_mevc"])
+            )
+            within_wide = ak.all(~at_trk_front | within_wide_seg, axis=-1)
+            data = self._append_array(data, mom, "mom_mag")
+            cut_manager.add_cut(
+                name="within_wide_win",
+                description=(
+                    f"Wide window ({self.thresholds['lo_wide_win_mevc']}"
+                    f" < p < {self.thresholds['hi_wide_win_mevc']} MeV/c)"
+                ),
+                mask=within_wide,
+                active=self.active_cuts["within_wide_win"],
+                group="Momentum",
+            )
+            data = self._append_array(data, within_wide, "within_wide_win")
+        except Exception as e:
+            self.logger.log(f"Error defining 'within_wide_win' cut: {e}", "error")
+            raise
+
+        # Extended momentum window
+        try:
+            mom = self.vector.get_mag(data["trkfit"]["trksegs"], "mom")
+            within_ext_seg = (
+                (mom > self.thresholds["lo_ext_win_mevc"])
+                & (mom < self.thresholds["hi_ext_win_mevc"])
+            )
+            within_ext = ak.all(~at_trk_front | within_ext_seg, axis=-1)
+            cut_manager.add_cut(
+                name="within_ext_win",
+                description=(
+                    f"Extended window ({self.thresholds['lo_ext_win_mevc']}"
+                    f" < p < {self.thresholds['hi_ext_win_mevc']} MeV/c)"
+                ),
+                mask=within_ext,
+                active=self.active_cuts["within_ext_win"],
+                group="Momentum",
+            )
+            data = self._append_array(data, within_ext, "within_ext_win")
+        except Exception as e:
+            self.logger.log(f"Error defining 'within_ext_win' cut: {e}", "error")
+            raise
+
+        # Signal momentum window
+        try:
+            mom = self.vector.get_mag(data["trkfit"]["trksegs"], "mom")
+            within_sig_seg = (
+                (mom > self.thresholds["lo_sig_win_mevc"])
+                & (mom < self.thresholds["hi_sig_win_mevc"])
+            )
+            within_sig = ak.all(~at_trk_front | within_sig_seg, axis=-1)
+            cut_manager.add_cut(
+                name="within_sig_win",
+                description=(
+                    f"Signal window ({self.thresholds['lo_sig_win_mevc']}"
+                    f" < p < {self.thresholds['hi_sig_win_mevc']} MeV/c)"
+                ),
+                mask=within_sig,
+                active=self.active_cuts["within_sig_win"],
+                group="Momentum",
+            )
+            data = self._append_array(data, within_sig, "within_sig_win")
+        except Exception as e:
+            self.logger.log(f"Error defining 'within_sig_win' cut: {e}", "error")
+            raise
+
+        self.logger.log("All cuts defined", "success")
+        return data
+
+    def apply_cuts(self, data, cut_manager, active_only=True):
+        """Apply the combined track-level mask from cut_manager and drop empty events."""
+        self.logger.log("Applying cuts to data", "info")
+        try:
+            data_cut = ak.copy(data)
+            trk_mask = cut_manager.combine_cuts(active_only=active_only)
+            data_cut["trk"] = data_cut["trk"][trk_mask]
+            data_cut["trkfit"] = data_cut["trkfit"][trk_mask]
+            data_cut["trkmc"] = data_cut["trkmc"][trk_mask]
+            data_cut = data_cut[ak.any(trk_mask, axis=-1)]
+            self.logger.log("Cuts applied successfully", "success")
+            return data_cut
+        except Exception as e:
+            self.logger.log(f"Error applying cuts: {e}", "error")
+            raise
+
+    def create_histograms(self, datasets):
+        """Create before/after histograms for the given labelled datasets."""
+        self.logger.log("Creating histograms", "info")
+        return self.hist_manager.create_histograms(datasets)
diff --git a/CrvCosmic/src/core/postprocess.py b/CrvCosmic/src/core/postprocess.py
new file mode 100644
index 0000000..1a19c43
--- /dev/null
+++ b/CrvCosmic/src/core/postprocess.py
@@ -0,0 +1,79 @@
+import awkward as ak
+
+from pyutils.pylogger import Logger
+from pyutils.pycut import CutManager
+
+
+class PostProcess:
+    """Combine per-file processing results into single arrays/histograms/cut flows."""
+
+    def __init__(self, on_spill, verbosity=1):
+        self.on_spill = on_spill
+        self.verbosity = verbosity
+        self.logger = Logger(print_prefix="[PostProcess]", verbosity=self.verbosity)
+        self.logger.log(f"Initialised with on_spill={self.on_spill}", "info")
+
+    def combine_cut_flows(self, results, format_as_df=True):
+        """Combine cut flows from a list of per-file results."""
+        if not results:
+            self.logger.log("results is None", "warning")
+            return None
+
+        if isinstance(results, list):
+            cut_flow_list = [r["cut_flow"] for r in results if "cut_flow" in r]
+        else:
+            cut_flow_list = [results["cut_flow"]]
+
+        cut_manager = CutManager()
+        combined = cut_manager.combine_cut_flows(
+            cut_flow_list=cut_flow_list,
+            format_as_df=format_as_df,
+        )
+        return combined.round(3)
+
+    def combine_hists(self, results):
+        """Sum histograms across per-file results."""
+        if not results:
+            self.logger.log("results is None", "warning")
+            return None
+
+        combined_hists = {}
+        for result in results:
+            hists = result.get("hists")
+            if not hists:
+                continue
+            for name, h in hists.items():
+                if name not in combined_hists:
+                    combined_hists[name] = h.copy()
+                else:
+                    combined_hists[name] += h
+
+        self.logger.log(
+            f"Combined {len(combined_hists)} histograms over {len(results)} results",
+            "success",
+        )
+        return combined_hists
+
+    def combine_arrays(self, results):
+        """Concatenate per-file event arrays into one awkward array."""
+        if not results:
+            self.logger.log("results is None", "warning")
+            return None
+
+        arrays = []
+        for result in results:
+            array = ak.Array(result["events"])
+            if len(array) == 0:
+                continue
+            arrays.append(array)
+
+        if not arrays:
+            self.logger.log("Combined array has zero length", "warning")
+            return arrays
+
+        combined = ak.concatenate(arrays)
+        self.logger.log(
+            f"Combined arrays, result contains {len(combined)} events",
+            "success",
+        )
+        return combined
diff --git a/CrvCosmic/src/ml/__init__.py b/CrvCosmic/src/ml/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/CrvCosmic/src/ml/assemble.py b/CrvCosmic/src/ml/assemble.py
new file mode 100644
index 0000000..5c5549f
--- /dev/null
+++ b/CrvCosmic/src/ml/assemble.py
@@ -0,0 +1,246 @@
+#
+# Sam Grant 2025
+# 
+
+# System tools  
+import os
+import sys
+from pathlib import Path
+import warnings
+warnings.filterwarnings("ignore") # suppress warnings
+
+# Data tools
+import numpy as np
+import pandas as pd
+import awkward as ak
+
+# ML tools
+from sklearn.model_selection import GroupShuffleSplit, GroupKFold
+
+# Internal modules
+script_dir = os.path.dirname(os.path.abspath(__file__))
+REPO_ROOT = Path(script_dir).parents[1]
+sys.path.extend([
+    os.path.join(script_dir, "..", "utils"),
+    os.path.join(script_dir, "..", "core")
+])
+
+import matplotlib.pyplot as plt
+
+from draw import Draw
+from load import LoadML
+from pyutils.pylogger import Logger
+from pyutils.pyplot import Plot
+
+class AssembleDataset():
+    """Load, label, and split CRY/CE mix data for ML training."""
+    def __init__(self, run="k", cutset_name="MLPreprocess", verbosity=1):
+        self.run = run
+        self.cutset_name = cutset_name
+        self.base_in_path = REPO_ROOT / f"output/ml/{self.run}/data/"
+        self.img_out_path = REPO_ROOT / f"output/images/ml/{self.run}/process/" 
+        self.logger = Logger(print_prefix="[Assemble]", verbosity=verbosity)
+        self.load_data()
+        self.logger.log(f"Initialised", "success")
+
+    def load_data(self):
+        """ Load input data from processing """
+        self.cry_data, self.ce_mix_data = LoadML(run=self.run).get_results()
+        # Convert to DataFrame
+        self.df_cry = ak.to_dataframe(self.cry_data["events"])
+        self.df_ce_mix = ak.to_dataframe(self.ce_mix_data["events"])
+        self.logger.log("Loaded data", "success")
+
+    def draw_cuts(self, ml=True):
+        draw = Draw(cutset_name=self.cutset_name)
+        if not ml:
+            draw.plot_summary(self.cry_data["hists"], out_path = self.img_out_path / "h1o_3x3_cuts_CRY.png")
+            draw.plot_summary(self.ce_mix_data["hists"], out_path = self.img_out_path / "h1o_3x3_cuts_CE_mix.png")
+        else:
+            draw.plot_summary_ml(self.cry_data["hists"], out_path = self.img_out_path / "h1o_3x3_cuts_CRY.png")
+            draw.plot_summary_ml(self.ce_mix_data["hists"], out_path = self.img_out_path / "h1o_3x3_cuts_CE_mix.png")
+
+
+    def draw_features(self, out_path=None, show=True):
+        """Plot CRV feature distributions for CRY vs CE mix."""
+        plotter = Plot(verbosity=0)
+
+        features = [
+            {"name": "crv_z", "xlabel": "CRV z-position [m]", "nbins": 100, "xmin": -15, "xmax": 10, "scale": 1e-3},
+            {"name": "crv_y", "xlabel": "CRV y-position [mm]", "nbins": 100, "xmin": -4000, "xmax": 4000},
+            {"name": "crv_x", "xlabel": "CRV x-position [mm]", "nbins": 100, "xmin": -4000, "xmax": 6000},
+            {"name": "angle", "xlabel": "Angle [rad]", "nbins": 80, "xmin": -3.14159, "xmax": 3.14159},
+            {"name": "nLayers", "xlabel": "Number of layers", "nbins": 6, "xmin": 0, "xmax": 6},
+            {"name": "PEs", "xlabel": "PEs", "nbins": 100, "xmin": 10, "xmax": 1e4, "log_x": True},
+            {"name": "nHits", "xlabel": "Number of hits", "nbins": 50, "xmin": 0, "xmax": 100},
+            {"name": "dT", "xlabel": r"$\Delta t$: track time $-$ CRV time [ns]", "nbins": 100, "xmin": -200, "xmax": 300},
+        ]
+
+        ncols = 4
+        nrows = (len(features) + ncols - 1) // ncols
+        fig, axes = plt.subplots(nrows, ncols, figsize=(ncols * 6.4, nrows * 4.8))
+        axes = np.atleast_2d(axes)
+
+        styles = {
+            "CRY": {"histtype": "stepfilled", "color": "C1", "alpha": 0.4, "linewidth": 2},
+            "CE Mix": {"histtype": "step", "color": "C0", "linewidth": 2},
+        }
+
+        for i, feat in enumerate(features):
+            row, col = i // ncols, i % ncols
+            ax = axes[row, col]
+
+            scale = feat.get("scale", 1.0)
+            cry_vals = np.array(ak.flatten(self.cry_data["events"][feat["name"]], axis=-1)) * scale
+            ce_vals = np.array(ak.flatten(self.ce_mix_data["events"][feat["name"]], axis=-1)) * scale
+
+            plotter.plot_1D_overlay(
+                {"CRY": cry_vals, "CE Mix": ce_vals},
+                nbins=feat["nbins"],
+                xmin=feat["xmin"],
+                xmax=feat["xmax"],
+                xlabel=feat["xlabel"],
+                ylabel="Normalized coincidences" if col == 0 else "",
+                log_y=True,
+                log_x=feat.get("log_x", False),
+                norm_by_area=True,
+                styles=styles,
+                ax=ax,
+                show=False,
+            )
+
+        # Hide unused axes
+        for i in range(len(features), nrows * ncols):
+            axes[i // ncols, i % ncols].set_visible(False)
+
+        plt.tight_layout()
+
+        if out_path is None:
+            out_path = self.img_out_path / "h1o_2x4_crv_features.png"
+        out_path = Path(out_path)
+        out_path.parent.mkdir(parents=True, exist_ok=True)
+        plt.savefig(out_path)
+        self.logger.log(f"Saved feature distributions to {out_path}", "success")
+
+        if show:
+            plt.show()
+
+    def get_cut_flows(self):
+        return {
+            "cry": self.cry_data["cut_flow"],
+            "ce_mix": self.ce_mix_data["cut_flow"]
+        }
+
+    def check_dT_window_results(self, dT_min = 0, dT_max = 150):
+        
+        def _apply_dT_window(df):
+
+            dT = df["dT"].values            
+            # Veto if dT is valid and in window
+            veto_condition = (~np.isnan(dT)) & (dT >= dT_min) & (dT <= dT_max)
+
+            # Event is vetoed if ANY coincidence passes cut
+            df_with_veto = df.copy()
+            df_with_veto["vetoed"] = veto_condition
+
+            event_veto = df_with_veto.groupby(["subrun", "event"])["vetoed"].max().reset_index()
+
+            total = len(event_veto)
+            unvetoed = total - event_veto["vetoed"].sum()
+
+            return [total, unvetoed, f"{(100*(1-(unvetoed/total))):.2f}"]
+    
+        return pd.DataFrame({
+            "Metric": ["Total", "Unvetoed", "Fraction [%]"],
+            "CRY": _apply_dT_window(self.df_cry),
+            "CE Mix": _apply_dT_window(self.df_ce_mix),
+        })
+    
+    def assemble_dataset(self, n_outer_folds=5):
+        """Combine, shuffle, and prepare data for nested CV.
+        Returns dict with full dataset and outer fold indices."""
+        # Use DataFrames already loaded in __init__
+        df_cry = self.df_cry
+        df_ce_mix = self.df_ce_mix
+
+        # SORT by event IDs to ensure reproducibility
+        df_cry = df_cry.sort_values(["subrun", "event"]).reset_index(drop=True)
+        df_ce_mix = df_ce_mix.sort_values(["subrun", "event"]).reset_index(drop=True)
+
+        # Add labels
+        df_cry["label"] = 1 # "signal"
+        df_ce_mix["label"] = 0 # "background"
+
+        self.logger.log("Got sorted and labelled DataFrames", "max")
+
+        # Combine and shuffle
+        df_full = pd.concat([df_cry, df_ce_mix], ignore_index=True)
+        df_full = df_full.sample(frac=1, random_state=42).reset_index(drop=True)
+
+        self.logger.log("Got combined dataset", "max")
+
+        self.logger.log(f"Columns: {df_full.columns}", "max")
+
+        # Replace inf with NaN (XGBoost handles NaN natively)
+        df_full.replace([np.inf, -np.inf], np.nan, inplace=True)
+
+        # Drop columns not used for training (but keep event/subrun for now)
+        # Would be better if this wasn't hardcoded
+        col_to_drop = ["d0", "tanDip", "maxr", "mom_mag", "PEs_per_hit", "t0", "timeStart", "timeEnd"]
+        df_ml = df_full.drop(columns=col_to_drop)
+
+        # Separate features, labels, and metadata
+        X = df_ml.drop(["label", "event", "subrun"], axis=1)
+        y = df_ml["label"]
+        metadata = df_ml[["event", "subrun"]]
+
+        # Event-level groups — all coincidences from the same event
+        # stay together in the same fold
+        groups = metadata["subrun"].astype(str) + "_" + metadata["event"].astype(str)
+
+        # Generate outer fold indices for nested CV
+        gkf = GroupKFold(n_splits=n_outer_folds)
+        outer_folds = list(gkf.split(X, y, groups=groups))
+
+        fold_sizes = [len(test_idx) for _, test_idx in outer_folds]
+        self.logger.log(
+            f"Prepared {n_outer_folds}-fold nested CV (event-level grouping)\n"
+            f"  Total: {len(X)} coincidences\n"
+            f"  Fold test sizes: {fold_sizes}",
+            "success"
+        )
+
+        # Return as dictionary
+        return {
+                "df_full": df_full,
+                "X": X,
+                "y": y,
+                "metadata": metadata,
+                "groups": groups,
+                "outer_folds": outer_folds,
+                }
+
+    @staticmethod
+    def get_fold_data(data, train_idx, test_idx):
+        """Slice full dataset into train/test dict for a given fold.
+        Compatible with Train and Optimise interfaces."""
+        return {
+            "X_train": data["X"].iloc[train_idx].reset_index(drop=True),
+            "X_test": data["X"].iloc[test_idx].reset_index(drop=True),
+            "y_train": data["y"].iloc[train_idx].reset_index(drop=True),
+            "y_test": data["y"].iloc[test_idx].reset_index(drop=True),
+            "metadata_train": data["metadata"].iloc[train_idx].reset_index(drop=True),
+            "metadata_test": data["metadata"].iloc[test_idx].reset_index(drop=True),
+        }
+
+    @staticmethod
+    def get_full_train_data(data):
+        """Return full dataset as train-only dict (for final model training)."""
+        return {
+            "X_train": data["X"].reset_index(drop=True),
+            "X_test": data["X"].reset_index(drop=True),  # evaluate on train set
+            "y_train": data["y"].reset_index(drop=True),
+            "y_test": data["y"].reset_index(drop=True),
+            "metadata_train": data["metadata"].reset_index(drop=True),
+            "metadata_test": data["metadata"].reset_index(drop=True),
+        }
\ No newline at end of file
diff --git a/CrvCosmic/src/ml/load.py b/CrvCosmic/src/ml/load.py
new file mode 100644
index 0000000..52afb16
--- /dev/null
+++ b/CrvCosmic/src/ml/load.py
@@ -0,0 +1,91 @@
+# System tools
+import os
+import sys
+from pathlib import Path
+
+# Python stack
+import pandas as pd
+import awkward as ak
+import joblib
+
+# pyutils
+from pyutils.pylogger import Logger
+
+# ML tools
+from sklearn.model_selection import train_test_split
+
+# Internal modules
+script_dir = os.path.dirname(os.path.abspath(__file__))
+REPO_ROOT = Path(script_dir).parents[1]
+sys.path.extend([
+    os.path.join(script_dir, "..", "utils"),
+    os.path.join(script_dir, "..", "core")
+])
+from io_manager import Load
+
+class LoadML():
+    """ Load up ML dataset """
+    def __init__(self, run="h", verbosity=1):
+        self.run = run
+        self.base_in_path = REPO_ROOT / f"output/ml/{self.run}/data/"
+        self.logger = Logger(print_prefix="[LoadML]", verbosity=verbosity)
+        self.logger.log("Initialised", "success")
+
+    def get_results(self):
+        """ Raw full processing output from pkl files
+        Can use be used seperately for validation
+        without cluttering main ML notebook """ 
+        # Define input paths
+        cry_in_path = self.base_in_path / "CRY_onspill-LH_aw"
+        ce_mix_in_path = self.base_in_path/ "CE_mix2BB_onspill-LH_aw"
+
+        # Load data
+        cry_data = Load(in_path=cry_in_path).load_pkl()
+        ce_mix_data = Load(in_path=ce_mix_in_path).load_pkl()
+
+        self.logger.log("Got full results", "success")
+
+        return cry_data, ce_mix_data
+
+    def load_training_results(self, tag, in_path=None):
+        """Load training results saved by Train._save_results().
+
+        For Keras models, also loads the separately saved .keras file.
+
+        Args:
+            tag: Model identifier (directory name under in_path)
+            in_path: Base directory containing saved results
+                     (default: output/ml/{run}/results)
+
+        Returns:
+            dict: Results dictionary matching Train.train() output
+        """
+        if in_path is None:
+            in_path = REPO_ROOT / f"output/ml/{self.run}/results"
+        else:
+            in_path = Path(in_path)
+
+        tag_path = in_path / tag
+        results_file = tag_path / "results.pkl"
+
+        if not results_file.exists():
+            self.logger.log(f"Results file not found: {results_file}", "error")
+            return None
+
+        results = joblib.load(results_file)
+
+        # Reload Keras model if saved separately
+        if results.get("model") is None and "keras_model_path" in results:
+            keras_path = Path(results["keras_model_path"])
+            if keras_path.exists():
+                import tensorflow as tf
+                results["model"] = tf.keras.models.load_model(keras_path)
+                self.logger.log(f"Loaded Keras model from {keras_path}", "info")
+            else:
+                self.logger.log(f"Keras model file not found: {keras_path}", "error")
+                return None
+
+        self.logger.log(f"Loaded training results for '{tag}' from {tag_path}", "success")
+
+        return results
+
diff --git a/CrvCosmic/src/ml/optimise.py b/CrvCosmic/src/ml/optimise.py
new file mode 100644
index 0000000..6e21bfa
--- /dev/null
+++ b/CrvCosmic/src/ml/optimise.py
@@ -0,0 +1,383 @@
+# Sam Grant 2025
+# Hyperparameter optimisation for ML models
+
+import os
+import itertools
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from pathlib import Path
+from sklearn.model_selection import GroupKFold
+
+REPO_ROOT = Path(os.path.dirname(os.path.abspath(__file__))).parents[1]
+
+from pyutils.pylogger import Logger
+from pyutils.pyplot import Plot
+from train import Train
+from validate import Validate
+
+
+class Optimise:
+    """Grid search over Train -> Validate pipeline. Minimises deadtime at target veto efficiency."""
+
+    def __init__(self, data, run="j", min_efficiency=0.999,
+                 model=None, scale_features=True, verbosity=1):
+        """Initialise with assemble_dataset() dict."""
+        self.data = data
+        self.run = run
+        self.min_efficiency = min_efficiency
+        self.model = model
+        self.scale_features = scale_features
+
+        self.results_table = []  # List of dicts for summary DataFrame
+        self.best_result = None
+
+        self.logger = Logger(print_prefix="[Optimise]", verbosity=verbosity)
+        self.logger.log("Initialised", "success")
+
+    def grid_search(self, param_grid, tag_prefix="scan",
+                    save_output=False, random_state=42, fixed_params=None):
+        """Run grid search. Returns best result (lowest deadtime meeting efficiency constraint)."""
+        # Build all combinations
+        param_names = list(param_grid.keys())
+        param_values = list(param_grid.values())
+        combinations = list(itertools.product(*param_values))
+
+        self.logger.log(
+            f"Grid search: {len(combinations)} combinations "
+            f"over {param_names}",
+            "info"
+        )
+
+        best_deadtime = np.inf
+
+        for i, combo in enumerate(combinations):
+            hyperparams = dict(zip(param_names, combo))
+            tag = f"{tag_prefix}_{i:03d}"
+
+            self.logger.log(
+                f"[{i+1}/{len(combinations)}] {tag}: {hyperparams}",
+                "info"
+            )
+
+            # Train
+            trainer = Train(
+                self.data,
+                model=self.model,
+                scale_features=self.scale_features,
+                run=self.run,
+                verbosity=0
+            )
+            all_params = {**(fixed_params or {}), **hyperparams}
+            training_results = trainer.train(
+                tag=tag,
+                random_state=random_state,
+                save_output=save_output,
+                **all_params
+            )
+
+            # Analyse — find threshold at minimum efficiency
+            ana = Validate(training_results, run=self.run, verbosity=0)
+            ana.roc_auc()
+            threshold_results = ana.find_threshold(
+                min_eff=self.min_efficiency,
+                plot=False,
+                show=False
+            )
+
+            # Record results
+            row = {
+                "tag": tag,
+                **hyperparams,
+                "train_auc": ana._train_auc,
+                "test_auc": ana._test_auc,
+                "overfit_gap": ana._train_auc - ana._test_auc,
+                "threshold": threshold_results["threshold"],
+                "veto_efficiency": threshold_results["veto_efficiency"],
+                "signal_efficiency": threshold_results["signal_efficiency"],
+                "deadtime": threshold_results["deadtime"],
+            }
+            self.results_table.append(row)
+
+            # Track best: lowest deadtime among combos meeting efficiency constraint
+            meets_constraint = threshold_results["veto_efficiency"] >= self.min_efficiency
+            if meets_constraint and threshold_results["deadtime"] < best_deadtime:
+                best_deadtime = threshold_results["deadtime"]
+                self.best_result = {
+                    "training_results": training_results,
+                    "threshold_results": threshold_results,
+                    "hyperparams": hyperparams,
+                    "tag": tag,
+                }
+
+        if self.best_result is not None:
+            self.logger.log(
+                f"Grid search complete. Best: {self.best_result['tag']} "
+                f"(deadtime: {best_deadtime*100:.3f}%, "
+                f"veto eff: {self.best_result['threshold_results']['veto_efficiency']*100:.3f}%)",
+                "success"
+            )
+            # Plot threshold overlay for winning scan only
+            best_ana = Validate(
+                self.best_result["training_results"],
+                run=self.run, verbosity=0
+            )
+            best_ana.find_threshold(
+                min_eff=self.min_efficiency,
+                plot=True,
+                show=True
+            )
+        else:
+            self.logger.log(
+                f"Grid search complete. No combination met "
+                f"{self.min_efficiency*100:.2f}% veto efficiency constraint",
+                "warning"
+            )
+
+        return self.best_result
+
+    def grid_search_cv(self, param_grid, n_folds=5, tag_prefix="scan_cv",
+                       random_state=42, fixed_params=None):
+        """K-fold CV grid search. Averages metrics across folds per hyperparam combo."""
+        param_names = list(param_grid.keys())
+        param_values = list(param_grid.values())
+        combinations = list(itertools.product(*param_values))
+
+        # CV folds over train set only, val/test held out
+        X = self.data["X_train"].reset_index(drop=True)
+        y = self.data["y_train"].reset_index(drop=True)
+        metadata = self.data["metadata_train"].reset_index(drop=True)
+
+        # Event-level groups for fold splitting
+        groups = metadata["subrun"].astype(str) + "_" + metadata["event"].astype(str)
+
+        gkf = GroupKFold(n_splits=n_folds)
+        folds = list(gkf.split(X, y, groups=groups))
+
+        self.logger.log(
+            f"CV grid search: {len(combinations)} combinations x {n_folds} folds "
+            f"= {len(combinations) * n_folds} fits over {param_names}",
+            "info"
+        )
+
+        best_deadtime = np.inf
+
+        for i, combo in enumerate(combinations):
+            hyperparams = dict(zip(param_names, combo))
+            tag = f"{tag_prefix}_{i:03d}"
+
+            fold_results = []
+
+            for k, (train_idx, test_idx) in enumerate(folds):
+                # Build fold data dict
+                fold_data = {
+                    "X_train": X.iloc[train_idx],
+                    "X_test": X.iloc[test_idx],
+                    "y_train": y.iloc[train_idx],
+                    "y_test": y.iloc[test_idx],
+                    "metadata_train": metadata.iloc[train_idx],
+                    "metadata_test": metadata.iloc[test_idx],
+                }
+
+                trainer = Train(
+                    fold_data,
+                    model=self.model,
+                    scale_features=self.scale_features,
+                    run=self.run,
+                    verbosity=0
+                )
+                all_params = {**(fixed_params or {}), **hyperparams}
+                training_results = trainer.train(
+                    tag=f"{tag}_fold{k}",
+                    random_state=random_state,
+                    save_output=False,
+                    **all_params
+                )
+
+                ana = Validate(training_results, run=self.run, verbosity=0)
+                ana.roc_auc()
+                threshold_results = ana.find_threshold(
+                    min_eff=self.min_efficiency,
+                    plot=False,
+                    show=False
+                )
+
+                fold_results.append({
+                    "train_auc": ana._train_auc,
+                    "test_auc": ana._test_auc,
+                    "threshold": threshold_results["threshold"],
+                    "veto_efficiency": threshold_results["veto_efficiency"],
+                    "deadtime": threshold_results["deadtime"],
+                    "signal_efficiency": threshold_results["signal_efficiency"],
+                })
+
+            # Average across folds
+            mean_train_auc = np.mean([r["train_auc"] for r in fold_results])
+            mean_test_auc = np.mean([r["test_auc"] for r in fold_results])
+            mean_deadtime = np.mean([r["deadtime"] for r in fold_results])
+            mean_veto_eff = np.mean([r["veto_efficiency"] for r in fold_results])
+            mean_signal_eff = np.mean([r["signal_efficiency"] for r in fold_results])
+            mean_threshold = np.mean([r["threshold"] for r in fold_results])
+            std_deadtime = np.std([r["deadtime"] for r in fold_results])
+            std_veto_eff = np.std([r["veto_efficiency"] for r in fold_results])
+
+            row = {
+                "tag": tag,
+                **hyperparams,
+                "train_auc": mean_train_auc,
+                "test_auc": mean_test_auc,
+                "overfit_gap": mean_train_auc - mean_test_auc,
+                "threshold": mean_threshold,
+                "veto_efficiency": mean_veto_eff,
+                "veto_efficiency_std": std_veto_eff,
+                "deadtime": mean_deadtime,
+                "deadtime_std": std_deadtime,
+                "signal_efficiency": mean_signal_eff,
+            }
+            self.results_table.append(row)
+
+            self.logger.log(
+                f"[{i+1}/{len(combinations)}] {tag}: "
+                f"deadtime={mean_deadtime*100:.3f}±{std_deadtime*100:.3f}%, "
+                f"veto_eff={mean_veto_eff*100:.3f}%, "
+                f"overfit_gap={mean_train_auc - mean_test_auc:.5f}",
+                "info"
+            )
+
+            # Track best: lowest mean deadtime meeting mean efficiency constraint
+            meets_constraint = mean_veto_eff >= self.min_efficiency
+            if meets_constraint and mean_deadtime < best_deadtime:
+                best_deadtime = mean_deadtime
+                self.best_result = {
+                    "hyperparams": hyperparams,
+                    "tag": tag,
+                    "mean_deadtime": mean_deadtime,
+                    "mean_veto_efficiency": mean_veto_eff,
+                    "fold_results": fold_results,
+                }
+
+        if self.best_result is not None:
+            self.logger.log(
+                f"CV grid search complete. Best: {self.best_result['tag']} "
+                f"(deadtime: {best_deadtime*100:.3f}%, "
+                f"veto eff: {self.best_result['mean_veto_efficiency']*100:.3f}%)",
+                "success"
+            )
+        else:
+            self.logger.log(
+                f"CV grid search complete. No combination met "
+                f"{self.min_efficiency*100:.2f}% mean veto efficiency constraint",
+                "warning"
+            )
+
+        return self.best_result
+
+    def get_summary(self):
+        """Results as DataFrame, sorted by deadtime ascending."""
+        if not self.results_table:
+            self.logger.log("No results yet — run grid_search first", "warning")
+            return None
+
+        df = pd.DataFrame(self.results_table)
+        df = df.sort_values("deadtime", ascending=True).reset_index(drop=True)
+        return df
+
+    def print_summary(self):
+        """Print results summary table."""
+        df = self.get_summary()
+        if df is not None:
+            print(f"\n{'='*80}")
+            print(f"Optimisation summary ({len(df)} runs, "
+                  f"min efficiency = {self.min_efficiency*100:.1f}%)")
+            print(f"{'='*80}")
+            print(df.to_string(index=False))
+            print(f"{'='*80}\n")
+
+    def save_summary(self, tag=None, out_path=None):
+        """Save results summary to CSV."""
+        df = self.get_summary()
+        if df is None:
+            return
+
+        if out_path is None:
+            fname = f"optimisation_summary_{tag}.csv" if tag else "optimisation_summary.csv"
+            out_path = REPO_ROOT / f"output/ml/{self.run}/results/{fname}"
+
+        out_path = Path(out_path)
+        out_path.parent.mkdir(parents=True, exist_ok=True)
+        df.to_csv(out_path, index=False)
+        self.logger.log(f"Summary saved to {out_path}", "success")
+
+    def plot_overfit_diagnostic(self, param_name, tag=None, out_path=None, show=True):
+        """Plot train vs test AUC and overfit gap as a function of a hyperparameter.
+        Other params are averaged over."""
+        df = self.get_summary()
+        if df is None or param_name not in df.columns:
+            self.logger.log(f"No results or '{param_name}' not in summary", "warning")
+            return
+
+        Plot(verbosity=0)  # Load Mu2e style
+
+        # Group by the parameter, averaging over all other hyperparam combinations
+        agg_dict = {
+            "train_auc": ("train_auc", "mean"),
+            "test_auc": ("test_auc", "mean"),
+            "train_auc_std": ("train_auc", "std"),
+            "test_auc_std": ("test_auc", "std"),
+            "overfit_gap": ("overfit_gap", "mean"),
+            "overfit_gap_std": ("overfit_gap", "std"),
+            "deadtime": ("deadtime", "mean"),
+            "deadtime_std": ("deadtime", "std"),
+        }
+        grouped = df.groupby(param_name).agg(**agg_dict).reset_index()
+
+        fig, axes = plt.subplots(1, 3, figsize=(5.3 * 3, 4.8))
+
+        # Train vs test AUC
+        ax = axes[0]
+        ax.errorbar(grouped[param_name], grouped["train_auc"],
+                     yerr=grouped["train_auc_std"],
+                     marker="o", label="Train", capsize=3)
+        ax.errorbar(grouped[param_name], grouped["test_auc"],
+                     yerr=grouped["test_auc_std"],
+                     marker="s", label="Test", capsize=3)
+        ax.set_xlabel(param_name)
+        ax.set_ylabel("AUC")
+        ax.legend(loc="best")
+        ax.set_title("Train vs test AUC")
+
+        # Overfit gap
+        ax = axes[1]
+        ax.errorbar(grouped[param_name], grouped["overfit_gap"],
+                    yerr=grouped["overfit_gap_std"],
+                    marker="o", color="purple", capsize=3)
+        ax.axhline(0, color="grey", linestyle="--", alpha=0.5)
+        ax.ticklabel_format(axis="y", style="scientific", scilimits=(0, 0),
+                            useMathText=True)
+        ax.set_xlabel(param_name)
+        ax.set_ylabel("Train AUC $-$ test AUC")
+        ax.set_title("Overfit gap")
+
+        # Deadtime
+        ax = axes[2]
+        ax.errorbar(grouped[param_name], grouped["deadtime"] * 100,
+                    yerr=grouped["deadtime_std"],
+                    marker="o", color="green", capsize=3)
+        ax.set_xlabel(param_name)
+        ax.set_ylabel("Deadtime [%]")
+        ax.set_title("Deadtime")
+
+        plt.tight_layout()
+
+        if out_path is None:
+            img_dir = REPO_ROOT / f"output/images/ml/{self.run}/optimise"
+            img_dir.mkdir(parents=True, exist_ok=True)
+            fname = f"overfit_{tag}_{param_name}.png" if tag else f"overfit_{param_name}.png"
+            out_path = img_dir / fname
+        out_path = Path(out_path)
+        out_path.parent.mkdir(parents=True, exist_ok=True)
+        plt.savefig(out_path)
+        self.logger.log(f"Saved to {out_path}", "success")
+
+        if show:
+            plt.show()
diff --git a/CrvCosmic/src/ml/process.py b/CrvCosmic/src/ml/process.py
new file mode 100644
index 0000000..e9884d5
--- /dev/null
+++ b/CrvCosmic/src/ml/process.py
@@ -0,0 +1,457 @@
+"""
+Prepare cosmic data for ML work
+Sam Grant 2025
+"""
+
+# External
+import sys
+import gc
+import os
+import awkward as ak
+import numpy as np
+
+# pyutils 
+from pyutils.pyprocess import Processor, Skeleton
+from pyutils.pylogger import Logger
+from pyutils.pyselect import Select
+
+script_dir = os.path.dirname(os.path.abspath(__file__))
+sys.path.extend([
+    os.path.join(script_dir, "..", "utils"),
+    os.path.join(script_dir, "..", "core")
+])
+
+from analyse import Analyse
+from postprocess import PostProcess
+from pyutils.pycut import CutManager
+
+# from pyutils.pylogger import Logger
+
+class MLProcessor(Skeleton):
+    """Class for core data processing
+    
+    This class inherits from the pyutils Skeleton template class
+
+     pyutils.Skeleton (base template)
+     ├── Provides: init(), process_file(), execute()
+     └── Expects: You to override member varibles and process_file() 
+     
+     CosmicProcessor(Skeleton)  
+     ├── Inherits: execute(), file handling, parallel processing
+     ├── Customises: __init__() with cosmic-specific parameters
+     └── Overrides: process_file() with cosmic analysis logic
+    """
+    def __init__(
+        self,
+        defname = None, 
+        file_list_path = None,
+        file_name = None,
+        cutset_name = "MLPreprocess",
+        branches = { 
+            "evt" : ["run", "subrun", "event"],
+            "crv" : ["crvcoincs.time", "crvcoincs.PEs", "crvcoincs.nHits", "crvcoincs.sectorType",
+                     "crvcoincs.angle", "crvcoincs.nLayers",  "crvcoincs.timeStart", "crvcoincs.timeEnd",
+                     "crvcoincs.pos.fCoordinates.fX", "crvcoincs.pos.fCoordinates.fY", "crvcoincs.pos.fCoordinates.fZ"],
+            "trk" : ["trk.nactive", "trk.pdg", "trkqual.valid", "trkqual.result"],
+            "trkfit" : ["trksegs", "trksegpars_lh"],
+            "trkmc" : ["trkmcsim"]
+        },
+        feature_set = "trk", # "crv" # TODO: better implementation? 
+        on_spill=True,
+        cuts_to_toggle = None,
+        groups_to_toggle = None,
+        use_remote = True,
+        location = "disk",
+        max_workers = None,
+        use_processes = True,
+        verbosity = 1,
+        worker_verbosity = 0
+        
+    ):
+        """Initialise your processor with specific configuration
+        
+        This method sets up all the parameters needed for this specific analysis.
+
+        Args:
+            defname (str, opt): Dataset definition name. Defaults to None.
+            file_list_path (str, opt): Path to file list. Defaults to None.
+            file_name (str, opt): Single file to process. Defaults to None.
+            cutset_name (str, opt): Which cutset to use. Defaults to "alpha".
+            branches (dict or list, opt): EventNtuple branches to extract.
+            on_spill (bool, opt): Apply on-spill timing cuts. Defaults to True.
+            cuts_to_toggle (dict, opt): Cuts to enable/disable. Defaults to None.
+            groups_to_toggle (dict, opt): Cut groups to enable/disable. Defaults to None.
+            use_remote (bool, opt): Use remote file access. Defaults to True.
+            location (str, opt): File location ("disk", etc.). Defaults to "disk".
+            max_workers (int, opt): Number of parallel workers. None lets pyprocess auto-scale to os.cpu_count().
+            use_processes (bool, opt): Use processes rather than threads. Defaults to True.
+            verbosity (int, opt): Logging verbosity level. Defaults to 2 (max).
+            worker_verbosity (int, opt): Verbosity for worker processes (debug only!). Defaults to 0.
+
+        Note: 
+            Could also use kwargs**, but this is self documenting 
+        """
+        # Call the Skeleton's __init__ method first
+        super().__init__()
+
+        # Override parameters from Skeleton
+
+        # Dataset, file list, or file name/path
+        self.defname = defname 
+        self.file_list_path = file_list_path
+        self.file_name = file_name 
+
+        # EventNtuple branches
+        # self.branches = { 
+        #     "evt" : ["run", "subrun", "event"],
+        #     "crv" : ["crvcoincs.time", "crvcoincs.PEs", "crvcoincs.nHits", "crvcoincs.pos.fCoordinates.fZ"],
+        #     "trk" : ["trk.nactive", "trk.pdg", "trkqual.valid", "trkqual.result"],
+        #     "trkfit" : ["trksegs", "trksegpars_lh"],
+        #     "trkmc" : ["trkmcsim"]
+        # }
+        self.branches = branches
+        
+
+        # Process parameters
+        self.on_spill = on_spill                    # Apply t0 cut for onspill
+        self.use_remote = use_remote                # Use remote file via mdh
+        self.location = location                    # File location
+        self.max_workers = max_workers              # Limit the number of workers
+        self.use_processes = use_processes          # Use processes rather than threads
+        self.verbosity = verbosity                  # Set verbosity 
+        self.worker_verbosity = worker_verbosity    # Set worker verbosity 
+        self.feature_set = feature_set              # Feature set (tracker or CRV)
+    
+        # Analysis methods
+        self.analyse = Analyse(
+            cutset_name=cutset_name,
+            on_spill=self.on_spill,
+            verbosity=self.worker_verbosity, # Reduce verbosity for workers
+        )
+        
+        # Cut manager
+        self.cut_manager = CutManager(verbosity=self.worker_verbosity)
+        
+        # Selector
+        self.selector = Select(verbosity=self.worker_verbosity)
+
+        # Postprocess
+        # BE VERY CAREFUL HERE SINCE THERE IS A METHOD IN SKELETON CALLED POSTPROCESS
+        # THIS ATTRIBUTE MUST BE CALLED SOMETHING DIFFERENT 
+        self.postprocessor = PostProcess(on_spill=self.on_spill)
+        
+        # Create unique logger
+        self.logger = Logger( 
+            print_prefix="[MLPreprocess]",
+            verbosity=self.verbosity
+        )
+
+        # Init printout
+        self.logger.log(
+            f"Initialised with:\n"
+            f"  on_spill        = {self.on_spill}\n"
+            f"  verbosity       = {self.verbosity}",
+            "info"
+        )
+
+        # Create unique logger
+        self.logger = Logger(
+            print_prefix = "[MLPreprocessor]"
+        )
+            
+        # Toggle cuts
+        self.cuts_to_toggle = cuts_to_toggle
+        self.groups_to_toggle = groups_to_toggle
+
+        init_summary = f"""
+        \tdefname         = {self.defname}
+        \tfile_list_path  = {self.file_list_path}
+        \tfile_name       = {self.file_name}
+        \tcutset_name     = {self.analyse.cutset_name}
+        \ton_spill        = {self.on_spill}
+        \tcuts_to_toggle  = {self.cuts_to_toggle}
+        \tgroups_to_toggle = {self.groups_to_toggle}
+        \tbranches        = {list(self.branches.keys()) if isinstance(self.branches, dict) else self.branches}
+        \tuse_remote      = {self.use_remote}
+        \tlocation        = {self.location}
+        \tmax_workers     = {self.max_workers}
+        \tverbosity       = {self.verbosity}
+        \tuse_processes   = {self.use_processes}"""
+
+        # Confirm init
+        self.logger.log(f"Initialised with:{init_summary}", "success")
+        
+    def _process_array(self, data, file_id):
+        """Helper to execute processing on array
+
+        Args:
+            data: The data to analyse
+            file_id: Identifier for the file
+            
+        Returns:
+            dict: filtered and flattened arrays for ML
+        """
+        
+        self.logger.log(f"Beginning preprocessing on file: {file_id}", "max") 
+        
+        try:
+            # Define cuts
+            self.logger.log(f"Defining cuts", "max")
+            data = self.analyse.define_cuts(data, self.cut_manager)
+            
+            # Toggle cuts
+            if self.cuts_to_toggle: 
+                self.logger.log(f"Toggling cuts", "max")
+                self.cut_manager.toggle_cut(self.cuts_to_toggle) 
+            if self.groups_to_toggle: 
+                self.logger.log(f"Toggling cut groups", "max")
+                self.cut_manager.toggle_group(self.groups_to_toggle) 
+
+            # Create main cut flow
+            self.logger.log("Creating cut flow", "max")
+            cut_flow = self.cut_manager.create_cut_flow(data)
+
+            ##########################################
+            # Apply cuts
+            ##########################################  
+
+            # Apply Preselect cuts
+            self.logger.log("Applying cuts", "max")
+            data_cut = ak.copy(data)
+            data_cut = self.analyse.apply_cuts(data_cut, self.cut_manager)
+
+            # Create verification plots (before flattening)
+            # pdg, pz, trk / event, t0, trkqual
+            hists = self.analyse.create_histograms(
+                datasets = {
+                    "Before cuts" : data,
+                    "After cuts" : data_cut
+                }
+            )
+            
+            # Container for processed data 
+            processed_data = {}
+
+            if self.feature_set == "trk":
+                # Tracker surfaces on trkfit level
+                at_trk_front = self.selector.select_surface(data_cut["trkfit"], surface_name="TT_Front") 
+                # Select
+                processed_data["d0"] = data_cut["trkfit"]["trksegpars_lh"]["d0"][at_trk_front]
+                processed_data["tanDip"] = self.analyse.get_pitch_angle(data_cut["trkfit"][at_trk_front])
+                processed_data["maxr"] = data_cut["trkfit"]["trksegpars_lh"]["maxr"][at_trk_front]
+                # Calculate momentum magnitude
+                mom_mag = self.analyse.vector.get_mag(data_cut["trkfit"][at_trk_front]["trksegs"], "mom")
+                processed_data["mom_mag"] = mom_mag
+
+            elif self.feature_set == "crv":
+
+                # Note that you MUST have has_trk_mid switched on for this 
+                
+                # Event parameters (nice to have)
+                processed_data["event"] = data_cut["evt"]["event"]
+                processed_data["subrun"] = data_cut["evt"]["subrun"]
+
+                # Work with event-level parameters
+                
+                # One coinc / event
+                # Based on central ∆t
+                # coinc_idx = data_cut["dev"]["cent_dT_idx"]
+
+                # Validate indexing (only if we have data with content)
+                # Check both that arrays exist and contain actual values
+                # has_data = (len(coinc_idx) > 0 and
+                #            ak.any(ak.num(data_cut["dev"]["dT"], axis=-1) > 0))
+
+                # if has_data and ak.all(data_cut["dev"]["dT"][coinc_idx]!=data_cut["dev"]["cent_dT"]):
+                #     self.logger.log(f"Central ∆T mismatch", "error")
+                #     raise ValueError()
+                
+                # CRV parameters using selected index
+                processed_data["crv_x"] = data_cut["crv"]["crvcoincs.pos.fCoordinates.fX"] # [coinc_idx]
+                processed_data["crv_y"] = data_cut["crv"]["crvcoincs.pos.fCoordinates.fY"] # [coinc_idx]
+                processed_data["crv_z"] = data_cut["crv"]["crvcoincs.pos.fCoordinates.fZ"] # [coinc_idx]
+                processed_data["PEs"] = data_cut["crv"]["crvcoincs.PEs"] # [coinc_idx]
+                processed_data["dT"] = data_cut["dev"]["dT"] # [coinc_idx]
+                processed_data["nHits"] = data_cut["crv"]["crvcoincs.nHits"] # [coinc_idx]
+                processed_data["nLayers"] = data_cut["crv"]["crvcoincs.nLayers"] # [coinc_idx]
+                processed_data["angle"] = data_cut["crv"]["crvcoincs.angle"] # [coinc_idx]
+                processed_data["timeStart"] = data_cut["crv"]["crvcoincs.timeStart"] # [coinc_idx]
+                processed_data["timeEnd"] = data_cut["crv"]["crvcoincs.timeEnd"] # [coinc_idx]    
+                processed_data["sector"] = data_cut["crv"]["crvcoincs.sectorType"] # [coinc_idx] 
+                
+                # Calculate PEs/nHits ratio (avoid division by zero)
+                processed_data["PEs_per_hit"] = ak.where(
+                    processed_data["nHits"] > 0, 
+                    processed_data["PEs"] / processed_data["nHits"], 
+                    0
+                )
+
+                # Pad empty coincidences here
+                has_coinc = ak.num(processed_data["crv_z"]) > 0
+                for key in ["crv_x", "crv_y", "crv_z", "PEs", "dT", "nHits", "nLayers",
+                            "angle", "timeStart", "timeEnd", "sector", "PEs_per_hit"]:
+                    processed_data[key] = ak.where(has_coinc, processed_data[key], [[np.nan]])
+                
+                # Tracker parameters
+                at_trk_front = self.selector.select_surface(data_cut["trkfit"], surface_name="TT_Front") 
+                at_trk_mid = self.selector.select_surface(data_cut["trkfit"], surface_name="TT_Mid")
+                processed_data["t0"] = data_cut["trkfit"]["trksegs"]["time"][at_trk_mid]
+                processed_data["d0"] = data_cut["trkfit"]["trksegpars_lh"]["d0"][at_trk_front]
+                processed_data["tanDip"] = self.analyse.get_pitch_angle(data_cut["trkfit"][at_trk_front])
+                processed_data["maxr"] = data_cut["trkfit"]["trksegpars_lh"]["maxr"][at_trk_front]
+                # Calculate momentum magnitude
+                mom_mag = self.analyse.vector.get_mag(data_cut["trkfit"][at_trk_front]["trksegs"], "mom")
+                processed_data["mom_mag"] = mom_mag
+
+                # Broadcast all features to match CRV coincidence shape [event, coincidence]
+                # Get target shape from any CRV array
+                coinc_shape = ak.ones_like(processed_data["crv_z"])
+                # Pad empty events with no coincidences with at least one slot
+                # coinc_shape = ak.where(ak.num(coinc_shape) == 0, [[1]], coinc_shape)
+
+                # Broadcast event-level parameters: [event] -> [event, coincidence]
+                for key in ["event", "subrun"]:
+                    processed_data[key] = processed_data[key][:, None] * coinc_shape
+                
+                # Broadcast track-level parameters: [event, track, segment] -> [event, coincidence]
+                # Flatten segments, broadcast to coincidences, then flatten again
+                for key in ["t0", "d0", "tanDip", "maxr", "mom_mag"]:
+                    arr = processed_data[key]
+                    arr = ak.flatten(arr, axis=-1)  # [event, track, segment] -> [event, track]
+                    arr = arr[:, None] * coinc_shape  # [event, track] -> [event, track, coincidence]
+                    arr = ak.flatten(arr, axis=-1)  # [event, track, coincidence] -> [event, coincidence] 
+                    # Override 
+                    processed_data[key] = arr
+
+                # def _test(events):
+
+                #     PEs = events["PEs"]
+                #     n_tot = len(PEs) #  ak.sum(is_none, axis=None
+                #     is_none = ak.is_none(events["PEs"], axis=-1)
+                #     n_is_none = ak.sum(is_none, axis=None)
+    
+                #     print("\n",50*"*")
+                #     print("PEs", PEs)
+                #     print("n_tot", n_tot)
+                #     print("is_none", is_none)
+                #     print("n_is_none", n_is_none)
+                #     print("diff", n_tot - n_is_none)
+                #     print(50*"*","\n")
+
+                # _test(processed_data)
+        
+            else: 
+                self.logger.log(f"Feature set '{self.feature_set}' invalid: {e}", "error")  
+                raise
+                
+            # Store results
+            self.logger.log("Creating result", "max")
+            result = {
+                "id": file_id,
+                "cut_flow": cut_flow,
+                "hists": hists, 
+                "events": processed_data 
+            }
+            
+            self.logger.log("Preprocessing completed", "max")
+            gc.collect()
+            return result
+            
+        except Exception as e:
+            self.logger.log(f"Error during preprocessing execution: {e}", "error")  
+            raise
+
+    def process_file(self, file_name): 
+        """Process a single file
+        
+        This method will be called for each file in our list.
+        It extracts data, processes it, and returns a result.
+        
+        Args:
+            file_name: Path to the ROOT file to process
+            
+        Returns:
+            A tuple containing the histogram (counts and bin edges)
+        """        
+        try:
+            # Create processor for this file
+            this_processor = Processor(
+                use_remote=self.use_remote,    
+                location=self.location,  
+                verbosity=self.worker_verbosity,
+                worker_verbosity=self.worker_verbosity
+            )
+            
+            # Extract the data 
+            this_data = this_processor.process_data(
+                file_name = file_name, 
+                branches = self.branches
+            )
+            
+            # Run processing on array
+            results = self._process_array(
+                this_data, 
+                file_name
+            )
+
+            # Clean up memory
+            gc.collect()
+
+            return results # this returns a list somehow?
+        
+        except Exception as e:
+            # Report any errors that occur during processing
+            self.logger.log(f"Error processing {file_name}: {e}", "error")
+            raise e
+    
+    def postprocess(self, results):
+        """ Combine histograms and arrays 
+            Borrow some methods from postprocess
+        """
+
+        # Ensure results is always a list
+        if not isinstance(results, list):
+            results = [results]
+            
+        postprocessed = {} 
+        postprocessed["cut_flow"] = self.postprocessor.combine_cut_flows(results)
+        postprocessed["hists"] = self.postprocessor.combine_hists(results)
+        postprocessed["events"] = self.postprocessor.combine_arrays(results)
+        
+        # Flatten (after combination!)
+        flattened_events = {}
+        lengths = {}
+        for field in postprocessed["events"].fields: 
+
+            # First fill None values with nan
+            filled_none = ak.fill_none(postprocessed["events"][field], np.nan)
+
+            # # For other fields: pad with NaN
+            # padded_array = ak.where
+            #     ak.num(postprocessed["events"][field]) == 0,
+            #     [[np.nan]],
+            #     filled_none
+            # )
+                
+            # Then flatten
+            flattened_events[field] = ak.flatten(filled_none, axis=None)
+
+            # Record lengths
+            lengths[field] = len(flattened_events[field])
+
+        # # Original method that does not preserve None
+        # for field in postprocessed["events"].fields: 
+        #     flattened_events[field] = ak.flatten(postprocessed["events"][field], axis=None)
+        #     lengths[field] = len(flattened_events[field])
+        
+        if len(set(lengths.values())) > 1:
+            self.logger.log(f"Length mismatch detected: {lengths}", "warning")
+            print(postprocessed["events"].type)
+
+        else:
+            self.logger.log(f"All field lengths match: {lengths}", "success")
+        
+        postprocessed["events"] = ak.Array(flattened_events) # enforce awkward type
+        self.logger.log(f"Postprocessing complete", "success")
+        
+        return postprocessed
\ No newline at end of file
diff --git a/CrvCosmic/src/ml/train.py b/CrvCosmic/src/ml/train.py
new file mode 100644
index 0000000..da915c9
--- /dev/null
+++ b/CrvCosmic/src/ml/train.py
@@ -0,0 +1,434 @@
+# Sam Grant 2025
+# Training class for ML models with pre-split data
+
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import GroupKFold
+from pathlib import Path
+import os
+import joblib
+import xgboost as xgb
+import numpy as np
+import pandas as pd
+
+REPO_ROOT = Path(os.path.dirname(os.path.abspath(__file__))).parents[1]
+
+from pyutils.pylogger import Logger
+
+class Train:
+    """Train ML models on pre-split data from assemble_dataset()."""
+
+    def __init__(self, data, model=None, scale_features=False,
+                 run="j", out_path=None, verbosity=1):
+        """Initialise with assemble_dataset() dict. Default model: XGBClassifier."""
+        # Set default model
+        if model is None:
+            model = xgb.XGBClassifier
+
+        self.model_class = model
+        self.scale_features = scale_features
+        self.run = run
+
+        # Extract data components
+        self.X_train = data["X_train"]
+        self.X_val = data.get("X_val", None)
+        self.X_test = data["X_test"]
+        self.y_train = data["y_train"]
+        self.y_val = data.get("y_val", None)
+        self.y_test = data["y_test"]
+        self.metadata_train = data["metadata_train"]
+        self.metadata_val = data.get("metadata_val", None)
+        self.metadata_test = data["metadata_test"]
+
+        # Optional full dataframe
+        self.df_full = data.get("df_full", None)
+
+        # Will be set during training
+        self.scaler = None
+        self.model = None
+        self.is_keras = self._is_keras_model()
+
+        # Output directory: output/ml/{run}/results/{tag}
+        self.out_path = Path(out_path) if out_path else REPO_ROOT / f"output/ml/{run}/results"
+
+        self.verbosity = verbosity
+        self.logger = Logger(
+            print_prefix = "[Train]",
+            verbosity = self.verbosity
+        )
+        self.logger.log("Initialised", "success")
+
+    def _is_keras_model(self):
+        """Check if the model is a Keras model"""
+        try:
+            import tensorflow as tf
+            # Check if it's a compiled Keras model instance
+            if isinstance(self.model_class, tf.keras.Model):
+                return True
+        except ImportError:
+            pass
+        return False
+
+    def train(self, tag, random_state=42, save_output=False,
+              epochs=50, batch_size=32, validation_split=0.0, verbose=1,
+              **hyperparams):
+        """Train model and return results dict. Pass hyperparams as kwargs."""
+        model_type = "Keras" if self.is_keras else self.model_class.__name__
+        self.logger.log(
+            f"Training model: {model_type}\n"
+            f"  Tag: {tag}\n"
+            f"  Random state: {random_state}\n"
+            f"  Scale features: {self.scale_features}\n",
+            "info"
+        )
+
+        if self.is_keras:
+            self.logger.log(
+                f"  Epochs: {epochs}\n"
+                f"  Batch size: {batch_size}\n"
+                f"  Validation split: {validation_split}\n",
+                "info"
+            )
+        else:
+            self.logger.log(
+                f"  Hyperparams: {hyperparams}\n",
+                "info"
+            )
+
+        # Scale features if requested
+        if self.scale_features:
+            X_train_scaled, X_test_scaled, X_val_scaled = self._scale_features()
+        else:
+            X_train_scaled = self.X_train.values
+            X_test_scaled = self.X_test.values
+            X_val_scaled = self.X_val.values if self.X_val is not None else None
+
+        # Train model based on type
+        if self.is_keras:
+            self.model = self._fit_keras_model(
+                X_train_scaled, self.y_train.values,
+                epochs=epochs,
+                batch_size=batch_size,
+                validation_split=validation_split,
+                verbose=verbose,
+                **hyperparams
+            )
+        else:
+            self.model = self._fit_model(
+                X_train_scaled, self.y_train,
+                random_state, **hyperparams
+            )
+
+        # Predict on train, val, and test sets
+        y_pred, y_proba = self._predict(X_test_scaled)
+        _, y_proba_train = self._predict(X_train_scaled)
+
+        y_pred_val, y_proba_val = None, None
+        if X_val_scaled is not None:
+            y_pred_val, y_proba_val = self._predict(X_val_scaled)
+
+        self.logger.log("Training complete!", "success")
+
+        # Package results
+        results = {
+            "tag": tag,
+            "model": self.model,
+            "feature_names": list(self.X_train.columns),
+            "y_train": self.y_train,
+            "y_val": self.y_val,
+            "y_test": self.y_test,
+            "y_pred": y_pred,
+            "y_proba": y_proba,
+            "y_pred_val": y_pred_val,
+            "y_proba_val": y_proba_val,
+            "y_proba_train": y_proba_train,
+            "X_val": self.X_val,
+            "X_test": self.X_test,
+            "scaler": self.scaler,
+            "metadata_val": self.metadata_val,
+            "metadata_test": self.metadata_test
+        }
+
+        # Save if requested
+        if save_output:
+            self._save_results(results, tag)
+
+        return results
+
+    def train_cv(self, tag, n_folds=5, min_eff=0.999, random_state=42,
+                 save_output=False, **hyperparams):
+        """Train with K-fold CV over train set for robust threshold estimation.
+        Val and test sets are held out entirely."""
+        from validate import Validate
+
+        # CV folds over train set only — val/test held out
+        X = self.X_train.reset_index(drop=True)
+        y = self.y_train.reset_index(drop=True)
+        metadata = self.metadata_train.reset_index(drop=True)
+
+        groups = metadata["subrun"].astype(str) + "_" + metadata["event"].astype(str)
+        gkf = GroupKFold(n_splits=n_folds)
+        folds = list(gkf.split(X, y, groups=groups))
+
+        self.logger.log(
+            f"CV training: {n_folds} folds, tag={tag}\n"
+            f"  Hyperparams: {hyperparams}",
+            "info"
+        )
+
+        fold_metrics = []
+
+        for k, (train_idx, test_idx) in enumerate(folds):
+            fold_data = {
+                "X_train": X.iloc[train_idx],
+                "X_test": X.iloc[test_idx],
+                "y_train": y.iloc[train_idx],
+                "y_test": y.iloc[test_idx],
+                "metadata_train": metadata.iloc[train_idx],
+                "metadata_test": metadata.iloc[test_idx],
+            }
+
+            fold_trainer = Train(
+                fold_data,
+                model=self.model_class,
+                scale_features=self.scale_features,
+                run=self.run,
+                verbosity=0
+            )
+            fold_results = fold_trainer.train(
+                tag=f"{tag}_fold{k}",
+                random_state=random_state,
+                save_output=False,
+                **hyperparams
+            )
+
+            ana = Validate(fold_results, verbosity=0)
+            ana.roc_auc()
+            threshold_results = ana.find_threshold(min_eff=min_eff, plot=False, show=False)
+
+            # Money table for this fold (using fold's own threshold)
+            money = ana.money_table(
+                X=fold_data["X_test"],
+                y=fold_data["y_test"],
+                metadata=fold_data["metadata_test"],
+                threshold=threshold_results["threshold"],
+                save_csv=False
+            )
+
+            fold_metrics.append({
+                "auc": ana._test_auc,
+                "threshold": threshold_results["threshold"],
+                "veto_efficiency": threshold_results["veto_efficiency"],
+                "deadtime": threshold_results["deadtime"],
+                "signal_efficiency": threshold_results["signal_efficiency"],
+                "thresholds": threshold_results["thresholds"],
+                "veto_efficiencies": threshold_results["veto_efficiencies"],
+                "signal_efficiencies": threshold_results["signal_efficiencies"],
+                "money_table": money,
+            })
+
+        # CV summary
+        cv_threshold = np.mean([m["threshold"] for m in fold_metrics])
+        cv_threshold_std = np.std([m["threshold"] for m in fold_metrics])
+        cv_deadtime = np.mean([m["deadtime"] for m in fold_metrics])
+        cv_deadtime_std = np.std([m["deadtime"] for m in fold_metrics])
+        cv_veto_eff = np.mean([m["veto_efficiency"] for m in fold_metrics])
+        cv_veto_eff_std = np.std([m["veto_efficiency"] for m in fold_metrics])
+        cv_auc = np.mean([m["auc"] for m in fold_metrics])
+
+        self.logger.log(
+            f"CV results ({n_folds} folds):\n"
+            f"  Threshold:       {cv_threshold:.4f} +/- {cv_threshold_std:.4f}\n"
+            f"  Veto efficiency: {cv_veto_eff*100:.3f} +/- {cv_veto_eff_std*100:.3f}%\n"
+            f"  Deadtime:        {cv_deadtime*100:.3f} +/- {cv_deadtime_std*100:.3f}%\n"
+            f"  AUC:             {cv_auc:.6f}",
+            "info"
+        )
+
+        # Train final model on full train set (val/test held out)
+        self.logger.log("Training final model on full train set...", "info")
+        results = self.train(
+            tag=tag,
+            random_state=random_state,
+            save_output=False,
+            **hyperparams
+        )
+
+        # CV-averaged money table metrics
+        # money_keys = ["veto_efficiency", "deadtime", "veto_purity",
+        #                "accuracy", "figure_of_merit"]
+        money_keys = ["veto_efficiency", "deadtime", "veto_purity", "figure_of_merit"]      
+        money_keys_dt = [f"{k}_dt" for k in money_keys]
+
+        cv_money = {}
+        for key in money_keys + money_keys_dt:
+            vals = [m["money_table"]["raw"][key] for m in fold_metrics]
+            cv_money[key] = np.mean(vals)
+            cv_money[f"{key}_std"] = np.std(vals)
+
+        # Attach CV metrics to results
+        results["cv_threshold"] = cv_threshold
+        results["cv_threshold_std"] = cv_threshold_std
+        results["cv_money_table"] = cv_money
+        results["cv_metrics"] = {
+            "auc": cv_auc,
+            "threshold": cv_threshold,
+            "threshold_std": cv_threshold_std,
+            "veto_efficiency": cv_veto_eff,
+            "veto_efficiency_std": cv_veto_eff_std,
+            "deadtime": cv_deadtime,
+            "deadtime_std": cv_deadtime_std,
+            "fold_metrics": fold_metrics,
+        }
+
+        # Evaluate on val and test sets with CV threshold
+        final_ana = Validate(results, run=self.run, verbosity=self.verbosity)
+
+        if self.X_val is not None:
+            val_money = final_ana.money_table(
+                X=self.X_val, y=self.y_val,
+                metadata=self.metadata_val,
+                threshold=cv_threshold,
+                save_csv=False
+            )
+            results["val_money_table"] = val_money
+            self.logger.log(
+                f"Validation set metrics (threshold={cv_threshold:.4f}):\n"
+                f"  Veto efficiency: {val_money['raw']['veto_efficiency']*100:.3f}%\n"
+                f"  Deadtime:        {val_money['raw']['deadtime']*100:.3f}%",
+                "info"
+            )
+
+        test_money = final_ana.money_table(
+            X=self.X_test, y=self.y_test,
+            metadata=self.metadata_test,
+            threshold=cv_threshold,
+            save_csv=False
+        )
+        results["test_money_table"] = test_money
+        self.logger.log(
+            f"Test set metrics (threshold={cv_threshold:.4f}):\n"
+            f"  Veto efficiency: {test_money['raw']['veto_efficiency']*100:.3f}%\n"
+            f"  Deadtime:        {test_money['raw']['deadtime']*100:.3f}%",
+            "info"
+        )
+
+        # Save after all metrics are attached
+        if save_output:
+            self._save_results(results, tag)
+
+        self.logger.log(
+            f"Use CV threshold: {cv_threshold:.4f} (not single-split threshold)",
+            "success"
+        )
+
+        # Plot CV threshold overlay
+        final_ana.plot_threshold_cv(fold_metrics, cv_threshold, min_eff=min_eff)
+
+        return results
+
+    def _scale_features(self):
+        """Scale features using StandardScaler (fit on train only)."""
+        self.scaler = StandardScaler()
+        X_train_scaled = self.scaler.fit_transform(self.X_train)
+        X_test_scaled = self.scaler.transform(self.X_test)
+        X_val_scaled = self.scaler.transform(self.X_val) if self.X_val is not None else None
+        return X_train_scaled, X_test_scaled, X_val_scaled
+
+    def _fit_model(self, X_train, y_train, random_state=42, **hyperparams):
+        """Fit sklearn-compatible model. Falls back if random_state unsupported."""
+        # Default to hist tree method for XGBoost (faster, required for GPU)
+        if self.model_class is xgb.XGBClassifier and "tree_method" not in hyperparams:
+            hyperparams["tree_method"] = "hist"
+
+        # Try to pass random_state if model supports it
+        try:
+            model = self.model_class(random_state=random_state, **hyperparams)
+        except TypeError:
+            # Model doesn't accept random_state parameter
+            self.logger.log(
+                f"Note: {self.model_class.__name__} doesn't accept random_state parameter",
+                "warning"
+            )
+            model = self.model_class(**hyperparams)
+
+        model.fit(X_train, y_train)
+        return model
+
+    def _fit_keras_model(self, X_train, y_train, epochs, batch_size,
+                        validation_split, verbose, **keras_params):
+        """Fit compiled Keras model instance."""
+        # Use the compiled model instance
+        model = self.model_class
+
+        # Fit the model
+        history = model.fit(
+            X_train, y_train,
+            epochs=epochs,
+            batch_size=batch_size,
+            validation_split=validation_split,
+            verbose=verbose,
+            **keras_params
+        )
+
+        # Store training history
+        model.history_ = history.history
+
+        return model
+
+    def _predict(self, X_test):
+        """Return (y_pred, y_proba) for sklearn or Keras models."""
+        if self.is_keras:
+            # Keras: predict returns probabilities
+            y_proba_raw = self.model.predict(X_test, verbose=0)
+
+            # Handle different output shapes
+            if y_proba_raw.shape[1] == 1:
+                # Binary classification with sigmoid (shape: n_samples, 1)
+                y_proba = y_proba_raw.flatten()
+            else:
+                # Binary classification with softmax (shape: n_samples, 2)
+                y_proba = y_proba_raw[:, 1]
+
+            # Convert probabilities to class predictions (threshold at 0.5)
+            y_pred = (y_proba >= 0.5).astype(int)
+
+        else:
+            # sklearn: predict returns classes, predict_proba returns probabilities
+            y_pred = self.model.predict(X_test)
+
+            # Get probabilities for positive class
+            proba_all = self.model.predict_proba(X_test)
+            pos_idx = list(self.model.classes_).index(1)
+            y_proba = proba_all[:, pos_idx]
+
+        return y_pred, y_proba
+
+    def _save_results(self, results, tag):
+        """Save results dict to {out_path}/{tag}/results.pkl."""
+        tag_path = self.out_path / tag
+        tag_path.mkdir(parents=True, exist_ok=True)
+
+        file_path = tag_path / "results.pkl"
+
+        if self.is_keras:
+            # Save Keras model separately
+            keras_model_path = tag_path / "keras_model.keras"
+            results["model"].save(keras_model_path)
+            self.logger.log(f"Keras model saved to {keras_model_path}", "success")
+
+            # Save results without the model (it's saved separately)
+            results_copy = results.copy()
+            results_copy["model"] = None  # Don't pickle the Keras model
+            results_copy["keras_model_path"] = str(keras_model_path)
+            joblib.dump(results_copy, file_path)
+        else:
+            # sklearn models can be pickled directly
+            joblib.dump(results, file_path)
+
+            # Save XGBoost model in UBJ format for deployment
+            if hasattr(results["model"], "get_booster"):
+                ubj_path = tag_path / "model.ubj"
+                results["model"].get_booster().save_model(str(ubj_path))
+                self.logger.log(f"XGBoost model saved to {ubj_path}", "success")
+
+        self.logger.log(f"Results saved to {file_path}", "success")
diff --git a/CrvCosmic/src/ml/validate.py b/CrvCosmic/src/ml/validate.py
new file mode 100644
index 0000000..5d95fc7
--- /dev/null
+++ b/CrvCosmic/src/ml/validate.py
@@ -0,0 +1,831 @@
+# Sam Grant 2025
+# Analyse ML model
+
+import os
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.metrics import (
+    roc_curve,
+    roc_auc_score
+)
+from pathlib import Path
+
+REPO_ROOT = Path(os.path.dirname(os.path.abspath(__file__))).parents[1]
+
+from pyutils.pylogger import Logger
+from pyutils.pyplot import Plot
+
+
+class Validate:
+    """Validate and analyse trained model. Works with results dict from Train.train()."""
+
+    def __init__(self, results, run="j", img_out_path=None, verbosity=1):
+        """Initialise with Train.train() results dict."""
+        self.model = results["model"]
+        self.scaler = results.get("scaler", None)
+        self.feature_names = results.get("feature_names", None)
+        self.y_test = results["y_test"]
+        self.y_val = results.get("y_val", None)
+        self.y_train = results["y_train"]
+        self.y_pred = results["y_pred"]
+        self.y_proba = results["y_proba"]
+        self.y_pred_val = results.get("y_pred_val", None)
+        self.y_proba_val = results.get("y_proba_val", None)
+        self.y_proba_train = results["y_proba_train"]
+        self.X_val = results.get("X_val", None)
+        self.X_test = results.get("X_test", None)
+        self.metadata_val = results.get("metadata_val", None)
+        self.metadata_test = results.get("metadata_test", None)
+        self.tag = results["tag"]
+
+        # Image output directory: output/images/ml/{run}/{tag}/
+        if img_out_path is not None:
+            self.img_out_path = Path(img_out_path)
+        else:
+            self.img_out_path = REPO_ROOT / f"output/images/ml/{run}/{self.tag}"
+
+        # Results output directory: output/ml/{run}/results/{tag}/
+        self.results_out_path = REPO_ROOT / f"output/ml/{run}/results/{self.tag}"
+
+        self.logger = Logger(print_prefix="[Validate]", verbosity=verbosity)
+        self.logger.log(f"Initialised analyser for model: {self.tag}", "success")
+
+        # Will be computed
+        self._train_auc = None
+        self._val_auc = None
+        self._test_auc = None
+
+        # Just for styles
+        plotter = Plot(verbosity=0)
+
+    def _save_fig(self, out_path, default_name):
+        """Save current figure, using default filename if no path given"""
+        if out_path is None:
+            out_path = self.img_out_path / default_name
+        out_path = Path(out_path)
+        out_path.parent.mkdir(parents=True, exist_ok=True)
+        plt.savefig(out_path)
+        self.logger.log(f"Saved to {out_path}", "success")
+
+    def roc_auc(self):
+        """Compute ROC AUC for train, val, and test sets."""
+        self._train_auc = roc_auc_score(self.y_train, self.y_proba_train)
+        self._test_auc = roc_auc_score(self.y_test, self.y_proba)
+
+        msg = (
+            f"ROC AUC:\n"
+            f"  Train: {self._train_auc:.6f}\n"
+        )
+
+        result = {
+            "train_auc": self._train_auc,
+            "test_auc": self._test_auc
+        }
+
+        if self.y_val is not None and self.y_proba_val is not None:
+            self._val_auc = roc_auc_score(self.y_val, self.y_proba_val)
+            msg += f"  Val:   {self._val_auc:.6f}\n"
+            result["val_auc"] = self._val_auc
+
+        msg += f"  Test:  {self._test_auc:.6f}"
+        self.logger.log(msg, "info")
+
+        return result
+
+    def plot_roc(self, out_path=None, show=True):
+        """Plot ROC curve for test set."""
+        # Compute ROC curve
+        fpr, tpr, _ = roc_curve(self.y_test, self.y_proba)
+
+        # Compute AUC if not already done
+        if self._test_auc is None:
+            self._test_auc = roc_auc_score(self.y_test, self.y_proba)
+
+        # Plot
+        plt.figure()
+        plt.plot(fpr, tpr, linewidth=2.5, label=f'ROC (AUC = {self._test_auc:.4f})')
+        plt.plot([0, 1], [0, 1], 'k--', linewidth=1.5, label='Random')
+        plt.xlabel('False positive rate')
+        plt.ylabel('True positive rate')
+        plt.legend(loc='lower right')
+        plt.grid(alpha=0.4)
+        plt.tight_layout()
+
+        self._save_fig(out_path, "roc_curve.png")
+
+        if show:
+            plt.show()
+
+    def plot_roc_cv(self, fold_curves, out_path=None, show=True):
+        """Overlay ROC curves from CV folds with transparent lines + bold mean.
+
+        Args:
+            fold_curves: list of dicts with keys "fpr", "tpr", "auc".
+            out_path: optional save path.
+            show: whether to display.
+        """
+        fig, ax = plt.subplots()
+
+        # Interpolate all folds onto common FPR grid for mean
+        mean_fpr = np.linspace(0, 1, 200)
+        interp_tprs = []
+
+        for k, fc in enumerate(fold_curves):
+            label = f"Fold {k}" if k == 0 else None
+            ax.plot(fc["fpr"], fc["tpr"], alpha=0.2, color="blue", label=label)
+            interp_tpr = np.interp(mean_fpr, fc["fpr"], fc["tpr"])
+            interp_tpr[0] = 0.0
+            interp_tprs.append(interp_tpr)
+
+        mean_tpr = np.mean(interp_tprs, axis=0)
+        std_tpr = np.std(interp_tprs, axis=0)
+        mean_auc = np.mean([fc["auc"] for fc in fold_curves])
+        std_auc = np.std([fc["auc"] for fc in fold_curves])
+
+        ax.plot(mean_fpr, mean_tpr, color="blue", linewidth=2.5,
+                label=f"Mean ROC (AUC = {mean_auc:.6f} $\\pm$ {std_auc:.6f})")
+        ax.fill_between(mean_fpr, mean_tpr - std_tpr, mean_tpr + std_tpr,
+                        alpha=0.15, color="blue")
+        ax.plot([0, 1], [0, 1], "k--", linewidth=1.5, label="Random")
+
+        ax.set_xlabel("False positive rate")
+        ax.set_ylabel("True positive rate")
+        ax.legend(loc="lower right")
+        ax.grid(alpha=0.4)
+        plt.tight_layout()
+
+        self._save_fig(out_path, "roc_curve_cv.png")
+
+        if show:
+            plt.show()
+
+    def plot_score_distribution_cv(self, fold_scores, threshold,
+                                   out_path=None, show=True, nbins=100):
+        """Overlay event-level score distributions from CV folds.
+
+        Args:
+            fold_scores: list of dicts with keys "signal" and "background"
+                         (arrays of max-probability-per-event).
+            threshold: CV-averaged threshold to draw.
+            out_path: optional save path.
+            show: whether to display.
+        """
+        fig, ax = plt.subplots(figsize=(6.4, 4.8))
+
+        for k, fs in enumerate(fold_scores):
+            bg_label = "CE mix" if k == 0 else None
+            sig_label = "CRY" if k == 0 else None
+            ax.hist(fs["background"], bins=nbins, range=(0, 1),
+                    alpha=0.1, color="blue", label=bg_label)
+            ax.hist(fs["signal"], bins=nbins, range=(0, 1),
+                    alpha=0.1, color="red", label=sig_label)
+
+        ax.axvline(threshold, color="grey", linestyle="--", alpha=0.8,
+                   linewidth=1.5, label=f"Threshold: {threshold:.4f}")
+        ax.set_xlabel("Maximum probability per event")
+        ax.set_ylabel("Events")
+        ax.legend()
+        ax.set_yscale("log")
+        plt.tight_layout()
+
+        self._save_fig(out_path, "score_distribution_cv.png")
+
+        if show:
+            plt.show()
+
+
+    def plot_feature_importance(self, importance_type="gain", feature_names=None,
+                                n_repeats=10, dataset="val", out_path=None, show=True):
+        """Plot feature importance.
+
+        Args:
+            importance_type: "gain", "weight", "cover" (XGBoost built-in),
+                             "perm" (permutation importance).
+            feature_names: List of feature names. Auto-detected if None.
+            n_repeats: Number of repeats for permutation importance.
+            dataset: "val" or "test" — which set to use for permutation importance.
+        """
+        # Get feature names: argument > results > model attribute
+        if feature_names is None:
+            if self.feature_names is not None:
+                feature_names = self.feature_names
+            elif hasattr(self.model, 'feature_names_in_'):
+                feature_names = list(self.model.feature_names_in_)
+            else:
+                self.logger.log(
+                    "No feature_names available (not in results or model)",
+                    "warning"
+                )
+                return None
+
+        errors = None
+
+        if importance_type == "perm":
+            from sklearn.inspection import permutation_importance
+            if dataset == "val" and self.X_val is not None:
+                X_perm, y_perm = self.X_val, self.y_val
+            else:
+                X_perm, y_perm = self.X_test, self.y_test
+            perm = permutation_importance(
+                self.model, X_perm, y_perm,
+                scoring="roc_auc", n_repeats=n_repeats, random_state=42,
+            )
+            importances = perm.importances_mean
+            errors = perm.importances_std
+            label = "Permutation importance ($\\Delta$ AUC)"
+        elif importance_type in ("gain", "weight", "cover") and hasattr(self.model, 'get_booster'):
+            booster = self.model.get_booster()
+            booster.feature_names = feature_names
+            scores = booster.get_score(importance_type=importance_type)
+            importances = np.array([scores.get(f, 0.0) for f in feature_names])
+            label = f"Feature importance ({importance_type})"
+        elif hasattr(self.model, 'feature_importances_'):
+            importances = self.model.feature_importances_
+            label = "Feature importance"
+        elif hasattr(self.model, 'coef_'):
+            coef = self.model.coef_
+            if coef.ndim > 1:
+                coef = coef[0]
+            importances = np.abs(coef)
+            label = "Coefficient magnitude"
+        else:
+            self.logger.log(
+                "Model has neither feature_importances_ nor coef_ attribute",
+                "warning"
+            )
+            return None
+
+        # Sort by importance
+        indices = np.argsort(importances)
+        sorted_names = [feature_names[i] for i in indices]
+        sorted_importances = importances[indices]
+        sorted_errors = errors[indices] if errors is not None else None
+
+        # Print feature importance
+        print(f"\nFeature importance ({importance_type}):")
+        for i in reversed(range(len(sorted_names))):
+            err_str = f" ± {sorted_errors[i]:.4f}" if sorted_errors is not None else ""
+            print(f"  {sorted_names[i]:15s}: {sorted_importances[i]:.4f}{err_str}")
+
+        # Plot
+        plt.figure()
+        plt.barh(range(len(sorted_names)), sorted_importances, height=0.8,
+                 xerr=sorted_errors, capsize=3)
+        plt.yticks(range(len(sorted_names)), sorted_names)
+        plt.xlabel(label)
+        plt.tight_layout()
+
+        fname = f"bar_feature_importance_{importance_type}.png"
+        self._save_fig(out_path, fname)
+
+        if show:
+            plt.show()
+
+        return dict(zip(feature_names, importances))
+
+    def find_threshold(self, min_eff=0.999, n_thresholds=10000, plot=True, out_path=None, show=True):
+        """Find highest threshold meeting min_eff event-level veto efficiency."""
+        if self.metadata_test is None:
+            self.logger.log(
+                "No metadata_test available — cannot do event-level threshold scan",
+                "error"
+            )
+            return None
+
+        thresholds = np.linspace(0, 1, n_thresholds)
+
+        y_test = np.asarray(self.y_test)
+        y_proba = np.asarray(self.y_proba)
+        metadata = self.metadata_test
+
+        # Pre-compute event-level quantities with a single groupby
+        df = metadata[["subrun", "event"]].copy()
+        df["label"] = y_test
+        df["proba"] = y_proba
+
+        event_df = df.groupby(["subrun", "event"]).agg(
+            max_proba=("proba", "max"),
+            label=("label", "first")
+        )
+
+        event_labels = event_df["label"].values
+        event_max_proba = event_df["max_proba"].values
+
+        is_signal = (event_labels == 1)
+        is_background = (event_labels == 0)
+        n_signal_events = is_signal.sum()
+        n_background_events = is_background.sum()
+
+        # Vectorised threshold scan — no groupby in the loop
+        veto_efficiencies = np.empty(n_thresholds)
+        deadtime_losses = np.empty(n_thresholds)
+
+        for i, thr in enumerate(thresholds):
+            # Event vetoed if its max coincidence score > threshold
+            event_vetoed = event_max_proba >= thr
+
+            tp = (is_signal & event_vetoed).sum()
+            fp = (is_background & event_vetoed).sum()
+
+            veto_efficiencies[i] = tp / n_signal_events if n_signal_events > 0 else 0
+            deadtime_losses[i] = fp / n_background_events if n_background_events > 0 else 0
+
+        signal_efficiencies = 1 - deadtime_losses
+
+        # Find threshold where veto efficiency >= min_eff
+        # (highest threshold that still meets the requirement)
+        above_target = np.where(veto_efficiencies >= min_eff)[0]
+        if len(above_target) > 0:
+            optimal_idx = above_target[-1]  # highest threshold still above target
+        else:
+            # Fall back to closest
+            optimal_idx = np.argmin(np.abs(veto_efficiencies - min_eff))
+            self.logger.log(
+                f"Target efficiency {min_eff*100:.2f}% not achievable, "
+                f"using closest: {veto_efficiencies[optimal_idx]*100:.2f}%",
+                "warning"
+            )
+
+        optimal_threshold = thresholds[optimal_idx]
+        actual_veto_eff = veto_efficiencies[optimal_idx]
+        actual_deadtime = deadtime_losses[optimal_idx]
+        actual_signal_eff = signal_efficiencies[optimal_idx]
+
+        # Precision, recall, F1 at the operating point
+        event_vetoed = event_max_proba >= optimal_threshold
+        tp = (is_signal & event_vetoed).sum()
+        fp = (is_background & event_vetoed).sum()
+        fn = (is_signal & ~event_vetoed).sum()
+        precision = tp / (tp + fp) if (tp + fp) > 0 else 0
+        recall = tp / (tp + fn) if (tp + fn) > 0 else 0
+        f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0
+
+        self.logger.log(
+            f"Event-level threshold (target {min_eff*100:.2f}% veto efficiency):\n"
+            f"  Threshold:         {optimal_threshold:.4f}\n"
+            f"  Veto efficiency:   {actual_veto_eff*100:.3f}%\n"
+            f"  Signal efficiency: {actual_signal_eff*100:.3f}%\n"
+            f"  Deadtime:          {actual_deadtime*100:.3f}%\n"
+            f"  Precision:         {precision*100:.3f}%\n"
+            f"  Recall:            {recall*100:.3f}%\n"
+            f"  F1:                {f1:.6f}",
+            "info"
+        )
+
+        if plot:
+            # Plot overlay of veto efficiency and signal efficiency
+            fig, ax = plt.subplots(figsize=(6.4, 4.8))
+
+            ax.plot(thresholds, veto_efficiencies, linewidth=2,
+                    color='blue', label='Veto efficiency (CRY vetoed)')
+            ax.plot(thresholds, signal_efficiencies, linewidth=2,
+                    color='red', label=r'$1-$'+'deadtime (CE mix passed)')
+
+            ax.axvline(optimal_threshold, color='grey', linestyle='--',
+                       alpha=0.8, linewidth=1.5,
+                       label=f'{min_eff*100:.2f}% efficiency: {optimal_threshold:.4f}')
+
+            # Mark operating point on both curves
+            ax.plot(optimal_threshold, actual_veto_eff, 'o', color='blue',
+                    markersize=8, zorder=5)
+            ax.plot(optimal_threshold, actual_signal_eff, 'o', color='red',
+                    markersize=8, zorder=5)
+
+            ax.set_xlabel('Event maximum probability')
+            ax.set_ylabel('Fraction')
+            ax.set_ylim([0.99, 1.01])
+            ax.legend(loc='best')
+            ax.grid(alpha=0.4)
+            ax.set_yscale("log")
+
+            plt.tight_layout()
+
+            self._save_fig(out_path, "threshold_overlay.png")
+
+            if show:
+                plt.show()
+
+        return {
+            "threshold": optimal_threshold,
+            "veto_efficiency": actual_veto_eff,
+            "deadtime": actual_deadtime,
+            "signal_efficiency": actual_signal_eff,
+            "precision": precision,
+            "recall": recall,
+            "f1": f1,
+            "thresholds": thresholds,
+            "veto_efficiencies": veto_efficiencies,
+            "signal_efficiencies": signal_efficiencies,
+        }
+
+    def plot_threshold_cv(self, fold_scans, cv_threshold, min_eff=0.999,
+                          out_path=None, show=True):
+        """Overlay threshold scans from K-fold CV on a single plot."""
+        fig, ax = plt.subplots(figsize=(6.4, 4.8))
+
+        # Horiztonal line at 1
+        ax.axhline(1.0, color="grey", alpha=.35, linewidth=1.0, linestyle="--")
+
+        # Per-fold curves (thin, transparent)
+        for k, scan in enumerate(fold_scans):
+            label_veto = 'Fold veto efficiency (cosmics passed)' if k == 0 else None
+            label_sig = r'Fold $1-$deadtime (pileup passed)' if k == 0 else None
+            ax.plot(scan["thresholds"], scan["veto_efficiencies"],
+                    linewidth=1, color='blue', alpha=0.35, label=label_veto)
+            ax.plot(scan["thresholds"], scan["signal_efficiencies"],
+                    linewidth=1, color='red', alpha=0.35, label=label_sig)
+            # Per-fold threshold (thin dashed)
+            fold_thres_label = "Fold thresholds (99.9% efficiency)" if k == 0 else ""
+            ax.axvline(scan["threshold"], color='grey', linestyle=':',
+                       alpha=0.35, linewidth=1, label=fold_thres_label)
+
+        # Mean curves
+        mean_veto = np.mean([s["veto_efficiencies"] for s in fold_scans], axis=0)
+        mean_signal = np.mean([s["signal_efficiencies"] for s in fold_scans], axis=0)
+        thresholds = fold_scans[0]["thresholds"]
+
+        ax.plot(thresholds, mean_veto, linewidth=1.5, color='blue',
+                label='Mean veto efficiency')
+        ax.plot(thresholds, mean_signal, linewidth=1.5, color='red',
+                label=r'Mean $1-$deadtime')
+
+        # Mean threshold
+        ax.axvline(cv_threshold, color='grey', linestyle='--',
+                   alpha=0.8, linewidth=1.5,
+                   label=f'Mean threshold: {cv_threshold:.4f}')
+
+
+
+        ax.set_xlabel('Maximum probability / event')
+        # ax.set_ylabel('Fraction')
+        ax.set_ylim([0.9925, 1.0075])
+        ax.legend(fontsize=12, loc='best')
+        # ax.grid(alpha=0.4)
+        ax.set_yscale("log")
+
+        plt.tight_layout()
+
+        self._save_fig(out_path, "threshold_overlay_cv.png")
+
+        if show:
+            plt.show()
+
+    def plot_score_distribution(self, threshold, out_path=None, show=True,
+                                nbins=100):
+        """Event-level max score distribution over full range [0, 1]."""
+        if self.metadata_test is None:
+            self.logger.log("No metadata_test — cannot compute event-level scores", "error")
+            return
+
+        # Group by event, take max score per event
+        df = self.metadata_test[["subrun", "event"]].copy()
+        df["label"] = np.asarray(self.y_test)
+        df["proba"] = np.asarray(self.y_proba)
+
+        event_df = df.groupby(["subrun", "event"]).agg(
+            max_proba=("proba", "max"),
+            label=("label", "first")
+        )
+
+        signal_scores = event_df.loc[event_df["label"] == 1, "max_proba"].values
+        background_scores = event_df.loc[event_df["label"] == 0, "max_proba"].values
+
+        fig, ax = plt.subplots(figsize=(6.4, 4.8))
+
+        ax.hist(background_scores, bins=nbins, range=(0, 1),
+                alpha=0.6, label='CE mix', color='blue')
+        ax.hist(signal_scores, bins=nbins, range=(0, 1),
+                alpha=0.6, label='CRY', color='red')
+        ax.axvline(threshold, color='grey', linestyle='--', alpha=0.8,
+                   linewidth=1.5, label=f'Threshold: {threshold:.4f}')
+        ax.set_xlabel('Maximum probability per event')
+        ax.set_ylabel('Events')
+        ax.legend()
+        ax.set_yscale('log')
+
+        plt.tight_layout()
+
+        self._save_fig(out_path, "h1_score_distribution.png")
+
+        if show:
+            plt.show()
+
+    def get_events_by_threshold(self, threshold, above=False):
+        """Select test events by event-level max score. Returns DataFrame with all coincidences."""
+        if self.metadata_test is None or self.X_test is None:
+            self.logger.log("No metadata_test or X_test available", "error")
+            return None
+
+        # Reconstruct test DataFrame from stored components
+        df_test = self.X_test.copy()
+        df_test["subrun"] = self.metadata_test["subrun"].values
+        df_test["event"] = self.metadata_test["event"].values
+        df_test["score"] = np.asarray(self.y_proba)
+        df_test["label"] = np.asarray(self.y_test)
+
+        # Event-level max score (same logic as find_threshold)
+        event_max = df_test.groupby(["subrun", "event"])["score"].max()
+
+        if above:
+            selected_events = event_max[event_max >= threshold].index
+        else:
+            selected_events = event_max[event_max < threshold].index
+
+        df_sel = df_test.set_index(["subrun", "event"]).loc[selected_events].reset_index()
+
+        n_cry = df_sel.loc[df_sel["label"] == 1].groupby(["subrun", "event"]).ngroups
+        n_mix = df_sel.loc[df_sel["label"] == 0].groupby(["subrun", "event"]).ngroups
+        direction = "above" if above else "below"
+        self.logger.log(
+            f"Events {direction} threshold {threshold:.4f}:\n"
+            f"  CRY:    {n_cry}\n"
+            f"  CE mix: {n_mix}",
+            "info"
+        )
+
+        return df_sel
+
+    def plot_physics_by_score(self, threshold, above=False, variables=None,
+                               nbins=50, out_path=None, show=True):
+        """Plot feature distributions for events above or below threshold, split by CRY/CE mix."""
+        df_sel = self.get_events_by_threshold(threshold, above=above)
+        if df_sel is None or len(df_sel) == 0:
+            return
+
+        score_label = f"event max score {'above' if above else 'below'} {threshold:.4f}"
+        default_fname = "h1_high_score_physics.png" if above else "h1_low_score_physics.png"
+
+        # Default to trained feature names
+        if variables is None:
+            if self.feature_names is not None:
+                variables = list(self.feature_names)
+            else:
+                self.logger.log("No feature_names or variables provided", "warning")
+                return
+        variables = [v for v in variables if v in df_sel.columns]
+
+        if len(variables) == 0:
+            self.logger.log("No matching columns found in DataFrame", "warning")
+            return
+
+        # Split by label
+        df_cry = df_sel[df_sel["label"] == 1]
+        df_mix = df_sel[df_sel["label"] == 0]
+
+        # Plot grid
+        ncols = 3
+        nrows = (len(variables) + ncols - 1) // ncols
+
+        fig, axes = plt.subplots(nrows, ncols, figsize=(6.4 * ncols, 4.8 * nrows))
+        axes = np.atleast_2d(axes).flatten()
+
+        for i, var in enumerate(variables):
+            ax = axes[i]
+            mix_vals = df_mix[var].dropna()
+            cry_vals = df_cry[var].dropna()
+
+            # Common range
+            all_vals = pd.concat([mix_vals, cry_vals])
+            lo, hi = all_vals.quantile(0.01), all_vals.quantile(0.99)
+
+            ax.hist(mix_vals, bins=nbins, range=(lo, hi),
+                    alpha=0.6, label="CE mix", density=True, color="blue")
+            ax.hist(cry_vals, bins=nbins, range=(lo, hi),
+                    alpha=0.6, label="CRY", density=True, color="red")
+            ax.set_xlabel(var)
+            ax.legend()
+            ax.grid(alpha=0.4)
+
+        # Hide unused axes
+        for i in range(len(variables), len(axes)):
+            axes[i].set_visible(False)
+
+        fig.suptitle(f"Feature distributions ({score_label})", y=1.01)
+        plt.tight_layout()
+
+        self._save_fig(out_path, default_fname)
+
+        if show:
+            plt.show()
+
+    @staticmethod
+    def event_level_confusion(y_pred, y_true, metadata):
+        """Event-level confusion matrix. Vetoes event if ANY coincidence is flagged."""
+        df = metadata[["subrun", "event"]].copy()
+        df["pred"] = np.asarray(y_pred).astype(int)
+        df["label"] = np.asarray(y_true).astype(int)
+
+        # Group by event: veto if ANY coincidence flagged, label from first row
+        event_df = df.groupby(["subrun", "event"]).agg(
+            vetoed=("pred", "max"),
+            label=("label", "first")
+        ).reset_index()
+
+        tp = ((event_df["label"] == 1) & (event_df["vetoed"] == 1)).sum()
+        tn = ((event_df["label"] == 0) & (event_df["vetoed"] == 0)).sum()
+        fp = ((event_df["label"] == 0) & (event_df["vetoed"] == 1)).sum()
+        fn = ((event_df["label"] == 1) & (event_df["vetoed"] == 0)).sum()
+
+        return {
+            "tp": int(tp),
+            "tn": int(tn),
+            "fp": int(fp),
+            "fn": int(fn),
+            "n_events": len(event_df),
+        }
+
+    def money_table(self, X, y, metadata, threshold=None,
+                    dT_min=0, dT_max=150, save_csv=True):
+        """Event-level comparison of ML model vs dT window cut."""
+        if self.feature_names is None:
+            self.logger.log("No feature_names available", "error")
+            return None
+
+        # --- Score coincidences with the ML model ---
+        X_scaled = X[self.feature_names].values
+        if self.scaler is not None:
+            X_scaled = self.scaler.transform(X_scaled)
+
+        # Get model probabilities
+        if hasattr(self.model, "predict_proba"):
+            proba = self.model.predict_proba(X_scaled)
+            pos_idx = list(self.model.classes_).index(1)
+            y_proba = proba[:, pos_idx]
+        else:
+            # Keras or similar
+            y_proba = self.model.predict(X_scaled, verbose=0).flatten()
+
+        if threshold is None:
+            threshold = 0.5
+        y_pred_ml = (y_proba >= threshold).astype(int)
+
+        # --- Simple dT cut: same logic as check_dT_window_results ---
+        dT = X["dT"].values
+        y_pred_dt = (
+            (~np.isnan(dT))
+            & (dT >= dT_min)
+            & (dT <= dT_max)
+        ).astype(int)
+
+        # --- Labels ---
+        y_true = np.asarray(y)
+
+        # --- Aggregate to event level ---
+        cm_ml = self.event_level_confusion(y_pred_ml, y_true, metadata)
+        tp, tn, fp, fn = cm_ml["tp"], cm_ml["tn"], cm_ml["fp"], cm_ml["fn"]
+
+        cm_dt = self.event_level_confusion(y_pred_dt, y_true, metadata)
+        tp_dt, tn_dt = cm_dt["tp"], cm_dt["tn"]
+        fp_dt, fn_dt = cm_dt["fp"], cm_dt["fn"]
+
+        # --- Compute metrics for both ---
+        veto_eff = tp / (tp + fn) if (tp + fn) > 0 else 0
+        deadtime = fp / (tn + fp) if (tn + fp) > 0 else 0
+        veto_purity = tp / (tp + fp) if (tp + fp) > 0 else 0
+        f1 = 2 * veto_eff * veto_purity / (veto_eff + veto_purity) if (veto_eff + veto_purity) > 0 else 0
+        fom = veto_eff * (1 - deadtime)
+
+        veto_eff_dt = tp_dt / (tp_dt + fn_dt) if (tp_dt + fn_dt) > 0 else 0
+        deadtime_dt = fp_dt / (tn_dt + fp_dt) if (tn_dt + fp_dt) > 0 else 0
+        veto_purity_dt = tp_dt / (tp_dt + fp_dt) if (tp_dt + fp_dt) > 0 else 0
+        f1_dt = 2 * veto_eff_dt * veto_purity_dt / (veto_eff_dt + veto_purity_dt) if (veto_eff_dt + veto_purity_dt) > 0 else 0
+        fom_dt = veto_eff_dt * (1 - deadtime_dt)
+
+        # --- Metrics comparison table ---
+        metrics_df = pd.DataFrame({
+            "Metric": [
+                "Veto efficiency (recall)",
+                "Deadtime",
+                "Veto purity (precision)",
+                "F1 score",
+                "Figure of merit",
+            ],
+            "ML model": [
+                f"{veto_eff*100:.3f}%",
+                f"{deadtime*100:.3f}%",
+                f"{veto_purity*100:.3f}%",
+                f"{f1*100:.3f}%",
+                f"{fom*100:.3f}%",
+            ],
+            f"dT cut [{dT_min}, {dT_max}] ns": [
+                f"{veto_eff_dt*100:.3f}%",
+                f"{deadtime_dt*100:.3f}%",
+                f"{veto_purity_dt*100:.3f}%",
+                f"{f1_dt*100:.3f}%",
+                f"{fom_dt*100:.3f}%",
+            ],
+            "Difference": [
+                f"{(veto_eff - veto_eff_dt)*100:+.3f}%",
+                f"{(deadtime - deadtime_dt)*100:+.3f}%",
+                f"{(veto_purity - veto_purity_dt)*100:+.3f}%",
+                f"{(f1 - f1_dt)*100:+.3f}%",
+                f"{(fom - fom_dt)*100:+.3f}%",
+            ],
+            "Description": [
+                "Fraction of cosmics vetoed (TP/(TP+FN))",
+                "Fraction of CE mix vetoed (FP/(FP+TN))",
+                "Of vetoed events, fraction that are cosmics (TP/(TP+FP))",
+                "Harmonic mean of precision and recall",
+                "eff_veto * (1 - deadtime)",
+            ],
+        })
+
+        # --- Confusion matrix comparison table ---
+        confusion_df = pd.DataFrame({
+            "Category": [
+                "True Positives (CRY vetoed)",
+                "True Negatives (CE mix passed)",
+                "False Positives (CE mix vetoed)",
+                "False Negatives (CRY passed)",
+            ],
+            "ML model": [tp, tn, fp, fn],
+            "dT cut": [tp_dt, tn_dt, fp_dt, fn_dt],
+            "Difference": [tp - tp_dt, tn - tn_dt, fp - fp_dt, fn - fn_dt],
+        })
+
+        # --- Save to CSV ---
+        if save_csv:
+            self.results_out_path.mkdir(parents=True, exist_ok=True)
+
+            metrics_path = self.results_out_path / "metrics_comparison.csv"
+            metrics_df.to_csv(metrics_path, index=False)
+
+            confusion_path = self.results_out_path / "confusion_comparison.csv"
+            confusion_df.to_csv(confusion_path, index=False)
+
+            self.logger.log(
+                f"Saved comparison tables to {self.results_out_path}",
+                "success"
+            )
+
+        accuracy = (tp + tn) / (tp + tn + fp + fn) if (tp + tn + fp + fn) > 0 else 0
+        accuracy_dt = (tp_dt + tn_dt) / (tp_dt + tn_dt + fp_dt + fn_dt) if (tp_dt + tn_dt + fp_dt + fn_dt) > 0 else 0
+
+        return {
+            "metrics": metrics_df,
+            "confusion": confusion_df,
+            "raw": {
+                "veto_efficiency": veto_eff,
+                "deadtime": deadtime,
+                "veto_purity": veto_purity,
+                "f1": f1,
+                "accuracy": accuracy,
+                "figure_of_merit": fom,
+                "tp": tp, "tn": tn, "fp": fp, "fn": fn,
+                "veto_efficiency_dt": veto_eff_dt,
+                "deadtime_dt": deadtime_dt,
+                "veto_purity_dt": veto_purity_dt,
+                "f1_dt": f1_dt,
+                "accuracy_dt": accuracy_dt,
+                "figure_of_merit_dt": fom_dt,
+                "tp_dt": tp_dt, "tn_dt": tn_dt, "fp_dt": fp_dt, "fn_dt": fn_dt,
+            },
+        }
+
+    def cv_money_table(self, fold_raws, save_csv=True):
+        """Display CV-averaged money table from list of fold raw dicts.
+
+        Args:
+            fold_raws: list of raw dicts from money_table()["raw"], one per fold.
+            save_csv: whether to save the table to CSV.
+        """
+        metrics = ["veto_efficiency", "deadtime", "veto_purity",
+                    "f1", "accuracy", "figure_of_merit"]
+        labels = ["Veto efficiency", "Deadtime", "Veto purity",
+                   "F1 score", "Overall accuracy", "Figure of merit"]
+        descriptions = [
+            "Fraction of cosmics vetoed (TP/(TP+FN))",
+            "Fraction of CE mix vetoed (FP/(FP+TN))",
+            "Of vetoed events, fraction that are cosmics (TP/(TP+FP))",
+            "Harmonic mean of precision and recall",
+            "Overall correct classification rate",
+            "eff_veto * (1 - deadtime)",
+        ]
+
+        ml_strs = []
+        dt_strs = []
+        diff_strs = []
+        for k in metrics:
+            vals = np.array([r[k] for r in fold_raws])
+            vals_dt = np.array([r[f"{k}_dt"] for r in fold_raws])
+            diffs = vals - vals_dt
+            dt_strs.append(f"{vals_dt.mean()*100:.3f} +/- {vals_dt.std()*100:.3f}%")
+            ml_strs.append(f"{vals.mean()*100:.3f} +/- {vals.std()*100:.3f}%")
+            diff_strs.append(f"{diffs.mean()*100:+.3f} +/- {diffs.std()*100:.3f}%")
+
+        df = pd.DataFrame({
+            "Metric": labels,
+            "dT cut": dt_strs,
+            "ML model": ml_strs,
+            "Improvement": diff_strs,
+            "Description": descriptions,
+        })
+
+        if save_csv:
+            self.results_out_path.mkdir(parents=True, exist_ok=True)
+            csv_path = self.results_out_path / "cv_money_table.csv"
+            df.to_csv(csv_path, index=False)
+            self.logger.log(f"Saved CV money table to {csv_path}", "success")
+
+        return df
diff --git a/CrvCosmic/src/utils/__init__.py b/CrvCosmic/src/utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/CrvCosmic/src/utils/draw.py b/CrvCosmic/src/utils/draw.py
new file mode 100644
index 0000000..509fc10
--- /dev/null
+++ b/CrvCosmic/src/utils/draw.py
@@ -0,0 +1,160 @@
+import os
+import matplotlib.pyplot as plt
+
+current_dir = os.path.dirname(os.path.abspath(__file__))
+plt.style.use(os.path.join(current_dir, "mu2e.mplstyle"))
+
+from analyse import Analyse
+from pyutils.pylogger import Logger
+
+
+class Draw:
+    """Plot the ML preselection summary using histograms produced by Analyse.create_histograms()."""
+
+    def __init__(self, cutset_name="MLPreprocess", on_spill=False,
+                 hist_styles=None, line_styles=None, verbosity=1):
+        self.verbosity = verbosity
+        self.logger = Logger(print_prefix="[Draw]", verbosity=self.verbosity)
+        self.on_spill = on_spill
+
+        # Used for threshold lines on the summary plots
+        self.analyse = Analyse(cutset_name=cutset_name, verbosity=0)
+
+        self.hist_styles = hist_styles or {
+            "Before cuts": {
+                "color": "#121212", "linewidth": 2.0, "alpha": 0.6,
+                "linestyle": "-", "histtype": "step",
+            },
+            "After cuts": {
+                "color": "#0173B2", "linewidth": 2.0, "alpha": 0.6,
+                "linestyle": "-", "histtype": "bar",
+            },
+        }
+
+        self.line_styles = line_styles or {
+            "linestyle": "--", "color": "black", "linewidth": 1.5, "alpha": 0.8,
+        }
+
+        plt.rcParams["savefig.dpi"] = 300
+        plt.rcParams["savefig.bbox"] = "tight"
+        self.logger.log("Initialised", "info")
+
+    def _format_axis(self, ax, labels, xlabel="", ylabel="", title="",
+                     y_ext_factor=1, ncols=1, leg=True, log=True,
+                     frameon=False, loc="upper right"):
+        if log:
+            ax.set_yscale("log")
+        if leg:
+            ax.legend(labels, frameon=frameon, ncols=ncols, loc=loc)
+        if xlabel:
+            ax.set_xlabel(xlabel)
+        if ylabel:
+            ax.set_ylabel(ylabel)
+        if title:
+            ax.set_title(title)
+        lo, hi = ax.get_ylim()
+        ax.set_ylim(lo, hi * y_ext_factor)
+
+    def _plot_histogram(self, hist_obj, ax, selection):
+        h_sel = hist_obj[{"selection": selection}]
+        labels = list(h_sel.axes["selection"])
+
+        for label in labels:
+            style = self.hist_styles.get(label, {
+                "color": "red", "linewidth": 1.5, "linestyle": "-",
+                "alpha": 0.9, "histtype": "step",
+            })
+            hist_obj[{"selection": label}].plot1d(
+                ax=ax,
+                yerr=False,
+                color=style["color"],
+                edgecolor=style["color"],
+                flow="none",
+                histtype=style["histtype"],
+                linewidth=style["linewidth"],
+                alpha=style["alpha"],
+                linestyle=style["linestyle"],
+            )
+
+        return labels
+
+    def _add_threshold_lines(self, ax, var_name, toggle_lines, line_kwargs):
+        """Draw vertical threshold markers for the active cuts on a given variable."""
+        if not toggle_lines.get(var_name, True):
+            return
+
+        active = self.analyse.active_cuts
+        thr = self.analyse.thresholds
+
+        if var_name == "mom_full":
+            ax.axvline(thr["lo_wide_win_mevc"], **line_kwargs)
+            ax.axvline(thr["hi_wide_win_mevc"], **line_kwargs)
+        elif var_name == "t0" and active.get("within_t0"):
+            ax.axvline(thr["lo_t0_ns"], **line_kwargs)
+            ax.axvline(thr["hi_t0_ns"], **line_kwargs)
+        elif var_name == "trkqual" and active.get("good_trkqual"):
+            ax.axvline(thr["lo_trkqual"], **line_kwargs)
+        elif var_name == "nactive" and active.get("has_hits"):
+            ax.axvline(thr["lo_nactive"], **line_kwargs)
+        elif var_name == "t0err" and active.get("within_t0err"):
+            ax.axvline(thr["hi_t0err"], **line_kwargs)
+        elif var_name == "d0" and active.get("within_d0"):
+            ax.axvline(thr["hi_d0_mm"], **line_kwargs)
+        elif var_name == "maxr":
+            if active.get("within_lhr_max_lo"):
+                ax.axvline(thr["lo_maxr_mm"], **line_kwargs)
+            if active.get("within_lhr_max_hi"):
+                ax.axvline(thr["hi_maxr_mm"], **line_kwargs)
+        elif var_name == "pitch_angle":
+            if active.get("within_pitch_angle_lo"):
+                ax.axvline(thr["lo_pitch_angle"], **line_kwargs)
+            if active.get("within_pitch_angle_hi"):
+                ax.axvline(thr["hi_pitch_angle"], **line_kwargs)
+
+    def plot_summary_ml(self, hists, out_path=None, toggle_lines=None):
+        """2x4 summary of the ML-relevant track-fit variables."""
+        fig, ax = plt.subplots(2, 4, figsize=(4 * 6.4, 2 * 4.8))
+        fig.subplots_adjust(hspace=0.3, wspace=0.25)
+
+        if toggle_lines is None:
+            toggle_lines = {
+                "mom_full": True, "t0": True, "trkqual": True, "nactive": True,
+                "t0err": True, "d0": True, "maxr": True, "pitch_angle": True,
+            }
+
+        var_info = {
+            "mom_full":    (r"Momentum [MeV/c]",                 "", "upper right",  20, 1),
+            "t0":          ("Track fit time [ns]",               "", "upper center", 20, 1),
+            "trkqual":     ("Track quality",                     "", "upper right",   5, 2),
+            "nactive":     ("Active tracker hits",               "", "upper right",  20, 2),
+            "t0err":       (r"$\sigma_{t_{0}}$ [ns]",             "", "upper right",   1, 1),
+            "d0":          (r"$d_{0}$ [mm]",                      "", "upper right",  20, 1),
+            "maxr":        (r"$R_{\text{max}}$ [mm]",             "", "upper left",   12, 1),
+            "pitch_angle": (r"$p_{z}/p_{T}$",                     "", "upper right",  20, 1),
+        }
+
+        plot_positions = [
+            ("mom_full",    (0, 0)), ("t0",   (0, 1)), ("trkqual", (0, 2)), ("nactive",     (0, 3)),
+            ("t0err",       (1, 0)), ("d0",   (1, 1)), ("maxr",    (1, 2)), ("pitch_angle", (1, 3)),
+        ]
+
+        for var_name, (row, col) in plot_positions:
+            labels = self._plot_histogram(
+                hists[var_name], ax[row, col],
+                list(hists[var_name].axes["selection"]),
+            )
+            xlabel, title, loc, y_ext_factor, ncols = var_info[var_name]
+            ylabel = "Tracks" if col == 0 else ""
+            self._format_axis(
+                ax[row, col], labels,
+                xlabel=xlabel, ylabel=ylabel, title=title,
+                loc=loc, y_ext_factor=y_ext_factor, ncols=ncols,
+            )
+            self._add_threshold_lines(ax[row, col], var_name, toggle_lines, self.line_styles)
+
+        plt.tight_layout()
+        if out_path:
+            out_path.parent.mkdir(parents=True, exist_ok=True)
+            plt.savefig(out_path)
+            self.logger.log(f"Wrote {out_path}", "success")
+        plt.show()
diff --git a/CrvCosmic/src/utils/hist_manager.py b/CrvCosmic/src/utils/hist_manager.py
new file mode 100644
index 0000000..74b6017
--- /dev/null
+++ b/CrvCosmic/src/utils/hist_manager.py
@@ -0,0 +1,166 @@
+import hist
+import numpy as np
+import awkward as ak
+
+from pyutils.pylogger import Logger
+
+
+class HistManager:
+    """Book and fill the histograms used by the ML preselection and summary plots."""
+
+    def __init__(self, analyse):
+        self.analyse = analyse
+        self.thresholds = analyse.thresholds
+        self.selector = analyse.selector
+        self.vector = analyse.vector
+        self.verbosity = analyse.verbosity
+
+        self.logger = Logger(print_prefix="[HistManager]", verbosity=self.verbosity)
+        self._define_histogram_configs()
+
+    def _define_histogram_configs(self):
+        """Histograms shown in plot_summary_ml plus dT (used as an ML feature)."""
+        self.histogram_configs = {
+            "mom_full": {
+                "axis": hist.axis.Regular(200, 0, 1000, name="mom", label="Momentum [MeV/c]"),
+                "param": "mom",
+            },
+            "trkqual": {
+                "axis": hist.axis.Regular(100, 0, 1, name="trkqual", label="Track quality"),
+                "param": "trkqual",
+            },
+            "nactive": {
+                "axis": hist.axis.Regular(101, -0.5, 100.5, name="nactive", label="Active tracker hits"),
+                "param": "nactive",
+            },
+            "t0": {
+                "axis": hist.axis.Regular(1400, 400, 1800, name="t0", label="Track fit time [ns]"),
+                "param": "t0",
+            },
+            "t0err": {
+                "axis": hist.axis.Regular(500, 0, 5.0, name="t0err",
+                                          label=r"Track $t_{0}$ uncertainty, $\sigma_{t_{0}}$ [ns]"),
+                "param": "t0err",
+            },
+            "d0": {
+                "axis": hist.axis.Regular(40, 0, 200, name="d0",
+                                          label=r"Distance of closest approach, $d_{0}$ [mm]"),
+                "param": "d0",
+            },
+            "maxr": {
+                "axis": hist.axis.Regular(170, 150, 1000, name="maxr",
+                                          label=r"Loop helix maximum radius, $R_{\text{max}}$ [mm]"),
+                "param": "maxr",
+            },
+            "pitch_angle": {
+                "axis": hist.axis.Regular(400, -1, 3.0, name="pitch_angle",
+                                          label=r"Pitch angle, $p_{z}/p_{T}$"),
+                "param": "pitch_angle",
+            },
+            "dT": {
+                "axis": hist.axis.Regular(500, -200, 300, name="dT",
+                                          label=r"Track time $-$ CRV time [ns]"),
+                "param": "dT",
+            },
+        }
+
+    def _select_electron_at_surface(self, data, surface_name="TT_Front"):
+        """Restrict trkfit to the named surface and to electron-hypothesis tracks."""
+        is_reco_electron = self.selector.is_electron(data["trk"])
+        at_surface = self.selector.select_surface(data["trkfit"], surface_name=surface_name)
+        has_surface = ak.any(at_surface, axis=-1)
+
+        data_cut = ak.copy(data)
+        data_cut["trkfit"] = data_cut["trkfit"][at_surface]
+
+        trk_mask = is_reco_electron & has_surface
+        data_cut["trk"] = data_cut["trk"][trk_mask]
+        data_cut["trkfit"] = data_cut["trkfit"][trk_mask]
+
+        return data_cut[ak.any(trk_mask, axis=-1)]
+
+    def _extract_data(self, data, param):
+        """Pull the flat 1D values for a histogram fill."""
+        if param == "mom":
+            sel = self._select_electron_at_surface(data, "TT_Front")
+            mom = self.vector.get_mag(sel["trkfit"]["trksegs"], "mom")
+            return ak.flatten(mom, axis=None) if mom is not None else ak.Array([])
+
+        if param == "trkqual":
+            return ak.flatten(data["trk"]["trkqual.result"], axis=None)
+
+        if param == "nactive":
+            return ak.flatten(data["trk"]["trk.nactive"], axis=None)
+
+        if param == "t0":
+            sel = self._select_electron_at_surface(data, "TT_Mid")
+            return ak.flatten(sel["trkfit"]["trksegs"]["time"], axis=None)
+
+        if param == "t0err":
+            sel = self._select_electron_at_surface(data, "TT_Mid")
+            return ak.flatten(sel["trkfit"]["trksegpars_lh"]["t0err"], axis=None)
+
+        if param == "d0":
+            sel = self._select_electron_at_surface(data, "TT_Front")
+            return ak.flatten(sel["trkfit"]["trksegpars_lh"]["d0"], axis=None)
+
+        if param == "maxr":
+            sel = self._select_electron_at_surface(data, "TT_Front")
+            return ak.flatten(sel["trkfit"]["trksegpars_lh"]["maxr"], axis=None)
+
+        if param == "pitch_angle":
+            sel = self._select_electron_at_surface(data, "TT_Front")
+            pitch = self.analyse.get_pitch_angle(sel["trkfit"])
+            return ak.flatten(pitch, axis=None)
+
+        if param == "dT":
+            sel = self._select_electron_at_surface(data, "TT_Mid")
+            try:
+                dT = self.analyse.get_trk_crv_dt(sel["trkfit"], data["crv"])["dT"]
+                return ak.flatten(dT, axis=None)
+            except Exception:
+                self.logger.log("Misalignment in dT calculation (returning [])", "warning")
+                return []
+
+        raise ValueError(f"Unknown parameter: {param}")
+
+    def create_histograms(self, datasets):
+        """Fill each booked histogram with one entry per labelled dataset.
+
+        Args:
+            datasets: dict mapping selection label -> awkward array.
+        """
+        self.logger.log("Creating histograms", "info")
+
+        selection_labels = list(datasets.keys())
+
+        histograms = {
+            name: hist.Hist(
+                cfg["axis"],
+                hist.axis.StrCategory(selection_labels, name="selection", label="Selection"),
+            )
+            for name, cfg in self.histogram_configs.items()
+        }
+
+        for label, data in datasets.items():
+            if len(data) == 0:
+                self.logger.log(f"Skipping empty dataset: {label}", "warning")
+                continue
+
+            for name, cfg in self.histogram_configs.items():
+                try:
+                    values = self._extract_data(data, cfg["param"])
+                    if len(values) == 0:
+                        continue
+                    histograms[name].fill(
+                        **{
+                            cfg["axis"].name: values,
+                            "selection": np.full(len(values), label),
+                        }
+                    )
+                except Exception as e:
+                    self.logger.log(f"Error filling {name} for {label}: {e}", "error")
+                    raise
+
+        self.logger.log("Histograms filled successfully", "success")
+        return {name: h.copy() for name, h in histograms.items()}
diff --git a/CrvCosmic/src/utils/io_manager.py b/CrvCosmic/src/utils/io_manager.py
new file mode 100644
index 0000000..de98da6
--- /dev/null
+++ b/CrvCosmic/src/utils/io_manager.py
@@ -0,0 +1,160 @@
+import copy
+import os
+import pickle
+import sys
+
+import awkward as ak
+import h5py
+import hist
+import numpy as np
+import pyarrow.parquet as pq
+import yaml
+
+from pyutils.pylogger import Logger
+
+
+class Write:
+    """Persist MLProcessor postprocessing output to pkl/csv/h5/parquet."""
+
+    def __init__(self, out_path="test_out", verbosity=1):
+        self.out_path = out_path
+        os.makedirs(self.out_path, exist_ok=True)
+        self.logger = Logger(print_prefix="[Write]", verbosity=verbosity)
+        self.logger.log(f"Initialised with out_path={out_path}", "success")
+
+    def write_pkl(self, results, out_name="results.pkl"):
+        path = os.path.join(self.out_path, out_name)
+        try:
+            with open(path, "wb") as f:
+                pickle.dump(results, f)
+            self.logger.log(f"Wrote {path}", "success")
+        except Exception as e:
+            self.logger.log(f"Failed to write {path}: {e}", "error")
+            raise
+
+    def write_cut_flow_csv(self, df_cut_flow, out_name="cut_flow.csv"):
+        if df_cut_flow is None:
+            self.logger.log("cut flow is None — skipping", "warning")
+            return
+        path = os.path.join(self.out_path, out_name)
+        try:
+            df_cut_flow.to_csv(path, index=True)
+            self.logger.log(f"Wrote {path}", "success")
+        except Exception as e:
+            self.logger.log(f"Failed to write {path}: {e}", "error")
+            raise
+
+    def write_hists_h5(self, hists, out_name="hists.h5"):
+        if not hists:
+            self.logger.log("hists is empty — skipping", "warning")
+            return
+        path = os.path.join(self.out_path, out_name)
+        try:
+            with h5py.File(path, "w") as f:
+                for name, h in hists.items():
+                    grp = f.create_group(name)
+                    grp.create_dataset("counts", data=h.values())
+                    # Save the primary variable axis edges
+                    var_axis = h.axes[0]
+                    grp.create_dataset("edges", data=var_axis.edges)
+                    grp.attrs["var_name"] = var_axis.name
+                    # Selection axis labels (StrCategory)
+                    sel_axis = h.axes["selection"]
+                    grp.attrs["selection_labels"] = list(sel_axis)
+            self.logger.log(f"Wrote {path}", "success")
+        except Exception as e:
+            self.logger.log(f"Failed to write {path}: {e}", "error")
+            raise
+
+    def write_events_parquet(self, events, out_name="events.parquet"):
+        if events is None or len(events) == 0:
+            self.logger.log("events is empty — skipping", "warning")
+            return
+        path = os.path.join(self.out_path, out_name)
+        try:
+            ak.to_parquet(events, path)
+            self.logger.log(f"Wrote {path}", "success")
+        except Exception as e:
+            self.logger.log(f"Failed to write {path}: {e}", "error")
+            raise
+
+    def write_all(self, results):
+        """Write the postprocessing dict in all available formats."""
+        self.logger.log("Saving all", "info")
+
+        # Always write the full pickle so loaders can reconstruct everything
+        self.write_pkl(results)
+
+        write_tasks = [
+            ("cut_flow", self.write_cut_flow_csv),
+            ("hists", self.write_hists_h5),
+            ("events", self.write_events_parquet),
+        ]
+        for key, fn in write_tasks:
+            if key in results and results[key] is not None:
+                fn(results[key])
+            else:
+                self.logger.log(f"Skipping {key} (not in results)", "warning")
+
+
+class Load:
+    """Read pickled MLProcessor output and the cut-config YAML."""
+
+    def __init__(self, in_path="test_out", verbosity=1):
+        self.in_path = in_path
+        self.logger = Logger(print_prefix="[Load]", verbosity=verbosity)
+        self.logger.log(f"Initialised with in_path={in_path}", "success")
+
+    def _get_path(self, in_name):
+        return os.path.join(self.in_path, in_name)
+
+    def load_cuts_yaml(self, ana, cut_config_path="../../config/cuts.yaml"):
+        """Resolve cuts.yaml relative to src/utils/ and apply the named cutset to ``ana``.
+
+        Sets ``ana.thresholds``, ``ana.active_cuts``, ``ana.cut_config``.
+        """
+        script_dir = os.path.dirname(os.path.abspath(__file__))
+        resolved = os.path.join(script_dir, cut_config_path)
+
+        if not os.path.exists(resolved):
+            self.logger.log(f"Cut config file not found: {resolved}", "error")
+            sys.exit(1)
+        ana.cut_config_path = resolved
+
+        with open(resolved, "r") as f:
+            config = yaml.safe_load(f)
+
+        if "defaults" not in config:
+            self.logger.log("No 'defaults' section in cuts.yaml", "error")
+            sys.exit(1)
+
+        if ana.cutset_name == "defaults":
+            final_config = config["defaults"]
+        else:
+            if "cutsets" not in config or ana.cutset_name not in config["cutsets"]:
+                self.logger.log(f"Cutset '{ana.cutset_name}' not found", "error")
+                sys.exit(1)
+
+            final_config = copy.deepcopy(config["defaults"])
+            override = config["cutsets"][ana.cutset_name]
+            for section in ("thresholds", "active"):
+                if section in override:
+                    final_config[section].update(override[section])
+            if "description" in override:
+                final_config["description"] = override["description"]
+
+        ana.thresholds = final_config["thresholds"]
+        ana.active_cuts = final_config["active"]
+        ana.cut_config = final_config
+
+    def load_pkl(self, in_name="results.pkl"):
+        """Load the pickled postprocess dict written by Write.write_pkl()."""
+        path = self._get_path(in_name)
+        try:
+            with open(path, "rb") as f:
+                results = pickle.load(f)
+            self.logger.log(f"Loaded {path}", "success")
+            return results
+        except Exception as e:
+            self.logger.log(f"Failed to load {path}: {e}", "error")
+            raise
diff --git a/CrvCosmic/src/utils/mu2e.mplstyle b/CrvCosmic/src/utils/mu2e.mplstyle
new file mode 100644
index 0000000..15a8209
--- /dev/null
+++ b/CrvCosmic/src/utils/mu2e.mplstyle
@@ -0,0 +1,46 @@
+# mu2e.mplstyle
+
+# Generic figure tweaks 
+axes.prop_cycle: cycler("color", ['red','blue','green','orange','violet','cyan','pink','gray','yellow'])
+
+mathtext.default: regular
+font.size: 18
+axes.labelsize: medium
+axes.unicode_minus: False
+axes.labelpad: 10
+axes.titlesize: 15
+axes.linewidth: 2
+grid.alpha: 0.8
+grid.linestyle: :
+savefig.transparent: False
+
+xaxis.labellocation: right
+yaxis.labellocation: top
+xtick.labelsize: small
+ytick.labelsize: small
+xtick.direction: in
+xtick.major.size: 12
+xtick.minor.size: 6
+xtick.major.pad: 6
+xtick.top: True
+xtick.major.top: True
+xtick.major.bottom: True
+xtick.minor.top: True
+xtick.minor.bottom: True
+xtick.minor.visible: True
+ytick.direction: in
+ytick.major.size: 12
+ytick.minor.size: 6.0
+ytick.right: True
+ytick.major.left: True
+ytick.major.right: True
+ytick.minor.left: True
+ytick.minor.right: True
+ytick.minor.visible: True
+
+legend.fontsize: small
+legend.handlelength: 1.5
+legend.borderpad: 0.5
+legend.frameon: True
+legend.loc: upper right
+