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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ORNL VENUS Neutron Transmission Analysis: Gold-197\n",
+    "\n",
+    "## Instrument: VENUS (Versatile Neutron Imaging Station)\n",
+    "## Facility: Oak Ridge National Laboratory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from dotenv import load_dotenv\n",
+    "load_dotenv('../../../.envrc')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Environment Configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Verify resolution function environment variable\n",
+    "resolution_file_path = Path(os.environ.get('VENUS_RES_FUNC', ''))\n",
+    "if not resolution_file_path.exists():\n",
+    "    raise FileNotFoundError(f\"Resolution file not found at VENUS_RES_FUNC: {resolution_file_path}\")\n",
+    "\n",
+    "# VENUS data paths\n",
+    "ipts_path = \"/SNS/VENUS/IPTS-35945\"\n",
+    "nexus_path = f\"{ipts_path}/nexus\"\n",
+    "autoreduce_base = f\"{ipts_path}/shared/autoreduce/mcp/images\"\n",
+    "\n",
+    "# Analysis directories\n",
+    "working_dir = \"./au_analysis\"\n",
+    "os.makedirs(working_dir, exist_ok=True)\n",
+    "spectra_dir = f\"{working_dir}/spectra\"\n",
+    "os.makedirs(spectra_dir, exist_ok=True)\n",
+    "twenty_dir = f\"{working_dir}/twenty\"\n",
+    "os.makedirs(twenty_dir, exist_ok=True)\n",
+    "sammy_working = Path(working_dir) / \"sammy_working\"\n",
+    "sammy_output = Path(working_dir) / \"sammy_output\"\n",
+    "sammy_working.mkdir(exist_ok=True)\n",
+    "sammy_output.mkdir(exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Processing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pleiades.processing.normalization import normalization\n",
+    "from pleiades.processing import Roi, Facility\n",
+    "\n",
+    "open_beam_folder = f\"{autoreduce_base}/Run_8021\"\n",
+    "sample_folders = [\n",
+    "    f\"{autoreduce_base}/Run_8022\",\n",
+    "    f\"{autoreduce_base}/Run_8023\",\n",
+    "    f\"{autoreduce_base}/Run_8024\"\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-09-25 18:21:52\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.normalization\u001b[0m:\u001b[36mnormalization\u001b[0m:\u001b[36m91\u001b[0m - \u001b[1mUsing ORNL-specific normalization\u001b[0m\n",
+      "olefile module not found\n",
+      "/SNS/users/8cz/github.com/PLEIADES/.pixi/envs/default/lib/python3.11/site-packages/dxchange/__init__.py:63: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n",
+      "  import pkg_resources\n",
+      "\u001b[32m2025-09-25 18:21:53\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mnormalization_ornl\u001b[0m:\u001b[36m199\u001b[0m - \u001b[1mLoading 3 sample folders\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:21:53\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.utils.load\u001b[0m:\u001b[36mload\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mloading 4367 files with extension .tif\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:22:38\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mload_spectra_file\u001b[0m:\u001b[36m42\u001b[0m - \u001b[34m\u001b[1mFound spectra file: Run_8022_20250423_April23_2025_MCP_TPX_Foil_Au_1_6C_Resonance_0001_2585092_Spectra.txt\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:22:38\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.utils.load\u001b[0m:\u001b[36mload\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mloading 4367 files with extension .tif\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:23:23\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mload_spectra_file\u001b[0m:\u001b[36m42\u001b[0m - \u001b[34m\u001b[1mFound spectra file: Run_8023_20250423_April23_2025_MCP_TPX_Foil_Au_1_6C_Resonance_0001_2586093_Spectra.txt\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:23:23\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.utils.load\u001b[0m:\u001b[36mload\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mloading 4367 files with extension .tif\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:08\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mload_spectra_file\u001b[0m:\u001b[36m42\u001b[0m - \u001b[34m\u001b[1mFound spectra file: Run_8024_20250423_April23_2025_MCP_TPX_Foil_Au_1_6C_Resonance_0001_2587094_Spectra.txt\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mload_multiple_runs\u001b[0m:\u001b[36m177\u001b[0m - \u001b[1mLoaded 3 runs\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mnormalization_ornl\u001b[0m:\u001b[36m202\u001b[0m - \u001b[1mLoading 1 OB folders\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.utils.load\u001b[0m:\u001b[36mload\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mloading 4367 files with extension .tif\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:53\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mload_spectra_file\u001b[0m:\u001b[36m42\u001b[0m - \u001b[34m\u001b[1mFound spectra file: Run_8021_20250423_April23_2025_OB_MCP_TPX1_Foil_1_6C_Resonance_0001_2584091_Spectra.txt\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:53\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mload_multiple_runs\u001b[0m:\u001b[36m177\u001b[0m - \u001b[1mLoaded 1 runs\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:53\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mnormalization_ornl\u001b[0m:\u001b[36m210\u001b[0m - \u001b[1mUsing individual mode\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:53\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mprocess_individual_mode\u001b[0m:\u001b[36m123\u001b[0m - \u001b[1mCombined 1 OB runs\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:53\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mprocess_individual_mode\u001b[0m:\u001b[36m128\u001b[0m - \u001b[34m\u001b[1mProcessing sample run 1/3\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:53\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mdetect_persistent_dead_pixels\u001b[0m:\u001b[36m193\u001b[0m - \u001b[34m\u001b[1mFound 20/262144 dead pixels (0.0%)\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:54\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mdetect_persistent_dead_pixels\u001b[0m:\u001b[36m193\u001b[0m - \u001b[34m\u001b[1mFound 13/262144 dead pixels (0.0%)\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:55\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mprocess_individual_mode\u001b[0m:\u001b[36m128\u001b[0m - \u001b[34m\u001b[1mProcessing sample run 2/3\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:55\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mdetect_persistent_dead_pixels\u001b[0m:\u001b[36m193\u001b[0m - \u001b[34m\u001b[1mFound 27/262144 dead pixels (0.0%)\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:55\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mdetect_persistent_dead_pixels\u001b[0m:\u001b[36m193\u001b[0m - \u001b[34m\u001b[1mFound 13/262144 dead pixels (0.0%)\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:56\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mprocess_individual_mode\u001b[0m:\u001b[36m128\u001b[0m - \u001b[34m\u001b[1mProcessing sample run 3/3\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:57\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mdetect_persistent_dead_pixels\u001b[0m:\u001b[36m193\u001b[0m - \u001b[34m\u001b[1mFound 28/262144 dead pixels (0.0%)\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:57\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mdetect_persistent_dead_pixels\u001b[0m:\u001b[36m193\u001b[0m - \u001b[34m\u001b[1mFound 13/262144 dead pixels (0.0%)\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mnormalization_ornl\u001b[0m:\u001b[36m249\u001b[0m - \u001b[1mSaved transmission to ./au_analysis/spectra/Run_8022_transmission.txt\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mnormalization_ornl\u001b[0m:\u001b[36m249\u001b[0m - \u001b[1mSaved transmission to ./au_analysis/spectra/Run_8023_transmission.txt\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mnormalization_ornl\u001b[0m:\u001b[36m249\u001b[0m - \u001b[1mSaved transmission to ./au_analysis/spectra/Run_8024_transmission.txt\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "transmissions = normalization(\n",
+    "    list_sample_folders=sample_folders,\n",
+    "    list_obs_folders=[open_beam_folder],\n",
+    "    nexus_path=nexus_path,\n",
+    "    facility=Facility.ornl,\n",
+    "    combine_mode=False,\n",
+    "    pc_uncertainty=0.005,\n",
+    "    output_folder=os.path.join(working_dir, \"spectra\"),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Transmission spectrum visualization\n",
+    "fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "for transmission in transmissions:\n",
+    "    label = transmission.metadata.get(\"sample_folder\", \"Unknown\").split(\"/\")[-1]\n",
+    "    ax.plot(transmission.energy, transmission.transmission, label=label)\n",
+    "ax.set_xlabel(\"Energy (eV)\")\n",
+    "ax.set_ylabel(\"Transmission\")\n",
+    "ax.set_xscale(\"log\")\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "ax.legend()\n",
+    "ax.set_title(\"Au-197 Transmission Spectra\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SAMMY Data Format Conversion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-09-25 18:24:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.data_manager\u001b[0m:\u001b[36mconvert_csv_to_sammy_twenty\u001b[0m:\u001b[36m53\u001b[0m - \u001b[1mConverting au_analysis/spectra/Run_8022_transmission.txt to SAMMY twenty format: au_analysis/twenty/Run_8022_transmission.twenty\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.data_manager\u001b[0m:\u001b[36mconvert_csv_to_sammy_twenty\u001b[0m:\u001b[36m123\u001b[0m - \u001b[1mConverted 4367 data points to twenty format\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.data_manager\u001b[0m:\u001b[36mvalidate_sammy_twenty_format\u001b[0m:\u001b[36m163\u001b[0m - \u001b[1mFile au_analysis/twenty/Run_8022_transmission.twenty is valid SAMMY twenty format\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.data_manager\u001b[0m:\u001b[36mconvert_csv_to_sammy_twenty\u001b[0m:\u001b[36m53\u001b[0m - \u001b[1mConverting au_analysis/spectra/Run_8023_transmission.txt to SAMMY twenty format: au_analysis/twenty/Run_8023_transmission.twenty\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.data_manager\u001b[0m:\u001b[36mconvert_csv_to_sammy_twenty\u001b[0m:\u001b[36m123\u001b[0m - \u001b[1mConverted 4367 data points to twenty format\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.data_manager\u001b[0m:\u001b[36mvalidate_sammy_twenty_format\u001b[0m:\u001b[36m163\u001b[0m - \u001b[1mFile au_analysis/twenty/Run_8023_transmission.twenty is valid SAMMY twenty format\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.data_manager\u001b[0m:\u001b[36mconvert_csv_to_sammy_twenty\u001b[0m:\u001b[36m53\u001b[0m - \u001b[1mConverting au_analysis/spectra/Run_8024_transmission.txt to SAMMY twenty format: au_analysis/twenty/Run_8024_transmission.twenty\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.data_manager\u001b[0m:\u001b[36mconvert_csv_to_sammy_twenty\u001b[0m:\u001b[36m123\u001b[0m - \u001b[1mConverted 4367 data points to twenty format\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.data_manager\u001b[0m:\u001b[36mvalidate_sammy_twenty_format\u001b[0m:\u001b[36m163\u001b[0m - \u001b[1mFile au_analysis/twenty/Run_8024_transmission.twenty is valid SAMMY twenty format\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Converting 3 transmission files\n",
+      "Converted: Run_8022_transmission.twenty\n",
+      "Converted: Run_8023_transmission.twenty\n",
+      "Converted: Run_8024_transmission.twenty\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pleiades.sammy.io.data_manager import convert_csv_to_sammy_twenty, validate_sammy_twenty_format\n",
+    "\n",
+    "input_dir = Path(spectra_dir)\n",
+    "output_dir = Path(twenty_dir)\n",
+    "\n",
+    "csv_files = list(input_dir.glob(\"*_transmission.txt\"))\n",
+    "print(f\"Converting {len(csv_files)} transmission files\")\n",
+    "\n",
+    "for csv_file in csv_files:\n",
+    "    twenty_file = output_dir / csv_file.name.replace(\".txt\", \".twenty\")\n",
+    "    convert_csv_to_sammy_twenty(csv_file, twenty_file)\n",
+    "    if validate_sammy_twenty_format(twenty_file):\n",
+    "        print(f\"Converted: {twenty_file.name}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SAMMY Configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m165\u001b[0m - \u001b[1mJsonManager initialized with Pydantic v2 models\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m214\u001b[0m - \u001b[1mCreating SAMMY workspace for 1 isotopes: ['Au-197']\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m215\u001b[0m - \u001b[1mWorking directory: au_analysis\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m218\u001b[0m - \u001b[1mStep 1: Staging ENDF files...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_isotope_info\u001b[0m:\u001b[36m167\u001b[0m - \u001b[1mGetting isotope parameters for Au-197\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m75\u001b[0m - \u001b[1mSearching for mass.mas20 in cached files for FileCategory.ISOTOPES: {PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/mass.mas20'), PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/neutrons.list'), PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/isotopes.info')}\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m77\u001b[0m - \u001b[1mChecking file: mass.mas20\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m79\u001b[0m - \u001b[1mFound file: /SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/mass.mas20\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m75\u001b[0m - \u001b[1mSearching for isotopes.info in cached files for FileCategory.ISOTOPES: {PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/mass.mas20'), PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/neutrons.list'), PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/isotopes.info')}\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m77\u001b[0m - \u001b[1mChecking file: mass.mas20\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m77\u001b[0m - \u001b[1mChecking file: neutrons.list\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m77\u001b[0m - \u001b[1mChecking file: isotopes.info\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m79\u001b[0m - \u001b[1mFound file: /SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/isotopes.info\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m75\u001b[0m - \u001b[1mSearching for neutrons.list in cached files for FileCategory.ISOTOPES: {PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/mass.mas20'), PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/neutrons.list'), PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/isotopes.info')}\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m77\u001b[0m - \u001b[1mChecking file: mass.mas20\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m77\u001b[0m - \u001b[1mChecking file: neutrons.list\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m79\u001b[0m - \u001b[1mFound file: /SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/neutrons.list\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m361\u001b[0m - \u001b[1mUsing cached ENDF data from /SNS/users/8cz/.pleiades/nuclear_data/DataRetrievalMethod.API/EndfLibrary.ENDF_B_VIII_0/n_079-Au-197_7925_resonance.dat\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m390\u001b[0m - \u001b[1mAPI method returned resonance data directly, no extraction needed\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m392\u001b[0m - \u001b[1mResonance parameters written to au_analysis/079-Au-197.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m396\u001b[0m - \u001b[1mNote: Only resonance data was downloaded (API method)\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m235\u001b[0m - \u001b[34m\u001b[1mStaged Au-197: 079-Au-197.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m244\u001b[0m - \u001b[1mStep 2: Creating JSON configuration with actual ENDF filenames...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_isotope_info\u001b[0m:\u001b[36m167\u001b[0m - \u001b[1mGetting isotope parameters for Au-197\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36madd_isotope_entry\u001b[0m:\u001b[36m95\u001b[0m - \u001b[34m\u001b[1mAdded isotope entry: 079-Au-197.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m272\u001b[0m - \u001b[34m\u001b[1mAdded Au-197 (MAT=7925) as 079-Au-197.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m285\u001b[0m - \u001b[1mJSON configuration written to: au_analysis/config.json\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m286\u001b[0m - \u001b[1mConfiguration contains 1 isotope entries\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m287\u001b[0m - \u001b[1mSAMMY workspace complete: 1 ENDF files + 1 JSON file\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "JSON configuration: au_analysis/config.json\n",
+      "ENDF files: 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pleiades.sammy.io.json_manager import JsonManager\n",
+    "\n",
+    "json_manager = JsonManager()\n",
+    "json_path = json_manager.create_json_config(\n",
+    "    isotopes=[\"Au-197\"],\n",
+    "    abundances=[1.0],\n",
+    "    working_dir=working_dir,\n",
+    "    custom_global_settings={\n",
+    "        \"forceRMoore\": \"yes\",\n",
+    "        \"purgeSpinGroups\": \"yes\", \n",
+    "        \"fudge\": \"0.7\"\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "print(f\"JSON configuration: {json_path}\")\n",
+    "endf_files = [f for f in os.listdir(working_dir) if f.endswith('.par')]\n",
+    "print(f\"ENDF files: {len(endf_files)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.utils.logger\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m145\u001b[0m - \u001b[1mlogging initialized...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.inp_manager\u001b[0m:\u001b[36mcreate_multi_isotope_inp\u001b[0m:\u001b[36m480\u001b[0m - \u001b[1mSuccessfully wrote multi-isotope SAMMY input file to au_analysis/au_fitting.inp\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INP file: au_analysis/au_fitting.inp\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pleiades.sammy.io.inp_manager import InpManager\n",
+    "\n",
+    "au_material_props = {\n",
+    "    'element': 'Au',\n",
+    "    'mass_number': 197,\n",
+    "    'density_g_cm3': 19.32,\n",
+    "    'thickness_mm': 0.025,\n",
+    "    'atomic_mass_amu': 196.966569,\n",
+    "    'abundance': 1.0,\n",
+    "    'min_energy': 1.0,\n",
+    "    'max_energy_eV': 200.0,\n",
+    "    'temperature_K': 293.6\n",
+    "}\n",
+    "\n",
+    "inp_file = Path(working_dir) / \"au_fitting.inp\"\n",
+    "InpManager.create_multi_isotope_inp(\n",
+    "    inp_file,\n",
+    "    title=\"Au-197 neutron transmission analysis - VENUS IPTS-35945\",\n",
+    "    material_properties=au_material_props,\n",
+    "    resolution_file_path=resolution_file_path\n",
+    ")\n",
+    "\n",
+    "print(f\"INP file: {inp_file}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pleiades.sammy.backends.local import LocalSammyRunner\n",
+    "from pleiades.sammy.config import LocalSammyConfig\n",
+    "from pleiades.sammy.interface import SammyFilesMultiMode\n",
+    "\n",
+    "data_file = Path(twenty_dir) / \"Run_8022_transmission.twenty\"\n",
+    "\n",
+    "files = SammyFilesMultiMode(\n",
+    "    input_file=inp_file,\n",
+    "    json_config_file=Path(json_path),\n",
+    "    data_file=data_file,\n",
+    "    endf_directory=Path(working_dir)\n",
+    ")\n",
+    "\n",
+    "config = LocalSammyConfig(\n",
+    "    sammy_executable=Path(\"/SNS/software/sammy/bin/sammy\"),\n",
+    "    working_dir=sammy_working,\n",
+    "    output_dir=sammy_output\n",
+    ")\n",
+    "\n",
+    "runner = LocalSammyRunner(config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SAMMY Execution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mprepare_environment\u001b[0m:\u001b[36m45\u001b[0m - \u001b[34m\u001b[1mValidating input files\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mprepare_environment\u001b[0m:\u001b[36m50\u001b[0m - \u001b[34m\u001b[1mPerforming JSON-ENDF mapping validation\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36m_validate_json_endf_mapping\u001b[0m:\u001b[36m100\u001b[0m - \u001b[34m\u001b[1mJSON-ENDF validation passed: 1 ENDF files verified\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mprepare_environment\u001b[0m:\u001b[36m54\u001b[0m - \u001b[34m\u001b[1mMoving files to working directory\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mmove_to_working_dir\u001b[0m:\u001b[36m214\u001b[0m - \u001b[34m\u001b[1mMoving JSON mode files to working directory: au_analysis/sammy_working\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mmove_to_working_dir\u001b[0m:\u001b[36m253\u001b[0m - \u001b[34m\u001b[1mSymlinked ENDF file: 079-Au-197.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mprepare_environment\u001b[0m:\u001b[36m58\u001b[0m - \u001b[34m\u001b[1mEnvironment preparation complete\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mexecute_sammy\u001b[0m:\u001b[36m110\u001b[0m - \u001b[1mStarting SAMMY execution 375628c1-9bce-4f3a-8037-a1dd38331dd4\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mexecute_sammy\u001b[0m:\u001b[36m111\u001b[0m - \u001b[34m\u001b[1mWorking directory: au_analysis/sammy_working\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:24:59\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mexecute_sammy\u001b[0m:\u001b[36m123\u001b[0m - \u001b[34m\u001b[1mUsing JSON mode command format\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Executing SAMMY fitting\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mexecute_sammy\u001b[0m:\u001b[36m156\u001b[0m - \u001b[1mSAMMY execution completed successfully for 375628c1-9bce-4f3a-8037-a1dd38331dd4\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m432\u001b[0m - \u001b[1mCollecting outputs for execution 375628c1-9bce-4f3a-8037-a1dd38331dd4\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m443\u001b[0m - \u001b[34m\u001b[1mFound known output file: SAMMY.IO\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m443\u001b[0m - \u001b[34m\u001b[1mFound known output file: SAMNDF.INP\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m443\u001b[0m - \u001b[34m\u001b[1mFound known output file: SAMNDF.PAR\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m443\u001b[0m - \u001b[34m\u001b[1mFound known output file: SAMMY.LST\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m443\u001b[0m - \u001b[34m\u001b[1mFound known output file: SAMMY.LPT\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m443\u001b[0m - \u001b[34m\u001b[1mFound known output file: SAMMY.ODF\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAMQUA.RED\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAMMY.PLT\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAMQUA.PAR\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAMMY.PAR\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAM46.DAT\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAM41.DAT\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAMMY.RED\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAMMY.COV\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: au_analysis/sammy_output/SAMMY.ODF\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved au_analysis/sammy_working/SAMMY.ODF to au_analysis/sammy_output/SAMMY.ODF\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: au_analysis/sammy_output/SAMNDF.INP\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved au_analysis/sammy_working/SAMNDF.INP to au_analysis/sammy_output/SAMNDF.INP\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: au_analysis/sammy_output/SAMMY.IO\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved au_analysis/sammy_working/SAMMY.IO to au_analysis/sammy_output/SAMMY.IO\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: au_analysis/sammy_output/SAMQUA.RED\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved au_analysis/sammy_working/SAMQUA.RED to au_analysis/sammy_output/SAMQUA.RED\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: au_analysis/sammy_output/SAMMY.LPT\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved au_analysis/sammy_working/SAMMY.LPT to au_analysis/sammy_output/SAMMY.LPT\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: au_analysis/sammy_output/SAMMY.PAR\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved au_analysis/sammy_working/SAMMY.PAR to au_analysis/sammy_output/SAMMY.PAR\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: au_analysis/sammy_output/SAMMY.LST\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved au_analysis/sammy_working/SAMMY.LST to au_analysis/sammy_output/SAMMY.LST\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: au_analysis/sammy_output/SAM46.DAT\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved au_analysis/sammy_working/SAM46.DAT to au_analysis/sammy_output/SAM46.DAT\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: au_analysis/sammy_output/SAMQUA.PAR\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved au_analysis/sammy_working/SAMQUA.PAR to au_analysis/sammy_output/SAMQUA.PAR\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: au_analysis/sammy_output/SAMMY.RED\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved au_analysis/sammy_working/SAMMY.RED to au_analysis/sammy_output/SAMMY.RED\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: au_analysis/sammy_output/SAMMY.PLT\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved au_analysis/sammy_working/SAMMY.PLT to au_analysis/sammy_output/SAMMY.PLT\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: au_analysis/sammy_output/SAMNDF.PAR\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved au_analysis/sammy_working/SAMNDF.PAR to au_analysis/sammy_output/SAMNDF.PAR\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: au_analysis/sammy_output/SAMMY.COV\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved au_analysis/sammy_working/SAMMY.COV to au_analysis/sammy_output/SAMMY.COV\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: au_analysis/sammy_output/SAM41.DAT\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved au_analysis/sammy_working/SAM41.DAT to au_analysis/sammy_output/SAM41.DAT\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m471\u001b[0m - \u001b[1mSuccessfully collected 14 output files in 0.08 seconds\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mcleanup\u001b[0m:\u001b[36m174\u001b[0m - \u001b[34m\u001b[1mPerforming cleanup for local backend\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Execution status: True\n",
+      "Runtime: 1.69 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Executing SAMMY fitting\")\n",
+    "runner.prepare_environment(files)\n",
+    "result = runner.execute_sammy(files)\n",
+    "\n",
+    "print(f\"Execution status: {result.success}\")\n",
+    "print(f\"Runtime: {result.runtime_seconds:.2f} s\")\n",
+    "\n",
+    "if result.error_message:\n",
+    "    print(f\"Error: {result.error_message[:200]}\")\n",
+    "\n",
+    "runner.collect_outputs(result=result)\n",
+    "runner.cleanup()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Results Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mprocess_lpt_file\u001b[0m:\u001b[36m479\u001b[0m - \u001b[1mSuccessfully read the file: au_analysis/sammy_output/SAMMY.LPT\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mprocess_lpt_file\u001b[0m:\u001b[36m490\u001b[0m - \u001b[34m\u001b[1mSplit LPT content into 3 blocks.\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_isotope_info\u001b[0m:\u001b[36m116\u001b[0m - \u001b[34m\u001b[1mExtracting isotope information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_results_from_string\u001b[0m:\u001b[36m415\u001b[0m - \u001b[1mIsotope results not found.\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_radius_info\u001b[0m:\u001b[36m189\u001b[0m - \u001b[34m\u001b[1mExtracting radius information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_results_from_string\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mRadius results not found.\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_broadening_info\u001b[0m:\u001b[36m261\u001b[0m - \u001b[34m\u001b[1mExtracting broadening information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_normalization_info\u001b[0m:\u001b[36m328\u001b[0m - \u001b[34m\u001b[1mExtracting normalization information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_chi_squared_info\u001b[0m:\u001b[36m377\u001b[0m - \u001b[34m\u001b[1mExtracting chi-squared information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_isotope_info\u001b[0m:\u001b[36m116\u001b[0m - \u001b[34m\u001b[1mExtracting isotope information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_results_from_string\u001b[0m:\u001b[36m415\u001b[0m - \u001b[1mIsotope results not found.\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_radius_info\u001b[0m:\u001b[36m189\u001b[0m - \u001b[34m\u001b[1mExtracting radius information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_results_from_string\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mRadius results not found.\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_broadening_info\u001b[0m:\u001b[36m261\u001b[0m - \u001b[34m\u001b[1mExtracting broadening information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_normalization_info\u001b[0m:\u001b[36m328\u001b[0m - \u001b[34m\u001b[1mExtracting normalization information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_chi_squared_info\u001b[0m:\u001b[36m377\u001b[0m - \u001b[34m\u001b[1mExtracting chi-squared information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_isotope_info\u001b[0m:\u001b[36m116\u001b[0m - \u001b[34m\u001b[1mExtracting isotope information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_results_from_string\u001b[0m:\u001b[36m415\u001b[0m - \u001b[1mIsotope results not found.\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_radius_info\u001b[0m:\u001b[36m189\u001b[0m - \u001b[34m\u001b[1mExtracting radius information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_results_from_string\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mRadius results not found.\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_broadening_info\u001b[0m:\u001b[36m261\u001b[0m - \u001b[34m\u001b[1mExtracting broadening information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_normalization_info\u001b[0m:\u001b[36m328\u001b[0m - \u001b[34m\u001b[1mExtracting normalization information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:25:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_chi_squared_info\u001b[0m:\u001b[36m377\u001b[0m - \u001b[34m\u001b[1mExtracting chi-squared information...\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Energy range: 6.673e+00 - 1.999e+02 eV\n",
+      "Data points: 3575\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pleiades.sammy.results.manager import ResultsManager\n",
+    "\n",
+    "lpt_file_path = sammy_output / \"SAMMY.LPT\"\n",
+    "lst_file_path = sammy_output / \"SAMMY.LST\"\n",
+    "\n",
+    "results_manager = ResultsManager(lpt_file_path=lpt_file_path, lst_file_path=lst_file_path)\n",
+    "data = results_manager.get_data()\n",
+    "\n",
+    "print(f\"Energy range: {data.energy.min():.3e} - {data.energy.max():.3e} eV\")\n",
+    "print(f\"Data points: {len(data.energy)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "results_manager.plot_transmission(\n",
+    "    show_diff = True,\n",
+    "    plot_uncertainty = True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = results_manager.plot_transmission(\n",
+    "      figsize=(12, 8),\n",
+    "      title=\"Hafnium Multi-Isotope Fit\",\n",
+    "      xscale=\"log\",\n",
+    "      # yscale=\"log\",\n",
+    "      data_color=\"blue\",\n",
+    "      final_color=\"red\",\n",
+    "      show=False,\n",
+    "      show_diff=True,\n",
+    "      plot_uncertainty=True,\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fitting Quality Metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fit iterations: 3\n",
+      "\n",
+      "Iteration 1:\n",
+      "  Chi-squared: 27372.9000\n",
+      "  Data points: 3575\n",
+      "  Reduced chi-squared: 7.656760\n",
+      "  Number density: 1.480000e-04 atoms/barn-cm\n",
+      "  Temperature: 293.60 K\n",
+      "\n",
+      "Iteration 2:\n",
+      "  Chi-squared: 2622.6100\n",
+      "  Data points: 3575\n",
+      "  Reduced chi-squared: 0.733597\n",
+      "  Number density: 1.957000e-05 atoms/barn-cm\n",
+      "  Temperature: 293.60 K\n",
+      "\n",
+      "Iteration 3:\n",
+      "  Chi-squared: 1946.8700\n",
+      "  Data points: 3575\n",
+      "  Reduced chi-squared: 0.544578\n",
+      "  Number density: 4.132800e-05 atoms/barn-cm\n",
+      "  Temperature: 293.60 K\n",
+      "\n",
+      "Final fit results:\n",
+      "  Reduced chi-squared: 0.544578\n",
+      "  Number density: 4.132800e-05 atoms/barn-cm\n"
+     ]
+    }
+   ],
+   "source": [
+    "if results_manager.run_results.fit_results:\n",
+    "    print(f\"Fit iterations: {len(results_manager.run_results.fit_results)}\")\n",
+    "    \n",
+    "    for i, fit_result in enumerate(results_manager.run_results.fit_results):\n",
+    "        print(f\"\\nIteration {i+1}:\")\n",
+    "        \n",
+    "        chi_sq = fit_result.get_chi_squared_results()\n",
+    "        if chi_sq.chi_squared is not None:\n",
+    "            print(f\"  Chi-squared: {chi_sq.chi_squared:.4f}\")\n",
+    "            print(f\"  Data points: {chi_sq.dof}\")\n",
+    "            print(f\"  Reduced chi-squared: {chi_sq.reduced_chi_squared:.6f}\")\n",
+    "        \n",
+    "        physics = fit_result.get_physics_data()\n",
+    "        if hasattr(physics, 'broadening_parameters'):\n",
+    "            broadening = physics.broadening_parameters\n",
+    "            if hasattr(broadening, 'thick') and broadening.thick is not None:\n",
+    "                print(f\"  Number density: {broadening.thick:.6e} atoms/barn-cm\")\n",
+    "                print(f\"  Temperature: {broadening.temp:.2f} K\")\n",
+    "\n",
+    "    # Final results\n",
+    "    if len(results_manager.run_results.fit_results) > 0:\n",
+    "        final_fit = results_manager.run_results.fit_results[-1]\n",
+    "        final_chi = final_fit.get_chi_squared_results()\n",
+    "        final_phys = final_fit.get_physics_data()\n",
+    "        \n",
+    "        print(\"\\nFinal fit results:\")\n",
+    "        if final_chi.reduced_chi_squared:\n",
+    "            print(f\"  Reduced chi-squared: {final_chi.reduced_chi_squared:.6f}\")\n",
+    "        if hasattr(final_phys, 'broadening_parameters'):\n",
+    "            if hasattr(final_phys.broadening_parameters, 'thick'):\n",
+    "                print(f\"  Number density: {final_phys.broadening_parameters.thick:.6e} atoms/barn-cm\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "default",
+   "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": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/Notebooks/ornl_venus/hf_analysis.ipynb b/examples/Notebooks/ornl_venus/hf_analysis.ipynb
new file mode 100644
index 00000000..4c9bfa1b
--- /dev/null
+++ b/examples/Notebooks/ornl_venus/hf_analysis.ipynb
@@ -0,0 +1,773 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ORNL VENUS Neutron Transmission Analysis: Hafnium Multi-Isotope System\n",
+    "\n",
+    "## Instrument: VENUS (Versatile Neutron Imaging Station)\n",
+    "## Facility: Oak Ridge National Laboratory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from dotenv import load_dotenv\n",
+    "load_dotenv('../../../.envrc')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Environment Configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Verify resolution function environment variable\n",
+    "resolution_file_path = Path(os.environ.get('VENUS_RES_FUNC', ''))\n",
+    "if not resolution_file_path.exists():\n",
+    "    raise FileNotFoundError(f\"Resolution file not found at VENUS_RES_FUNC: {resolution_file_path}\")\n",
+    "\n",
+    "# VENUS data paths\n",
+    "ipts_path = \"/SNS/VENUS/IPTS-35945\"\n",
+    "nexus_path = f\"{ipts_path}/nexus\"\n",
+    "autoreduce_base = f\"{ipts_path}/shared/autoreduce/mcp/images\"\n",
+    "\n",
+    "# Analysis directories\n",
+    "working_dir = \"./hf_analysis\"\n",
+    "os.makedirs(working_dir, exist_ok=True)\n",
+    "spectra_dir = f\"{working_dir}/spectra\"\n",
+    "os.makedirs(spectra_dir, exist_ok=True)\n",
+    "twenty_dir = f\"{working_dir}/twenty\"\n",
+    "os.makedirs(twenty_dir, exist_ok=True)\n",
+    "sammy_working = Path(working_dir) / \"sammy_working\"\n",
+    "sammy_output = Path(working_dir) / \"sammy_output\"\n",
+    "sammy_working.mkdir(exist_ok=True)\n",
+    "sammy_output.mkdir(exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Processing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pleiades.processing.normalization import normalization\n",
+    "from pleiades.processing import Roi, Facility\n",
+    "\n",
+    "open_beam_folder = f\"{autoreduce_base}/Run_8045\"\n",
+    "sample_folders = [\n",
+    "    f\"{autoreduce_base}/Run_8046\"\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-09-25 16:21:26\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.normalization\u001b[0m:\u001b[36mnormalization\u001b[0m:\u001b[36m91\u001b[0m - \u001b[1mUsing ORNL-specific normalization\u001b[0m\n",
+      "olefile module not found\n",
+      "/SNS/users/8cz/github.com/PLEIADES/.pixi/envs/default/lib/python3.11/site-packages/dxchange/__init__.py:63: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n",
+      "  import pkg_resources\n",
+      "\u001b[32m2025-09-25 16:21:27\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mnormalization_ornl\u001b[0m:\u001b[36m199\u001b[0m - \u001b[1mLoading 1 sample folders\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:21:27\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.utils.load\u001b[0m:\u001b[36mload\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mloading 4367 files with extension .tif\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:22:16\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mload_spectra_file\u001b[0m:\u001b[36m42\u001b[0m - \u001b[34m\u001b[1mFound spectra file: Run_8046_20250424_April23_2025_MCP_TPX_Foil_Hf_1_6C_Resonance_0001_2609116_Spectra.txt\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:22:16\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mload_multiple_runs\u001b[0m:\u001b[36m177\u001b[0m - \u001b[1mLoaded 1 runs\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:22:16\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mnormalization_ornl\u001b[0m:\u001b[36m202\u001b[0m - \u001b[1mLoading 1 OB folders\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:22:16\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.utils.load\u001b[0m:\u001b[36mload\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mloading 4367 files with extension .tif\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:05\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mload_spectra_file\u001b[0m:\u001b[36m42\u001b[0m - \u001b[34m\u001b[1mFound spectra file: Run_8045_20250424_April23_2025_OB_MCP_TPX1_Foil_1_6C_Resonance_0001_2608115_Spectra.txt\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:05\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mload_multiple_runs\u001b[0m:\u001b[36m177\u001b[0m - \u001b[1mLoaded 1 runs\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:05\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mnormalization_ornl\u001b[0m:\u001b[36m210\u001b[0m - \u001b[1mUsing individual mode\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:05\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mprocess_individual_mode\u001b[0m:\u001b[36m123\u001b[0m - \u001b[1mCombined 1 OB runs\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:05\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mprocess_individual_mode\u001b[0m:\u001b[36m128\u001b[0m - \u001b[34m\u001b[1mProcessing sample run 1/1\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:05\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mdetect_persistent_dead_pixels\u001b[0m:\u001b[36m193\u001b[0m - \u001b[34m\u001b[1mFound 80/262144 dead pixels (0.0%)\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:05\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mdetect_persistent_dead_pixels\u001b[0m:\u001b[36m193\u001b[0m - \u001b[34m\u001b[1mFound 74/262144 dead pixels (0.0%)\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mnormalization_ornl\u001b[0m:\u001b[36m249\u001b[0m - \u001b[1mSaved transmission to ./hf_analysis/spectra/Run_8046_transmission.txt\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "transmissions = normalization(\n",
+    "    list_sample_folders=sample_folders,\n",
+    "    list_obs_folders=[open_beam_folder],\n",
+    "    nexus_path=nexus_path,\n",
+    "    facility=Facility.ornl,\n",
+    "    combine_mode=False,\n",
+    "    pc_uncertainty=0.005,\n",
+    "    output_folder=os.path.join(working_dir, \"spectra\"),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Transmission spectrum visualization\n",
+    "fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "for transmission in transmissions:\n",
+    "    label = transmission.metadata.get(\"sample_folder\", \"Unknown\").split(\"/\")[-1]\n",
+    "    ax.plot(transmission.energy, transmission.transmission, label=label)\n",
+    "ax.set_xlabel(\"Energy (eV)\")\n",
+    "ax.set_ylabel(\"Transmission\")\n",
+    "ax.set_xscale(\"log\")\n",
+    "ax.set_yscale(\"log\")\n",
+    "ax.grid(True, which=\"both\", ls=\"--\", lw=0.5)\n",
+    "ax.legend()\n",
+    "ax.set_title(\"Hafnium Multi-Isotope Transmission Spectrum\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SAMMY Data Format Conversion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.data_manager\u001b[0m:\u001b[36mconvert_csv_to_sammy_twenty\u001b[0m:\u001b[36m53\u001b[0m - \u001b[1mConverting hf_analysis/spectra/Run_8046_transmission.txt to SAMMY twenty format: hf_analysis/twenty/Run_8046_transmission.twenty\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.data_manager\u001b[0m:\u001b[36mconvert_csv_to_sammy_twenty\u001b[0m:\u001b[36m123\u001b[0m - \u001b[1mConverted 4367 data points to twenty format\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.data_manager\u001b[0m:\u001b[36mvalidate_sammy_twenty_format\u001b[0m:\u001b[36m163\u001b[0m - \u001b[1mFile hf_analysis/twenty/Run_8046_transmission.twenty is valid SAMMY twenty format\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Converting 1 transmission files\n",
+      "Converted: Run_8046_transmission.twenty\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pleiades.sammy.io.data_manager import convert_csv_to_sammy_twenty, validate_sammy_twenty_format\n",
+    "\n",
+    "input_dir = Path(spectra_dir)\n",
+    "output_dir = Path(twenty_dir)\n",
+    "\n",
+    "csv_files = list(input_dir.glob(\"*_transmission.txt\"))\n",
+    "print(f\"Converting {len(csv_files)} transmission files\")\n",
+    "\n",
+    "for csv_file in csv_files:\n",
+    "    twenty_file = output_dir / csv_file.name.replace(\".txt\", \".twenty\")\n",
+    "    convert_csv_to_sammy_twenty(csv_file, twenty_file)\n",
+    "    if validate_sammy_twenty_format(twenty_file):\n",
+    "        print(f\"Converted: {twenty_file.name}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multi-Isotope SAMMY Configuration\n",
+    "\n",
+    "Natural hafnium isotopic composition:\n",
+    "- Hf-174: 0.16%\n",
+    "- Hf-176: 5.26%\n",
+    "- Hf-177: 18.60%\n",
+    "- Hf-178: 27.28%\n",
+    "- Hf-179: 13.62%\n",
+    "- Hf-180: 35.08%"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m165\u001b[0m - \u001b[1mJsonManager initialized with Pydantic v2 models\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m214\u001b[0m - \u001b[1mCreating SAMMY workspace for 6 isotopes: ['Hf-174', 'Hf-176', 'Hf-177', 'Hf-178', 'Hf-179', 'Hf-180']\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m215\u001b[0m - \u001b[1mWorking directory: hf_analysis\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m218\u001b[0m - \u001b[1mStep 1: Staging ENDF files...\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_isotope_info\u001b[0m:\u001b[36m167\u001b[0m - \u001b[1mGetting isotope parameters for Hf-174\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m75\u001b[0m - \u001b[1mSearching for mass.mas20 in cached files for FileCategory.ISOTOPES: {PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/neutrons.list'), PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/mass.mas20'), PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/isotopes.info')}\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m77\u001b[0m - \u001b[1mChecking file: neutrons.list\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m77\u001b[0m - \u001b[1mChecking file: mass.mas20\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m79\u001b[0m - \u001b[1mFound file: /SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/mass.mas20\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m75\u001b[0m - \u001b[1mSearching for isotopes.info in cached files for FileCategory.ISOTOPES: {PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/neutrons.list'), PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/mass.mas20'), PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/isotopes.info')}\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m77\u001b[0m - \u001b[1mChecking file: neutrons.list\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m77\u001b[0m - \u001b[1mChecking file: mass.mas20\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m77\u001b[0m - \u001b[1mChecking file: isotopes.info\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m79\u001b[0m - \u001b[1mFound file: /SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/isotopes.info\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m75\u001b[0m - \u001b[1mSearching for neutrons.list in cached files for FileCategory.ISOTOPES: {PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/neutrons.list'), PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/mass.mas20'), PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/isotopes.info')}\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m77\u001b[0m - \u001b[1mChecking file: neutrons.list\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m79\u001b[0m - \u001b[1mFound file: /SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/neutrons.list\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m361\u001b[0m - \u001b[1mUsing cached ENDF data from /SNS/users/8cz/.pleiades/nuclear_data/DataRetrievalMethod.API/EndfLibrary.ENDF_B_VIII_0/n_072-Hf-174_7225_resonance.dat\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m390\u001b[0m - \u001b[1mAPI method returned resonance data directly, no extraction needed\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m392\u001b[0m - \u001b[1mResonance parameters written to hf_analysis/072-Hf-174.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m396\u001b[0m - \u001b[1mNote: Only resonance data was downloaded (API method)\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m235\u001b[0m - \u001b[34m\u001b[1mStaged Hf-174: 072-Hf-174.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_isotope_info\u001b[0m:\u001b[36m167\u001b[0m - \u001b[1mGetting isotope parameters for Hf-176\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m361\u001b[0m - \u001b[1mUsing cached ENDF data from /SNS/users/8cz/.pleiades/nuclear_data/DataRetrievalMethod.API/EndfLibrary.ENDF_B_VIII_0/n_072-Hf-176_7231_resonance.dat\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m390\u001b[0m - \u001b[1mAPI method returned resonance data directly, no extraction needed\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m392\u001b[0m - \u001b[1mResonance parameters written to hf_analysis/072-Hf-176.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m396\u001b[0m - \u001b[1mNote: Only resonance data was downloaded (API method)\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m235\u001b[0m - \u001b[34m\u001b[1mStaged Hf-176: 072-Hf-176.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_isotope_info\u001b[0m:\u001b[36m167\u001b[0m - \u001b[1mGetting isotope parameters for Hf-177\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m361\u001b[0m - \u001b[1mUsing cached ENDF data from /SNS/users/8cz/.pleiades/nuclear_data/DataRetrievalMethod.API/EndfLibrary.ENDF_B_VIII_0/n_072-Hf-177_7234_resonance.dat\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m390\u001b[0m - \u001b[1mAPI method returned resonance data directly, no extraction needed\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m392\u001b[0m - \u001b[1mResonance parameters written to hf_analysis/072-Hf-177.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m396\u001b[0m - \u001b[1mNote: Only resonance data was downloaded (API method)\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m235\u001b[0m - \u001b[34m\u001b[1mStaged Hf-177: 072-Hf-177.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_isotope_info\u001b[0m:\u001b[36m167\u001b[0m - \u001b[1mGetting isotope parameters for Hf-178\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m361\u001b[0m - \u001b[1mUsing cached ENDF data from /SNS/users/8cz/.pleiades/nuclear_data/DataRetrievalMethod.API/EndfLibrary.ENDF_B_VIII_0/n_072-Hf-178_7237_resonance.dat\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m390\u001b[0m - \u001b[1mAPI method returned resonance data directly, no extraction needed\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m392\u001b[0m - \u001b[1mResonance parameters written to hf_analysis/072-Hf-178.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m396\u001b[0m - \u001b[1mNote: Only resonance data was downloaded (API method)\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m235\u001b[0m - \u001b[34m\u001b[1mStaged Hf-178: 072-Hf-178.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_isotope_info\u001b[0m:\u001b[36m167\u001b[0m - \u001b[1mGetting isotope parameters for Hf-179\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m361\u001b[0m - \u001b[1mUsing cached ENDF data from /SNS/users/8cz/.pleiades/nuclear_data/DataRetrievalMethod.API/EndfLibrary.ENDF_B_VIII_0/n_072-Hf-179_7240_resonance.dat\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m390\u001b[0m - \u001b[1mAPI method returned resonance data directly, no extraction needed\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m392\u001b[0m - \u001b[1mResonance parameters written to hf_analysis/072-Hf-179.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m396\u001b[0m - \u001b[1mNote: Only resonance data was downloaded (API method)\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m235\u001b[0m - \u001b[34m\u001b[1mStaged Hf-179: 072-Hf-179.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_isotope_info\u001b[0m:\u001b[36m167\u001b[0m - \u001b[1mGetting isotope parameters for Hf-180\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m361\u001b[0m - \u001b[1mUsing cached ENDF data from /SNS/users/8cz/.pleiades/nuclear_data/DataRetrievalMethod.API/EndfLibrary.ENDF_B_VIII_0/n_072-Hf-180_7243_resonance.dat\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m390\u001b[0m - \u001b[1mAPI method returned resonance data directly, no extraction needed\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m392\u001b[0m - \u001b[1mResonance parameters written to hf_analysis/072-Hf-180.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m396\u001b[0m - \u001b[1mNote: Only resonance data was downloaded (API method)\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m235\u001b[0m - \u001b[34m\u001b[1mStaged Hf-180: 072-Hf-180.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m244\u001b[0m - \u001b[1mStep 2: Creating JSON configuration with actual ENDF filenames...\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_isotope_info\u001b[0m:\u001b[36m167\u001b[0m - \u001b[1mGetting isotope parameters for Hf-174\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36madd_isotope_entry\u001b[0m:\u001b[36m95\u001b[0m - \u001b[34m\u001b[1mAdded isotope entry: 072-Hf-174.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m272\u001b[0m - \u001b[34m\u001b[1mAdded Hf-174 (MAT=7225) as 072-Hf-174.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_isotope_info\u001b[0m:\u001b[36m167\u001b[0m - \u001b[1mGetting isotope parameters for Hf-176\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36madd_isotope_entry\u001b[0m:\u001b[36m95\u001b[0m - \u001b[34m\u001b[1mAdded isotope entry: 072-Hf-176.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m272\u001b[0m - \u001b[34m\u001b[1mAdded Hf-176 (MAT=7231) as 072-Hf-176.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_isotope_info\u001b[0m:\u001b[36m167\u001b[0m - \u001b[1mGetting isotope parameters for Hf-177\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36madd_isotope_entry\u001b[0m:\u001b[36m95\u001b[0m - \u001b[34m\u001b[1mAdded isotope entry: 072-Hf-177.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m272\u001b[0m - \u001b[34m\u001b[1mAdded Hf-177 (MAT=7234) as 072-Hf-177.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_isotope_info\u001b[0m:\u001b[36m167\u001b[0m - \u001b[1mGetting isotope parameters for Hf-178\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36madd_isotope_entry\u001b[0m:\u001b[36m95\u001b[0m - \u001b[34m\u001b[1mAdded isotope entry: 072-Hf-178.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m272\u001b[0m - \u001b[34m\u001b[1mAdded Hf-178 (MAT=7237) as 072-Hf-178.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_isotope_info\u001b[0m:\u001b[36m167\u001b[0m - \u001b[1mGetting isotope parameters for Hf-179\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36madd_isotope_entry\u001b[0m:\u001b[36m95\u001b[0m - \u001b[34m\u001b[1mAdded isotope entry: 072-Hf-179.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m272\u001b[0m - \u001b[34m\u001b[1mAdded Hf-179 (MAT=7240) as 072-Hf-179.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_isotope_info\u001b[0m:\u001b[36m167\u001b[0m - \u001b[1mGetting isotope parameters for Hf-180\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36madd_isotope_entry\u001b[0m:\u001b[36m95\u001b[0m - \u001b[34m\u001b[1mAdded isotope entry: 072-Hf-180.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m272\u001b[0m - \u001b[34m\u001b[1mAdded Hf-180 (MAT=7243) as 072-Hf-180.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m285\u001b[0m - \u001b[1mJSON configuration written to: hf_analysis/config.json\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m286\u001b[0m - \u001b[1mConfiguration contains 6 isotope entries\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:07\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m287\u001b[0m - \u001b[1mSAMMY workspace complete: 6 ENDF files + 1 JSON file\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "JSON configuration: hf_analysis/config.json\n",
+      "ENDF files: 6\n",
+      "  072-Hf-174.B-VIII.0.par\n",
+      "  072-Hf-176.B-VIII.0.par\n",
+      "  072-Hf-177.B-VIII.0.par\n",
+      "  072-Hf-178.B-VIII.0.par\n",
+      "  072-Hf-179.B-VIII.0.par\n",
+      "  072-Hf-180.B-VIII.0.par\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pleiades.sammy.io.json_manager import JsonManager\n",
+    "\n",
+    "json_manager = JsonManager()\n",
+    "json_path = json_manager.create_json_config(\n",
+    "    isotopes=[\"Hf-174\", \"Hf-176\", \"Hf-177\", \"Hf-178\", \"Hf-179\", \"Hf-180\"],\n",
+    "    abundances=[0.0016, 0.0526, 0.1860, 0.2728, 0.1362, 0.3508],\n",
+    "    working_dir=working_dir,\n",
+    "    custom_global_settings={\n",
+    "        \"forceRMoore\": \"yes\",\n",
+    "        \"purgeSpinGroups\": \"yes\",\n",
+    "        \"fudge\": \"0.7\"\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "print(f\"JSON configuration: {json_path}\")\n",
+    "endf_files = [f for f in os.listdir(working_dir) if f.endswith('.par')]\n",
+    "print(f\"ENDF files: {len(endf_files)}\")\n",
+    "for f in sorted(endf_files):\n",
+    "    print(f\"  {f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-09-25 16:23:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.utils.logger\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m145\u001b[0m - \u001b[1mlogging initialized...\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.inp_manager\u001b[0m:\u001b[36mcreate_multi_isotope_inp\u001b[0m:\u001b[36m480\u001b[0m - \u001b[1mSuccessfully wrote multi-isotope SAMMY input file to hf_analysis/hf_fitting.inp\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INP file: hf_analysis/hf_fitting.inp\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pleiades.sammy.io.inp_manager import InpManager\n",
+    "\n",
+    "hf_material_props = {\n",
+    "    'element': 'Hf',\n",
+    "    'mass_number': 178,  # Weighted average\n",
+    "    'density_g_cm3': 13.31,\n",
+    "    'thickness_mm': 0.05,\n",
+    "    'atomic_mass_amu': 178.49,  # Weighted average\n",
+    "    'abundance': 1.0,\n",
+    "    'min_energy': 1.0,\n",
+    "    'max_energy_eV': 200.0,\n",
+    "    'temperature_K': 293.6\n",
+    "}\n",
+    "\n",
+    "inp_file = Path(working_dir) / \"hf_fitting.inp\"\n",
+    "InpManager.create_multi_isotope_inp(\n",
+    "    inp_file,\n",
+    "    title=\"Hafnium multi-isotope neutron transmission analysis - VENUS IPTS-35945\",\n",
+    "    material_properties=hf_material_props,\n",
+    "    resolution_file_path=resolution_file_path\n",
+    ")\n",
+    "\n",
+    "print(f\"INP file: {inp_file}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pleiades.sammy.backends.local import LocalSammyRunner\n",
+    "from pleiades.sammy.config import LocalSammyConfig\n",
+    "from pleiades.sammy.interface import SammyFilesMultiMode\n",
+    "\n",
+    "data_file = Path(twenty_dir) / \"Run_8046_transmission.twenty\"\n",
+    "\n",
+    "files = SammyFilesMultiMode(\n",
+    "    input_file=inp_file,\n",
+    "    json_config_file=Path(json_path),\n",
+    "    data_file=data_file,\n",
+    "    endf_directory=Path(working_dir)\n",
+    ")\n",
+    "\n",
+    "config = LocalSammyConfig(\n",
+    "    sammy_executable=Path(\"/SNS/software/sammy/bin/sammy\"),\n",
+    "    working_dir=sammy_working,\n",
+    "    output_dir=sammy_output\n",
+    ")\n",
+    "\n",
+    "runner = LocalSammyRunner(config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multi-Isotope SAMMY Execution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-09-25 16:23:08\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mprepare_environment\u001b[0m:\u001b[36m45\u001b[0m - \u001b[34m\u001b[1mValidating input files\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:08\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mprepare_environment\u001b[0m:\u001b[36m50\u001b[0m - \u001b[34m\u001b[1mPerforming JSON-ENDF mapping validation\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:08\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36m_validate_json_endf_mapping\u001b[0m:\u001b[36m100\u001b[0m - \u001b[34m\u001b[1mJSON-ENDF validation passed: 6 ENDF files verified\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:08\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mprepare_environment\u001b[0m:\u001b[36m54\u001b[0m - \u001b[34m\u001b[1mMoving files to working directory\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:08\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mmove_to_working_dir\u001b[0m:\u001b[36m214\u001b[0m - \u001b[34m\u001b[1mMoving JSON mode files to working directory: hf_analysis/sammy_working\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:08\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mmove_to_working_dir\u001b[0m:\u001b[36m253\u001b[0m - \u001b[34m\u001b[1mSymlinked ENDF file: 072-Hf-177.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:08\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mmove_to_working_dir\u001b[0m:\u001b[36m253\u001b[0m - \u001b[34m\u001b[1mSymlinked ENDF file: 072-Hf-180.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:08\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mmove_to_working_dir\u001b[0m:\u001b[36m253\u001b[0m - \u001b[34m\u001b[1mSymlinked ENDF file: 072-Hf-174.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:08\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mmove_to_working_dir\u001b[0m:\u001b[36m253\u001b[0m - \u001b[34m\u001b[1mSymlinked ENDF file: 072-Hf-178.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:08\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mmove_to_working_dir\u001b[0m:\u001b[36m253\u001b[0m - \u001b[34m\u001b[1mSymlinked ENDF file: 072-Hf-176.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:08\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mmove_to_working_dir\u001b[0m:\u001b[36m253\u001b[0m - \u001b[34m\u001b[1mSymlinked ENDF file: 072-Hf-179.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:08\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mprepare_environment\u001b[0m:\u001b[36m58\u001b[0m - \u001b[34m\u001b[1mEnvironment preparation complete\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mexecute_sammy\u001b[0m:\u001b[36m110\u001b[0m - \u001b[1mStarting SAMMY execution b8a922b7-1097-4b31-be67-027b6fa9f623\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:08\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mexecute_sammy\u001b[0m:\u001b[36m111\u001b[0m - \u001b[34m\u001b[1mWorking directory: hf_analysis/sammy_working\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:08\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mexecute_sammy\u001b[0m:\u001b[36m123\u001b[0m - \u001b[34m\u001b[1mUsing JSON mode command format\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Executing multi-isotope SAMMY fitting\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mexecute_sammy\u001b[0m:\u001b[36m156\u001b[0m - \u001b[1mSAMMY execution completed successfully for b8a922b7-1097-4b31-be67-027b6fa9f623\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m432\u001b[0m - \u001b[1mCollecting outputs for execution b8a922b7-1097-4b31-be67-027b6fa9f623\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m443\u001b[0m - \u001b[34m\u001b[1mFound known output file: SAMNDF.PAR\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m443\u001b[0m - \u001b[34m\u001b[1mFound known output file: SAMMY.ODF\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m443\u001b[0m - \u001b[34m\u001b[1mFound known output file: SAMNDF.INP\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m443\u001b[0m - \u001b[34m\u001b[1mFound known output file: SAMMY.LST\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m443\u001b[0m - \u001b[34m\u001b[1mFound known output file: SAMMY.IO\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m443\u001b[0m - \u001b[34m\u001b[1mFound known output file: SAMMY.LPT\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAMQUA.RED\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAMMY.PLT\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAMQUA.PAR\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAMMY.PAR\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAM46.DAT\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAM41.DAT\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAMMY.RED\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAMMY.COV\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: hf_analysis/sammy_output/SAMMY.RED\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved hf_analysis/sammy_working/SAMMY.RED to hf_analysis/sammy_output/SAMMY.RED\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: hf_analysis/sammy_output/SAMMY.PAR\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved hf_analysis/sammy_working/SAMMY.PAR to hf_analysis/sammy_output/SAMMY.PAR\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: hf_analysis/sammy_output/SAMMY.ODF\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved hf_analysis/sammy_working/SAMMY.ODF to hf_analysis/sammy_output/SAMMY.ODF\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: hf_analysis/sammy_output/SAMNDF.PAR\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved hf_analysis/sammy_working/SAMNDF.PAR to hf_analysis/sammy_output/SAMNDF.PAR\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: hf_analysis/sammy_output/SAMMY.IO\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved hf_analysis/sammy_working/SAMMY.IO to hf_analysis/sammy_output/SAMMY.IO\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: hf_analysis/sammy_output/SAMMY.LPT\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved hf_analysis/sammy_working/SAMMY.LPT to hf_analysis/sammy_output/SAMMY.LPT\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: hf_analysis/sammy_output/SAMQUA.RED\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved hf_analysis/sammy_working/SAMQUA.RED to hf_analysis/sammy_output/SAMQUA.RED\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: hf_analysis/sammy_output/SAMMY.LST\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved hf_analysis/sammy_working/SAMMY.LST to hf_analysis/sammy_output/SAMMY.LST\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: hf_analysis/sammy_output/SAMMY.COV\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved hf_analysis/sammy_working/SAMMY.COV to hf_analysis/sammy_output/SAMMY.COV\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: hf_analysis/sammy_output/SAMNDF.INP\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved hf_analysis/sammy_working/SAMNDF.INP to hf_analysis/sammy_output/SAMNDF.INP\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: hf_analysis/sammy_output/SAM46.DAT\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved hf_analysis/sammy_working/SAM46.DAT to hf_analysis/sammy_output/SAM46.DAT\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: hf_analysis/sammy_output/SAMMY.PLT\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved hf_analysis/sammy_working/SAMMY.PLT to hf_analysis/sammy_output/SAMMY.PLT\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: hf_analysis/sammy_output/SAM41.DAT\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved hf_analysis/sammy_working/SAM41.DAT to hf_analysis/sammy_output/SAM41.DAT\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: hf_analysis/sammy_output/SAMQUA.PAR\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved hf_analysis/sammy_working/SAMQUA.PAR to hf_analysis/sammy_output/SAMQUA.PAR\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m471\u001b[0m - \u001b[1mSuccessfully collected 14 output files in 0.08 seconds\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mcleanup\u001b[0m:\u001b[36m174\u001b[0m - \u001b[34m\u001b[1mPerforming cleanup for local backend\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Execution status: True\n",
+      "Runtime: 3.39 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Executing multi-isotope SAMMY fitting\")\n",
+    "runner.prepare_environment(files)\n",
+    "result = runner.execute_sammy(files)\n",
+    "\n",
+    "print(f\"Execution status: {result.success}\")\n",
+    "print(f\"Runtime: {result.runtime_seconds:.2f} s\")\n",
+    "\n",
+    "if result.error_message:\n",
+    "    print(f\"Error: {result.error_message[:200]}\")\n",
+    "\n",
+    "runner.collect_outputs(result=result)\n",
+    "runner.cleanup()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Results Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mprocess_lpt_file\u001b[0m:\u001b[36m479\u001b[0m - \u001b[1mSuccessfully read the file: hf_analysis/sammy_output/SAMMY.LPT\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mprocess_lpt_file\u001b[0m:\u001b[36m490\u001b[0m - \u001b[34m\u001b[1mSplit LPT content into 3 blocks.\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_isotope_info\u001b[0m:\u001b[36m116\u001b[0m - \u001b[34m\u001b[1mExtracting isotope information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_results_from_string\u001b[0m:\u001b[36m415\u001b[0m - \u001b[1mIsotope results not found.\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_radius_info\u001b[0m:\u001b[36m189\u001b[0m - \u001b[34m\u001b[1mExtracting radius information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_results_from_string\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mRadius results not found.\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_broadening_info\u001b[0m:\u001b[36m261\u001b[0m - \u001b[34m\u001b[1mExtracting broadening information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_normalization_info\u001b[0m:\u001b[36m328\u001b[0m - \u001b[34m\u001b[1mExtracting normalization information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_chi_squared_info\u001b[0m:\u001b[36m377\u001b[0m - \u001b[34m\u001b[1mExtracting chi-squared information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_isotope_info\u001b[0m:\u001b[36m116\u001b[0m - \u001b[34m\u001b[1mExtracting isotope information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_results_from_string\u001b[0m:\u001b[36m415\u001b[0m - \u001b[1mIsotope results not found.\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_radius_info\u001b[0m:\u001b[36m189\u001b[0m - \u001b[34m\u001b[1mExtracting radius information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_results_from_string\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mRadius results not found.\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_broadening_info\u001b[0m:\u001b[36m261\u001b[0m - \u001b[34m\u001b[1mExtracting broadening information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_normalization_info\u001b[0m:\u001b[36m328\u001b[0m - \u001b[34m\u001b[1mExtracting normalization information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_chi_squared_info\u001b[0m:\u001b[36m377\u001b[0m - \u001b[34m\u001b[1mExtracting chi-squared information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_isotope_info\u001b[0m:\u001b[36m116\u001b[0m - \u001b[34m\u001b[1mExtracting isotope information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_results_from_string\u001b[0m:\u001b[36m415\u001b[0m - \u001b[1mIsotope results not found.\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_radius_info\u001b[0m:\u001b[36m189\u001b[0m - \u001b[34m\u001b[1mExtracting radius information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_results_from_string\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mRadius results not found.\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_broadening_info\u001b[0m:\u001b[36m261\u001b[0m - \u001b[34m\u001b[1mExtracting broadening information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_normalization_info\u001b[0m:\u001b[36m328\u001b[0m - \u001b[34m\u001b[1mExtracting normalization information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 16:23:11\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_chi_squared_info\u001b[0m:\u001b[36m377\u001b[0m - \u001b[34m\u001b[1mExtracting chi-squared information...\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Energy range: 6.673e+00 - 1.999e+02 eV\n",
+      "Data points: 3575\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pleiades.sammy.results.manager import ResultsManager\n",
+    "\n",
+    "lpt_file_path = sammy_output / \"SAMMY.LPT\"\n",
+    "lst_file_path = sammy_output / \"SAMMY.LST\"\n",
+    "\n",
+    "results_manager = ResultsManager(lpt_file_path=lpt_file_path, lst_file_path=lst_file_path)\n",
+    "data = results_manager.get_data()\n",
+    "\n",
+    "print(f\"Energy range: {data.energy.min():.3e} - {data.energy.max():.3e} eV\")\n",
+    "print(f\"Data points: {len(data.energy)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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b97PxySfHjUnTNBYuaqGvt494LEY6nS58zuPxYFkWlmXh8/lYunw5733/+wrvAgwNDXHXF7/ElldfJZvNHtP3gwY0uV0UaxoORUEDHPlwmLVtsrZNyrbZq2dIvEmZRr3LSb3TSdSyKHVoBFSNg5ksL6dS1DqdnB8M4FIUdNsiadkczGbZkkqRtMb/uqtxOrkkHKRY07Dz4xgwTLqyWZ5JJEjkr1/ucLDW70NVoM8w2ZFO02fkXsxV19bg8/pIJJMkE3EyegbLtnG73SiKQiwWJZvJfY18qsKHiouodDoZNEzaMhl26zptmSzvKwqzLhQgor2+FMXCJmpaHMhk2JRIMGyYrAsFOcPvw5OfmU6YFr2GwUvJFPsyOks8HjK2zcPDI3Rnc98f10aK+FBxMS41FzASlkVX1mAIiBsGLySSPJVMYihqbqbb46GurpZUKk3voUN8NBTg/aHgMT3PI6bJc3qGjWkdze1GqavDHQrT2dlJf18fV6nw3vy5UrZN0uMhlMngzP8p6jUMnjVMHnO5aTvUiw2oisJlwQAf9XnQ8iHJtG02JZJ8czhKzLa5wOPmo8VhTGBDMsXPhqNk8mNa4HJyfVGYsKZh2jZuTSWuqKRUlSLbpl9R+INlsxC4UrEZsW0GFQVV1VAzGXyqik9V8OXHAhC1LAxFwQ+4FYWMovBsaTmPWja7d+zgcyE/K10uDhgG2OBWwKuqqIBLUfAoChnbJqMoBBUFG3guXMSDwyMkUikcDmfuHQLbxrItkskUpUaW9wf9nO/1oikKw6aFroBb0zAdDoarq/FedgXhBQuO+vto7Iv3gN9PV1cXPp+38HutoqKiMGtsWzZdW1/j4/EoxYrCfhuy2SwBh0a1quLJfz1026ZPUajIvwjcFQzRnc0yomdIZXTQNILhIuLJJNF4nIBDoyYYJGgYREyTcgUC+d9nFtDh8fJqIsFIMkWlqrDc46Zc0wAwbJsB26bfhkNZgz7boiebKwvzmwZ1LhctLhe1Wu7rnbJsOkyTEWzcNqiqwtZAkKFFi/H6vLz26haS6TQejxunw0l5ZeVhkxCTQcLz7DGnwvOTTz7JP//zP/Piiy/S3d3NL37xC6688sqj3uaJJ57gtttuY+vWrVRXV/O3f/u33Hzzzcd8n1MVng8cOMAdf/c5DvX0nLT7nAwOIPe7WCF7hG9Nj6Kg27k3LxtcTkKqhoHNkGHSaxiMxq7KykqypsFAXz9OBWqdLjyqQtQ06c6+fr1gMIgej+NRFKL50LZg4QJAIdbZySn5X8BtmQyvpXVqa2t5/wevJh6N0d3dhW3bLF68pDCLPSqTyfDf3/4O/Vtf45XePs50qLQ6HRiZLBnLotMweEbP4vX7KGppYX5zM5s2PYNzeBhnaSlFZWXs27sXfXiYT4UC1DscZGybzmyWb/UP8BeRYs7w+TCxiZsWndksm1MpHjMtmpcs5f1XvZ/GefO468tfZt9LL3FBMEhIU9mV1nkmkWRsPJ3vcnF1pIioafLDwWEytsUZfj9bUikGLJtAIMBpZ5zOjTfdRFlZGY/8+tf82z3fIJvN4lEU3hUKEnFoHMhkeSIWx8yf98JggFvKStBQcCrgOoaNASzbZqeu82oyzSq/l+0pnW/1D5DOfz+c4ffx5erKcbfJ2jZOReFgJkuZQ0MHkraN0wa/quBRVRKmxZd6DvHnZAqAiKZxf30tNjYd2SwK4FJUIppGiUNjxLT4dGcXvVmD7zfWEcgHr9HQ+moqxb/3DdDrdBEMhXLvrhgGlmViGCbYdv7YKvyxvKW0hEvDIbal05Q6NKqcTjRFwcp/P/8uGuNgJlt4blQFijSNxR4Piz1uVEXhgJ7hsVicQdNAU1R8ikK9y8npfh+lDgf79QxBTcWlKPx1RyflDgdfq63m4aERtus6Lk2j0uOhSlMJKwphRaHF4yZpWTwZS/DHWJwaj5v5Xg8/G4lSYlncXVvNgwNDPJtIYNq5n1GN/Ky4kp9VVBT8qkqrx82FwSDlztyLgBHL4o6hKPvicZodGv9aU8UPh4bZkUrT4HJR6nTQZ8MBPYPLMlnp83JRMECfafLJ9k50VWW5x81Xqyr4n+Eo/xuL49M0Frkc/EVRETHLZGM8yYcjRTwWjZGw4V2hAAcyGe7o6sGjqNxbV0OvabA1lc49L0CF241PUegzTZa5XVTluw39PJ7gx/Hcz4een90ffTGZSaeJuN3oqkrGstAcDgI+HxWKwjqfh/VOB/+bTGEYBlcGA/xj3wAvJlOYloXT4SjU+9u2jW3bOBwOHA4HAYfGZQE/H/B6+KNp8V96luGhITLZDJrmIOLxcGMoyLluJ4OWxS+icfqyWepcTryaStZWCGkqZ3k9+BSVDRWVLPzYx1iwcOG4n5NEIsHw0BCvvvoqh3p6SCaSbNnyKslEkvnN82ldvJiRoWFCRWE62jvYsX07w4OD/FM4SIWm8oVYgvZkClVVMUwjFzRdTqpQaNBUGvw+ht0enjFMokAwECAWjwM26bROPB4DRSHgD+D2uNHTev5rnKW4qJiA00mRQ2PYtHCFgvT2HCIxprTKpyi4bYtMIEA4XEQ8Hs/NyOd/zrLZ19/9Cvj9kE5Rk83S7HbT6NTwAzq579NlDo0XLJvvqSojsTihUBiXy4VpGFRWV7PqtNX4fT5Wn376pJV1SHiePeZUeP7tb3/L008/zamnnsr73ve+Nw3P+/fvZ+nSpXz84x/npptu4umnn+aWW27hxz/+Me973/uO6T6nIjxHo1H+9tOfYc+uXW/pdhpQ7NAIqRp+VcWh5GYG05bNDj3NPJebpV4PLkUhZpnUO124VAXH6EyMafJCMsVLyRRLPG4CqspzyRReReG6kggLPC5cikp3NkvasrGw+Vb/ICnL4qbSEi4vCuEcswtjzDRpy2QxbZvfRGM8l0hyS1kJFwUDRE2LP8XjXFkUHvcYMpbNU4kE/97bXwjCFQ4H/15XTfGYVnz9hsE3evt5JpHkTL+Pz1eW41ZV9uo6X+g+RFd+xu4rNZWs8vkwbRtNUfjZ0DAAfq+Xbw9HOdehcsgGZdEiTj/9DC5618WFRVbfvecePvjaqwTzsyWmbfNaKl0Ies1uN34tF8QeGhrh+/EEZ3nc3F5WQmcmyyc6OolZFleGQ3yivJSfDA6jKrDW76fW5QTgR4NDJC2LgKrR6HZyqtfLTl3n9s4elpy2mpUrT+HH//Vf3FdZTplDy8/UOnglmeJzXT2FFyj/1VCHS1FwqQo9WYOubJZ3BANkbZtHR2Lc2z9AuKSE2z77GZrmz+cr//f/8vJLmwH4cnUlq31e+g2DCqeTP8bi/N+eXlo9bu6preapeILtaR0jH4p7swYmNqYNJjaWDU5FwaUo+DWVRW437wwGOMXn5flEkpU+L7+Pxvjm0DBup4uvlEawgH/q6ibscDKUzRIHlnm9XBMpots0+aXmJGPbOFwuSiMRvMkEl0ZHmOd0cn1bBzHL4jPlZZzu9/Lx9oOMmK+/lFBVlVKHg7uqyhkwTB6PxflUeSkfOdBBj2EQ1lRW+f18qChMiUPjU32DZINBBgYG8Ng2i7weXkqmMEwT27K4Ihyi2unkx0PDPNhYx38PjfDA4BCQm7FsdrtY6vWw1VZos0wUFLLZLLZtj3s3IKSqFDscdOSDwqjRMKbkz5e2bQJqLjAOmAZZ2yakatzS2Y3m0PB4fbS0LCQcLuK5Z54hkUhQqWlcGApwUTBIdf57y7RtDmUNFAVGTIu/6ejkWP9YqECTy4VDVbi9opxeI8vfdvbwdxVltHo8XN/W8fqLN0UpvBtl2zaqplGrqvxrXTVPJ5Lc2z/If9RV02eYfPpgV66UJf9Rqan8v+pKqpxOHhmJ8vW+AWxsml1uvlxdgW7ZZMmVq3yiq4eRfGnR6Dtflm3jcjqxTZPFLidR02LfmHepRstgnE4npmliWhaaphVqxk3LwuVyYVsWiqJwRSjAX+Z/J31rYIhfJpKF+zNNE8MwcDqdudvkn9/Rd3ecTieXhIJ8zOXgqXSGYiwWuNw8l0qxwO0iqKo8MBJjQyJBKv/9oSqvvxh1ulwEXC5uDgU42+XkO4EQZ15/faGH92gLyD27dxNOp2koLWFTVzeDI8O4HLl3CdP5FwvBYIB0Kk0ikeCdis31fh9fjKd4LZslaxj4PB5GolEUQHM4CuUxmqri9XpJp9M4nE68Xi+WZeJwOBkZGUHPrxtwezx4fV5iI1HIP++aquJwOPD5fLjcbtweN26ni6GREbBtkokEHp+PUDBIfUMD8XiceDxO76FDqKpKPBYjk8ngcDjweDw0zZ/PyNAQmWyWdDqNnk6j6zoejwe3x8NKBT7pcfF81uCekRj+UJBgKISmOQgFA7zjggvQdZ3TzziD4kjkGL/zj07C8+wxp1rVrV+/nvXr1x/z9f/jP/6D+vp67rnnHgBaW1t54YUX+Jd/+ZdjDs9T4fE/Pj5hcC53ODjF56HO6aLK6cCtKmxOpnl4eIRriov4cKQI9xFmBgcMg6L8W9tJyyKkaXRns0RNk6yd+2NZ4vXwgeIi/qmrh89XVaApCt8fGEK3La4oCvF4LI5u27wzGMCbv5+LQ0H+EItzYTDAjwaH6cpmC3+gF7rdvLc4zLBh8ncVZTwRT3C238d/9g9yUSjAlUVhXkmm+HpvP05FIeLQmO9y8YHiIu6oKufvOnOz7ucH/XhVlVs7uohbJhGHg/cVhbmjspxrD3Tw3qIw+zMZfj40wg2lEW4pK+EngyM0uJws9nj4dv8APx0a4dpIMdeWFBe+Jgm3k6uKiwD49Cuv8Ju+fk47IzdL8W//+q8kH3+cYGU5nz3YRb3LxfOJJN1j6lzDmsqpXi+1LicfLYnwVDzOGR43g4ZBmcPBulCQRpeT9eEQG+MJvj0wCMCGaJz/bKhlczLFdweGxj1PrR43X6mp4r76Gj778su0H2hjmUOjxuXk5vaD7NEzrPB6uKu6iveEQ/x0eIQSLff5f+jqIWZafL2umhaPm+8NDJK1bT4aKcbE5t6BQZ588kmGBgfZvTP3/VXvcnKG38dXenrZEItzUTDA31WW8+uRKFcVhdmvZ/hyTy9KfgGcqqoYgPmGsGo5HLhDIRKKwlPJJI/3DaDYNqZt8+FIER8Mh9gQCnNRayuLd27n3zIGSriIEdtGAxZWVhLPZPhBIEBGT9NQXYPP5+Oc885lfnMz0ViMXz7wXyxq28ctZSXc3dvH2QE//xuL44xEuOTMNYSLwmx66mlM06SutpZH+3q5yWVxqs/L86k0/eT6tTvcbrY7nXwhkeTr4RAfDAd5yOFCVVU+FvRzWTjEgwNDPDg4xEK3i78pLwXgiqLci+dHU6lC/3fdttma1tlpmixY2MI8pxMbGMzX/o/+wc9kMiQti6Rl4fF6cWga6XS6ELI1hwPTMEjnW/klbJuv9fXztZrcRitf6unF7XZTVFzMvHnzuOqDV6On0+zcsYNUOkW3YfD9wWG+PzjMfJeLhGXhUhX+rbYGr6pw9/AQDpeLbL5ue5QNOPLrEDRNQ9VU7HypSztgZLI8MDDI/1dVwUKvlzP9Pv5nOJoLzvnQrI7OvI8GCtumwzD41sAQny4v5YyAHzfw//UN4HK7yRpGrkxLUegxLW5sO0ipw0GPZeH1ekmlUuzRdT7V2cPflJXgURW+0dtPwrLHBeHRevFM/jFsNczcuw/5YKuoCthgGAaGYeRqzkeDvqrmAqNhkM1kcLvdWLbNzweGeDWVRlNU9mazqJqGpqpU19agkOv2kkwkcTgdYNn09/djmiahcJh4PM4vR6KkXU7e4/fSljF5OBZnhcdNe9bgP6MxEj4fvlAYKx5Hgfw4VSzTxO1ykbYsvhmNU1YUYn08yk9/9zv8fj+VVVX5hZcxKhJxPplOQiKKkdL531SaZi3Dh1WwLZNvjMTpyZ+/DLimKMjv9AwHXC4sPZ3r1OJ0Ul1TzdDAIJZloagqKArZbIZYfnOi2poa9GyWvr5e4okEmqLi9XrAzr3oGM6/gPT5fGiaRiqVQtd1NE2jvrGRdCqFZVn4fV6isRhFRUWcsupUwkVFxGNxDCNLOp2ipKSEYDBIIpHA4/EUAnJRcRG2ZeH3++ju7qE7mSgsvi2OFPPCoV7+1bL4pM/D58IB/juVQtMcxJ1OHJEIyVQKfz7IC/FGcyo8v1XPPPMM69atG3fZxRdfzHe+8x2y2SxOp/Ow2+h67m2oUdFo9ISPc6xEIsEvx7SiC6gq7woFqXQ6uDQcwqEodGezdGcNbGz+qqyEqGlybUkxT8TiPB6LM2zm/kgbdm7WZrnXy99XlgNwzf52eg2DoKoSm6Au9QeNdfxDVQUAf4zFuSwc4tVUildSaf7foVxf6R8ODtPkdtHgdPKXZSVcHArySjLFfw2OD4J/iMX5+fAIMdPkf+Y38s5ggOcTSX42PMK+TIY7qyq4v3+Qg/ma2/0ZeDGZ4pBh8PmqCqqcDrqzufq3fXqG1/KzeAcyWXaldX42r4FzAn6aXC5+NRLlT/EExQ6NvywtocXtLsxU79IzWMCvR6KF8JwwLa4qLmLENOnIZLmxNMLf7N3Lb379ayKREn79P//LjSURerMGm1NpNqfSvNGIafF4PIECvDsU4ryAnwaXk6cTSaocDs4P+mn1eCB/36P2ZzJc39bBoGEeds7taZ2/6ejkntpq3hMM8p+9vbyjOEzCtNij5ypAX0mleSqR4MJQgJ8OjxRmsQ9msrRnc1+b+W4XvxmJMmxaaChcGyniwaERtr32WqHd3DKPh7+tLGPAMHg8luuosSEW54PFRVwSCrLE6+G/h0dQNI3Gpkbqauuorq0hk82y5dUt2JaJx+dDT6YIFRVRV1uL0+0ilUiiORwYpkFfXx9dI1FcepJ3z5/POzraGQwX0XDOuVzQ1cW+3XvImgbhcBFFRWGGh4fRdQ8ti1s55ZRTCj2YiyMRzrvyCn76b//KdaEgiqIQ0FSeSeucetZarr7mQ1RXV3PZFVcQj8VQNY3nn3mW3j/8nnJsDtTVc8Was1h12mqSySS/+eWv6O7qYkMqxSU+H98dGqasvJzzHLlQfEk4yPcHh7ggFKTPMPh9LM6Hi4v4UzqNs6yccl3PLfQjF4BC4TDNC5pZunQpI8MjbHr6afr6+oiUlOB0OMhms3R1dRVCXCgUoqamBtOyME2TrGHg9XgYGhoknUpjK9DlcvMQCmXhMMniEi5taqKsooIVK1Ywv7mZRCLBWeecze8f/R3RkZHCbPa+bBYUBQ2Vm7p6UG2LQU2juroKIx8wR98ez2SzOF0uHJqGkc3mShsymcIMayqVYlMqTb9h8i/VFfhUlacSScg/dvIzwEq+5lfLh1sbeNKyaTZMljsc/CStM+J0EfDkZotNM1cW43A6UFWNpGFQrGmomobP58O0LFK2zddNG5/Lg1VZRb3TSTwex8rPkA4NDRX6hkMuQIfCIQKBIOl0Gp/Py8jwCBY2I0PDaA4HTqeTdCqFw+EgUlKCx+ulp6sLyM3AaqrKAcPEtg00TcMfCLB4yRJWrFyBgkJtfR07tu9g29bXiEVjlJeXkzVN9HQan89HKpnk92mdX0djhUWgG4uL0RxOTjvrbM48+2wU2+a1V7fQ0dFBX38f/f39xKMxLNvGyGaxHRo/Tev8U9BPQyzGju3b8Xg8DA+PEI/HWZbNkLRtfh2N8+FwkGZN5RSng12ZDEFV5Sulxfzb0Ahx0+T/FBcxZFn8KKXj9PuprasjFAjidLsAyOi5tScupzP3glANkM1kKC0ro7a+HofDQV1dLW1tbcRjcRwOR6FmHHILVhOJBF6vFyW/5sUfCOByOCitq+PQoUPU1tdRX1/P/PnzWbhoESMjI+zeuZNAMMDuHTspLS1lUWsrTfPmoSoKTz/9NNlMhrr6esrKy9HTaUZGRhgYGCAWjdLdnZtUWbhoEQejUe5PJfmADV8K5x4TtsHvjCwuj4cFLS3SH1pMSMLzUfT09FBRUTHusoqKCgzDoL+/f8Ltc++66y6+8IUvnKwhHmbv3tzuaADNbhdfrq6kZEy5wnv3HiiUMwD8v+pKPhwpwqkoPBaL8+dkCqfTSUlpCT35XzJ/zv+xg1y5AzBhcAbYr2eodDp5JZniT7E47wwGqHU56ci8vqis1zDoNQz+DJzi83Ka30dbJjPh+Xrz99edzVLrchXu/8Vkikv3HpjwbeSX8jWtiz0eurNxQqpK1BofNONW7u3ZpV4PxY7cLDrAnnQGp6KMK/HYk869GBowTZ5NJMhYNj5VZbXfx349w69Gony+qoJyh8Yf//AYmqbxj1UVnBPw89yYr92R2MAOXafR5aLM4aAnGydpWlwdKQLgH7p6eDH/mEa1Z468SK8tk+XpeJLT/F7+cwBKNAcD5vjODi8lU5wf8ONWFGqcTkzbptvInfMznV0Uabm6Q8i9CLqhNMJSr5fXhoaJRaMUFRdznaYQUjW+2HMIAwqdUJ6IJ/hI/kXGTsOiorKC897xDs4480wqq6rI6Drvu+qqwiJOl9udC1/5VntjW+4BtO3bR+bf/5Vl/f14kwniN/8VF7QuLrTn6zt0iKeffhpsWLSolYamJuob6g/7o7d8xQqiH/koL3//QS4MBhhUVd750Y9y8bvXF65bVlZW6C3udDrZrdjs7+0j3NzM2StWFNpxDQ8P88Tjj/NKdzcfUBWqLYu0qhJSFTYk01zk8zDf7abF42F7JsvvNCf9FsQb53FKpISlK5azbcsWMlmDmrpaGhubmDd/HmVlZSQSCc5YcyapZO5757WtW+np6qZx/jwOtncQj8fxeD0URyL4AwHmNc9n/9691Nc34Pf7cXs89Pf3MTQ0RDoQQC8v53xVY8mypRQVFxceq9/v550XXEAikWDP7j1ER0bw+rxYppV7AeHQyGazmIaJV1VZvfo0Tlm9ioH+fpLJFJqmUlVVRSAQYNfOnfQcOoRt23Qd7GT37t0ogNfrJZPJ8MtkkutDQfZYFo7587hswQJ2bNvOwc5ObMsiGA5TWlaKkcmgqRppXcfldPJHr5cnHA4IBKkrjqAoEAiG8Pl8VNdUs/LUU8lmMjz37LOk02mqKqtobGrkUG8vHW1teL0+SstKcTqdVFRWsn3bNra8ugUzm6Wyuop0KrdYOZlMYGQNgsEQPr8Pl8uF5nDg8WYoLi6meX4zLrcLj8dLOp3CMk0qq2tIxGNks1n0VAqPz5erAdc0nA4HC1oW5tsIluPJB7Hq6moWtrRw7nnnkkomeW3rVrZv3cquXbsxTYPKykpaFrXQ3XOIrq4u0skkpmXTWFvLBevWFV4MlpSVsXvnTnp6emjbf4CDBw8SjcWIx2KYhkGHx0uPw8GKjM72/M+xw6ExODBAfTbLHtvm104XVtbkPIfGH02L7yfT+Jwu/hqFz+V/9+w1Te5O6hRVVLD69NOZ3zyf3Tt3kUjEqauv513r19N24ABdXV24PR4G+vtpO9CGy+kimUpSFC6idckSGufN45XNm9HTOuFwCJfbg+bIbUDUefAguq4TDodxuly5F4iaRl19HYsXL57w+3Z0ofaF69Yd1p6zoalp3CLuN/YjHx7KTdL0dHfzi//+b/YC9xcVsbqyAtWyWFpTzZlLluKorpbgLI5IwvObUMa8SgYKby2+8fJRt99+O7fddlvh+GTvZf/S838GwK+qfKm6kn7D4NMHu3mgMTeG6BtC705dZ7XfB+QCJUB1TQ3/3z/+A6+88grf+89vExuzYOPNtqoYrRV+JpGkMx9I57nd7NIPD8cWuQVSp/l9Rwzjo3qyBrUuFwNjZluPVDUWsyz6sgZ1+XcGgppKT/bwFn3dWYPG/KzraHeH0TGPNfZr9vmuQ9jAX5WWsNqfC/WjwXaF18vjw0PYWYNzFswDoOMILwreqCuT5dygn4CqEjetcbPKzx9DAH+jLekU7woHcefLWUbPpygKLpeL/XoGVVFocDmpdjrpNQyyNiiqmi8PeP3rdcgwGDAMlvg87LFz3/elpaU0JGP8dyzBDlWjqLgYj9eDgsJrYx7zSHExH/jAVbzjHe8Y98fvrVi8bBlmUxN1e/Zguz2UnboK8i0HIRd43/gH80jOPvdcBsrLyX7nP3FccBHvfde7jnjd6upqwpdedth5/X4/LS2L2L1zJx2pFIZlcGpJCTvy35BP6zrv8LpZEfTT7Hbxs2QKh9tNrL6BFaesZPGSJcxvbuaMM8+ccMx+v3/cscfn47Hfb0DTNBrqG0il0/T39ZFMJCgtK6UkEiEcDI0LGaPdFDJ6Bo/Hc8SuAfObm7nk0kt5bMMf0HWdefPn4XK5efyxx+ju7ibg81NeWUEwEOTdl19GQ0PDhP1xR8NcOp1m4YKFXHrFFRQVhUkmk/z52ed4bvNLxEwbrbqGay+9lDPXrKGtrY0nHn+cnq5uIiUllJaVYlkW5ZWVPP/Msxw4sB+fz8fipUuZ3zyfl154kUOHeohEIjQ0NnLGmjMLj2lRa+u4MXV1dVFcVEQ6nR4XXFsXL+adF1xAKplk9549HGzvwOV2YZkWe/bsJuAP5EKc24WRyRIIBYlEIixqbS288Ovv72fTU0+TyWRoaGjEHwiye9dO4rE45eXlLGptJRQOcfqZZ+Lz+Sb8/hn9vy8QwOlwgA3ZTIaKqkpWnXYau3ftoqqygoxhUN/QMO5dlML3Zj5AGqaZ22EzmaStrY0Nj/6OWCJBm9fD0kScrnCIouJiiiMRDvUcYp7XxW8zuXdOnw0EeMXnw+P1EOzqxsbm/w4M0ujQKCkupnLt2ZyhKKw+/XQaGhvx+/2sWLly3GOqrK7Ofa9lMrS0LKKisor9+/aSTqWpXFTF6tNOw7IsensOsXv3LjJZgyXLFjK/eT4jw8O0V1bR199HWVkZToeTgwcP4vP5iEQiR/y+fePPyNE+N9ExQFFxMWf19dG2/wDBUBBHVRWaphGcxAWCYvaS8HwUlZWV9LyhW0Vvby8Oh4OSkpIJb+N2u3FPYY3U7x59FIDLwyFCqsonug/RP8Hb+6PGhspYfoaiqChMeUUFV77nPQSDQe764peO+f7NfKTtyGYZGnO/6SOE45dTafoNg8diR99IY3QGetA8tj7VPUaWyvxq/4CqETUPD7GDhsma/AuH0fA8ZL4+5iHD4H9HxpfdjAb2jmzufH2GSdyy6MlmaXC5MGK5me5Rr01QrjGRQ4ZBVT7sRy2LgTG10cfyiF0uF5kxoXW0RVmZw0FE0xiybaqqq/EHAgT8fjr27AagzuWixuWkM5MlEAgQCoUKnQDKK3IbbxzYt589eoZ5LidFviDhUJiQ10d47y600lJOLa+gqamJhYsWkUgmeOqPj9N3qJu4280HPvx+zn/HO47pa3A0WtM82LMHZd48mKAu/2h/TN+oZNEi+OevcXjR1eGOdN76hnqa5s+no72DQ+ksJYaRbzGnkAwEaMsavNPvw6MoDASCvOOss3jnhRceNoN2LGOura3l/Vd/YNzsWUdbG23t7WDbaJrGosWLx4WMN97maPezYOFCqmtqxl3XH/Dz0I9+hGGYVFZUcta559DQ0HDEcY8Nc2+8v9bFi7moc91hu4Y2NDRw4bp1hdA9NuSecsophRnC0a/ZipUrD7vsSM/TkcYz9noen4/enkNk9AzNC5o59/zziEQiE74DMvb2xZHIuK8XwK6dOzmwbx+aw0k4HGJBS0vhcR7ta19dXV3oz93d3UNxcRHRkRGqq6snnFUda6LnoXnhQhqbmti2dSu9L7+Mv6ON+kDuOtlMhnMXtRDoaGMPCm6Xi7KKcgKBIJVVVTQ0NtHf10uXrwtTUQgtXEBdfX3uRVs+OE90vxN9f070PF3/8RsPuzyRSLB8xYrD3m06GZuXjH5P+fILHDVNkzINccwkPB/FmjVr+NWvfjXust///vesXr16wnrnk800TZ56cmOhL3BFZWVhY4qzA36eSiQLwfmL3YfommBWdWy5xGiAXNjaWvgFsnzFCs5cu5ZH9+1BBQLBICUlJfi8Xg4cOEAqNb6c4HfRGOcG/GxLpYlbVqFLReoI4XnYNPng/vY3fazJ/O0nqvOdSHfWoGbMzHN8gvsfMs3CAsnR84+dzb6zu5ct6YnD7+507uvWnv/6Hcxkqc6H9dFWZp/v6uGZMbPGo4uHHE4nwWCAquoa2toOMDw4NC60x02Tg/myjNiYy30+H2Z+FzeX242qqjg0DW9+UUsyHsfl8XCwvZ3efHiucDoIaRodhoHL6aS6upqa2hqSqSTRTJoyh0aN08HWtI6qaaw5+yxaFy9m29atOB1O3C4XmqrSEx1hleYgHotx6NAhPvju9bB3F+dfcQXnLR3/tuqyZcuIDw0RCIc5/w1lT2/bkiWw4fewYMHknO84+f1+Fi1axAvPPc+hRIwybLosE0tzULdsOb17drPGyP9sNTSw9uyzqamtPa77G/tHfdHixdQ1NBw1ZLyVFxRvvO7Z55xDy6JFxGMxAsFgIQi+lXOMvbz5DS3TRh1LyH07j+dYrv9WXmC82blPOfVUFra0vK1zlZWV5Wab8y8iRkPcsXzNJzK/uRnLsnjqQBt2Rxvpl1+h3R8klUrTUloCHW14GuoJ6DotixaxZOky6hvqgVzo7e3tpbe3t/DC7FgC5ZFmd492nSNddqTbnwhHe9EnxNHMqfAcj8fZs2dP4Xj//v28/PLLRCIR6uvruf322+ns7OTBBx8E4Oabb+bf//3fue222/j4xz/OM888w3e+8x1+/OMfT9VDGGfzSy/xs4ceIp1KseWVV7noXReTyWTybdBcPDJm1vSJ+OHbEBcXF7Nt6PVFerqdC5CRMW15ysrKeO/738fPfvpTUokEZ51ax0Xr1rFg4UJefuklNm3aRDwW45XNLxOPx2nLZPmLAx2F2w+ZJqUOBynrSEUWE1MUBUd+oRRAJl8ukx5TNjO6cA1y9amRkhKaFzTz9Man6MkarPZ5gVyLr6zLxZJly/D7fIWdBEfG1EFnnU5KgiEG+vsLreT6jSPP+e7Uda470EFHfnyDpkl1Pqx78xtRjOTH5vF6Oe2MMygKh3G5ci2hiouKeedFF7L5pZe479+/ydDwcOHcMcsialnc3z/Aa3ru7dVAIMDipUsoL69goL8fj9dDUVExA/399PX1kU6lKK6txRfw49Q0Og4cwLJtKpxOPJqKormpq2vgsssvIxAMsnXLawykEpQ7cmUbjyXTeL1elixdyrnnncdpp5+e39Y4SXt7G519fbzb7yPWN0CqJMJwd26RVPX8efCGUDi2bnjSLFkKt/w1LF8xuec9DjU1Nbm3nkeGaDFN+p1O4rZNpKIcr9sJr7xM2uXmsg9+cNxb7pPlrYbJt+qEPI8TONGP42Td9/GcazJDXCKR4GBHB0owQMzrxdnXy/Zdu3C4nKj5Beyta9awkNy24mOfY7/fT01t7ZRsXT1VpvL7T8xccyo8v/DCC7xjzFvIo7XJH/3oR3nggQfo7u6mvf31WdCmpiYeeeQRPvWpT/HNb36T6upq/vVf/3VatKnr6+vjFz9/mN35lnTpjM7gYK6VWammoSlK4a37iQRDQRYvXcrTGzcWLsvkA675htndU1etoq6+/rBZqLPOOYeVp55KRtf53aO/49vf+tZh9zM8Gp7t12d+Q+EQyURywq3CNU2jtq6OyspKgqEgfYd6eeWVV/j1SIyFbjc78jPB1bW1rF+/nl27dtLbcwi3z0tzczMul4unNz5FdzZLxOEgpKq4VJXeRIKoc5gPXvMhBvr7ue+b9xbKVABSpsWKFcv542N/zLVwVaA/H34rKirwer0UFRfR19dPT3c3pmkWgjPkZsSXenOdMTz53qum00GRx8s555/HpZddxsGOjnFvT/v9fiKRSG4l/LiZ59y4fj4Sw+v1UltfT1NTI5decQXz58/PPVf5P2zJZJL9e/fSc+gQqWSSwf4BaqqrUTWNhG1Q5vXiczhQHU5aWxexcNEi/H4/H7zmQ8Tv/w9afV48qkqmuJilDY20Ll4MvP4HJRqLMTg4RMo0cSoKRZZJT3cP/Qdz4Rm3Z+JvsMmmKHDqqpNzX8fI5XZTWVXFoKpRapl4kkmGfT4sy6Ji3cVYe/eivvOdJyQ4i9lnskJcRtdJpXJtDLuyBnR1sdft4dRTV+HLZEg6HLh9PlpaW4/44kgCpRBHN6fC8/nnn3/U5uQPPPDAYZedd955vPTSSydwVG9Pe1sbr77ySmHmtaerm/TozmmO3KYcb+ywMNb7r7qKbdu2j7ts9NqxeOyw6x9pFmr0l+z8efPwBwIk4uNrl0dnX0dnniurqoiUlDA8NERXZ+e46yqKwjUfuZZ3X3IJDk1jcHCQR379a1599VV6DYPPdb1ef97QUM9lV14BjK//3Lt3Lw6Ho9A9Y6EnV0MXM036+/vp7++ntq4Op8tJbEz4jRsGnZ1daJrGl3oOcUF+gxCA8ooK1qxZw6mrc+Gto6ODJ594kq2vvUYqmSSVSjFimoRVDbfbTSh/n2U1dSxZupQr3/deqqurCwuOxs7muJxOnC4nA2PCc9Qy8fv9lJSW4A8Gqa+t46J3Xczy5cvHfd1H/x3t0FDoPtHbS2dnF8nODvyaisOGjE0hOENuxunQE3+iYnfuxVdg/nzOete7DnuOLdPE5XJxKB/oqzSN11IpBrtzz13SNPEd9l0xN/j9fhoaG3lc13E5VBZoGj22TU9XN45IBPXr9+Ca6kGKOcflzu1E2dlxkAFFZYFp4HS6SOlpTquqxGmanH7mmRKOhTgOcyo8zybDw8OH1RsP59/6D+R3tIuaE9cZV1VVcfZ553GgvWPCz4+2yHorFi9byhlnnsmmp54qBPpsNoueD82jCwarqqv4yEc/SiAY5PHHH+d3v/0t6WQKj9/H+vXr+ejHPlY4Z3EkwopTTuGPj/2ReL5pv6IouD0e1p51VuGX/9j6T4fTid/vpy/fIeTm0tzCzhHTJGta7Nu7l/nz51NWVk784OuP33I4SOtpUOCZZIpnUuncLmQOB/UN9Zx9/nmFBVnNCxdy+pln0t3ZSVt7O//1vQdIxWN41dy2sDXFuTZtH7n5JioWLjxqHWdVTQ3z5zezZ9fuwmWGw0lNeTkf+osPU1lVNW6R1ZG88dw+rxddUQi73TgVcAcC1L6h60vFokWwexe2qvLu666jrLLyjaclEAxSVFTE1myWlGXR7HLxajLFSG8veD1sfu01mtzuCVfEzwUlJSWolZXQ30u1Q2Of203WyOba8B2hI48QJ5Lf76ehqYkdO7YTTyUpjg7TvKA5V8McjeEoLcUhwVmI4yLheYZ67PcbAFjscVPhcPD4mJpmLf+v9YZmbj6/n+qaGq76wFU0NDTQcIQFTJGi4gkvPxq/38/HbriexsZGBgb6Sad1fvfb3xZqlUcXDLo9XiIlJdTU1tI0bx5rzzqL/t5eSsvLWbRo0WHnXbV6NatOO42nn3wSy7YJBgKsXLWKc84777D79/v9LF+xgobGRvZt2QJAY76Z/6BpoqoaLqcLl9NJU2MjOzsPFm6vqioKCiWREgYGBoBcrXIkEuHCiy46LByOLoLy+nzU1NZg7t2LqihUFhfjdTrAMiitqTmmhTZrz1rLM08/XbisvKIcXyBAfX39ERdaHY1D06hraMDsaMOr67gAU9MY6OsbH8KLigBQSkomDM6Qe8dh8bKlPPvMM2xKpnhPUYjHHE7cuS0uMDSN3Tt3Eg6H5+xMlh2JQH9v7qC4GL8/MLUDEnNefUM9La2tmLqOc2QIn66zs+cQ6Z4eWLRI3hER4jhJeJ6hXnn5ZQBuryynyunk8d37Cp8bne8au0bv4nevZ926dRRFIoWWU2euXXPY4sei4mLOXLvmbY2purqaK977HjK6zsYnN/K73/62UPqQyA/G5/VSVPx6OF+0aBFMEJpH+f1+Pnb9dXg8bmLDIzQvauGCCy88YlArKyvj3ZdewrfG1K73Zg36DRO324E/4KeqpoZTV6/i5U2bCtfx5ssfKlcs5/lnn8UyLcrKyzh19WnMe5Oa1fKyMvyxGGTStNTX486H8hdfeYWWJUvedFa2samJZStWQE+uFKKqqppgKIjX9/YKIlxuN5HiYtIKFDs0sG1cfj9t7e3UNTS8/rUbfR483qOeb9WqVTy/7Fk27t/LBZpGs99PUFMBm0AkQv/QEBldn5Phuai4mKqGRrK7duAERhxO6hsaxn2PC3Gy+f1+li1fzp/37QfA7O4hncngiMfoTCRwd3XN2XeLhJgMEp5nqNEtwEd7A/tUhWQ+oI5ufzpatKGqKu9617tyAW2MqpoarnjPe+CF54DcbMW557+Dqpqatz2u0Rlgd77uN1vokmGhaRrzm5vfcsiqra3lr/76r4959Xfr4sWUlpeRNDL4VLWwtbXL5aKyqqrwh6V2QTOkcjP2bpcTh9PJ2eecg9PpJJFIsGDhQk4/44yj3l9RcTG1dfV07t0LQHJokHq3GyOrk8pkjmlWtqi4mGXLlvHT/l50FMorymlqmve2A5jf78fr9xHLGlRaFqgK3nCYhG2PD7mj7zCEgkc9X1FxMaeuWsVey4TBfooVG7/HA3qKkVgMj8dT6M861/j9ftactRbnhlx/9XBzM/PWSD2pmHq1tbWErvkQ9ssvYnZ34q6oxGcYpD1e2uf4u0VCHC8Jz7NEseYgaeUWwI1uIWHnyzbq6usmnD31+/28+9JL+HEsihaPcfnKU940LB6r8nx/3+1pnXeHcxuwuNxuFi5qeVvneyurv11uNx63h2QmjU9VSdkWTpeL8soK5jU1AWCYJolkqjBNn06lGRwYoLSsjI9ed90xB3W/30/TvCb2/+lPAFj5evGUYtPZcZBQOPSms7J+v5+155zNa6EQ8UScCn+ApSuWv+3nIZFIkEok8Xg8FKXTgM1wPI5TUcaH3OpqWHcxnH/0TUz8fj9nnrWWbDqN9cQfqfJ4cagqFrkdCef6xgK1tbVkLrkUZc9uTrn8cvwBKdsQ00M0mUR1OmFgED3fBUgrLSGdTs/Zd4uEmAwSnmeJIk2lM9884o1lGx/6i7844i/J2tpaLr/llknv6WmZJh6Ph0ejMV5NpTlkGBQFAljmsW1ycjwyuk4oHCYVzfWwzqDg9XhYumxZYVY9EY+jqSrYo7sqFqEqCol4nIaxpQ1vIpFIEItGaVi4ELa+StDrQYvFyLqcpPU0Rn8W4xge82T2ec3oOpZt4wyGCKVzi0rTlklzff348zoc8IGrj+mc4XCYUEmErKqxoK4WTyCA/doWli5fflJ6AU93rve8F+CYdi0U4mRIJBLs3rmTRS434XSaTH633J5MFk9k7r5bJMRkkPA8Q3m9XlKpVGEHP19+VzuXolDmyD2tFrnWcCtWrjzquU5ET089lcLldpPNZukl99a/pmnob+gQcqI4HA7Iz7TYLicV4WLOPuecwuN0ulyoqgr5XGtaubISp+utLaUZ7akaKC4CwKWouICsouDz+QgEAjg07ajnGDVZz4PL7SaZSNAzZgOcQFExdfla97cjo+vYlo3pdOBBIej3Y+d3OBRCTD+jv5viHjfFySQl+b8RPXqas+f4u0VCHC/1za8ipqN5+c0yRmuKR7eE/j/lpdxUlmvPZmMzMjxMR1vbSR9feUUFkZISNE1DVVWcDgeRSKRQznEiudxuhoeGsKzXe0wHgsFxrdqKioooikT4yuAwn+8fwrYsiiIRivIdKN7KfXm9HqL5cg1rcJBmBTKKis/ro7KyckpmeGwgOWYTmoO9vfR0d7/t840+zoyq4jCy6MkktqrK7JUQ09Rov+eudIYSbOp9PpKahqc4QjgcnurhCTGjSXieoRbnd4IbDc/e/CLBi8cs/jJtSKVSpPO78p1MVTU1nLlmDaWlpRQVFVFZVckZa9Yc12LEYzUyNER/X1+hUV/SNBgaHGRkzEysQ9NYvGQJqSVLMRYu4JRVq2hd3PqWZ1L9fj8tra24vLnOGDd7XMzTNOKZDId6eqitqzvpMzwZXcfhcKD5Xu+iMRCL8twzz5BIHL5N+7EYfZyqz4dPVdEA1eGQ2SshpqnRfs+ZUJCwbVNq22RDIdwuV64PuRDibZOyjRkqnO/EoOYrnEfLNsYaDY/B4NG7KZwIo4sRyyvKiY6MEAqHWX3aaSclbPUeOkQsHoei/ON2uojF4/QeOlTom+xyu6mqqiQRj+NyuygvL8fn872tmdRwOIziHb9NtWbbxOJxspnMcT+et8rldmOZJsPxxOsvjzUnXd3dDA8Nve3noLq6GjNSgsfng+pqtAP7J2/QQohJV99QT2z+fJTuLhqNLKmiojndHUeIySLheYYKh0IAaPnVgd6jhGd7inY6q66u5oILL5z0xYhvxu31oqoqozuxR3UdzePF7X19Jtbv97OgpYX2tnYyegaPx/O2u0ZkdB3FMX6pmGJk6Whr45VXXhnfW/kkGJ1x6jIMcOV+xFOmiUs/viBvmibRdBorlSJQXg5S7yzEtOb3+2lYvRqe2kgwnWY4GJzz3XGEmAwSnmeokrJSABz5YOw8SkCeykVdJ2Ix4pspLSsjUlyMw8y1H0kqKpHiYkrf0BWitraW91/9geMO9y63G1VVydp24XlwKQrJVIr9+/cf12zv29XQ2MDOmmroy+18Z2q5uvPjmXHq7u7GSCbRslninZ3U2Lb8AhFimqtY1IodCKDE45S3tuKWzVGEOG5S8zxDBQIBgsHgmPB85OvqU1A6MJVCwSALFi3COdqBpCjM/IULCE1QvuL3+ymORI4r3Pr9fsJF4cJW5ABdhkk6lSKVX0h4sjk0jdLq1+vLaxqbqG9seNsvpEbbXpmKgltVGR4YIJ5Mcijf/koIMU05HCgfuw4Ad/3b77gjhHidhOcZqqy8nOIxK6adisJE+dnpdM65J9kwTXweD//rcrNHVWlsacHjch9Tv+W3K5vNkhmzH/p/DA6RNU0qyiumZKtml9tNpKqycNwwf95xdf4YbXuluFyQzXXb0A2D5597jq6urskathDiRFh5CvzL12DJkqkeiRCzwlzLVbNGWVkZjfPn85mDXQwZBg5FmbB0w+1xUxSJTMEIp45D0wgXF7M3GOQ+X5BISQmR0pITVr6SSCRoO9BGNl9l/odojK60TjgcYs3ZZ01JfaHf76e+ZVHh2OfzHVet42irulTWwMpmsE0TRdXQHA5279z5trt4CCFOkqJimKL1L0LMNhKeZ7D5CxfwcipNj2HgPEJ4jpSU0HAcm2PMRC63m5qaahYsWMjyFcupra09of2Wh4eG6O3pIZuf+zc0jUAgQEN9/bje0idbxZj7Xn366VQfR63jaKs6byiI0waXqqG6nJSXlxe2+hVCCCHmAlnvM4Nl84HFyC9Umyg8qygkEok5tbq60JNYVUmn08fVSeNYqZqKobwenktKS6mrf/s1xpNizIuFyXjs1dXVJOrqoLsLr8eNkskQHRmR1ldCCCHmFAnPM5im5Z6+rG3jUBRcE4TnVDpFV1cXCxYsONnDm1LV1dWEw+GT0iavqLiYhsYmrAP7ARvV7aaupo6GpsapDZUn4L6dXi+KktueW7VzW5pL6yshhBBziZRtzGCj7eqyNkecec7omSnZqGM6mIxOGsd6P+e98x248veTtCy8Hg8VFRVTGyon6P19PA4ePMir27ZhptMoloXL6zvuchAhhBBippHwPION9i3OlW2Aa4K1INlsFlU2szjhEvE4ifyLFE84TF19PSPDw7NmId1oqzpLUdAADcgY2akelhBCCHHSSXiewToPHgTyZRtMPPMMuWAnTpxEIsGO7dsx819+xe0mmUwyMhKd+oV0Z66Baz9y3KcZbVXncLtRbRuXQ8O07al/fEIIIcRJJjXPM1gikduAY3Rnu4lqnvVMhpGhoZM9tDklo+sYhomq5mb4Tc1BLBqlsurEdfg4Zjd+fFJOM9qqLp3NoloWRiaDepw7FgohhBAzkcw8z2AV5bmyDQvQjlDzbFsWuj43a55PFpfbjWkYZNJpADp6D5HJZFjU2jprFtL5/X4WtLRgKyoKoFoWvlBo1jw+IYQQ4lhJeJ7BmpqaALBsGxUmDM8ulwuv33eSRzb3mJaFbVkAFJdX0tDYSGVV1RSPanLV1tayes2ZABT5/Xh9EpyFEELMPRKeZ7D9Bw4AuZlnVZk4PHu93lkX4qabjK6TzWZxOp0AeMJBUJiV9cBuX+6FmGaYoMmvDyGEEHOP1DzPYPv27AHAtG1UFLQ3hGdVVamuriYYCEzF8OYMwzRzdeWWCcBwKo3eP4BhmlM8shNgtHNLNvP6/4UQQog5ZM5NHd177700NTXh8XhYtWoVGzduPOr1v/nNb9La2orX66WlpYUHH3zwJI30zVmWnfsX0JRc+7CxPD4fHqlJPeEcmkYilSSbzs007+joIK2np3Z3wRMl/5gsXUfPGrOmFZ8QQghxrOZUeH7ooYe49dZbueOOO9i8eTPnnHMO69evp729fcLr33fffdx+++380z/9E1u3buULX/gCf/3Xf82vfvWrkzzyiS1c1AKAaee24VbfMPOcSiQY6OubnTOg00g0FmOgr58H0hmeN0xMf4C+3j6isdhUD23y5cNzeiTKvgMHeP7ZZ+nq6priQQkhhBAnz5wKz3fffTc33HADN954I62trdxzzz3U1dVx3333TXj973//+9x0001cffXVzJs3jw9+8IPccMMNfOUrXznJI59YTW0tfr8fCxtVOfzJVBSFeCzGQF/flIxvrrBME4/XSzIc5juak+KSEjxeL9YsfNGSyuY2RnFYJk6PB9u22b1zp8xACyGEmDPmTHjOZDK8+OKLrFu3btzl69atY9OmTRPeRtd1PB7PuMu8Xi/PP/882ezEu6vpuk40Gh33caK4XS4qq6owbdAmqHm2ye0wmM63UBMnRiAYpLysDKfbRbgojM/vo6y0lEAwONVDm3SGmesoolkWtqoSCodJp9OzcnGkEEIIMZE5E577+/sxTZOKiopxl1dUVNDT0zPhbS6++GK+/e1v8+KLL2LbNi+88ALf/e53yWaz9Pf3T3ibu+66i3A4XPioq6ub9McySs9kMLJZbCVX8/zGJ9O2LFRNIxAKnbAxCCgrK2PN2WdRFilB0xx4XO7ccX779NnCNE327N8PgGbbWEB0ZASPxyObpQghhJgz5ly3DeWNs7O2fdhloz7/+c/T09PDmWeeiW3bVFRU8LGPfYyvfvWraEdYDHb77bdz2223FY6j0egJC9BulwuHw4Fp5fo8qxM8DIfDge8Ns+di8p26ahV19fXEYzECweCsC84A3d3ddHb3cEr+2FIUNE1jQUuLbJYihBBizpgzM8+lpaVomnbYLHNvb+9hs9GjvF4v3/3ud0kmkxw4cID29nYaGxsJBoOUlpZOeBu3200oFBr3caLomQzDw8OY5FrV5fZ+G0NRyGSzDA3K9twnQ1lZGU3z5s3K4JxIJNi9cyfmmN8YDrebxUuXUl1dPXUDE0IIIU6yOROeXS4Xq1atYsOGDeMu37BhA2vXrj3qbZ1OJ7W1tWiaxk9+8hMuvfRSVHXqv3SJeJxMJoNlv162Ydl24fNujweHqmLa1tQNUswKGV0nlUrjHrOroKJps7MdnxBCCHEUc6ps47bbbuPaa69l9erVrFmzhvvvv5/29nZuvvlmIFdy0dnZWejlvGvXLp5//nnOOOMMhoaGuPvuu3nttdf4r//6r6l8GAXpdJpUKoUVDBQ2STFscOUnoG3TxOF0UlRcPLUDFTOey+3G6/UwOGbxqeZ0Sq2zEEKIOWdOheerr76agYEB7rzzTrq7u1m6dCmPPPIIDQ0NQK6mc2zPZ9M0+drXvsbOnTtxOp284x3vYNOmTTQ2Nk7RIxjPNE0Mw8iVbeRnnk1sGFO+UVxcLDsMiuPm9/tpaW2lPZmEzS8CUFJeLrXOQggh5pw5FZ4BbrnlFm655ZYJP/fAAw+MO25tbWXz5s0nYVRvj2XlyjFeb1UHWdvGm/+80+Uka2RJJZNTN0gxa1RXV1Nk2/C/vwAgJO9oCCGEmIOmvnBXvG2jddevb5KiYI6peTayBrGRKKlUaqqGKGYZ39je1VLvLIQQYg6S8DyDOTQNl8uVWzBIrlXd2D3tNIcDVVWPuKGLEG/Z2MA8DRbNCiGEECeb/PWbwTweD16fLz/znGtVZ70+8Uw2k8EfCJzQdnlijhkTnnXDmMKBCCGEEFNDwvMMVlJWRjAUwrRzT6Sm5Eo4RlmWhcftJiy1qWKSHBzTJ33X7j10dXVN4WiEEEKIk0/C8wzm0DSqa6pxejyoioL2hpnnktJSLNtmZEg2SRHHL5FI8OyzzxWOe/p6ef6ZZ0kkElM4KiGEEOLkkvA8wyXjCTK6DuRqnsfOPKczOul0mozUPItJMDw0REdHR+FYc7poazvAsLw4E0IIMYdIeJ7BYvE4gwMDjO4f6EAZt2AwEYuRSiZRpSuCmCxjdoC3lDdcIIQQQswBEp5nMD2VwuPxoGq5p9GhMK5sIxAI4vV6sUzzCGcQ4tgVFRdT39BYOE7pGeobGmQHSyGEEHPKnNskZTYpikTQMxky2VzXA4eijCvbqK2rxe1243I6p2qIYhbx+/00zWsqHFuqQtO8JtllUAghxJwiM88znGka2PnArCkK5piZZ9u2WbJkKVU1NVM0OjGbJBIJRoaHC8fVtXWMDA/LgkEhhBBzioTnGay7qwsja6AouadRY/yCwWzWoLauVmYGxaTI6DqpVLpwHI4U5xak5hesCiGEEHOBhOcZTFNUsqZRCMwOZXyrOsM0eOmFF+nr65uiEYrZxOV24/V6CsfxZK7m3uV2T+GohBBCiJNLwvMMVl1bQ3l5BXY+MGuKUui8ATDQ28ee3bvp6+2dkvGJ2cXv97OgpeX1CzSVBS0t8s6GEEKIOUUWDM5gLrebkkgEu6sTOLxsIxqNgqKQiMWnaIRitqmtrS38v3X5crzV1VM4GiGEEOLkk5nnGSyj65imCWruaVQVCrPQo9KpFBlDNkkRk88bDE71EIQQQoiTTsLzDJZKJonGoij58KxMsGFF1sjSK2Ub4kTQ5I0rIYQQc4+E5xlPQR2deZ7gs7Zlo6fTE3xGiOPkkPAshBBi7pHwPIN5fT4qKivRnLkQoypgT3A9t8czwaVCHCcJz0IIIeYgCc8zWFFxMUuWLKGktAwAFeWw8KwoCh6XtBITJ4CEZyGEEHOQhOcZzO/3s+astbQuXQqApqmF8NymZwBwOBxkDWOKRihmNQnPQggh5iD56zfDVVRUsDOTC8pel5tYKsU1+9tJWCaqquL1eikpLZniUYpZScKzEEKIOUhmnme4H//wh/zp8ccB8gsDbXoNg7SiEgyHWdjSwsKxG1sIMVkkPAshhJiD5K/fDLZjxw7+uOEPVOXLMhQFsBUCwSAOh4MlS5fynve9l7KysqkdqJhdQiGIRgv9xYUQQoi5RMLzDNbf20ssFqMif6zauW4bFZWVhMNhLr/yClaecspUDlHMRp/+LDz/3FSPQgghhJgSMnU0g/mDQQzDwMjPPOd2GLQxslkcmkapzDiLE6GmBt7z3qkehRBCCDEl5lx4vvfee2lqasLj8bBq1So2btx41Ov/8Ic/ZMWKFfh8PqqqqrjuuusYGBg4SaM9uvKyMkrKy0AZv8NgdGQEn99PSLZPFkIIIYSYVHMqPD/00EPceuut3HHHHWzevJlzzjmH9evX097ePuH1n3rqKT7ykY9www03sHXrVn72s5/x5z//mRtvvPEkj3xiqWQSt9NV2J579MnUNI2hgQGisdjUDU4IIYQQYhaaU+H57rvv5oYbbuDGG2+ktbWVe+65h7q6Ou67774Jr//ss8/S2NjIJz/5SZqamjj77LO56aabeOGFF07yyI9MUZRxOwyiKAQCAfRMhkQ8PrWDE0IIIYSYZeZMeM5kMrz44ousW7du3OXr1q1j06ZNE95m7dq1HDx4kEceeQTbtjl06BA///nPueSSS454P7quE41Gx32cKF6fj4qqKjxeLwCaouTCcyiIz+/D5XSesPsWQgghhJiL5kx47u/vxzRNKioqxl1eUVFBT0/PhLdZu3YtP/zhD7n66qtxuVxUVlZSVFTEv/3bvx3xfu666y7C4XDho66ublIfx1hFxcVUVVWhjpZtKAqqpqEqKkuWLKWqpuaE3bcQQgghxFw0Z8LzKEVRxh3btn3YZaO2bdvGJz/5Sf7hH/6BF198kUcffZT9+/dz8803H/H8t99+OyMjI4WPjo6OSR3/GxUXF1NRXQXkwjNAaVkpK05Zid/vP6H3LYQQQggx18yZPs+lpaVomnbYLHNvb+9hs9Gj7rrrLs466yw++9nPArB8+XL8fj/nnHMOX/rSl6iqqjrsNm63G7fbPfkPYAIZXcfr9VJVUwtDg7hdbnyqSmNTEyPDwyQSCQnQQgghhBCTaM7MPLtcLlatWsWGDRvGXb5hwwbWrl074W2SyWShJGKUpmlAbsZ6qrncbrxeD7F8XbVpZEnrOgMDA/T09JDR9SkeoRBCCCHE7DJnZp4BbrvtNq699lpWr17NmjVruP/++2lvby+UYdx+++10dnby4IMPAnDZZZfx8Y9/nPvuu4+LL76Y7u5ubr31Vk4//XSqq6un8qEA4Pf7SafTbNnyGoQD2KaFYVkcbGsnnUxhmOZUD1EIIYQQYlaZU+H56quvZmBggDvvvJPu7m6WLl3KI488QkNDAwDd3d3jej5/7GMfIxaL8e///u98+tOfpqioiHe+85185StfmaqHME5fXx/79uzFHwwAuVZ1lm2DopA1sjLzLIQQQggxyRR7OtQfzGLRaJRwOMzIyAihUGhSz71/3z5++pOH0Hfv4h80hT7DYG8my8/KyqlvaOT9H7iKmtraSb1PIYQQQrx1o3lAYtfMN2dqnmejQDCIqigMj4wAuSfTtm3a2ztwupwUFRdP7QCFEEIIIWYZCc8zWFlZGUtXLMfn9QG5VnVOpxOfz4fL5Zri0QkhhBBCzD4Snme41aedRm1jrmZby2/VXVVVRVFRkdQ8CyGEEEJMMgnPM1xZWRmLWlsBUGybVDJFKplE0xy4TlK/aSGEEEKIuULC8wyXSCSIxeMAKORqnru6ujhwYP/UDkwIIYQQYhaS8DzDDQ8NcWB/LiirioKqaaAotO3bT1dX1xSPTgghhBBidpHwPAtYpgXkZp5RFJwuJ6Zpks1kpnRcQgghhBCzjYTnGa6ouJj6pkbg9bINp+agqraGsvLyKR2bEEIIIcRsM6d2GJyN/H4/pWVlr1+gKJSUlPLuSy6hbOzlQgghhBDiuMnM8wzX19dH24E2IFfz7HA4UACH0zm1AxNCCCGEmIUkPM9w8ViMaDQK5Mo23G43FrDttddIJBJTOjYhhBBCiNlGwvMMFwgGx7WqSyQSWKaJqqqySYoQQgghxCST8DwLqI5c6boCWLZNIpnE5XbLJilCCCGEEJNMwvMMF4/FCAaDhWPLthkZHiYZj+P3+6dwZEIIIYQQs4+E5xkuEAySTqUA0BQFRVHwejwc7Oykr69vikcnhBBCCDG7SHie4Xw+H4FQqHBsA5GSEoysQTwWm7qBCSGEEELMQhKeZ7iMruPxegvHlmUx0N+PaWQJjCnnEEIIIYQQx0/C8wxnmCZ6OlU4VlUV0zQpjpTg8/mmcGRCCCGEELOPhOcZzqFpoCivHzscNM2fR2VVpbSqE0IIIYSYZBKeZzjDNLHt1481h4NsJovD6ZRWdUIIIYQQk0zC8wzn0DRKysoKx4ZhkE6niRQVS6s6IYQQQohJJuF5hnO53RQVFRWOfX4/8+bPJ5FKyvbcQgghhBCTTMLzDOf3+/H4Xu+2kc1miZSUgG1LzbMQQgghxCST8DzDJRIJ0snXu21kMlm2bdnC8PCw1DwLIYQQQkwyCc8zXEbX6entLRwrqkIylcIwjCkclRBCCCHE7CTheYYzTJPhoaHCsaJphIJB3G63lG0IIYQQQkyyORee7733XpqamvB4PKxatYqNGzce8bof+9jHUBTlsI8lS5acxBEfnUPTcHs8hWPTMDEtE6/XK2UbQgghhBCTbE6F54ceeohbb72VO+64g82bN3POOeewfv162tvbJ7z+N77xDbq7uwsfHR0dRCIRrrrqqpM88iMzTJOxjZ5N02CgfwB/MCit6oQQQgghJtmcCs933303N9xwAzfeeCOtra3cc8891NXVcd999014/XA4TGVlZeHjhRdeYGhoiOuuu+4kj/zIHJqGLxAoHHu9Xmrq6kjEYtKqTgghhBBikjmmegDHYnh4mOeff57e3l4syxr3uY985CPHdI5MJsOLL77I5z73uXGXr1u3jk2bNh3TOb7zne9w4YUX0tDQcMTr6LqOPqbWOBqNHtO53y6X201xpLhw7HS7qa2txTQtMrous89CCCGEEJNo2ofnX/3qV3z4wx8mkUgQDAZRFKXwOUVRjjk89/f3Y5omFRUV4y6vqKigp6fnTW/f3d3Nb3/7W370ox8d9Xp33XUXX/jCF45pTJPB7/dTXBIpHKfTabKGQTgckppnIYQQQohJNu3LNj796U9z/fXXE4vFGB4eZmhoqPAxODj4ls83NnwD2LZ92GUTeeCBBygqKuLKK6886vVuv/12RkZGCh8dHR1veYxvRSKRGNdVwzAM2vbvx+3xyKyzEEIIIcQkm/Yzz52dnXzyk5/E5/Md13lKS0vRNO2wWebe3t7DZqPfyLZtvvvd73LttdficrmOel232437JM74ZnSdtrYOzs4fO5xOPF4vA/39JBIJCdBCCCGEEJNo2s88X3zxxbzwwgvHfR6Xy8WqVavYsGHDuMs3bNjA2rVrj3rbJ554gj179nDDDTcc9zgmm2GajAwPF44tyyKbyZBKpaTPsxBCCCHEJJv2M8+XXHIJn/3sZ9m2bRvLli3D6XSO+/zll19+zOe67bbbuPbaa1m9ejVr1qzh/vvvp729nZtvvhnIlVx0dnby4IMPjrvdd77zHc444wyWLl16/A9okjk0DceY2fBkMklfXx8ZXZeaZyGEEEKISTbtw/PHP/5xAO68887DPqcoCqZpHvO5rr76agYGBrjzzjvp7u5m6dKlPPLII4XuGd3d3Yf1fB4ZGeHhhx/mG9/4xnE8ihPHME3MMVtxaw4HtmVhvqEriRBCCCGEOH7TPjy/sTXd8brlllu45ZZbJvzcAw88cNhl4XCYZDI5qWOYTA5Nwx8MFo49Xi/z6hvx+/3Sqk4IIYQQYpJN+5pncXQut5vq6urCcSgcJhQK4Q8EpGxDCCGEEGKSzYjw/MQTT3DZZZfR3NzMggULuPzyy9m4ceNUD2ta8Pv9lFaUF46Hh4YAWLZ8ucw6CyGEEEJMsmkfnn/wgx9w4YUX4vP5+OQnP8knPvEJvF4vF1xwwZtuWDIXJBIJoiMjhWPbhrb9+2lva5vCUQkhhBBCzE7Tvub5y1/+Ml/96lf51Kc+Vbjs//yf/8Pdd9/NF7/4Ra655popHN3Uy+g6fb19Yy6xMS2LTU8/zfzmZsrKyqZsbEIIIYQQs820n3net28fl1122WGXX3755ezfv38KRjS9uNxuhsf0edYzGTweDxk9QzwWm7qBCSGEEELMQtM+PNfV1fHYY48ddvljjz1GXV3dFIxo+vF4vYX/W7ZNPBbD6/MSGNOFQwghhBBCHL9pX7bx6U9/mk9+8pO8/PLLrF27FkVReOqpp3jggQembe/lkymj66iO159Gh8OBDaw6dZWUbAghhBBCTLJpH57/6q/+isrKSr72ta/x05/+FIDW1lYeeughrrjiiike3dQzTJPomLKNUDhMfVUNLYtbp25QQgghhBCz1LQPzwDvec97eM973jPVw5iWHJpGUSRSOHa7PZRXVuDQtCkclRBCCCHE7DTta57F0bncbiqrKgvHI9ERYiNRBgcHp3BUQgghhBCz07SceY5EIuzatYvS0lKKi4tRFOWI153rIdHv949bMGhbFh6vl4MdHVRWVclGKUIIIYQQk2hahuevf/3rBPOdIr7+9a8fNTzPdYlEgv6+1/s8O91ubNtmZCRKRtclPAshhBBCTKJpGZ4/+tGPFv7/sY99bOoGMgNkdJ2+/oHCsa7rHDzYQWVVJS63ewpHJoQQQggx+0z7mueXXnqJLVu2FI7/93//lyuvvJK///u/J5PJTOHIpgfDNEklk4XjZCJJZ8dB/MGgzDoLIYQQQkyyaR+eb7rpJnbt2gXkdhu8+uqr8fl8/OxnP+Nv//Zvp3h0U8+hafjGbIbi9nqoqKokEYuRSCSmcGRCCCGEELPPtA/Pu3btYuXKlQD87Gc/47zzzuNHP/oRDzzwAA8//PDUDm4acLndlJWWFI5t20ZTVAYHB8no+hSOTAghhBBi9pn24dm2bSzLAuAPf/gD7373u4Hctt39/f1TObRpwe/34wsECscZPYMFxKMxDNOcuoEJIYQQQsxC0z48r169mi996Ut8//vf54knnuCSSy4BYP/+/VRUVEzx6KZeIpEgFosVjl0eNwoQCAZloxQhhBBCiEk27cPzPffcw0svvcQnPvEJ7rjjDpqbmwH4+c9/ztq1a6d4dFMvo+sYxuszzF6fD4/HTaQkIt02hBBCCCEm2bRsVTfW8uXLx3XbGPXP//zPaDKzisvtpqgoXDjO6DqxWJyS0lLptiGEEEIIMcmm/cxzR0cHBw8eLBw///zz3HrrrTz44IM4nc4pHNn04Pf7qW9sLBw7XS6WLV+Onk5Ltw0hhBBCiEk27cPzNddcw+OPPw5AT08PF110Ec8//zx///d/z5133jnFo5se0unXu2qoqkaktIR0Oi3dNoQQQgghJtm0D8+vvfYap59+OgA//elPWbp0KZs2bSq0q5vrEokEHW1tWPljG5s9u3aDokjNsxBCCCHEJJv24TmbzeLOh8A//OEPXH755QAsWrSI7u7uqRzatJDRdfoHxmzPnckwMDhAQ3291DwLIYQQQkyyaR+elyxZwn/8x3+wceNGNmzYwLve9S4Aurq6KCkpeZNbz36GaRIdHi4cp9Np4iNRDMs68o2EEEIIIcTbMu3D81e+8hW+9a1vcf755/OhD32IFStWAPDLX/6yUM7xVtx77700NTXh8XhYtWoVGzduPOr1dV3njjvuoKGhAbfbzfz58/nud7/7th7LieDQNHyBAHb+2OVyEy4upm3/flkwKIQQQggxyaZ9q7rzzz+f/v5+otEoxcXFhcv/8i//Ep/P95bO9dBDD3Hrrbdy7733ctZZZ/Gtb32L9evXs23bNurr6ye8zQc+8AEOHTrEd77zHZqbm+nt7cUwjON6TJPJ5XbjGtN1xLItgsEgpmmR0XUp3RBCCCGEmETTPjwDaJo2LjgDNI5pz3as7r77bm644QZuvPFGILcBy+9+9zvuu+8+7rrrrsOu/+ijj/LEE0+wb98+IpHI277fE01RlMLMs8fjIZ1O4/a4ZcGgEEIIIcQkm5bh+dRTT+Wxxx6juLiYU045BUVRjnjdl1566ZjOmclkePHFF/nc5z437vJ169axadOmCW/zy1/+ktWrV/PVr36V73//+/j9fi6//HK++MUv4vV6J7yNruvoY1rERaPRYxrf25XRdcJFRbmvkW3j9flxu12yYFAIIYQQ4gSYluH5iiuuKHTYuPLKKyflnP39/ZimSUVFxbjLKyoq6OnpmfA2+/bt46mnnsLj8fCLX/yC/v5+brnlFgYHB49Y93zXXXfxhS98YVLGfCxcbjder6dw7PV5mTdvPnUNDSdtDEIIIYQQc8W0DM//+I//OOH/J8MbZ7Ft2z7izLZlWSiKwg9/+EPC4dwW2HfffTfvf//7+eY3vznh7PPtt9/ObbfdVjiORqPU1dVN4iMYz+/309LaCvmZ595DvWQWZRkZGZGZZyGEEEKISTYtw/ORxONxrDe0YAuFQsd029LSUjRNO2yWube397DZ6FFVVVXU1NQUgjNAa2srtm1z8OBBFixYcNht3G53Ydb8ZEnkvy4a4HA6AJvdO3cSDoclQAshhBBCTKJp36pu//79XHLJJfj9fsLhMMXFxRQXF1NUVHTYIsKjcblcrFq1ig0bNoy7fMOGDaxdu3bC25x11ll0dXURj8cLl+3atQtVVamtrX17D2iSJRIJdmzfXjg2TYu+Q72MjERle24hhBBCiEk27WeeP/zhDwPw3e9+l4qKiqMuHnwzt912G9deey2rV69mzZo13H///bS3t3PzzTcDuZKLzs5OHnzwQQCuueYavvjFL3LdddfxhS98gf7+fj772c9y/fXXH3HB4MmW0XUGBgYL3Tai0RG2bdtGKBySbhtCCCGEEJNs2ofnV199lRdffJGWlpbjPtfVV1/NwMAAd955J93d3SxdupRHHnmEhvziuu7ubtrb2wvXDwQCbNiwgb/5m79h9erVlJSU8IEPfIAvfelLxz2WyWKYJsODg1i2DYqCadmkUylM2WFQCCGEEGLSTfvwfNppp9HR0TEp4Rnglltu4ZZbbpnwcw888MBhly1atOiwUo/pxKFpBEKhwoy81+dlXl0Dfr9fNkkRQgghhJhk0z48f/vb3+bmm2+ms7OTpUuX4hyzmx7A8uXLp2hk04PL7aa2pqbQbaO0tJRQKIQ/EJCyDSGEEEKISTbtw3NfXx979+7luuuuK1ymKEqhxZxpmlM4uqnn9/tZfspK1F/+AkyTTCZLVXU1y5Yvl1lnIYQQQohJNu3D8/XXX88pp5zCj3/84+NeMDibjX5dAqEg4YULqK6unuIRCSGEEELMPtM+PLe1tfHLX/6S5ubmqR7KtJRIJNi5fTslloUD6OvtZdfjf8Lr9TJfvmZCCCGEEJNq2vd5fuc738krr7wy1cOYtjK6zvDwCHa+V53X5yOTybBj+3YSicTUDk4IIYQQYpaZ9jPPl112GZ/61KfYsmULy5YtO2zB4OWXXz5FI5seXG43DoeGne/0bBgGwVAI07Sk24YQQgghxCRTbHt0znJ6UtUjT47PhAWD0WiUcDjMyMjIMW8l/lbt3bOH2n/+Cm7T5MW6etqXLiMSibD69NMlPAshhBDTwGgemOaxSxyDaT/zbMlmH29qfnMzhsMBponb7SYSibCgpUWCsxBCCCHEJJv2Nc8TGR4enuohTCtdXV2FV7KWbVNSWirdNoQQQgghToBpH56/8pWv8NBDDxWOr7rqKiKRCDU1NbKQkNe7bRj58pWe7m6efPxP7N2zZ4pHJoQQQggx+0z78Pytb32Luro6ADZs2MAf/vAHHn30UdavX89nP/vZKR7d1Hu920Zu5lm6bQghhBBCnDjTvua5u7u7EJ5//etf84EPfIB169bR2NjIGWecMcWjm3qFbhv58KzrOh6PR7ptCCGEEEKcANN+5rm4uJiOjg4AHn30US688EIAbNue9p02Tga/38/iJUsKOwxmDYNoLIZpmrjc7ikenRBCCCHE7DLtZ57f+973cs0117BgwQIGBgZYv349AC+//LLsOphnWRZWfuY5nU6DbSObmAshhBBCTL5pH56//vWv09jYSEdHB1/96lcJBAJArpzjlltumeLRTb1EIsGO7dupyh8HAkFCoRCqpknZhhBCCCHEJJv24dnpdPKZz3zmsMtvvfXWkz+YaSij6xiGCYoCto3D6SSdTqNpqpRtCCGEEEJMsmkfngF27drFn/70J3p7ew/bNOUf/uEfpmhU04PL7UaBQs1zIpkgm82yqLVVZp2FEEIIISbZtA/P//mf/8lf/dVfUVpaSmVlZSEkQi4wzvXwDGCOqXkOBILUNzRQWVX1JrcSQgghhBBv1bQPz1/60pf48pe/zN/93d9N9VCmpYyu4/V60TQNLIvq2hr04iKpdxZCCCGEOAGmfau6oaEhrrrqqqkexrTlcrvJ6Hqhz3NXZycjQ8NS7yyEEEIIcQJM+/B81VVX8fvf/36qhzGtmZZV2J47Go3S1tZGT3f3FI9KCCGEEGL2mfZlG83NzXz+85/n2WefZdmyZTidznGf/+QnPzlFI5seMrqOqqooigq2iT8QwOl0smP7diqrqqR0QwghhBBiEk378Hz//fcTCAR44okneOKJJ8Z9TlGUOR+eXW43LpcTRVXAAhsIhkKyPbcQQgghxAkw7cPz/v37p3oI05rf72dRayv85tcA+LxeIiURwuGQ1D0LIYQQQkyyaV/zLN6c1+dD03JPZUrXAYUFLS0y6yyEEEIIMcmm/cwzwMGDB/nlL39Je3s7mUxm3OfuvvvuKRrV9JBIJNi5fTun5ZptEC4qIqlphMPhqR2YEEIIIcQsNO1nnh977DFaWlq49957+drXvsbjjz/O9773Pb773e/y8ssvv+Xz3XvvvTQ1NeHxeFi1ahUbN2484nX/9Kc/oSjKYR87duw4jkc0uTK6TiqVxiaXnvt6e9m7dw8dbW1TPDIhhBBCiNln2ofn22+/nU9/+tO89tpreDweHn74YTo6OjjvvPPecv/nhx56iFtvvZU77riDzZs3c84557B+/Xra29uPerudO3fS3d1d+FiwYMHxPKRJ5XK7URUF08i1qlNVDafTRVt7O4lEYopHJ4QQQggxu0z78Lx9+3Y++tGPAuBwOEilUgQCAe68806+8pWvvKVz3X333dxwww3ceOONtLa2cs8991BXV8d999131NuVl5dTWVlZ+NA07W0/nsnm9/tpaGpCzdc8m5ZFXX0d2DYZXZ/i0QkhhBBCzC7TPjz7/X70fAisrq5m7969hc/19/cf83kymQwvvvgi69atG3f5unXr2LRp01Fve8opp1BVVcUFF1zA448/ftTr6rpONBod93Gi1TfUo6q5QO/xejjYcZBkMiXdNoQQQgghJtm0D89nnnkmTz/9NACXXHIJn/70p/nyl7/M9ddfz5lnnnnM5+nv78c0TSoqKsZdXlFRQU9Pz4S3qaqq4v777+fhhx/mv//7v2lpaeGCCy7gySefPOL93HXXXYTD4cJHXV3dMY/x7erp7sYwDQD6e/uIx2IoJ/xehRBCCCHmnmnfbePuu+8mHo8D8E//9E/E43Eeeughmpub+frXv/6Wz6co42OlbduHXTaqpaWFlpaWwvGaNWvo6OjgX/7lXzj33HMnvM3tt9/ObbfdVjiORqMnNEAnEgm2bd3KWZYFgD8QIBQKoWqabJIihBBCCDHJpnV4Nk2Tjo4Oli9fDoDP5+Pee+99W+cqLS1F07TDZpl7e3sPm40+mjPPPJMf/OAHR/y82+3GfRLLJTK6jmGYKKoKlkV5ZQV7dB1NU6VsQwghhBBikk3rsg1N07j44osZHh4+7nO5XC5WrVrFhg0bxl2+YcMG1q5de8zn2bx5M1VVVcc9nsnicruJRIpxulwAxKIxXC4Xi1pbZdZZCCGEEGKSTeuZZ4Bly5axb98+mpqajvtct912G9deey2rV69mzZo13H///bS3t3PzzTcDuZKLzs5OHnzwQQDuueceGhsbWbJkCZlMhh/84Ac8/PDDPPzww8c9lsni9/tZ0NKCquReBxVHIqxb/y7mNzdP8ciEEEIIIWafaR+ev/zlL/OZz3yGL37xi6xateqw2dRQKHTM57r66qsZGBjgzjvvpLu7m6VLl/LII4/Q0NAAQHd397iez5lMhs985jN0dnbi9XpZsmQJv/nNb3j3u989OQ9uEtn5sm2Xx43X55vawQghhBBCzFKKbdv2VA/iaFT19cqSsQv7Rhf6maY5FcM6ZtFolHA4zMjIyFsK+scqkUjw/LPPcuovHiacTvPKsuUcWriIs849R8o2hBBCiGliNA9M89gljsG0n3n+3ve+R11d3WEbk1iW9aY7A84Fo9tz53fnpr2jg72GSX1DPYsWL57awQkhhBBCzDLTPjxff/31dHd3U15ePu7ygYEBLrzwwsLug3OVy+3GoWlY+Veymvb69tx1DQ0y+yyEEEIIMYmmdbcNOHIf5ng8jsfjmYIRTS9+v5+6hoZCeLYsm+aFC2R7biGEEEKIE2DazjyPbjSiKAqf//zn8Y1ZBGeaJs899xwrV66cotFNLy6Xs/ACIxAKMpRIEIlEpM+zEEIIIcQkm7bhefPmzUBu5nnLli248n2MIdezecWKFXzmM5+ZquFNG4lEgva2NiL5yflkIklHWzvLV6yQkg0hhBBCiEk2bcPz448/DsB1113HN77xjRPSqWI2GF0wODrznNbT6BmdbCYzxSMTQgghhJh9pn3N8/e+9z0JzkfhcrtRFQUjawDgdLnw+fy0tbeTSCSmeHRCCCGEELPLtA/P4uj8fj8NTU0oo/2wbVkwKIQQQghxokh4ngXGLhh0e70MDw7i8XhkwaAQQgghxCST8DzDjS4YtG0LgM6Og7y8+WXcHo8sGBRCCCGEmGQSnme4jK4zPDxS2GGwpKyMSCTCQH+/1DwLIYQQQkwyCc8znMvtxuHQsPIzz4oCkZISTNOSmmchhBBCiEkm4XmG8/v9LF6yBIem5Y8DREoihMMhqXkWQgghhJhkEp5nAa/Ph+bItexO6WlAYUFLi9Q8CyGEEEJMMgnPM1wikWDn9u3YVq7oubKyCr/PRzgcnuKRCSGEEELMPhKeZ7jXdxjMHTucTtJ6WuqdhRBCCCFOAAnPM5zL7Saj62SN3A6DW197jZ3bdzA4ODjFIxNCCCGEmH0kPM8CqVQKI5sFwMhmsWybPXv2SKs6IYQQQohJJuF5hsvoOg6nEy3fbaO4JEI4FCIRj0vphhBCCCHEJJPwPMO53G7CoVCh20YgGMKybfyBgLSqE0IIIYSYZBKeZzi/38/SFctx5MNzLBqlpLSEZcuXS6s6IYQQQohJJuF5Fqiursadn2UuKSuluKh4ikckhBBCCDE7SXieBRKJRGHBoKppOJwOdu/cKQsGhRBCCCEmmYTnWSCj65iWBYBhmITCYdJp6fUshBBCCDHZJDzPAoODg5j5Ps/ZbJaO9nY8Ho8sGBRCCCGEmGRzLjzfe++9NDU14fF4WLVqFRs3bjym2z399NM4HA5Wrlx5Ygf4FiUSCdrb2rBzu3OTTCbp6eqmtq5OFgwKIYQQQkyyORWeH3roIW699VbuuOMONm/ezDnnnMP69etpb28/6u1GRkb4yEc+wgUXXHCSRnrsRrfnHm1V19jURE1dLZFIZIpHJoQQQggx+8yp8Hz33Xdzww03cOONN9La2so999xDXV0d991331Fvd9NNN3HNNdewZs2akzTSY+dyu/H7fWiqAkA8HsfjlpINIYQQQogTYc6E50wmw4svvsi6devGXb5u3To2bdp0xNt973vfY+/evfzjP/7jiR7i2+L3+wkXFaHnFwfu2rmTru5uRkZGpnhkQgghhBCzj2OqB3Cy9Pf3Y5omFRUV4y6vqKigp6dnwtvs3r2bz33uc2zcuLGwCcmb0XW9EGQBotHo2x/0MUgkEvR0dVOT77bh9njoj8fZ8uqrhMNhqXsWQgghhJhEc2bmeZSiKOOObds+7DIA0zS55ppr+MIXvsDChQuP+fx33XUX4XC48FFXV3fcYz6ajK4Ti8cLj8Hr86GpKol4XFrVCSGEEEJMsjkTnktLS9E07bBZ5t7e3sNmowFisRgvvPACn/jEJ3A4HDgcDu68805eeeUVHA4Hf/zjHye8n9tvv52RkZHCR0dHxwl5PKNcbjfhUAhN0wBwOJ3ouo4/EJC6ZyGEEEKISTZnwrPL5WLVqlVs2LBh3OUbNmxg7dq1h10/FAqxZcsWXn755cLHzTffTEtLCy+//DJnnHHGhPfjdrsJhULjPk4kv9/P0hXLCzPPnZ2dZDIZKioqpGRDCCGEEGKSzZmaZ4DbbruNa6+9ltWrV7NmzRruv/9+2tvbufnmm4HcrHFnZycPPvggqqqydOnScbcvLy/H4/EcdvlUSyWTePI1zz6fj0BdHSPDwyQSCQnQQgghhBCTaE6F56uvvpqBgQHuvPNOuru7Wbp0KY888ggNDQ0AdHd3v2nP5+kmkUiwbetWzjRNAFxuF9FkkpGRKBldl/AshBBCCDGJFNse3ZtOnAjRaJRwOMzIyMgJKeEYGhzkT4//iXP/8DtKslmeWLaClw2T5oULOP+d75TwLIQQQkwDo3lAYtfMN2dqnmcrl9tNJFKMy+UCoK+3F0VRWNTaKsFZCCGEEGKSSXie4fx+PwtaWgqvZE3LoqKyEq/PN8UjE0IIIYSYfSQ8zwKpZBLDMACwLBuH08HunTtJJBJTPDIhhBBCiNlFwvMMN7pg0MwvGNQ0leGh4cKCQSGEEEIIMXkkPM9wGV3HMExUNfdUlldWEI/F0DRVNkkRQgghhJhkEp5nuDcuGOzt7ZMFg0IIIYQQJ4iE5xnujQsGLdOUBYNCCCGEECeIhOdZYOyCQdOWBYNCCCGEECeKhOcZThYMCiGEEEKcPBKeZ7iMrmNZNi6nE4DKqmpZMCiEEEIIcYJIeJ7hXG43RUVhVE0D4FBPjywYFEIIIYQ4QSQ8z3CjCwbHkgWDQgghhBAnhoTnWSAcDqPk/9/S2kpFZYUsGBRCCCGEOAEkPM8C7W3tZLJZAHbs2IGeyZBOp2XBoBBCCCHEJJPwPMMlEgna9u/HMi0AguEQe3fvAUWRBYNCCCGEEJNMwvMMl9F1UBS8Pi8A6XSaZDJJQ329LBgUQgghhJhkEp5nOJfbjdfrwbJyM8/ZtI7L6cSZ365bCCGEEEJMHgnPM5zf76e2rg4jX/OcNUxq6mo52NEhCwaFEEIIISaZhOdZIJPJYtt27kBVCASDsmBQCCGEEOIEkPA8wxUWDFq58FwciciCQSGEEEKIE0TC8wyX0XUs28bhdABgmgbZbEYWDAohhBBCnAASnmc4l9uN3+/D5XQCEAiGmDdvPnUNDVM8MiGEEEKI2UfC8ww3uj23me+2MTg4gGGajIyMTPHIhBBCCCFmHwnPs0A4HC783+8PEA6HZHtuIYQQQogTQMLzLNDe1k4236pucGiIrGFItw0hhBBCiBNAwvMM98btub1eLzu2bpNuG0IIIYQQJ8CcC8/33nsvTU1NeDweVq1axcaNG4943aeeeoqzzjqLkpISvF4vixYt4utf//pJHO2bG+22oWm5p/LQoR46uzrxe33SbUMIIYQQYpI5pnoAJ9NDDz3Erbfeyr333stZZ53Ft771LdavX8+2bduor68/7Pp+v59PfOITLF++HL/fz1NPPcVNN92E//9v787Dm6jWP4B/J0vTPd3oRkuhlL3I0lIoyuJCAUU2ryJ6WWRRVBSoKKKCFFTcQPQqCF4EEa7gVVAURKpS4GdB2ZHlFqyFArYUCt3bpE3m98eQtKFbGpqkTb6f58kDOTkz856cpHlzcuaMhwcef/xxO7SgOheVCgq5HDcukQKF0gWhwSEoLi1BcXExE2giIiKiRiSIxkvTOb7evXujZ8+eWLFihbGsU6dOGDlyJBYvXmzWPkaPHg0PDw98/vnnZtUvKCiAWq1Gfn4+vL29LYq7Pnv37EHH9evQQq/Hav8W8OrRE2q1N+J694avn59VjklERETmM+QDTpR2OSynmbah1Wpx6NAhJCQkmJQnJCQgNTXVrH0cOXIEqampGDBgQK11NBoNCgoKTG7WVFxcjOLCIgiCAADw8PJC1qVLnPNMREREZAVOkzxfvXoVOp0OQUFBJuVBQUHIzs6uc9uwsDCoVCrExsbi6aefxpQpU2qtu3jxYqjVauMtPDy8UeKvTeWcZzkAIPfqVc55JiIiIrISp0meDQwjtAaiKFYru9nevXtx8OBBfPzxx1i2bBm++OKLWuvOnTsX+fn5xtuFCxcaJe7a3DznWa5UIjS0pXHOMxERERE1Hqc5YTAgIAByubzaKHNOTk610eibtWnTBgDQtWtXXL58GQsWLMDYsWNrrKtSqaCy4XQJDw8PuHm4Q1deAQAoLSlBSEgIIIrQajQcfSYiIiJqRE6TPLu4uCAmJgbJyckYNWqUsTw5ORkjRowwez+iKELThC4+YpzzLBMAHRAeEYGc3Fz4+vlyzjMRkZPR6XTGi2aRbSmVSuMUSnJsTpM8A0BiYiLGjRuH2NhYxMfHY9WqVcjMzMS0adMASFMuLl26hHXr1gEAPvroI7Rq1QodO3YEIK37/O677+KZZ56xWxtuptVoAEGAm7s7UFiI8opylJSUIKJVK446ExE5CVEUkZ2djby8PHuH4tR8fHwQHBxc73RQat6cKnkeM2YMcnNzsXDhQmRlZSE6Ohrbt29HREQEACArKwuZmZnG+nq9HnPnzkVGRgYUCgXatm2LN998E0888YS9mlCNi0oFNzdXiDeuMKgt08DFxx1KFxc7R0ZERLZiSJwDAwPh7u7O5M3GRFFESUkJcnJyAECaPkkOy6nWebYHW6zznP7nn/Bb+i58tVr8OzAYvj16ICAgALFxcRx9JiJycDqdDmfOnEFgYCD8/f3tHY5Ty83NRU5ODtq3b19tCgfXeXYcTrfahiPSassh6qU3o7ePD7zVapSVlUlTOoiIyKEZ5ji7u7vbORIy9AHnnTs2Js/NXHFxMf44ehR6vQ4AcC03F0cOHeZFUoiInAynatgf+8A5MHlu5vKuX8elS5eMPwOVl1fg6tUrCAwM5JQNIiIiokbG5NkBCDIBshtzq3z9/RAYGITAwEA7R0VERNRwAwcOxMyZM+0dBlGtmDw3cz6+vvDx9YOol1bbuHLlCgJatICPr6+dIyMiIrKulJQUCILAJfrIppg8OwC9Xg/9jeRZz7N4iYiIiKyGyXMzl3f9Ogry8yBXSEt2h4WFIT8vD3nXr9s5MiIioroVFxdj/Pjx8PT0REhICJYsWWLy+Pr16xEbGwsvLy8EBwfjkUceMa6lfO7cOdx5550AAF9fXwiCgIkTJwIAduzYgTvuuAM+Pj7w9/fHsGHDkJ6ebtO2keNi8uwIREAwDDgLglRARERkgeLiYly/dg3FxcVWP9bzzz+PXbt2YcuWLdi5cydSUlJw6NAh4+NarRaLFi3CsWPH8M033yAjI8OYIIeHh+Prr78GAKSlpSErKwvvv/++sQ2JiYk4cOAAfv75Z8hkMowaNcr4Ky3RrXCqKww6Ih9fX/j6+UEvSn8QLly8iBa3deOcZyIiarC///4bZ9PSUFpaBjc3V7Tr0AGhoaFWOVZRURFWr16NdevWYdCgQQCAzz77DGFhYcY6kyZNMv4/MjISH3zwAeLi4lBUVARPT0/4+fkBAAIDA+Hj42Os+8ADD5gca/Xq1QgMDMSpU6cQHR1tlfaQ8+DIswPQVZnzDL3IcWciImqw4uJinE1LgyiKCAwKhCiKOJuWZrUR6PT0dGi1WsTHxxvL/Pz80KFDB+P9I0eOYMSIEYiIiICXlxcGDhwIAMjMzKx334888ggiIyPh7e2NNm3amLUdkTmYPDdzedevIz/vOmQ3FmZvGc45z0RE1HBajQalpWXwVqshk8msfrXa+i5TXVxcjISEBHh6emL9+vU4cOAAtmzZIsWq1da57f3334/c3Fx88skn+O233/Dbb7+ZtR2ROZg8OwABAmQyaZ1nQSYD5zwTEVFDuahUcHNzRUF+PvR6PQry8+Hq6mq1q9VGRUVBqVRi//79xrLr16/jzJkzAID//e9/uHr1Kt58803069cPHTt2NJ4saIzZxQUAoNPpjGW5ubk4ffo0XnnlFdx9993o1KkTrnNAiRoRk+dmrtqc5wsX4OPjyznPRETUIB4eHmjXoQMEQcCVnBwIgoB2HTpY7Wq1np6emDx5Mp5//nn8/PPPOHHiBCZOnAiZTEpNWrVqBRcXF/zrX//CX3/9ha1bt2LRokUm+4iIiIAgCPj+++9x5coVFBUVwdfXF/7+/li1ahX+/PNP/PLLL0hMTLRKG8g5MXl2AFXnPIsi5zwTEZFlQkNDERsXh7jevREbF2e1kwUN3nnnHfTv3x/Dhw/HPffcgzvuuAMxMTEAgBYtWmDt2rX473//i86dO+PNN9/Eu+++a7J9y5YtkZSUhBdffBFBQUGYPn06ZDIZNm7ciEOHDiE6OhqzZs3CO++8Y9V2kHMRxPomHdEtKSgogFqtRn5+Pry9vRt9/5cuXsSXGzdiXNppBIgiPmvXAXlKF/zjoQfRssoZy0RE5JjKysqQkZGBNm3awNXV1d7hOLW6+sKQDzDtav448uwASkvLjD9z5efl2WRtTiIiIiJnxOS5mXNRqaBUKIzTNoqKiqBUKKx2ggcRERGRM2Py3Mwp5HLIFHLodVLyXFxSAoVSAYVcbufIiIiIiBwPk+dmrqCwEFdzrkCQSes8e3h64krOFRQUFto5MiIiIiLHw8tzN3N6nQ4qV1cIEACIcHV1RYWrG/RV1rwkIiIiosbBkedmztPLS0qbb6zzfDknB6JeD08vL/sGRkREROSAmDw7AH2VdZ71ei6BQ0RERGQtTJ6buaLCQgiCcOOy3IB/QAAEmQxFnPNMRERE1Og457mZk8nlKCwogAzSCYN516+hvLwcMq62QURERNToOPLczCnkcri6u1desUgmg7uHB5eqIyKiJk8URTz++OPw8/ODIAg4evQoBg4ciJkzZzbqcRYsWIDu3bvXWefXX39F165doVQqMXLkSKSkpEAQBOTl5TVqLNT8OV3yvHz5cuNlM2NiYrB3795a627evBmDBg1CixYt4O3tjfj4ePz44482jNY87u7ulUvVeXjCzd3dzhERERHVb8eOHVi7di2+//57ZGVlITo6Gps3b8aiRYtsHktiYiK6d++OjIwMrF27Fn379kVWVhbUajUAYO3atfDx8bF5XNT0OFXyvGnTJsycORMvv/wyjhw5gn79+mHo0KHIzMyssf6ePXswaNAgbN++HYcOHcKdd96J+++/H0eOHLFx5LVzUalQrtUaTxjMy89DuVbLKwwSEVGTl56ejpCQEPTt2xfBwcFQKBTw8/ODlx1WjEpPT8ddd92FsLAw+Pj4wMXFBcHBwRAEweaxUNPmVMnz0qVLMXnyZEyZMgWdOnXCsmXLEB4ejhUrVtRYf9myZXjhhRfQq1cvtGvXDm+88QbatWuH7777zsaR106r0aC8ouLGOs/SHOhynQ5ajcbOkREREdVu4sSJeOaZZ5CZmQlBENC6dWsAqDZto3Xr1njjjTcwadIkeHl5oVWrVli1apXJvubMmYP27dvD3d0dkZGRmDdvHsrLy82K49y5cxAEAbm5uZg0aRIEQcDatWtNpm2kpKTgscceQ35+vnSSviBgwYIFjfRMUHPjNCcMarVaHDp0CC+++KJJeUJCAlJTU83ah16vR2FhIfz8/KwRokVKS0qgKSsz3vf2VqNQU4bSkhI7RkVERHan0QDZ2bY/bnAwYMavn++//z7atm2LVatW4cCBA5DXca7OkiVLsGjRIrz00kv46quv8OSTT6J///7o2LEjAMDLywtr165FaGgo/vjjD0ydOhVeXl544YUX6o0jPDwcWVlZ6NChAxYuXIgxY8ZArVbjt99+M9bp27cvli1bhvnz5yMtLQ0A4OnpWe++yTE5TfJ89epV6HQ6BAUFmZQHBQUh28w/LkuWLEFxcTEeeuihWutoNBpoqoz6FhQUWBawmdzc3eHm5gahMF86Xn4e3IKCOe+ZiMjZZWcDi5Jsf9x5rwIREfVWU6vV8PLyglwuR3BwcJ117733Xjz11FMApFHm9957DykpKcbk+ZVXXjHWbd26NZ577jls2rTJrOTZcHxBEKBWq2uMxcXFBWq1GoIg1BsrOT6nSZ4Nbp67JIqiWfOZvvjiCyxYsADffvstAgMDa623ePFiJCXZ7o9V5ZxnHSCXo6CgEAo/f855JiJydsHBUiJrj+M2sttuu834f0MCm5OTYyz76quvsGzZMvz5558oKipCRUUFvL29Gz0OIsCJkueAgADI5fJqo8w5OTnVRqNvtmnTJkyePBn//e9/cc8999RZd+7cuUhMTDTeLygoQHh4uOWB1yMvLw+lpaUQBGn6uoeXF0rLypCXl4cWLVpY7bhERNTEqVRmjQA3B0ql0uS+IAjGE+X379+Phx9+GElJSRg8eDDUajU2btyIJUuW2CNUcgJOkzy7uLggJiYGycnJGDVqlLE8OTkZI0aMqHW7L774ApMmTcIXX3yB++67r97jqFQqqGw46luu1aKiogIQReDGSYO6igqUa7U2i4GIiMhefv31V0RERODll182lp0/f77Rj+Pi4gKdTtfo+6Xmx2mSZ0Baw3HcuHGIjY1FfHw8Vq1ahczMTEybNg2ANGp86dIlrFu3DoCUOI8fPx7vv/8++vTpYxy1dnNzM677aG8enp7QlpcbL5KSe/UqVC1awIMnMhARkROIiopCZmYmNm7ciF69emHbtm3YsmVLox+ndevWKCoqws8//4xu3brB3d0d7jy/yCk51VJ1Y8aMwbJly7Bw4UJ0794de/bswfbt2xFx42etrKwskzWfV65ciYqKCjz99NMICQkx3mbMmGGvJlSj1WqhkMthmLbtolJBIZdDy5FnIiJyAiNGjMCsWbMwffp0dO/eHampqZg3b16jH6dv376YNm0axowZgxYtWuDtt99u9GNQ8yCIxus6kzUUFBRArVYjPz/fKicvnDp5Ess/+BcWlJchQCbDTJ0IncoVTz4zHZ27dGn04xERUdNSVlaGjIwM49VzyX7q6gtDPsC0q/lzqpFnR+Th6QmNVgvopTfj5Zwr0Gi1nLZBREREZAVMnpu5co0GmrIyGBbbK6+ogKasDOW8wiARERFRo2Py3MzlXL6MkpIS45xnmUKOkpIS5Fy+bN/AiIiIiByQU6224YhEQQaNRmP8FlSq0UAmSuVERERE1LiYYTVzSqUCCoXCOG1DD0ChUECp5PciIiJnwhPR7I994ByYPDdznp6eULm6VibPehEuKhU8ecIgEZFTMFx9r6SkxM6RkKEPbr4iIjkWDk82cxqtFlqNBjIvaaH2ClFEuVYrrcBBREQOTy6Xw8fHBzk5OQAAd3d3CIYTYcgmRFGUzjfKyYGPjw/kcrm9QyIrYvLczGnKyqDT6yHcGHsW9XrodDpoysrsHBkREdlKcHAwABgTaLIPHx8fY1+Q42Ly3MwV5RdIS9VVGWQoKytDUX6B/YIiIiKbEgQBISEhCAwMRHl5ub3DcUpKpZIjzk6CyXMzpxf1qKioQNUf6HQ6HfSi3m4xERGRfcjlciZwRFbGEwabubzreQBMO1Kv1xvLiYiIiKjxMHlu5rKzswEAiptODjGUExEREVHjYfLczF36+28AgPym5NlQTkRERESNh3OercywYHpBgXVO4NPrdaioqEDShUsYovZGRUWFsdxaxyQiIqKG4Wey4xBEXg7Hqi5evIjw8HB7h0FERERNwIULFxAWFmbvMOgWMHm2Mr1ej7///hteXl5ctJ6IiMhJiaKIwsJChIaGQibjrNnmzKmS5z179uCdd97BoUOHkJWVhS1btmDkyJF1brN7924kJibi5MmTCA0NxQsvvIBp06bZJmAiIiIialKc6qtPcXExunXrhg8//NCs+hkZGbj33nvRr18/HDlyBC+99BKeffZZfP3111aOlIiIiIiaIqcaea5KEIR6R57nzJmDrVu34vTp08ayadOm4dixY9i3b58NoiQiIiKipsSpRp4bat++fUhISDApGzx4MA4ePMjLnxIRERE5IS5VV4fs7GwEBQWZlAUFBaGiogJXr15FSEhItW00Gg00Go3xvl6vx7Vr1+Dv788TBomIiJwUTxh0HEye63FzwmuY5VJbIrx48WIkJSVZPS4iIiJqfpx0tqxDYfJch+Dg4GqXuc7JyYFCoYC/v3+N28ydOxeJiYnG+/n5+WjVqhUuXLgAb29vq8ZLRERETVNBQQGv++AgmDzXIT4+Ht99951J2c6dOxEbGwulUlnjNiqVCiqVqlq5t7c3k2ciIiKiZs6pJt0UFRXh6NGjOHr0KABpKbqjR48iMzMTgDRqPH78eGP9adOm4fz580hMTMTp06fx6aefYvXq1Zg9e7Y9wiciIiIiO3OqkeeDBw/izjvvNN43TK+YMGEC1q5di6ysLGMiDQBt2rTB9u3bMWvWLHz00UcIDQ3FBx98gAceeMDmsRMRERGR/TntOs+2UlBQALVajfz8fE7bICIiclKGfIBpV/PnVNM2iIiIiIhuBZNnIiIiIiIzMXkmIiIiIjITk2ciIiIiIjMxeSYiIiIiMpNNlqorKCho8DZcmYKIiIiImhqbJM8+Pj4QBMHs+oIg4MyZM4iMjLRiVEREREREDWOzi6R89dVX8PPzq7eeKIq49957bRAREREREVHD2CR5joiIQP/+/eHv729W/cjISCiVSitHRURERETUMDZJnjMyMhpU/8SJE1aKhIiIiIjIcjZfbWPdunU4d+6crQ9LRERERHTLBNHGF1mXy+X48MMPodVqcfz4ccjlcnTu3BmTJk1yyBU2DNeyz8/Pd8j2ERERUf0M+YCN0y6yApsnzzKZDIIgIDQ0FHFxcdDpdDh48CA0Gg127tyJHj162DIcq2PyTEREREyeHYfNVtuoasqUKVi+fDnkcjkAoKKiAlOmTMGMGTOwZ88ee4RERERERFQvu4w8nz59Gh06dDApP3XqFGJjY1FSUmLLcKyOI89ERETEkWfHYfMTBn18fHDx4sVq5RcuXGBySURERERNms2T54SEBEyePBlfffUVLl26hMzMTHzxxReYMmUKHn30UVuHQ0RERERkNpvPeV6+fDlmzJiBMWPGGMtUKhWmT5+O119/3dbhEBERERGZzeZzng0KCwuRnp4OpVKJtm3bwtXV1R5hWB3nPBMRERHnPDsOu6y2AQBeXl7o3r27vQ5PRERERNRgNpnzPHr0aBQUFJhd/9FHH0VOTo4VIyIiIiIiajibTNuQy+U4c+YMWrRoUW9dURQRHh6Oo0ePIjIy0tqhWR2nbRARERGnbTgOm4w8i6KI9u3bw9fXt96bn58fiouLrRbL8uXL0aZNG7i6uiImJgZ79+6ttW5KSgoEQah2+9///me1+IiIiIio6bLJnOddu3Y1eJuWLVs2ehybNm3CzJkzsXz5ctx+++1YuXIlhg4dilOnTqFVq1a1bpeWlmYyamzOCDoREREROR67rbZhD71790bPnj2xYsUKY1mnTp0wcuRILF68uFr9lJQU3Hnnnbh+/Tp8fHwsOianbRARERGnbTgOm18kxV60Wi0OHTqEhIQEk/KEhASkpqbWuW2PHj0QEhKCu+++u95RdI1Gg4KCApMbERERETkGp0mer169Cp1Oh6CgIJPyoKAgZGdn17hNSEgIVq1aha+//hqbN29Ghw4dcPfdd2PPnj21Hmfx4sVQq9XGW3h4eKO2g4iIiIjsx27rPNuLIAgm90VRrFZm0KFDB3To0MF4Pz4+HhcuXMC7776L/v3717jN3LlzkZiYaLxfUFDABJqIiIjIQdh05FkURZw/fx6lpaW2PCwAICAgAHK5vNooc05OTrXR6Lr06dMHZ8+erfVxlUoFb29vkxsREREROQabJ8/t2rXDxYsXbXlYAICLiwtiYmKQnJxsUp6cnIy+ffuavZ8jR44gJCSkscMjIiIiombAptM2ZDIZ2rVrh9zcXLRr186WhwYAJCYmYty4cYiNjUV8fDxWrVqFzMxMTJs2DYA05eLSpUtYt24dAGDZsmVo3bo1unTpAq1Wi/Xr1+Prr7/G119/bfPYiYiIiMj+LEqeL1y4AEEQEBYWBgD4/fff8Z///AedO3fG448/Xue2b7/9Np5//nmsWLEC0dHRlhzeYmPGjEFubi4WLlyIrKwsREdHY/v27YiIiAAAZGVlITMz01hfq9Vi9uzZuHTpEtzc3NClSxds27YN9957r03jJiIiIqKmwaJ1nvv164fHH38c48aNQ3Z2Njp06IAuXbrgzJkzePbZZzF//vxat/X19UVJSQkqKirg4uICNzc3k8evXbvW8FY0YVznmYiIiLjOs+OwaOT5xIkTiIuLAwB8+eWXiI6Oxq+//oqdO3di2rRpdSbPy5YtsyhQIiIiIiJ7syh5Li8vh0qlAgD89NNPGD58OACgY8eOyMrKqnO7lJQUzJs3D5GRkZYcmoiIiIjIbixabaNLly74+OOPsXfvXiQnJ2PIkCEAgL///hv+/v61bqdUKrFlyxbLIiUiIiIisjOLkue33noLK1euxMCBAzF27Fh069YNALB161bjdI7ajBo1Ct98840lhyUiIiIisiuLpm0MHDgQV69eRUFBAXx9fY3ljz/+ONzd3evcNioqCosWLUJqaipiYmLg4eFh8vizzz5rSUhERERERFZn0Wobt6JNmza1PiYIAv766y8bRmN9XG2DiIiIuNqG4zB75LlHjx4QBMGsuocPH671sYyMDHMPSURERETUpJidPI8cObJRD6zVapGRkYG2bdtCobDphQ6JiIiIiCxi82kbJSUleOaZZ/DZZ58BAM6cOYPIyEg8++yzCA0NxYsvvmjLcKyO0zaIiIiI0zYch0WrbdyKuXPn4tixY0hJSYGrq6ux/J577sGmTZtsHQ4RERERkdksmi+h0+nw3nvv4csvv0RmZia0Wq3J43VdYvubb77Bpk2b0KdPH5M51J07d0Z6erol4RARERER2YRFI89JSUlYunQpHnroIeTn5yMxMRGjR4+GTCbDggUL6tz2ypUrCAwMrFZeXFxs9gmJRERERET2YFHyvGHDBnzyySeYPXs2FAoFxo4di3//+9+YP38+9u/fX+e2vXr1wrZt24z3DQnzJ598gvj4eEvCISIiIiKyCYumbWRnZ6Nr164AAE9PT+Tn5wMAhg0bhnnz5tW57eLFizFkyBCcOnUKFRUVeP/993Hy5Ens27cPu3fvtiQcIiIiIiKbsGjkOSwsDFlZWQCkKwbu3LkTAHDgwAGoVKo6t+3bty9+/fVXlJSUoG3btti5cyeCgoKwb98+xMTEWBIOEREREZFNWDTyPGrUKPz888/o3bs3ZsyYgbFjx2L16tXIzMzErFmzatxm586duPPOO6FUKtG1a1fjUnVERERERM1Fo6zzvH//fqSmpiIqKgrDhw+vsU5kZCSuXbuGwYMHY8SIEbj33nvh4+Nzq4du8rjOMxEREXGdZ8dh04ukHD9+HFu3bsXWrVtx/Phx3H777RgxYgSGDx+O1q1b2yoMm2LyTEREREyeHYdFyfO6devqfHz8+PH17uPvv/82JtK7du1C+/btjYl0bGxsQ0Nqspg8ExEREZNnx2FR8uzr62tyv7y8HCUlJXBxcYG7u3udF0mpSXFxMXbs2IFvv/0W27dvR2JiIl566aWGhtUkMXkmIiIiJs+Oo9GmbZw9exZPPvkknn/+eQwePNji/ej1euTm5qJFixaNEZbdMXkmIiIiJs+Ow6Kl6mrSrl07vPnmm5gxY0ad9c6dO4fPP/8cixYtwty5c7F06VLs2rULZWVlUkAymVUT5+XLl6NNmzZwdXVFTEwM9u7dW2f93bt3IyYmBq6uroiMjMTHH39stdiIiIiIqGmzaKm62sjlcvz99981Pvaf//wHH3zwAX7//XcEBgaiZcuWcHNzw7Vr15Ceng5XV1c8+uijmDNnDiIiIhozLKNNmzZh5syZWL58OW6//XasXLkSQ4cOxalTp9CqVatq9TMyMnDvvfdi6tSpWL9+PX799Vc89dRTaNGiBR544AGrxNggKz8GDvxu+fZduwJubkBYONChAxDRGlDU8JKoqADOnwPS0oC/0oHMTKCBU3PsSqEAZDJAq629jiAAjTUaoFACoaGAVgNkZ5u/nSAAnp6ATgdoNNJ9tVraX+5VqR8MZDLAxQWQy6X6gBS/Xi/9K5cDHh5A69ZAZFvg5EmgsADwDwB69ACu5QIVOiA4GIiJAeQKqW8PHwaKiwBBBuh10jHd3IHSUukY5eWAQg60bAncMwhw9wA0ZcChQ0BODhAYKO1P5do4z6U5KiqAixeA/Hzp+QoLr/l13FSOaXg/pacDAqT+qfreu/nxiNZSfxYVSa8PQDpuWRng6gaovSvLSkqkegIAD0+pf/x8a49PUwYcOACcOQNUlEv1oqKk19SxY9LjbaOAXr2k18j5c8DZs0B+HuDlDXh7S8fw9JD6PycHgAiUlkmvf7UP4OoKKBVSO8PCgIsXpb8lmeelNuj1UqxKpbStq5v0+vLyrowdqDz29WuATi+VyWSAnx/Qrp2076ws6XkwPE9FRZX9Y9iH4XkNCweuXAH+/FOK1csb8PWVti0qktpYXiH9v1wLuKgAd3eguFi67+oGhIRI/XXtmvR+qfq39ObjVW1/ejog6qX3luFYImpuS02vL8Pr79o1qc8LCqR9GJ6Xqm5+jgzHr+m1Z87rtmr/V32uRAA+PlJZSQkgl1U+D3/9ZdpvNcVY27bnzpnGClS23fAeqPo6qet9aY+/FeSQLJq2sXXrVpP7oigiKysLH374IcLDw/HDDz+YPN6zZ0/IZDJMnDgRw4cPr5aoajQa7Nu3Dxs3bsTXX3+N5cuX48EHH7SgOXXr3bs3evbsiRUrVhjLOnXqhJEjR2Lx4sXV6s+ZMwdbt27F6dOnjWXTpk3DsWPHsG/fPrOOabVpG7eaOBMJQmUCbkhizGX4wJHLpUS/tscFoXK/glD5r+HPjuH/giB9gBrqKhTSFzvD9goF0CIQ8PaSPgBzcqSEwRC3IED6hBWlNvn4SI9ptTc+sEVp/zK59MGsUkkJvkIhffiXlUr7FVFZV5BJCY5MDsgE6Rg6nXSr0EmHk8ul7eQKIDwc8PKUkqsrV6Tt1d5SgnQlR4oHkOprtZVfdgCprf7+0hela7mVX1b0+hv7l0t1NBqpTC43fV71einm8nLTMtWNhE8mk75MabXS8+TrCwS2AP44cSPp0lX2iVwu/d/wfOl1lccvL5duen3lMRVKqY6hHwyPKxSVdQxfCkUR0ItSslr1S1/V14dhO09PKXadToqjpFhKZivKpT41PP9yubSNXC4l8woFkHtN6jsfH6m9ol7qM63mxvNUIX1JBKR+0lVUfuk0tF0UTb+wVo3z5tewTCZta2irq2vl8VzdpNdUSUnl86JylY5fUiLtQya78YVYJb1+hRuvCQjS/11cgJDQyn7NzgLKNEBRofS6MsRi6H8DQag8nqdn5XMr6ivb5+1due/aGI6ZXyC1SRQrX4syWZX3+Y33oJub9BorKKjsd42menyGGOXymrfV66XXACDF6uUl9ZdMJn2BMfSD0kV6DeJGX6hcpPekixIIDpEGDsorgMvZ0mvJ10d6r2vKAG91ZR/4+FR+IazrS6eFOG3DcViUPMtkprM9BEFAixYtcNddd2HJkiUICQkxeXzbtm247777zNr31atXkZGRgV69ejU0rDpptVq4u7vjv//9L0aNGmUsnzFjBo4ePVrjpcH79++PHj164P333zeWbdmyBQ899BBKSkqgVCrrPa7VkucpkxpvX0REZD01/bJlzq9dVRNaQ5Ja9UtX1e0Nn8uiWL3c8GXA8EVVrpCSzapldSXPNcVZ9TiG7at+oTN8qTDUM3xZqqltVb9EG76EGLaTySoTcEMdw6+Jhi93hi+Zhi9ACkXlYwbaG8m2Uik9BlRua/hSI7/xpT0oCIiIkEbsb7+j0RJoQz5wcvLjZtUfLwtqlOPa0sFVC+0dgk1Y9IrQN2SECjA7cQaAgIAABAQENDSkel29ehU6nQ5BQaYvxqCgIGTX8tN6dnZ2jfUrKipw9erVal8SAGkUXVNlJK6goKARoiciomartuSzIdvdnHzerLbPZUNyWFV9+2qom9siQhpI1tUw0lzTNob/G36ZEG78WlAbw/S02h6raYTboOqvCTdvJ9dJvzIV5Esj3zKZNM3FTtehWKe/bJX9do4Itcp+AQCLkhpWf96r1onDyuw62ae0tBTlN73Irb0ihXDTt2tRFKuV1Ve/pnKDxYsXIympgS8eIiKi+tQ3Wl3T51JDf1yubZS8IdsbR8FlUhIN1J3wmrutyYi6vMpUsCp1BEGa2mHcJ6Rk3pCMC6a/nANi5XQsnb4ygQ8MkqZxFOSb33YrqDXRbaZJp6MwO3lOTEw0e6dLly6t9bGSkhK88MIL+PLLL5Gbm1vtcV1jfyO+ISAgAHK5vNooc05OTrXRZYPg4OAa6ysUCvj7+9e4zdy5c02eq4KCAoSHh99i9ERE1CQYErab52qbk6TWN32jtqkXVY9TG8O0jqr1DPupbVT65qkVdR3r5v0ajlf1OIYyQzuqTq0wTJu4ed83T/2oui0EaT66YT591TZWnetv2I9x+keV5wS4kVgbjnHTc3bzc+HqKiXNbm5SuWFOdCOqOh3DWaY5OBqzk+cjR46Y3D906BB0Oh06dOgAADhz5gzkcjliYmLq3M/zzz+PXbt2Yfny5Rg/fjw++ugjXLp0CStXrsSbb75pQRPM4+LigpiYGCQnJ5vMeU5OTsaIESNq3CY+Ph7fffedSdnOnTsRGxtb63xnlUoFlUrVeIHXJiAAuHrV+schIudh7qozxqQJN0b2qpxkZ5hXWlFReYJaebnpvg3zUn18qszJxY0VGW4MoNy8Oo4hYVIqK08iKyuTjlN1aoIo3pjTKlYmXTcnbAqFdBIeUHmynyCTttGWS6OUKpV0kp1h1RulCvC8sdqGViudPOeqko5RXCKddGpYFaW8vHL+bdXYq87bdXGREjTDfF6FQirXaKR2lZdXPr81JZyGEVaVi3QyqI+PtMpP1bp6PZCRAVy/XnmSatVk2tVVOn5d0xwM8SsU0gmNKhfpvq+v9BxptJWjuoZVgDRaoLSkMp68fCmBDgmp3ha9XlqVyPCcVN1WFIFz56WTK+XyyjaoXKWTcxUKKf6CAukxvV56TpQK6XUiCNJzqdNVvh5dVNLUDEMiXlxc+fzLb5xc6a2WThz08ZFWKLESJs7Nl9nJ865du4z/X7p0Kby8vPDZZ58ZrzZ4/fp1PPbYY+jXr1+d+/nuu++wbt06DBw4EJMmTUK/fv0QFRWFiIgIbNiwAY8++qiFTalfYmIixo0bh9jYWMTHx2PVqlXIzMzEtGnTAEijxpcuXTJefnzatGn48MMPkZiYiKlTp2Lfvn1YvXo1vvjiC6vFaLbX3gBmPlt5Br+zMvyxNnxAN8ZZzAqF9IfW8OHk5Skdw3BWu0JRmRRU6KQ/8oMHS2e//2c9UFhoGl+nTsCgQdLZ3mVl0geWt1r6g5557sbKATJgfypw+bL0IREeLp2Jf/myFIePj7RkWMcOQFY2cPYMcO26tHKE4UNCEKQPMxcXaTWKGn7ZqZdSWbn8nSHBMYzuaLV1L/fn7n5jdQud6WiS4TkzrBxQ12obhp9PG3JehaH9FTrcWC5DYkicDAmM4YO26v4NH+SG1SiqjgBWXR3EsA9D2wwjVHJ55ZJZVU9uqnrik0wmxaZUSM+RSnVj+Tal9JotLZNWkjAcV6+XHgsLk/ZTWCj9jKxUVK4I4qKU2lOuAyq0lWVKpbT6hYAbK2PoAIVM+rdcA5SUSqtrAJXJo0IBxMQCHu7A9TxpZYKKCsDXHwgNAS5dBC79LbVPfSOh8FZLSU7eNelf2Y2k1scPcHeVXs9u7lISpNdLbbx2VWqLQgEEBAKxMUCbSCmGixeln8dd3aTX7uVsKQnVaqR/5XJA5QaEtwS8faRYDe+hv/4EzmdKqy+ofQEPNyAvT3q/ed7o06KiyjaFtaxc3s3HB4hqJ5VdvARkpEvvRw9P6Ri+flI/1LaEpyFubzUQEizt42xaZTz+LYAe3aX+OXYEuJp7Y7nI7lKCn3lOeiyybWUM6WcrVwupj0wmxRjVTjrBrbblCLd+B/zvlJSYy2RS0t4mUvpSkJfX8GOFtZT+Dhnabkgwb34+bq5T24l3NT2Xhm3vvFvq53N/Sc+LANM4qj5nAqTlEb28pS82Go3Uz4Y2GtpgeN0Z9ml4zLBtfX1PTs+i1TZatmyJnTt3okuXLiblJ06cQEJCQq1rPQOAp6cnTp48iYiICISFhWHz5s2Ii4tDRkYGunbtiqKiooa3ogGWL1+Ot99+G1lZWYiOjsZ7772H/v37AwAmTpyIc+fOISUlxVh/9+7dmDVrFk6ePInQ0FDMmTPHmGybw6pXGKz6B0ejBX7YLi1zFRAAPP6ENGfrZiXFQHKytO7vX+nmH8vFRVoTeNqTgJ+/6R+6hvxRtOUfI00ZcPCgtI6rqyvQs6f0R7PqerqNHZumTFor2bDmcc+etl3z2KCkGFjyrrRkmuGLgK8vMOdF6cOhpBj47jtpjd+KCiAyEhg/HvDxrX2f6X8C6z6TRomqJr9eXsCo0dJZ6cCtP68VFcD585WJjNIF2LNbSuoMo2aubsCQIUBcHJBzRTqWh6fpF5KbEyBAiuv6NSmZunRRet+0jQJ6xVreTxUV0jq2Rw9LSWLbKKBnD+nDPyO9MjmqLblpjOesIZrKa5Rsp+p7ypzXI1mFIR+ImTqPo87NnEXJs5eXF7799lvcddddJuW//PILRowYgcKqI283ue222/Cvf/0LAwYMQEJCAm677Ta8++67+OCDD/D222/j4sWLDW9FE8bLc5PdlBQDP/8sXWwhJAS4+25p/VJLVVQAv/ws3YpLANz4+bRtW2DSJOsmYEz4iKiZq5o8A5y20ZxZ9LVz1KhReOyxx7BkyRL06dMHALB//348//zzGD16dJ3bPvbYYzh27BgGDBiAuXPn4r777sO//vUvVFRU1HmiIRE1kLsHcP/wxtufQgHcdTfQuo3pKOutjNqaS+UKxPe17jGIiGyEiXPzZtHIc0lJCWbPno1PP/3UuNScQqHA5MmT8c4778DDw/zRrczMTBw8eBBt27ZFt27dGhpKk8eRZyIiIuIVBh2HRcmzQXFxMdLT0yGKIqKiosxKmtetW4cxY8ZUW5FCq9Vi48aNGD9+vKXhNElMnomIiIjJs+O4peTZEnK5HFlZWQgMDDQpz83NRWBgoNXWebYXJs9ERETE5NlxmD3nefTo0Vi7di28vb3rnde8efPmWh+r7Yp+Fy9ehFrd+IuRExERERE1FrOTZ7VabUx6LUlye/ToAUEQIAgC7r77biiqLJGj0+mQkZGBIUOGNHi/RERERES2YnbyvGbNmhr/b66RI0cCAI4ePYrBgwfD09PT+JiLiwtat26NBx54oMH7JSIiIiKyFYuWqistLYUoinC/cYnU8+fPY8uWLejcuTMSEhJq3ObVV18FALRu3RpjxoyBqyvXaCUiIiKi5kVmyUYjRowwXsI6Ly8PcXFxWLJkCUaMGIEVK1bUue2ECROYOBMRERFRs2RR8nz48GH069cPAPDVV18hODgY58+fx7p16/DBBx/Uua1Op8O7776LuLg4BAcHw8/Pz+RGRERERNRUWZQ8l5SUwMvLCwCwc+dOjB49GjKZDH369MH58+fr3DYpKQlLly7FQw89hPz8fCQmJhq3X7BggSXhEBERERHZhEXJc1RUFL755htcuHABP/74o3Gec05OTr1rGW/YsAGffPIJZs+eDYVCgbFjx+Lf//435s+fj/3791sSDhERERGRTViUPM+fPx+zZ89G69atERcXh/j4eADSKHSPHj3q3DY7Oxtdu3YFAHh6eiI/Px8AMGzYMGzbts2ScIiIiIiIbMKi5Pkf//gHMjMzcfDgQfz444/G8rvvvhvvvfdenduGhYUhKysLgDSCvXPnTgDAgQMHql2ym4iIiIioKbEoeQaA4OBgeHl5ITk5GaWlpQCAXr16oWPHjnVuN2rUKPz8888AgBkzZmDevHlo164dxo8fj0mTJlkaDhERERGR1QmiBRdZz83NxUMPPYRdu3ZBEAScPXsWkZGRmDx5Mnx8fLBkyRKz97V//36kpqYiKioKw4cPb2goTZ7hWvb5+fn1zgcnIiIix2TIByxIu6iJsWjkedasWVAqlcjMzDReKAUAxowZgx07djRoX3369EFiYqJDJs5ERERE5FgsusLgzp078eOPPyIsLMykvF27djUuVbd161az980kmoiIiIiaKouS5+LiYpMRZ4OrV6/WeNLfyJEjTe4LglDtZwtBEABIF1EhIiIiImqKLJq20b9/f+PluQEp8dXr9XjnnXdw5513Vquv1+uNt507d6J79+744YcfkJeXh/z8fPzwww/o2bNng6d8EBERERHZkkUnDJ4+fRoDBgxATEwMfvnlFwwfPhwnT57EtWvX8Ouvv6Jt27a1bhsdHY2PP/4Yd9xxh0n53r178fjjj+P06dMNb0UTxhMGiYiIiCcMOo4GjzyXl5fjqaeewtatWxEXF4dBgwahuLgYo0ePxpEjR+pMnAEgPT0darW6Wrlarca5c+caGg4RERERkc00OHlWKpU4ceIE/P39kZSUhO+//x7bt2/Ha6+9hpCQkHq379WrF2bOnGm8UAogXXXwueeeQ1xcXEPDMdv169cxbtw4qNVqqNVqjBs3Dnl5eXVuM3HiRAiCYHLr06eP1WIkIiIioqbNojnP48ePx+rVqy064KeffoqcnBxEREQgKioKUVFRaNWqFbKysizepzkeeeQRHD16FDt27MCOHTtw9OhRjBs3rt7thgwZgqysLONt+/btVouRiIiIiJo2i1bb0Gq1+Pe//43k5GTExsbCw8PD5PGlS5fWum1UVBSOHz+O5ORk/O9//4MoiujcuTPuuece44obje306dPYsWMH9u/fj969ewMAPvnkE8THxyMtLQ0dOnSodVuVSoXg4GCrxEVEREREzYtFyfOJEyfQs2dPAMCZM2dMHjMnARYEAQkJCejcuTNCQkIgl8stCcNs+/btg1qtNibOgHRxFrVajdTU1DqT55SUFAQGBsLHxwcDBgzA66+/jsDAwFrrazQaaDQa4/2CgoLGaQQRERER2Z1FyfOuXbsa5eCdO3fG0aNHERkZ2Sj7q012dnaNCW9gYCCys7Nr3W7o0KF48MEHERERgYyMDMybNw933XUXDh06VON61gCwePFiJCUlNVrsRERERNR0WDTnubHc6nItCxYsqHZC3823gwcPAqh5RFwUxTpHyseMGYP77rsP0dHRuP/++/HDDz/gzJkz2LZtW63bzJ07F/n5+cbbhQsXbqmNRERERNR0WDTy3FRMnz4dDz/8cJ11WrdujePHj+Py5cvVHrty5QqCgoLMPl5ISAgiIiJw9uzZWuuoVKpaR6WJiIiIqHmza/L80ksvwc/Pz+LtAwICEBAQUG+9+Ph45Ofn4/fffzcuh/fbb78hPz8fffv2Nft4ubm5uHDhgllL8hERERGR47H5tI2FCxeipKQEgDTFwcfHBwBQWlqKhQsXWuWYnTp1wpAhQzB16lTs378f+/fvx9SpUzFs2DCTkwU7duyILVu2AACKioowe/Zs7Nu3D+fOnUNKSgruv/9+BAQEYNSoUVaJk4iIiIiaNpsnz0lJSSgqKqpWXlJSYtUT7TZs2ICuXbsiISEBCQkJuO222/D555+b1ElLS0N+fj4AQC6X448//sCIESPQvn17TJgwAe3bt8e+ffvg5eVltTiJiIiIqOmy+bSN2k7SO3bs2C1N4aiPn58f1q9fX2edqicwurm54ccff7RaPERERETU/Ngsefb19TWugNG+fXuTBFqn06GoqAjTpk2zVThERERERA1ms+R52bJlEEURkyZNQlJSEtRqtfExFxcXtG7dGvHx8bYKh4iIiIiowWyWPE+YMAEA0KZNG/Tt2xdKpdJWhyYiIiIiahQ2SZ4LCgrg7e0NAOjRowdKS0tRWlpaY11DPSIiIiKipsYmybOvry+ysrIQGBgIHx+fOq/2p9PpbBESEREREVGD2SR5/uWXX4wraezatcsWhyQiIiIianQ2SZ7ff/999OjRA97e3jh//jzGjBnDS1gTERERUbNjk4ukfP/99yguLgYAPPbYY8YLkRARERERNSc2GXnu2LEj5s6dizvvvBOiKOLLL7+s9cTA8ePH2yIkIiIiIqIGE8Sql9WzktTUVCQmJiI9PR3Xrl2Dl5dXjScNCoKAa9euWTscmyooKIBarUZ+fj5XEiEiInJShnzABmkXWZlNkueqZDIZsrOzERgYaMvD2g2TZyIiImLy7DhsMue5qoyMDLRo0cLWhyUiIiIiumU2SZ4zMzON/4+IiKhxykZVly5dsnZIREREREQNZpPkuVevXpg6dSp+//33Wuvk5+fjk08+QXR0NDZv3myLsIiIiIiIGsQmq22cPn0ab7zxBoYMGQKlUonY2FiEhobC1dUV169fx6lTp3Dy5EnExsbinXfewdChQ20RFhERERFRg9j0hMGysjJs374de/fuxblz51BaWoqAgAD06NEDgwcPRnR0tK1CsRmeMEhEREQ8YdBx2Hy1DWfD5JmIiIiYPDsOm6+2QURERETUXDF5JiIiIiIyE5NnIiIiIiIzMXkmIiIiIjITk2ciIiIiIjM5TfL8+uuvo2/fvnB3d4ePj49Z24iiiAULFiA0NBRubm4YOHAgTp48ad1AiYiIiKjJcprkWavV4sEHH8STTz5p9jZvv/02li5dig8//BAHDhxAcHAwBg0ahMLCQitGSkRERERNldOt87x27VrMnDkTeXl5ddYTRRGhoaGYOXMm5syZAwDQaDQICgrCW2+9hSeeeMKs43GdZyIiIuI6z47DJpfnbo4yMjKQnZ2NhIQEY5lKpcKAAQOQmppaa/Ks0Wig0WiM9/Pz8wFIbxoiIiJyTswDHAeT51pkZ2cDAIKCgkzKg4KCcP78+Vq3W7x4MZKSkqqVh4eHN26ARERE1OxcvHgRYWFh9g6DbkGzTp4XLFhQY6Ja1YEDBxAbG2vxMQRBMLkvimK1sqrmzp2LxMRE4329Xo9r167B39+/2nYFBQUIDw/HhQsXnGZKB9vMNjsiZ2svwDY7Q5udrb2AddssiiIKCwsRGhraqPsl22vWyfP06dPx8MMP11mndevWFu07ODgYgDQCHRISYizPycmpNhpdlUqlgkqlMimrb3UPb29vp/nDZMA2Owdna7OztRdgm52Bs7UXsF6b1Wp1o++TbK9ZJ88BAQEICAiwyr7btGmD4OBgJCcno0ePHgCkFTt2796Nt956yyrHJCIiIqKmzWmWqsvMzMTRo0eRmZkJnU6Ho0eP4ujRoygqKjLW6dixI7Zs2QJAmq4xc+ZMvPHGG9iyZQtOnDiBiRMnwt3dHY888oi9mkFEREREdtSsR54bYv78+fjss8+M9w2jybt27cLAgQMBAGlpacbVMQDghRdeQGlpKZ566ilcv34dvXv3xs6dO+Hl5dUoMalUKrz66qvVpnk4MrbZOThbm52tvQDb7Aycrb2Ac7aZGs7p1nkmIiIiIrKU00zbICIiIiK6VUyeiYiIiIjMxOSZiIiIiMhMTJ6JiIiIiMzE5NmOli9fjjZt2sDV1RUxMTHYu3evvUNqFIsXL0avXr3g5eWFwMBAjBw5EmlpaSZ1Jk6cCEEQTG59+vSxU8S3bsGCBdXaY7jQDiBdWWrBggUIDQ2Fm5sbBg4ciJMnT9ox4lvXunXram0WBAFPP/00gObfx3v27MH999+P0NBQCIKAb775xuRxc/pUo9HgmWeeQUBAADw8PDB8+HBcvHjRhq1omLraXF5ejjlz5qBr167w8PBAaGgoxo8fj7///ttkHwMHDqzW7/VdzMqe6utnc17Hzamf62tvTe9pQRDwzjvvGOs0tz425zPJEd/PZD1Mnu1k06ZNmDlzJl5++WUcOXIE/fr1w9ChQ5GZmWnv0G7Z7t278fTTT2P//v1ITk5GRUUFEhISUFxcbFJvyJAhyMrKMt62b99up4gbR5cuXUza88cffxgfe/vtt7F06VJ8+OGHOHDgAIKDgzFo0CAUFhbaMeJbc+DAAZP2JicnAwAefPBBY53m3MfFxcXo1q0bPvzwwxofN6dPZ86ciS1btmDjxo34v//7PxQVFWHYsGHQ6XS2akaD1NXmkpISHD58GPPmzcPhw4exefNmnDlzBsOHD69Wd+rUqSb9vnLlSluEb5H6+hmo/3XcnPq5vvZWbWdWVhY+/fRTCIKABx54wKRec+pjcz6THPH9TFYkkl3ExcWJ06ZNMynr2LGj+OKLL9opIuvJyckRAYi7d+82lk2YMEEcMWKE/YJqZK+++qrYrVu3Gh/T6/VicHCw+OabbxrLysrKRLVaLX788cc2itD6ZsyYIbZt21bU6/WiKDpWHwMQt2zZYrxvTp/m5eWJSqVS3Lhxo7HOpUuXRJlMJu7YscNmsVvq5jbX5PfffxcBiOfPnzeWDRgwQJwxY4Z1g7OSmtpc3+u4OfezOX08YsQI8a677jIpa859LIrVP5Oc4f1MjYsjz3ag1Wpx6NAhJCQkmJQnJCQgNTXVTlFZj+HCM35+fiblKSkpCAwMRPv27TF16lTk5OTYI7xGc/bsWYSGhqJNmzZ4+OGH8ddffwEAMjIykJ2dbdLfKpUKAwYMcJj+1mq1WL9+PSZNmgRBEIzljtbHBub06aFDh1BeXm5SJzQ0FNHR0Q7T7/n5+RAEAT4+PiblGzZsQEBAALp06YLZs2c3619YgLpfx47cz5cvX8a2bdswefLkao815z6++TOJ72dqKKe5wmBTcvXqVeh0OgQFBZmUBwUFITs7205RWYcoikhMTMQdd9yB6OhoY/nQoUPx4IMPIiIiAhkZGZg3bx7uuusuHDp0qFle2al3795Yt24d2rdvj8uXL+O1115D3759cfLkSWOf1tTf58+ft0e4je6bb75BXl4eJk6caCxztD6uypw+zc7OhouLC3x9favVcYT3eVlZGV588UU88sgj8Pb2NpY/+uijaNOmDYKDg3HixAnMnTsXx44dM07raW7qex07cj9/9tln8PLywujRo03Km3Mf1/SZxPczNRSTZzuqOkIHSG/qm8uau+nTp+P48eP4v//7P5PyMWPGGP8fHR2N2NhYREREYNu2bdX+UDcHQ4cONf6/a9euiI+PR9u2bfHZZ58ZTy5y5P5evXo1hg4ditDQUGOZo/VxTSzpU0fo9/Lycjz88MPQ6/VYvny5yWNTp041/j86Ohrt2rVDbGwsDh8+jJ49e9o61Ftm6evYEfr5008/xaOPPgpXV1eT8ubcx7V9JgHO+36mhuO0DTsICAiAXC6v9m01Jyen2jff5uyZZ57B1q1bsWvXLoSFhdVZNyQkBBERETh79qyNorMuDw8PdO3aFWfPnjWuuuGo/X3+/Hn89NNPmDJlSp31HKmPzenT4OBgaLVaXL9+vdY6zVF5eTkeeughZGRkIDk52WTUuSY9e/aEUql0iH4Hqr+OHbWf9+7di7S0tHrf10Dz6ePaPpOc+f1MlmHybAcuLi6IiYmp9hNXcnIy+vbta6eoGo8oipg+fTo2b96MX375BW3atKl3m9zcXFy4cAEhISE2iND6NBoNTp8+jZCQEOPPm1X7W6vVYvfu3Q7R32vWrEFgYCDuu+++Ous5Uh+b06cxMTFQKpUmdbKysnDixIlm2++GxPns2bP46aef4O/vX+82J0+eRHl5uUP0O1D9deyI/QxIvybFxMSgW7du9dZt6n1c32eSs76f6RbY6URFp7dx40ZRqVSKq1evFk+dOiXOnDlT9PDwEM+dO2fv0G7Zk08+KarVajElJUXMysoy3kpKSkRRFMXCwkLxueeeE1NTU8WMjAxx165dYnx8vNiyZUuxoKDAztFb5rnnnhNTUlLEv/76S9y/f784bNgw0cvLy9ifb775pqhWq8XNmzeLf/zxhzh27FgxJCSk2bbXQKfTia1atRLnzJljUu4IfVxYWCgeOXJEPHLkiAhAXLp0qXjkyBHjyhLm9Om0adPEsLAw8aeffhIPHz4s3nXXXWK3bt3EiooKezWrTnW1uby8XBw+fLgYFhYmHj161OS9rdFoRFEUxT///FNMSkoSDxw4IGZkZIjbtm0TO3bsKPbo0aNZttnc13Fz6uf6XteiKIr5+fmiu7u7uGLFimrbN8c+ru8zSRQd8/1M1sPk2Y4++ugjMSIiQnRxcRF79uxpspRbcwagxtuaNWtEURTFkpISMSEhQWzRooWoVCrFVq1aiRMmTBAzMzPtG/gtGDNmjBgSEiIqlUoxNDRUHD16tHjy5Enj43q9Xnz11VfF4OBgUaVSif379xf/+OMPO0bcOH788UcRgJiWlmZS7gh9vGvXrhpfxxMmTBBF0bw+LS0tFadPny76+fmJbm5u4rBhw5r0c1BXmzMyMmp9b+/atUsURVHMzMwU+/fvL/r5+YkuLi5i27ZtxWeffVbMzc21b8PqUFebzX0dN6d+ru91LYqiuHLlStHNzU3My8urtn1z7OP6PpNE0THfz2Q9giiKopUGtYmIiIiIHArnPBMRERERmYnJMxERERGRmZg8ExERERGZickzEREREZGZmDwTEREREZmJyTMRERERkZmYPBMRERERmYnJMxFREyUIAgRBgI+PT6Pve+3atcb9z5w5s9H3T0TkqJg8ExE1YWvWrMGZM2fMqqvVahEQEIDXXnutxscXL16MgIAAaLVajBkzBllZWYiPj2/McImIHB6TZyKiW1BeXm7V/fv4+CAwMNCsui4uLvjnP/+JtWvXoqaLx65Zswbjxo2Di4sL3NzcEBwcDBcXl8YOmYjIoTF5JiKHIIoi3n77bURGRsLNzQ3dunXDV199ZXw8JSUFgiDg559/RmxsLNzd3dG3b1+kpaWZ7Oe7775DTEwMXF1dERkZiaSkJFRUVBgfFwQBH3/8MUaMGAEPDw/jKO9rr72GwMBAeHl5YcqUKXjxxRfRvXt3AMCePXugVCqRnZ1tcqznnnsO/fv3b3Bb64px8uTJSE9Px549e0y22bt3L86ePYvJkyc3+HhERFSJyTMROYRXXnkFa9aswYoVK3Dy5EnMmjUL//znP7F7926Tei+//DKWLFmCgwcPQqFQYNKkScbHfvzxR/zzn//Es88+i1OnTmHlypVYu3YtXn/9dZN9vPrqqxgxYgT++OMPTJo0CRs2bMDrr7+Ot956C4cOHUKrVq2wYsUKY/3+/fsjMjISn3/+ubGsoqIC69evx2OPPdagdtYXY9euXdGrVy+sWbPGZLtPP/0UcXFxiI6ObtDxiIjoJiIRUTNXVFQkurq6iqmpqSblkydPFseOHSuKoiju2rVLBCD+9NNPxse3bdsmAhBLS0tFURTFfv36iW+88YbJPj7//HMxJCTEeB+AOHPmTJM6vXv3Fp9++mmTsttvv13s1q2b8f5bb70ldurUyXj/m2++ET09PcWioqJa2wVA3LJli0mZOTGuWLFC9PDwEAsLC0VRFMXCwkLRw8NDXLlyZbVjDBgwQJwxY0atMRARkSmOPBNRs3fq1CmUlZVh0KBB8PT0NN7WrVuH9PR0k7q33Xab8f8hISEAgJycHADAoUOHsHDhQpN9TJ06FVlZWSgpKTFuFxsba7LPtLQ0xMXFmZTdfH/ixIn4888/sX//fgDSSPBDDz0EDw+PBrXVnBjHjh0LvV6PTZs2AQA2bdoEURTx8MMPN+hYRERUncLeARAR3Sq9Xg8A2LZtG1q2bGnymEqlMrmvVCqN/xcEwWR7vV6PpKQkjB49utoxXF1djf+vKeE17MtAvOmEvcDAQNx///1Ys2YNIiMjsX37dqSkpNTXtGrMiVGtVuMf//gH1qxZg8mTJ2PNmjX4xz/+AW9v7wYfj4iITDF5JqJmr3PnzlCpVMjMzMSAAQMs3k/Pnj2RlpaGqKioBm3XoUMH/P777xg3bpyx7ODBg9XqTZkyBQ8//DDCwsLQtm1b3H777VaLcfLkyRg4cCC+//57/Prrr3jjjTcafCwiIqqOyTMRNXteXl6YPXs2Zs2aBb1ejzvuuAMFBQVITU2Fp6cnJkyYYNZ+5s+fj2HDhiE8PBwPPvggZDIZjh8/jj/++KPWtZMB4JlnnsHUqVMRGxuLvn37YtOmTTh+/DgiIyNN6g0ePBhqtRqvvfYaFi5caFFbzY1xwIABiIqKwvjx4xEVFWXRqh5ERFQd5zwTkUNYtGgR5s+fj8WLF6NTp04YPHgwvvvuO7Rp08bsfQwePBjff/89kpOT0atXL/Tp0wdLly5FREREnds9+uijmDt3LmbPno2ePXsiIyMDEydONJnqAQAymQwTJ06ETqfD+PHjLWpnQ2KcNGkSrl+/brKiCBER3RpBvHliHhER3bJBgwYhODjYZHk6AJg6dSouX76MrVu31rsPQRCwZcsWjBw50kpRAgMHDkT37t2xbNkyqx2DiMiRcNoGEdEtKikpwccff4zBgwdDLpfjiy++wE8//YTk5GRjnfz8fBw4cAAbNmzAt99+a/a+x44dC39/f1y8eLFRY96wYQOeeOIJlJaWGi/mQkRE9ePIMxHRLSotLcX999+Pw4cPQ6PRoEOHDnjllVdMVsQYOHAgfv/9dzzxxBN47733zNrvn3/+CQCQy+UNmn5ijsLCQly+fBmAdAnwgICARt0/EZGjYvJMRERERGQmnjBIRERERGQmJs9ERERERGZi8kxEREREZCYmz0REREREZmLyTERERERkJibPRERERERmYvJMRERERGQmJs9ERERERGZi8kxEREREZKb/B3xgxmng3U4WAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 800x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "results_manager.plot_transmission(\n",
+    "    show_diff = True,\n",
+    "    plot_uncertainty = True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = results_manager.plot_transmission(\n",
+    "      figsize=(12, 8),\n",
+    "      title=\"Hafnium Multi-Isotope Fit\",\n",
+    "      xscale=\"log\",\n",
+    "      # yscale=\"log\",\n",
+    "      data_color=\"blue\",\n",
+    "      final_color=\"red\",\n",
+    "      show=False,\n",
+    "      show_diff=True,\n",
+    "      plot_uncertainty=True,\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multi-Isotope Fitting Quality Metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fit iterations: 3\n",
+      "\n",
+      "Iteration 1:\n",
+      "  Chi-squared: 721159.0000\n",
+      "  Data points: 3575\n",
+      "  Reduced chi-squared: 201.723000\n",
+      "  Number density: 2.250000e-04 atoms/barn-cm\n",
+      "  Temperature: 293.60 K\n",
+      "\n",
+      "Iteration 2:\n",
+      "  Chi-squared: 9205.0400\n",
+      "  Data points: 3575\n",
+      "  Reduced chi-squared: 2.574840\n",
+      "  Number density: 1.591200e-04 atoms/barn-cm\n",
+      "  Temperature: 293.60 K\n",
+      "\n",
+      "Iteration 3:\n",
+      "  Chi-squared: 4017.0300\n",
+      "  Data points: 3575\n",
+      "  Reduced chi-squared: 1.123640\n",
+      "  Number density: 1.589900e-04 atoms/barn-cm\n",
+      "  Temperature: 293.60 K\n",
+      "\n",
+      "Final multi-isotope fit results:\n",
+      "  Reduced chi-squared: 1.123640\n",
+      "  Number density: 1.589900e-04 atoms/barn-cm\n"
+     ]
+    }
+   ],
+   "source": [
+    "if results_manager.run_results.fit_results:\n",
+    "    print(f\"Fit iterations: {len(results_manager.run_results.fit_results)}\")\n",
+    "    \n",
+    "    for i, fit_result in enumerate(results_manager.run_results.fit_results):\n",
+    "        print(f\"\\nIteration {i+1}:\")\n",
+    "        \n",
+    "        chi_sq = fit_result.get_chi_squared_results()\n",
+    "        if chi_sq.chi_squared is not None:\n",
+    "            print(f\"  Chi-squared: {chi_sq.chi_squared:.4f}\")\n",
+    "            print(f\"  Data points: {chi_sq.dof}\")\n",
+    "            print(f\"  Reduced chi-squared: {chi_sq.reduced_chi_squared:.6f}\")\n",
+    "        \n",
+    "        physics = fit_result.get_physics_data()\n",
+    "        if hasattr(physics, 'broadening_parameters'):\n",
+    "            broadening = physics.broadening_parameters\n",
+    "            if hasattr(broadening, 'thick') and broadening.thick is not None:\n",
+    "                print(f\"  Number density: {broadening.thick:.6e} atoms/barn-cm\")\n",
+    "                print(f\"  Temperature: {broadening.temp:.2f} K\")\n",
+    "        \n",
+    "        # Multi-isotope abundances\n",
+    "        nuclear = fit_result.get_nuclear_data()\n",
+    "        if hasattr(nuclear, 'isotopes') and nuclear.isotopes:\n",
+    "            print(\"\\n  Isotopic abundances:\")\n",
+    "            hf_isotopes = [\"Hf-174\", \"Hf-176\", \"Hf-177\", \"Hf-178\", \"Hf-179\", \"Hf-180\"]\n",
+    "            natural_abundances = [0.0016, 0.0526, 0.1860, 0.2728, 0.1362, 0.3508]\n",
+    "            \n",
+    "            for j, isotope in enumerate(nuclear.isotopes):\n",
+    "                if j < len(hf_isotopes) and hasattr(isotope, 'abundance'):\n",
+    "                    fitted = isotope.abundance\n",
+    "                    natural = natural_abundances[j]\n",
+    "                    ratio = fitted / natural if natural > 0 else 0\n",
+    "                    print(f\"    {hf_isotopes[j]}: fitted={fitted:.6f}, natural={natural:.4f}, ratio={ratio:.3f}\")\n",
+    "\n",
+    "    # Final results\n",
+    "    if len(results_manager.run_results.fit_results) > 0:\n",
+    "        final_fit = results_manager.run_results.fit_results[-1]\n",
+    "        final_chi = final_fit.get_chi_squared_results()\n",
+    "        final_phys = final_fit.get_physics_data()\n",
+    "        \n",
+    "        print(\"\\nFinal multi-isotope fit results:\")\n",
+    "        if final_chi.reduced_chi_squared:\n",
+    "            print(f\"  Reduced chi-squared: {final_chi.reduced_chi_squared:.6f}\")\n",
+    "        if hasattr(final_phys, 'broadening_parameters'):\n",
+    "            if hasattr(final_phys.broadening_parameters, 'thick'):\n",
+    "                print(f\"  Number density: {final_phys.broadening_parameters.thick:.6e} atoms/barn-cm\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "default",
+   "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": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/Notebooks/ornl_venus/ta_analysis.ipynb b/examples/Notebooks/ornl_venus/ta_analysis.ipynb
new file mode 100644
index 00000000..617d8cab
--- /dev/null
+++ b/examples/Notebooks/ornl_venus/ta_analysis.ipynb
@@ -0,0 +1,706 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ORNL VENUS Neutron Transmission Analysis: Tantalum-181\n",
+    "\n",
+    "## Instrument: VENUS (Versatile Neutron Imaging Station)\n",
+    "## Facility: Oak Ridge National Laboratory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from dotenv import load_dotenv\n",
+    "load_dotenv('../../../.envrc')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Environment Configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Verify resolution function environment variable\n",
+    "resolution_file_path = Path(os.environ.get('VENUS_RES_FUNC', ''))\n",
+    "if not resolution_file_path.exists():\n",
+    "    raise FileNotFoundError(f\"Resolution file not found at VENUS_RES_FUNC: {resolution_file_path}\")\n",
+    "\n",
+    "# VENUS data paths\n",
+    "ipts_path = \"/SNS/VENUS/IPTS-35945\"\n",
+    "nexus_path = f\"{ipts_path}/nexus\"\n",
+    "autoreduce_base = f\"{ipts_path}/shared/autoreduce/mcp/images\"\n",
+    "\n",
+    "# Analysis directories\n",
+    "working_dir = \"./ta_analysis\"\n",
+    "os.makedirs(working_dir, exist_ok=True)\n",
+    "spectra_dir = f\"{working_dir}/spectra\"\n",
+    "os.makedirs(spectra_dir, exist_ok=True)\n",
+    "twenty_dir = f\"{working_dir}/twenty\"\n",
+    "os.makedirs(twenty_dir, exist_ok=True)\n",
+    "sammy_working = Path(working_dir) / \"sammy_working\"\n",
+    "sammy_output = Path(working_dir) / \"sammy_output\"\n",
+    "sammy_working.mkdir(exist_ok=True)\n",
+    "sammy_output.mkdir(exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Processing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pleiades.processing.normalization import normalization\n",
+    "from pleiades.processing import Roi, Facility\n",
+    "\n",
+    "open_beam_folder = f\"{autoreduce_base}/Run_8109\"\n",
+    "sample_folders = [\n",
+    "    f\"{autoreduce_base}/Run_8111\"\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-09-25 18:26:02\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.normalization\u001b[0m:\u001b[36mnormalization\u001b[0m:\u001b[36m91\u001b[0m - \u001b[1mUsing ORNL-specific normalization\u001b[0m\n",
+      "olefile module not found\n",
+      "/SNS/users/8cz/github.com/PLEIADES/.pixi/envs/default/lib/python3.11/site-packages/dxchange/__init__.py:63: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n",
+      "  import pkg_resources\n",
+      "\u001b[32m2025-09-25 18:26:03\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mnormalization_ornl\u001b[0m:\u001b[36m199\u001b[0m - \u001b[1mLoading 1 sample folders\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:26:03\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.utils.load\u001b[0m:\u001b[36mload\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mloading 4367 files with extension .tif\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:26:52\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mload_spectra_file\u001b[0m:\u001b[36m42\u001b[0m - \u001b[34m\u001b[1mFound spectra file: Run_8111_20250425_April24_2025_MCP_TPX_Foil_Ta_Far_1_6C_Resonance_0001_2674034_Spectra.txt\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:26:52\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mload_multiple_runs\u001b[0m:\u001b[36m177\u001b[0m - \u001b[1mLoaded 1 runs\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:26:52\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mnormalization_ornl\u001b[0m:\u001b[36m202\u001b[0m - \u001b[1mLoading 1 OB folders\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:26:52\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.utils.load\u001b[0m:\u001b[36mload\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mloading 4367 files with extension .tif\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:40\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mload_spectra_file\u001b[0m:\u001b[36m42\u001b[0m - \u001b[34m\u001b[1mFound spectra file: Run_8109_20250424_April24_2025_OB_MCP_TPX1_Foil_1_6C_Resonance_0001_2672032_Spectra.txt\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:40\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mload_multiple_runs\u001b[0m:\u001b[36m177\u001b[0m - \u001b[1mLoaded 1 runs\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:40\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mnormalization_ornl\u001b[0m:\u001b[36m210\u001b[0m - \u001b[1mUsing individual mode\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:40\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mprocess_individual_mode\u001b[0m:\u001b[36m123\u001b[0m - \u001b[1mCombined 1 OB runs\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:40\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mprocess_individual_mode\u001b[0m:\u001b[36m128\u001b[0m - \u001b[34m\u001b[1mProcessing sample run 1/1\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:40\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mdetect_persistent_dead_pixels\u001b[0m:\u001b[36m193\u001b[0m - \u001b[34m\u001b[1mFound 95/262144 dead pixels (0.0%)\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:41\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mdetect_persistent_dead_pixels\u001b[0m:\u001b[36m193\u001b[0m - \u001b[34m\u001b[1mFound 93/262144 dead pixels (0.0%)\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mnormalization_ornl\u001b[0m:\u001b[36m249\u001b[0m - \u001b[1mSaved transmission to ./ta_analysis/spectra/Run_8111_transmission.txt\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "transmissions = normalization(\n",
+    "    list_sample_folders=sample_folders,\n",
+    "    list_obs_folders=[open_beam_folder],\n",
+    "    nexus_path=nexus_path,\n",
+    "    facility=Facility.ornl,\n",
+    "    combine_mode=False,\n",
+    "    pc_uncertainty=0.005,\n",
+    "    output_folder=os.path.join(working_dir, \"spectra\"),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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aKSkpuOKKK3DnnXciJiYGf/3rX7F169aAfV5++eWIi4vDMcccg5ycHPT09ODBBx9EUlKSx6xkKFm1ahXOPPNMVFRUICUlBc3NzXjppZdw9NFHQ6/Xo6mpCddccw3OO+88zJ8/H1qtFh9//DGamppw8803+/R70UUXYcOGDbj44ovR0dGB8vJybNy4EQ888ABOP/10nHjiiSGJ//7778eXX36J733ve9JxAO3t7XjiiScwMDCA3/zmNy7ltVotHn30UYyOjqK6ulranfK0007DscceC2D6TMKf/vSnuPTSS1FfX481a9bAYDCgu7sbGzduRHl5Oa688krEx8fj0UcfxWWXXYYTTzwRl19+ObKysrB3715s3boVTzzxBABISd/DDz+M0047DWq1GhUVFdIyWG9kZ2fjxBNPxIMPPoiUlBQUFhbio48+kpbsEgRBzBqzuKkKQRAE4QdvOzX++9//FpcuXSrqdDpxzpw54q9+9Svxvffe87qDni9weKdJb/+cGR4eFm+77TaxrKxM1Ov1YmZmprhu3Trx3Xff9fDp2EHS2z+lcfm65rVr14qLFy/2Wn7Tpk3i0UcfLer1ejEjI0O87LLLxIaGBq87B3rb6fPOO+90ue4XXnhBXL9+vZiVlSVqtVoxNzdXPP/888WmpiapjGN3yrq6Oq+++vr6ZK8JbrtT3nzzzWJVVZWYkpIixsbGinPnzhWvv/56sb+/XxRFUTx06JB4ySWXiKWlpaLBYBDj4+PFiooK8bHHHhOtVqtLXznvTimK0zshXnHFFWJOTo6o0WjEwsJC8ZZbbhHNZrNHTFdffbVHHxUWFsruNvr111+LV199tbh06VIxNTVVVKvVYkZGhnjqqad63C+O/mhqahLXrVsnxsXFiampqeKVV14pjo6Oevh+9tlnxVWrVokGg0GMi4sTS0pKxIsuukisr693Kffuu++Ka9euFQ0Gg6jX68VFixaJDz/8sGSfnJwUL7vsMjEjI0MUBEEEIO2c6evaRVEUu7u7xe9+97tiamqqmJSUJF544YVifX294nvM1/1bWFgonnHGGT77lCAIwh+CKPpZe0AQBEEQBBFCLrnkErz22msYHR2d7VAIgiCOWOidOIIgCIIgCIIgiCMISuIIgiAIgiAIgiCOIGg5JUEQBEEQBEEQxBEEzcQRBEEQBEEQBEEcQVASRxAEQRAEQRAEcQRBSRxBEARBEARBEMQRBB32PYvY7XYcPHgQCQkJEARhtsMhCIIgCIIgCGKWEEURIyMjyM3NhUrlf66NkrhZ5ODBg8jPz5/tMAiCIAiCIAiCiBC6urqQl5fntwwlcbNIQkICgGmhEhMTPez19fWoqqpi8qm0jlw5f3ZWWyDXEQ54xhWM72jRnSWucEK6s9lJd76+SXe+kO5sdtKdr2/SnS/RoLvJZEJ+fr6UI/hFJGYNo9EoAhCNRqNXe3d3N7NPpXXkyvmzs9oCuY5wwDOuYHxHi+4scYUT0p3NTrrz9U2684V0Z7OT7nx9k+58iQbd5XIDZ2hjkwjGbrdzqyNXzp+d1RbIdYQDnnEF4ztadGeJK5yQ7mx20p2vb9KdL6Q7m5105+ubdOdLNOmuBEriIpiuri5udeTK+bOz2gK5jnDAM65gfEeL7ixxhRPSnc1OuvP1TbrzhXRns5PufH2T7nyJJt2VQEkcQRAEQRAEQRDEEYQgiqI420F8UzGZTEhKSoLRaPS6scnk5CRiY2OZfCqtI1fOn53VFsh1hAOecQXjO1p0Z4krnJDubHbSna9v0p0vpDubnXTn65t050s06C6XGzhDM3ERzN69e7nVkSvnz85qC+Q6wgHPuILxHS26s8QVTkh3Njvpztc36c4X0p3NTrrz9U268yWadFcCJXERzOjoKLc6cuX82VltgVxHOOAZVzC+o0V3lrjCCenOZifd+fom3flCurPZSXe+vkl3vkST7kqgJC6CMRgM3OrIlfNnZ7UFch3hgGdcwfiOFt1Z4gonpDubnXTn65t05wvpzmYn3fn6Jt35Ek26K4HeiZtF5Na9Tk1NQavVMvlUWkeunD87qy2Q6wgHPOMKxne06M4SVzgh3dnspDtf36Q7X0h3Njvpztc36c6XaNCd3omLEhobG7nVkSvnz85qC+Q6wgHPuILxHS26s8QVTkh3Njvpztc36c4X0p3NTrrz9U268yWadFcCJXEEQRAEQRAEQRBHEJTERTB5eXnc6siV82dntQVyHeGAZ1zB+I4W3VniCiekO5uddOfrm3TnC+nOZifd+fom3fkSTborgZK4CEatVnOrI1fOn53VFsh1hAOecQXjO1p0Z4krnJDubHbSna9v0p0vpDubnXTn65t050s06a4ESuIimH379nGrI1fOn53VFsh1hAOecQXjO1p0Z4krnJDubHbSna9v0p0vpDubnXTn65t050s06a4ESuKC5O2338bChQsxf/58/PnPf57tcAiCIAiCIAiCiHLoiIEgsFqtWLRoET755BMkJiaisrISNTU1SE1NVVRfbhvRiYkJxMXFMcWktI5cOX92Vlsg1xEOeMYVjO9o0Z0lrnBCurPZSXe+vkl3vpDubHbSna9v0p0v0aA7HTEQJmpra7F48WLMmTMHCQkJOP300/H++++HzH9HRwe3OnLl/NlZbYFcRzjgGVcwvqNFdyXtzQakO5uddOfrm3TnC+nOZifd+fom3fkSTbor4RudxH3++ec466yzkJubC0EQ8MYbb3iUefLJJ1FcXAydTocVK1bgiy++kGwHDx7EnDlzpN/z8vJw4MCBkMVnMpm41ZEr58/OagvkOsIBz7iC8R0turPEFU5IdzY76c7XN+nOF9KdzU668/VNuvMlmnRXwjc6iRsbG8PSpUvxxBNPeLW/8soruO6663DbbbehsbERxx13HE477TR0dnYCALytRBUEIWTx6XQ6bnX8ldvbO4KbPzHib7Wd+P1He9A1OI7xKStufG0rPtnd67euN1sg1xEOgo3rH/Vd+PUb22C3e94HwfieLd3l7IHYIlF7njGR7mxxhRPSnc1OuvP1TbrzhXRns5PufH3zioveiTuMIAj417/+hXPOOUf6bNWqVaisrMRTTz0lfVZWVoZzzjkHDz74IDZt2oTf/OY3+Ne//gUA+J//+R+sWrUKF1xwgdc2JicnMTk5Kf1uMpmQn5/vc92r1WqFRqNhug6lddzL9Y6YsfL+j3yWv/aE+fj9R3sAAB9dfxwK0+OhUXv+DcBb+4FcRzgINq6im98BADx9YSVOXZITMt/h1J3FHogtErXnGRPpzhZXOCHd2eykO1/fpDtfSHc2O+nO1zdLXZZ34iKr9yOIqakpbN68GTfffLPL5yeffDI2bdoEAFi5ciW2b9+OAwcOIDExEe+++y7uuOMOnz4ffPBB3H333R6f19fXw2AwoLKyEs3NzZiYmEBCQgKGh4elsyUKCwtht9vR1dUFAFi2bBn27t2L0dFRGAwGLFiwAI2NjRgaGkJ5eTnUarW0pWlFRQU6OjpgMpmg0+mwePFifPjhh0hJSUFubi50Oh1WPlrrtz8cCRwAnPDYzJLSDd8qQLpqHGq1GlVVVZLfjIwMpKSkoKWlBUNDQzjqqKMwODiIvr4+qFQqVFdXo76+HjabDWlpacjMzERzczMAYP78+TCZTDh06BCA6WS6oaEBFotFinnHjh0AgMw5hXippgsVyRZkG1QwzFmIn/+1Dmk6AWkJsfjvnhEp1hMXpKAqLx4lujEkxqpgt9sxpY7DxMQE8tISUFJSgqamJgBAQUEBAEizrkuXLkVraytGR0cRFxeHsrIyye+fP2vBohRg6+52JMaqUL28Aps2bcKooEdOchwqlixB05YGAEBOTg70ej1aW1sBAIsXL8bBgwcxNDSEmJgYVFZWSn2YlZWFxMRE7Nkz3fdlZWXo7e3FwMCA1N91dXWw2+0YGxtDZWUldu/eDQBYsGABhoaG0NfXB0EQsHLlSmzevBl9fX0oKSlBVlaW1N/z5s3D6Ogoenp6MDQ0hFNOOQVbtmzB1NQUkpOTkZeXh+3bt2NoaAgrVqyA2WzGwYMHAQArVqzAjh070N3djcLCQhQVFUl9WFhYiN27d0t/gVq+fDlaWlowNjaG+Ph4zJs3D1u2bAEA5OfnQ6VSudyz7e3tGBkZkfq7oWG6D+fMmQOtVov29nYAQHl5Obq6ujA8PIzY2FhUVFSgrq4OAJCdnQ2DwSD196JFi1BbW4v4+Hipv2tqaqbvpcxMJCUlSf1dWlqK/v5+9Pf3S/eso7/T09ORnp6OXbt2Sfes0WjE7t27kZKS4nLPpqamIjs7Gzt37gQAlJSUYGxsDD09PQCA6upqNDU1oaenB8XFxcjPz8e2bdsAAMXFxZiampKWaQc6RphMJqxduxaNjY0Appd+O48RNpsNKSkpLmPE5s2bAUy/kL148WK0tbUBAJYsWYL9+/djeHgYRqMRJ510Empra6X+jo+PR11dHVJSUlBWVoZDhw5hcHAQGo0GK1asQG1tLURRdBkjAGDhwoVcxoiSkhKMj49j586dSElJQVVVFbZv3w6z2YykpCQUFBRI/V1UVASr1Yr9+/dL/b1r1y6Mj48jPj4eJSUl2Lp1q8cYMTQ0hHXr1kljhF6vR2lpqXTP5uXlQaPRSO9FlJeXo7OzE0ajEUajESeccALq6+tDPkbYbDaoVNNjXUZGBlJTU13GiMbGRhgMBpcxwmq1IjU1Fb29vdKXDucxAphehRIbG8s0RpjNZiQmJnqMETabTepvHmPE0NAQ1qxZwzRG9PT0YHBwUHaMsFgsyMnJYRojent74SAmJobLGPHpp58iMTERCQkJKC4udulvu92OpqYmpKSkeP0e8fHHHyMlJcVjjKioqMCXX36JhIQEpjFCq9Vi2bJlHmPE3r17pXuWxxgxNTWF/Px8pjGiu7sbAGTHiObmZmmTCqVjBDD9PWLjxo1ISEjgMka0tbUhMzPT5Z51HiOGhoawevVqr98jBgYGsGDBAo8xYmhoCC0tLUhNTWUaI1auXOn1ewQAzJ07l9sYsWfPHuk7R6i/R9TX1yMlJYVpjHB8j9izZw/S0tIUjRGOmJRAM3GHcZ+Jc7zv9uWXX2L16tVSuQceeAAvvPCCdJO/9dZb+OUvfwm73Y4bb7wRP/3pT322wToTV1NTg1WrVjFdh9I67uUcs0rBsOveU9FQX4fVRx8VUExK6R+dxAc7D+Gspbm4483teL2B/T3E3fedirraOlz41oD0WZpBi7ULM/C785f5rNdjNOOUxz9HYZoeTfuNXsucX5WHV+v3u3x28qIs/PGiKkWxhVN3FnsgtlBrHwp4xhSMb9KdL6Q7m5105+ubdOcL6c5mJ935+mapSzNxIcT9HTdRFF0+O/vss3H22Wcr8hUbG4vY2FjFbefm5iouy1onEN9ylN7+H2TFx2DjSjtinJZahrqtHz1Ti+ZuE255fVvAPhb++j/42w8XAphJ4gbGpvB6wwHcfFopMhM81y+PT1lx1IPTS059JXAAPBI4APjvzkOKY5tN3f3ZA7HxuM+ChWdMwfgm3flCurPZSXe+vkl3vpDubHbSna9vXnF9ozc28Ud6ejrUarU0Neygt7cXWVlZYYlhNl+ADZRDoxbsGxgPuq2vWgewpWvYq625OzS7/Nzx3y6vn696wPXdQFEUMWW144zfbwyqvfK73ve6GY473vrLarPDarPjw52H8O+tBzExZZOto9Q3ANjtIrbtN2KfyYYpq11R3Q93HkJH/xhsdhH/aTGhtW9UcXuBMj5lxXNftuPWf23Dq/VdsHnZVEaOaHrxmV54Vw7pzmYn3fn6Jt35Qrqz2Ul3vr55xUVJnA+0Wi1WrFiBDz74wOXzDz74wGV5JU8c68t51HEv94cfLGduyxfuX6xZr6N/dBI/+NPXOGfDly6fN3QOhWTZp4M9feNePxdFYMRskX4vvuVdLPj1e2jvHwuqvRGzFSc99rn0u9VmxyGT2aNcW1sbjOMWbD9gxLc2fImim99B+V3/xUXP1uKyF+vx87814pLnpt8vmLLace/bO3HvW9vQY/T0NXNNouTbm+0nL9ThrCc24sKXdmDBr99D0c3vYNI6kygaJywudZ//sh2XvViPdb/9FK/Wd+H+DzpwwqOfeb0WR5x7e0cxNDblN8Z3t3XjF69uxa3/2gZRFPHzvzXishfqMT5lBQA8+t8W3P3vnXi5phM3vtaEirvex/4hXzqKME5YPD4P5LlSSjC+Weoaxy1oOTQS8PPOYg/ExrOPAyUadGetEy7d/Y0vrHjb7dcbFpsdw+NTsNlF2O0idveM4MDwhIsfu11EW1sbTGYL9g2MwWT2HA86+sdQ2z6IrV3D0h+wfMVgnLDgwPAEBkanX4sI5npb9raivX8Mzd0mbD9gxN7eEfSNTHqUOzA8gX/Ud+GDnYfw2ZYWmC02jzLGCQvqOgaxb2AMVpsdbW1tMFts0rjpjq//D4xPWdG0a6/PP+TJPe9DY1P4rKUPGz7Ziz9+3oqatgGfMQSLxWZ3+X+U0lhDwTf9eWeNK5xEk+5K+EYvpxwdHZVergWA9vZ2bNmyBampqSgoKMANN9yAH/3oR6iqqsLRRx+NP/7xj+js7MQVV1wxi1Hz4aylufj53xpD4uuUxz/HjrtPgSFW+e1ls4sQAKhUAnpNM/8jc16+eu6Tm0ISnxJOePQzbLzpeGg1of07x97emdmqS5+vwxd7+vHaFUejqijVpdzKBz7EpNP/SCcsNmxqnVn6WdM+COOEBUvv/q/02VsPfoTS7AQUpunx8Hcq0No3CrPFjh/+efqF2wfPLcfcw2XfaDyA617Zgnu+tRh3vLnDa6wLf/0fdDx0Bs57ehPqOoaQFqfC5lVAXccg7vr3Tqnch05LRXf3jGBhdgKAae3sooiXvt6H29/YLpXZeufJSIqLAQB0Dozj/z5vxU/XzMWLX+3DMxtnXuhdMz8d/946/eLzojvex5kVOXi7qdslxrEpG77/x6/x2a/WQ60SpHb/sXk/XvpqH7YdMKIoTY9Pf7Xe6zX6QxRF/KWmE3Exanx3RZ7XMq9t3o9nNrbjjz9agfxUPXMbcoxPWREXo/ZY1n3iY5+hb2QSDx+fjMh6I2F2sNtFqFSeS9+nbHZYbSLTWOSNTa39GDFbccribHQOjOOh/zTjR0cV4eiSNK+z6wOjk0iMi4FGJWDfwDgK0/Ro7RtFTlKcFMvQ2BReru3EXGHmi2jX4DjitGrYRRFJcTEYMVvRNTiO+FgNitINAIAYtQr13ZN4/I9f49vL5+C7K/Kka+81mfF1+yDmJOtQWZACs1XEK3Wd2NUzglGzFfOz4pEUF4P+0SlcsLIA9sOxHzKZ8WpdFw4azRibtCIvJQ7FKium9vRhcW4SNnyyFx39Y1g8Jwmp+hh8uX0U1370IU5elA1DrAYvbOrAaUuyMTI8iqs/+AB5KXqYLTaoBAHdxgkUpBkwLyMeoihiUW4iyuck4b87D2HUbMWObiOGxiyYm2GARiXgiz39yErUYXh8CjnJ0/01abFJ4+HcdAPGpqzY2mXEhMUGQQBiNSqYLdP2pXlJSIyLQcO+IdhEETqViOF/TY+TsRoVzlk2B3v7RqEWBGjUgsu4GhejhkYtYMRsRaJuWieLTcTS/CTY7CLqOoakssvyk5GuNuO5ls0YGJvE0LgFdrsIXYwaC7LiMTppxZYuIyw2O+Ji1NBr1dAd/q/FZsfOg0ZY7J963Ds5SToszk2EVqNC38ikS5sA8MuP/oPMhFhkJ+mQFBcD04QF2w4Y4cg7NSoBgAjrv/4DAEiI1SA7SYdkfQziYzVQq1TY3zsM+6bPMGW1wy5OJ0SDY1Mz/8955z3EqAXEqFXT/18WBEAA7DYbYt7/r/TZ9LAkwGa1QP3Bh+gf9UxCVQKQn6pHXIwasTFq6DQqpMfHIitRh6zEWCTrYxCjVkGjVkGjEjA2aYVxwgKT2QrThAWmCcvh3w//d2LaPnE4mU2P1yI3OQ65SXHT/03WYU5yHAaHLCg2maFRqw7/vwgQIcJmF2GxipiyTd9TFtv0ShuLzY4pqx2TVjusdju0ahXitGrExUzrFqdVIydJB73W/1giiiJGJq2w2qa/0wjC9Gs5gjDdZ3bahoIIId/ojU0+/fRTrF/v+eXu4osvxvPPPw9g+rDvRx55BN3d3ViyZAkee+wxrFmzJiTty728ODY2BoPBwORTaR1v5UI5y7U4NxHvXHucopisNjvm3fYeACA+VoPRyZm/3F17/Dxce8J8aNSqkManhPzUOLx19bFYfu8H8oUZ6HjoDAAz/X1mRQ6euKBSso+NjWHxvZ+GtE0H959divk5KTj//75SVL7joTNc+n3DBZW4+uUG2ToAcMGfvkavaQJ73WY8tRoVmu48Gbt7RvAtt9nWQDmjPAfnVs7B8aWZeLm2E7f9a7uLve62E5GRMP0+qtz92NY3ii/39iNBF4PrXtkCADixLBN/vrjao6xz32y54yQMmUaxpXscZ1Xkej2Co71/DJmH43h/Rw9OXJSFiSkbnvxkL767LBOtgxa83ngA//u9Zeg2mnHWExtx7vI5+M15SzE2aYVeO53QOdr9+boi/OLUxbL9MzBswkd7jDi1PBuJuukEespql/5I4eiTl2s6seGTvXj+0mrMz0pwsXUbJ5ARH+tyXSOjo7AIWtS2DyAvRY8lc5I8+riuYxCxGhUq8pIBANv2G3HIZMbg+BT0WjWOL82EXqtB58A45qTE4cDQBH795nakx2uRqIvB+JQV316eh5IMA8wWOzbu7ccx89JQmDbt/6Wv9+HON7ejON2AcyvzUJRmwH939uDNLQc9+uEXJy3A8WWZ+Ef9fulL6DtN3egxmZGbpMNBoxlVhSk4oSwL2w8Y0XJoBCNmK3oOz5gXpxu8zsjPSdahIi8Zogj8Z0ePh92ZuRkGrJmfgec3dQAA8pJ1yE6KQ8fAuNcvwQ5i1AJEEdDFqF3GyHmZ8UiOi0F7/xgG/Mx0+/JpiNVgeNxzhupIIy5GDbPVBl/faGI1Kpc/jDlQqwQUpuoxND6FIQX9oNWoYLHZfbbDgl47ndSpBAGTVjtGzBa4TwIKArA8PxlTNjv29Y9hZNL77FN2og6D41M+Z9HCRXG6AeVzkjBptWFrl1F6dqKF7EQdClN1mJeViOJ0A9QqAV2DE+gaGkfX4Dj2D024PJ/eiItRI16nQXysBoZYNQxajZTg2cXpRNMmzsxwx6oFJOm1iI/VIF6ngSFWg4TY6f/GatTQalTTY7l1CgnxesSqp3/XqFVQCwLUqul/k+YJxBv0UKsEqAQBeq0aqQYtBEHw+/9FX7ZAvqPyhmdMwfhmqcuysck3OombbeSE2r17NxYuXMjkU2kdb+Xa+8ew/refAgBeOq8Qb7XZ8I/N+7EoJxH//vmxqG0fxA/+9LXiWBznp/mLadPefvzh4734qm3Aq92BezLhj0tWF+H5TR04oyIH7zR1oyBVjxGzBdeeMB8d/WN44at9iq/B+Xw8FpZmx0EXF4ea9kEPm3sS5/wZAPz8+Y349y7fG6eEk+Z7TkXZHf9hqrP1zpMRq1Gh9Ha2eqGgsiAZ+wbGPb7MVhel4B9XrMYzG9tx79vTs4gXHlWAu85aLM3gCYKAzfuG8J2nvM/4OmvkwFnDs5bmYs/BAezqm8Sy/GRcekwR6joGce3x85GZqMMrdZ246Z+um/GsKEyBWhBQ2zGIhFgVRianv4BddmwxUgxa/Ob96V1w7/3WYtzx1g78+oxF+MmxxVK7369IweUnVaCxcxjfqZyDsSkbbnxtK0YnbdhwwXIk6GIgiiKKb3kXALBkTiKW5Cbh73XT74M+/r1lOGf5HOkZdb4erUaF969bg6te+BrNfTNfxC46uhA3nlqK+FgNbn55E/7eNDNTsP3uU9DRP4Yn32/CD9eU4ed/a8TgYS123H0Kuo1mnPzYZx5fVANFEBCSL9NHInEx0zN2zomJt/4QBGDN/AxoNSr0j06isXPYq7/lBckoTjNgymZH19AEtnYNIy5GjQmLDRqVgKqiFBSmGrBvcAwGwYK1SwrwwqYOdA1OYM2CdExa7fi6bQDnV+Vj1dw0JOo0mLTakZOkQ+fgODr6x2AXgc37hrB1/zCW5SdjeX4K8lLiUJRuwNtNB7F/aAJXriuBzS4iRR+D/UMTsNhE6GJU0MWoYbHa0do3inidBqXZiSjJiIdxwoIRswV5KXr0jU6iYd8QRsxWLM5NRFJcDJp27cX6qkWIj9Xgo+ZefLK7F4tzk6BRC+gfncRZFbnIT9XDbhfR2jcKQQASD89wCYIAm11EbfsgRFHEiYuykJMUh94RM97f3oNd+w6itDAXafGxSI6LgUatwvD4FPb0jiJWo0JlYQoSdTGYmLJh4vDyRrNlOtHUmQewrmqxyyz7+OEZxr19oxBFETFqFdYuyEBu8vR29rt27UJW/lzsGxxH/8gkjBMWaNQCVhanIicpDna7iB6TGa2trVi6eCEEAIdMkzhkMsM4YcGo2Yopmx3jw30oKymELmY6gdSoBKTotUiN16KzbS/yikswNmmFxSpChAhRBOyiiLb2dhQVFUMURYiA9Hl7RwcKCwuRl6xHkj7G5b7qMZrROTiOSev0+9bjUzb0jUzi0IgZh4xmmMxWWA7PmNvsIvSxaiTFxSBRF4PEOI3TzzHSz0lx0za7CBwcnpj5ZzTjwOGfO/tHMDjhmdRrVIKU9MSoVdCqVYh1/Hz4c41KwJTNjomp6dm6iSkbxiatGJFJzo5EtGoVspJikRQjYm52KnKSdMhK1CEnSXd4BleLg50dWFK6ADrtdH857tlAvqPyhmdMwfhmqUtJ3BGCnFCzsRXt9gNGpMVr0bmrye92s7b0Evz0xc2yf3HqeOgMr231GM34W20n/ldhgrTn/tMw//BsnS8uWFWAy44txtyMeNjsovTl3B1vyaBBq8aPji7C05+1uny+vCDZ55eeX52yUPqSffTcNKwoTMETn0wvz/1OaRweveR4NHebcN3ft2D3oZnz6rwlcRtvWo+8FD1GJ61Ycuf7fq8znBw3Px1f7OlnqqPXqvGvq47BKY9/Ll+YAyUZBrT2uc6WJOg02HbXKR7a3/Otxfhg5yFMWuz4+0+Pwtxb3/Xp961rjsF/dxzCZccVY1fPCF7Y1IH3ts/MusSoBVhs3ofTzb8+ESvu+5DpOi5YVYCXazo9Pj96bpr0R4+lmTHY0W+F1S7iFyctwKMftEjlTijNxNiUFVeum4eLn/V9DuTfLj8KTTt24riqpTj991/4LOfOExcsxzUvuy7Bdp9Jd+eUxVl4f4fynVqDYU5ynPSOVCB/jInVqHB+VT4aOoeQn6JHarwWGfGxGJu0YlPrAHYe3mCpsiAZS5IsUCVkYGBsCv/eehALsxKgj1VjxGzFD1YWYHlBMiYtdvyzYT9i1AKMExYYtBqkJ8RiW+t+dI5Nz0gWpOpx3Px0fLm3H29uPYjLjp2LMypy0HJoBBbbdLJmsYno2rMTZx2/Gn0jk3h/Rw8S42IwN92A4nQD4mLUeL3xAL5uG0BxjBHfO3EV0uNndkWetNowNGbB3t5RDHS2YHF5OWLUKhSk6l0Sii+/+hrHHH0UhsamEBujcllG5jymOy95/+rrr3H0Ua5HzMw20bDlOGsd2mp+Oqbq6pUQMb2k031JeiAMjU2hfWAMH9Y0QZWYjfb+MdhFEfmpeuSnxCEvVY/8FD3yUuKgVasOJ7rTCa9dFGG3Axu/rsXCxUsxMmnB2OR0cjg6aYVdnP7OohYEqKT/Tre7dcdu5OQXYXTSihHzdPlRsxWjU1ZpCeiU1YbBYRO0cXpMWe2Hl4g6ZvWm3w+dnLJAUKthP/yZYwkyCyph+g9IcVo1BLsVyfH66eWmMWrotGrExagku/S5kz0pLgZpBi3S4rVIM8QiRR/jddVKoETD805HDEQJWq2WWx1f5RzLoXr8+NFqtVheko7td58Ck9mCy1+o9zrjBAD7h8a9tuXYql8pVi9fjp/8YSX29o7i6vXzMD5lRYJu5i+AvhI4b1x34nxcd+ICAPBI4rwlcKeXZ+OJH1RCpRLw42OKYRen37lp7jZJSVzc4S88ZTmJeP/6NTjmoY9dXro3ui3bOfbhT9Dx0BkYHGVbDsUb1gQOAManbPj1G4Ef/6CEnfecgs929+Gqlxs8/tLqnsABwIKsBPzi1a0enzu/D1jX4f0ednD2E9NLP2s7BlHr5X73lcABwH3vNPv17Y06H8+U86z11t6Z+8g5gQOAj3ZNHyr8dZvvBA6ANLv+4CblCRwAjwQOgOwfdZwTuPzUOHQNTniUyUyIxVXrSnB8aRbyUuLwl5p9eHZjOzoO73p78qIstPWPubxf+v51a5Csj0GvaRJxWhUyEqbfF7LY7LCLImI1aiyMNaJuWI9u4wRuOa0MKXotth0wYtJqw4HhCZxflQ9dzPTB6VOH//ruPqvgTEf/GPJS4qBRq9DY2Ijly6eXtfrbJOrokjSPzxobJ7B8uWud+VkJuOSYYun3BYeXtjoQ+6dnZjISYnHhUYUePr+7Ig/fXZGHxsZGlwQOAGI1amQnqZGdpEPjaBfmZSZ41AcAvW66XorBc/x2HtOdvxzrGI7QCReB/H80HL5n4//vSuyB2Hj2caBotVqP92SDJcWgRYpBC2EgEcuXK52RcY0hPUGHgjS296dTJg5i+fIC2XLT45Dv8cfdPmW1o3fEjB6jGV9v3QVdaja6jdO/95jM6B6ewIjZirEpq7R6wi5Ov4s+dniH7L5xz12pWRAEIDkuBmnxsS7JXVq8FjlJOpy6OMfvOOxOND3vSqCZuFlELtt2P5NOCUrryJXzZ3e3jU1acdwjn0jLptxpf/B0l/L7Bsaw9jefysbozNK8JGx1Opttyx0nIVkf2EPx5y/aXL5U33p6KX66pmT6539t8zr7AQD3f3sJzlk2x+cmCR39Y1h3eDnqHWeW4cfHzpVsHzUfwk9eqJ8u99AZeLvpoMeX4I6HzsANr2zB643+Dy9ve+B0jxmjirwkv2fXyXHx0YU4vzof72/vwbmVeTj58c8DfrdiaX4yUvUx+GR3X0D1/3HF0Tjv6Zl39hJ1GsxJ0bscLdH2wOlQqQSMmC0ov+u/3txgzYIM/PS4ubjwmRpF7ZZkGNBtNGN8yveOZ9HCCz9e6Xd2Ls2glZakCgJQc8sJAIDekUn0jU7i0ufqXMo//J1yvPT1Pmw/4Hn8h7f3KO87Zwl+sLIAQ+NTODg8gbe2HMTLtZ1465pjUZSml/3LrNVmh0oQYD08467kjzaBjKdKCcb3kTLOK7Hx7ONAId3Z7KQ7X99Hqu5Wu4gJiw3mKRvMFrvT8mA7zJbp5cKOZcPmw/+mP7NLn41PTW9KMzA6hcGxKQyOT8kuh9dr1Ti/Kh8/PqZYUfIbDbqzzMTREQMRTG2t/7+gB1NHrpw/u7vNEKtBw+0nKS7PmsABcEngtt55csAJHACcvdT10MVYjVr6+cSyTJ/1friq0O8ud3HaGT9dna6JYF6K6+CT4iN+bwncP644Wvr5hNJMj78unrwoC7dVx6Dg8O6Iz11ajff+5ziXMuf52F3Rwa9OLcXi3CQckzSMonRDUC/H56fEeU2EHj1vKcpyPAekqsIUl98XuZUpSNPjb5e7LkNw9EG8Hz1e/PFKrJqbCqVjbmvfmNe49U66AkAh419Rw82V60r82n9ybDHWLshAcbr3l6wfPLccm28/CZ/9ah1uOrUUvz0+GZmJOmQm6rBkThLWL8zEj5xmf9LjtfhedQHe/vlxaH3gdDx7SRU2nJKCK9aWYMMFlTijIgcdD52BO89aBGB6970friqAWiUgPT4WFXnJuO2MMuy4+xTMy4xXtLRGo1ZBdfjdFqWz7oGMp0oJxveRMs4rsfHs40Ah3dnspDtf30eq7jFqFRJ1MchMnJ5JXJidgKnuFhxdkob1pZk4vTwH31mRhwuPKsRlx83FNcfPx69OKcUdZy3Cg+eW47HvLcP//agKf//p0fjghrXYfPtJ2Hv/6aj/9Yl4/7o1ePmyVfjDD5bjrrMW4efHz8MPVhagNDsB41M2PL+pA+t++wmu/MtmbN435DNOJX0QDOHWXQm0nJIIGd9alut1R7hQ49iePlDckyDnYwScEzpWnJctjUy5JkEx6pk2+0cnMWWTT5Ic78kB07N0XYPjyEmaPjDSsXkLAJy5NBcwd+HDG9bCbLUhURcDm11EWU4itGoBr125GjFqFa47aQGOeehjyb/zlv3+kiGlPPDtctz6r23YNzAuvb/jTFG6AesXZngc1n5GRQ7qnQbmuBg1DFq1tFxDo1K5JMjO+PrLliPxilGroI+Z8aWUS48pwnNfduD08mxs7TJifGpm2d/BYc8lgKFkXma8y1JBZy5ZXYTvr8zHxj39ePLTVmn2++HvlOP8qnwIggBRFPHHz9t8HoRekhEPAPj4F2vx7rYefLK7F69t3o+l+cn4++VHSX1dmGbAletKUFPjuaT2uhPn46WvpzcIKj+8BBuYXsZ8fGkWaowduHl9qUfsczPiMSdZ56FbpP0VnyAIguCL4w9509+dPJd1i6KIL/cO4E9ftOGzlj68t70H723vQWVBMn66Zi5OWpTN9OpMNEJJXASTnZ3NrY5cOX92X7ZHvluBeYnAo58f9Fn+a5ldKIHpzTRe/PFKiCL8bjQRKO6PvNbpL//u58JpVNNLtnydE+aM82CSnOg6IGlUTtuym62wKJjpcp+9cz6H7Jh56VISp1ULyM7Ontlm+HAs7/z8WOmMGgDIPZwAOrhibQkuXl0kzeABM1o9/J1yj50U5chOOryF/6TV64G0GpWAOSlxHp+7z0oKwvTsriPxilELLhopIdZJR72TL6XcdGop7jxr+h0n981Q/L37xoKv7eorC5IxMWVzeYfSwV1nT8dUmp2IL/b047OW6SWrVUWpks6CIMCgVcNknn4/7ap1JSgyWJGZmYkv9/bjvKo8qdwZFTk4oyIHlyxNwKJ5xV7fIfH2vKfFx+L1q1bjsfe247YzyhTVEQQBaxdk+OwP3gQynobD95E2zvuz8ezjQCHd2eykO1/fpLtyBEHAsfPTcez8dOzuGcGfv2jDm1sOoqFzGFf8pQGFaXr8+JhinFeVJ228FE26K4GSuAgmPj6eWx25cv7svmyxGjUuWFWAsqJcXPZivdfyj7ltvuDMhzesQW5ynPQw8vrjvM1tEbZz4ua+nM56eDZDw/jXntLcZJff85ySF7XgexdDpehiZmLWqFReNXH/Qi4IAjLjteg9vHlKnFYtbWTjwOEnzSC/QUFpdgJ29czsuimdP2azSwmEM2qVAIOXg1JTDe5J3PTZVRiZPjOrc3CceabGeUmeQasG69t57ol9qM5eWlWcikfPX4qkuBi83nAAd77ledC6XqtBsj7GaxLnjPPMr+MMPAfxsRpJg+9V5yMeZqSlpWHdQu/LhXPSknxuAuDrea8sSMHj312EtDTPv6AGMnbxhmdMwfg+0sZ5fzbSnW9d0l05pDubPdJ1X5idgN+ctxS/OnUhXty0D3+p2Yd9A+O4860d+N0HLfjhqgJcsrooqnRXAr0TF8Hs3buXWx25cv7scrYTF2W5fLZnz8zW3t52sXz8e8uw7a6TMS8zwWUba14k6lyXYzoncXYfb9kqTSKeu6QaP1szF3PgOuOoUglI1juSHBumbJ4zQ847+71+1Wq/7Tgv+4zRqJTfK/aZNmI1no+/w4/7jKQ3bjujDCuc3mdz7OxnsdkxafW8Psc5PM7cfuYi6QwkYHr7YgAwxM5cn+MA3t+dv1Q2JgfOSTfrPRWjFlwSmhd/vFK2ziWrixT5/tvlRyEvRY8EXQxO8PH+ZZxWjdJs/y8zA9NnvjlIcFsOq3f6PVaj5vq8s9aZLXjGFIzvI3WcDyaucEK6s9lJd76+SffgyEzQ4ZenLMSmm4/HPd9ajMI0PYwTFjz5aSuOefhj3PBKI3Yc5HPObrh1VwLNxBFcUAmQtqTd2muBt5ODLj+uGH0jkzh7aa7PWQB/7wcFiiPZcOA867IkN8m9OABA6Wq+9aWZWF+aiZoazx0Rhw8nI7t6RmCxilLbjvfjxqdmEiz3LcXdcZ6Jc37fTg7nov7Wkse4XfAff7QCP31ps8tnSXExePVnR+PtpoNQDe6TEjTH+TTA9NJVR1qsVgkuiWNBqh4/ObYYPcaZg6QdybLLu4mHHZxbmYdEXYxs3wCAyinpdk4IleD+XmT5HO/3xJkVOTizIhfHl2ZCrRLw6e5eaRt8b7jf53kperx/3RqMmC34rtNunDEqAauKU/HPhv0AZs69K812ve4frCzAlt3tOLW6zOOPDM6bsShJyAmCIAjiSEGv1eCio4vww1WF+GDnIfz5izbU7xvCZ52T+Oz3G7GyOBU/PqYo6t+bo/+7RzBlZZ7vmoSqjlw5f3Yltt+eNzNrMq5N9Vr2tjMW4fHvL/d7lovSGQ5W8lNnZn+cv/9mJMRi403rce3x81zKqxmX8/nro2tebpQSt7KcmS/mbzbOvEuoj/GfeDgnojFqlWLd43Qzy+68XZPDj/sXf2+zWY6dAb+1bA7WVi2REr8hpzPwkuJm6mkO7ybowCG73inJcmzG4RyZiJnZ0RMXZSnaZlij9j0TpxL8n+Xlfu3eZiwBYP3CTJy6JFvqh8qCFK/lPvnlOlx7/Dzc+60lHraF2QmH32eb+UzE9DENDp6/dCV+efICPHdptUtdXYwa9363CqeV53j4dZ6JjNXI3x/BPu8sdWYLnjEF4/tIHucDjSuckO5sdtKdr2/SPbSoVQJOXZKN165cjdevWo1Ty9KhVgmobR/EFX9pwJpHPsEfP2/1OJs3EMKtuxIoiYtgDh06JF8owDpy5fzZldicN+XoHgh8avu97d0uv59R4fmFNRCcNxpx/ytNXooeGYmum4CwvpMl17+O3RudNyupaZ9Zgil3SKlzYhGjVinW3W6fWeborQ2HH/eNRFReRgrnGatDhw55nRGM08x8pla5blDiaN/bpiXO3e1jk0WJZy6uwpzkOFQWJM+05WcmTq0ScJbbMRPOuMfja8t7q931PTnne+R7VfkAgB8dVYjidANuOHmh3wNL/3nlzPJZuyhiYXYCHv/eMrzy06OQn6rHNcfPR06S56YwvnR3vr9jNfL3R7DPO0ud2YJnTMH4PpLH+UDjCiekO5uddOfrm3TnR2VBCn6xOhUbb1qPq9aVIOXwu+UPvLsLRz34EX79xragVnaFW3clUBIXwQwOer4/Fqo6cuX82ZXYqotmZiVe3Ta9ffyQ02Hgj3ynQlGc5XOSXX5n3WDEF86Jm7cZKffZF9bpeLn+dSRxWrVK2pRiuY+ZHG+4zsQJinW3O72L5+26HX7cZ6O8lXUuMzg46DUZU2Em0XF/J04l7aboP2ZfW+U7OKEsC1/efDzWOO186KxXXIzbLKKX9x5LMmbOTFOqtbtezpf/wLnlePrCFfjFyQsU+XKexctMmP4DwjnL52DV3DS/9Xzp7px0a9Qq7s87S53ZgmdMwfg+ksf5QOMKJ6Q7m5105+ubdOfL4OAgcpLicOOppfjqlhPw0LnlWJiVgAmLDX/5uhMn/u4zXPRsLT7Z3Qu73F+IvfgOJi4eUBIXwWg07K8sKq0jV86fXYnN28zV8MTMdPb8LGU79cx1O5BYFaItK52TQW8zUu7vzbEmcd766GinL+SOXS+d3xObPLwtf0mKvIbO56YJEBTr7tx/3vrS4cc9H9N4mWVznnnTaDQe79EB08cfOJi+1pm4HYmhtwRR8DgIQh5nzZz1cp8h9DZs//vnxzK35/5unnN/OpZ4sBxKf/txKTh3+RxFx1k48KW7++6nvJ93ljqzBc+YgvF9JI/zgcYVTkh3Njvpztc36c4X55h0MWp8f2UB/nPdcXj58lU4aVEWBAH4vKUPlz5XhxMf+wwvftWBMadN5ZT6DiauUBJ5ChASK1as4FZHrpw/e6C2M37/hfSzt/PCvHHcgnSX30N17IDLTJyXBM19Jo61XW/98P2V+fiqbQCrS9KkySBBmJnRcpxl5n7GnDcSYjVYlJOIg8YJFKTpEZ+rTPc4vR4wTh+27W2JpCNu9wRPLnlesWIFRsyua87VKgEGgx4Ynm7P/Z04h0uvvgPQWedllg/wftSCO87Jpehjh1I55JbAyvGTM/zvSOoNX8+b+4x1uJ/3QMYu3vCMKRjf0TTOk+5865LuyiHd2ezRrLsgCFhdko7VJenoHBjHC1914NW6LrT1jeGON3fgN+/vxveq8nHx6iKXV1yU+A4mrlBAM3ERTG1tLbc6cuX82QO1OZ/B5lgyJofGLdMI2Uyc87tZXlx6LKdkbNdbPzhmqqxOsyQCZt4Tc/w1yDwmv2ZbEAS8dc0x+PKm4xEfq1Gs+8T4zO6J3pJXhx93m/vvpy3JRobTOWW1tbUeM3EalYCx0Zn2VG67UzqSqWCTHwfOM3HOM4fu2nlbQuF8fYGe4Md6j7gTyuf9+NJMReWU2AOxBXItvOEZUzC+o2mcJ9351iXdlUO6s9m/KboXpOlx+5mL8NWtJ+DusxejON2AEbMVf97YjnW//dRlt2xW38HEFSg0ExfBBDIjoLSOXDl/dqW2rMRYHDJNKorHF+4zCqHaKdZlOaXM+14A+3JKb33kaNNit0t2QZhJGEcOJ3FKjzPQqFVSMqpUd0GYKeftuh1+3G3u1//Uha5/VRJF0SOJ06pVLjtLqt1n4vzF6cfmC+clpu5LG1kIcCIO5yyfg798vQ/HOb2bx9Zu6J73nxxbDJUgSDPZ4XjeldaZLXjGFIzvI32cDySucEK6s9lJd76+SXe+KI0pPlaDi1cX4UdHFeKzlj48+2U7BEFAdpLvCYZw664ESuIimIwM9i+DSuvIlfNnV2pzT+A0KkF6F0wparX/ZCJQ5JdTur4Tx7o7pbc+cswO2eyitOOiIAjSlvx9h/tL73QMQDDteSNGEwNgekbU23U7/LjPjsn1e0ZGhkcZlUqARqOW2lMJrn6UdqnSd8ScN1aR09cfope5uDMqcvBOU7eX0jOsKEzBPy6cj2Vl8/yW80Uon3eNWoXL18xV7DsUz7vSOrMFz5iC8X2kj/OBxBVOSHc2O+nO1zfpzhfWmFQqQTrfd9Jq81s23LorgZZTRjApKcp3K2StI1fOn12pzf3L92XHzXUvLov7TBxrMqXEr7cZqWCXU3rrI8fSUItNdHknrnNweslhbcf07kV6nfLNMPy15w2Nl41FvPlxt8ldv7f2VYLry7wCBMXLYZ2L3fOtxQrrOGs687l7Eid3D3n7O8Pvzl+Kj3+xVjaGkjkZPo8kkONIf94DiSuc8IwpGN+kO19IdzY76c7XN+nOl2Bicv/jfSh98+orSuIimJaWFm515Mr5syu1neF2CLFjKd2FRxUoCRGA5xfwUC2nlJup8XwnjM2/tz5yzMRZbXZptsfb5UwoeCdOSXvemJycmR319i6aw4/7pidys1mOevd/e+ZAa5UgYHJyZn25IAS2TNLbQePecA7ReXdL98RRfsmJ52exGjXmZsjvqBrIMxtM3Uh63gOJK5zwjIl0Z4srnJDubHbSna9v0p0v0aS7EiiJI7ixpWvY5XfHFvpxMf7/2uEMr41NYtTedzJ0wDp7w9Km1e46E+c4GDr58GHQoToLzxtKPcu9E+eLGJXr7pPuyZSz31AvEXf27Rw++8RY5K3zJwiCIAiCcIaSuAhm4cKF3OrIlfNnV2rTa12TtX81HgAA9I4o3+zEPXcIVRLnOhPn3+7tdzm89ZG0sYnNLqUJAgTpvT/z4SQ3LSWJqS1f7XkjLs7/rqAOPx7LKWWuX6rnUk6Azun9PkEABIUjTiDnxKl9LJF1v5bLVuf79RNMchnIMxtM3Uh63gOJK5zwjIl0Z4srnJDubHbSna9v0p0v0aS7EiiJi2ACOeFdaR25cv7sSm1nLc11sTmStze3HFQSIoDpGTBNAJthyCH3ThzrO2HueOsj5yMGZnaBnInFbLEDAGyWKaa2fLXnDZvMi7sOP6wbmzjquSZSgM1mc/rdbSZOUcTKcZbIeQLX+VouWFWA75a5HiDvTjBxBfLMBlM3kp73QOIKJzxjIt3Z4gonpDubnXTn65t050s06a4ESuIimL6+Pm515Mr5syu1OZ/bZWPcldIZ5x0t+czEKVlOyebfWx85fLoupxQ82rJM+j6nhKU9b1itFr92hx+PmUiZDvBWTxAAi8Uq/a5ieCcuEJldDvj2MRO3LD8ZgwP9fv1YrHb2xg8TyDMbTN1Iet4DiSuc8IyJdGeLK5yQ7mx20p2vb9KdL9GkuxIoiYtgVO67S4Swjlw5f3althin4wEsNjviY6c3qPj1GWWKYvRGqI4YkJ2JC3I5pbc+cszETVltLtvYu78DF6Nmv0alusu92+fw4365ctfvqOe+pNG5OcHjnTjfiX2wSZzLTpVuMTliTdBN34+LchIBALqY6c+LM/zP1PmNIYBnNpi6kfS8BxJXOOEZE+nOFlc4Id3Z7KQ7X9+kO1+iSXcl0Dlxs8CGDRuwYcMGaalZfX09DAYDKisr0dzcjImJCSQkJGDJkiWoqakBABQWFsJut6OrqwsAsGzZMuzduxejo6MwGAxYsGABGhsbAQAHDhyAWq3Gvn37AAAVFRXo6OiAyWSCTqfD4sWLYbfbUVNTg9zcXOh0OrS1tQEAlixZgv3798Nut6OxsRHLli2TTprPzs5GfHy8VLesrAyHDh3C4OAgNBoNVqxYAVEUUVNTg4yMDOgTZt7t6h8cQnFKDLb1WDHefwDAXNTX18NmsyEtLQ2ZmZlobm4GAMyfPx8mkwmHDh3y6LvhoSGMjo5ix44dAICSkhKMj4+ju3v6DK+qqips374dZrMZSUlJKCgowLZt2wAARUVFsFqt2L9/P4YGRySfTVu3wJSZhJKSEmzduhUAoE9z3VnTbrNix44dGB0dhV6vR2lpKRoaGgAAeXl50Gg06OjoAACUl5cjISEBNTU10Ol0WLJkCerr69E7Nq33pNWOAweml5RarRZMjIy7tKUWgJqaGmRlZSExMRF79uwBAJSVlaG3txcDAwNQq9WoqqpCXV0d7HY7MjIyMDw8jN27dwMAFixYgKGhIfT19UEQBKxcuRKbN2+G4JQ8Ou6tefPmYXR0FD09PQCmk6ttTU0uMW1pbHD53VF3xYoV2LFjB+x2O5qbm2GzziRAdrsNsTotgOnZuKmpKTQ375bsjnvFnZqaGtjtM7NhTU1NKCsrk/p7zpw50Gq1aG9vl/q7q6sLu/f0SnVMxmHU1NQgOzsb5omZ/p00m5GcmYyamhrcuzYZmwZ0OC51DDU1NXjynCK8ut2IU+ZM39+lpaXo7+9Hf38/VCoVqqurXeI0Go3YtWsXgOl71mg0Ss/GqlWr0NDQAIvFgtTUVGRnZ2Pnzp0Apu/ZsbExqb+rq6vR1NQEu92O3bt3Iz8/X7pni4uLMTU1hQMHpt8nDWaMmJqaksaIvLw8jzGiubnZZYzYvHkzACA3Nxd9fX0eY8Tw8DC0Wi1EUWQaI2prayGKIjIyMpCSkiLt2rVw4UIMDg6ir69P6m+lY4Rzf6ekpCA3N9djjHDEpHSMcPT3rl27MD4+jvj4eJcxoqBgepfdzs5OAIDZbEZra6viMaKzsxNGoxE6nQ42mw319fUAgJycHOj1erS2tgIAFi9ejIMHD2JoaAgxMTGorKyUrkVujKiurnYZI1JTU13GiLS0NNTU1LiMEVarFampqVi4cKHPMWLlypVobGzE1NQUkpOTkZeXh+3btwOY/ouz2WzGwYPTY5xjjDCbzUhMTERRURGaDo8vhYWFsNlsUn8vX74cLS0tGBsbQ3x8PObNm4ctW7YAAPLz86FSqVzu2fb2doyMjCAuLs7vGDE+Po6uri4MDw8jNjYWFRUVqKurk+5Zg8Eg9feiRYvQ09ODwcFBqb8d/ZCZmYmkpCSpv0tLS9Ha2uoyRjj6Oz09Henp6R5jRG9vr8c9G+oxIjY2FjU1NUhISEBxcbFLf9vtdun+8fY9wmHzNkbEx8dL/19jGSO8fY/Yu3evdM/yGiM6OjqYxgil3yNyc3Ole4JljFi6dCn0ej1qamq4jBF2ux0NDQ0u96z7GGEymXx+j2hra/MYI4aGhmC321FbW8s8RmzZssXrGDF37lxuY0R+fr4UE8sY4fge4W+McDwbrGNEf38/7HY76urqFI0RjpgUIRKzhtFoFAGIRqPRq72uro7Zp9I6cuX82ZXarDa7WHjT22LhTW+LA6OT4jkbNoqFN70tvr+9W1GMDhw+Cm96W3zovWamur648R9bJZ/dwxMe9v4Rs0u7L37VweTfWx/1GCfEwpveFufe8o740HvNYuFNb4t3v7VD/M1/drm09cvnP2G+HqW6n/qb96V2/PmZstpcYjJOTLn87qveBzt6pDKrH/xIPOGh/7jUmbTM+D3lsc+k+u6+L/zz137j9Mamvf1SnUufq5U+/+NnrdLnrzd0BfRc+YrTnWB8H+nPeyBxhROeMZHubHGFE9KdzU668/VNuvMlGnSXyw2ciby5UELCeVOIUNeRK+fPrtSmVs2872W12WG1Tc8CxTDu+Z6XEif9HKrd9103vvC0s74T5o63PnIs97OLIuxOG5t4noXH/v6g4ntFZutFhx/365U79kCqp3Ze0uhZTql+gSybda7i8rPbcspAnisHjn4oStN7tQfj+0h/3gOJK5zwjIl0Z4srnJDubHbSna9v0p0v0aS7EiiJi2DS0tK41ZEr58/OYnO83zVls0sblDC/X+Zjw4rg8L0FPRD8IePe+sjhQxThck6ce4KUGM/+TpZS3bXaGEV+3LtErt8d9dRuWmk0riu2lZ63F4jOKpdNVZz1dS0XyHPl4PWrVuOkRVl45pJqr/ZgfEfD884aVzjhGRPpzhZXOCHd2eykO1/fpDtfokl3JdA7cRFMZmYmtzpy5fzZWWwxahXMFjssNhFW2/R7ThrGjTucc5xQHLrtjpLdKVnx1kfOPh27dQqC4DJ7BQDJiQkhac8bWq0WwISsH/d+lusPRz334yCmk8aZ3Tad3fibFAwoiXPe2MTpc/fNVgJ5rhxU5CXjTxdV+bQH4zsannfWuMIJz5hId7a4wgnpzmYn3fn6Jt35Ek26K4Fm4iIYxwu6POrIlfNnZ7FpDy+dtDjNxLEup3SdVQl9Eud+JhoQ/BED3vrI+TqkJA6e19TX67mhSyDteWNifNyv3ZcfOc0c9VxmwwBMjLsmjM59IPo5kS2QHNp1OaXv3SkDea6UEozvaHjeWeMKJ6Q7m5105+ubdOcL6c5mJ935+uYVFyVxBFdmttW3w+KYiWP8hu5yiHPocziviaHG7UU5QfEJZ75xjl06N0/wnHXicY0OAnFdkOr9/S9vuB/d4C/55TkT5yyfs74c/gZAEARBEAQRdiiJi2Dmz5/PrY5cOX92FluMZvpbsyWIjU18zaqECl/LKbUapzgZm/XWR87tOGYlvSU62QFMuyvVXa/3n5B58+Nvxsy9nos+AqDT6RTF5U4gR6ooOycusOdKKcH4jobnnTWucEK6s9lJd76+SXe+kO5sdtKdr29ecVESF8GYTCZudeTK+bOz2GKk5ZRiEBubzPwcupmUmcTE16yPWz7ChLc+cm7H7rSc0r39yUkzWFGqu91mDYkfX/XcN6GxB7gjUyAzca4ztt6X4AqCEPA1KiEY39HwvLPGFU5IdzY76c7XN+nOF9KdzU668/XNKy5K4iIYb4ddh6qOXDl/dhZbjMr5nbjp5ZQxzBub8NidcgZfSaXzEkrWDVW89ZFz7FZpYxPPxHR0ZASsKNV9amqK2Y/MqQQu9dwTX4vF4rOOP7eBHTHgOuPmzZeAwJ4rpQTjOxqed9a4wgnpzmYn3fn6Jt35Qrqz2Ul3vr55xUVJHMEVx3LKKZsdE1PTszKxGjWTD8HHl/NgcE5MfPkMdb7o+k7cdEIrQPBITI/k97acE1+5hFv0kx0GdsSA9/ruG5sQBEEQBEEc6VASF8GsWrWKWx25cv7sLDZpOaXVLh1wHcxySh5fwn3Nsgk+flaCtz7y9k6cIHgmkfNLShhbU657cnIysx8lM3GOes5dKQhAQgL7cQkA8J3KPABAabby+q7vxM187nJ2nSqw50opwfiOhuedNa5wQrqz2Ul3vr5Jd76Q7mx20p2vb15xURIXwTQ0NHCrI1fOn53F5kjirHZRSgZYEzHeyymVwNqstz5yThYdCa0gCB5JZGdnB3N8SnWXW5cdyD3nr97Y2GhA/o6dn44Pb1iDN64+RnEdX/eJ8z46AoSAr1EJwfiOhuedNa5wQrqz2Ul3vr5Jd76Q7mx20p2vb15x0WHfEYy/94mCrSNXzp+dxeZ8TpwjcWFdEuk6E8dW1xdKZpcEHzM7SvDVRyoBsIuQdur0trFJIJuBKNVdFO3Mfvwte3Sv5765iN1PXTmv8zLZZvF8bUTjPkMXyHOllGB8R8PzzhpXOCHd2eykO1/fpDtfSHc2O+nO1zevuGgmLoJJSUnhVkeunD87i82xicmU1Q7paDTGjMjXdvG8cV1Oydaurz5yLKmcmYnzTEwT4uOZ2vLXnjvamJiQ+PFVz3UzGECj8fN3IgWJNAu+Z+JcPw/0GpUQjO9oeN5Z4wonpDubnXTn65t05wvpzmYn3fn65hUXJXERTG5uLrc6cuX82Vls0mHftpkZoGBm4lgTQF8oOfsMLu2y+ffVR474pXfivGxskpaaytaYn/bckTu3zZsfJbmWo577TJwuNlZRXKHA12Hfzom/IAT2XCklGN/R8LyzxhVOSHc2O+nO1zfpzhfSnc1OuvP1zSsuSuIimB07dnCrI1fOn53FFnP4wOxJi3MSF8w7cUxVgyKYpnz1kWOTDZufIwYO7O8KWXvujI76P74gkHvOuZ77xiZjY2MB+QsEwUeyr3aboQv0GpUQjO9oeN5Z4wonpDubnXTn65t05wvpzmYn3fn65hUXJXEEVxzvxE1aQ5XEhWgmjvGduFDhSEIdSZxK8LymSNsFX0lfOXA/W8/ftYR4NaXbUQIzn6vdZuIIgiAIgiCOdCiJi2BKAthqXmkduXL+7Cw2xztxk9aZzToExrvO13bx4YQ1ofPVR45EY+aIAc9EJycnmzk+pbob9IaQ+PFVz2U2DEBcXJzPOko2TGHB11EULhubQAj4GpUQjO9oeN5Z4wonpDubnXTn65t05wvpzmYn3fn65hUXJXERzPj4OLc6cuX82VlsMV5m4ljTMF/nf/HGPSFhwVcfqdyWUzp/5mBqcoqxNeW6awT/iZM3P0reH3TUc99J1G7zvxtmKFG2sUlgz5VSgvEdDc87a1zhhHRns5PufH2T7nwh3dnspDtf37zioiQugunu7uZWR66cPzuLTUrignknznmTipBtbCKPy+6UjM366iO1x0ycp++hoUG2xvy058535qlQmKbHnWctUuxHyYTZTD3X5ZSTU+wJaaD4POzbbZOTQJ4rpQTjOxqed9a4wgnpzmYn3fn6Jt35Qrqz2Ul3vr55xUXnxBFckY4YcDr7jDURc37PShXGPzsIbsvwQoFjUsjuZ3dKnpONGXo1PvvVeqY6LIse3Wcv/V1LyN+Jc2nb+6ycSgDCNzdIEARBEATBB5qJi2Cqqqq41ZEr58/OYvM2E8c6q+W+bX0oODA0Id9uEP599ZHgtpzS28Ym8+fPD1l7rOUCueec6zlfiUoQkJjo+8DuEL8S5/PeULtsaSoEfI1KCMZ3NDzvrHGFE9KdzU668/VNuvOFdGezk+58ffOKi5K4CGb79u3c6siV82dnsXk/Jy7w3SlDtWNkeoL8+WXu2+Wz4KuPvB0x4H5sQue+fWyN+WmPtZw3uyPZ8tcFjnqC21RcOI8Y8HVfuc/EBfJcKSUY39HwvLPGFU5IdzY76c7XN+nOF9KdzU668/XNKy5K4iIYs9nMrY5cOX92FpvW6zlxSiKcwX2zjFCQm+T/0OtpBC8/KcNXHznit9rth/0KHompxWJhbC08uiup5zoTB9jtYVy86EMk141NhICvUQnB+I6G5501rnBCurPZSXe+vkl3vpDubHbSna9vXnHRO3ERTFJSErc6cuX82VlsjnfizNbA34lz2WkwRDNxVx8/D1vaevDD4xYqKs/arK8+ciRsjs0pvc3E6Q16tsb8tMdazrt9ZtbQ1xJIRz3XiTgBGo0GgPekVMmulyy4vBPn55y4QJ4rpQTjOxqed9a4wgnpzmYn3fn6Jt35Qrqz2Ul3vr55xUVJXARTUFDArY5cOX92Fpu3LfXZ34kL/XLKRF0MnvvxKuj1vhMm16bY2pXrX+cz0tyvKTMjk6ktJe0pLefPrhIE2H1kcY56rod9A3E6HQDv7x+G650496MHAnmulBKM72h43lnjCiekO5uddOfrm3TnC+nOZifd+frmFRctp4xgtm3bxq2OXDl/dhab+0Yezp8phcdySkC+D4I5YsCXb4cfR3eoBMHjmvbt62BrzE97rOW82R3Jlr/DuR313N8jHBsbVRRXKPClkftMXCDPlVKC8R0NzztrXOGEdGezk+58fZPufCHd2eykO1/fvOKiJI7giuPrs/NujKz4OsSZNzyamkninDc2CeMJ5gEQ6IRZqI5lUIrLBjhOn6tdPo/sviYIgiAIglACJXERTFFREbc6cuX82Vls7klLIAmLy2HfIbxj5fpACGJjEznfzjs+undJdnYWY2t8dXfMwKn8ZOCOeiq3WS+dLs5nnVAvp/R1a7nfP4E8V0oJxnc0PO9K25sNSHc2O+nO1zfpzhfSnc1OuvP1zSsuSuIiGKvVyq2OXDl/dhab+ztxgSRxPN6JA+T7wHVpIFu7vnw7EsOZmTjPw75tTgejB9seazm/dj9Jl6OeZy+F+khv3/iaZXPfnTKQ50opwfiOhuedNa5wQrqz2Ul3vr5Jd76Q7mx20p2vb15xURIXwezfv59bHbly/uwsNkd+4nwuGis8dqcE2PqXtVVfvpUspxzo72dsLTy6+0vIHPXcD2afDOMWxL4mCtVuyywDea6UEozvaHjeWeMKJ6Q7m5105+ubdOcL6c5mJ935+uYVFyVxBFekjU2CWU7plhiEi2A2NpHDeTllKDdr4YEjdVMSp/vulOF83c/n7pRuSzwJgiAIgiCOdCiJi2AqKyu51ZEr58/OYnN8Z7baQrWxCXt9X8j1gesyztD4drhxXk7pvlRzwfz5bI35aY+1nDe7I+GM08bI1nM9Jw5IS0n2WcffbpeB4EujWM3MMDdlFQN6rpQSjO9oeN5Z4wonpDubnXTn65t05wvpzmYn3fn65hUXJXERzK5du7jVkSvnz85iC8XGJsG8m+aPQPo3WN9KDvvu6uoKWXus5bzZ7YeDLUpSy9Zznb0UMB7WIwa83xvxsTPHYZrMllnRnVfdSHveWeMKJ6Q7m5105+ubdOcL6c5mJ935+uYVFyVxEcz4+Di3OnLl/NlZbO4bmwT7TlwoZ+Lk+sB1VomtYTnfjlkoAZ4zcZOT7O+R8dD9/Ko8AMD/nDg9M3jFcj2+X52Pt39+rO96bjNxsPvepIXnlifuif+aBRnIS4nDsvzkgJ4rpQTjOxqed9a4wgnpzmYn3fn6Jt35Qrqz2Ul3vr55xaWRL0KEmg0bNmDDhg3SLoT19fUwGAyorKxEc3MzJiYmkJCQAJ1Oh5qaGgBAYWEh7Ha7NEuzbNky7N27F6OjozAYDFiwYAEaGxthMplw4MABqNVq7Nu3DwBQUVGBjo4OmEwm6HQ6LF68GCaTCTU1NcjNzYVOp0NbWxsAYMmSJdi/fz9MJhMaGxuxbNky1NbWAgCys7MRHx8v1S0rK8OhQ4cwODgIjUaDFStWYGRkBDU1NcjIyEBKSgra29sBABbr9LXabDbU1dWhuroa9fX1sNlsSEtLQ2ZmJpqbmwEA8+fPh8lkwqFDhwAAKkEn9d2B/fsxmhmLHTt2AABKSkowPj6O7u5uAEBVVRW2b98Os9mMpKQkFBQUSIcsFhUVwWq1Si+YxsXFYdu2bRgfH0d8fDxKSkqwdetWAEBBQQHsTrtEWixT2LFjB0ZHR6HX61FaWoqGhgYAQF5eHjQaDTo6OgAA5eXlmJycRE1NDXQ6HZYsWYL6+noAgNVqAQBMWaZ3KpqanMSB/UMu98f42DhqamqQlZWFxMRE7NmzBwBQVlaG3t5eDAwMQK1Wo6qqCnV1dbDb7bBarRgeHsbu3bsBAAsWLMDQ0BD6+vogCAJWrlyJzZs3w2QyYc+ePcjKypL6e968eRgdHUVPTw9MJhNEUcSWLVvwrTmTOC4nD+uWZqKmpgbqqVH8Ys08mM0m1NRM/1VpxYoV2LFjB0wmE5qbmxGfnitdx+TUJM5eoMdXB6ZwTF4spqam0NLSItntoijd387U1NSgoqIC7e3tGBkZQVxcHMrKyqT+njNnDrRarXRvlZeXo6urC8PDw5KP7u4e1NSMIDs7GwaDAVctssMu6mExj8NisaCmpgYxMTGorKyUYsjMzERSUpLU36Wlpejv70d/fz9UKhWqq6ul/k5PT0d6err017X58+fDaDRKz8aqVavQ0NAAi8WC1NRUZGdnY+fOndI9OzY2hp6eHgBAdXU1mpqaYDKZsHv3buTn50v3bHFxMaampnDgwAEACHiMmJiYwNTUFBobG6V71nmM0Ol0aG5udhkjNm/eDGD6Dw59fX0eY8Tw8DDGx8chiiLTGFFbWwtRFKUxwnFPLFy4EIODg+jr65P6W+kY4dzfKSkpyM3N9RgjHDGxjBGVlZXYtWuXzzECADo7O2EymWA2m9Ha2qp4jOjs7ITRaMT4+DhsNps0RuTk5ECv16O1tRUAsHjxYhw8eBBDQ0PSPeu4FrkxIj4+XrpnMzIykJqa6jJGWK1W1NTUuIwRVqsVqamp0Gq10r3lPEYAgMFgQGNjI6amppCcnIy8vDxs374dJpMJfX19MJvNOHjwIICZMcJsNiMxMRFFRUVoamqS7lmbzSb19/Lly9HS0oKxsTHEx8dj3rx52LJlCwAgPz8fKpXK5f9rSsYIk8mE8fFxaYyIjY1FRUUF6urqpHvWYDBI/b1o0SL09PRgcHBQdoyIiYlBa2sr0xjR29sLAIiPj+c2RkxMTKCmpgYJCQkoLi526W+73S7dP96+Rzhs7mNERUWFy//XlI4RWq3W6/eIvXv3SvcsjzFCrVajo6ODaYxQ+j1CdPp/l9IxAgCWLl0Ks9mMmpoaLmOEyWRCQ0ODyz3rPEaYTCaYTCav3yNMJhPa2to8xoihoSGYTCbU1tYyjRErV67Eli1bPMYIAJg7dy63MUIQBCmmQL9H+BojHM8Gyxjh+B5hMpmk775yY4QjJiUIYqhfTCEUYzKZkJSUBKPRiMTERA+72WyGTqfzUtM3SuvIlfNnZ7G9WteFG//ZhIJUPToHx5Gij0HjHSfLxufMTa814ZX66S+m/7zyaKwoTGWq7wu5PljzyCfoHJz+68nzl1Zj3cLMoH0f/+inaOsbQ4JOgxGzFQ+dW475WQn4zlObpDIvXFyJtWU5DFcSObr3j06i6r4PAQAnlmXhie8twRTUSIjVSDOORTe/AwDITtTh61tPcPkMADoeOkP2Onzh8HP5ccW47YxFzNcRLMH4jobnnTWucEK6s9lJd76+SXe+kO5sdtKdr2+WunK5gTO0nDKCcfw1h0cduXL+7Ew2tyMGgj7sO4TvxLH0L+u7eLK+/bwT19bWytSWovYUlgtWd/de2rp1KxJ1MSF9l1EJ/toL5LlSSjC+o+J5Z4wrnJDubHbSna9v0p0vpDubnXTn65tXXJTEEVxxfJWeeScukI1NnN+JC+MRA+7vd4XC5+H/2p3eiQvnNfGGRV8xjAeBEwRBEARBRBOUxEUwjvXUPOrIlfNnZ7FJG5uIwRwx4OkvFMj1QTAt+fLtSHKc0xf3a8rKUr5sU6491nLB6u6ur786s7WQO5DnKhy+o+F5Z40rnJDubHbSna9v0p0vpDubnXTn65tXXJTEEVxx5CdBLacM4ry2YAjmnDg5pATGy4HYrDthRhJHcuwEQRAEQRBHCpTERTCOHY141JEr58/OYvNM4hSF54KK03JKuT5wOfOMMTnx5dvhxXkpofs19R7eTSsU7bGWC1p3wcfnMpxZMb2RS1VhiqLywRDIcxUO39HwvLPGFU5IdzY76c7XN+nOF9KdzU668/XNKy46YoDgiuc5ccEd9u28yQl3XM4aC5HLw37MFrvUhMc1HcGTWSz95Lya8qHvVGDNggycvCgr5DERBEEQBEFEG5TERTBLly7lVkeunD97ILbQHfYdugxHrg8EHz+HwrfkVxA8Zvnmz5vH2Frk6O7eT/7qOL8TFx+rwflV+X5jCxWBPFfh8B1NzzvPPg4U0p3NTrrz9U2684V0Z7OT7nx984qLllNGMI6DHHnUkSvnz85i89zYJJB34jz9hQKm/mVs1pdvb8sy3ZeYOg5tDUV7rOWC1d19pjWQezgU+JOLZ0zB+I6G5501rnBCurPZSXe+vkl3vpDubHbSna9vXnFREhfBjI6OcqsjV86fncXm+E5vD9k7cez1fSHXBy4bmzBmcb58e25i4pn4mCcmmNry1x5ruWB1d70SMaB7mDc8YwrGdzQ876xxhRPSnc1OuvP1TbrzhXRns5PufH3ziouSuAhGr9dzqyNXzp+dxeZIfoKZieN1TpxcHwTTEot27ompLk7HrT3eurvL47+92TljIJDnKhy+o+F5Z40rnJDubHbSna9v0p0vpDubnXTn65tXXJTERTClpaXc6siV82dnsTkSFOct9VnhtZxSrg9cDvtmbFapDoLgeU1FhYVsjTG0x1t39xlLf3W4nhPnR69AniulBOM7Gp531rjCCenOZifd+fom3flCurPZSXe+vnnFRUlcBNPQ0MCtjlw5f3YWm3vyE0nnxMn1gXNCwtqsL9/uSye9JXEtu3czthY5urvrE8g9zBueMQXjOxqed9a4wgnpzmYn3fn6Jt35Qrqz2Ul3vr55xUVJHMGVQI4UcMdlJi6UL8XJ4DoTx6/dcB5gzptouhaCIAiCIIhIhZK4CCYvL49bHbly/uwsNvfv9IF8x3dOoNQhzBJY+pe1WV++PftD8EhMs7LYz0qLFN0Ft4MZ/NUxW2x+Y+FFIM9VOHxHw/POGlc4Id3Z7KQ7X9+kO19IdzY76c7XN6+4KImLYDQa9mP8lNaRK+fPzmLztnyQFV67U7L0L2uzvnx7u373jzRqNWNrkaO7+/V5q7OyKBUAcEZFjt9YeBHIcxUO39HwvLPGFU5IdzY76c7XN+nOF9KdzU668/XNKy5K4iKYjo4ObnXkyvmzs9jcky7WrfrdfYRyWaNcHwTTllIdvL0T193dza093rq795i3On+6qAqPnrcUd5y12G8svAjkuQqH72h43pW2NxuQ7mx20p2vb9KdL6Q7m5105+ubV1yRl0YTUUUoci5nH2F8Jc51YWCI2vXmx+OajuD3ypQkvkn6GHxnReQtwyAIgiAIgjhSoJm4CKa8vJxbHbly/uwstlAsp3QmlEcMyPWBa1Ns7fry7W0m0r2P5s+bx9SWv/ZYywWru/vVBXIPhwJ/M748YwrGdzQ876xxhRPSnc1OuvP1TbrzhXRns5PufH3ziouSuAims7OTWx25cv7sLLZQTyqFMomT64NgzolTqoMgCB4zcT09PWyNMbTHW3fXfhIDuod5wzOmYHxHw/POGlc4Id3Z7KQ7X9+kO19IdzY76c7XN6+4KImLYIxGI7c6cuX82Vlsod6aXwjhHSvXB8GcE+fLt/fllK4fjo2OMrYWObq76x3IPcwbnjEF4zsannfWuMIJ6c5mJ935+ibd+UK6s9lJd76+ecVFSVwEo9PpuNWRK+fPzmLz2NgkyKQulEcMsPQva9y+fHt7/c09iYuN1TK15a891nKh0p01rlDjTy6eMQXjOxqed9a4wgnpzmYn3fn6Jt35Qrqz2Ul3vr55xSWIoihy8UzIYjKZkJSUBKPRiMTERA+7zWaDmnG7eaV15Mr5s7PYNu7px4XP1Ei/L8pJxLv/c5xsfM488fEe/Pa/LQCA5ntORZyWfQt+JbG6c/YTG9G0f/qvJ29cfQyW5ScH7ftbT2zE1v0zf5F54oLlWLMgAxV3/Vf67J2fH4PFc5S35a891nKh0L3o5ncAACeWZeL/LqxkvoeDwdH2FWtLcPNppV7LBPJcKSUY39HwvLPGFU5IdzY76c7XN+nOF9KdzU668/XNUlcuN3CGZuIimPr6em515Mr5s7PYQrGbpPOfGUK5OlOuDwQfPwfl28sFuM/E7dyxg7G1yNOdNa5wwjOmYHxHw/POGlc4Id3Z7KQ7X9+kO19IdzY76c7XN6+4KIkj+OKxnDI4d6Hc2EQWp7ZCdsSAx++BnJxH+OLcyjlQCcCPji6c7VAIgiAIgiC4QefERTA5OTnc6siV82dnsbmnKIEkQ87rfUN5TpxcH7jOxLE1zKKde59kZGQwtcXSXrh0Z40rVDx63lI8dG4FtBrff5/iGVMwvqPheWeNK5yQ7mx20p2vb9KdL6Q7m5105+ubV1yUxEUwer2eWx25cv7sLDaPjU2CnHcK5UycXB8Ec8SAL9/ufgTBs0/i4thfgI003VnjChWCIECr8S8Wz5iC8R0NzztrXOGEdGezk+58fZPufCHd2eykO1/fvOKi5ZQRTGtrK7c6cuX82VlsoT5iQBXCqbhA+jdY3952p3Rnf1dXyNpjLRcq3VnjCiezoTuvuqS7ckh3Njvpztc36c4X0p3NTrrz9c0rLkriCK54m3k6UghXqEdSn7BA+94SBEEQBEHwgZK4CGbx4sXc6siV82dnsXkup4wc5PpACGJjE1++3WcmvfmdN38eW2N+2mMtFyrdWeMKJzxjCsZ3NDzvrHGFE9KdzU668/VNuvOFdGezk+58ffOKi5K4CObgwYPc6siV82dns0VS2uaKXB8Es7GJL99KvPT19jG15a891nKh050trnDCM6ZgfEfH884WVzgh3dnspDtf36Q7X0h3Njvpztc3r7goiYtghoaGuNWRK+fPzmLzeIUtgtYOyvVBMKH68u3p07OREZPR47NA22MtFyrdWeMKJzxjCsZ3NDzvrHGFE9KdzU668/VNuvOFdGezk+58ffOKi5K4CCYmJoZbHbly/uwsNo/lgwpic4fXu1VyfeA8+8aa0CnVQRA8fUeD7sD0dQVyLbzhGVMwvqNFd5a4wgnpzmYn3fn6Jt35Qrqz2Ul3vr55xSWIIm0/MFuYTCYkJSXBaDQiMTFxtsPhwtauYXxrw5fS78sLkvGvq45h8vG/H+7BYx+2AAA6HjojpPH54/z/+wq17YMAgP9cdxxKs4PX6Pynv0Jtx6D0+//9aAXWLczAwl//R/rsk1+uQ3G6Iei2Zouim98BAJxYlok/X1w9y9EQBEEQBEEcGbDkBjQTF8HU1NRwqyNXzp+dxRbKc91CDUv/sr4T59O3l41e3H1v3bqVqS2/7TGWC5XurHGFE54xBeM7Gp531rjCCenOZifd+fom3flCurPZSXe+vnnFRUkcwRWPIwZmJ4yAcNnYJIyBH0l9RBAEQRAEQYQfSuIimKysLG515Mr5s7PYPM+Ji5wURa4PggnVl2/PfV4Ej3bS09ND1h5ruVDpzhpXOOEZUzC+o+F5Z40rnJDubHbSna9v0p0vpDubnXTn65tXXJTERTCBvCentI5cOX92FhvrMsRwItcHLhubhMi3kpnJhIR4xtYiT3dgekOaSHzXk2dMwfiOhuedNa5wQrqz2Ul3vr5Jd76Q7mx20p2vb15xURIXwezZs4dbHbly/uwstkheTinXB86xs87KsWjn7npfRwdbYwzthUt31rjCCc+YgvEdDc87a1zhhHRns5PufH2T7nwh3dnspDtf37zioiSO4Ir7xiaBLFEUMTsbqLrGGpr0031mMoJWlxIEQRAEQRBHCJrZDuCbyIYNG7BhwwbYbDYAQH19PQwGAyorK9Hc3IyJiQkkJCRg7ty50o42hYWFsNvt6OrqAgAsW7YMe/fuxejoKAwGAxYsWIDGxkZYLBYcOHAAarUa+/btAwBUVFSgo6MDJpMJOp0OixcvhsViQU1NDXJzc6HT6dDW1gYAWLJkCfbv3w+LxYLGxkYsW7YMtbW1AIDs7GzEx8dLdcvKynDo0CEMDg5Co9FgxYoVsFqtqKmpQUZGBlJSUrBt206Xax8ZGUFdXR2qq6tRX18Pm82GtLQ0ZGZmorm5GQAwf/58mEwmHDp06HCtFKl+S0sLcnNzsWPHDgBASUkJxsfH0d3dDQCoqqrC9u3bYTabkZSUhIKCAmzbtg0AUFRUBKvViv3790vtbNu2DePj44iPj0dJSYm0M2RBQQEmJ6ekdqcmJ7Fjxz6Mjo5Cr9ejtLQUDQ0NAIC8vDxoNBp0HJ5BKy8vh06nQ01NDXQ6HZYsWYL6+noAwOSk2aU/Jsxmj7/QOPowKysLiYmJkr2srAy9vb0YGBiAWq1GVVUV6urqYLfbkZCQgOHhYezevRsAsGDBAgwNDaGvrw+CIGDlypXYvHkzLBYL9uzZg6ysLKm/582bh9HRUfT09MBisUAURWzZsgVTU1NITk5GXl4etm/fDovFgr6+PpjNZhw8eBAAsGLFCuzYsQMWiwXNzc0oKiqSrsM8OYm0tGzpHl6+fDlaWlowNjaG+Ph4zJs3D1u2bAEA5OfnQ6VSudyz7e3tGBkZQVxcHMrKyqT+njNnDrRaLdrb26X+7urqwvDwMGJjY1FRUYG6ujrpnjUYDGhtbQUALFq0CHq9HjU1NYiJiUFlZaUUX2ZmJpKSkqT+Li0tRX9/P/r7+6FSqVBdXS31d3p6OtLT07Fr1y7pXjIajdKzsWrVKjQ0NMBisSA1NRXZ2dnYuXOndM+OjY2hp6cHAFBdXY2mpiZYLBbs3r0b+fn50j1bXFyMqakpHDhwAAACHiM0Gg2mpqbQ2Ngo3bPOY0RxcTGam5tdxojNmzcDAFJSUtDX1+cxRgwPD0MQBIiiyDRG1NbWQhRFaYxoaZk+PmThwoUYHBxEX1+f1N9Kxwjn/k5JSfE6RjhiYhkjKisrsWvXLp9jBAB0dnbCYrHAbDajtbVV8RjR2dkJo9EIlUoFm80mjRE5OTnQ6/XSPbt48WIcPHgQQ0ND0j3ruBa5MaKsrEy6ZzMyMpCamuoyRiQkJKCmpsZljLBarUhNTUVBQYF0bzmPEY5no7GxkWmMMJvNSExMRFFREZqamqR71mazSf3NY4ywWCwYHx9nGiN6enowODgoO0bk5eWhtbWVaYzo7e2VtOI1RqjVatTU1CAhIQHFxcUu/W2326X7x9v3CIfNfYyoqKhAbGys9P81pWOEVqv1+j1i7969Uj/wGCNyc3PR0dHBNEYo/R6Rmpoq3RNKxwgAWLp0KbRaLWpqariMERaLBQ0NDS73rPMYYbFYYDKZvH6PsFgsaGtr8xgjhoaGYLFYUFtbyzRGrFy50uv3CACYO3cutzEiIyNDiinU3yMczwbLGOH4HmGxWKTvvnJjhCMmRYjErGE0GkUAotFo9Grfs2cPs0+ldeTK+bOz2PYcMomFN70t/TvvqU2K4nPmsQ92S/VDiVwf/PBPX0vt7u0dCYnvH/zxK5f++Ki5R7Ta7C6fbWzYydSWv/ZYy4VCd8d1/Pi52oDuYd7wjCkY39HwvLPGFU5IdzY76c7XN+nOF9KdzU668/XNUlcuN3CGllNGMAMDA9zqyJXzZ2ezRe5LcXJ94PJOXIh8e74j6Ol5aHiIsbVI1J0trnDCM6ZgfEfH884WVzgh3dnspDtf36Q7X0h3Njvpztc3r7goiYtg1Go1tzpy5fzZWWyRvLEJS/+yHo3gy7e3pM39k2jQnTWucMIzpmB8k+58Id3Z7KQ7X9+kO19IdzY76c7XN6+4BFEUZ2fXCAImkwlJSUkwGo0RuVVrKGjvH8P6334q/b6qOBWv/OxoJh+Pf9iCxz+cXl/c8dAZoQzPLxc9W4vPW/oAAJ/+ch2K0g1B+7zwzzXYuLdf+v25S6uxdn4G5t76rvTZlzcfjznJcUG3NVsU3fwOAOCE0kw8c0n1LEdDEARBEARxZMCSG9BMXATjeKmSRx25cv7sLDbPw61lQwsbcn0QTKi+fHu7fvfPmg6/FB2K9ljLhUp31rjCCc+YgvEdDc87a1zhhHRns5PufH2T7nwh3dnspDtf37zioiQugrHb7dzqyJXzZ2exKXkHTA41p8xPrg+COSdOqQ7e3EaD7qxxhROeMQXjm3TnC+nOZifd+fom3flCurPZSXe+vnnFRUlcBJORkcGtjlw5f3YWm/s5cYHwo6MLkZ8ah5+tmRu0L2dY+pc1+VTqWxAEj/ft0tPSmNpiaS9cugOAyBBXOOEZUzC+o+F5Z40rnJDubHbSna9v0p0vpDubnXTn65tXXHROXASTmprKrY5cOX/2QG1AYMspk/VafP6r9cybi8ghG6vzz4xN+/Kt5BqSU5LZGvPTHmu5UOseyD3MG54xBeM7mp530p1vXdJdOaQ7m5105+ubdOdLNOmuBJqJi2AcBy7yqCNXzp+dxaZSuSYtgeZhoU7gAPk+CKZNX7493hH0Uqbt8AGeoWiPtVyodGeNK5zwjCkY39HwvLPGFU5IdzY76c7XN+nOF9KdzU668/XNKy5K4giueCYtEbSziQxHTqQEQRAEQRDENwlK4iKYBQsWcKsjV86fncUWSbtRuiPXB8FsbOLLt8dGL4Ln53PnlrA15qc91nKh0h2YToIDuYd5wzOmYHxHw/POGlc4Id3Z7KQ7X9+kO19IdzY76c7XN6+4KImLYIaGhrjVkSvnz85ic9/YJJKSOvm+mgmWdWmlL99KZiZNpmGmtvy1x1ouVLqzxhVOeMYUjO9oeN5Z4wonpDubnXTn65t05wvpzmYn3fn65hUXJXERTF9fH7c6cuX82VlsEZSzeSDXBy4zcSH27dGW088DA4OMrUWe7qxxhROeMQXjOxqed9a4wgnpzmYn3fn6Jt35Qrqz2Ul3vr55xUVJXAQTyMYaSuvIlfNnZ7Hx2JAkVPCMzZdvz/7xLBPIsQyRpjtrXOFkNnTnVZd0Vw7pzmYn3fn6Jt35Qrqz2Ul3vr55xSWIoihy8UzIYjKZkJSUBKPRiMTExNkOhwv9o5Oouu9D6fc1CzLw4o9XzmJEyvnpi/X4785DAICvbjkeOUlxQfv8yfN1+GhXr/T7Xy9bhWPmpaPk1ndhs08/inW3nYiMhNig25otim5+BwBwQmkmnrmkepajIQiCIAiCODJgyQ1oJi6C2bx5M7c6cuX82VlsSrbUny3k+sB1OSVb5Ep18Oa1qWkrU1ss7YVLd2D6sO9A7mHe8IwpGN/R8LyzxhVOSHc2O+nO1zfpzhfSnc1OuvP1zSsuSuIiGKvVyq2OXDl/dhZbJG9sItcHgsvGJqHx7eFHcPkPAMBms7E15qc91nKh0p01rnDCM6ZgfEfD884aVzgh3dnspDtf36Q7X0h3Njvpztc3r7goiYtgAjnhXWkduXL+7Cy2SEra3JHrg2A2NvHtW95TcnIyY2uRpztrXOGEZ0zB+I6G5501rnBCurPZSXe+vkl3vpDubHbSna9vXnFREhfBZGVlcasjV86fncXmsZGHgtjChVwfBJOAKtXBMdvn3FZmRga39sKlO2tc4YRnTMH4jobnnTWucEK6s9lJd76+SXe+kO5sdtKdr29ecVESF8E0NzdzqyNXzp+dxeZ5uHXkpHFM/csYti/fvi7feXuhPXv2sDXmpz3WcqHSnTWucMIzpmB8R8PzzhpXOCHd2eykO1/fpDtfSHc2O+nO1zevuCiJI7gSOSkbOy7vxIXoSjxeiTuSO4ggCIIgCIKYFSiJi2DmzZvHrY5cOX92FpvHxiYKYgsXsn3l/E4cY+BKdfDmdu7cYrbGGNoLl+6scYUTnjEF4zsannfWuMIJ6c5mJ935+ibd+UK6s9lJd76+ecVFSVwEMzo6yq2OXDl/dhab53JK+djChVwfBBOqL99Krn9sbCxk7bGWC5XurHGFE54xBeM7Gp531rjCCenOZifd+fom3flCurPZSXe+vnnFRUlcBNPT08Otjlw5f3YWm+cyxMjJ4lj6lzVqX77d+8PbO4J9vb0enwXaHmu5UOnOGlc44RlTML6j4XlnjSuckO5sdtKdr2/SnS+kO5uddOfrm1dclMQRXImkmbdgiKQNWQiCIAiCIIhvNoIoOu+LR4QTk8mEpKQkGI1GJCYmethFUWROHpTWkSvnz85im7TasPDX/5F+P2lRFv50UZVsfOFArg+ufrkB7zR1AwAabz8JKQZt0L6v/MtmvLd95i8y/7jiaFQXpWLere/Cap9+FLfccRKS9crb8tcea7lQ6F508zsAgPULM/DsJdURlwAH8lyFw3c0PO+scYUT0p3NTrrz9U2684V0Z7OT7nx9s9SVyw2coZm4CGbLli3c6siV82dnsUXyxiYs/cv63Pry7fGOoJcy27ZtY2vMT3us5UKlu9L2ZgOeMQXjOxqed6XtzQakO5uddOfrm3TnC+nOZifd+frmFRclcRHM1NQUtzpy5fzZWWyRlLS5I9cHwcTOqp3zdLglCnRnjSuc8IwpGN/R8LyzxhVOSHc2O+nO1zfpzhfSnc1OuvP1zSsuSuIimOTkZG515Mr5s7PY3KePI2nmnaV/Wc+J8+Xbc2MTb3WTmNry1x5ruVDprrS92YBnTMH4jobnXWl7swHpzmYn3fn6Jt35Qrqz2Ul3vr55xUVJXASTl5fHrY5cOX92FpvKY/lg5GRxTP3LGLZP3wr85ObOYWvMX3uM5UKlOzCdwAdyD/OGZ0zB+I6G5501rnBCurPZSXe+vkl3vpDubHbSna9vXnFREhfBbN++nVsduXL+7Cy2SHvp1RmW/mW9DOW+PR03N+9ka4yhvXDpzhpXOOEZUzC+o+F5Z40rnJDubHbSna9v0p0vpDubnXTn65tXXJTEEdxxTlMiOKcLCx6n5n3D+4MgCIIgCIJgh5K4CGbu3Lnc6siV82dntTknKpGUtLD0L2vYvnwrOc+juLiYsbXI1J0lrnDCM6ZgfEfL884SVzgh3dnspDtf36Q7X0h3Njvpztc3r7goiYtgzGYztzpy5fzZWW0uM3ER9E4cS/+yLgtV6tub18ko0Z0lrnDCM6ZgfEfL884SVzgh3dnspDtf36Q7X0h3Njvpztc3r7goiYtgDh48yK2OXDl/9kBtkQZLrKyppy/fSvz09PTIF1LYHmu5UOouimJE3g88YwrGdzQ976Q737qku3JIdzY76c7XN+nOl2jSXQmUxBHcEVyn4o5IQrUM1H05ZSRv/EIQBEEQBEFEJoIoikpe0yE4YDKZkJSUBKPRiMTERA+71WqFRqNh8qm0jlw5f3ZW28Jfv4dJqx0AcGZFDp64oFI2vnAg1wc//1sj/r11+q8nO+85BXqtci18+b765Qa809Qt/f7m1cdgaX4y5t7yDuyHn8SmO05Aol6nuC1/7bGWC4XuRTe/AwBYvzADf/pRJfM9zJtAnqtw+I6W550lrnBCurPZSXe+vkl3vpDubHbSna9vlrpyuYEzNBMXwezYsYNbHbly/uystkj9OwFL/7K+y+fLtxIvu5qbmdry1x5ruVDqzhJXOOEZUzC+o+V5Z4krnJDubHbSna9v0p0vpDubnXTn65tXXJTERTDR8gKs4LSIMJKWD8r1gXPyyRq2L9+eyym91Z1ka8xPe6zl6MXn2fMdLc87S1zhhHRns5PufH2T7nwh3dnspDtf37SxyTcQuWnUYOrIlfNnZ7U5J26Rk8IF1r/h8J2YmMCtvXDqzhJXOIlc3aPjeWeJK5yQ7mx20p2vb9KdL6Q7m5105+ubV1yUxEUwRUVF3OrIlfNnZ7WphJnbLIIm4mT7IJhZQ1++PQ779pLWFhYWhqw91nKh1F1Je7MBz5iC8R0tz7uS9mYD0p3NTrrz9U2684V0Z7OT7nx984qLkrgQ8u1vfxspKSn47ne/GxJ/TU1N3OrIlfNnZ7XZRbuimMINS/+y5nNKfXvzu337drbGGNoLh+5z0w0AgLOX5QZ0D/OGZ0zB+I6W550lrnBCurPZSXe+vkl3vpDubHbSna9vXnFREhdCrr32Wrz44ouzHUZEE0ETcUyE6pDyyNziJbS8ec0x+OeVR+OcZXNmOxSCIAiCIIiohJK4ELJ+/XokJLC/z+SLQJbVKa0jV86fndWmUjm9ExdB6ylZ+pc17EC0k+oWFLDXiSDdE3QxWFGYCkEQguoHXvCMKSjdo+R5Z4krnJDubHbSna9v0p0vpDubnXTn65tXXBGRxB04cAAXXngh0tLSoNfrsWzZMmzevDlk/j///HOcddZZyM3NhSAIeOONN7yWe/LJJ1FcXAydTocVK1bgiy++CFkMgWCz2bjVkSvnz85si9DpJ5b+ZU09ffn2eCfOi2O7PUp0Z4grnPCMKRjfUfO8M8QVTkh3Njvpztc36c4X0p3NTrrz9c0rrllP4oaGhnDMMccgJiYG7733Hnbu3IlHH30UycnJXst/+eWXsFgsHp/v2rULPT09XuuMjY1h6dKleOKJJ3zG8corr+C6667DbbfdhsbGRhx33HE47bTT0NnZKZVZsWIFlixZ4vHv4MGDbBetkP3793OrI1fOn53V5rJVv4LYwkUg/RusbyX57IEDB0LWHmu5UOrOElc4mQ3dedUl3ZVDurPZSXe+vkl3vpDubHbSna9vXnHN+lHrDz/8MPLz8/Hcc89Jn/naxcVut+Pqq6/G/Pnz8fe//x1qtRoA0NLSgvXr1+P666/HjTfe6FHvtNNOw2mnneY3jt/97nf4yU9+gssuuwwA8Pjjj+P999/HU089hQcffBAAQjo7+I0lkrI4BngtAw3Vu3YEQRAEQRDEN4dZT+LeeustnHLKKTjvvPPw2WefYc6cObjqqqtw+eWXe5RVqVR49913sWbNGlx00UV46aWX0N7ejuOPPx5nn3221wROCVNTU9i8eTNuvvlml89PPvlkbNq0KSCf/tiwYQM2bNggTa/W19fDYDCgsrISzc3NmJiYQEJCAsrKylBTUwNgej2t3W5HV1cXAGDZsmXYu3cvRkdHYTAYsGDBAjQ2NsJut+PAgQNQq9XYt28fAKCiogIdHR0wmUzQ6XRYvHgx7HY7ampqkJubC51Oh7a2NgDAkiVLsH//ftjtdjQ2NmLZsmWora0FAGRnZyM+Pl6qW1ZWhkOHDmFwcBAajQYrVqyAKIqoqalBRkYGUlJS0NLS4pKmDPT3o66uDtXV1aivr4fNZkNaWhoyMzPR3NwMAJg/fz5MJhMOHToEAFi1ahUaGhpgsViQkpKC3Nxc7NixAwBQUlKC8fFxdHd3AwCqqqqwfft2mM1mJCUloaCgANu2bQMw/ccBq9Uq/UVkyZIl2LZtG8bHxxEfH4+SkhJs3boVAFBQUADzxIQU96TZjPb2NoyOjkKv16O0tBQNDQ0AgLy8PGg0GnR0dAAAysvLER8fj5qaGuh0OixZsgT19fUAgIlx11nk8YlxtLT0wGmyUurfrKwsJCYmYs+ePQCAsrIy9Pb2YmBgAGq1GlVVVairq4PdbkdKSgqGh4exe/duAMCCBQswNDSEvr4+CIKAlStXYvPmzbDb7dizZw+ysrKk/p43bx5GR0fR09MDu90OURSxZcsWTE1NITk5GXl5edi+fTvsdjv6+vpgNpul2ecVK1Zgx44dsNvtaG5uRlFRkbQLU2FhIbKzs6V7ePny5WhpacHY2Bji4+Mxb948bNmyBQCQn58PlUrlcs+2t7djZGQEcXFxKCsrk/p7zpw50Gq1aG9vl/q7q6sLw8PDiI2NRUVFBerq6qR71mAwoLW1FQCwaNEiJCYmoqamBjExMaisrJTiy8zMRFJSktTfpaWl6O/vR39/P1QqFaqrq6X+Tk9PR3p6Onbt2iXds0ajUdLO+Z5NTU1FdnY2du7cKd2zY2Nj0sqB6upqNDU1wW63Y/fu3cjPz5fu2eLiYkxNTUmzs4GOETqdDlNTU2hsbJTuWecxorS0FM3NzS5jhOOPVpmZmejr6/MYI4aHh6HRaCCKItMYUVtbC1EUXcYIAFi4cCEGBwfR19cn9XcoxwhHTCxjRGVlJXbt2uVzjACAzs5O2O12mM1mtLa2Kh4jOjs7YTQaERMTA5vNJo0ROTk50Ov10j27ePFiHDx4EENDQ9I9q3SMWL58uXTPZmRkIDU11WWMSE1NRU1NjcsYYbVakZqainnz5kn3lvMY4bi3GhsbmcYIs9mMxMREjzHCZrNJ/c1jjLDb7RgfH2caI3p6ejA4OCg7RpSUlKC1tZVpjOjt7ZWuldcYERsbi5qaGiQkJKC4uNilv+12u3T/ePse4bC5jxEVFRUwGAzS/9eUjhFardbr94i9e/dK9yyPMaKoqAgdHR1cvkc4/39N6RgBAEuXLkVcXBxqamq4jBF2ux0NDQ0u96zzGGG322EymXx+j2hra/MYI4aGhmC321FbW8s0RqxcudLr9wgAmDt3LrcxIjc3V4op1N8jHM8Gyxjh+B5ht9ul775yY4QjJkWIs0xsbKwYGxsr3nLLLWJDQ4P49NNPizqdTnzhhRd81tm3b59YWFgofu973xMLCgrEiy66SLTb7YraAyD+61//cvnswIEDIgDxyy+/dPn8/vvvFxcsWKD4Wk4++WQxPT1djIuLE+fMmSPW1tb6LW80GkUAotFo9Grftm2b4rZZ68iV82dntS369Tti4U1vi4U3vS3e8MoWRfGFA7k+uOblBilupfeXnO+r/rpZ8ll409vizoPT2hffPPPZlq1NTG35a4+1XCh1Z4krnPCMKRjf0fK8s8QVTkh3Njvpztc36c4X0p3NTrrz9c1SVy43cGbWZ+LsdjuqqqrwwAMPAJjOsnfs2IGnnnoKF110kdc6BQUFePHFF7F27VrMnTsXzzzzTEiWu7n7EEWRye/7778fdAzOjI2NcasjV86fPVAbEFmHfcvGytG31IaXRqJJ90CuhTc8YwrGN+nOF9KdzU668/VNuvOFdGezk+58ffOKa9Y3NsnJycGiRYtcPisrK3PZUMSdQ4cO4ac//SnOOussjI+P4/rrrw8qhvT0dKjVao+NUXp7e5GVlRWU72CIj4/nVkeunD87q805EY6gHI6pf1n/SODLty8vzhueRIvuLHGFE54xBeObdOcL6c5mJ935+ibd+UK6s9lJd76+ecU160ncMcccI62/ddDS0uLzTIX+/n6ccMIJKCsrw+uvv46PP/4Yr776Kn75y18GHINWq8WKFSvwwQcfuHz+wQcfYPXq1QH7DZZ58+ZxqyNXzp+d1aZWz/pt5hW5PgjmZARfvt19etvYZH6U6M4SVzjhGVMwvqPleWeJK5yQ7mx20p2vb9KdL6Q7m5105+ubV1yz/u36+uuvx9dff40HHngAe/fuxcsvv4w//vGPuPrqqz3K2u12nHrqqSgsLMQrr7wCjUaDsrIyfPjhh3j++efx2GOPeW1jdHQUW7ZskV5+bG9vx5YtW1xm+2644Qb8+c9/xrPPPovm5mZcf/316OzsxBVXXMHlupXgiJdHHbly/uysNpt15nyMSFpOGUj/hsP3lq3sdSNRdyXtzQYRq3uUPO9K2psNSHc2O+nO1zfpzhfSnc1OuvP1zSuuWX8nrrq6Gv/6179wyy234J577kFxcTEef/xx/PCHP/Qoq1Kp8OCDD+K4446DVquVPi8vL8eHH36ItLQ0r23U19dj/fr10u833HADAODiiy/G888/DwD43ve+h4GBAdxzzz3o7u7GkiVL8O6770bkifRHMt/0LfWVHPb9ze4hgiAIgiAIQo5ZT+IA4Mwzz8SZZ56pqOxJJ53k9fNly5b5rLNu3TqXA6d9cdVVV+Gqq65SFEc4yM/P51ZHrpw/O6tNrVYBFj6n1QeDXB8Ek0z58u25nFJ53UDaYy0XSt1Z4gonPGMKxjfpzhfSnc1OuvP1TbrzhXRns5PufH3zimvWl1MSvlGp2OVRWkeunD97oDYgspZTBtK/4fDtOMSeR3vh1p1nHwdKpOoeTc876c63LumuHNKdzU668/VNuvMlmnRX5DeQSi0tLfjjH/+I++67D/fcc4/LPyJ0OA7Z5FFHrpw/O6vNbrdLP0dSEhdI/4bat7f+iBbdWeIKJ5Gge6jqku7KId3Z7KQ7X9+kO19IdzY76c7XN6+4mJdT/ulPf8KVV16J9PR0ZGdnu24fLwi44447QhogEW1EUBYXQShY7UsQBEEQBEEQAABBVPKymBOFhYW46qqrcNNNN/GK6RuDyWRCUlISjEYjEhMTPewTExOIi4tj8qm0jlw5f3ZWW+U9/8XguAUA8IOVBXjw3HLZ+MKBXB9c+7dGvLX1IACg46EzQuL76pcb8E5Tt/T7hzesxbzMeBTd/I70WfOdx0eF7ixxhROeMQXjO1qed5a4wgnpzmYn3fn6Jt35Qrqz2Ul3vr5Z6srlBs4wL6ccGhrCeeedx1qNCID29nZudeTK+bOz2mz2yDxiIJD+DbVvb/0RLbqzxBVOIkH3UNUl3ZVDurPZSXe+vkl3vpDubHbSna9vXnExJ3HnnXce/vvf//KIhXBjZGSEWx25cv7szDanyd4IyuEC6t9w+I4a3RniCiekO5uddOfrm3TnC+nOZifd+fom3fkSTborgfmduHnz5uH222/H119/jfLycsTExLjYr7322pAF900nkGlbpXXkyvmzM9sEAZ6b688+PJcBKPXtLamNGt0Z4gonkaB7qOqS7soh3dnspDtf36Q7X0h3Njvpztc3r7iY34krLi727UwQ0NbWFnRQ3xTk1r1aLBaPJFkOpXXkyvmzs9qq7vsA/aNTAIALjyrAfedExjtxcn0QzDtxvny7vxP38S/WYm6G6ztxe+49OSp0Z4krnPCMKRjf0fK8s8QVTkh3Njvpztc36c4X0p3NTrrz9c1Sl+s7ce3t7T7/UQIXWhoaGrjVkSvnz85qs1gs0s9CBC2olOuDYN7fU6qD4KWRaNGdJa5wwjOmYHyT7nwh3dnspDtf36Q7X0h3Njvpztc3r7iCOn1OFEUwTuQR30Cc05RI2thEjnDc2kdQdxAEQRAEQRARQkBJ3Isvvojy8nLExcUhLi4OFRUVeOmll0Id2zeeOXPmcKsjV86fndWmVqsVxRRuAunfcPiOFt1Z4gonpDubnXTn65t05wvpzmYn3fn6Jt35Ek26K4F5Y5Pf/e53uP3223HNNdfgmGOOgSiK+PLLL3HFFVegv78f119/PY84v5FotVpudeTK+bOz2lwOhFcQW7gIpH9D7dvbzGS06M4SVziJBN1DVZd0Vw7pzmYn3fn6Jt35Qrqz2Ul3vr55xcU8E/eHP/wBTz31FB5++GGcffbZ+Na3voVHHnkETz75JH7/+9/ziPEbS7ScJ2KzOZ8TFzlpXDSdJxKJurPEFU5IdzY76c7XN+nOF9KdzU668/VNuvMlmnRXAnMS193djdWrV3t8vnr1anR3d3upQRBHJuHINyNpoxeCIAiCIAjiyID5iIElS5bgggsuwK233ury+X333YdXXnkF27ZtC2mA0YzcNqLj4+PQ6/VMPpXWkSvnz85qW3n/B+gdmT5i4JLVRbjr7MWy8YUDuT74n7834s0tgR0x4Mu3+xEDX9y4HvmpepcjBnbesT4qdGeJK5zwjCkY39HyvLPEFU5IdzY76c7XN+nOF9KdzU668/XNUpfrEQN333037rjjDpx66qm49957cd999+HUU0/F3XffjXvuuYfVHeGHrq4ubnXkyvmzs9rsLsspFQQXJgLp33D4jhbdWeIKJ6Q7m5105+ubdOcL6c5mJ935+ibd+RJNuiuBOYn7zne+g5qaGqSnp+ONN97A66+/jvT0dNTW1uLb3/42jxi/sQwPD3OrI1fOn53VZrfPTPZG0vLBQPo3HL6jRXcl7c0GpDubnXTn65t05wvpzmYn3fn6Jt35Ek26K4F5d0oAWLFiBf7yl7+EOhbCjdjYWG515Mr5s7PaImkzE2cC6d9w+I4W3VniCiekO5uddOfrm3TnC+nOZifd+fom3fkSTborQdE7cSaTSVqXaTKZ/JaVW79JzCC37tVut0OlYpssVVpHrpw/O6vt6Ac/QrfRDAD4ybHFuP3MRbLxhQO5PgjmnThfvt3fidt403rkpbi+E9f2wGlRoTtLXOGEZ0zB+I6W550lrnBCurPZSXe+vkl3vpDubHbSna9vlrohfycuJSUFvb29AIDk5GSkpKR4/HN8ToSOuro6bnXkyvmzs9qmpqaknyNpTi6Q/g21b2+zlNGiO0tc4SQSdA9VXdJdOaQ7m5105+ubdOcL6c5mJ935+uYVl6LllB9//DFSU1MBAJ988gmXQAiCIAiCIAiCIAh5FCVxa9eu9fozwZfs7GxudeTK+bOz2jRqNQA7gMjanTKQ/g21b2/dES26s8QVTiJB91DVJd2VQ7qz2Ul3vr5Jd76Q7mx20p2vb15xMS/u/M9//oONGzdKv2/YsAHLli3DBRdcgKGhoZAG903HYDBwqyNXzp+d1ea8ZDCSNjkJpH/D4TtadGeJK5yQ7mx20p2vb9KdL6Q7m5105+ubdOdLNOmuBOYk7le/+pW0ucm2bdtwww034PTTT0dbWxtuuOGGkAf4Taa1tZVbHbly/uysNpvNKv0cOSlcYP0btG+3bYS85bTRojtLXOFkVnTnVJd0Vw7pzmYn3fn6Jt35Qrqz2Ul3vr55xcV8xEB7ezsWLZreXfCf//wnzjrrLDzwwANoaGjA6aefHvIACSKaiaRz8wiCIAiCIIgjA+aZOK1Wi/HxcQDAhx9+iJNPPhkAkJqaKnv8AMGGI1nmUUeunD87q00bEzPzSwTlLIH0b9C+FVx/tOjOElc4mRXdOdUl3ZVDurPZSXe+vkl3vpDubHbSna9vXnExJ3HHHnssbrjhBtx7772ora3FGWdMn5/V0tKCvLy8kAf4Taanp4dbHbly/uysNpvdLv0cSTNPcn0QTKQ+fStYThkturPEFU54xhSMb9KdL6Q7m5105+ubdOcL6c5mJ935+uYVF3MS98QTT0Cj0eC1117DU089hTlz5gAA3nvvPZx66qkhD/CbzODgILc6cuX82VltdptNUUzhJpD+DYfvaNGdJa5wQrqz2Ul3vr5Jd76Q7mx20p2vb9KdL9GkuxKY34krKCjA22+/7fH5Y489FpKAiBlinJchhriOXDl/dlab6+6UCoILE4H0b9C+Bb+/+q8bSHuM5UKpO0tc4WRWdOdUl3RXDunOZifd+fom3flCurPZSXe+vnnFJYiiKMoXm6GhoQExMTEoLy8HALz55pt47rnnsGjRItx1113QarVcAo1GTCYTkpKSYDQakZiYONvhcGPNI5+gc3D6Pcqr1pXgxlNLZzkiZfzP3xvx5paDAICOh84Iic+r/9qAd7Z1S7/X3nYCMhN0KLr5HemzULVFEARBEARBHDmw5AbMyyl/9rOfoaWlBQDQ1taG73//+9Dr9fjHP/6BG2+8MbCICa/U1NRwqyNXzp+d1TY5aVYUU7gJpH/D4TtadGeJK5yQ7mx20p2vb9KdL6Q7m5105+ubdOdLNOmuBOYkrqWlBcuWLQMA/OMf/8CaNWvw8ssv4/nnn8c///nPUMdHRBmRtJxSDi6heiynPII6hCAIgiAIgogImJM4URRhP7zb4IcffiidDZefn4/+/v7QRvcNJzMzk1sduXL+7Kw2jXrm1ctISloC6d+gfStYvBwturPEFU5mRXdOdUl35ZDubHbSna9v0p0vpDubnXTn65tXXMxJXFVVFe677z689NJL+Oyzz6QjBtrb25GVlRXyAL/JJCUlcasjV86fndWmUs3cZpE0ExdI/4bat7f+iBbdWeIKJ5Gge6jqku7KId3Z7KQ7X9+kO19IdzY76c7XN6+4mJO4xx9/HA0NDbjmmmtw2223Yd68eQCA1157DatXrw55gN9k9uzZw62OXDl/dlab1TKlKKZwE0j/htq3t5w2WnRniSucRILuoapLuiuHdGezk+58fZPufCHd2eykO1/fvOJiPmKgoqIC27Zt8/j8N7/5DdRqdUiCIqKXCJqIk0WIpGlDgiAIgiAIgjgM80ycL3Q6XUSeGXEkU1rKvhW/0jpy5fzZWW3a2NiZXyIoMQqkf0Pt21uiGC26s8QVTiJB91DVJd2VQ7qz2Ul3vr5Jd76Q7mx20p2vb15xKUriUlNTpU1LUlJSkJqa6vMfEToC2ShGaR25cv7srDabzaYopnDDcyOeYHxHi+4scYUT0p3NTrrz9U2684V0Z7OT7nx9k+58iSbdlaAoiXvssceQkJAAYPqduMcee8znPyJ0RMvDbndK4iJnHm6WHnaPIwYY6gbSHmM5GuRnzzfpzhfSnc1OuvP1TbrzhXRns5PufH3zikvRO3EXX3yx158Jvjjv6hjqOnLl/NkDtQERtZoyoP4N2rfbEQPe+iOadOfZx4EyK7pzqku6K4d0Z7OT7nx9k+58Id3Z7KQ7X9+84hJEUVRwcpUnvb296O3tlc6Mc1BRURGSwL4JmEwmJCUlwWg0IjExcbbD4cYJj36K1r4xAMD1Jy7A/5w4f5YjUsYNr2zB640HAAAdD50REp9X/7UB72zrln7fcsdJSNZrUXTzO9JnoWqLIAiCIAiCOHJgyQ2YU8PNmzdjyZIlyMnJQUVFBZYtWyb9W758ecBBE57U1dVxqyNXzp+d1WY2mxXFFG4C6d+gfXssp/SciosW3VniCiezojunuqS7ckh3Njvpztc36c4X0p3NTrrz9c0rLuYjBi699FIsWLAAzzzzDLKysmgbdo64z3KGso5cOX/2QG1AZC2nDKR/g/atYN47mnTn2ceBMiu6c6pLuiuHdGezk+58fZPufCHd2eykO1/fvOJiTuLa29vx+uuvS4d8E/xIT0/nVkeunD87q236/MDI26EykP4NuW8vSW206M4SVziJCN1DVJd0Vw7pzmYn3fn6Jt35Qrqz2Ul3vr55xcW8nPKEE07A1q1becRCuBEtD7tGM/O3ggiaiJPvqyCC9enbfTklJXFhJ5oGedJdOaQ7m5105+ubdOcL6c5mJ935+o6YJO7Pf/4znn32Wdx999345z//ibfeesvlHxE6du3axa2OXDl/dlbb1OSk9HMkLacMpH+D9q1gOWW06M4SVziZFd051SXdlUO6s9lJd76+SXe+kO5sdtKdr29ecTEvp9y0aRM2btyI9957z8MmCELEHuxMEJFIBOW0BEEQBEEQxBEC80zctddeix/96Efo7u6G3W53+UcJXGiZP599K36ldeTK+bOz2mJjtdLPkbQRTiD9Gw7f0aI7S1zhhHRns5PufH2T7nwh3dnspDtf36Q7X6JJdyUwz8QNDAzg+uuvR1ZWFo94vhFs2LABGzZskJLe+vp6GAwGVFZWorm5GRMTE0hISIBGo8GePXsAAIWFhbDb7ejq6gIALFu2DHv37sXo6CgMBgMWLFiAxsZGjI2NYeHChVCr1di3bx+A6bP7Ojo6YDKZoNPpsHjxYmzZsgUGgwG5ubnQ6XRoa2sDACxZsgT79+/HgQMHkJKSgmXLlqG2thYAkJ2djfj4eGzduhUGgwFlZWU4dOgQBgcHodFosGLFCmzduhV6vR4ZGRlISUlBS0uLyxEDXV1dqKsbQnV1Nerr62Gz2ZCWlobMzEw0NzcDmL7ZTSYTDh06BABYtWoVGhoaYLFYkJKSgtzcXOzYsQMAUFJSgvHxcXR3T5+9VlVVhe3bt8NsNiMpKQkFBQXYtm0bAKCoqAhWqxX79+8HAKSlpeHAgQMYHx9HfHw8SkpKpPc9CwoKMDExIcVtNpvR2tqK0dFR6PV6lJaWoqGhAQCQl5cHjUaDjo4OAEB5eTl2794NlUoFnU6HJUuWoL6+HgAwPm5xuRfGx8bQ3dnr8plDm6ysLCQmJkr3QFlZGXp7ezEwMAC1Wo2qqirU1dXBbrdDpVJBpVJh9+7dAIAFCxZgaGgIfX19EAQBK1euxObNm2E0GpGfn4+srCypv+fNm4fR0VH09PRgbGwM69evx5YtWzA1NYXk5GTk5eVh+/btGBsbQ3l5OcxmMw4ePAgAWLFiBXbs2IGBgQHk5OSgqKgITU1N0j3b3d0txb98+XK0tLRgbGwM8fHxmDdvHrZs2QIAyM/Ph0qlcrln29vbMTIygri4OJSVlUn9PWfOHGi1WrS3t0v93dXVheHhYcTGxqKiokLazjc7OxsGgwGtra0AgEWLFknxxMTEoLKyEjU1NQCAzMxMJCUlSfbS0lL09/ejv78fKpUK1dXVUn+np6cjPT1dWiIxf/58GI1GtLe3w2AwuNyzqampyM7Oxs6dO6V7dmxsDD09PQCA6upqNDU1YXBwEHPmzEF+fr50zxYXF2NqagoHDkyfVxjoGGG1WlFdXY3GxkbpnnUeI5KTk3Ho0CGXMWLz5s1SP9lsNo8xYnh4GJOTkzjuuOOYxoja2lqIougyRgDAwoULMTg4iL6+Pqm/QzlG7N27FwaDgWmMqKysxK5du3yOEQDQ2dmJsbExrF69mmmM6OzshNFoxNTUFFavXi2NETk5OdDr9dI9u3jxYhw8eBBDQ0PSPat0jEhLS0NrayvsdjsyMjKQmprqMka0trZiz549LmOE1WpFamoqrFar5Nd5jACAjIwM7Nu3j2mMMJvNSExM9BgjbDab1N88xoixsTEcddRRTGNET08PBgcHZceIhIQEDA0NMY0Rvb29kq+Ojg4uY8TOnTsRExODhIQEFBcXu/S33W7Hrl27YDAYvH6PcNxb7mNERUWFy//XlI4RWq3W6/eIvXv3SvcsjzHCYDBw+x5x8OBB6R5QOkYAwNKlS7Fr1y6o1WouY8T+/fuRnJzscs86jxFjY2Ooqqry+j1iZGQERUVFHmPE0NAQOjo6EB8fzzRGrFy50uv3CACYO3cutzGir69PiinU3yOamppgMBiYxgjH94h9+/YhISFB8fcIxYiMXHTRReKf/vQn1mqEF4xGowhANBqNXu1ff/01s0+ldeTK+bOz2o69/z2x8Ka3xcKb3haf+HiPovjCgVwf3PDKFinuUPm+6i+bJZ+FN70tjpotoiiKLp9Fi+4scYUTnjEF45t05wvpzmYn3fn6Jt35Qrqz2Ul3vr5Z6srlBs4wz8QtWLAAt9xyCzZu3Ijy8nLExMS42K+99lpWl0SU47yAMoJWUxIEQRAEQRDE/7f35uFVlef6/72TkAQyQQijQEABQaZAwP60DuAA4lCH49FWj4hiW0Vbh2MrLae2WhVP26O2X6dardOpLWpLW4disYpYtAFCwhjmRAaZCQnJzpz1+4OTbWLCWutZez/vXlm5P9fF1U3u9332k/cTdvO6pk5JyLIsF/fL+4Jhw4aduFgoFDmcTpyprKxEVlYWKioqkJmZGe921LjoiWXYtO8YAOC+i0bhtqmnxLkjd/zn62vwx9XHD+GXPXpJTGre/rvVeGfd3sjfNz44Az2SkzB03juRr8XqvQghhBBCSOdBsjcQ39iktLT0hH+4gYstLefuasxxGmeXS7PW15b5CS/ra6J2ULxL+jIJvctyetetTe+60Lssp3fd2vSuS5C8u0G8ifsyTU1NKC4uRnl5eSz6Ia1oaGhwHuRxjtM4u1yefXGw10+nU3pZ31jXDnXwkIHgeNddY6/4wXus5tK7e+hdltO7bm1614XeZTm969bW6ku8ibvrrrvwwgsvADi+gTvnnHMwadIkDB48GEuXLo11f12a7OxstTlO4+xyaZaY+MWllz7awzmuQTQbTrceOnqPoHiX9GUSzZ6iqU3vutC7LKd33dr0rgu9y3J6162t1Zd4E/fmm29iwoQJAIC33noLZWVl2LRpE+666y7Mnz8/5g12Zfr37682x2mcXS7NkruJ759jBC/ra6J2ULxL+jIJvctyetetTe+60Lssp3fd2vSuS5C8u0G8iTt06FCkmXfffRf//u//jpEjR2LOnDmR52iQ2NDyzBiNOU7j7HJp1vo5cX46ndLL+pqoHRTvkr5MQu+ynN51a9O7LvQuy+ldtza96xIk724Qb+L69euHjRs3oqmpCYsXL8YFF1wAAAiHw0hMTIx5gyRYdHQNGCGEEEIIIcQ94vPcbrrpJlxzzTUYMGAAQqEQLrzwQgBAQUEBRo0aFfMGuzKnnCK/Fb/bOU7j7HJplpKSAiDsqi+TOK1BNNtNtx46OjIZFO+Svkyi2VM0teldF3qX5fSuW5vedaF3WU7vurW1+hIfifvJT36C559/Ht/61rewfPny//sFHUhMTMS8efNi3mBXprq6Wm2O0zi7XJpZzc2R1346ndLL+sa6dkdHJoPiXdKXSfzgPVZz6d099C7L6V23Nr3rQu+ynN51a2v15ekRA1dffTXuvvtuDBo0KPK1G2+8EZdffnnMGiPAvn371OY4jbPLpVlDo/9uQwt4W18TtYPiXdKXSehdltO7bm1614XeZTm969amd12C5N0Nnm4b+I9//AP/+Mc/cODAATS3OsoCAL/97W9j0hgJDrwO7sT46cgkIYQQQgjpHIQsy7Kch33BAw88gAcffBCTJ0+OXBfXmkWLFsW0wSBTWVmJrKwsVFRUIDMzs13e3NyMhATZwVK3c5zG2eXS7NL/9zHW76kEAPzo0tMw56xhjv2ZwGkNvvfGGrxRuBsAUPboJTGpffvvVuOddXsjf9/68Ex0S0zA0HnvRL6245GZgfAu6cskmj1FUzso/94lfZmE3mU5vevWpndd6F2W07tubclcp71Ba8TdPPvss3jppZdQUFCAP//5z1i0aFGbPyR2rF27Vm2O0zi7XJrV1tREXvvpwJOX9Y117Y7WIyjeJX2ZxA/eYzWX3t1D77Kc3nVr07su9C7L6V23tlZf4k1cfX09zjzzTI1eyJeoq6tTm+M0zi6XZs3NooO9xvCyvrGu/eUj2ZK5XuaY9C7pyyR+8B6rufTuHnqX5fSuW5vedaF3WU7vurW1+hJv4m655Ra89tprGr2QL9GzZ0+1OU7j7HJplpT0xfMD/XQNmJf1NVE7KN7dvF88oHdZTu+6teldF3qX5fSuW5vedQmSdzeIb2xSW1uL5557Du+//z7Gjx+Pbt26tckfe+yxmDXX1Rk8eLDaHKdxdrk0S0lOBuC//2LjZX2jrh2y/av9XC/vJxwXS++SvkwSF+9Kc+ndPfQuy+ldtza960LvspzedWtr9SU+Erd27Vrk5eUhISEB69evR1FRUeRPcXGxQotdl3Xr1qnNcRpnl0uzGp9eE+e0Bt8852QAwNcmDIxZbTfff1C8S/oyiWZP0dSmd13oXZbTu25teteF3mU5vevW1upLfCTuww8/1OiDdBE6ugbMr4zsl4GND85A926JzoM90omWgxBCCCGE+AR/3RuUtGHYMPmt+N3OcRpnl0uz1JQUVz2Zxs1a9UhO8rTxdOuho9pB8S7pyySaPUVTm951oXdZTu+6teldF3qX5fSuW1urL08P+165ciXeeOMN7Ny5E/X19W2yP/3pTzFpjKDd2sZyjtM4u1yaNbd6FKGfjjx5Wd9oa7vZEAbFu6Qvk8TDu9ZcencPvctyetetTe+60Lssp3fd2lp9iY/E/eEPf8BXv/pVbNy4EYsWLUJDQwM2btyIDz74AFlZWRo9dln27NmjNsdpnF0uzRpa/fD6aA/naX2jre3m+w+Kd0lfJomHd6259O4eepfl9K5bm951oXdZTu+6tbX6Em/iHnnkETz++ON4++23kZycjF/+8pcoKSnBNddcgyFDhmj0SAghhBBCCCHk/whZliV6EnNaWho2bNiAoUOHIicnBx9++CHGjRuHkpISnHfeedi7d69Wr4GjsrISWVlZqKioQGZmZru8oaGh3SMcnHA7x2mcXS7NrnzqnyjaVQEA+OkVY3HD/5fr2J8JvKxvtLXv/EMR/lL8eeTvZY9eAgAYOu+dyNe2/nR6ILxL+jJJPLxrzaV399C7LKd33dr0rgu9y3J6160tmeu0N2iN+EhcdnY2jh07BgA46aSTsH79egDA0aNHEQ6HpeWIDSUlJWpznMbZ5dLMr48Y8LK+0dZ28/0HxbukL5PEw7vWXHp3D73LcnrXrU3vutC7LKd33dpafYlvbHL22WdjyZIlGDduHK655hrceeed+OCDD7BkyRKcf/75Gj12WVpvfmI9x2mcXS7NmpubXfVkGi/ra6J2ULxL+jIJvctyetetTe+60Lssp3fd2vSuS5C8u0G8iXvyySdRW1sLAPjBD36Abt264Z///Ceuuuoq/OhHP4p5g12ZjIwMtTlO4+xyaZaYmAigEYC/7k7pZX1N1A6Kd0lfJqF3WU7vurXpXRd6l+X0rlub3nUJknc3iK6Ja2xsxO9+9zvMmDED/fv3V2moK+F03mtNTQ26d+8uqul2jtM4u1yaXfXUP7H6/66Je+TKcbjuK/64AY6X9Y229t0Li7Go6Iu7FHV0TVzJj88LhHdJXyaJh3etufTuHnqX5fSuW5vedaF3WU7vurUlc9WuiUtKSsJtt92Guro6yTTikbVr16rNcRpnl0uz6upqVz2Zxsv6mqgdFO+SvkxC77Kc3nVr07su9C7L6V23Nr3rEiTvbhDf2OQrX/kKioqKNHohAaX1KZR+Op0yHnTxb58QQgghhMQA8TVxc+fOxX/+539i9+7dyM/PR1paWpt8/PjxMWuuq5ObK78Vv9s5TuPscmmWkpIKoAqAvzYxXtbXRO2geJf0ZRJ6l+X0rlub3nWhd1lO77q16V2XIHl3g+tN3M0334wnnngC1157LQDgu9/9biQLhUKwLAuhUAhNTU2x77KL4uWujm7nOI2zy+WZ6FGExtC8a+YJa7vYxQbHuz/vTBoX70pz6d099C7L6V23Nr3rQu+ynN51a2v15fp0ypdffhm1tbUoLS1t92fHjh2R/yWxY9euXWpznMbZ5dKsvtU1lH46ndLL+pqoHRTvkr5MQu+ynN51a9O7LvQuy+ldtza96xIk725wfSSu5SaWfjx8SjoPIV+dUGmerv79E0IIIYSQ6HH9iIGEhATs378fffr00e6py+B0G9G6ujqkpKSIarqd4zTOLpdm1zy7HCvKjgIAfvZv43HNlMGO/ZnAy/pGW/veN9bgzcLdkb939IiBzQ9cEAjvkr5MEg/vWnPp3T30LsvpXbc2vetC77Kc3nVrS+aqPWJg5MiRyM7Otv1DYse2bdvU5jiNs8ulWZsn1fvoQJSX9TVROyjeJX2ZhN5lOb3r1qZ3XehdltO7bm161yVI3t0gujvlAw88gKysLJVGSHuqqqrU5jiNs8ulWVOT/y5+Bbytb7S13exhg+Jd0pdJ4uFday69u4feZTm969amd13oXZbTu25trb5Em7ivf/3r6Nu3r0ojpD1ffnxDLOc4jbPLpVli4hcHfH10IM7T+kZb282NXYLiXdKXSeLhXWsuvbuH3mU5vevWpndd6F2W07tuba2+XF8Tl5iYiL1793ITF0Ocznutr69HcnKyqKbbOU7j7HJp9vVff4J/lZYDAH7x7xNwdf4gx/5M4GV9o639/Mc78NA7JZG/d3RN3JYHLwyEd0lfJomHd6259O4eepfl9K5bm951oXdZTu+6tSVzVa6Jc7nXIzGkqKhIbY7TOLtcmh075r9D7oC39Y229qwzhmL0APt/lEHxLunLJPHwrjWX3t1D77Kc3nVr07su9C7L6V23tlZfrjdxzc3NPApHPNH6FEI/nU4ZD5KTEnDn+cPj3QYhhBBCCOnEiO5OScwyaJD8tEO3c5zG2eXSrPVtVf30sG8v62uidlC8S/oyCb3LcnrXrU3vutC7LKd33dr0rkuQvLuBmzgfk5iYqDbHaZxdLs1Cftq5tcLL+pqoHRTvkr5MQu+ynN51a9O7LvQuy+ldtza96xIk727gJs7HfPbZZ2pznMbZ5dKstrY28tpP+zkv62uidlC8S/oyCb3LcnrXrU3vutC7LKd33dr0rkuQvLuBmziiTqjNax/t4gghhBBCCOmEuH7EAIk9TrcRrampQffu3UU13c5xGmeXS7NvPPcJPt1x/BEDT1ybhysmnuTYnwm8rG8sai9evxe3/u9qAB0/YqDkx+cFwrukL5PEy7vGXHp3D73LcnrXrU3vutC7LKd33dqSuSqPGCDmKSsrU5vjNM4ul2a1Nf48ndLL+pqoHRTvbt4vHtC7LKd33dr0rgu9y3J6161N77oEybsbuInzMZWVlWpznMbZ5dKsqanRVU+m8bK+saltv5MNindJXyaJn/fYz6V399C7LKd33dr0rgu9y3J6162t1Rc3cT4mNTVVbY7TOLtcmiUk+PPHzMv6xqa2/RnMQfEu6csk8fMe+7n07h56l+X0rlub3nWhd1lO77q1tfriNXFxxOm818bGRiQlJYlqup3jNM4ul2b/8fy/8M9thwEAv/rGRHxtwkDH/kzgZX1jUdvpmrhtD80IhHdJXyaJl3eNufTuHnqX5fSuW5vedaF3WU7vurUlc3lNXEAoLCxUm+M0zi6XZsdaHUb20SVxntY3NrXtVyEo3iV9mSR+3mM/l97dQ++ynN51a9O7LvQuy+ldt7ZWX9zEEUIIIYQQQkgnwl/HQUkbBg6Un3bodo7TOLtcmh0/F7gBgL/uTullfWNR+6wROejeLRHjTsoSz/XyfpJxsfQu6csk8fKuMZfe3UPvspzedWvTuy70LsvpXbe2Vl/cxPmYoFwA2/rGJn562He8LoBNT0nCmh9PR7fEjtciKN4lfZkkSBc+07t76F2W07tubXrXhd5lOb3r1tbqi6dT+pgdO3aozXEaZ5dLs5qasKueTONlfWNVOzkpAaETHJYMindJXyaJp/dYz6V399C7LKd33dr0rgu9y3J6162t1Rc3ccQofjqdkhBCCCGEkM4IHzEQR5xuI1pdXY20tDRRTbdznMbZ5dJs1vOfYtm2IwCAZ66fhJnjBjj2ZwIv66tVu/UjBjb8aGogvEv6MomfvEc7l97dQ++ynN51a9O7LvQuy+ldt7ZkLh8xEBB2796tNsdpnF0uzWpra131ZBov62uidlC8S/oyCb3LcnrXrU3vutC7LKd33dr0rkuQvLuBmzgfc/ToUbU5TuPscmnW2NAQee2n0ym9rK+J2kHx7ub94gG9y3J6161N77rQuyynd93a9K5LkLy7gZs4H5OcnKw2x2mcXS7NEhL9+WPmZX1N1A6Kd0lfJqF3WU7vurXpXRd6l+X0rlub3nUJknc38Jq4GHLllVdi6dKlOP/88/Hmm286jnc679WyrBPewfBEuJ3jNM4ul2Y3v7QSH2w6AAB49j/ycdHY/o79mcDL+mrVbn1NXOmCiwPhXdKXSfzkPdq59O4eepfl9K5bm951oXdZTu+6tSVzeU1cnPjud7+LV155JWb1VqxYoTbHaZxdLs0qWh1G9tO/dy/ra6J2ULxL+jIJvctyetetTe+60Lssp3fd2vSuS5C8u4GbuBgybdo0ZGRkxLsNQgghhBBCSIDx1SZuwYIFCIVCuOuuu2Jad9myZbjsssswcOBAhEIh/PnPf+5w3NNPP41hw4YhNTUV+fn5+Pjjj2Pah5T+/eWnHbqd4zTOLpdmqakpkdc+OhDnaX1N1A6Kd0lfJqF3WU7vurXpXRd6l+X0rlub3nUJknc3+GYTt3LlSjz33HMYP3687bjly5ejodXdDlvYtGkT9u3b1+Gc6upqTJgwAU8++eQJ6y5cuBB33XUX5s+fj6KiIpx99tmYOXMmdu7cGRmTn5+PsWPHtvvz+eefu/wuZaSnp6vNcRpnl0uzpKSkyGs/nT/tZX1N1A6Kd0lfJqF3WU7vurXpXRd6l+X0rlub3nUJknc3+GITV1VVheuvvx6/+c1v0KtXrxOOa25uxu23347rrrsOTU1Nka9v2bIF06ZNO+H1aDNnzsRDDz2Eq6666oS1H3vsMcyZMwe33HILRo8ejSeeeAKDBw/GM888ExlTWFiI9evXt/szcOBAD9+1M9u2bVOb4zTOLpdm1dXVrnoyjZf1NVE7KN4lfZmE3mU5vevWpndd6F2W07tubXrXJUje3ZDkPESf22+/HZdccgkuuOACPPTQQyccl5CQgHfffRfnnHMOZs2ahVdffRWlpaU477zz8LWvfQ3f//73Pb1/fX09CgsLMW/evDZfnz59Oj755BNPNe146qmn8NRTT0U2oqtWrUJaWhomTZqEkpIS1NTUICMjA01NTSgoKAAA5Obmorm5Gbt27QIA5OXlYdu2baiqqkJaWhpGjhyJoqIilJeXY8+ePUhMTMRnn30GABg/fjzKyspQWVmJ1NRUjBkzBuXl5SgoKMDAgQORmpqKHTt2AADGjh2L3bt3o7y8HEVFRcjLy4tckNm/f3+kp6dH5o4ePRr79+/HkSNHkJSUhPz8/EjWp08f9OrVC1u2bEFDfX3ke9+6ZTN6Vu/ElClTsGrVKjQ1NaF3797o27cvSkpKAAAjRoxAZWUl9u/fDwD4yle+gtWrV6OhoQG9evXCwIEDsWHDBgDAKaecgnA4jL179wIAJk+ejPXr16O2thZZWVkYMmQI1q1bBwAYOnQoGhsbIw9dbG5uxrp16xAOh5Geno5TTjkFa9asAQAMGTIEACJHYidMmIDt27ejqqoKPXr0wKhRo7B69WoAwKBBg5CUlISysjIAwLhx43Ds2DEUFBQgNTUVY8eOxapVqwAAAwYMQI8ePbB9+3YAwJgxY9odyW1Zw379+iEzMxNbt24FAIwePRoHDhzA4cOHkZiYiMmTJ2PlypVobm5GdXU1jh49is2bNwMARo4cifLychw8eBChUAinn346CgsLUV5ejq1bt6Jfv36R9R4+fDiqqqqwb98+lJeXw7IsFBcXo76+Hj179sSgQYOwfv36SL3a2tpIz/n5+diwYQPKy8tRUlKCoUOHYu3atZGf2dra2sjP8MSJE7FlyxZUV1cjPT0dw4cPR3FxMQBg8ODBSEhIaPMzW1paimPHjqF79+4YPXp0ZL1POukkJCcno7S0NLLeu3btwtGjR5GSkoLx48dj5cqVkZ/ZtLS0yHqfdtppqKqqQkFBAbp164ZJkyZF+uvbty+ysrIi6z1q1CgcOnQIhw4dQkJCAqZMmRJZ75ycHOTk5GDTpk2Rn9mKioqIu9Y/s9nZ2ejfvz82btwY+Zmtrq6OnDkwZcoUrF27FuXl5di8eTMGDx4c+ZkdNmwY6uvrsWfPHgDw/BlRWVmJ+vp6FBUVRX5mW39GNDU1oaSkpM1nRGFhIQCgpqYGBw8ebPcZcfToUVRUVMCyLNFnxIoVK2BZVpvPCAA49dRTceTIERw8eDCy3rH8jGjpSfIZMWnSJGzatMnxM6K8vBy1tbWiz4idO3eioqICFRUVaGpqcvyMKC8vj/zMuv2MABD5me3Tpw+ys7PbfEZUV1ejoKCgzWdEY2MjsrOz0djYGPnZav0ZARy/01pRUZHoM6K2thaZmZntPiOampoi663xGVFeXo5wOCz6jNi3bx+OHDni+BnR0NCA7du3iz4jDhw4fqdmAGqfEZWVlSgoKEBGRgaGDRvWZr2bm5sjPz8d/R7Rkn35M2L8+PFt/n/N7WdEcnJyh79HtPxSq/UZUV9fj7KyMpXfI2pqaiI/E24/I4Djv0e0rKHWZ8Tq1avb/My2/owoLy9HZWVlh79HlJeXY8eOHe0+I8rLy1FeXo4VK1aIPiNOP/30Dn+PAICTTz5Z7TOi9e8csf49ouXfhuQzouX3iPLycqxcudLVZ0RLT66w4szvf/97a+zYsVZNTY1lWZZ17rnnWnfeeaftnM8++8zKzc21rr32WmvIkCHWrFmzrObmZlfvB8BatGhRm6/t2bPHAmAtX768zdcffvhha+TIka6/l+nTp1s5OTlW9+7drZNOOslasWKF7fiKigoLgFVRUXHCXIrbOU7j7HJpdtMLn1q5971t5d73tvX+xn2u+jOBl/XVqt2yPrn3vR0Y75K+TOIn79HOpXf30Lssp3fd2vSuC73LcnrXrS2Z67Q3aE1cT6fctWsX7rzzTvzv//4vUlNTXc8bMmQIXnnlFSxcuBBJSUl44YUXYnKt1ZdrWMJnQrz33ns4ePAgwuEwdu/ejSlTpkTVT8t/QdKY4zTOLpdmdXW1rnoyjZf1NVE7KN4lfZmE3mU5vevWpndd6F2W07tubXrXJUje3RDXTVxhYSEOHDiA/Px8JCUlISkpCR999BF+9atfISkpqc11b63Zv38/vvWtb+Gyyy5DOBzG3XffHVUfOTk5SExMbHdjlAMHDqBfv35R1Y6GI0eOqM1xGmeXS7P6+i9uROOj+5p4Wl8TtYPiXdKXSehdltO7bm1614XeZTm969amd12C5N0Ncb0m7vzzz4+cZ9zCTTfdhFGjRuG+++6LnNPfmkOHDuH888/H6NGj8cYbb2Dr1q2YOnUqUlJS8Itf/MJTH8nJycjPz8eSJUtw5ZVXRr6+ZMkSXH755Z5qxoLWd3WM9RyncXa5NEtI+GLnFvLRQwa8rK+J2kHxLunLJPQuy+ldtza960LvspzedWvTuy5B8u6GkGVZlkplj0ydOhV5eXl44okn2mXNzc04/fTT0a9fPyxatAjJyckAgHXr1mHatGmYP39+h0flqqqqIhfRTpw4EY899himTZuG7OzsyIWnCxcuxA033IBnn30WZ5xxBp577jn85je/wYYNG5Cbm6vyvVZWViIrKwsVFRXIzMxUeQ8/cOurhVi84fhRzhdnT8G0UX3j3JH/GDrvncjrskcviWMnhBBCCCEkHkj2Br54xIBbEhISsGDBAvzxj3+MbOCA43eVef/993H11Vd3OG/VqlWYOHEiJk6cCAC45557MHHiRNx///2RMddeey2eeOIJPPjgg8jLy8OyZcvw7rvvqm3g3NByNyeNOU7j7HJpdqS81WFk/xyI87S+JmoHxbukL5PQuyynd93a9K4LvctyetetTe+6BMm7G3x3LHTp0qW2+YUXXtjh1/Py8k44Z+rUqXBzwHHu3LmYO3eu4zhTeDlI6naO0zi7XJqFTvA63mgehI6mdlC8S/oyCb3LcnrXrU3vutC7LKd33dr0rkuQvLuhUx2J62r06dNHbY7TOLtcmqWkpLjqyTRe1tdE7aB4l/RlEnqX5fSuW5vedaF3WU7vurXpXZcgeXcDN3E+plevXmpznMbZ5dKs9amvsXgURKzwsr4magfFu6Qvk9C7LKd33dr0rgu9y3J6161N77oEybsbuInzMVu2bFGb4zTOLpdmx44di7z2zxbO2/qaqB0U75K+TELvspzedWvTuy70LsvpXbc2vesSJO9u4CaOqOOjg2+EEEIIIYR0eriJ8zGnnnqq2hyncXa5NMvIyIi89tOGzsv6mqgdFO+SvkxC77Kc3nVr07su9C7L6V23Nr3rEiTvbuAmzsd4ecK72zlO4+xyadZQX++qJ9N4WV8TtYPiXdKXSehdltO7bm1614XeZTm969amd12C5N0N3MT5mIMHD6rNcRpnl0uzurq6yOuQj66K87K+JmoHxbukL5PQuyynd93a9K4LvctyetetTe+6BMm7G7iJ8zEJCXI9buc4jbPLpVnrO1L66XRKL+tronZQvEv6Mgm9y3J6161N77rQuyynd93a9K5LkLy7IWT58Wl9XYTKykpkZWWhoqICmZmZ8W5HjTteW4231+4FAPzulq/gq8Nz4tyR/xg6753I67JHL4ljJ4QQQgghJB5I9gb+20aTCKtWrVKb4zTOLpdm5eVfnAvsowNxntbXRO2geJf0ZRJ6l+X0rlub3nWhd1lO77q16V2XIHl3AzdxPqapqUltjtM4u1yatTnY66NdnJf1NVE7KN4lfZmE3mU5vevWpndd6F2W07tubXrXJUje3cBNnI/p3bu32hyncXa5NEtJSXHVk2m8rK+J2kHxLunLJPQuy+ldtza960LvspzedWvTuy5B8u4GbuJ8TN++fdXmOI2zy6VZakpq5LWf7k7pZX1N1A6Kd0lfJqF3WU7vurXpXRd6l+X0rlub3nUJknc3cBPnY0pKStTmOI2zy6VZZWVl5LWf7k7pZX1N1A6Kd0lfJqF3WU7vurXpXRd6l+X0rlub3nUJknc3cBNH1PHTxo0QQgghhJDODjdxPmbEiBFqc5zG2eXSLCMjI/LaT/s5L+tronZQvEv6Mgm9y3J6161N77rQuyynd93a9K5LkLy7gZs4H9P6NMRYz3EaZ5dLs8aGhsjrkI8Oy3lZXxO1g+Jd0pdJ6F2W07tubXrXhd5lOb3r1qZ3XYLk3Q3cxPmY/fv3q81xGmeXS7Pa2lpXPZnGy/qaqB0U75K+TELvspzedWvTuy70LsvpXbc2vesSJO9u4CaOGMVHB+IIIYQQQgjplISsNk9iJiaprKxEVlYWKioqkJmZGe921LhnYTH+VLQHAPDmrWdg8tDsOHfkP4bOeyfyuuzRS+LYCSGEEEIIiQeSvQGPxPmY1atXq81xGmeXS7MjR4646sk0XtbXRO2geJf0ZRJ6l+X0rlub3nWhd1lO77q16V2XIHl3AzdxPqah1Q1BYj3HaZxdLs2am5sjr/10OqWX9TVROyjeJX2ZhN5lOb3r1qZ3XehdltO7bm161yVI3t3ATZyP6dWrl9ocp3F2uTRLSUlu9Tf/7OK8rK+J2kHxLunLJPQuy+ldtza960LvspzedWvTuy5B8u4GbuJ8zMCBA9XmOI2zy6VZjx49XPVkGi/ra6J2ULxL+jIJvctyetetTe+60Lssp3fd2vSuS5C8u4GbOB+zYcMGtTlO4+xyaXb06NHIaz+dTullfU3UDop3SV8moXdZTu+6teldF3qX5fSuW5vedQmSdzdwE0cIIYQQQgghnQhu4nzMKaecojbHaZxdLs1a3yLVRwfiPK2vidpB8S7pyyT0LsvpXbc2vetC77Kc3nVr07suQfLuBm7ifEw4HFab4zTOLpdmTY2NkdchH51P6WV9TdQOindJXyahd1lO77q16V0Xepfl9K5bm951CZJ3N3AT52P27t2rNsdpnF0uzWpq/PcPHfC2viZqB8W7pC+T0Lssp3fd2vSuC73LcnrXrU3vugTJuxu4iSNG8c9xOEIIIYQQQjonIcuyrHg30VWprKxEVlYWKioq2lw31kJTUxMSExNFNd3OcRpnl0uz77+xBq8X7gYA/PWOr2L8oJ6O/ZnAy/pq1R46753I6+0PXxQI75K+TOIn79HOpXf30Lssp3fd2vSuC73LcnrXrS2Z67Q3aA2PxPmY9evXq81xGmeXS7Mj5Udc9WQaL+tronZQvEv6Mgm9y3J6161N77rQuyynd93a9K5LkLy7gZs4H1NbW6s2x2mcXS7NmpuaIq9DPjqh0sv6mqgdFO+SvkxC77Kc3nVr07su9C7L6V23Nr3rEiTvbuAmzsdkZWWpzXEaZ5dLs+Tk5MhrH92c0tP6mqgdFO+SvkxC77Kc3nVr07su9C7L6V23Nr3rEiTvbuA1cXHE6bzXcDiMHj16iGq6neM0zi6XZt97fTXeWH38zjxvf+csjD3JH//wvayvVu3W18RtvH9aILxL+jKJn7xHO5fe3UPvspzedWvTuy70LsvpXbe2ZC6viQsI69atU5vjNM4ul2ZHjvjzmjgv62uidlC8S/oyCb3LcnrXrU3vutC7LKd33dr0rkuQvLuBmzhigC/OofTT6ZSEEEIIIYR0RriJ8zFDhw5Vm+M0zi6XZhkZ6a56Mo2X9TVROyje3bxfPKB3WU7vurXpXRd6l+X0rlub3nUJknc3cBPnYxobG9XmOI2zy6VZ68su/XR3Si/ra6J2ULxL+jIJvctyetetTe+60Lssp3fd2vSuS5C8u4GbOB+ze/dutTlO4+xyaVZdVR157afTKb2sr4naQfEu6csk9C7L6V23Nr3rQu+ynN51a9O7LkHy7gZu4og6ftq4EUIIIYQQ0tnhIwbiiNNtRBsaGtCtWzdRTbdznMbZ5dLsB39cg9+vPP5fIRbfdTZG9be/ZaopvKyvVu3WjxjY+tPpgfAu6cskfvIe7Vx6dw+9y3J6161N77rQuyynd93akrl8xEBA2LRpk9ocp3F2uTQ7evRo5LWfronzsr4magfFu6Qvk9C7LKd33dr0rgu9y3J6161N77oEybsbuInzMeFwWG2O0zi7XJo1+fDiV8Db+pqoHRTvkr5MQu+ynN51a9O7LvQuy+ldtza96xIk727gJs7HpKfLb83vdo7TOLtcmrU+hOyn6+O8rK+J2kHxLunLJPQuy+ldtza960LvspzedWvTuy5B8u4GXhMXR5zOe62trUVqaqqopts5TuPscmn2wz+uwWv/d03ckrvPwYh+GY79mcDL+mrVbn1N3KafnB8I75K+TOIn79HOpXf30Lssp3fd2vSuC73LcnrXrS2Zy2viAsKaNWvU5jiNs8ul2eHDh1z1ZBov62uidlC8S/oyCb3LcnrXrU3vutC7LKd33dr0rkuQvLuBmzhiFD+dTkkIIYQQQkhnhJs4HzNkyBC1OU7j7HJplpHhj9Mnv4yX9TVROyjeJX2ZhN5lOb3r1qZ3XehdltO7bm161yVI3t3ATRxRJ2TzN0IIIYQQQogMbuJ8zM6dO9XmOI2zy6XZsWPHIq/9dDqll/U1UTso3iV9mYTeZTm969amd13oXZbTu25tetclSN7dwE0cUcdPGzdCCCGEEEI6O3zEQBzpKo8Y+NGiNXi14PgjBj74z3Nxch9/PFvET7ei5SMGzOEn79HOpXf30Lssp3fd2vSuC73LcnrXrc1HDHRBtm/frjbHaZxdLs0qKyoir0M+OiznZX1N1A6Kd0lfJqF3WU7vurXpXRd6l+X0rlub3nUJknc3cBPnY6qqqtTmOI2zy6VZfUODq55M42V9TdQOindJXyahd1lO77q16V0Xepfl9K5bm951CZJ3N3AT52N69OihNsdpnF0uzbp16xZ57Z/jcN7W10TtoHiX9GUSepfl9K5bm951oXdZTu+6teldlyB5dwOviYsjTue9NjQ0tNkAucHtHKdxdrk0+8lf1uGlT4/fmeej701Fbu80x/5M4GV9tWq3viZu60+nB8K7pC+T+Ml7tHPp3T30LsvpXbc2vetC77Kc3nVrS+bymriAsHr1arU5TuPscml28MABVz2Zxsv6mqgdFO+SvkxC77Kc3nVr07su9C7L6V23Nr3rEiTvbuAmjhgl5KsTKgkhhBBCCOl8cBPnYwYNGqQ2x2mcXS7NMjIyIq99dHNKT+tronZQvEv6Mgm9y3J6161N77rQuyynd93a9K5LkLy7gZs4H5OUlKQ2x2mcXS7NEhL9+WPmZX1N1A6Kd0lfJqF3WU7vurXpXRd6l+X0rlub3nUJknc3+PO3awIAKCsrU5vjNM4ul2bHKipd9WQaL+tronZQvLt5v3hA77Kc3nVr07su9C7L6V23Nr3rEiTvbuAmjqjT+hRKP51OSQghhBBCSGeEjxiII063EQ2Hw+JnS7id4zTOLpdmD/11HZ7/5PgjBv553zQM6uWPZ4t4WV+t2re8vBLvlxzAqP4Z+NO3JgfCu6Qvk/jJe7Rz6d099C7L6V23Nr3rQu+ynN51a0vm8hEDAWHnzp1qc5zG2eXSrPLYF6dThnx0KM7L+mrV/p9/z8OPLj0Nr8w5PTDeJX2ZxE/eo51L7+6hd1lO77q16V0Xepfl9K5bW6sv/12VSCJUVFSozXEaZ5dLs/q6ushr/2zhvK2vVu2sHt0w56xhAIDSgHiX9GUSP3mPdi69u4feZTm969amd13oXZbTu25trb54JM7HpKamqs1xGmeXSzM/3sEI8La+JmoHxbukL5PQuyynd93a9K4LvctyetetTe+6BMm7G3hNXBxxOu+1qakJiYmJoppu5ziNs8ul2c8Wl+DppTsAAJ/+4DwMyOru2J8JvKyvidpB8S7pyyT0LsvpXbc2vetC77Kc3nVr07suQfDOa+ICwqpVq9TmOI2zy6XZvr17XfVkGi/ra6J2ULxL+jIJvctyetetTe+60Lssp3fd2vSuS5C8u4GbOKJOqM1rP10VRwghhBBCSOeDmzgfM2DAALU5TuPscmmWkZERee2jm1N6Wl8TtYPiXdKXSehdltO7bm1614XeZTm969amd12C5N0N3MT5GC/Po3A7x2mcXS7NunXr5qon02g+3ySa2kHxLunLJPQuy+ldtza960LvspzedWvTuy5B8u4GbuJ8zPbt29XmOI2zy6XZ0aPlkdc+OhDnaX1N1A6Kd0lfJqF3WU7vurXpXRd6l+X0rlub3nUJknc3cBNH1Amd8C+EEEIIIYQQKXzEQBxxuo1oVVUV0tPTRTXdznEaZ5dLs//52wb8v4/KAAAr5p+Pvhn+eLaIl/U1UTso3iV9mYTeZTm969amd13oXZbTu25tetclCN75iIE4ceWVV6JXr164+uqrY1Lv888/V5vjNM4ul2bHjh2LvPbT3Sm9rK+J2kHxLunLJPQuy+ldtza960LvspzedWvTuy5B8u4GbuJiyHe/+1288sorMatXXl7uPMjjHKdxdrk0q62tibz2090pvayvidpB8S7pyyT0LsvpXbc2vetC77Kc3nVr07suQfLuBm7iYsi0adPa3E4/Wrzc1dHtHKdxdrk08/qEe20075oZTe2geJf0ZRJ6l+X0rlub3nWhd1lO77q16V2XIHl3Q9yviXvmmWfwzDPPoKysDAAwZswY3H///Zg5c2bM3mPZsmX4+c9/jsLCQuzduxeLFi3CFVdc0W7c008/jZ///OfYu3cvxowZgyeeeAJnn3226L2WLl2KJ598Em+++abjWMl5r52Zp5duw88WbwYAFP7XBeidnhLnjgghhBBCCPEXneqauEGDBuHRRx/FqlWrsGrVKpx33nm4/PLLsWHDhg7HL1++HA0NDe2+vmnTJuzbt6/DOdXV1ZgwYQKefPLJE/axcOFC3HXXXZg/fz6Kiopw9tlnY+bMmdi5c2dkTH5+PsaOHdvuj9a5rgUFBWpznMbZ5dJs185dkdchH51P6WV9TdQOindJXyahd1lO77q16V0Xepfl9K5bm951CZJ3NySpVBVw2WWXtfn7ww8/jGeeeQb/+te/MGbMmDZZc3Mzbr/9dowYMQJ/+MMfIqfpbdmyBdOmTcPdd9+N73//++3eY+bMmY5H9h577DHMmTMHt9xyCwDgiSeewHvvvYdnnnkGCxYsAAAUFhZ6/j67Mj7atxFCCCGEENLpifsmrjVNTU144403UF1djTPOOKNdnpCQgHfffRfnnHMOZs2ahVdffRWlpaU477zz8LWvfa3DDZwb6uvrUVhYiHnz5rX5+vTp0/HJJ594qmnHU089haeeegpNTU0AgFWrViEtLQ2TJk1CSUkJampqkJGRgezs7MjuPTc3F83Nzdi16/hRrby8PGzbtg1VVVVIS0vDyJEjUVRUhHA4jD179iAxMRGfffYZAGD8+PEoKytDZWUlUlNTMWbMGITDYRQUFGDgwIFITU3Fjh07AABjx47F7t27EQ6HUVRUhLy8PKxYsQIA0L9/f6Snp0fmjh49Gvv378eRI0eQlJSE/Px81NTUoKCgAH369EGvXr2wZcsW1Nd/ceR0dWEhsronYcqUKVi1ahWamprQu3dv9O3bFyUlJQCAESNGoLKyEvv37wcAfOUrX8Hq1avR0NCAXr16YeDAgZEjtaeccgrC4TD27t0LAJg8eTLWr1+P2tpaZGVlYciQIVi3bh0AYOjQoWhsbMTu3bsBADk5OVi3bh3C4TDS09NxyimnYM2aNQCAIUOGAEDkSOyECROwfft2VFVVoUePHhg1ahRWr14N4PjR5KSkpMgpwePGjYNlWSgoKEBqairGjh2LVatWAQAGDBiAHj16RB78OGbMGHz++ecoLy9Ht27dMGnSpMj69uvXD5mZmdi6dSsAYPTo0Thw4AAOHz6MxMRETJ48GStXrkRzczMSEhJw9OhRbN58/LTVkSNHory8HAcPHkQoFMLpp5+OwsJChMNhbN26Ff369Yus9/Dhw1FVVYV9+/YhHA7DsiwUFxejvr4ePXv2xKBBg7B+/XqEw2EcPHgQtbW1kaPP+fn52LBhA8LhMEpKSjB06FCsXbs28jObkpIS+RmeOHEitmzZgurqaqSnp2P48OEoLi4GAAwePBgJCQltfmZLS0tx7NgxdO/eHaNHj46s90knnYTk5GSUlpZG1nvXrl04evQoUlJSMH78eKxcuTLyM5uWlhZZ79NOOw2hUAgFBQWR9W7pr2/fvsjKyoqs96hRo3Do0CEcOnQICQkJmDJlSmS9c3JykJOTg02bNkV+ZisqKiLuWv/MZmdno3///ti4cWPkZ7a6ujpy5sCUKVOwdu1ahMNhbN68GYMHD478zA4bNgz19fXYs2cPAHj+jGhsbER9fT2KiooiP7OtPyN69eqFkpKSNp8RLf/RKjk5GQcPHmz3GXH06FHU19fDsizRZ8SKFStgWVabzwgAOPXUU3HkyBEcPHgwst6x/Ixo6UnyGTFp0iRs2rTJ8TMiHA6jtrZW9Bmxc+dOVFRUoKGhAU1NTSqfEf369Yv8zPbp0wfZ2dltPiMSExNRUFDQ5jOisbER2dnZyMrKivxstf6MaPm3UlRUJPqMqK2tRWZmZrvPiKampsh6a3xGtLiXfEbs27cPR44ccfyMyMzMxPbt20WfEQcOHAAA9OvXT+0zoqGhAQUFBcjIyMCwYcParHdzc3Pk56ej3yNasi9/RowfPx7Nzc2R/19z+xmRnJzc4e8R27Zti/zManxGpKeno6ysTOX3iOTk5MjPhNvPCOD47xFNTU0oKChQ+YwIh8NYvXp1m5/Z1p8R4XAYlZWVHf4eEQ6HsWPHjnafEeXl5QiHw1ixYoXoM+L000/v8PcIADj55JPVPiNSU1MjPcX694iWfxuSz4iW3yPC4TBWrlzp6jOipSdXWD5g7dq1VlpampWYmGhlZWVZ77zzju34zz77zMrNzbWuvfZaa8iQIdasWbOs5uZmV+8FwFq0aFGbr+3Zs8cCYC1fvrzN1x9++GFr5MiRrr+P6dOnWzk5OVb37t2tk046yVqxYoXt+IqKCguAVVFR0WF++PBh1+8tneM0zi6XZv/z7lor9763rdz73rbKq+tc9WcCL+tronZQvEv6Mgm9y3J6161N77rQuyynd93a9K5LELw77Q1aE/dr4oDj/3WluLgY//rXv3DbbbfhxhtvjPwXqY4YMmQIXnnlFSxcuBBJSUl44YUXYnKt1ZdrWJYlqvvee+/h4MGDCIfD2L17N6ZMmRJVPy07eY05TuPscml2+NAhVz2Zxsv6mqgdFO+SvkxC77Kc3nVr07su9C7L6V23Nr3rEiTvbvDFJi45ORnDhw/H5MmTsWDBAkyYMAG//OUvTzh+//79+Na3voXLLrsM4XAYd999d1Tvn5OTg8TExHY3Rjlw4AD69esXVW3SFj897JsQQgghhJDOiC82cV/GsizU1dV1mB06dAjnn38+Ro8ejT/96U/44IMP8Prrr+Pee+/1/H7JycnIz8/HkiVL2nx9yZIlOPPMMz3XjZbRo0erzXEaZ5dLs76tN8I+2sN5WV8TtYPiXdKXSehdltO7bm1614XeZTm969amd12C5N0Ncd/E/fCHP8THH3+MsrIyrFu3DvPnz8fSpUtx/fXXtxvb3NyMiy66CLm5uZFTKUePHo33338fL730Eh5//PEO36OqqgrFxcWRix9LS0tRXFzc5vEB99xzD55//nn89re/RUlJCe6++27s3LkTt956q8r37YaWC6A15jiNs8ulWXVVlaueTONlfU3UDop3SV8moXdZTu+6teldF3qX5fSuW5vedQmSdzfEfRO3f/9+3HDDDTj11FNx/vnno6CgAIsXL8aFF17YbmxCQgIWLFiAP/7xj0hOTo58fdy4cXj//fdx9dVXd/geq1atwsSJEzFx4kQAxzdsEydOxP333x8Zc+211+KJJ57Agw8+iLy8PCxbtgzvvvsucnNzY/wdu+fw4cNqc5zG2eXSLByujrz20+MGvKyvidpB8S7pyyT0LsvpXbc2vetC77Kc3nVr07suQfLuhrg/YuCFF14Qje9ocwccv532iZg6dSosy3KsPXfuXMydO1fUjyYtz8HTmOM0zi6XZqHQF/+twEd7OE/ra6J2ULxL+jIJvctyetetTe+60Lssp3fd2vSuS5C8uyFkudndEBUqKyuRlZWFiooKZGZmxrsdNX6zbAcefvf4s1vW/WQ6MlK7xbkjQgghhBBC/IVkbxD30ynJiWl50KDGHKdxdrk0a3n4MND+MQ7xxMv6mqgdFO+SvkxC77Kc3nVr07su9C7L6V23Nr3rEiTvbuAmzsc0NzerzXEaZ5dLM78e7PWyviZqB8W7pC+T0Lssp3fd2vSuC73LcnrXrU3vugTJuxu4ifMxffr0UZvjNM4ul2bp6WmR1/45DudtfU3UDop3SV8moXdZTu+6teldF3qX5fSuW5vedQmSdzdwE+djsrOz1eY4jbPLpVmPHj0ir310NqWn9TVROyjeJX2ZhN5lOb3r1qZ3XehdltO7bm161yVI3t3ATZyP2bx5s9ocp3F2uTQ7ePCgq55M42V9TdQOindJXyahd1lO77q16V0Xepfl9K5bm951CZJ3N3ATR4wS8tUJlYQQQgghhHQ+uInzMSNHjlSb4zTOLpdmfVudC+yn0ym9rK+J2kHxLunLJPQuy+ldtza960LvspzedWvTuy5B8u4GbuJ8THl5udocp3F2uTSrqalx1ZNpvKyvidpB8S7pyyT0LsvpXbc2vetC77Kc3nVr07suQfLuBm7ifIyXa8ncznEaZ5dLs6qqKlc9mUbzWr1oagfFu6Qvk9C7LKd33dr0rgu9y3J6161N77oEybsbuInzMV4ejO12jtM4u1yatf6Sn06n1HzweDS1g+Jd0pdJ6F2W07tubXrXhd5lOb3r1qZ3XYLk3VVdy69PYu4CVFZWIisrCxUVFcjMzIx3O2q8uLwUD7y1EQCw+aGLkJKUGOeOCCGEEEII8ReSvQGPxPmYwsJCtTlO4+xyabZr167Iaz/dndLL+pqoHRTvkr5MQu+ynN51a9O7LvQuy+ldtza96xIk727gJs7HNDY2qs1xGmeXS7Pm5ubIaz8dffeyviZqB8W7pC+T0Lssp3fd2vSuC73LcnrXrU3vugTJuxu4ifMxXp7w7naO0zi7XJqlpfVw1ZNpvKyvidpB8S7pyyT0LsvpXbc2vetC77Kc3nVr07suQfLuBm7ifEy/fv3U5jiNs8ulWWbGF+f0+uhAnKf1NVE7KN4lfZmE3mU5vevWpndd6F2W07tubXrXJUje3cBNnI8pKSlRm+M0zi6XZvv27Yu89tPdjLysr4naQfEu6csk9C7L6V23Nr3rQu+ynN51a9O7LkHy7gZu4og6Ptq3EUIIIYQQ0unhJs7HDB8+XG2O0zi7XJr1yekTee2n/ZyX9TVROyjeJX2ZhN5lOb3r1qZ3XehdltO7bm161yVI3t3ATZyPqaqqUpvjNM4ul2Z19XWR1346KudlfU3UDop3SV8moXdZTu+6teldF3qX5fSuW5vedQmSdzdwE+djWl9LFus5TuPscmlWWVnpqifTeFlfE7WD4l3Sl0noXZbTu25teteF3mU5vevWpnddguTdDdzEEaP46cYmhBBCCCGEdEZClmVZ8W6iq1JZWYmsrCxUVFQgMzOzXW5ZlnjT43aO0zi7XJq9+mkZfvSXDQCAskcvcezNFF7W10TtoHiX9GUSepfl9K5bm951oXdZTu+6teldlyB4d9obtIZH4nxMcXGx2hyncXa5NNu9e7ernkzjZX1N1A6KdzfvFw/oXZbTu25teteF3mU5vevWpnddguTdDdzE+Zj6+nq1OU7j7HJpVlvf6Kon03hZXxO1g+Jd0pdJ6F2W07tubXrXhd5lOb3r1qZ3XYLk3Q3cxPmYnj17qs1xGmeXS7PU7t1d9WQaL+tronZQvLt5v3hA77Kc3nVr07su9C7L6V23Nr3rEiTvbuA1cXHE6bzX6upqpKWliWq6neM0zi6XZk/9YxN+vmQ7AH9dE+dlfU3UDop3SV8moXdZTu+6teldF3qX5fSuW5vedQmCd14TFxDWr1+vNsdpnF0uzfbu3euqJ9N4WV8TtYPiXdKXSehdltO7bm1614XeZTm969amd12C5N0N3MQRQgghhBBCSCciKd4NkBNz8sknq81xGmeXS7Ps7N4Awq76MomX9TVROyjeJX2ZhN5lOb3r1qZ3XehdltO7bm2v3puamtDQ0GA7bvDgwaitrfWUe8mc3i8eaPYUTe3Wc7t164bExMSY9MRNnI/x8sPido7TOLtcmjl98MQLzQ+faGoHxbukL5PQuyynd93a9K4LvctyetetLZ1rWRYOHz6MQ4cOOY5tbGxERUWFp9xL5vR+8UCzp2hqf3luz5490b9//6ifacdNnI/5/PPPMXjwYJU5TuPscmlWUVnp2E888LK+JmoHxbukL5PQuyynd93a9K4LvctyetetLZ27b98+1NXVYdCgQejRo4ftL/28sYn/b2xiWRbC4TAOHDgAABgwYEBUPXETRwghhBBCiI9oamrC0aNHkZOTg969ezuOb2xsRGpqqqfcS+b0fvFAs6doaree2/3/Hrt14MAB9O3bN6pTK/mIgTjidBvRxsZGJCXJ9tlu5ziNs8ul2VMfbMHP/74VgL8eMeBlfU3UDop3SV8moXdZTu+6teldF3qX5fSuW1syt7a2FqWlpcjNzUWPHj0cx1uWZXukzi73kjm9XzzQ7Cma2l+eW1NTg7KyMgwbNqzdxpCPGAgIGzZsUJvjNM4ul2b79u931ZNpvKyvidpB8S7pyyT0LsvpXbc2vetC77Kc3nVre5lbV1fnalxNTY3n3Evm9H7xQLOnaGp/eW6sNprcxPmYoFz43NDQ6Kon0wTlwmfJHF7wTu/SnN51a9O7LvQuy+ldt7aXuc3NzTEZZ5d7ydz2ZRLNnqKprdUXN3E+xukwajRznMbZ5dIsJSXFVU+m8bK+JmoHxbukL5PQuyynd93a9K4Lvctyetet7WWu22umnMbZ5V6yWN0mP5Zo9hRNba2+uInzMUOHDlWb4zTOLpdmPXv2ctWTabysr4naQfHu5v3iAb3LcnrXrU3vutC7LKd33dpe5iYnJ7sa5/QfzO1yL5ndnNmzZyMUCiEUCiEpKQlDhgzBbbfdhvLyctseo6V1T1u2bMHll1+OnJwcZGZm4qtf/So+/PDDNuPvvPNO5OfnIyUlBXl5ee3q1dbWYvbs2Rg3bhwyMjJwxRVXtBuzd+9eXHfddTj11FORkJCAu+66y7avWMJNnI9Zu3at2hyncXa5NNu3f5+rnkzjZX1N1A6Kd0lfJqF3WU7vurXpXRd6l+X0rlvby1y312KFw2HPuZfM6f0uuugi7N27F2VlZXj++efx1ltvYe7cubZzoqV1T5dccgkaGxvxwQcfoLCwEHl5ebj00kuxb98Xv5NaloWbb74Z1157bYf1mpqa0L17d3z3u9/FtGnTOhxTV1eHPn36YP78+ZgwYYJjX7GEmziiDu9/SgghhBDSdUhJSUH//v0xaNAgTJ8+Hddeey3+/ve/AwCmTp3a7ojVFVdcgdmzZ0f+PnToUDzyyCO4+eabkZGRgSFDhuC5555z9d6HDh3Ctm3bMG/ePIwfPx4jRozAo48+inA43ObmMr/61a9w++234+STT+6wTlpaGp555hl885vfRN++fTscM3ToUPzyl7/ErFmzkJWV5aq/WMFNnI/Jzc1Vm+M0zi6XZj179nTVk2m8rK+J2kHxLunLJPQuy+ldtza960LvspzedWt7mdv6dErLshCub+zwT1Mo8YSZU+6UdfQ0Mskpgjt27MDixYvRrVs30ff+P//zP5g8eTKKioowd+5c3Hbbbdi0adMJx7f01Lt3b4wePRqvvPIKqqur0djYiF//+tfo168f8vPzRT20EM11bVqnU/rrwR6kDU1NTWpznMbZ5dLswhFZeO7TzzFlqL+ujfOyviZqB8W7pC+T0Lssp3fd2vSuC73LcnrXre1lbusNVE1DE067/z3P7++VjQ/OQI/ktlsGp8dMv/3220hPT0dTU1PkrpyPPfaY6H0vvvjiyCmY9913Hx5//HEsXboUo0aN6nB8S0+hUAhLlizB5ZdfjoyMDCQkJKBfv35YvHhxXA4saD2Sm0fifMzu3bvV5jiNs8ulWfOxg1j7k+lY+K0zXPVmCi/ra6J2ULxL+jIJvctyetetTe+60Lssp3fd2l7mNjQ0eH4/Terr623zadOmobi4GAUFBfjOd76DGTNm4Dvf+Y7oPcaPHx95HQqF0L9/fxw4cMCxJ8uyMHfuXPTt2xcff/wxVqxYgcsvvxyXXnop9u7dK+qhhWg2705r5RUeiSNGyEyVHUInhBBCCCFf0L1bIjY+OKPDrKqqGunpaSeca5c7Zd27yU8lTEtLw/DhwwEcv/Zs2rRpeOCBB/DTn/4UCQkJ7Y5OdbRZ/fLpl6FQyNUz1z744AO8/fbbKC8vjzzW4emnn8aSJUvw8ssvY968eeLvx49wE+djJk6cqDbHaZxdLs28fB8m0OwrmtpB8S7pyyT0LsvpXbc2vetC77Kc3nVre5nbvXv3yOtQKNTutMYWUnseP23wRNjlTlkoFGr39R49eti13Y4f//jHmDlzJm677Tb06dOnzRGxpqYmrF+//oR3gHRLS08td4P88veUkJDg+cHbSUnet0zStXILT6f0MVu2bFGb4zTOLpdmXr4PE2j2FU3toHiX9GUSepfl9K5bm951oXdZTu+6tb3MdXsqXl1dnefcS+b0fl9m6tSpGDNmDB555BGcd955eOedd/DOO+9g06ZNmDt3Lo4ePSqqZ9fTGWecgV69euHGG2/EmjVrsGXLFnzve99DaWkpLrnkksj4bdu2obi4GPv27UNNTQ2Ki4tRXFzcZs03btyI4uJiHDp0CBUVFZExrWn5WlVVFQ4ePIji4mJs3LixXV+xhkfifEx1dbXaHKdxdrk08/J9mECzr2hqB8W7pC+T0Lssp3fd2vSuC73LcnrXre1lbpBuaHPPPffgpptuwrZt27BmzRrMmjULSUlJuPvuu6M+Cte6p5ycHCxevBjz58/Heeedh4aGBowZMwZ/+ctf2jzL7ZZbbsFHH30U+XvLkdLS0tLIg9kvvvhifPbZZ+3GtD4dtPUR1sLCQrz22mvIzc1FWVlZm75iDTdxPiY9PV1tjtM4u1yaefk+TKDZVzS1g+Jd0pdJ6F2W07tubXrXhd5lOb3r1vYy1+2t7Z3G2eVeMrs5L730Uodfv+6663DdddcBOH6N2tNPP33CGi0boNZ8+QiYXU+TJ0/Ge+/Z38lz6dKltnnrPmpqatqc2toap7tPRvN4AjtCltZ9L4kjlZWVyMrKQkVFReTCy9bU1dWJny3hdo7TOLtcmnn5Pkyg2Vc0tYPiXdKXSehdltO7bm1614XeZTm969aWzK2trUVpaSlyc3NdXVPV3Nxse02cXe4lc3q/eKDZUzS1vzy3xe2wYcOQmpraZqzT3qA1/lp90gan/+IQzRyncXa5NPPyfZhAs69oagfFu5v3iwf0LsvpXbc2vetC77Kc3nVre5lbU1PjalzLzTy85F4yp/fT5JFHHkF6enq7P5mZmZg5c6bKe0bz/WqtFU+nJIQQQgghhHQKbr31VlxzzTXtvl5dXY2cnJw4dBQfuInzMYMHD1ab4zTOLpdmXr4PE2j2FU3toHiX9GUSepfl9K5bm951oXdZTu+6tb3M/fKz0k5EcnKy59xL5vR+mmRnZyM7O7vd1+vr69X6iqauVk88ndLHeDn31u0cp3F2uTTz2znTLWj2FU3toHiX9GUSepfl9K5bm951oXdZTu+6tTX76uhZbm5zL5nT+8UDzZ6iqf3lubG6HYn//uWRCK1vaRrrOU7j7HJp5uX7MIFmX9HUDop3SV8moXdZTu+6teldF3qX5fSuW1syt+UI3LFjx1yN9/tz4kyg2VM0tb88t+UaObdHWU8ET6ckhBBCCCHERyQmJqJnz544cOAAUlNT0aNHD9ujQXV1dUhKOvGv9Xa5l8zp/eKBZk/R1G6Za1kWwuEwDhw4gJ49e0b96AE+YiCOON1G1O6ZFCfC7RyncXa5NPPyfZhAs69oagfFu6Qvk9C7LKd33dr0rgu9y3J6160tnWtZFnbt2uXqIeGWZdlu8uxyL5nT+8UDzZ6iqf3luT179kT//v07rCd5xIC/ttCkDaWlpTjttNNU5jiNs8ulmZfvwwSafUVTOyjeJX2ZhN5lOb3r1qZ3XehdltO7bm3p3FAohKqqKpx66qloaGiwHbtt2zYMHz7cU+4lc3q/eKDZUzS1W8/t1q1bzB7+zU2cj3F7HrSXOU7j7HJp5uX7MIFmX9HUDop3SV8moXdZTu+6teldF3qX5fSuW9ur98TERMdf/Kurq9s9ONpt7iVzer94oNlTNLW1+uKNTXyMl8P1buc4jbPLpZnfTrNoQbOvaGoHxbukL5PQuyynd93a9K4LvctyetetTe+6BMm7G3hNXBxxOu+1oaFBfOcat3Ocxtnl0szL92ECzb6iqR0U75K+TELvspzedWvTuy70LsvpXbc2vesSBO+Sa+J4JM7HrF69Wm2O0zi7XJp5+T5MoNlXNLWD4l3Sl0noXZbTu25teteF3mU5vevWpnddguTdDbwmLo60HAStrKzsMK+urj5hdiLcznEaZ5dLMy/fhwk0+4qmdlC8S/oyCb3LcnrXrU3vutC7LKd33dr0rksQvLeMc3OiJDdxcaTlAtfBgwfHuRNCCCGEEEKIHzh27BiysrJsx/CauDjS3NyMzz//HBkZGR0+K2LKlClYuXKlqKbbOU7j7HJJVllZicGDB2PXrl2O5/aaxsv6mqgdBO+Af93Tuyynd93a9K4LvctyetetTe+6BMG7ZVk4duwYBg4ciIQE+6veeCQujiQkJGDQoEEnzBMTE8X/ONzOcRpnl3vJMjMzffUPHfC2viZqB8k74D/39C7L6V23Nr3rQu+ynN51a9O7LkHx7nQErgXe2MTH3H777WpznMbZ5V4zv6HZazS16V0Xepfl9K5bm951oXdZTu+6teldlyB5dwNPpySqSG6VSoIF3XdN6L1rQu9dE3rvmtC7P+CROKJKSkoKfvzjHyMlJSXerRDD0H3XhN67JvTeNaH3rgm9+wMeiSOEEEIIIYSQTgSPxBFCCCGEEEJIJ4KbOEIIIYQQQgjpRHATRwghhBBCCCGdCG7iCCGEEEIIIaQTwU0cIYQQQgghhHQiuIkjceXKK69Er169cPXVV8e7FWKIXbt2YerUqTjttNMwfvx4vPHGG/FuiRjg2LFjmDJlCvLy8jBu3Dj85je/iXdLxCDhcBi5ubm49957490KMURSUhLy8vKQl5eHW265Jd7tEEOUlpZi2rRpOO200zBu3DhUV1fHu6XAwkcMkLjy4YcfoqqqCi+//DLefPPNeLdDDLB3717s378feXl5OHDgACZNmoTNmzcjLS0t3q0RRZqamlBXV4cePXogHA5j7NixWLlyJXr37h3v1ogB5s+fj61bt2LIkCH4xS9+Ee92iAFycnJw6NCheLdBDHPuuefioYcewtlnn40jR44gMzMTSUlJ8W4rkPBIHIkr06ZNQ0ZGRrzbIAYZMGAA8vLyAAB9+/ZFdnY2jhw5Et+miDqJiYno0aMHAKC2thZNTU3gf0PsGmzduhWbNm3CxRdfHO9WCCGKbNiwAd26dcPZZ58NAMjOzuYGThFu4ohnli1bhssuuwwDBw5EKBTCn//853Zjnn76aQwbNgypqanIz8/Hxx9/bL5RElNi6X3VqlVobm7G4MGDlbsm0RIL70ePHsWECRMwaNAgfP/730dOTo6h7olXYuH93nvvxYIFCwx1TGJBLLxXVlYiPz8fZ511Fj766CNDnZNoiNb71q1bkZ6ejq997WuYNGkSHnnkEYPddz24iSOeqa6uxoQJE/Dkk092mC9cuBB33XUX5s+fj6KiIpx99tmYOXMmdu7cabhTEkti5f3w4cOYNWsWnnvuORNtkyiJhfeePXtizZo1KC0txWuvvYb9+/ebap94JFrvf/nLXzBy5EiMHDnSZNskSmLx772srAyFhYV49tlnMWvWLFRWVppqn3gkWu8NDQ34+OOP8dRTT+HTTz/FkiVLsGTJEpPfQtfCIiQGALAWLVrU5munn366deutt7b52qhRo6x58+a1+dqHH35o/du//Zt2i0QBr95ra2uts88+23rllVdMtEliTDT/3lu49dZbrddff12rRaKAF+/z5s2zBg0aZOXm5lq9e/e2MjMzrQceeMBUyyQGxOLf+0UXXWStXLlSq0WigBfvn3zyiTVjxoxI9rOf/cz62c9+pt5rV4VH4ogK9fX1KCwsxPTp09t8ffr06fjkk0/i1BXRxo13y7Iwe/ZsnHfeebjhhhvi0SaJMW6879+/P/Jf4isrK7Fs2TKceuqpxnslscON9wULFmDXrl0oKyvDL37xC3zzm9/E/fffH492SYxw4728vBx1dXUAgN27d2Pjxo04+eSTjfdKYocb71OmTMH+/ftRXl6O5uZmLFu2DKNHj45Hu10CXm1IVDh06BCamprQr1+/Nl/v168f9u3bF/n7jBkzsHr1alRXV2PQoEFYtGgRpkyZYrpdEiPceF++fDkWLlyI8ePHR863f/XVVzFu3DjT7ZIY4cb77t27MWfOHFiWBcuycMcdd2D8+PHxaJfECLef8yRYuPFeUlKCb3/720hISEAoFMIvf/lLZGdnx6NdEiPceE9KSsIjjzyCc845B5ZlYfr06bj00kvj0W6XgJs4okooFGrzd8uy2nztvffeM90SMYCd97POOgvNzc3xaIsoY+c9Pz8fxcXFceiKaOP0Od/C7NmzDXVETGDn/cwzz8S6devi0RZRxunf+8yZMzFz5kzTbXVJeDolUSEnJweJiYnt/mvsgQMH2v1XHBIc6L1rQu9dE3rvmtB714Te/Qc3cUSF5ORk5Ofnt7sr0ZIlS3DmmWfGqSuiDb13Tei9a0LvXRN675rQu//g6ZTEM1VVVdi2bVvk76WlpSguLkZ2djaGDBmCe+65BzfccAMmT56MM844A8899xx27tyJW2+9NY5dk2ih964JvXdN6L1rQu9dE3rvZMTrtpik8/Phhx9aANr9ufHGGyNjnnrqKSs3N9dKTk62Jk2aZH300Ufxa5jEBHrvmtB714Teuyb03jWh985FyLIsy8RmkRBCCCGEEEJI9PCaOEIIIYQQQgjpRHATRwghhBBCCCGdCG7iCCGEEEIIIaQTwU0cIYQQQgghhHQiuIkjhBBCCCGEkE4EN3GEEEIIIYQQ0ongJo4QQgghhBBCOhHcxBFCCCGEEEJIJ4KbOEIIIaQTc/jwYfTt2xdlZWUxrbtu3ToMGjQI1dXVMa1LCCEkeriJI4QQ0iWYPXs2QqFQuz8XXXRRvFuLigULFuCyyy7D0KFDXY2/7LLLcMEFF3SYffrppwiFQli9ejXGjRuH008/HY8//ngMuyWEEBILQpZlWfFughBCCNFm9uzZ2L9/P1588cU2X09JSUGvXr3U3re+vh7JyckqtWtqajBw4EC8++67OOOMM1zN+fOf/4yrrroKpaWlyM3NbZN985vfxKpVq1BUVAQAeOutt3Drrbdi586dSExMjHn/hBBCvMEjcYQQQroMKSkp6N+/f5s/rTdwoVAIzz//PK688kr06NEDI0aMwF//+tc2NTZu3IiLL74Y6enp6NevH2644QYcOnQokk+dOhV33HEH7rnnHuTk5ODCCy8EAPz1r3/FiBEj0L17d0ybNg0vv/wyQqEQjh49iurqamRmZuLNN99s815vvfUW0tLScOzYsQ6/n7/97W9ISkpqt4Gz6/HSSy9F37598dJLL7WZEw6HsXDhQsyZMyfytRkzZuDw4cP46KOPXK4wIYQQE3ATRwghhLTigQcewDXXXIO1a9fi4osvxvXXX48jR44AAPbu3Ytzzz0XeXl5WLVqFRYvXoz9+/fjmmuuaVPj5ZdfRlJSEpYvX45f//rXKCsrw9VXX40rrrgCxcXF+Pa3v4358+dHxqelpeHrX/96u6OEL774Iq6++mpkZGR02OuyZcswefLkNl9z6jEpKQmzZs3CSy+9hNYn47zxxhuor6/H9ddfH/lacnIyJkyYgI8//tjDShJCCFHDIoQQQroAN954o5WYmGilpaW1+fPggw9GxgCw/uu//ivy96qqKisUCll/+9vfLMuyrB/96EfW9OnT29TdtWuXBcDavHmzZVmWde6551p5eXltxtx3333W2LFj23xt/vz5FgCrvLzcsizLKigosBITE609e/ZYlmVZBw8etLp162YtXbr0hN/T5Zdfbt18881tvuamx5KSEguA9cEHH0TGnHPOOdY3vvGNdu9x5ZVXWrNnzz5hD4QQQsyTFNcdJCGEEGKQadOm4Zlnnmnztezs7DZ/Hz9+fOR1WloaMjIycODAAQBAYWEhPvzwQ6Snp7ervX37dowcORIA2h0d27x5M6ZMmdLma6effnq7v48ZMwavvPIK5s2bh1dffRVDhgzBOeecc8Lvp6amBqmpqW2+5qbHUaNG4cwzz8Rvf/tbTJs2Ddu3b8fHH3+Mv//97+3mdO/eHeFw+IQ9EEIIMQ83cYQQQroMaWlpGD58uO2Ybt26tfl7KBRCc3MzAKC5uRmXXXYZ/vu//7vdvAEDBrR5n9ZYloVQKNTua1/mlltuwZNPPol58+bhxRdfxE033dRuXmtycnJQXl7e5mtue5wzZw7uuOMOPPXUU3jxxReRm5uL888/v92cI0eO4JRTTjlhD4QQQszDa+IIIYQQl0yaNAkbNmzA0KFDMXz48DZ/vrxxa82oUaOwcuXKNl9btWpVu3H/8R//gZ07d+JXv/oVNmzYgBtvvNG2n4kTJ2Ljxo2eerzmmmuQmJiI1157DS+//PIJN4zr16/HxIkTbfsghBBiFm7iCCGEdBnq6uqwb9++Nn9a31nSidtvvx1HjhzBN77xDaxYsQI7duzA3//+d9x8881oamo64bxvf/vb2LRpE+677z5s2bIFr7/+euTukK03Tr169cJVV12F733ve5g+fToGDRpk28+MGTOwYcOGNkfj3PaYnp6Oa6+9Fj/84Q/x+eefY/bs2e3ql5WVYc+ePSd8rhwhhJD4wE0cIYSQLsPixYsxYMCANn/OOuss1/MHDhyI5cuXo6mpCTNmzMDYsWNx5513IisrCwkJJ/6/1GHDhuHNN9/En/70J4wfPx7PPPNM5O6UKSkpbcbOmTMH9fX1uPnmmx37GTduHCZPnozXX3/dU49z5sxBeXk5LrjgAgwZMqRd/d///veYPn16u+fJEUIIiS982DchhBASBx5++GE8++yz2LVrV5uv/+53v8Odd96Jzz//3NVDwt99913ce++9WL9+ve1GUkpdXR1GjBiB3//+9/jqV78as7qEEEKihzc2IYQQQgzw9NNPY8qUKejduzeWL1+On//857jjjjsieTgcRmlpKRYsWIBvf/vbrjZwAHDxxRdj69at2LNnDwYPHhyzfj/77DPMnz+fGzhCCPEhPBJHCCGEGODuu+/GwoULceTIEQwZMgQ33HADfvCDHyAp6fh/T/3JT36Chx9+GOeccw7+8pe/dPiIAEIIIQTgJo4QQgghhBBCOhW8sQkhhBBCCCGEdCK4iSOEEEIIIYSQTgQ3cYQQQgghhBDSieAmjhBCCCGEEEI6EdzEEUIIIYQQQkgngps4QgghhBBCCOlEcBNHCCGEEEIIIZ0IbuIIIYQQQgghpBPBTRwhhBBCCCGEdCL+f8KXexaRMMMtAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Transmission spectrum visualization\n",
+    "fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "for transmission in transmissions:\n",
+    "    label = transmission.metadata.get(\"sample_folder\", \"Unknown\").split(\"/\")[-1]\n",
+    "    ax.plot(transmission.energy, transmission.transmission, label=label)\n",
+    "ax.set_xlabel(\"Energy (eV)\")\n",
+    "ax.set_ylabel(\"Transmission\")\n",
+    "ax.set_xscale(\"log\")\n",
+    "ax.set_yscale(\"log\")\n",
+    "ax.grid(True, which=\"both\", ls=\"--\", lw=0.5)\n",
+    "ax.legend()\n",
+    "ax.set_title(\"Ta-181 Transmission Spectrum\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SAMMY Data Format Conversion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.data_manager\u001b[0m:\u001b[36mconvert_csv_to_sammy_twenty\u001b[0m:\u001b[36m53\u001b[0m - \u001b[1mConverting ta_analysis/spectra/Run_8111_transmission.txt to SAMMY twenty format: ta_analysis/twenty/Run_8111_transmission.twenty\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.data_manager\u001b[0m:\u001b[36mconvert_csv_to_sammy_twenty\u001b[0m:\u001b[36m123\u001b[0m - \u001b[1mConverted 4367 data points to twenty format\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.data_manager\u001b[0m:\u001b[36mvalidate_sammy_twenty_format\u001b[0m:\u001b[36m163\u001b[0m - \u001b[1mFile ta_analysis/twenty/Run_8111_transmission.twenty is valid SAMMY twenty format\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Converting 1 transmission files\n",
+      "Converted: Run_8111_transmission.twenty\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pleiades.sammy.io.data_manager import convert_csv_to_sammy_twenty, validate_sammy_twenty_format\n",
+    "\n",
+    "input_dir = Path(spectra_dir)\n",
+    "output_dir = Path(twenty_dir)\n",
+    "\n",
+    "csv_files = list(input_dir.glob(\"*_transmission.txt\"))\n",
+    "print(f\"Converting {len(csv_files)} transmission files\")\n",
+    "\n",
+    "for csv_file in csv_files:\n",
+    "    twenty_file = output_dir / csv_file.name.replace(\".txt\", \".twenty\")\n",
+    "    convert_csv_to_sammy_twenty(csv_file, twenty_file)\n",
+    "    if validate_sammy_twenty_format(twenty_file):\n",
+    "        print(f\"Converted: {twenty_file.name}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SAMMY Configuration\n",
+    "\n",
+    "Ta-180m lacks resonance data in ENDF-B/VIII.0. Analysis proceeds with Ta-181 only (99.988% natural abundance)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m165\u001b[0m - \u001b[1mJsonManager initialized with Pydantic v2 models\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m214\u001b[0m - \u001b[1mCreating SAMMY workspace for 1 isotopes: ['Ta-181']\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m215\u001b[0m - \u001b[1mWorking directory: ta_analysis\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m218\u001b[0m - \u001b[1mStep 1: Staging ENDF files...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_isotope_info\u001b[0m:\u001b[36m167\u001b[0m - \u001b[1mGetting isotope parameters for Ta-181\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m75\u001b[0m - \u001b[1mSearching for mass.mas20 in cached files for FileCategory.ISOTOPES: {PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/isotopes.info'), PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/neutrons.list'), PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/mass.mas20')}\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m77\u001b[0m - \u001b[1mChecking file: isotopes.info\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m77\u001b[0m - \u001b[1mChecking file: neutrons.list\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m77\u001b[0m - \u001b[1mChecking file: mass.mas20\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m79\u001b[0m - \u001b[1mFound file: /SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/mass.mas20\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m75\u001b[0m - \u001b[1mSearching for isotopes.info in cached files for FileCategory.ISOTOPES: {PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/isotopes.info'), PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/neutrons.list'), PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/mass.mas20')}\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m77\u001b[0m - \u001b[1mChecking file: isotopes.info\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m79\u001b[0m - \u001b[1mFound file: /SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/isotopes.info\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m75\u001b[0m - \u001b[1mSearching for neutrons.list in cached files for FileCategory.ISOTOPES: {PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/isotopes.info'), PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/neutrons.list'), PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/mass.mas20')}\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m77\u001b[0m - \u001b[1mChecking file: isotopes.info\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m77\u001b[0m - \u001b[1mChecking file: neutrons.list\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m79\u001b[0m - \u001b[1mFound file: /SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/neutrons.list\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m361\u001b[0m - \u001b[1mUsing cached ENDF data from /SNS/users/8cz/.pleiades/nuclear_data/DataRetrievalMethod.API/EndfLibrary.ENDF_B_VIII_0/n_073-Ta-181_7328_resonance.dat\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m390\u001b[0m - \u001b[1mAPI method returned resonance data directly, no extraction needed\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m392\u001b[0m - \u001b[1mResonance parameters written to ta_analysis/073-Ta-181.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m396\u001b[0m - \u001b[1mNote: Only resonance data was downloaded (API method)\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m235\u001b[0m - \u001b[34m\u001b[1mStaged Ta-181: 073-Ta-181.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m244\u001b[0m - \u001b[1mStep 2: Creating JSON configuration with actual ENDF filenames...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_isotope_info\u001b[0m:\u001b[36m167\u001b[0m - \u001b[1mGetting isotope parameters for Ta-181\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36madd_isotope_entry\u001b[0m:\u001b[36m95\u001b[0m - \u001b[34m\u001b[1mAdded isotope entry: 073-Ta-181.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m272\u001b[0m - \u001b[34m\u001b[1mAdded Ta-181 (MAT=7328) as 073-Ta-181.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m285\u001b[0m - \u001b[1mJSON configuration written to: ta_analysis/config.json\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m286\u001b[0m - \u001b[1mConfiguration contains 1 isotope entries\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:42\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m287\u001b[0m - \u001b[1mSAMMY workspace complete: 1 ENDF files + 1 JSON file\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "JSON configuration: ta_analysis/config.json\n",
+      "ENDF files: 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pleiades.sammy.io.json_manager import JsonManager\n",
+    "\n",
+    "json_manager = JsonManager()\n",
+    "json_path = json_manager.create_json_config(\n",
+    "    isotopes=[\"Ta-181\"],\n",
+    "    abundances=[1.0],\n",
+    "    working_dir=working_dir,\n",
+    "    custom_global_settings={\n",
+    "        \"forceRMoore\": \"yes\",\n",
+    "        \"purgeSpinGroups\": \"yes\",\n",
+    "        \"fudge\": \"0.7\"\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "print(f\"JSON configuration: {json_path}\")\n",
+    "endf_files = [f for f in os.listdir(working_dir) if f.endswith('.par')]\n",
+    "print(f\"ENDF files: {len(endf_files)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-09-25 18:27:43\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.utils.logger\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m145\u001b[0m - \u001b[1mlogging initialized...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:43\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.inp_manager\u001b[0m:\u001b[36mcreate_multi_isotope_inp\u001b[0m:\u001b[36m480\u001b[0m - \u001b[1mSuccessfully wrote multi-isotope SAMMY input file to ta_analysis/ta_fitting.inp\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INP file: ta_analysis/ta_fitting.inp\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pleiades.sammy.io.inp_manager import InpManager\n",
+    "\n",
+    "ta_material_props = {\n",
+    "    'element': 'Ta',\n",
+    "    'mass_number': 181,\n",
+    "    'density_g_cm3': 16.65,\n",
+    "    'thickness_mm': 0.127,\n",
+    "    'atomic_mass_amu': 180.94788,\n",
+    "    'abundance': 1.0,\n",
+    "    'min_energy': 1.0,\n",
+    "    'max_energy_eV': 200.0,\n",
+    "    'temperature_K': 293.6\n",
+    "}\n",
+    "\n",
+    "inp_file = Path(working_dir) / \"ta_fitting.inp\"\n",
+    "InpManager.create_multi_isotope_inp(\n",
+    "    inp_file,\n",
+    "    title=\"Ta-181 neutron transmission analysis - VENUS IPTS-35945\",\n",
+    "    material_properties=ta_material_props,\n",
+    "    resolution_file_path=resolution_file_path\n",
+    ")\n",
+    "\n",
+    "print(f\"INP file: {inp_file}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pleiades.sammy.backends.local import LocalSammyRunner\n",
+    "from pleiades.sammy.config import LocalSammyConfig\n",
+    "from pleiades.sammy.interface import SammyFilesMultiMode\n",
+    "\n",
+    "data_file = Path(twenty_dir) / \"Run_8111_transmission.twenty\"\n",
+    "\n",
+    "files = SammyFilesMultiMode(\n",
+    "    input_file=inp_file,\n",
+    "    json_config_file=Path(json_path),\n",
+    "    data_file=data_file,\n",
+    "    endf_directory=Path(working_dir)\n",
+    ")\n",
+    "\n",
+    "config = LocalSammyConfig(\n",
+    "    sammy_executable=Path(\"/SNS/software/sammy/bin/sammy\"),\n",
+    "    working_dir=sammy_working,\n",
+    "    output_dir=sammy_output\n",
+    ")\n",
+    "\n",
+    "runner = LocalSammyRunner(config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SAMMY Execution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-09-25 18:27:43\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mprepare_environment\u001b[0m:\u001b[36m45\u001b[0m - \u001b[34m\u001b[1mValidating input files\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:43\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mprepare_environment\u001b[0m:\u001b[36m50\u001b[0m - \u001b[34m\u001b[1mPerforming JSON-ENDF mapping validation\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:43\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36m_validate_json_endf_mapping\u001b[0m:\u001b[36m100\u001b[0m - \u001b[34m\u001b[1mJSON-ENDF validation passed: 1 ENDF files verified\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:43\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mprepare_environment\u001b[0m:\u001b[36m54\u001b[0m - \u001b[34m\u001b[1mMoving files to working directory\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:43\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mmove_to_working_dir\u001b[0m:\u001b[36m214\u001b[0m - \u001b[34m\u001b[1mMoving JSON mode files to working directory: ta_analysis/sammy_working\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:43\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mmove_to_working_dir\u001b[0m:\u001b[36m253\u001b[0m - \u001b[34m\u001b[1mSymlinked ENDF file: 073-Ta-181.B-VIII.0.par\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:43\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mprepare_environment\u001b[0m:\u001b[36m58\u001b[0m - \u001b[34m\u001b[1mEnvironment preparation complete\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:43\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mexecute_sammy\u001b[0m:\u001b[36m110\u001b[0m - \u001b[1mStarting SAMMY execution 142c46a8-830d-412f-8e9e-00a9cb81de83\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:43\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mexecute_sammy\u001b[0m:\u001b[36m111\u001b[0m - \u001b[34m\u001b[1mWorking directory: ta_analysis/sammy_working\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:43\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mexecute_sammy\u001b[0m:\u001b[36m123\u001b[0m - \u001b[34m\u001b[1mUsing JSON mode command format\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Executing SAMMY fitting\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mexecute_sammy\u001b[0m:\u001b[36m156\u001b[0m - \u001b[1mSAMMY execution completed successfully for 142c46a8-830d-412f-8e9e-00a9cb81de83\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m432\u001b[0m - \u001b[1mCollecting outputs for execution 142c46a8-830d-412f-8e9e-00a9cb81de83\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m443\u001b[0m - \u001b[34m\u001b[1mFound known output file: SAMMY.LPT\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m443\u001b[0m - \u001b[34m\u001b[1mFound known output file: SAMNDF.PAR\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m443\u001b[0m - \u001b[34m\u001b[1mFound known output file: SAMNDF.INP\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m443\u001b[0m - \u001b[34m\u001b[1mFound known output file: SAMMY.ODF\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m443\u001b[0m - \u001b[34m\u001b[1mFound known output file: SAMMY.IO\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m443\u001b[0m - \u001b[34m\u001b[1mFound known output file: SAMMY.LST\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAMQUA.RED\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAMMY.PLT\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAMQUA.PAR\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAMMY.PAR\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAM46.DAT\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAM41.DAT\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAMMY.RED\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAMMY.COV\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: ta_analysis/sammy_output/SAMMY.LST\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved ta_analysis/sammy_working/SAMMY.LST to ta_analysis/sammy_output/SAMMY.LST\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: ta_analysis/sammy_output/SAMMY.PAR\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved ta_analysis/sammy_working/SAMMY.PAR to ta_analysis/sammy_output/SAMMY.PAR\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: ta_analysis/sammy_output/SAMNDF.PAR\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved ta_analysis/sammy_working/SAMNDF.PAR to ta_analysis/sammy_output/SAMNDF.PAR\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: ta_analysis/sammy_output/SAMQUA.RED\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved ta_analysis/sammy_working/SAMQUA.RED to ta_analysis/sammy_output/SAMQUA.RED\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: ta_analysis/sammy_output/SAMMY.PLT\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved ta_analysis/sammy_working/SAMMY.PLT to ta_analysis/sammy_output/SAMMY.PLT\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: ta_analysis/sammy_output/SAMMY.RED\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved ta_analysis/sammy_working/SAMMY.RED to ta_analysis/sammy_output/SAMMY.RED\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: ta_analysis/sammy_output/SAMMY.IO\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved ta_analysis/sammy_working/SAMMY.IO to ta_analysis/sammy_output/SAMMY.IO\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: ta_analysis/sammy_output/SAMQUA.PAR\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved ta_analysis/sammy_working/SAMQUA.PAR to ta_analysis/sammy_output/SAMQUA.PAR\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: ta_analysis/sammy_output/SAMMY.LPT\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved ta_analysis/sammy_working/SAMMY.LPT to ta_analysis/sammy_output/SAMMY.LPT\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: ta_analysis/sammy_output/SAM46.DAT\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved ta_analysis/sammy_working/SAM46.DAT to ta_analysis/sammy_output/SAM46.DAT\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: ta_analysis/sammy_output/SAMMY.COV\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved ta_analysis/sammy_working/SAMMY.COV to ta_analysis/sammy_output/SAMMY.COV\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: ta_analysis/sammy_output/SAMMY.ODF\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved ta_analysis/sammy_working/SAMMY.ODF to ta_analysis/sammy_output/SAMMY.ODF\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: ta_analysis/sammy_output/SAM41.DAT\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved ta_analysis/sammy_working/SAM41.DAT to ta_analysis/sammy_output/SAM41.DAT\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m462\u001b[0m - \u001b[34m\u001b[1mRemoving existing output file: ta_analysis/sammy_output/SAMNDF.INP\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved ta_analysis/sammy_working/SAMNDF.INP to ta_analysis/sammy_output/SAMNDF.INP\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m471\u001b[0m - \u001b[1mSuccessfully collected 14 output files in 0.08 seconds\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mcleanup\u001b[0m:\u001b[36m174\u001b[0m - \u001b[34m\u001b[1mPerforming cleanup for local backend\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Execution status: True\n",
+      "Runtime: 2.40 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Executing SAMMY fitting\")\n",
+    "runner.prepare_environment(files)\n",
+    "result = runner.execute_sammy(files)\n",
+    "\n",
+    "print(f\"Execution status: {result.success}\")\n",
+    "print(f\"Runtime: {result.runtime_seconds:.2f} s\")\n",
+    "\n",
+    "if result.error_message:\n",
+    "    print(f\"Error: {result.error_message[:200]}\")\n",
+    "\n",
+    "runner.collect_outputs(result=result)\n",
+    "runner.cleanup()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Results Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mprocess_lpt_file\u001b[0m:\u001b[36m479\u001b[0m - \u001b[1mSuccessfully read the file: ta_analysis/sammy_output/SAMMY.LPT\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mprocess_lpt_file\u001b[0m:\u001b[36m490\u001b[0m - \u001b[34m\u001b[1mSplit LPT content into 3 blocks.\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_isotope_info\u001b[0m:\u001b[36m116\u001b[0m - \u001b[34m\u001b[1mExtracting isotope information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_results_from_string\u001b[0m:\u001b[36m415\u001b[0m - \u001b[1mIsotope results not found.\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_radius_info\u001b[0m:\u001b[36m189\u001b[0m - \u001b[34m\u001b[1mExtracting radius information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_results_from_string\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mRadius results not found.\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_broadening_info\u001b[0m:\u001b[36m261\u001b[0m - \u001b[34m\u001b[1mExtracting broadening information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_normalization_info\u001b[0m:\u001b[36m328\u001b[0m - \u001b[34m\u001b[1mExtracting normalization information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_chi_squared_info\u001b[0m:\u001b[36m377\u001b[0m - \u001b[34m\u001b[1mExtracting chi-squared information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_isotope_info\u001b[0m:\u001b[36m116\u001b[0m - \u001b[34m\u001b[1mExtracting isotope information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_results_from_string\u001b[0m:\u001b[36m415\u001b[0m - \u001b[1mIsotope results not found.\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_radius_info\u001b[0m:\u001b[36m189\u001b[0m - \u001b[34m\u001b[1mExtracting radius information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_results_from_string\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mRadius results not found.\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_broadening_info\u001b[0m:\u001b[36m261\u001b[0m - \u001b[34m\u001b[1mExtracting broadening information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_normalization_info\u001b[0m:\u001b[36m328\u001b[0m - \u001b[34m\u001b[1mExtracting normalization information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_chi_squared_info\u001b[0m:\u001b[36m377\u001b[0m - \u001b[34m\u001b[1mExtracting chi-squared information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_isotope_info\u001b[0m:\u001b[36m116\u001b[0m - \u001b[34m\u001b[1mExtracting isotope information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_results_from_string\u001b[0m:\u001b[36m415\u001b[0m - \u001b[1mIsotope results not found.\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_radius_info\u001b[0m:\u001b[36m189\u001b[0m - \u001b[34m\u001b[1mExtracting radius information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_results_from_string\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mRadius results not found.\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_broadening_info\u001b[0m:\u001b[36m261\u001b[0m - \u001b[34m\u001b[1mExtracting broadening information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_normalization_info\u001b[0m:\u001b[36m328\u001b[0m - \u001b[34m\u001b[1mExtracting normalization information...\u001b[0m\n",
+      "\u001b[32m2025-09-25 18:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_chi_squared_info\u001b[0m:\u001b[36m377\u001b[0m - \u001b[34m\u001b[1mExtracting chi-squared information...\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Energy range: 6.673e+00 - 1.999e+02 eV\n",
+      "Data points: 3575\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pleiades.sammy.results.manager import ResultsManager\n",
+    "\n",
+    "lpt_file_path = sammy_output / \"SAMMY.LPT\"\n",
+    "lst_file_path = sammy_output / \"SAMMY.LST\"\n",
+    "\n",
+    "results_manager = ResultsManager(lpt_file_path=lpt_file_path, lst_file_path=lst_file_path)\n",
+    "data = results_manager.get_data()\n",
+    "\n",
+    "print(f\"Energy range: {data.energy.min():.3e} - {data.energy.max():.3e} eV\")\n",
+    "print(f\"Data points: {len(data.energy)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "results_manager.plot_transmission(\n",
+    "    show_diff = True,\n",
+    "    plot_uncertainty = True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = results_manager.plot_transmission(\n",
+    "      figsize=(12, 8),\n",
+    "      title=\"Hafnium Multi-Isotope Fit\",\n",
+    "      xscale=\"log\",\n",
+    "      # yscale=\"log\",\n",
+    "      data_color=\"blue\",\n",
+    "      final_color=\"red\",\n",
+    "      show=False,\n",
+    "      show_diff=True,\n",
+    "      plot_uncertainty=True,\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fitting Quality Metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fit iterations: 3\n",
+      "\n",
+      "Iteration 1:\n",
+      "  Chi-squared: 436342.0000\n",
+      "  Data points: 3575\n",
+      "  Reduced chi-squared: 122.054000\n",
+      "  Number density: 7.040000e-04 atoms/barn-cm\n",
+      "  Temperature: 293.60 K\n",
+      "\n",
+      "Iteration 2:\n",
+      "  Chi-squared: 77205.3000\n",
+      "  Data points: 3575\n",
+      "  Reduced chi-squared: 21.595900\n",
+      "  Number density: 8.172900e-04 atoms/barn-cm\n",
+      "  Temperature: 293.60 K\n",
+      "\n",
+      "Iteration 3:\n",
+      "  Chi-squared: 25096.0000\n",
+      "  Data points: 3575\n",
+      "  Reduced chi-squared: 7.019870\n",
+      "  Number density: 7.876200e-04 atoms/barn-cm\n",
+      "  Temperature: 293.60 K\n",
+      "\n",
+      "Final fit results:\n",
+      "  Reduced chi-squared: 7.019870\n",
+      "  Number density: 7.876200e-04 atoms/barn-cm\n"
+     ]
+    }
+   ],
+   "source": [
+    "if results_manager.run_results.fit_results:\n",
+    "    print(f\"Fit iterations: {len(results_manager.run_results.fit_results)}\")\n",
+    "    \n",
+    "    for i, fit_result in enumerate(results_manager.run_results.fit_results):\n",
+    "        print(f\"\\nIteration {i+1}:\")\n",
+    "        \n",
+    "        chi_sq = fit_result.get_chi_squared_results()\n",
+    "        if chi_sq.chi_squared is not None:\n",
+    "            print(f\"  Chi-squared: {chi_sq.chi_squared:.4f}\")\n",
+    "            print(f\"  Data points: {chi_sq.dof}\")\n",
+    "            print(f\"  Reduced chi-squared: {chi_sq.reduced_chi_squared:.6f}\")\n",
+    "        \n",
+    "        physics = fit_result.get_physics_data()\n",
+    "        if hasattr(physics, 'broadening_parameters'):\n",
+    "            broadening = physics.broadening_parameters\n",
+    "            if hasattr(broadening, 'thick') and broadening.thick is not None:\n",
+    "                print(f\"  Number density: {broadening.thick:.6e} atoms/barn-cm\")\n",
+    "                print(f\"  Temperature: {broadening.temp:.2f} K\")\n",
+    "\n",
+    "    # Final results\n",
+    "    if len(results_manager.run_results.fit_results) > 0:\n",
+    "        final_fit = results_manager.run_results.fit_results[-1]\n",
+    "        final_chi = final_fit.get_chi_squared_results()\n",
+    "        final_phys = final_fit.get_physics_data()\n",
+    "        \n",
+    "        print(\"\\nFinal fit results:\")\n",
+    "        if final_chi.reduced_chi_squared:\n",
+    "            print(f\"  Reduced chi-squared: {final_chi.reduced_chi_squared:.6f}\")\n",
+    "        if hasattr(final_phys, 'broadening_parameters'):\n",
+    "            if hasattr(final_phys.broadening_parameters, 'thick'):\n",
+    "                print(f\"  Number density: {final_phys.broadening_parameters.thick:.6e} atoms/barn-cm\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "default",
+   "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": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/Notebooks/pleiades_venus_demo.ipynb b/examples/Notebooks/pleiades_venus_demo.ipynb
deleted file mode 100644
index 41f107c6..00000000
--- a/examples/Notebooks/pleiades_venus_demo.ipynb
+++ /dev/null
@@ -1,761 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "ce9b367c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from pathlib import Path"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8fd9a91c",
-   "metadata": {},
-   "source": [
-    "## Pre-process Raw Data into Transmission Spectra"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "9e8d58c6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# VENUS IPTS-35945 paths\n",
-    "ipts_path = \"/SNS/VENUS/IPTS-35945\"\n",
-    "nexus_path = f\"{ipts_path}/nexus\"\n",
-    "autoreduce_base = f\"{ipts_path}/shared/autoreduce/mcp/images\"\n",
-    "\n",
-    "# Working directory for output\n",
-    "working_dir = \"./ipts_35945\"\n",
-    "os.makedirs(working_dir, exist_ok=True)\n",
-    "spectra_dir = f\"{working_dir}/spectra\"\n",
-    "os.makedirs(spectra_dir, exist_ok=True)\n",
-    "twenty_dir = f\"{working_dir}/twenty\"\n",
-    "os.makedirs(twenty_dir, exist_ok=True)\n",
-    "# Create SAMMY working and output directories\n",
-    "sammy_working = Path(working_dir) / \"sammy_working\"\n",
-    "sammy_output = Path(working_dir) / \"sammy_output\"\n",
-    "sammy_working.mkdir(exist_ok=True)\n",
-    "sammy_output.mkdir(exist_ok=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "0ce164f3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Test the dual-export (individual + combined) implementation\n",
-    "from pleiades.processing.normalization import normalization\n",
-    "from pleiades.processing import Roi, Facility\n",
-    "\n",
-    "# All sample runs\n",
-    "open_beam_folder = f\"{autoreduce_base}/Run_8021\"\n",
-    "sample_folders = [\n",
-    "    f\"{autoreduce_base}/Run_8022\",\n",
-    "    f\"{autoreduce_base}/Run_8023\",\n",
-    "    f\"{autoreduce_base}/Run_8024\",\n",
-    "    # f\"{autoreduce_base}/Run_8025\",\n",
-    "    # f\"{autoreduce_base}/Run_8026\",\n",
-    "    # f\"{autoreduce_base}/Run_8027\"\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "2464208b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m2025-09-18 19:22:50\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.normalization\u001b[0m:\u001b[36mnormalization\u001b[0m:\u001b[36m91\u001b[0m - \u001b[1mUsing ORNL-specific normalization\u001b[0m\n",
-      "olefile module not found\n",
-      "/SNS/users/8cz/github.com/PLEIADES/.pixi/envs/default/lib/python3.11/site-packages/dxchange/__init__.py:63: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n",
-      "  import pkg_resources\n",
-      "\u001b[32m2025-09-18 19:22:51\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mnormalization_ornl\u001b[0m:\u001b[36m199\u001b[0m - \u001b[1mLoading 3 sample folders\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:22:51\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.utils.load\u001b[0m:\u001b[36mload\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mloading 4367 files with extension .tif\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:23:36\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mload_spectra_file\u001b[0m:\u001b[36m42\u001b[0m - \u001b[34m\u001b[1mFound spectra file: Run_8022_20250423_April23_2025_MCP_TPX_Foil_Au_1_6C_Resonance_0001_2585092_Spectra.txt\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:23:36\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.utils.load\u001b[0m:\u001b[36mload\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mloading 4367 files with extension .tif\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:24:24\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mload_spectra_file\u001b[0m:\u001b[36m42\u001b[0m - \u001b[34m\u001b[1mFound spectra file: Run_8023_20250423_April23_2025_MCP_TPX_Foil_Au_1_6C_Resonance_0001_2586093_Spectra.txt\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:24:24\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.utils.load\u001b[0m:\u001b[36mload\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mloading 4367 files with extension .tif\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:25:13\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mload_spectra_file\u001b[0m:\u001b[36m42\u001b[0m - \u001b[34m\u001b[1mFound spectra file: Run_8024_20250423_April23_2025_MCP_TPX_Foil_Au_1_6C_Resonance_0001_2587094_Spectra.txt\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:25:13\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mload_multiple_runs\u001b[0m:\u001b[36m177\u001b[0m - \u001b[1mLoaded 3 runs\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:25:13\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mnormalization_ornl\u001b[0m:\u001b[36m202\u001b[0m - \u001b[1mLoading 1 OB folders\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:25:13\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.utils.load\u001b[0m:\u001b[36mload\u001b[0m:\u001b[36m21\u001b[0m - \u001b[1mloading 4367 files with extension .tif\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:25:58\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mload_spectra_file\u001b[0m:\u001b[36m42\u001b[0m - \u001b[34m\u001b[1mFound spectra file: Run_8021_20250423_April23_2025_OB_MCP_TPX1_Foil_1_6C_Resonance_0001_2584091_Spectra.txt\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:25:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mload_multiple_runs\u001b[0m:\u001b[36m177\u001b[0m - \u001b[1mLoaded 1 runs\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:25:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mnormalization_ornl\u001b[0m:\u001b[36m210\u001b[0m - \u001b[1mUsing individual mode\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:25:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mprocess_individual_mode\u001b[0m:\u001b[36m123\u001b[0m - \u001b[1mCombined 1 OB runs\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:25:58\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mprocess_individual_mode\u001b[0m:\u001b[36m128\u001b[0m - \u001b[34m\u001b[1mProcessing sample run 1/3\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:25:58\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mdetect_persistent_dead_pixels\u001b[0m:\u001b[36m193\u001b[0m - \u001b[34m\u001b[1mFound 20/262144 dead pixels (0.0%)\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:25:59\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mdetect_persistent_dead_pixels\u001b[0m:\u001b[36m193\u001b[0m - \u001b[34m\u001b[1mFound 13/262144 dead pixels (0.0%)\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:26:00\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mprocess_individual_mode\u001b[0m:\u001b[36m128\u001b[0m - \u001b[34m\u001b[1mProcessing sample run 2/3\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:26:00\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mdetect_persistent_dead_pixels\u001b[0m:\u001b[36m193\u001b[0m - \u001b[34m\u001b[1mFound 27/262144 dead pixels (0.0%)\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:26:00\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mdetect_persistent_dead_pixels\u001b[0m:\u001b[36m193\u001b[0m - \u001b[34m\u001b[1mFound 13/262144 dead pixels (0.0%)\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:26:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mprocess_individual_mode\u001b[0m:\u001b[36m128\u001b[0m - \u001b[34m\u001b[1mProcessing sample run 3/3\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:26:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mdetect_persistent_dead_pixels\u001b[0m:\u001b[36m193\u001b[0m - \u001b[34m\u001b[1mFound 28/262144 dead pixels (0.0%)\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:26:01\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.processing.helper_ornl\u001b[0m:\u001b[36mdetect_persistent_dead_pixels\u001b[0m:\u001b[36m193\u001b[0m - \u001b[34m\u001b[1mFound 13/262144 dead pixels (0.0%)\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:26:02\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mnormalization_ornl\u001b[0m:\u001b[36m249\u001b[0m - \u001b[1mSaved transmission to ./ipts_35945/spectra/Run_8022_transmission.txt\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:26:02\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mnormalization_ornl\u001b[0m:\u001b[36m249\u001b[0m - \u001b[1mSaved transmission to ./ipts_35945/spectra/Run_8023_transmission.txt\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:26:02\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.processing.normalization_ornl\u001b[0m:\u001b[36mnormalization_ornl\u001b[0m:\u001b[36m249\u001b[0m - \u001b[1mSaved transmission to ./ipts_35945/spectra/Run_8024_transmission.txt\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "transmissions = normalization(\n",
-    "    list_sample_folders=sample_folders,\n",
-    "    list_obs_folders=[open_beam_folder],\n",
-    "    nexus_path=nexus_path,\n",
-    "    facility=Facility.ornl,\n",
-    "    combine_mode=False,\n",
-    "    pc_uncertainty=0.005,\n",
-    "    output_folder=os.path.join(working_dir, \"spectra\"),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "75485cec",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "for transmission in transmissions:\n",
-    "    label = transmission.metadata.get(\"sample_folder\", \"Unknown\").split(\"/\")[-1]\n",
-    "    ax.plot(transmission.energy, transmission.transmission, label=label)\n",
-    "ax.set_xlabel(\"Energy (eV)\")\n",
-    "ax.set_ylabel(\"Transmission\")\n",
-    "ax.set_xscale(\"log\")\n",
-    "ax.grid()\n",
-    "ax.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1fa3ea77",
-   "metadata": {},
-   "source": [
-    "convert to sammy twenty format"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "4766fcfb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m2025-09-18 19:26:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.data_manager\u001b[0m:\u001b[36mconvert_csv_to_sammy_twenty\u001b[0m:\u001b[36m40\u001b[0m - \u001b[1mConverting ipts_35945/spectra/Run_8022_transmission.txt to SAMMY twenty format: ipts_35945/twenty/Run_8022_transmission.twenty\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:26:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.data_manager\u001b[0m:\u001b[36mconvert_csv_to_sammy_twenty\u001b[0m:\u001b[36m56\u001b[0m - \u001b[1mConverted 4367 data points to twenty format\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:26:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.data_manager\u001b[0m:\u001b[36mvalidate_sammy_twenty_format\u001b[0m:\u001b[36m96\u001b[0m - \u001b[1mFile ipts_35945/twenty/Run_8022_transmission.twenty is valid SAMMY twenty format\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:26:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.data_manager\u001b[0m:\u001b[36mconvert_csv_to_sammy_twenty\u001b[0m:\u001b[36m40\u001b[0m - \u001b[1mConverting ipts_35945/spectra/Run_8023_transmission.txt to SAMMY twenty format: ipts_35945/twenty/Run_8023_transmission.twenty\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:26:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.data_manager\u001b[0m:\u001b[36mconvert_csv_to_sammy_twenty\u001b[0m:\u001b[36m56\u001b[0m - \u001b[1mConverted 4367 data points to twenty format\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:26:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.data_manager\u001b[0m:\u001b[36mvalidate_sammy_twenty_format\u001b[0m:\u001b[36m96\u001b[0m - \u001b[1mFile ipts_35945/twenty/Run_8023_transmission.twenty is valid SAMMY twenty format\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:26:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.data_manager\u001b[0m:\u001b[36mconvert_csv_to_sammy_twenty\u001b[0m:\u001b[36m40\u001b[0m - \u001b[1mConverting ipts_35945/spectra/Run_8024_transmission.txt to SAMMY twenty format: ipts_35945/twenty/Run_8024_transmission.twenty\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:26:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.data_manager\u001b[0m:\u001b[36mconvert_csv_to_sammy_twenty\u001b[0m:\u001b[36m56\u001b[0m - \u001b[1mConverted 4367 data points to twenty format\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:26:59\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.data_manager\u001b[0m:\u001b[36mvalidate_sammy_twenty_format\u001b[0m:\u001b[36m96\u001b[0m - \u001b[1mFile ipts_35945/twenty/Run_8024_transmission.twenty is valid SAMMY twenty format\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Input directory: ipts_35945/spectra\n",
-      "Input directory exists: True\n",
-      "Found 3 CSV files to convert:\n",
-      "  - Run_8022_transmission.txt\n",
-      "  - Run_8023_transmission.txt\n",
-      "  - Run_8024_transmission.txt\n",
-      "\n",
-      "Converting to twenty format...\n",
-      "✅ Run_8022_transmission.txt -> Run_8022_transmission.twenty\n",
-      "✅ Run_8023_transmission.txt -> Run_8023_transmission.twenty\n",
-      "✅ Run_8024_transmission.txt -> Run_8024_transmission.twenty\n",
-      "\n",
-      "Conversion complete! Output files in: ipts_35945/twenty\n"
-     ]
-    }
-   ],
-   "source": [
-    "from pleiades.sammy.io.data_manager import convert_csv_to_sammy_twenty, validate_sammy_twenty_format\n",
-    "\n",
-    "# Define paths - use absolute path to be safe\n",
-    "input_dir = Path(spectra_dir)\n",
-    "output_dir = Path(twenty_dir)\n",
-    "\n",
-    "# Debug: Check if input directory exists\n",
-    "print(f\"Input directory: {input_dir}\")\n",
-    "print(f\"Input directory exists: {input_dir.exists()}\")\n",
-    "\n",
-    "# Convert all transmission CSV files to twenty format\n",
-    "csv_files = list(input_dir.glob(\"*_transmission.txt\"))\n",
-    "\n",
-    "print(f\"Found {len(csv_files)} CSV files to convert:\")\n",
-    "for csv_file in csv_files:\n",
-    "    print(f\"  - {csv_file.name}\")\n",
-    "\n",
-    "if len(csv_files) == 0:\n",
-    "    print(\"No files found! Check the input directory path.\")\n",
-    "    # List all files in the directory for debugging\n",
-    "    all_files = list(input_dir.glob(\"*\"))\n",
-    "    print(f\"All files in directory: {[f.name for f in all_files]}\")\n",
-    "else:\n",
-    "    print(\"\\nConverting to twenty format...\")\n",
-    "    for csv_file in csv_files:\n",
-    "        # Create output filename: Run_8022_transmission.txt -> Run_8022_transmission.twenty\n",
-    "        twenty_file = output_dir / csv_file.name.replace(\".txt\", \".twenty\")\n",
-    "\n",
-    "        try:\n",
-    "            # Convert using existing function\n",
-    "            convert_csv_to_sammy_twenty(csv_file, twenty_file)\n",
-    "\n",
-    "            # Validate the output\n",
-    "            if validate_sammy_twenty_format(twenty_file):\n",
-    "                print(f\"✅ {csv_file.name} -> {twenty_file.name}\")\n",
-    "            else:\n",
-    "                print(f\"❌ {csv_file.name} -> validation failed\")\n",
-    "\n",
-    "        except Exception as e:\n",
-    "            print(f\"❌ {csv_file.name} -> conversion failed: {e}\")\n",
-    "\n",
-    "    print(f\"\\nConversion complete! Output files in: {output_dir}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ca30a51f",
-   "metadata": {},
-   "source": [
-    "## Configure Sammy Runner"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "7659fd06",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m165\u001b[0m - \u001b[1mJsonManager initialized with Pydantic v2 models\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m214\u001b[0m - \u001b[1mCreating SAMMY workspace for 1 isotopes: ['Au-197']\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m215\u001b[0m - \u001b[1mWorking directory: ipts_35945\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m218\u001b[0m - \u001b[1mStep 1: Staging ENDF files...\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_isotope_info\u001b[0m:\u001b[36m167\u001b[0m - \u001b[1mGetting isotope parameters for Au-197\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m75\u001b[0m - \u001b[1mSearching for mass.mas20 in cached files for FileCategory.ISOTOPES: {PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/isotopes.info'), PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/mass.mas20'), PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/neutrons.list')}\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m77\u001b[0m - \u001b[1mChecking file: isotopes.info\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m77\u001b[0m - \u001b[1mChecking file: mass.mas20\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m79\u001b[0m - \u001b[1mFound file: /SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/mass.mas20\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m75\u001b[0m - \u001b[1mSearching for isotopes.info in cached files for FileCategory.ISOTOPES: {PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/isotopes.info'), PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/mass.mas20'), PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/neutrons.list')}\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m77\u001b[0m - \u001b[1mChecking file: isotopes.info\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m79\u001b[0m - \u001b[1mFound file: /SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/isotopes.info\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m75\u001b[0m - \u001b[1mSearching for neutrons.list in cached files for FileCategory.ISOTOPES: {PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/isotopes.info'), PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/mass.mas20'), PosixPath('/SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/neutrons.list')}\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m77\u001b[0m - \u001b[1mChecking file: isotopes.info\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m77\u001b[0m - \u001b[1mChecking file: mass.mas20\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m77\u001b[0m - \u001b[1mChecking file: neutrons.list\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_file_path\u001b[0m:\u001b[36m79\u001b[0m - \u001b[1mFound file: /SNS/users/8cz/github.com/PLEIADES/src/pleiades/nuclear/isotopes/files/neutrons.list\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m361\u001b[0m - \u001b[1mUsing cached ENDF data from /SNS/users/8cz/.pleiades/nuclear_data/DataRetrievalMethod.API/EndfLibrary.ENDF_B_VIII_0/n_079-Au-197_7925_resonance.dat\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m390\u001b[0m - \u001b[1mAPI method returned resonance data directly, no extraction needed\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m392\u001b[0m - \u001b[1mResonance parameters written to ipts_35945/079-Au-197.B-VIII.0.par\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.manager\u001b[0m:\u001b[36mdownload_endf_resonance_file\u001b[0m:\u001b[36m396\u001b[0m - \u001b[1mNote: Only resonance data was downloaded (API method)\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m235\u001b[0m - \u001b[34m\u001b[1mStaged Au-197: 079-Au-197.B-VIII.0.par\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m244\u001b[0m - \u001b[1mStep 2: Creating JSON configuration with actual ENDF filenames...\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.nuclear.isotopes.manager\u001b[0m:\u001b[36mget_isotope_info\u001b[0m:\u001b[36m167\u001b[0m - \u001b[1mGetting isotope parameters for Au-197\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36madd_isotope_entry\u001b[0m:\u001b[36m95\u001b[0m - \u001b[34m\u001b[1mAdded isotope entry: 079-Au-197.B-VIII.0.par\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m272\u001b[0m - \u001b[34m\u001b[1mAdded Au-197 (MAT=7925) as 079-Au-197.B-VIII.0.par\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m285\u001b[0m - \u001b[1mJSON configuration written to: ipts_35945/config.json\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m286\u001b[0m - \u001b[1mConfiguration contains 1 isotope entries\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:08\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.json_manager\u001b[0m:\u001b[36mcreate_json_config\u001b[0m:\u001b[36m287\u001b[0m - \u001b[1mSAMMY workspace complete: 1 ENDF files + 1 JSON file\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "🔧 Step 1: Creating JSON configuration and ENDF files for Au-197...\n",
-      "✅ JSON config created: ipts_35945/config.json\n",
-      "✅ ENDF files staged in: ./ipts_35945\n",
-      "📄 Found 1 ENDF files: ['079-Au-197.B-VIII.0.par']\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Step 1: Create JSON configuration and ENDF files using JsonManager\n",
-    "from pleiades.sammy.io.json_manager import JsonManager\n",
-    "\n",
-    "print(\"🔧 Step 1: Creating JSON configuration and ENDF files for Au-197...\")\n",
-    "\n",
-    "json_manager = JsonManager()\n",
-    "json_path = json_manager.create_json_config(\n",
-    "    isotopes=[\"Au-197\"],\n",
-    "    abundances=[1.0],  # 100% abundance for single isotope\n",
-    "    working_dir=working_dir,\n",
-    "    custom_global_settings={\n",
-    "        \"forceRMoore\": \"yes\",\n",
-    "        \"purgeSpinGroups\": \"yes\", \n",
-    "        \"fudge\": \"0.7\"\n",
-    "    }\n",
-    ")\n",
-    "\n",
-    "print(f\"✅ JSON config created: {json_path}\")\n",
-    "print(f\"✅ ENDF files staged in: {working_dir}\")\n",
-    "\n",
-    "# List the files that were created\n",
-    "import os\n",
-    "endf_files = [f for f in os.listdir(working_dir) if f.endswith('.par')]\n",
-    "print(f\"📄 Found {len(endf_files)} ENDF files: {endf_files}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "d8f9e8e3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m2025-09-18 19:27:15\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.utils.logger\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m145\u001b[0m - \u001b[1mlogging initialized...\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "🔧 Step 2: Generating INP file using PLEIADES...\n",
-      "📊 Material properties: {'element': 'Au', 'mass_number': 197, 'density_g_cm3': 19.32, 'thickness_mm': 0.025, 'atomic_mass_amu': 196.966569, 'abundance': 1.0, 'min_energy': 1.0, 'max_energy_eV': 200.0, 'temperature_K': 293.6}\n",
-      "📄 Resolution file path: venus_resolution.dat\n",
-      "📄 Resolution file exists: True\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m2025-09-18 19:27:15\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.inp_manager\u001b[0m:\u001b[36mcreate_multi_isotope_inp\u001b[0m:\u001b[36m480\u001b[0m - \u001b[1mSuccessfully wrote multi-isotope SAMMY input file to ipts_35945/au_fitting.inp\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "✅ Generated INP file: ipts_35945/au_fitting.inp\n",
-      "\n",
-      "📝 Sample INP content:\n",
-      "   PLEIADES-generated Au-197 fitting for VENUS IPTS-35945\n",
-      "   Au197      196.96657     0.001     200.0\n",
-      "   REICH-MOORE FORMALISM IS WANTED\n",
-      "   USE NEW SPIN GROUP Format\n",
-      "   USE TWENTY SIGNIFICANT DIGITS\n",
-      "   BROADENING IS WANTED\n",
-      "   INPUT IS ENDF/B FILE 2\n",
-      "   SOLVE BAYES EQUATIONS\n",
-      "   DO NOT SUPPRESS ANY INTERMEDIATE PRINTOUT\n",
-      "   CHI SQUARED IS WANTED\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Step 2: Generate INP file using PLEIADES multi-isotope generator\n",
-    "from pleiades.sammy.io.inp_manager import InpManager\n",
-    "\n",
-    "print(\"🔧 Step 2: Generating INP file using PLEIADES...\")\n",
-    "\n",
-    "# Define Gold material properties (from IPTS-35945 sample characteristics)\n",
-    "au_material_props = {\n",
-    "    'element': 'Au',               # Element symbol\n",
-    "    'mass_number': 197,            # Mass number for Au-197\n",
-    "    'density_g_cm3': 19.32,        # Gold density (g/cm³)\n",
-    "    'thickness_mm': 0.025,         # Sample thickness: 0.025 mm (0.001 in)\n",
-    "    'atomic_mass_amu': 196.966569, # Au-197 atomic mass (AMU)\n",
-    "    'abundance': 1.0,              # 100% abundance for single isotope mode\n",
-    "    'min_energy': 1.0,            # Minimum energy in eV (to cover thermal range)\n",
-    "    'max_energy_eV': 200.0,       # Maximum energy in eV (based on transmission data range)\n",
-    "    'temperature_K': 293.6         # Room temperature (K)\n",
-    "}\n",
-    "\n",
-    "print(f\"📊 Material properties: {au_material_props}\")\n",
-    "\n",
-    "# Define resolution file with absolute path for security/control\n",
-    "resolution_file_path = Path(\".\") / \"venus_resolution.dat\"\n",
-    "print(f\"📄 Resolution file path: {resolution_file_path}\")\n",
-    "print(f\"📄 Resolution file exists: {resolution_file_path.exists()}\")\n",
-    "\n",
-    "# Generate INP file using PLEIADES with absolute resolution path\n",
-    "inp_file = Path(working_dir) / \"au_fitting.inp\"\n",
-    "InpManager.create_multi_isotope_inp(\n",
-    "    inp_file,\n",
-    "    title=\"PLEIADES-generated Au-197 fitting for VENUS IPTS-35945\",\n",
-    "    material_properties=au_material_props,\n",
-    "    resolution_file_path=resolution_file_path  # Use absolute path\n",
-    ")\n",
-    "\n",
-    "print(f\"✅ Generated INP file: {inp_file}\")\n",
-    "\n",
-    "# Show a sample of the generated INP content\n",
-    "print(\"\\n📝 Sample INP content:\")\n",
-    "with open(inp_file, 'r') as f:\n",
-    "    for i, line in enumerate(f):\n",
-    "        if i >= 10:  # Show first 10 lines\n",
-    "            break\n",
-    "        print(f\"   {line.rstrip()}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "2d09b54c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "🔧 Step 3: Setting up SAMMY execution...\n",
-      "📄 Using combined data file: ipts_35945/twenty/Run_8022_transmission.twenty\n",
-      "📄 Data file exists: True\n",
-      "📁 SAMMY working directory: ipts_35945/sammy_working\n",
-      "📁 SAMMY output directory: ipts_35945/sammy_output\n",
-      "✅ LocalSammyRunner configured and ready\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Step 3: Set up SAMMY execution using LocalSammyRunner\n",
-    "from pleiades.sammy.backends.local import LocalSammyRunner\n",
-    "from pleiades.sammy.config import LocalSammyConfig\n",
-    "from pleiades.sammy.interface import SammyFilesMultiMode\n",
-    "\n",
-    "print(\"🔧 Step 3: Setting up SAMMY execution...\")\n",
-    "\n",
-    "# Use the combined transmission data file\n",
-    "combined_data_file = Path(twenty_dir) / \"Run_8022_transmission.twenty\"\n",
-    "\n",
-    "print(f\"📄 Using combined data file: {combined_data_file}\")\n",
-    "print(f\"📄 Data file exists: {combined_data_file.exists()}\")\n",
-    "\n",
-    "# Create SammyFilesMultiMode container\n",
-    "files = SammyFilesMultiMode(\n",
-    "    input_file=inp_file,\n",
-    "    json_config_file=Path(json_path),\n",
-    "    data_file=combined_data_file,\n",
-    "    endf_directory=Path(working_dir)\n",
-    ")\n",
-    "\n",
-    "print(f\"📁 SAMMY working directory: {sammy_working}\")\n",
-    "print(f\"📁 SAMMY output directory: {sammy_output}\")\n",
-    "\n",
-    "# Create LocalSammyRunner configuration\n",
-    "config = LocalSammyConfig(\n",
-    "    sammy_executable=Path(\"/SNS/software/sammy/bin/sammy\"),\n",
-    "    working_dir=sammy_working,\n",
-    "    output_dir=sammy_output\n",
-    ")\n",
-    "\n",
-    "runner = LocalSammyRunner(config)\n",
-    "print(\"✅ LocalSammyRunner configured and ready\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "56d1a7d4",
-   "metadata": {},
-   "source": [
-    "## Fit for Number Densities"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "0be35b2e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m2025-09-18 19:27:43\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mprepare_environment\u001b[0m:\u001b[36m45\u001b[0m - \u001b[34m\u001b[1mValidating input files\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:43\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mprepare_environment\u001b[0m:\u001b[36m50\u001b[0m - \u001b[34m\u001b[1mPerforming JSON-ENDF mapping validation\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:43\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36m_validate_json_endf_mapping\u001b[0m:\u001b[36m100\u001b[0m - \u001b[34m\u001b[1mJSON-ENDF validation passed: 1 ENDF files verified\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:43\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mprepare_environment\u001b[0m:\u001b[36m54\u001b[0m - \u001b[34m\u001b[1mMoving files to working directory\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:43\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mmove_to_working_dir\u001b[0m:\u001b[36m214\u001b[0m - \u001b[34m\u001b[1mMoving JSON mode files to working directory: ipts_35945/sammy_working\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:43\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mmove_to_working_dir\u001b[0m:\u001b[36m253\u001b[0m - \u001b[34m\u001b[1mSymlinked ENDF file: 079-Au-197.B-VIII.0.par\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:43\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mprepare_environment\u001b[0m:\u001b[36m58\u001b[0m - \u001b[34m\u001b[1mEnvironment preparation complete\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:43\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mexecute_sammy\u001b[0m:\u001b[36m110\u001b[0m - \u001b[1mStarting SAMMY execution e5c1142b-f2d0-4e43-8e65-f5423ad00251\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:43\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mexecute_sammy\u001b[0m:\u001b[36m111\u001b[0m - \u001b[34m\u001b[1mWorking directory: ipts_35945/sammy_working\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:43\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mexecute_sammy\u001b[0m:\u001b[36m123\u001b[0m - \u001b[34m\u001b[1mUsing JSON mode command format\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "🚀 Step 4: Executing SAMMY JSON mode fitting...\n",
-      "🔄 Preparing SAMMY environment...\n",
-      "⚡ Running SAMMY...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mexecute_sammy\u001b[0m:\u001b[36m156\u001b[0m - \u001b[1mSAMMY execution completed successfully for e5c1142b-f2d0-4e43-8e65-f5423ad00251\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m432\u001b[0m - \u001b[1mCollecting outputs for execution e5c1142b-f2d0-4e43-8e65-f5423ad00251\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m443\u001b[0m - \u001b[34m\u001b[1mFound known output file: SAMNDF.PAR\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m443\u001b[0m - \u001b[34m\u001b[1mFound known output file: SAMMY.IO\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m443\u001b[0m - \u001b[34m\u001b[1mFound known output file: SAMMY.ODF\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m443\u001b[0m - \u001b[34m\u001b[1mFound known output file: SAMMY.LPT\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m443\u001b[0m - \u001b[34m\u001b[1mFound known output file: SAMMY.LST\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m443\u001b[0m - \u001b[34m\u001b[1mFound known output file: SAMNDF.INP\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAMQUA.RED\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAMMY.PLT\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAMQUA.PAR\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAMMY.PAR\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAM46.DAT\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAM41.DAT\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAMMY.RED\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m449\u001b[0m - \u001b[34m\u001b[1mFound additional output file: SAMMY.COV\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved ipts_35945/sammy_working/SAM41.DAT to ipts_35945/sammy_output/SAM41.DAT\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved ipts_35945/sammy_working/SAMMY.PAR to ipts_35945/sammy_output/SAMMY.PAR\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved ipts_35945/sammy_working/SAMMY.IO to ipts_35945/sammy_output/SAMMY.IO\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved ipts_35945/sammy_working/SAMMY.RED to ipts_35945/sammy_output/SAMMY.RED\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved ipts_35945/sammy_working/SAMMY.LST to ipts_35945/sammy_output/SAMMY.LST\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved ipts_35945/sammy_working/SAMMY.PLT to ipts_35945/sammy_output/SAMMY.PLT\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved ipts_35945/sammy_working/SAMMY.COV to ipts_35945/sammy_output/SAMMY.COV\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved ipts_35945/sammy_working/SAMMY.LPT to ipts_35945/sammy_output/SAMMY.LPT\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved ipts_35945/sammy_working/SAMMY.ODF to ipts_35945/sammy_output/SAMMY.ODF\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved ipts_35945/sammy_working/SAMQUA.RED to ipts_35945/sammy_output/SAMQUA.RED\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved ipts_35945/sammy_working/SAMQUA.PAR to ipts_35945/sammy_output/SAMQUA.PAR\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved ipts_35945/sammy_working/SAM46.DAT to ipts_35945/sammy_output/SAM46.DAT\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved ipts_35945/sammy_working/SAMNDF.PAR to ipts_35945/sammy_output/SAMNDF.PAR\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m466\u001b[0m - \u001b[34m\u001b[1mMoved ipts_35945/sammy_working/SAMNDF.INP to ipts_35945/sammy_output/SAMNDF.INP\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.interface\u001b[0m:\u001b[36mcollect_outputs\u001b[0m:\u001b[36m471\u001b[0m - \u001b[1mSuccessfully collected 14 output files in 0.04 seconds\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:45\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.backends.local\u001b[0m:\u001b[36mcleanup\u001b[0m:\u001b[36m174\u001b[0m - \u001b[34m\u001b[1mPerforming cleanup for local backend\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "📊 SAMMY Execution Results:\n",
-      "   Success: True\n",
-      "   Execution time: 1.65s\n",
-      "   Console output length: 5489 chars\n",
-      "   ✅ SAMMY completed normally\n",
-      "\n",
-      "📥 Step 5: Collecting SAMMY output files...\n",
-      "\n",
-      "📂 SAMMY Output Files in ipts_35945/sammy_output:\n",
-      "   ✅ SAMMY.LPT (15.5 KB)\n",
-      "   ✅ SAMMY.LST (911.2 KB)\n",
-      "   ✅ SAMMY.ODF (191.0 KB)\n",
-      "   ✅ SAMMY.PAR (19.1 KB)\n",
-      "   ✅ SAMNDF.PAR (23.5 KB)\n",
-      "   ✅ SAMNDF.INP (1.8 KB)\n",
-      "\n",
-      "✅ Generated 6/6 expected files\n",
-      "🧹 Cleaning up working directory...\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Step 4: Execute SAMMY fitting\n",
-    "print(\"🚀 Step 4: Executing SAMMY JSON mode fitting...\")\n",
-    "\n",
-    "# Prepare the environment (copy/symlink files to working directory)\n",
-    "print(\"🔄 Preparing SAMMY environment...\")\n",
-    "runner.prepare_environment(files)\n",
-    "\n",
-    "# Execute SAMMY\n",
-    "print(\"⚡ Running SAMMY...\")\n",
-    "result = runner.execute_sammy(files)\n",
-    "\n",
-    "# Display execution results\n",
-    "print(f\"\\n📊 SAMMY Execution Results:\")\n",
-    "print(f\"   Success: {result.success}\")\n",
-    "print(f\"   Execution time: {result.runtime_seconds:.2f}s\")\n",
-    "print(f\"   Console output length: {len(result.console_output)} chars\")\n",
-    "\n",
-    "if result.error_message:\n",
-    "    print(f\"   ⚠️  Error: {result.error_message[:200]}...\")\n",
-    "    \n",
-    "if result.console_output and \"Normal finish to SAMMY\" in result.console_output:\n",
-    "    print(f\"   ✅ SAMMY completed normally\")\n",
-    "elif result.console_output:\n",
-    "    print(f\"   ⚠️  Console output (last 300 chars): {result.console_output[-300:]}\")\n",
-    "\n",
-    "# Step 5: Collect outputs (copy results from working_dir to output_dir)\n",
-    "print(f\"\\n📥 Step 5: Collecting SAMMY output files...\")\n",
-    "runner.collect_outputs(result=result)\n",
-    "\n",
-    "# Check generated output files in the output directory\n",
-    "print(f\"\\n📂 SAMMY Output Files in {sammy_output}:\")\n",
-    "expected_outputs = [\"SAMMY.LPT\", \"SAMMY.LST\", \"SAMMY.ODF\", \"SAMMY.PAR\", \"SAMNDF.PAR\", \"SAMNDF.INP\"]\n",
-    "\n",
-    "found_outputs = []\n",
-    "for output_file in expected_outputs:\n",
-    "    output_path = sammy_output / output_file\n",
-    "    if output_path.exists():\n",
-    "        found_outputs.append(output_file)\n",
-    "        size_kb = output_path.stat().st_size / 1024\n",
-    "        print(f\"   ✅ {output_file} ({size_kb:.1f} KB)\")\n",
-    "    else:\n",
-    "        print(f\"   ❌ {output_file} (missing)\")\n",
-    "\n",
-    "print(f\"\\n✅ Generated {len(found_outputs)}/{len(expected_outputs)} expected files\")\n",
-    "\n",
-    "# Cleanup\n",
-    "print(\"🧹 Cleaning up working directory...\")\n",
-    "runner.cleanup()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f1c1d220",
-   "metadata": {},
-   "source": [
-    "## Analyze Results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "b8b2becc",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m2025-09-18 19:27:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mprocess_lpt_file\u001b[0m:\u001b[36m479\u001b[0m - \u001b[1mSuccessfully read the file: ipts_35945/sammy_output/SAMMY.LPT\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:58\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mprocess_lpt_file\u001b[0m:\u001b[36m490\u001b[0m - \u001b[34m\u001b[1mSplit LPT content into 3 blocks.\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:58\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_isotope_info\u001b[0m:\u001b[36m116\u001b[0m - \u001b[34m\u001b[1mExtracting isotope information...\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_results_from_string\u001b[0m:\u001b[36m415\u001b[0m - \u001b[1mIsotope results not found.\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:58\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_radius_info\u001b[0m:\u001b[36m189\u001b[0m - \u001b[34m\u001b[1mExtracting radius information...\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_results_from_string\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mRadius results not found.\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:58\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_broadening_info\u001b[0m:\u001b[36m261\u001b[0m - \u001b[34m\u001b[1mExtracting broadening information...\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:58\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_normalization_info\u001b[0m:\u001b[36m328\u001b[0m - \u001b[34m\u001b[1mExtracting normalization information...\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:58\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_chi_squared_info\u001b[0m:\u001b[36m377\u001b[0m - \u001b[34m\u001b[1mExtracting chi-squared information...\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:58\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_isotope_info\u001b[0m:\u001b[36m116\u001b[0m - \u001b[34m\u001b[1mExtracting isotope information...\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_results_from_string\u001b[0m:\u001b[36m415\u001b[0m - \u001b[1mIsotope results not found.\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:58\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_radius_info\u001b[0m:\u001b[36m189\u001b[0m - \u001b[34m\u001b[1mExtracting radius information...\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_results_from_string\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mRadius results not found.\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:58\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_broadening_info\u001b[0m:\u001b[36m261\u001b[0m - \u001b[34m\u001b[1mExtracting broadening information...\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:58\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_normalization_info\u001b[0m:\u001b[36m328\u001b[0m - \u001b[34m\u001b[1mExtracting normalization information...\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:58\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_chi_squared_info\u001b[0m:\u001b[36m377\u001b[0m - \u001b[34m\u001b[1mExtracting chi-squared information...\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:58\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_isotope_info\u001b[0m:\u001b[36m116\u001b[0m - \u001b[34m\u001b[1mExtracting isotope information...\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_results_from_string\u001b[0m:\u001b[36m415\u001b[0m - \u001b[1mIsotope results not found.\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:58\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_radius_info\u001b[0m:\u001b[36m189\u001b[0m - \u001b[34m\u001b[1mExtracting radius information...\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:58\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_results_from_string\u001b[0m:\u001b[36m418\u001b[0m - \u001b[1mRadius results not found.\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:58\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_broadening_info\u001b[0m:\u001b[36m261\u001b[0m - \u001b[34m\u001b[1mExtracting broadening information...\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:58\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_normalization_info\u001b[0m:\u001b[36m328\u001b[0m - \u001b[34m\u001b[1mExtracting normalization information...\u001b[0m\n",
-      "\u001b[32m2025-09-18 19:27:58\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mpleiades.sammy.io.lpt_manager\u001b[0m:\u001b[36mextract_chi_squared_info\u001b[0m:\u001b[36m377\u001b[0m - \u001b[34m\u001b[1mExtracting chi-squared information...\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Energy range: 6.673e+00 - 1.999e+02 eV\n",
-      "Experimental transmission: 0.8508 - 1.0236\n",
-      "Theoretical transmission: 0.8593 - 1.0025\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from pleiades.sammy.results.manager import ResultsManager\n",
-    "\n",
-    "lpt_file_path = sammy_output / \"SAMMY.LPT\"\n",
-    "lst_file_path = sammy_output / \"SAMMY.LST\"\n",
-    "\n",
-    "results_manager = ResultsManager(lpt_file_path=lpt_file_path, lst_file_path=lst_file_path)\n",
-    "data = results_manager.get_data()\n",
-    "\n",
-    "print(f\"Energy range: {data.energy.min():.3e} - {data.energy.max():.3e} eV\")\n",
-    "print(f\"Experimental transmission: {data.experimental_transmission.min():.4f} - {data.experimental_transmission.max():.4f}\")\n",
-    "print(f\"Theoretical transmission: {data.theoretical_transmission.min():.4f} - {data.theoretical_transmission.max():.4f}\")\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.plot(data.energy, data.experimental_transmission, '.', markersize=1, label='Experimental')\n",
-    "plt.plot(data.energy, data.theoretical_transmission, '-', linewidth=1, label='Theoretical')\n",
-    "plt.xlabel('Energy (eV)')\n",
-    "plt.ylabel('Transmission')\n",
-    "plt.legend()\n",
-    "plt.grid(True, alpha=0.3)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6ec3417c",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "default",
-   "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": "3.11.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/pleiades/__main__.py b/src/pleiades/__main__.py
index 744dfe07..0310f0d4 100644
--- a/src/pleiades/__main__.py
+++ b/src/pleiades/__main__.py
@@ -5,7 +5,8 @@
 
 
 def main():
-    print("Placeholder for PLEIADES main function")
+    # TODO: Implement PLEIADES CLI
+    raise NotImplementedError("PLEIADES CLI not yet implemented")
 
 
 if __name__ == "__main__":
diff --git a/src/pleiades/nuclear/manager.py b/src/pleiades/nuclear/manager.py
index 2a6adb9e..6f5c93ea 100644
--- a/src/pleiades/nuclear/manager.py
+++ b/src/pleiades/nuclear/manager.py
@@ -144,7 +144,7 @@ def clear_cache(self, method: Optional[DataRetrievalMethod] = None, library: Opt
                         # Call unlink directly on the file path
                         file.unlink()
                         logger.info(f"Deleted cached file: {file}")
-                    except Exception as e:
+                    except (OSError, PermissionError) as e:
                         logger.error(f"Failed to delete {file}: {str(e)}")
 
     def _get_data_from_direct(
diff --git a/src/pleiades/results/models.py b/src/pleiades/results/models.py
index c58ec32c..922fc155 100644
--- a/src/pleiades/results/models.py
+++ b/src/pleiades/results/models.py
@@ -18,12 +18,12 @@ class AbundanceInfo(BaseModel):
 
 class BackgroundInfo(BaseModel):
     # TODO: Add attributes and methods for background information
-    pass
+    ...
 
 
 class NormalizationInfo(BaseModel):
     # TODO: Add attributes and methods for normalization information
-    pass
+    ...
 
 
 class PixelInfo(BaseModel):
diff --git a/src/pleiades/sammy/backends/local.py b/src/pleiades/sammy/backends/local.py
index aad2a997..d7622dea 100644
--- a/src/pleiades/sammy/backends/local.py
+++ b/src/pleiades/sammy/backends/local.py
@@ -1,9 +1,8 @@
 #!/usr/bin/env python
 """Local backend implementation for SAMMY execution."""
 
-import shlex
+import os
 import subprocess
-import textwrap
 from datetime import datetime
 from pathlib import Path
 from typing import List, Union
@@ -22,14 +21,6 @@
 
 logger = loguru_logger.bind(name=__name__)
 
-# Known SAMMY output file patterns
-SAMMY_OUTPUT_FILES = {
-    "SAMMY.LPT",  # Log file
-    "SAMMIE.ODF",  # Output data file
-    "SAMNDF.PAR",  # Updated parameter file
-    "SAMRESOLVED.PAR",  # Additional parameter file
-}
-
 
 class LocalSammyRunner(SammyRunner):
     """Implementation of SAMMY runner for local installation."""
@@ -111,36 +102,34 @@ def execute_sammy(self, files: Union[SammyFiles, SammyFilesMultiMode]) -> SammyE
         logger.debug(f"Working directory: {self.config.working_dir}")
 
         # Generate command based on file type
+        # Prepare input text for SAMMY based on mode
         if isinstance(files, SammyFilesMultiMode):
-            # JSON mode command format
-            sammy_command = textwrap.dedent(f"""\
-                {self.config.sammy_executable} <<EOF
-                {shlex.quote(files.input_file.name)}
-                #file {shlex.quote(files.json_config_file.name)}
-                {shlex.quote(files.data_file.name)}
-
-                EOF""")
-            logger.debug("Using JSON mode command format")
+            # JSON mode input format
+            sammy_input = f"{files.input_file.name}\n#file {files.json_config_file.name}\n{files.data_file.name}\n\n"
+            logger.debug("Using JSON mode input format")
         else:
-            # Traditional mode command format
-            sammy_command = textwrap.dedent(f"""\
-                {self.config.sammy_executable} <<EOF
-                {shlex.quote(files.input_file.name)}
-                {shlex.quote(files.parameter_file.name)}
-                {shlex.quote(files.data_file.name)}
-
-                EOF""")
-            logger.debug("Using traditional mode command format")
+            # Traditional mode input format
+            sammy_input = f"{files.input_file.name}\n{files.parameter_file.name}\n{files.data_file.name}\n\n"
+            logger.debug("Using traditional mode input format")
 
         try:
+            # Ensure libcrypto.so.1.1 is found by adding /usr/lib64 to LD_LIBRARY_PATH
+            env = dict(os.environ)
+            env.update(self.config.env_vars)
+            if "LD_LIBRARY_PATH" in env:
+                env["LD_LIBRARY_PATH"] = f"/usr/lib64:{env['LD_LIBRARY_PATH']}"
+            else:
+                env["LD_LIBRARY_PATH"] = "/usr/lib64"
+
+            # Use safer subprocess call without shell
             process = subprocess.run(
-                sammy_command,
-                shell=True,
-                executable=str(self.config.shell_path),
-                env=self.config.env_vars,
+                [str(self.config.sammy_executable)],
+                input=sammy_input,
+                shell=False,
+                text=True,
+                env=env,
                 cwd=str(self.config.working_dir),
                 capture_output=True,
-                text=True,
             )
 
             end_time = datetime.now()
diff --git a/src/pleiades/sammy/data/options.py b/src/pleiades/sammy/data/options.py
index 6cbe9383..9d003102 100644
--- a/src/pleiades/sammy/data/options.py
+++ b/src/pleiades/sammy/data/options.py
@@ -12,6 +12,10 @@
 
 logger = loguru_logger.bind(name=__name__)
 
+# Plot constants
+RESIDUAL_YLIM = (-1, 1)  # Y-axis limits for residual plots
+HISTOGRAM_BIN_RANGE = (-1, 1, 0.01)  # Range and step for histogram bins
+
 
 class DataTypeOptions(str, Enum):
     TRANSMISSION = "TRANSMISSION"
@@ -108,21 +112,44 @@ def validate_columns(self):
                 if col in self.data.columns:
                     raise ValueError(f"Unexpected transmission column for cross-section data: {col}")
 
-    def plot_transmission(self, show_diff=False, plot_uncertainty=False):
+    def plot_transmission(
+        self,
+        show_diff=False,
+        plot_uncertainty=False,
+        figsize=None,
+        title=None,
+        xscale="linear",
+        yscale="linear",
+        data_color="#433E3F",
+        final_color="#ff6361",
+        show=True,
+    ):
         """
         Plot the transmission data and optionally the residuals.
 
         Args:
             show_diff (bool): If True, plot the residuals.
             plot_uncertainty (bool): (Unused, for compatibility)
+            figsize (tuple): Figure size (width, height) in inches.
+            title (str): Plot title.
+            xscale (str): X-axis scale ('linear' or 'log').
+            yscale (str): Y-axis scale ('linear' or 'log').
+            data_color (str): Color for experimental data points.
+            final_color (str): Color for fitted theoretical curve.
+            show (bool): If True, display the plot. If False, return figure object.
+
+        Returns:
+            matplotlib.figure.Figure: The figure object if show=False, None otherwise.
         """
         if self.data is None:
             raise ValueError("No data loaded to plot.")
 
         data = self.data
-        data_color = "#433E3F"
         initial_color = "#003f5c"
-        final_color = "#ff6361"
+
+        # Use provided figsize or default
+        if figsize is None:
+            figsize = (8, 6)
 
         # Column name mapping for compatibility
         col_exp = "Experimental transmission (dimensionless)"
@@ -135,12 +162,12 @@ def plot_transmission(self, show_diff=False, plot_uncertainty=False):
                 2,
                 2,
                 sharey=False,
-                figsize=(8, 6),
+                figsize=figsize,
                 gridspec_kw={"width_ratios": [5, 1], "height_ratios": [5, 2]},
             )
             ax = np.ravel(ax)
         else:
-            fig, ax = plt.subplots(figsize=(8, 6))
+            fig, ax = plt.subplots(figsize=figsize)
             ax = [ax]
 
         # Plot experimental transmission as scatter with error bars if available
@@ -165,11 +192,20 @@ def plot_transmission(self, show_diff=False, plot_uncertainty=False):
                 color=final_color,
                 lw=1,
             )
+        # Apply scale settings first
+        ax[0].set_xscale(xscale)
+        ax[0].set_yscale(yscale)
+
+        # Then remove x-axis labels and ticks (for show_diff layout)
         ax[0].set_xlabel("")
         ax[0].set_xticks([])
         ax[0].legend(["data", "final fit"])
         ax[0].set_ylabel("transmission")
 
+        # Apply title if provided
+        if title:
+            ax[0].set_title(title)
+
         # Determine y-axis limits
         max_y = data[col_exp].max()
         min_y = data[col_exp].min()
@@ -202,13 +238,15 @@ def plot_transmission(self, show_diff=False, plot_uncertainty=False):
             )
             ax[2].set_ylabel("residuals\n(fit-data)/err [σ]")
             ax[2].set_xlabel("energy [eV]")
-            ax[2].set_ylim(-1, 1)
+            ax[2].set_ylim(*RESIDUAL_YLIM)
+            # Apply same x-scale to residual plot
+            ax[2].set_xscale(xscale)
 
             # Plot histograms of residuals
             if "residual_initial" in data.columns:
                 data.plot.hist(
                     y=["residual_initial"],
-                    bins=np.arange(-1, 1, 0.01),
+                    bins=np.arange(*HISTOGRAM_BIN_RANGE),
                     ax=ax[3],
                     orientation="horizontal",
                     legend=False,
@@ -235,7 +273,12 @@ def plot_transmission(self, show_diff=False, plot_uncertainty=False):
             ax[3].spines["left"].set_visible(False)
 
         plt.subplots_adjust(wspace=0.003, hspace=0.03)
-        plt.show()
+
+        if show:
+            plt.show()
+            return None
+        else:
+            return fig
 
     def plot_cross_section(self, show_diff=False, plot_uncertainty=False):
         """Plot the cross-section data."""
diff --git a/src/pleiades/sammy/fitting/config.py b/src/pleiades/sammy/fitting/config.py
index 373f214f..d3a1c348 100644
--- a/src/pleiades/sammy/fitting/config.py
+++ b/src/pleiades/sammy/fitting/config.py
@@ -48,23 +48,3 @@ def append_isotope_from_string(self, isotope_string: str) -> None:
             self.nuclear_params.isotopes.append(isotope_info)
         else:
             logger.error(f"Could not append Isotope {isotope_string}.")
-
-
-# example usage
-if __name__ == "__main__":
-    example_config = FitConfig(
-        fit_title="Example SAMMY Fit",
-        tolerance=1e-5,
-        max_iterations=100,
-        i_correlation=10,
-        max_cpu_time=3600.0,
-        max_wall_time=7200.0,
-        max_memory=8.0,
-        max_disk=100.0,
-        nuclear_params=nuclearParameters(),
-        physics_params=PhysicsParameters(),
-        data_params=SammyData(),
-        options_and_routines=FitOptions(),
-    )
-
-    print(example_config)
diff --git a/src/pleiades/sammy/interface.py b/src/pleiades/sammy/interface.py
index 53951746..8425037e 100644
--- a/src/pleiades/sammy/interface.py
+++ b/src/pleiades/sammy/interface.py
@@ -1,4 +1,4 @@
-#!/usr/env/bin python
+#!/usr/bin/env python
 """
 Interface definitions for SAMMY execution system.
 
@@ -396,15 +396,6 @@ def cleanup(self, files: SammyFiles) -> None:
         """
         raise NotImplementedError
 
-    async def __aenter__(self) -> "SammyRunner":
-        """Allow usage as async context manager."""
-        return self
-
-    async def __aexit__(self, exc_type, exc_val, exc_tb) -> None:
-        """Ensure cleanup on context exit."""
-        if hasattr(self, "files"):
-            await self.cleanup(self.files)
-
     @abstractmethod
     def validate_config(self) -> bool:
         """
diff --git a/src/pleiades/sammy/io/data_manager.py b/src/pleiades/sammy/io/data_manager.py
index 2066607b..f4952621 100644
--- a/src/pleiades/sammy/io/data_manager.py
+++ b/src/pleiades/sammy/io/data_manager.py
@@ -6,7 +6,6 @@
 experimental transmission data.
 """
 
-import csv
 from pathlib import Path
 from typing import Union
 
@@ -21,7 +20,7 @@ def convert_csv_to_sammy_twenty(csv_file: Union[str, Path], twenty_file: Union[s
     """
     Convert transmission spectra from CSV to SAMMY twenty format.
 
-    This function supports both tab- and comma-separated CSV files, with either two columns
+    This function supports tab-, comma-, and space-separated files, with either two columns
     (energy, transmission) or three columns (energy, transmission, uncertainty).
     If only two columns are present, the uncertainty column will be filled with 0.0.
 
@@ -30,11 +29,13 @@ def convert_csv_to_sammy_twenty(csv_file: Union[str, Path], twenty_file: Union[s
         twenty_file: Path to output SAMMY twenty format file
 
     File Formats:
-        Input CSV (tab or comma separated):
+        Input CSV (tab, comma, or space separated):
             "energy_eV,transmission,uncertainty\n6.673,0.932,0.272\n"
             or
             "energy_eV\ttransmission\tuncertainty\n6.673\t0.932\t0.272\n"
             or
+            "# Energy(eV)  Transmission  Uncertainty\n6.673240e+00 1.003460e+00 7.242967e-03\n"
+            or
             "energy_eV,transmission\n6.673,0.932\n"
         Output twenty:
             "        6.6732397079        0.9323834777        0.2727669477\n"
@@ -51,21 +52,54 @@ def convert_csv_to_sammy_twenty(csv_file: Union[str, Path], twenty_file: Union[s
     """
     logger.info(f"Converting {csv_file} to SAMMY twenty format: {twenty_file}")
 
-    # Use csv.Sniffer to detect delimiter
-    with open(csv_file, "r", newline="") as f:
-        sample = f.read(2048)  # Read a sample of the file for delimiter detection
-        f.seek(0)  # Reset file pointer to start
-
-        sniffer = csv.Sniffer()
-        dialect = sniffer.sniff(sample, delimiters=[",", "\t"])  # Detect comma or tab delimiter
-        delimiter = dialect.delimiter
-
-        # Create a CSV reader with the detected delimiter
-        reader = csv.reader(f, delimiter=delimiter)
-        header = next(reader)  # Skip header row
-
-        # Read the remaining rows, skipping empty lines
-        data = [row for row in reader if row and any(field.strip() for field in row)]
+    data = []
+
+    with open(csv_file, "r") as f:
+        lines = f.readlines()
+
+    # Skip header lines (comments starting with # or containing non-numeric first field)
+    for line in lines:
+        # Strip whitespace and skip empty lines
+        line = line.strip()
+        if not line:
+            continue
+
+        # Skip comment lines
+        if line.startswith("#"):
+            continue
+
+        # Try to parse the line with different delimiters
+        # First try splitting by whitespace (most common for scientific data)
+        fields = line.split()
+
+        # If that doesn't give us 2 or 3 fields, try comma
+        if len(fields) not in [2, 3]:
+            fields = line.split(",")
+
+        # If still not right, try tab
+        if len(fields) not in [2, 3]:
+            fields = line.split("\t")
+
+        # Skip lines that don't have the right number of fields
+        if len(fields) not in [2, 3]:
+            # Check if this might be a header line
+            try:
+                float(fields[0])
+            except (ValueError, IndexError):
+                continue  # Skip header lines
+            logger.warning(f"Skipping line with {len(fields)} fields: {line[:50]}...")
+            continue
+
+        # Try to convert to floats
+        try:
+            numeric_fields = [float(field) for field in fields]
+            data.append(numeric_fields)
+        except ValueError:
+            # This is likely a header line, skip it
+            continue
+
+    if not data:
+        raise ValueError(f"No valid data found in {csv_file}")
 
     # Convert data to numpy array of floats
     data = np.array(data, dtype=float)
diff --git a/src/pleiades/sammy/io/inp_manager.py b/src/pleiades/sammy/io/inp_manager.py
index bbcafd63..64f0f743 100644
--- a/src/pleiades/sammy/io/inp_manager.py
+++ b/src/pleiades/sammy/io/inp_manager.py
@@ -14,6 +14,20 @@
 
 logger = loguru_logger.bind(name=__name__)
 
+# Default constants for SAMMY parameters
+DEFAULT_DENSITY = 9.000000  # Default material density (g/cm³)
+DEFAULT_NUMBER_DENSITY = 1.797e-03  # Default number density (atoms/barn-cm)
+
+# TZERO default parameters
+DEFAULT_T0_VALUE = 0.86000000  # Time offset t₀ (μs)
+DEFAULT_T0_UNCERTAINTY = 0.00200000  # Uncertainty on t₀ (μs)
+DEFAULT_L0_VALUE = 1.0020000  # L₀ value (dimensionless)
+DEFAULT_L0_UNCERTAINTY = 2.00000e-5  # Uncertainty on L₀
+
+# Normalization default parameters
+DEFAULT_NORM_CONSTANT = 1.00  # Default normalization constant
+DEFAULT_NORM_UNCERTAINTY = 0.00  # Default normalization uncertainty
+
 
 class InpManager:
     """
@@ -147,7 +161,7 @@ def generate_sample_density_section(self, material_properties: Dict = None) -> s
             return f"  {density:8.6f} {number_density:.6e}"
 
         # Default values (matching reference format)
-        return "  9.000000 1.797e-03"
+        return f"  {DEFAULT_DENSITY:.6f} {DEFAULT_NUMBER_DENSITY:.3e}"
 
     def generate_reaction_type_section(self) -> str:
         """
@@ -262,10 +276,10 @@ def generate_misc_parameters_section(self, flight_path_m: float = 25.0) -> str:
         # Create TzeroParameters with proper values
         # TZERO values (rounded uncertainties required - SAMMY cannot use zero uncertainty)
         tzero_params = TzeroParameters(
-            t0_value=0.86000000,  # Time offset t₀ (μs)
-            t0_uncertainty=0.00200000,  # Uncertainty on t₀ (μs)
-            l0_value=1.0020000,  # L₀ value (dimensionless)
-            l0_uncertainty=2.00000e-5,  # Uncertainty on L₀
+            t0_value=DEFAULT_T0_VALUE,  # Time offset t₀ (μs)
+            t0_uncertainty=DEFAULT_T0_UNCERTAINTY,  # Uncertainty on t₀ (μs)
+            l0_value=DEFAULT_L0_VALUE,  # L₀ value (dimensionless)
+            l0_uncertainty=DEFAULT_L0_UNCERTAINTY,  # Uncertainty on L₀
             flight_path_length=flight_path_m,  # Flight path (m)
             t0_flag=VaryFlag.YES,  # Allow SAMMY to vary t₀
             l0_flag=VaryFlag.YES,  # Allow SAMMY to vary L₀
diff --git a/src/pleiades/sammy/io/lpt_manager.py b/src/pleiades/sammy/io/lpt_manager.py
index b1239b7f..a51d6b2c 100644
--- a/src/pleiades/sammy/io/lpt_manager.py
+++ b/src/pleiades/sammy/io/lpt_manager.py
@@ -1,7 +1,3 @@
-# This module imports a results class from the pleiades.sammy.results module
-# to create a new class called LptData. The LptData class is filled by reading
-# a .LPT file.
-
 import re
 from collections import defaultdict
 
@@ -14,7 +10,7 @@
 logger = loguru_logger.bind(name=__name__)
 
 
-def parse_value_and_varied(s):
+def parse_value_and_varied(s: str) -> tuple[float, bool]:
     """
     Parse a value that may have a parenthesis indicating it was varied.
     Returns (float_value, varied_flag)
@@ -27,7 +23,7 @@ def parse_value_and_varied(s):
     raise ValueError(f"Could not parse value: {s}")
 
 
-def split_lpt_values(line):
+def split_lpt_values(line: str) -> list[str]:
     """
     Splits a line into values, where each value may be followed by a parenthesis group.
     Example: '2.9660E+02(  4)  1.1592E-01(  5)' -> ['2.9660E+02(  4)', '1.1592E-01(  5)']
@@ -97,7 +93,6 @@ class LptManager:
         "***** INITIAL VALUES FOR PARAMETERS",
         "***** INTERMEDIATE VALUES FOR RESONANCE PARAMETERS",
         "***** NEW VALUES FOR RESONANCE PARAMETERS",
-        "***** NEW VALUES FOR RESONANCE PARAMETERS",
     ]
 
     # If initialize with a filepath then start processing the file
diff --git a/src/pleiades/sammy/parameters/background.py b/src/pleiades/sammy/parameters/background.py
index 954f36ae..10ba92d4 100644
--- a/src/pleiades/sammy/parameters/background.py
+++ b/src/pleiades/sammy/parameters/background.py
@@ -63,6 +63,7 @@ def from_lines(cls, lines: List[str]) -> "BackgroundParameters":
         Raises:
             NotImplementedError: This class is not yet implemented
         """
+        # TODO: Implement Card Set 13 background parameter parsing
         raise NotImplementedError("Card Set 13 background parameter parsing is not yet implemented")
 
     def to_lines(self) -> List[str]:
@@ -71,4 +72,5 @@ def to_lines(self) -> List[str]:
         Raises:
             NotImplementedError: This class is not yet implemented
         """
+        # TODO: Implement Card Set 13 background parameter formatting
         raise NotImplementedError("Card Set 13 background parameter formatting is not yet implemented")
diff --git a/src/pleiades/sammy/parameters/det_efficiency.py b/src/pleiades/sammy/parameters/det_efficiency.py
index 518c1263..acfc9306 100644
--- a/src/pleiades/sammy/parameters/det_efficiency.py
+++ b/src/pleiades/sammy/parameters/det_efficiency.py
@@ -57,6 +57,7 @@ def from_lines(cls, lines: List[str]) -> "DetectorEfficiencyParameters":
         Raises:
             NotImplementedError: This class is not yet implemented
         """
+        # TODO: Implement Card Set 15 detector efficiency parameter parsing
         raise NotImplementedError("Card Set 15 detector efficiency parameter parsing is not yet implemented")
 
     def to_lines(self) -> List[str]:
@@ -65,4 +66,5 @@ def to_lines(self) -> List[str]:
         Raises:
             NotImplementedError: This class is not yet implemented
         """
+        # TODO: Implement Card Set 15 detector efficiency parameter formatting
         raise NotImplementedError("Card Set 15 detector efficiency parameter formatting is not yet implemented")
diff --git a/src/pleiades/sammy/parameters/last.py b/src/pleiades/sammy/parameters/last.py
index db6d5abf..571e4e1f 100644
--- a/src/pleiades/sammy/parameters/last.py
+++ b/src/pleiades/sammy/parameters/last.py
@@ -41,6 +41,7 @@ def from_lines(cls, lines: List[str]) -> "LastParameters":
         Raises:
             NotImplementedError: This feature needs to be implemented
         """
+        # TODO: Implement Last Card parameter parsing
         raise NotImplementedError("Last Card parsing not yet implemented")
 
     def to_lines(self) -> List[str]:
@@ -49,6 +50,7 @@ def to_lines(self) -> List[str]:
         Raises:
             NotImplementedError: This feature needs to be implemented
         """
+        # TODO: Implement Last Card parameter formatting
         raise NotImplementedError("Last Card formatting not yet implemented")
 
 
diff --git a/src/pleiades/sammy/parameters/resolution.py b/src/pleiades/sammy/parameters/resolution.py
index f63d5411..77076918 100644
--- a/src/pleiades/sammy/parameters/resolution.py
+++ b/src/pleiades/sammy/parameters/resolution.py
@@ -73,6 +73,7 @@ def from_lines(cls, lines: List[str]) -> "ResolutionParameters":
         Raises:
             NotImplementedError: This class is not yet implemented
         """
+        # TODO: Implement Card Set 14 resolution parameter parsing
         raise NotImplementedError("Card Set 14 resolution parameter parsing is not yet implemented")
 
     def to_lines(self) -> List[str]:
@@ -81,6 +82,7 @@ def to_lines(self) -> List[str]:
         Raises:
             NotImplementedError: This class is not yet implemented
         """
+        # TODO: Implement Card Set 14 resolution parameter formatting
         raise NotImplementedError("Card Set 14 resolution parameter formatting is not yet implemented")
 
 
diff --git a/src/pleiades/sammy/parameters/resonance.py b/src/pleiades/sammy/parameters/resonance.py
index 344825f0..6b7a249b 100644
--- a/src/pleiades/sammy/parameters/resonance.py
+++ b/src/pleiades/sammy/parameters/resonance.py
@@ -8,7 +8,6 @@
 from pleiades.utils.helper import VaryFlag, safe_parse
 from pleiades.utils.logger import loguru_logger
 
-# setup logging
 logger = loguru_logger.bind(name=__name__)
 
 
diff --git a/src/pleiades/sammy/parfile.py b/src/pleiades/sammy/parfile.py
index 19f1f405..980154ca 100644
--- a/src/pleiades/sammy/parfile.py
+++ b/src/pleiades/sammy/parfile.py
@@ -481,4 +481,5 @@ def print_parameters(self) -> None:
 
 
 if __name__ == "__main__":
-    print("TODO: usage example for SAMMY parameter file handling")
+    # TODO: Add usage example for SAMMY parameter file handling
+    raise NotImplementedError("Example usage not yet implemented")
diff --git a/src/pleiades/sammy/results/manager.py b/src/pleiades/sammy/results/manager.py
index d393987f..3748eb82 100644
--- a/src/pleiades/sammy/results/manager.py
+++ b/src/pleiades/sammy/results/manager.py
@@ -79,17 +79,56 @@ def print_results_data(self):
             logger.warning("No results data available.")
 
     def plot_transmission(
-        self, override_data_type: bool = False, show_diff: bool = False, plot_uncertainty: bool = False
+        self,
+        override_data_type: bool = False,
+        show_diff: bool = False,
+        plot_uncertainty: bool = False,
+        figsize=None,
+        title=None,
+        xscale="linear",
+        yscale="linear",
+        data_color="#433E3F",
+        final_color="#ff6361",
+        show=True,
     ):
-        """Plot the transmission data from the results."""
+        """
+        Plot the transmission data from the results.
+
+        Args:
+            override_data_type (bool): Force plotting even if data type is not transmission.
+            show_diff (bool): If True, plot the residuals.
+            plot_uncertainty (bool): If True, plot error bars.
+            figsize (tuple): Figure size (width, height) in inches.
+            title (str): Plot title.
+            xscale (str): X-axis scale ('linear' or 'log').
+            yscale (str): Y-axis scale ('linear' or 'log').
+            data_color (str): Color for experimental data points.
+            final_color (str): Color for fitted theoretical curve.
+            show (bool): If True, display the plot. If False, return figure object.
+
+        Returns:
+            matplotlib.figure.Figure: The figure object if show=False, None otherwise.
+        """
         if self.run_results.data:
             # Check if data type is transmission
             if self.run_results.data.data_type == "TRANSMISSION" or override_data_type:
-                self.run_results.data.plot_transmission(show_diff=show_diff, plot_uncertainty=plot_uncertainty)
+                return self.run_results.data.plot_transmission(
+                    show_diff=show_diff,
+                    plot_uncertainty=plot_uncertainty,
+                    figsize=figsize,
+                    title=title,
+                    xscale=xscale,
+                    yscale=yscale,
+                    data_color=data_color,
+                    final_color=final_color,
+                    show=show,
+                )
             else:
                 logger.warning("Data type is not transmission. Cannot plot.")
+                return None
         else:
             logger.warning("No results data available for plotting.")
+            return None
 
     def plot_cross_section(
         self, override_data_type: bool = False, show_diff: bool = False, plot_uncertainty: bool = False
diff --git a/src/pleiades/sammy/results/models.py b/src/pleiades/sammy/results/models.py
index ad73485d..d51d6148 100644
--- a/src/pleiades/sammy/results/models.py
+++ b/src/pleiades/sammy/results/models.py
@@ -49,13 +49,6 @@ def get_chi_squared_results(self) -> ChiSquaredResults:
         """Retrieve the current chi-squared results."""
         return self.chi_squared_results
 
-    def __init__(self, **data):
-        super().__init__(**data)
-        # Initialize nuclear and physics data with default values
-        self.nuclear_data = nuclearParameters()
-        self.physics_data = PhysicsParameters()
-        self.chi_squared_results = ChiSquaredResults()
-
 
 class RunResults(BaseModel):
     """RunResults is a container for aggregating multiple fit results from a given SAMMY execution.
@@ -69,15 +62,6 @@ class RunResults(BaseModel):
         default_factory=SammyData, description="Container for LST data loaded from a SAMMY .LST file."
     )
 
-    def __init__(self, **data):
-        super().__init__(**data)
-
-        # Initialize the list of fit results
-        self.fit_results = []
-
-        # Initialize the data container
-        self.data = SammyData()
-
     def add_fit_result(self, fit_result: FitResults):
         """Add a FitResults object to the list of fit results."""
         self.fit_results.append(fit_result)
diff --git a/src/pleiades/utils/config.py b/src/pleiades/utils/config.py
index 5ffe67af..93d4b6b4 100644
--- a/src/pleiades/utils/config.py
+++ b/src/pleiades/utils/config.py
@@ -97,7 +97,7 @@ def load(cls, path: Optional[Path] = None) -> "PleiadesConfig":
 
 
 # Global configuration instance
-_config = None
+_config: Optional[PleiadesConfig] = None
 
 
 def get_config() -> PleiadesConfig:
diff --git a/src/pleiades/utils/helper.py b/src/pleiades/utils/helper.py
index 4291adbb..d0cca935 100644
--- a/src/pleiades/utils/helper.py
+++ b/src/pleiades/utils/helper.py
@@ -14,7 +14,7 @@ class VaryFlag(Enum):
     USE_FROM_OTHERS = -2  # do not vary, use value from other sources (INP, COV, etc.)
 
 
-def check_pseudo_scientific(val):
+def check_pseudo_scientific(val: str) -> float:
     """Check for pseudo scientific notation sometimes found in SAMMY files.
 
     Args:
@@ -35,7 +35,7 @@ def check_pseudo_scientific(val):
         s_fixed = f"{m.group(1)}e{m.group(2)}{m.group(3)}"
         try:
             return float(s_fixed)
-        except Exception:
+        except (ValueError, TypeError):
             raise ValueError(f"Cannot convert pseudo-scientific string '{val}' to float (fixed: '{s_fixed}')")
     # Case 2: 5.00000-5 or -1.23+4 (no dot before sign, no E)
     m2 = re.match(r"^([+-]?\d*\.?\d+)([+-]\d+)$", s)
@@ -43,12 +43,12 @@ def check_pseudo_scientific(val):
         s_fixed = f"{m2.group(1)}e{m2.group(2)}"
         try:
             return float(s_fixed)
-        except Exception:
+        except (ValueError, TypeError):
             raise ValueError(f"Cannot convert pseudo-scientific string '{val}' to float (fixed: '{s_fixed}')")
     # Case 3: already valid scientific notation or normal float
     try:
         return float(s)
-    except Exception as e:
+    except (ValueError, TypeError) as e:
         raise ValueError(f"Cannot convert string '{val}' to float. Original error: {e}")
 
 
diff --git a/src/pleiades/utils/plotter.py b/src/pleiades/utils/plotter.py
deleted file mode 100644
index bf0656cd..00000000
--- a/src/pleiades/utils/plotter.py
+++ /dev/null
@@ -1 +0,0 @@
-# This file will contain the image plotting functions for Pleiades.
diff --git a/tests/unit/pleiades/sammy/backends/test_local.py b/tests/unit/pleiades/sammy/backends/test_local.py
index c9b593a4..e4eabb48 100644
--- a/tests/unit/pleiades/sammy/backends/test_local.py
+++ b/tests/unit/pleiades/sammy/backends/test_local.py
@@ -236,11 +236,14 @@ def test_json_mode_validation_missing_endf(self, local_config, mock_multimode_fi
 
     def test_json_mode_command_generation(self, local_config, mock_multimode_files, monkeypatch):
         """Should generate correct SAMMY command for JSON mode."""
-        # Mock subprocess to capture command
+        # Mock subprocess to capture command and input
         executed_commands = []
+        captured_inputs = []
 
         def mock_run(command, **kwargs):
             executed_commands.append(command)
+            if "input" in kwargs:
+                captured_inputs.append(kwargs["input"])
             mock_result = MagicMock()
             mock_result.returncode = 0
             mock_result.stdout = ""
@@ -255,15 +258,19 @@ def mock_run(command, **kwargs):
         # Execute (will be mocked)
         runner.execute_sammy(files)
 
-        # Verify command format
+        # Verify command format - now using list format without shell
         assert len(executed_commands) == 1
         command = executed_commands[0]
-
-        # Should contain #file for JSON mode
-        assert "#file" in command
-        assert "test.json" in command
-        assert "test.inp" in command
-        assert "test.dat" in command
+        assert isinstance(command, list)
+        assert str(local_config.sammy_executable) in command[0]
+
+        # Verify input contains the JSON mode format
+        assert len(captured_inputs) == 1
+        input_text = captured_inputs[0]
+        assert "#file" in input_text
+        assert "test.json" in input_text
+        assert "test.inp" in input_text
+        assert "test.dat" in input_text
 
     def test_prepare_environment_json_mode(self, local_config, mock_multimode_files):
         """Should prepare environment for JSON mode successfully."""