diff --git a/CNAP-updated.ipynb b/CNAP-updated.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "37898e50-beb6-4ca2-affb-b74b3a8f1da2",
+   "metadata": {},
+   "source": [
+    "# Updating pySNOW's CNAPs list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "53dbc299-0d7e-4be0-9740-ec6cc3224d35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from collections import Counter\n",
+    "\n",
+    "import sys\n",
+    "from pathlib import Path\n",
+    "# Define the absolute path to the directory containing the 'snow' folder\n",
+    "local_packages_path = Path(\"/Users/davidegay/pySNOW/\")\n",
+    "#Convert to an absolute string and inject it into Python's search path\n",
+    "sys.path.append(str(local_packages_path.resolve()))\n",
+    "\n",
+    "from snow.io.xyz import read_xyz, write_xyz, read_xyz_movie\n",
+    "#from snow.descriptors.coordination import coordination_number\n",
+    "from snow.descriptors import cna\n",
+    "\n",
+    "natoms= 98\n",
+    "\n",
+    "growth_movie = \"1_growth_98/movie.xyz\"\n",
+    "#melt_movie = \"2_melt_98/movie.xyz\"\n",
+    "melt_movie = \"melting_98/movie.xyz\"\n",
+    "#freeze_movie = \"3_freeze_98/movie.xyz\"\n",
+    "freeze_movie = \"freezing_98/movie.xyz\"\n",
+    "quench_xyz = \"4_quench_98/out11.xyz\"\n",
+    "\n",
+    "growth_data = \"1_growth_98/energy.out\"\n",
+    "#melt_data = \"2_melt_98/energy.out\"\n",
+    "melt_data = \"melting_98/energy.out\"\n",
+    "#freeze_data = \"3_freeze_98/energy.out\"\n",
+    "freeze_data = \"freezing_98/energy.out\"\n",
+    "quench_data = \"4_quench_98/energy.out\"\n",
+    "\n",
+    "alat   = 4.087077141\n",
+    "cutoff = 0.85 * alat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a7f7e25-cbbc-4725-b229-aa5103db844c",
+   "metadata": {},
+   "source": [
+    "## \"NEW\" CNAPS RECAP:\n",
+    "(from Ferrando paper SI; 17, 18 were already implemented)\n",
+    "- **17** is \"*StepA 111 fcc*\": 1x[2, 1, 1], 4x[3, 1, 1], 5x[4, 2, 1]]\n",
+    "- **18** is \"*subsusrface Ih chiral type 2*\": 4x[4,2,1], 4x[4,2,2], 2x[4,3,3], 2x[5,4,4]\n",
+    "- **19** is \"*vertex 111_111 fcc sharp-wide*\": 2x[2,0,0], 1x[2,1,1], 2x[3,1,1]\n",
+    "- **20** is \"*terrace hcp 10m11*\": 1x[2,1,1], 2x[3,1,1], 2x[3,2,2], 2x[4,2,1], 2x[4,2,2]\n",
+    "- **21** is \"*edge 10m11_10m11 hcp*\": 1x[2,0,0], 2x[2,1,1], 1x[3,1,1], 1x[3,2,2], 1x[4,2,1], 1x[4,2,2]\n",
+    "- **22** is \"*terrace hcp 10m11 & edge anti-Mackay*\": 2x[2,0,0], 1x[2,1,1], 2x[3,1,1], 1x[4,2,1], 2x[4,2,2]\n",
+    "- **23** is \"*edge 111-111 sharp fcc*\": 2x[2,1,1], 4x[3,1,1]\n",
+    "- **24** is \"*edge anti-Mackay close to vertex*\": 2x[2,0,0], 3x[3,2,2], 2x[4,2,2], 1x[5,5,5]\n",
+    "- **25**: 2x[2,0,0], 1x[2,1,1], 1x[3,1,1], 1x[3,2,2], 1x[4,2,2]  **UNRECOGNIZED** by Ferrando"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5391c7d2-760a-4acf-bfad-fadac59b6486",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "CNAP_mapping = {\n",
+    "    0: \"Unrecognised combination\",\n",
+    "    1: \"Vertex between two (111) facets and a (100) facet\",\n",
+    "    2: \"Edge between (100) and a slightly distorted (111)\",\n",
+    "    3: \"Atoms lying on a (555) symmetry axis\",\n",
+    "    4: \"FCC bulk\",\n",
+    "    5: \"Intersection of six five-fold axes\",\n",
+    "    6: \"Edge between (100) facets\",\n",
+    "    7: \"Vertex on twinning planes shared by (111) facets\",\n",
+    "    8: \"Edge between (111) re-entrances and (111) facets\",\n",
+    "    9: \"Re-entrance delimited by (111) facets\",\n",
+    "    10: \"Edge between (100) and (111) facets\",\n",
+    "    11: \"Vertex shared by (100) and (111) facets\",\n",
+    "    12: \"(100) facet\",\n",
+    "    13: \"Five-fold symmetry axis (without center)\",\n",
+    "    14: \"Five-fold vertex\",\n",
+    "    15: \"(111) facet\",\n",
+    "    16: \"Twinning plane\",\n",
+    "    17: \"StepA 111 fcc\",\n",
+    "    18: \"Subsusrface Ih chiral type 2\",\n",
+    "    19: \"Vertex 111_111 fcc sharp-wide\",\n",
+    "    20: \"Terrace hcp 10m11\",\n",
+    "    21: \"Edge 10m11_10m11 hcp\",\n",
+    "    22: \"Terrace hcp 10m11 & edge anti-Mackay\",\n",
+    "    23: \"Edge 111-111 sharp fcc\",\n",
+    "    24: \"Edge anti-Mackay close to vertex\",\n",
+    "    25: \"Unrecognized\"\n",
+    "}\n",
+    "#print(CNAP_mapping.values())\n",
+    "# Convert dictionary items to a list of tuples and print as markdown\n",
+    "#markdown_table = tabulate(CNAP_mapping.items(), headers=[\"CNAp\", \"Description\"], tablefmt=\"github\")\n",
+    "#print(markdown_table)\n",
+    "\n",
+    "# Function to map CNAp number to description\n",
+    "CNAP_mapping_func = np.vectorize(lambda x: CNAP_mapping.get(x, \"Unknown\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ac7cb4c-cb1f-49cd-a9e7-402a8195d95e",
+   "metadata": {},
+   "source": [
+    "### All CNAPs:\n",
+    "\n",
+    "|   CNAp | Description                                       |\n",
+    "|--------|---------------------------------------------------|\n",
+    "|      0 | Unrecognised combination                          |\n",
+    "|      1 | Vertex between two (111) facets and a (100) facet |\n",
+    "|      2 | Edge between (100) and a slightly distorted (111) |\n",
+    "|      3 | Atoms lying on a (555) symmetry axis              |\n",
+    "|      4 | FCC bulk                                          |\n",
+    "|      5 | Intersection of six five-fold axes                |\n",
+    "|      6 | Edge between (100) facets                         |\n",
+    "|      7 | Vertex on twinning planes shared by (111) facets  |\n",
+    "|      8 | Edge between (111) re-entrances and (111) facets  |\n",
+    "|      9 | Re-entrance delimited by (111) facets             |\n",
+    "|     10 | Edge between (100) and (111) facets               |\n",
+    "|     11 | Vertex shared by (100) and (111) facets           |\n",
+    "|     12 | (100) facet                                       |\n",
+    "|     13 | Five-fold symmetry axis (without center)          |\n",
+    "|     14 | Five-fold vertex                                  |\n",
+    "|     15 | (111) facet                                       |\n",
+    "|     16 | Twinning plane                                    |\n",
+    "|     17 | StepA 111 fcc                                     |\n",
+    "|     18 | Subsusrface Ih chiral type 2                      |\n",
+    "|     19 | Vertex 111_111 fcc sharp-wide                     |\n",
+    "|     20 | Terrace hcp 10m11                                 |\n",
+    "|     21 | Edge 10m11_10m11 hcp                              |\n",
+    "|     22 | Terrace hcp 10m11 & edge anti-Mackay              |\n",
+    "|     23 | Edge 111-111 sharp fcc                            |\n",
+    "|     24 | Edge anti-Mackay close to vertex                  |\n",
+    "|     25 | Unrecognized                                      |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12aef184-f46e-4f93-8b64-140ef9480121",
+   "metadata": {},
+   "source": [
+    "### Updated CNAP function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "66443adc-94d8-4b9d-a505-9d4172879aab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#================= NEW VERSION of CNAP ================#\n",
+    "def NEW_cnap_peratom(\n",
+    "    coords: np.ndarray,\n",
+    "    cut_off: float,\n",
+    "    pbc: bool = False,\n",
+    "    box: np.ndarray = None,\n",
+    "    display_progress: bool = False) -> np.ndarray:\n",
+    "    \"\"\"\n",
+    "    Computes the per-atom CNA patterns and assigns an integer structure ID.\n",
+    "\n",
+    "    Tries to match the cna per atom patterns to known patterns in a database for atomic \n",
+    "    environment characterization (see README.md for ID-structure mapping).\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    coords : np.ndarray\n",
+    "        (N, 3) array with atomic coordinates\n",
+    "    cut_off : float\n",
+    "        Cutoff radius for neighbor determination. If None, an adaptive cutoff is used\n",
+    "    pbc : bool, default False\n",
+    "        Whether to use or not periodic boundary conditions\n",
+    "    box : np.ndarray, default None\n",
+    "        Simulation box. Only needed if you enable PBC\n",
+    "    display_progress: bool, default False\n",
+    "        Wheter to display a progress bar - needs the tqdm optional dependency library.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    pattenrs_ids : np.ndarray\n",
+    "        Array of (integers) structure IDs per atom\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Compute CNA info\n",
+    "    new_cna = cna.cna_peratom(coords, cut_off, pbc=pbc, box=box)\n",
+    "    n_atoms = len(coords)\n",
+    "    cna_atom = np.zeros(n_atoms, dtype=int)\n",
+    "\n",
+    "    # --- Define pattern rules as a lookup table ---\n",
+    "    # Each rule is a tuple (required signatures, required counts) -> assigned ID\n",
+    "    PATTERNS = [\n",
+    "        # n_sigs == 1\n",
+    "        ((([5, 5, 5],), (12,)), 5),\n",
+    "        (([[4, 2, 1]], (12,)), 4),\n",
+    "        # n_sigs == 2\n",
+    "        (([[4, 2, 2], [5, 5, 5]], (10, 2)), 3),\n",
+    "        (([[4, 2, 1], [3, 1, 1]], (3, 6)), 15),\n",
+    "        (([[2, 1, 1], [4, 2, 1]], (4, 1)), 11),\n",
+    "        (([[2, 1, 1], [4, 2, 1]], (4, 4)), 12),\n",
+    "        (([[3, 2, 2], [5, 5, 5]], (5, 1)), 14),\n",
+    "        (([[4, 2, 1], [4, 2, 2]], (6, 6)), 16),\n",
+    "        (([[2, 1, 1], [3, 1, 1]], (2, 4)), 23),\n",
+    "        # n_sigs == 3\n",
+    "        (([[1, 0, 0], [2, 1, 1], [4, 2, 2]], (2, 2, 2)), 6),\n",
+    "        (([[2, 0, 0], [3, 1, 1], [4, 2, 1]], (2, 4, 1)), 8),\n",
+    "        (([[2, 1, 1], [3, 1, 1], [4, 2, 1]], (3, 2, 2)), 10),\n",
+    "        (([[3, 1, 1], [3, 2, 2], [4, 2, 2]], (4, 2, 2)), 13),\n",
+    "        (([[2, 1, 1], [3, 1, 1], [4, 2, 1]], (1, 4, 5)), 17),\n",
+    "        (([[2, 0, 0], [2, 1, 1], [3, 1, 1]], (2, 1, 2)), 19),\n",
+    "        # n_sigs == 4\n",
+    "        (([[1, 0, 0], [2, 1, 1], [3, 2, 2], [4, 2, 2]], (1, 2, 1, 1)), 1),\n",
+    "        (([[2, 0, 0], [2, 1, 1], [3, 1, 1], [4, 2, 1]], (1, 2, 2, 1)), 2),\n",
+    "        (([[3, 0, 0], [3, 1, 1], [4, 2, 1], [4, 2, 2]], (2, 4, 2, 2)), 9),\n",
+    "        (([[4, 2, 1], [4, 2, 2], [4, 3, 3], [5, 4, 4]], (4, 4, 2, 2)), 18),\n",
+    "        (([[2, 0, 0], [3, 2, 2], [4, 2, 2], [5, 5, 5]], (2, 3, 2, 1)), 24),\n",
+    "        # n_sigs == 5\n",
+    "        (([[2, 0, 0], [3, 0, 0], [3, 1, 1], [3, 2, 2], [4, 2, 2]],\n",
+    "                (2, 1, 2, 1, 1)), 7),\n",
+    "        (([[2, 1, 1], [3, 1, 1], [3, 2, 2], [4, 2, 1], [4, 2, 2]],\n",
+    "                (1, 2, 2, 2, 2)), 20),\n",
+    "        (([[2, 0, 0], [2, 1, 1], [3, 1, 1], [4, 2, 1], [4, 2, 2]],\n",
+    "                (2, 1, 2, 1, 2)), 22),\n",
+    "        (([[2, 0, 0], [2, 1, 1], [3, 1, 1], [3, 2, 2], [4, 2, 2]],\n",
+    "                (2, 1, 1, 1, 1)), 25),\n",
+    "        # n_sigs ==6\n",
+    "        (([[2, 0, 0], [2, 1, 1], [3, 1, 1], [3, 2, 2], [4, 2, 1], [4, 2, 2]],\n",
+    "                (1, 2, 1, 1, 1, 1)), 21),\n",
+    "    ]\n",
+    "\n",
+    "    def match_pattern(sigs, counts):\n",
+    "        \"\"\"Try to match CNA signatures/counts to a known structure pattern.\"\"\"\n",
+    "        for (req_sigs, req_counts), struct_id in PATTERNS:\n",
+    "            if len(req_sigs) != len(sigs):\n",
+    "                continue\n",
+    "            # Convert both to sets of tuples for order-insensitive comparison\n",
+    "            sig_dict = {tuple(sig): cnt for sig, cnt in zip(sigs, counts)}\n",
+    "            if all(\n",
+    "                tuple(rs) in sig_dict and sig_dict[tuple(rs)] == rc\n",
+    "                for rs, rc in zip(req_sigs, req_counts)\n",
+    "            ):\n",
+    "                return struct_id\n",
+    "        return 0  # default (unidentified)\n",
+    "\n",
+    "    # --- Process atoms ---\n",
+    "    iterator = tqdm(range(n_atoms), desc=\"Processing CNA patterns\") if display_progress \\\n",
+    "            else range(n_atoms)\n",
+    "    for i in iterator:\n",
+    "        sigs = np.array(new_cna[i][0])\n",
+    "        counts = np.array(new_cna[i][1]).flatten()\n",
+    "\n",
+    "        if len(sigs) == 0:\n",
+    "            continue\n",
+    "\n",
+    "        cna_atom[i] = match_pattern(sigs, counts)\n",
+    "\n",
+    "    return cna_atom"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0dc4380f-66d2-4f2e-8a33-39806120f0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def CNAP_analysis(el, coords, cut, natoms, outmovie= \"CNAP_movie.xyz\"):\n",
+    "    \"\"\"\n",
+    "    Returns a matrix with entries (nframe, number of atoms corresponding to cnap);\n",
+    "    also writes to file movie with cnap.\n",
+    "    \"\"\"\n",
+    "    #el, coords = read_xyz_movie(inmovie)\n",
+    "    tot_frames = len(coords)\n",
+    "    \n",
+    "    CNAP_matrix = np.zeros(shape = (tot_frames, 26))\n",
+    "    \n",
+    "    #outmovie= \"CNAP_movie.xyz\"\n",
+    "    with open(outmovie, \"w\") as outfile:\n",
+    "        for frame_index, frame in enumerate(coords):\n",
+    "            outfile.write(str(natoms) + \"\\n\" + str((frame_index+1)*1000) +\n",
+    "                          \" \" + str(el[0][0]) +  \" \" + str(natoms) + \"\\n\")\n",
+    "            cnaps = NEW_cnap_peratom(frame, cut)\n",
+    "            \n",
+    "            for atom in range(len(frame)):\n",
+    "                outfile.write(\n",
+    "                        str(el[0][atom]) + \" \" +\n",
+    "                        str(frame[atom][0]) + \" \" +\n",
+    "                        str(frame[atom][1]) + \" \" +\n",
+    "                        str(frame[atom][2]) + \" \" +\n",
+    "                        str(cnaps[atom]) + \"\\n\"\n",
+    "                    )\n",
+    "                CNAP_matrix[frame_index, cnaps[atom]]= CNAP_matrix[frame_index, cnaps[atom]] +1\n",
+    "\n",
+    "    return CNAP_matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4fcea701-a882-45db-a3df-d66712b41149",
+   "metadata": {},
+   "source": [
+    "## LOAD DATA & COMPUTE CNAP"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "589dfc7e-5c8e-406d-a0a1-aafdd6fd8f45",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1750 1850\n"
+     ]
+    }
+   ],
+   "source": [
+    "# === MELT ===\n",
+    "melt_el, melt_coords = read_xyz_movie(melt_movie)\n",
+    "CNAP_melt = CNAP_analysis(melt_el, melt_coords, cutoff, natoms,\n",
+    "                          outmovie= \"CNAP/latest_CNAP_melt.xyz\")\n",
+    "#np.savetxt('test.dat', matrix, delimiter=' ', fmt='%d')\n",
+    "# Normalize matrix\n",
+    "CNAP_melt /= natoms\n",
+    "\n",
+    "# === FREEZE ===\n",
+    "freeze_el, freeze_coords = read_xyz_movie(freeze_movie)\n",
+    "CNAP_freeze = CNAP_analysis(freeze_el, freeze_coords, cutoff, natoms,\n",
+    "                            outmovie= \"CNAP/latest_CNAP_freeze.xyz\")\n",
+    "#np.savetxt('test.dat', matrix, delimiter=' ', fmt='%d')\n",
+    "# Normalize matrix\n",
+    "CNAP_freeze /= natoms\n",
+    "\n",
+    "\n",
+    "# Check len of CNAP lists\n",
+    "print(len(CNAP_melt), len(CNAP_freeze))\n",
+    "\n",
+    "# Load TEMPERATURES\n",
+    "melt_temp = np.loadtxt(melt_data, usecols=6)\n",
+    "freeze_temp = np.loadtxt(freeze_data, usecols=6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd13b2ef-ffd8-4387-8807-de1be01e305f",
+   "metadata": {},
+   "source": [
+    "## PLOTTING"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a9aeb333-b103-4047-897e-30054b8403e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datasets = [\n",
+    "    (melt_temp, CNAP_melt[:,0],\n",
+    "     freeze_temp, CNAP_freeze[:,0],\n",
+    "     \"Fraction of zero-tag CNAPs\"),\n",
+    "    (melt_temp,\n",
+    "     CNAP_melt[:,19]+CNAP_melt[:,20]+CNAP_melt[:,21]+CNAP_melt[:,22]+CNAP_melt[:,23]+CNAP_melt[:,24]+CNAP_melt[:,25],\n",
+    "     #CNAP_melt[:,25],\n",
+    "     freeze_temp,\n",
+    "     CNAP_freeze[:,19]+CNAP_freeze[:,20]+CNAP_freeze[:,21]+CNAP_freeze[:,22]+CNAP_freeze[:,23]+CNAP_freeze[:,24]+CNAP_freeze[:,25],\n",
+    "     #CNAP_freeze[:,25],\n",
+    "     \"Fraction of newly added CNAPs\")\n",
+    "]\n",
+    "this_xlim = [\n",
+    "    (200, 800),\n",
+    "    (200, 800)\n",
+    "]\n",
+    "this_ylim = [\n",
+    "    (0, 1),\n",
+    "    (0, 0.5)\n",
+    "]\n",
+    "leg_loc = [\n",
+    "    'upper left',\n",
+    "    'upper right'\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a017c1b3-634f-4ab9-add0-1ff0f2980dfe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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lELPM4F6aQ8sHT4B/5tfgHQ7wViuwaxfbvSlpbvfuBVpb2c/NzcCOHYp9cRzw9G8ncfJ5J4ZyBVx18VHw2zf5NK7CoSG8MPlBtB7LB+Zm0JfTix3rD4EvmocwmY2Dx9ZBxBhGx9bDavXMTbCgFwCefBJ45hkI45nYtzgBsfI8cOcUo2UXwDeya3oOjUNsfRs9ENHS7AS/Y1vEG0QQZMOcm0Vzdi92FD4L/tgLQNpa7Ju7DGJOBbjpkz6tNK+glQ5/H1o9LxnyCfQKiL1/6LS0gOeC5n7TJvaXg1zv7bHjW1+zC5bZM0BRIVwFJf65JA0wQRAEYTTORl3wihmzR3M5tGhFbtoccs2zGBzNgJhTGSIcFQdnIeZWMv3u0Fx4navKflVpR73nciohjgH1tRLTox4+yZ7AbtnC/vVqUZU0t4ODQG4ue3k0wmHluUOzqMvph5i7DuLgbIDGVRycxZB9gZmyzGPwlMmnixXtk+rmxqubdbshZldAnLSgfs0kG5N90j81cp304GzUeQ0YpmUOQ8NpEE0lwNgYxLXnQhTdqJ89EpdWOuL6KWiBQ+bebg/Vewev75pJDIoZGFpaEziXSYA2wARBEIQmzkZd8IoZs0dzWbbKgemlTExLWSgvmQd3pj9EOMqVZ4Gb7kfndAW4skx2KtaJUGinaMp77kw/uGKgs9sMzmoBt3kde6J66BD712ZT9oXjmG51epq9ysrC9sU0xVnoOlMJbvokuPIsv93OTnDlWSizpTNTrgyUr3WDEzqA8nKmW1UzN17drMkEbmYAXL4LncP5bEy2fP/UlGUyW9P9zI8o8xowTFcmytYsgXOPAsXF4E79AxxnQmfWBeDcox6tdJm+AmPZPIWde5uN9Rsy6bL1Hc5HOTePsrThwLlMApQHWAbllE09lAfYuFAeYOOSirUhDbD6dob77CRSAxyl3+B2kfLHCqdNEKfSfXpZxYS54fLuqsxVOzQk4a2/HsV55VlMA+yRf3gvFMCH6Gy9OzTVcyMITBYwOQkhtwbiKt4/Jnkzea7kGDXAOH0amJqCkFfL9LVx5ksO6VA+NjVzD4QNIPCtb94CYDaHzCXlASYIgiAMR1wa1nh3z97/QKVS9p+8FjNh2qpxKWDMGsYgCEB3dzry8tgDMY3NdSGwPx78R3jwH/GfaxcBDkCAKzwPnufhracABBZXCJvjVe3ABAG8KIIPE13JN/CB/litLFGt08mu8y5IyAaXB79VZq+93ROQx/vzI7d7g/RKMfWOHPB5eYHBdJ6bghfbwdcx/4Q24MDBbCB/FeqaOfBbefCCAIjtALgQf+TziI94JxuAyC4PvoTnSwCUwGsy0hR6x1pXF7QmDQ0BSwCU+NbWe5kksSkEwuyLZX/4SEUl6Dk4hklhBpV1WVhd3+C7T9ocPOx2HjYJaOT9TglF9eyz5RmX0DEGcX8HuIYxlrfZ0yEPEfzGXNn7wLEl+m9F2gATCaGqqgpf+cpX8JWvfCXVrhAEYSTijSbztBf65rBvoBFiRSa4qnx1ZsK0DYrniW5LwxgEAXj2WeDEiVwIAnDVVex4MoMII7kbbSgB5y3jaMF+8K5BFRdH6Uhp0iNNTEDUF3zBbfLNb0yFKa68EuaREeCVV9iuMMx1gu0i7H1wEq0dBUCGG80fXIUdH5kDf1D9Qqq5bTRfE7Qm8qISFs8Oz+Xy/+x0sge0MzNAZmbINAYEP/bNzeCtaQv6epawOJ+OnGwJl1wxjU3vzIXNBjz2GJNjW63Arp2jaLQrfLYKunBw9+sQxwCu2I6W7wP8xuKwg5SPLdFf6pIGeIVwww03wGQyhbx6enpS4s/Bgwfxuc99LiV9EwRhYOKNJvMG0fAbITpcqOed6s2EaasUz6PXGLynq6sXguOAkhZQF6m/aL4EnB+aYwFaqi6O0lG4IKpI7RWC2yJ2rXRQ4Zh5fDzqdWK7gMFTJuRac5DrnsaQfYEFsWlYSDXrrvmaoDWRF5WQ17AYGmLveR4YHmYb4txcdiygD9Ef/MibRzA44Ia0sIT8IjPEyVXIwQxEkT1UD4hJbJ9R/mwdHmfBbudlQBwDxHYh4iDlp8bGIk5n3NAGeAVx5ZVXYnh4OOBls9kCrllYWEiKL6WlpcjOzk5KXwRBLCPijSbzBtEIHeCsFnQKRerNhGmrFM+j1xi8p3t704PjgJIWUBepv2i+BJwvy2QBWqoujtJRuCCqSO0Vgtsidq10UOGYVFgY9TqugUf5WjemHWcwbcpFmY1pk7UspJp113xN0JpwtnzfufJyfwxaWRl7LwjAmjXsqfD0NDsW0AfnD34UpFKUV5hgTk/DpFMCl7+IM8j2Ki0CYxIbspU/W5sLWbDbW/PgigGugY84SPmp4uKI0xk3FAQnYzkHVN1www0YHx/Hn//854Dj733ve7Fp0yakp6fjscceQ0NDAw4cOICOjg7ceuutePnll5GTk4PLL78c99xzD0pKSgCwYLS7774be/bswfDwMNavX49vf/vb+OhHP+rr79FHHw3x48UXX8R73/veEAmEyWTCr371K+zbtw//93//h7KyMvzkJz/B1Vdf7Wv71FNP4atf/SoGBwfxrne9CzfccANuuOEGOJ1OFBYWKo57Oa/ZcsNwgTyEj2W3NqnSAHsCdARxFbpQB3CcTwOp2SUNDYaGJBw+fBKbN69DWZk5sLnkCV5SEmbqKBQOZ0opZskTr4XKSmD1al9cFYs9s0bXAAtdE/5L5AFdgeJU1rEosoII3HoWACUXqsoqNggC0PqsEwOv9KEi14marWUw19cFuBEcSCaAh9jaBW7gKPjcaaCmhg3Sm0bNZoPU0MA+O3l5MI+PQ5BK0eVk1SHqnG+wtdm8GWhshNDmQNfhM0B+PtMAy3Svcn8F8P5hOgLbADIfi+YDq1LI508WEOYblyygTd5HsC7bu6beB6scRNRxo4AkQZxKx+nFIvRPc5Ak/5QELKUgoK11Gof7SyC5gYluB2bnzah7VxFK17NdaV2RAx0vDKG9LwcN7+Gw7aNcwOdLBMfGyC1CGDFD7BqFlJUL85bzA9c56D4SBKCrVQQmJ7H6nEXUv6eOguCWI0aJkn700UfxhS98Aa+99hrcbjeGh4exdetWfPazn8Xu3bsxOzuLb3zjG7j22muxf/9+AMC3vvUtPPnkk3jggQdQW1uLl19+GZ/4xCdQWlqKrVu34qc//Sl++MMf+vr44Q9/iN/97nfYsGFDWD/uuOMO/PjHP8bdd9+Nn//85/jnf/5nnDx5EsXFxejr68NHP/pRfOlLX8InP/lJdHR04Gtf+1rC54YgiBQQbxUIT3seQUFbkQjR/64CV+Xf9Gl2SUMDngdqaxcCLmfFITyCx76+UGHm9u0ahcna3Q3WmxYVsZoNTz/NXMnNBS64gFVLq6jw1HZo4cE3RNC5gse+Q5kQW0+ACy7qIAsu845bODGJfWe2Qsy0eIpt8Kygglx/23QV9jxpxZ8ez8bocC0K0mdQ8/IMzts2hapNeb6p4SGA72HthEPl2Dd5CcQ3BXD9w2jBs+Ar04ELL/QXZxgd9W/GeR6CuYzJjPfPAP2jaIYDOypfAz82Blit4Bt58I0KEwu/v4KlHPvwAYiuQlhmJjB5eADH7NlAuhvNV1mw4yPzaOh5mq35wACb2KqqAN2xLzCwrgWCwGPf3nGI+9vBDR8LKGrhX4fQxT10yFskYwbNOd2oW3sAvLMTKKrHG85m9BWdjwFnHvr6WN2PALk1eLzQx+ZhbmAE2W4JdZVn0D9Ugv4pwOWcxKG32oG+PrjcZtiF1RDWXwDeCqCnB3xfH/gTJ4AzZ4DMTPCbNgHgse/IOoitvf51DrqPBAFsrK1vg4OI1ecPRrmr44MkEAnC+/n2vgQh8X0+88wzyM3N9b0+9rGPAQBqamrw4x//GHV1ddiwYQMeeOABbN68GT/4wQ+wYcMGXHDBBXj44Yfx4osv4sSJEzhz5gx2796Nhx9+GFdccQWqq6txww034BOf+AQefPBBAEBBQYEnopZHa2sr9uzZg//5n/9hEcNhuOGGG/Dxj38cNTU1+MEPfoAzZ87g73//OwBgz549qKurw913343169fj+uuvxw033JDwOSMI4iwhHu1wgn3yCTPNZr++VbMwOfbu5bJcux0wmYCCArY/NJnYA1OeV+eGKCJ6UQfZuMVT8xAtqyMWlBDtk7DbgcWFJWSZ5zGflo3B0XTwOVOBPsnaiYOzEO0TqLf0QJQKIUqFbH57e0OLM8iaDw6yohe50gSGXKxUb6hQNvxEioOzbPz1wKB9EfbBVci1ZiPXNI1B+6JfN8zzoROroI31zae5W3VRC1FklwQU8MipBBwOVoDC4QKfPRV2XeXzYF6cgyCVMj2wfZFNHe/E0CAwKPGot45BFGR6aO/YTp3yC43tdoi9E6GFQ6LcO2On5iLfbHFCG+AEoUbIrjeXXnopjh496nv97Gc/AwBceOGFAdcdOnQIL774YsBm2fvk9u2330ZHRwfm5ubwvve9L+Caxx57DG+//XaArSNHjmDnzp2477778O53vzuif42N/j+dc3JykJeXB4fDAQDo6upCU1NTwPXveMc7YpsIgiCIYOLRDifYJ58wU5L8+lbNwuTYu5fLcm02wO0GJiaAkhL2s9XKXFTjBschelEH2bi5tRngXKcjFpTgbPmw2YBV6WmYlTKQsTSD8pIFCGfyAn2StePKs8DZCtDpqgFnHgdnHmfzW10dWpxB1ry8nBW9mDYXoMwisCISIULZ8BPJlWex8XcC5bZVsJUvYtoxg2l3Lsptq/y6YUEInVgFbaxvPqVa1UUtOI5dElDA40w/YLWyAhRWC4SZvLDrKp8HaVUmePMI0wPbVrGpE4pQVg6UmwV0OorB8TI9tHdsa9f6hcY2G7jqgtDCIVHuneK1iZU1kgQiQagRsutNTk4OampqFI/LWVpawlVXXYUf/ehHIdeuWbMGx44dAwDs27cPZd6klR4yMjJ8PwuCgKuvvhqf/vSn8elPfzqqf6tWrQp4bzKZsLS0BIBpjoMLX5xl8nSCOGvRmDo26rWK1/A80NICXhTRIpVCNOcr25BpWCWYYeYKA7SYCvUVVDlnHhlhWgKr1X/O45NPQ+p0Mv0ktx6ctQR8i9UvA3CkQfxbT0gBhYh9B2ulJVkhB4cDvN2OFlstxBor02taC2C9iUdFBWtaV8ce4njlrZLkf5gTKVdy0/ZC2ItrYcsvBmrycaDLCnTJNcY8Jqf+CZVVTqwu7kbT6VE4V1uAzYXMbfAQa64BV8O0vDxvxU1WoLFyBu2vjCEvR0LjezmsLp2H1HMc4l8AbF7HvoKvqWGvETNq3jyGmnoJddus4KffjbbpatjNjbAVzKOxaCDkP2YeArbjGIpXZ0DawKGm4nygog5orvan6QrWATvagMOHmYGaGvAAWtDh0zTDUeG7vqimAKJzFCiqZ9IdjmNC6+Zm/4S2tPg1wGBa2aaqadi3V8IxaMUfFt6HhkoO2xxDwF/+ggAhLwB45BM7zh/DltkxgOdR11QN3pwHnG4CPzWFljwO4uo8n7ZbKQXy9u1AcXE2xk4WYnokAwVZLlzaOAJrzTy7ly5dDfzDAdFdDG7bef570uv/BUx7DMCnB25pBLpwDgDJ80Eq8PkMjuVpbtlRiNbCWgycKMdzqFe+yXSCNsAJQv57LdUa4GA2b96M//mf/0FVVRUsltBbYOPGjcjIyEB/fz+2bt2qaGNubg7XXHMNNmzYgN27d8ft04YNG/Dss88GHHvzzTfjtksQhLHRkhY47jyq0bTD3hyo+zPR12/GAMpRUTmFqm0WNG0vxAsvhE1DGzUHbs6BA+wr4dLSwHNBhRz27QPEHoDrAVo8eluhzYF9u7sgOlzgrBa07ELgJlipbyBQ81zkAud8Gy0VbWwT3NEBTE+Dz80Fv3EjkJ0N9HBA01UArFi1ij0F9v7/pXadBMErXS7C22NFmGwDPM9UsGkTeyD44ovAzEwhck0WvNvdhQr3NJB3HK5+Hocq1gIAXK4ScFwJWurgWS8BzeP74ZwqgjjFwX4sCyXT+3HwxWmIC3ngbKfQsnkYfPYkBEca9j2fDvH0IjjLBOrO6ULbUgl2298Fh7QIa9ESdtUcRuN5JuDECZg3bADy8iDc8zsc/MMZ9M1YMZC9AX1X1KMnl0eL534R2hzYe2evPxdwk4gdQz8Hb3+DaUV4HkhPB5+ZCb65GajbAXh0w359ay+4iX60OP8EvmCWaYBravz3AHjs6/EEuB0aR9NkKw4ey8LfHFX468kamFatgvXANL5f+CS2OR5nYu3aWuDd7/Zrm2dmwHd2gp+aYn9wNe0COCvwxhueDfIoUFKCN+zWsFLkgwfZur36agGmx7OQvTAB55E+3PSBv6Nh+ybAfhCYF8Fz04D1XP/6y/y3WABMTsJ17Dg4TKBp0yx6UAHx2DB6IKJl00nw+TPMZ89N5XDw+P1fivCPfxRhYsKp9CnVDZJAJBCeZ6lCjLT5BYB/+Zd/wdjYGD7+8Y/j73//O3p7e/Hcc8/hxhtvhCRJyMvLw6233opbbrkFjz76KN5++20cOXIE9913ny/zw+c//3kMDAzgZz/7GUZGRiAIAgRBiDnN2uc//3kcP34c3/jGN9Dd3Y0nnngCjzzyCACoKolMEMTyRItcTHOOVK3yM28OVMtq8K5BOKRi8OYRiENzsNtlusow+VPDdiyKsExMABs2RHQqnAnRPsl0y1tyIDpcTG8ZrWGw5jm7n7XlNzIdrCCwJK6CwN4r5JEN8EHlvMqvGxxkmmLvnNntQHd3oMY4Z8GJwYKNGBrNQH12vy9fbUg/oj8/bX3uAET7BOwnXBBNJUyHOjTL5qW+HmL3GMRRN+pXOyEu5EEcnIV9fg0ci0XYYvkHHAv5sA+u8glgvXmAxRMiRFch+KIFOCYzwON0yDoE5AI+Pg1xaJ5tMiWJ7SS9ulelPMVefetSITtVW+vPSKG0lENzsPe6IeZWAnNzmJx0o6YGGBtdQvtAvn/Du7QE3w1aX89+PnVKlqTXrqirjiZFzslha1SQtQiTaxH2JY+mO4I+PXj9h+wLPk2vvdfNdNlefXjvRIge225ncviCAvaFSSKhDfBZyNq1a/Haa69BkiRcccUV2LRpE7785S+joKAAaWnslvje976H22+/HXfddRfq6+txxRVX4Omnn/blFT5w4ACGh4exceNGrFmzxvdq9T4e0YjNZsMf//hH/OlPf8I73vEO7NmzB9/85jcBBMouCIJYWWiRi+mVRzVSB1x5FjjXaQiWcljNYxCkUnBlmbDZZLrKMPlTw3bMcXAVFADHj0d0KpwJzpbPdMuHzoCzWpjeMlrDYM3zTCVrK3QwHSzPs1QBPM/eK+SRDfBB5bzKrysvZ5pi75zZbGzPJ9cYn0kvQvlEB8pK5tE5U+nLVxvSD+fPT9s5XQHOVgDbegs49yjToZZlsXnp7ARXWwyuxITO00Xg0qfAlWfBljEM6yonDrnOhTV9ErbyRZ8A1psHmFvPgbOMQ3Cmw5o/DwGrQ9YhIBfwhlxwZRlsB2k2s8eoXt2rUp5ir741bZyd6u72JNG1Kc4fV5YJW7UJ3HQ/kJmJ/HwTenqA4pI0NFRMslx1FguQlgbfDdrZyX5eu1aWpNemqKuOJkU+c4at0cTsKrgtq2BL82i6I+jTg9e/zJbu0/Taqk1Ml+3Vh1cXhOixbTYmh5+YANLTle8xvaA8wDIop2zqcbvdmJubQ2ZmJn7wgx9gz549GBgYCHs9rVnyWHa5Zs8iDL82UYS7kXLUeo8D/tym3rymATlmg+3J8sFG+xpOnpMXBw+i9WgWJrN4VNZlYXXRAvPLk1c2Fg2wJEnobW1F9iKHcbMVUlFJSMrfaL4LbQ6I9km2ybNa/d148qkKp00Q+8+Ay18E79WsyjXAzjRwPX/z5bUVRi0Q2wVwa9LBl0oBzrS1sYd8eXlsXweRzbdD4mCfKkFeHssP7F2X4CG37R+FvX0GtoZsWDeWsDkTRdT1/R/gcKA1axsm1zUiNxeY7uhH/swwai4s9OX19do83Smiv2sW+a4xNK87BeTmomu6DJAk1NVI4IvmIfRMo+sNJzA7i7oSESgshFhxPqQlwPlmL7t2gwl80TzanBU4fCIH+aYpNF+4CKSlwTFuxnSxG++8qgnm9nYIf34D4kwWpAvfEZBn2Jfu9/QonP1T/ry+r74K8eV2cMVuJktxOtmkbd4Mwdrou6+8gWSiM42t7cg/2KPOhgZg27aA/L9SUQnMPV3gJu3gK9MhmMsggkPnSAmGuybQwI9hm80O9PcDkgShqB5dqIPoNIEzT6Bucw67Lw4fRttYOezFW5CXu4TV073Ml5pclkvYkQbRPgkprxDm1SUh8vHWVqaamBmdRnXmKVTx8+A2rWVz7+z03zNBN0Hw51bsGoV06CjMs9OQrGtgXpXmv08BtLVOwz7Jwba5CI2NQJtHVj056cSXv1xMeYCJlc/999+PCy+8ELm5uXjzzTdx991344tf/GKq3SIIIh5UiEej5ai1WNjDrmPH4M9ruv4Q0JOpbE+WDxY9kYXFvn76JmF5qx2T3VM4NlkIcGloXkrHjrWvg+8ZBHo48C0t4LdG2ExHyA0sgMfxt6vR32+GLEVqiJY4nO98oxV8ozVwOi3jaMF+wOnEvq4aiDOZ4DJn0NK3PyD3Li8I4F/4rU/ALPTNYV/+9RCd5eAOvYWWimPgqzLZpsij4fWlJnbOInNsApuKHMgvdsJZloEBZx4qKljeYCBAwgk4HDj4GNMrjx6yoGVXHbbWLQH/+Z/AH/4AzM/jI+W/hfBv92Jv/7vR2lYJoBLNVmDHu/3z4OgYxYM/nEBHXwYyZi14LzeOtdYxuLbw4OaHUdfXBlRlAgV16HmxH2LfFA655oBSCa71AiwXng9kroHr2HH02EW0NM/Ben4OpFdE9Dhc6Otg/SwsAqsyp7EupwNl/QfBZ46DLzMD7y7wCcUDb+EStLR4giLbHNj3jBviwDpws0NoOXUU/EI/UFEBYSwd+1DJNrwDXUx7XZUJvqUFQAHQMwHMeuQEbW0QXjiGfa1FEDEBrmoELQN7wE+xJ8T8rl3gG0vQIAjAhMcRsAkXwGPvXmD/frafXrOmGNvGgB3bAUdfPnY/bcXAxDhmF1fhnfwCNpkFtLxzFHzFIaat9i7exsDPiMMBPP440PHWAtKm59CdbcaqzHxkvpKG5uJu7FjvuWeKikLyVbMUqbKbv8OBfX88yTTs7g60XDYH/p3rAFSjzcFj91OsP+tLwK5dQGMjex0/Hv6jpgckgSAMQ3d3Nz70oQ9h8+bN+P73v4+vfvWr+M53vpNqtwiCiIcYRbnyZt60uAF5TfmNugiBfZfyTqZZXViL3Kwl5EqTGOyeYZpHHfJZjo+bfZpLeYrUkJSuUXwP1oiKg7NM5zs8z3LeKuXeFcUAAbPYO8H0qLzTrwv2aoY99r1PPc3SInJN0+iV1mFoOC0gf6ySXldRryyKwIkTbKe8ejXr5/BJlms2V3ke7O0zOCWugjV3Dm6XC91pdRgUM1Dv7gzwWTx8kul9ix0YWlyNwbkS1Ju7Wc5amf5UHJyF2C74fBscZH1u2JyFiTETxHZBla5VUZtdK0EcA0RTiU9Q68sHrDDHIQbt9kB9c7cI8dR8oIY3jCPepbVYmLbabPbo0+2TsPe64TBZUZs9jLFJC3IK05mfOZXKiyfDKyO2Fs5jcXEJA3MlMLvm2OdC/vlTka9abBcgjgH1NS6IkxY2TzLNr8MROlQAGBvT9BHTDG2ACcNwzz33YGhoCE6nE11dXfj2t7+tmKWCIIhlRIyiXHkzb1rcgLymQocuQmDfpUIR06ymn8L0bBqmzfkor81mmkcd8lkWFko+zaU8RWpIStcovgdrRLnyLKbzXZPBct4q5d7luAABM1ddwPSoQpFfF+zVDHvsCwLb5ErmVZh256LafBJla5YC8scq6XUV9cocB6xfz3Zpp0+zfjavY7lmp5XnwdaQjbXcIhzTmTBZLKhd6kI5N49OU32Az9zmdUzvO2ZF2arTKM8cRadUy3LWyvSnXHkWuAbe51t5Oevz+OFZFBS7wTXwqnStitrsbjO4YoBzj/oEtb58wApzHGLQZgvUN9dy4NZmBGp4wzjiXVqXi2mrJcmjT7flw1ZtgtXtQPfMGhTnu3BmfIH5eaZfefFkeGXEjvEMrFqVhorMUUiWTPa5kH/+VOSr5hp4cMVAZ48FXL6LzZNM82u1hg4VAIqLNX3ENEMaYBmkJ009cg2wmuwPtGbJw/A607MYw69NjHXhlTTAAFBXJMtnqykZcJRLJQfQ04Ou/iygspLleUVsvsvxrk9eXi3Gx83elL9sLBrzCYec9mqA5fpSJd1zUBJjAXyAPlXel0/v6vXTowFGURFEs9WXFzicBliuV/ala/PqCAQBuOgiYNu2qHmV2/aP4vDrc34NcGUlxNUbQ3wW9ndAfP0EuOxZYN06fw5ehOqpA7TUABw9TkynOZkGeGQk7LyH1al77eUtgF/thnxyIs1xiEGZBpirK2H5he12tiOUFZFScsQ7j97DvrkUBJ++Ni93CatXOf1+hls8+fx7tLj5S07UFIpwogjguNDPn4rPmrC/w685ry8KuLYtzFCdTieKixOnAaYNsAzvZqqqqgpZWVkp8vDsRusGeHZ2Fn19fbQBTgKG32SdxRhubWLc8CbaBVVuRboo6H9qtcOMZ32Cg+K8gUvBm8uAzRMvb6wQnKS0IYN/MycPigL8bRw9kwHBSgEbWM+mSDhtgjjFKoPx1iX/BUVFvl2/UFQfUJjDu3mXxHGYuUKclkowNeXfEAXMgTQE/OMfLE3Apk1oK3wP7FMl/s1TWxuEw6cg5tvANbMALbFrFJx4AjwEtikF5wtslEpL2R8nswUY7z/jm9dwm7KwayTCV1jC+xTTF0g4LgKTk6irnA3cJJ8+DaF/AV1SDcQiVsSKg4g65xu+YEVYrf4/cMzWwCDB08BUvxO2fBGNzbkhf8BE+/sw4I8P7x96Yf66CbeZ1/WjHrR23d1OvOc9FASXFLyVymZmZmgDvEyYmZkBEFpljiCIFKGlskUSXQBUuBXJ97Y2YPduT7SOFcLOr2OffWNChxlQPAEimqocONjPQ5xO9xfEsC6x4h3eAKpmC1p2FLINjWwsQtNV2HfQCrFvMiAoy+u4t9hG30AaBmaXUPHODBRV5AEAXM5JzLw1gI6+bEy7p2CtT8fOm3Jw9Kgnrm5uFs3Zvdhe+CYOHsuCmFUGjjOhpfIf4PveAObmWKqupSVWKCH7YxDLzgXnfBtNRT04OLQWfc58DIzlID17Ecdm85BVkIGKCmDnTsB+1DMHE71oGfoly6DgcqGtaCt259ngKMmBtSILuz7YBesfHsK+DhvEjAlY3psL5ObB9WY7uP4jaFp8HQdNTRAtq8FV5qBl2xxKr9uKsePjeOVPp+EcWQJntcD2QTMee4bzLrUvMCvSvXbsGPC3vwFZWSx/LsACHSdH51GwMINCTKM59x/YccFxFiiXng7h8CnsHf8A9s+tRn/2DGC2oFIawbaZTuzIfQb8+idZNgmplBUzqciEpYhtBAcGgL+9Mo+sMzOoyJ7Brqva0HhTM5tfFcVi9u6VFXXZNIkd+ftZZgdvVQxZhKNgKcc+fCAkoM93T+nxGWhrg3Cnf+1mmvLxlpDYh1qkAZZhNptRWFgIh8MBURQxOzuLubk5eiX5NT8/H/Wa2dlZiKIIh8OBwsJCYzz1Iggi5qC3RLugyq1IFwVF64jtQsKHKYrwF0/IrWSFH4SFkACzgACqoTnmS9BYfAUulIKy4A/o4mvz4RhjwW6+OCneid7BVRCkUmyxDsIhSGhvhz+IzTKHoeE02E02FuxUK0EcnofYPcYusFjYxRYLRLOVjcFTmMOesxGisADeNQSHyQrT/CzGxCVfjYj2dgQWkBh1+3Lf2mdXwzEKbKllgXn2w+MQT81DtG5AvbsTg90zLBDO0gPRVQD7QhnE+VzUS+0QLat9wYKTQ7MQHUu+eW0/vBA2MCvcLZOTw4K2amv98WW1tayQxNKChNwCM4ZGM/yBcp62g9k1sCzMQJp1wbUowTw7hcGF1RALqn3Rn75iJrzTZzsnB2ye8h1wmKyw97oDghijFYsJCEC0L7C5kFfFkAXJhQvoC1c0JSbs9oC16z2+AEGIw54K6AlwELznzxeH5wYlkovb7YbL5YLFYlElgSgsLPStGUEQBiDGoLdkuBDVrUi+B0XrcA08OHtih8lxLNCt82QFOPTDtt6C0f70oACzJRZA1d+PTlSAa8z0+BI4Fs6WD24U6OzzBmW1AVV+x70BXX3dk7AWZ0CYyUNZBfOjUyhCdfko5vpGcMhRDmu9GQ0NwOIiS0ULVyYa1yzB5rZjtDiLBYWtsYCrLAb6PNkfyssBlwscHOD4dE9hjrdhO9OBUX4t+pwlsLodcGdkoZhLQ3c3exDZ0ADYFz1zkNYLrsQEjLDqZ7as07DmAYe682CtAGybC8G9nQGu4zg6M+pRXpsN5Kaj880acJYjsLmHMGpai05zAzjXaV+wYH7ZDDjrvG9ebZvTceiUcmBWuFtmeJgFbXV3s/gygP1cUgKkLZgxPSGhsWTeHyjnaVs+3oMT6RfBnGUBzGZIaXkod58GN9ELrGfRn6yYSSM6hSKf7YEBsHmatKIi2wFbtYmtM6Lf5xzHlqO/n71v3JQOLj8LEPoQEOEIsMIi5eXgkIlOITvg3vHdU3p8Bmw2cGv9a1e9IR1TCd4AkwY4DJIkYXFxMYmeEQCb95MnT2LdunVRn+quWrWKnvwmEcPpTAkf8a6Nrjq+aJFNCXQiIGjOERiIJQ8UAoKChZSMyLWQXm3lH1+FePgkuM3rwL+7Rll3q0DEIDipA+jv9+keed4jNT7MtJ3WmvyA4La2DouvyETjthKfz/JiAh7ZaKBeE4DQ2suKZbhHgbQ0iBXngyuUWL7ZxUUIQxJEdzGk8ko4p9OB/HwU1XBsGiQHHAdPwi5kwnbRGlbgolVEz+sOVvfhwjQ01rtYH/ZJNkfr81mCWpMJQtkWiIOzkIYEOPMqgU2bWEEFr264/wyksQmYiwtwOrcaU6s8GQLgQNeLQ8DEJIpyF2E+fQqcoxN89iSwfj3ayq6EfVWdvzjHaTafXVINUFMDSQL628aRPzmA5rJ+OGCF/VQGbPmjaLy0BFJDQ6gG2LrkDx6rLPIV/YgWb+nTAOeNAuPjvvZKGmChZxriC0cgOSfRU7YV/QUNyMMUipbGwI10oShrDuaL3gGupgi8sxOCuCowuE+mAc4b68PqYheb80aropbbG6vm/WhKEtsA5+ezXNQ82EmhZ5ppjTevC9BwB+i2nZ1MlwwereP1mEwrwubNQTIRjy69SyzxBc7B6fR9Xrxj8M2rp/KGMLDI7svmOnS/0YP3fLiWNMDJxmw203/yKUCSJKSlpSEzM5PmnyCSgK6S3WBj3ipRSXAitEDEC2hwDQKjHAQwraKvuMOMchEKAP43Qf4IjjRW9MCxFlzPGbQc+zP47EnwHAfUtcBXNSEMIyNmvPIK23T4CmHMO7HJcRz586Nw5U6A+2A2bO+pwGN7zsDROQVrul/bCd5TpMIOiLPAqB2welKVCeBx0MncffsF1h+rb2BFS4uVbW727gW/fz/4/n4Iixz2rboGYlEOuDMDaDH/BfzoMfBr1oAvK4NQ0YSDfQ1ss9Js9miKl8CjDY2rRAhHy7Hv5UvQ99ogBk7MoiJrFFJvAazX5oB/7X/AOxxATy5QWQn09UGYK8S+tCr0jRdg4GQZKrIdqDr3TdR96wKA48C/sQ98X59Pf9pQVeWf8+8fgdh5GhbXHGBKg8udBm7VarRUjYIvHkVj7quwNhVh30Er3nwT4LiNaGraiJ6DQN8L3vUuRGZmIdqWzkU+JuA61oVRzMMqHUOp548DvtGKsgvMvhupURRhtZRj39gH8KarMOJt6a1/0tAQeCM2cpyswIT/8ajQ5sC+R16HeMgFi2QGijvgKpvGBFyYcI7jSCaPgaVyVLjWoKpPQguGwLsG2cazzlMYxdOf0LaIfbun8abDBe6lLrTsHAVvfx3om/Pohs8DV5WPpibghReY7ndujkko1q8Hqqpkn6FDQx4tuRvcWDpati+A7+kBRBE81+Mp4gHghUMQ9ndgb//FaAUHVGZgbCwbVqt/Myvs3Y+9+1ejdXgVUJyGTVkDyF8ahytzApZN7I8rXwGVJgf4g/s8/XDgmwsACMjof1blpz82SANMEARxFqOrZDdWYzo4EWDCUyAiWP/K8+yrarM5TBGKCP4EFHgYnmf6Ww3+KhbCkCZhH83FYMFGVuDBPoH2dsAhSExrK9N2Rpom+XFvgYeAa0SRHTSbAUmCuJAHUSpE/ewRiKIbYuE5rNTe2rXAqVMQu8ciaorFwVmI9gnw0ik4XMXgC+bYnBw+6ddJCwLb6efmMt3v0Cz4+X52ff6sfw69duX6U/mcD8+j3jqGwYVSDI1moD7/FERXAcTBOd+1wVpUb22G4PW224FB+2JAcYyQtQse59CctttSxb0s2ichDs2iPqsPg+lVGBpNR73UjqGlNRgUM8CvNft02MH3crC9kMIjnoIect2wd068tVAsFnYPyqZaWUvuXZ9gMf3QEESzFUMuHrmucVacZlDmmsfWoHkdck3T7D4fXIVB8zrU5w5gyL4QWIMjXD8JroRBG2CCIIizGF0lu7Ea08GJABOeAhEB+leO7cnWrGFf/yoWoYjgT0CBhzUZTH+rwV/FQhjmfNhKplE+0cEKPNgK0NAAWHkz09q6/drOSNMkP+4t8BBwDcexgx5ZB5c+Bc48js6sC8BxJnDjb7Pvwk+dAtauBVdb7C/KUJbpt+HphCvPAmcrgGBeC6tlDMJEJpuTzev8OmmeZ48Yp6fBSQ5wZVkQMirZ9ZNZ/jn02hUEv/5UPudrMtDpKEZ5+gjKSubRObkWnGUCXHmm71rv+srqSiiut80GlNtWBRTHCFm74HGWZWq7LVXcy5wtH1xZFjpnq1C+0IeykgV0mhtQljaMcm4ewikJ1mJWdCT4Xg62F1J4xFPQg+mGLawQh2dOvLVQXC52D8qmmrWRF+Moy/SvT0ClE3YvcZIDZRYB05ZCVpymXOaax1a5dBLT7lx2n5cvolw6ic7pCpTZ0gNrcITrJ8GVMEgDTBgK0pkaF1ob42I4DbBGY8G5bpVy0wYk8A+XbF/eNQJz4wJ+aa/TGajSCDAn0wALzgyI4CAVlbCHp51dMA8PsU1GSUloPl7Alyu4bXED7KvqYLMBDQ0Seltbkb3IoWd8Nd4e5zA1xYK8mpdeBbq6IK7ZBO4D7/RpgA+/OI6liWmcsymLFeTw5jJuc6Dr8JlAfa5n4+HTP0NWGAEi6tDFZBDeIToz0DW9FqiohCQ6MWUX4RDTMLeUgTV1eahvysPpnilMmQt9OX+Dqy20SQ2w95uRN9YHsxlARSXT9Pa8yp4mV1ayR689PcDUFIS8WohFNZD6BmCenQZ30Xrw2zb657yri11rNkMuKBXaHBAPn2TrhiJgagp1lbNAYSErWJKfj7pmTlHzrbTeRUWA2em514rmIQHonZhAdXOz/7PjaSxIpehyMkF1EZwwc4W+eyHi7R3hM+Dz6/QozG1HwJnG4Cg7H/ZpK/KkcYxPmjBwOh1LWTlIKyxARQVQUzgK5z8GADdQt60s8H6Tf0a896JXf/s2y5ZRtznHNyetrf7l8WqbffeOxHS6XWIJ4HSiztzDnuCv4n3aaKFrgl3r7AGmp9GVdyFwzjmKnyPNGmD5AUGA8+BBFF99NWmACYIgiMTg1TCmwphfMlkCjitBS51fTevNTSsOnAE3O4SWd46C31QSVowp71oQeOzr8STv72FNGhoC++zpYS+fOZl+05v7tM9ZyKSpRVOocjrQUtEJLAosL6qrBtwo0OLVPnpyBbf1ZGP3cDEca3lYzynAl/95FKv/dgDu2QIc6m5C60w2kJmF5k0TaM7vB79KBO8+BmAdAB5WKzAmFaK1rRBoA5r7PFplCMAL+9HTWoS+OR4D2ZmoqMuBRzIbML5Dh4DW/TNA/yia0YUdla+B37YRwvZPsNyt88BMG9DZyaGvrwa9vUwXnb4fuOgoYLGUoKICGJVYIBp/cB/g0ekKRfU46EyDWHEeLEUXABLgOjKJnqc60FIxDL5onn197XT6BM985qvgN21iT5pXuQC7CAjF/gU7dMifmHZsDF5BKd9oBazWAEl2UY1fzwoAzW2T2JHv13zD2gLeo5MNXm+m5S0BDxc7ODKCHJcLqK72Zz7geX8+3b5JWE50A2dm4MQUBrKzAuZc8VYP8xkIlLqXoOX69wEAvFN74kQRRJEpQVwuJlWwWoHstGwsCcXIdM+gubUXO76FgE0w32gNeC+Ax75DmRBbT7A/gMacgHUbAN73x4AkARs9f3/4fbKiqcmKnhcHID5zCIemJ4DcXLguqQb3djqaJvezPM9z2eCyLWipG8XW3JeAupzQ8fI8WwPfASsAa4BSPkR7H7xgb76pMLn6QRIIgiAIImVEkkz69I21EsQxQMypVC3GjGg33DkF/adPmpo95cuBGlYb6skVbF97MRyTGdiyZpjlkW2fgWViAuLqjRgaTkOuZQ65uUyPqqTv9Ep2vXlaffpKmU6TN4/AIUiBOk7ZMAYHgVzLPHKlCQy5eIhmKzA4GKCXtduZ6qGwkAUGlpayp4Pz80FyXK9G0zMZYk6lT1/q0xzLc8R6c8gGCJ5z/UJUJRFzQGLaQHF28HrZ7WHy2IbTySqtt/eHDRtgmZgI34Z3YvCUCUOWyHOuBiU/5BLoU6eApSW2+Z2fZ/8uLbGxmqU55FqzMTTMKgFG7UeWP9qrdY7Uf4B+2j6Bencnhgo3YlDMYDmbh+Zg73UzjbC5G6KwEJJLWje8NhNckIw2wARBEETKiCSZ9Okbu83gigHuTL9qMWZEu+HOKeg/fdLUmTxPDtSO8NpQT65g26nXYM2fx6HhNSyPbEM2XAUF4E53oGzNEqZdmZieZnpUJX2nV7I7Pc1ePn2lTKcpSKWw8uZAHadsGOXlwLQrA9PmApRZBPb1dnl5gF7WZmNa0PFxIDsbGBlhD2gzMoLkuF6NpmcyuDP9Pn2pT3MsFPnmB2VlzIEAwfO0X4iqJGIuL/cPOEicHbxeNlvQ5bb0yDpZpfX2/nD8OFwFBeHbCEUoX+tGmSvynKtByQ+5BHrtWlYwz2Jha+Cp94HyckAyZ2LaMYOyNUtsPaL1U5YZonWO1H+AftpWgE5TPcrGO1DOzbOczWWZsFWbmEZYqgXHp7O1jmUi1E7U7Ky+doMgDTBhKEhnalxobYzLcl+bSLJhLRpgTXbDnZOdEMD7vi725sL15tX1ngtpH0EDXF1QgBHwTFcKj/4Yyo6ETacs+LXNkfSovvaiRwPMLfoMycfurXTmcLD0WGvWsKeBIWmQg/IjC1IpywsbpCGV5x0OEOF6B+K7OMjpKPmjFSSigZeHmceI6y0IkByOUA1wcBvJr19VpQGOgJIfSnplL17dsrNHZLmEPXpeVf0o6Ooj9S+fW7G1C9ykHaishLh6Y6iuvmjJv9aJKEYlCHD29qL44osTpgGmDTBhKJb7f+QrGVob47Ls10bHKDzF/8xjNR1QWQPRA5uCN42ecw6HhImJXjQ3V8e+PkG7JHlhBMUNIR+4gQu3WYn2R4LXZpE0CvPUeGjQX5jrg/ew4frR/EeKigWVb/5QVOQLZKvjRkOCLIM/O2HnzPOHDWw2f4BetDGFm3e1xWIUOtDrfg7+Ay7SPazStYA/zoKLw0QKdA1n0Ol0ori4mILgCIIgiBWIjpU4gk01NQEHD8ZoWm7M4vmv0pe5vyVgQ7LPHx+Gigr4AqQAX5wVXK6cgDirmAbmqeQhjKVjn/MiiGsWwW07D03bCwODwpqB7ds9Y++bBDfQhZaKNvBVmYq+K82PIAB793oKJ0zMInt8AnX5p1FVMYyWXQjNRCC73uuDt8hIuH6i9R9yDtHvFUEA9u0dh9jaC8vcNCbTpnFsdhUwNoHmNYPYse0Q+B3bIgepBc+ZzQY89hh7TG61Art2QbA2Rh5TuHmPNFGKzvg78AXmxXk/ewM8RU+BD+/nROkeVrIfbm2Evfs9hTQmwDVbWAEVPnBNOIhoaXYGroGu1XjUQxpggiAIInVEilaL05S3IEJMpuXGFKtLBF6mUMdBHmeFiQlL7EOTdyIIEKVCiKYSFow0NBcaFDYkG7s8OC2M7+ECBb02LdIihkdXga/NZ7YUgrDk1wfHsYXrJ1r/IedU3CvyALAhSyXsg+nIlSaRa5rGoHmdcvGL4D6D56y93V/gw6MZiTqmcPMuimEiHMM5IyvGEs9HRdY4OIhTXjgk+B6OYipgbUIKacjnRCEoL7LBxEMbYIIgCCJ1RIpWi9OUtyBCTKblxhSrSwReplDHQR5nhYICV+xDk3fC8+DM4+DcoywYqSwzNCisTDZ2eXBaGN/DBQp6bbrMq7CmZBFC9ySzpRCEJb8+OI4tXD/R+g85p+JekQeAlbn6YStfwLQ5H9PuXJRLJ5WLXwT3GTxnDQ3+Ah9WK2CzRR9TuHnnuDARjuGckRVjieejImscHMQpLxwSfA9HMRWwNiGFNORzohCUF9lg4iENMGEolr2WcQVDa2NcjLg2mrSKcQgbo2l+I+lIFQO5wulNPRfI23iDw7wJ/0WwTYpc1qmoAY6m7YSCllJeoKNnGl0DWaz4RDPzTVajghV7MAOnTwNTU4AtbxSNq08HDs6jQ23ryYbdXOMveBE0tz4N8Ii/CIivgEWQ4wL4AD988xBJGyov4gAhQGertHZt+0dhb5+BrSEbjdtKwgd1dY1CEsfhRBHrF6EaYPnaVFdXY3zc7NPASqeDNM/797Mnwd4owSg6WqeT3S9KumNW6eQwS7nR3By/BlhF4Q2OA3iHX8csWBsDmnglznl5wGpzmKI0oR+HED9UaYCL5pXF8kEGSQNMEARBLCs0S/rCFA6ItR+5qRDTnkZC3xz2DTRCrMiEpYj95xoi8Q1qHKLBbPIWiJhDz0AjxIrz4KzK9yU78PZfWgp0d0thnQ6waxlH02QrKzgQoKXkfRkc9h0ExHmAcwJFjkCdc01NqJ5ztKoE1o0lgRumvXsh7O/AweELIK6ZwejYebBaC5XnThCAnleBWRGwc/4CFkFj4VtagDo+sMiIZ454UQTPcUBdCwD/psd7DkdmgI4O9lTUo7PlGxtD9o27HythUtxDwE74v74PXf8S7NtX4jvX3MKB5/1PFr2uj4wATmceSktZzl2O82hi7SUQxRJW6AQO8N5UGYcOAW+9BVRVgW9pAd/AB9gTRblknEOPkwso7gJB8C+YJEW+wRU+F+HuZ6UPW8ApyzhacAy8axAYHQXfYg3w3XcPWcaxEc+x63r89pS68RZekTsXWPwi2O8SQHCF/9AGjfX06cjTEy8kgSAIgiB0JVmSvpj68eop+Y2hxRyi2Anpz1MgQm4rqh/RtJ3yggNBWkql5sE6Z1V6To8OVTRbA7TEYf2OKniNoFP1FtGIJvTt7WU7MZnONhjvHtR7SXu7OtNKa+I9tmEDcPr0qoD1D9GOBxUCUZpUeX/eOiCKfev94YhgL/i+ClcsRM11urmtwdDYWIx9qIQ2wARBEISuJEvSF1M/Xj2l0BFazCGKnZD+PAUi5Lai+hFN2ykvOBCkpVRqHqxzVqXn9OhQOckRoCUO63dUwWsEnaq3iEY0oW91NdtYynS2wXjqjPguaWhQZ1ppTbzHjh8HVq9eDFj/EO14UCEQpUmV9+etA6LYt94fjgj2gu+rcMVC1Fynm9saDBUXx9iHSkgDTBgKI2oZCQatjXFJ9NrEItFVo1UMzg0atriEvGlQblXVif3l13jErfJcuoCyfUWfg22Jfm1wQB5VT3EGqbAQ3VNTqK2txciIOSBHrVyDLC9cAYD55i04IDMcTnvq9d3rS2cnMDzMNorbtoXqjIPnQHFtvbpOEZBghpkrDLxWjU5VUcwrF52uZue8T35luXaDCU7HG2n9o+W0VdIAh5tTvzg4vFGVaaPDO60mN7BSPxHy6yqtuVIfAd0XKecv1isHMXM6uqFEa4BpA0wYCtpkGRdaG+OSyLXRLUVnkCGh6SrseyEdYusJcBDRtGkWB/O3+XKTKuaE7ZsEN/CWYk7bcP4q5gIOl082zkTCoZrLZ8G7BiEVFaF3wwZkVzfjL38xQxRDUws3NQEHXxj3zUdLsxP89k3qE7QG+d5mu8avl7UCO3cq62WjrZk3t2vfHI+B7PWoqMuJmCNWFW1twO7dAXl1w214taL1fo342UlWflq1uYGDXIqQnjp8P2r0wnoPNUbjid4AkwSCIAiCCEuitH+ifTIgN6i91x2Qm1RRzxkhp204fxVzAavVtGpMJBxJS2keHw84H6w7ttsRmitVS4LWIN/t7TOq9bKRBuTN7cqbR+AQpKg5YlURLOZV0PvGiq4SW731upH6UZMbOMgltdp1xcaRdMB6DzVZ86gR2gATBEEQYUmU9o+z5QfkBrVVmwJykyrqOSPktA3nr2IuYLWaVo2JhCNpKaXCwoDzwbpjmw2huVK1JGgN8t3WkK1aLxtpQN7croJUCitvjpojVhXBYl4FvW+s6Cqx1VuvG6kfNbmBg1xSq11XbBxJB6z3UJM1jxohCQRhKOhrduNCa2NcjKgBVmNIDw2wGn815U5V1VjlEMNpgMNIIYPnIyS5bCRBq4LvavSyagbkze0qFZVEdUE1wc7piJZxRv3s6HbzRyEeDbAW99TmDNZ7qDEYJw2wztAG2NjQJsu40NoYFz3WRu1+MPqJODrT0YTihhnwa5A9RRuAqPuN+BwTBAx1jOGtwVmcd9n5KCsLXZ+wGxrPBjqk+IZSt/KCEo3WsO7EPR5VjofpSO2mN1mbTgBDQxIOHz6JzZvXoazMrKrrJLqnjSQ4lsyxUyEMgiAIIqGEi1EJG7sST8SMDtE20UwEBs11saC5onl20uWCYCnH3skWtB4rABAx5kgbCkF0wgvH8OxrBTg5nYEhxySu+kSRsq/BQU2WcbRgP+B0+gp2cFX5yvFvbQ7s290F0eECZ7WgZRfAN1rjn+pIBrRGY6kNfEtW4Jmnq2efBU6cyIUgAO98Z/SYxyS6p40kOGbYsccIaYAJgiDOctTGhPliV+IJatEhICaaCd95edCcrDqBODiLQfuiL+ZoaEinuJxgx+x2TxDZOtTl9EMcmg3va3ABBU8QnZoiG6J9kl2zJYeN1z6pap40j0duIKzjYTpSG/iWxIApr/nq6gXvckXt2qDxXElxzLBjjxHaABMEQZzlqI0J88WuxBPUokNATDQTvvPyoDlZdQKuPAvltlW+mKOyMp3icoIds9k8QWQn0XWmElxZVnhfgwsoeILo1BTZ4Gz57JpDZ9h4bfmq5knzeOQGwjoepiO1gW9JDJjymu/tTfcuV9SuDRrPlRTHDDv2GCENMGEoSGdqXGhtjIt3bfLyagOS+WshoRrgtjbg8GEgP5/pDYCIAT+qdJhhNK/BNqTTozBPjbPrrEt+DbAjDV2HzwD5+ahr5kLj3aBhfEFaWG/QGFdXAh4xaoAlB3hnJztfVK+rBjhsoQqlQDu1lSZ8jidAA5xA4emRIxJaW0+huXktLrggMRrgeN1X3Z40wJpI+Qb4/vvvx913343h4WE0NDTg3nvvxSWXXBL2+v/+7//Gj3/8Y3R3d6OgoABXXnkl/vM//xOcyj9FaANsbGiTZVxobYyLJElobe3F8ePVcDrNxtLntbUBd94JdHQAGRnABz8IfOQjYcWWqnSGKsWIWjTMAnjFIhZRJ1OpwMdBa0CfpaUaPzsJFFuGmG5ygD/4tLpiGwn2TZ3D+s7F009LOHFCxPr1HK66yqz7UOJ1f6XpbrWwogthPP744/jKV76Cb37zmzhy5AguueQSvP/970d/f7/i9a+++ip27tyJT3/602hvb8cf/vAHHDx4EJ/5zGeS7DlBEISxGB83G1OfZ7cDp06xr73dbvY+gthSlc5QpRhRi4Y54FBQEYuIkxlkS7RPxr8OCRRbhpi2T7If1BTbSLBvye4vWAOciKHE6/5K090aiZRugHfv3o1Pf/rT+MxnPoP6+nrce++9qKiowAMPPKB4/RtvvIGqqip86Utfgs1mw7vf/W58/vOfx5tvvplkzwmCIIxFYaFkTH2ezQasXcs2VyYTex9BbKlKZ6hSjKhFwxxwKKiIRcTJDLLF2fLjX4cEii1DTNvy2Q9qim0k2Ldk9xesAU7EUOJ1f6Xpbo1EyiQQCwsLyM7Oxh/+8Ad8+MMf9h3/8pe/jKNHj+LAgQMhbVpbW3HppZfiT3/6E97//vfD4XDg2muvRX19Pfbs2aPYz/z8PObn533vJycnUVFRgZGREZJAGBBJktDT04Oamhr6mt1g0NoYF+/a5ObWRNUA66Hh02zDowEWlqwQz3kHK6jgDCr4oNK+T0aaN4rG1acDLlKld/UeCy44EXytI4xeVclgkLY1+BL5Z6e93Qz7ESds+SIaL8qNb6FiXEwlDbDQNQFxzASu2A0UFYVqjuOqvhCn30oFVMJIlrX2NTQk4ejRAZx/foWiPlsPkqYBTrFNvXE6nSgtLV15eYBHR0chSRJWr14dcHz16tUQBEGxTXNzM/77v/8b1113Hebm5uByuXD11Vfj5z//edh+7rrrLtxxxx0hx3t7exMyoUR8LC0tYWxsDD09PUhLoyQlRoLWxrh41wboQXp6GqamgKmp0OtGRsw4cCAHExMWFBS4sHXrGZSWSpr6islGVhZG1r8HBw7k4NQRM4aHJ8HzaSgrK8TWVWdQOtUd0iQ9HSHjOH48HY8+WoSxMQuKi9Px//5fLjaks4si+SW3FXhdaP/p6cBM7whOHzgAy8QEXB0dODM9Dam0FOaREeR4jxcU4MzWrQDgPya7Vt6nd32eeaYfv/1lLiZ6J1GyagKffe9LWHf9RkilpcrzpjQJHpR8CWsniumRETMOtBZiYsKCtLQlAMDSkuibRx6Ccl/hbrQIxOy3x+GR3hl2H51Kx/CwGTzvQlnZouJ9qKavpaUlFBePYXp6Ht3difu9FmEpk9Jejh6/B5LB5ORkQu2nvBCGyWQKeO92u0OOeeno6MCXvvQl3H777bjiiiswPDyMr33ta7jpppvw0EMPKba57bbbsGvXLt977xPg6upqegJsQOgpo3GhtTEuatdmYYHVLLj4YuD4caCgAKit1dZXrDa87TZsYA9L6+uBmRltPnR0AHNzwLvfzZJKLC3l+9qq9UvVdeEuUjoORDXoXZ/Tp9dh9swM3lX+Ng5PVGNyqh/VsSyClgFrNOX98nXrVr/Zaszq1le8fmu6j1T0dTb+XtPx1kkoTqczofZTtgEuKSmB2WwOedrrcDhCngp7ueuuu3DxxRfja1/7GgCgsbEROTk5uOSSS/D9738fa9asCWmTkZGBjIyMkONms/msudmXG2lpabQ+BoXWxrioWRurFSgtBU6cYP9arewrZC3EasPbrq8PWL0aGBlhyQa0+FBTw9oeOcL+ranxt1Xrl6rrwl2kdBxQ1XFaWhqqq9Oweo0FRzorsDp9BDXVaTDHsghaBqzRVEUFOxZgFvr1Fa/fmu4jlX2dbb/XdLx1Ekqi1yNlG+D09HRs2bIFzz//fIAG+Pnnn8c111yj2GZmZgYWS6DL3gk6y9IZEwRBaIbnWRqleLR/sdqQt1Ol3VSgsZFV0FWS5qr1S9V14S4Kd1zlhDQ2Aru+lQP74QXY8hfQ2NwcuwBTj8UMYwoINqtfX/H6rek+0nGOVhI0LYyUSiB27dqFT37yk7jwwgtx0UUX4Ze//CX6+/tx0003AWDyhaGhITz22GMAgKuuugqf/exn8cADD/gkEF/5ylfwjne8A2vXrk3lUAiCIAxDpHoDPO+vLdDeHtt/gF4bwUSrXxCunRq8tqzWoDHJOuE9xr2poiJtglUFTTU0RG+sMZqosRFobCwCoCzB02QungmNYio0iJAH36DTTilOvzU113GOAAMEj+nkgM7TsixJ6Qb4uuuugyiK+O53v4vh4WFs2rQJzz77LNatWwcAGB4eDsgJfMMNN2Bqagq/+MUv8NWvfhWFhYXYtm0bfvSjH6VqCARBEIairQ3YvZtlHbNa2RPT4E1wIpLrB9tsagpb6yJu2+EKWigVodDUp9aJ0XkijVb0wGj+pJqUz0fKHVhZpDyU++abb0ZfXx/m5+dx6NAhvOc97/Gde+SRR/DSSy8FXP+v//qvaG9vx8zMDE6dOoXf/va3KCsrS7LXBEEQxsRuZ5vfLVvYv3Z76DWJSK4fbDNCrYu4bYcraBF3EQqtE6PzRBqt6IHR/Ek1KZ+PlDuwskj5BpggCILQD5uNPfk9dIj9a7OFXpOI5PrBNiPUuojbdriCFnEXodA6MTpPpNGKHhjNn1ST8vlIuQMri5SnQSMIgjAiKdf6xYg3UOzwYSA/35+oQE5wEIzDAfztb/7Ncyz1F5QCa9TYCmc7OMBJMWgn6ATPW9ESQ58BHdtsQEkJYLNBAA+xPSgwTHKAN4+EdSye+yaW4KRwRT30YKUFS8X7mU75fKTcgZUFbYAJgiCCWO5SO6uVbSB7egCnU9l/bxCMXDOcl8e+Xc3ODj/uSHOjFEilZd68tvv6gIEBlpKrqsrfR9hIf9mJmIJ7FDoW3j6DfaiE6CqEN/mQyzkJbqALLRVt4KsyQxzT477R4r8gAPv2jkNs7QUHES3NTvA7tum+CV5O93449PpMp3w+Uu7AyoEkEARBEEEsd6mdFv/lmuFTp/zFBcK1S+TceO3xPPOJ55M0/wodi4OzEIfmUF8PDA4CQ0NAPe+E6HBB5DcqOpbs+0YUwXzMHYCYWwlxcHb53axJYrl/pgn9oQ0wQRBEEMtdaqfFf7lmeO1a9j5Su0TOjdeeIDCfBCFJ86/QMVeeBa4sE52dQHk5UFYGdApF4KwWcEKHomPJvm84DszH6Qpw0/3gyrOW382aJJb7Z5rQH5JAEARBBLHcpXZa/A8uLuHV7UqScj7dSLbDaSzVai+DixwoVUKNV8cpCEBXF/u5rs5jQ6m6glSKGmchajzXAYAo5oOT6sCbixUdSMR9I7Q5INonwdnywTcGCrp5HmjZUQhxSzU4FOiuAV5J8BDQUjMBsYYDV1dC00TQBpggCEKJ5S610+I/K84QeCySXlLJdjiNpVbtpbxQhzePcE8PaxfNr2gIArB3L9Dayt43NwM7dsg2wWG0vN6NMjtt9bwi+68HQpsD+3Z3QXS4wFktaNkFxU0wz5cAKNGn05WIZ0F5UQTPcUBdC4Bl/OEmdIEkEARBEEQAseglw7WJVXup1C5eHacoMi1vbi57DQ4mX+esBdE+CdHhQv2WHKY9tk+mxpHljlEWlDAUtAEmCIIgAohFLxmuTazaS6V28eo4OY5peaen2au8PPk6Zy1wtnxwVgs6D51h2mNbfmocWe4YZUEJQ0ESCIIgCCKAWLSs4drEqosN1y4ejS3PM8nDli3svU8DrLLvZMM3WtGyC2E1wIRKjLKghKGgDTBBEMQKQRCA8XF9/o+PpGUNV6wiXJvg42oC2cJdI9cItwcXqVAxbh4C+BLR37BduaFeWl5BYJnVenvTsbDAggwD7EaZDL7Rqnnja9QiLnK/gCT7uNxF/cnAqDdOgqANMEEQxApgZMSMV15hmRMSWbwjWrEKte0jBbJFu0Z+3lekwqVi3DE3jA1vd729QEdHERoaWKYNX1cJqLhi1CIuSZ56QitGvXESCGmACYIgVgDj4+akxPnEW6xCTTxStGvk531FKtSMW95waIg1TuCEec2uXg2MjVl8KeZ8XSUgOMuo8V4xrxmRHIx64yQQegJMEASxAigslJIS5+O13dfnL1ZRVRVfcJvWa+Tny8vZMVXjljcsK9PQMDa8Znt7geJiFxwO9gTY11UCgrOMGu8V85oRycGoN04CMbndbneqnUgmk5OTKCgowNjYGIqKilLtDhGEJEno7u5GbW0tzGZzqt0hZJyta7McZHGSJKG1tReLi9Uwm81hg7v0QF5IoqgoUAOsxUZMGmCZ+FhwZkAEK2oAaFijJAtRmQZYQm/vSVRXr4PVatakAY61TyPesynVAIfhbP29pojBbhyn04ni4mJMTEwgP1//DCj0BJggCCIMy0UWJwjAgQM5sFiA0lJ/5bJE9KPHfKiJRwq5Jkh8zFdUgK+qYkUNeF69H8GGE7ygPM/WJD19AbW17A+GiP7o1KcR79MkTz2hFaPeOAmCNMAEQRBhWC6yOFEEJiYs2LAhOfrflMxHvOJjgiAIGbQBJgiCCMNykcVxHFBQ4MLx48nR/6ZkPrwdCoJffGzkRSEIwtCQBIIgCCIMyyl/fmXlIsrLgY0bI/sZj8wveD4A4MAB9m9CdMfBCYebmoB3vSs0AbFGc/Jmsc6HweSSBEFohDbABEEQETC6LE4QgGefBU6cyML8PNsAR7o2Xg2vvBDF3r1Aays73tzMqqzpNlfxJhwOY04+diC2+Vgu2nCCIMJDEgiCIIhljFcGW129EFUSq6eGVxRZPtfcXPYaGtJZjquz5ldp7LHOx3LRhhMEER7aABMEQSxj/Llm06NKYvXU8HIcy+c6Pc1eZWU6y3F11vwqjT3W+Vgu2nCCIMJDEgiCIIhlDM8DH/gAwPPT2LyZi/hVvJ6aZp5nkoctW9h73TXAcmdj1PyGMyc3E8t8LCdtOEEQytAGmCAIYpnD80Bt7YKqjVg4TXNwUJeaogWJ1Eez/nlwnIYcv1Ht6TcGo2vDCYKIDG2ACYIgznKCg7qamoCDB9l7i+d/CZcreQFfegeZUdAaQRDBkAaYIAjiLCc4qMtu978fHGQBbskM+NI7yIyC1giCCIaeABMEQZzlBAd12WzA6Ch7X17OrklmwJfeQWYUtEYQRDC0ASYIgkgBaosyKGlzu7rYOXng2ciIGQsLLGECEN6OJAFOZ2B7paAuqzVUAyxJrO+uLnVBb7EWi9A7yCzEHgSgXYQglUI0WwP7iMFpTU0MVkHDYO4QRNKgDTBBEESSUVuUIfhYUxPwwguhxSckCThwIAcWC5Cezs7JNbteO319wIkTwJkzQGZmYPGK4KAupSAvLYUv4tXd6h1k5rPncUzom8O+gUaIFZngqvKZf9DutKZxCgLwl78YRoxM2mjibEazBnhgYACDg4O+93//+9/xla98Bb/85S91dYwgCGKlorYog5I2V6n4hCgCExMWbNigrNmV15Q4dYoFtmktXiGK7Hpv34ODySu6oSseZ0R+I0SHC/W80+9fDE5ramKwSTGYOwSRVDRvgHfs2IEXX3wRACAIAt73vvfh73//O/793/8d3/3ud3V3kCAIYqWhtiiDkjZXqfgExwEFBS4cP87Ol5Up2xEEYO1a9nRYa/EKjmPXe/suL09e0Q1d8TjDCR3grBZ0CkV+/2JwWlMTg02KwdwhiKRicrvdbi0NioqK8MYbb6Curg4/+9nP8Pjjj+O1117Dc889h5tuugm9vb2J8lUXJicnUVBQgLGxMRQVFaXaHSIISZLQ3d2N2tpamM3mVLtDyKC1iYxaLaVcixtc20GN3lfpmCRJaG3tRUFBNaxWtjZaNMBqxyZvW1QUvTaFFn1pUrWons6SpQEO+OyMjBhKdJuIeV9OumL6vWZcnE4niouLMTExgfz8fN3ta9YALy4uIiMjAwDwwgsv4OqrrwYAbNiwAcPDw/p6RxAEsQxQq6WMdp1c9xp8bV1d6DVySksl1NayTan3OjmxamrV6pWVbKvtM+laVI9jPICQbmKYKE1NDFZBQ293SFdMLBc0SyAaGhqwZ88evPLKK3j++edx5ZVXAgBOnToFjr4/IQjiLEStllKL5tIo+ky1emW9+yCWJ7SWxHJB8wb4Rz/6ER588EFs3boVH//4x3HeeecBAJ566im84x3v0N1BgiAIo6NWS6lFc2kUfaZavbLefRDLE1pLYrmgWQLx3ve+F6Ojo5icnAzQ0H7uc59Ddna2rs4RBEEsB9TmrdWS31bvXLixEs6PhObppa/Mly20lsRyQfUGeGlpCT/5yU/w5z//GYuLi9i+fTtuv/12ZGZmAgCqqqoS5SNBEIThUaulDNb5Rtoo6KHPjNRHuHPBxzX7IQgQuiYgggNXV6I8XgR2okUvnMjNVbhCI/HYcziAiQkzamvj989oKK2HwWTOBKGI6g3wj370I3zrW9/CZZddhqysLOzevRujo6OU/5cgCCIGkhEsFKmPcOfU+BXxGkGAsHc/9rUWQcQEuGYLWnYUApC1sYyjBfvBuwY1DT7RcyYI2op9qPV3ZARwuXJQXc1Sya0UKOCNWM6o1gA/8sgj+PnPf47nnnsO/9//9//hz3/+Mx577DFozKJGEARBIDnBQpH6CHdOjV8RrxFFiIOzEHMrUZ87AHFoLjRwbmgO4uCs5sEnes60FvtQY08UgQ0bWKGSlRYQRgFvxHJG9Qb45MmT+OAHP+h7f8UVV8DtduPUqVMJcYwgCGIlk4xgoUh9hDunxq+I13AcuPIscNP96JyuAFeWGRo4V5YJrjxL8+ATPWdai32oscdxwPHjrFDJSgsIo4A3YjmjWgKxsLCArKws33uTyYT09HTMz88nxDGCIM5ellMi/ViJNVgomqZ3fDxQuxuuj3DnfMe7RsFBBI8CBGfLjeg7z4PfsQ0tW7wa4EIA7NqmJm/xjELw2KZp8N5x+23of2/wPJM8bNnC3odogDUKhL3zxDTAZ1bcvUwBb8RyRlMWiG9/+9sBmR4WFhZw5513oqCgwHds9+7d+nlHEMRZx9mkK9QaLBRpbkZGzHjlFVapTX4uUh/hzvEQwPd4OupRXoSIvvM8eE+hifA+qx98Mu+JsOOKUSDM80BpKdDdLenvrAGggDdiuaJ6A/ye97wHXd6/fD00NzcbvvQxQRDLC7musLOT/Uz/wTIizc34uBmiCDQ06DBvOi6CHqYMcU+IIhMF5+ay90NDdHMSxDJG9Qb4pZdeSqAbBEEQDNIVhifS3BQWSvrNm46LoIcpQ9wTHMdEwf397H1jI92cBLGM0VwIIxz/+Mc/8NBDD+Hee+/VyyRBEGchwbpCAGhvT4zG0Mha43D5VcNpLktLJVRXB2qAY++TBx/UUVsbYLcDNhvb+6nxV8lnADhwgP1bVKROz8vzQEuTA6J9EpwtHzxv1T64eIkqEE4uRr53CWI5ENcGeHJyEr/73e/w0EMP4c0330Sj0m9FgiAIjXh1hYnUfhpZaxzJt2ia3ljzzIb2yYNvYB21tQG7d7NgLqsV2LUrcBMcbS7l6+mV0c7NAdnZbB9ZVRVl/gUB/MF94EURGOUAa4oWyyCCVyPfuwSxXFCdBk3OgQMHsHPnTqxZswY333wztm3bhhMnTuDo0aM6u0cQxNlMIvOMGjmHaSp8i9Sn3c42v1u2sH/t9tj8lctoLRZgeJht3KKO0ciLlQJoOggiflRvgIeHh/GDH/wANTU1uP7661FSUoIDBw4gLS0NO3fuRE1NTSL9JAjiLCSR2k9D6ErDkArfIvVps7Env4cOsX9tttj89cpop6cBlwtYs4Y9zYw6RiMvVgqg6SCI+FEtgbDZbPjYxz6G++67D+973/uQlhbTw2OCIAjVJDLPqJFzmKbCt0h9NjYy2UM4DbBaf4NltGo1wIZerBRA00EQ8aN6A7xu3Tq8+uqrqKysxLp167Bhw4ZE+kUQBAEguuwynmCgWCSd0frTKzgpFXLT4D7lY2lsVA5+C9dWax/ec3F3EAHfeCQHePMIIEmJq6oRJ9HuI4PIkQli2aJ6A9zV1YXXXnsNDz30EJqamrB+/Xp84hOfAMCqwhEEQSSbZAcDRetvJQUnJWMsyZwvX199k+AGutBS1Are2QlUVKiIwksuK+k+IgijoknHcPHFF+Phhx/G8PAwbrrpJjzxxBOQJAk333wzfvWrX2FkZCRRfhIEQYSQ7GCgaP2tpOCkZIwlmfPl64t3QnS4IOZUsog+VVF4yWUl3UcEYVRiEvLm5ubis5/9LF5//XW0t7djy5Yt+Na3voW1a9fq7R9BEERYkh0MFK2/lRSclIyxJHO+fH0JReCsFnBn+llEn6oovOSyku4jgjAqJrfb7dbD0OLiIp5++ml85CMf0cNcwpicnERBQQHGxsZQVFSUaneIICRJQnd3N2pra2E2m1PtDiHDqGuT7IIAWjTAgDrf4h2DlrXR0leiro25XZwTlSoNcMT1CTOmZMw9YdzfawTgdDpRXFyMiYkJ5Ofn625ft0pwq1atMvzmlyCIlUeyg4Gi9ae1iEdKdLAq+1I7t/GMQfX66TBR/r6snleKiTCmZMw9QZzNqJZApKWlwWw2R3xZLLrtpwmCIJY1WopDJF0Hq3NfSRnDShTG6jCmlTgtBJEMVO9Y//SnP4U919raip///OfQSU1BEASx7NFSHCLpOlid+0rKGFaiMFaHMa3EaSGIZKB6A3zNNdeEHDt+/Dhuu+02PP300/jnf/5nfO9739PVOYIgiEikQvuots9IxQqCbQRfp1X/6XAAExNm1NZGvjacT+H6Cz4e7jotY41lLqN2ooV4xMpdXeznujp9bjgdxkRFMQgiNmLSLJw6dQr/8R//gUcffRRXXHEFjh49ik2bNuntG0EQRFhSoX3UQ0MbzoZ8M6q2D++1IyOAy5WD6mqgrCzyGJQKUSj1F3y8qQk4eDC8X1rGGstchu1EC7HeNIIA7N0LtLay983NrKSdXpvgOO1QUQyC0I6mNGgTExP4xje+gZqaGrS3t+Ovf/0rnn76adr8EgSRdFKhfdSjTz1zCXvPb9gATExYdPUn+Ljdrn3skcaSEu1qrJ2KIjA4COTmstfQEIltCWKZo3oD/OMf/xjV1dV45pln8Lvf/Q6tra245JJLEukbQRBEWFKhfdSjTz1zCXvPHz8OFBS4dPUn+LjNpn3skcaSEu1qrJ1yHFBeDkxPs1dZGYltCWKZozoPcFpaGrKysrB9+/aIufKefPJJ3ZxLBJQH2NhQTkbjYsS1MbIGOB4b2jXAEiYmetHcXB3T2sSrAY7FdrRzCSMFGmAjfnYIBq2NcTFMHuCdO3fCZDLp7gBBEIQWgvcvkfSxiSgkEFVvGcGY/FRDQxx9BF1bWgp0d0sQBGB8XPveLlx/wce9P3d1sVddHXsfMNy2NqaXsNmAxsaAvMjt7YG+KfYbNH+x/LEQsU2sgtlY2nkdKSzU3h9BEAlF9Qb4kUceSaAbBEEQ0UlEcQldg+kiGEt00N7IiBmvvAI4nYkLCgyOBdu0CcjPB1wuT5+2DvCP7WZpKaxWYNcuoLFR/diDLhSarsK+g9aw7ZTsAgYpDCF3rqgI5g0bEDVNB0EQSUO1BliSJLS1tWF2djbk3MzMDNra2rC0tKSrcwRBEHLUxjDFEkimSzBWBGOJDvoaHzcnPKgsOBbMbmfvfX22C2zzu2UL+9du97VT5VvQhaJ9UnPAYEqC65QIcsQ8Pp4iRwiCUEL1Bvg3v/kNbrzxRqSnp4ecy8jIwI033oi9e/fq6hxBEIQctTFMsQSS6RKMFcFYooO+CgulhAeVBceC2Wzsva/PBp49+T10iP1rs/naqfIt6ELOlq85YDAlwXVKBDkikQyCIAyF6iC4Sy65BP/yL/+C66+/XvH8E088gV/84hd4+eWXdXVQbygIzthQQIJxMcraqNXrqrnOe40kAWazumvDBYr5bEgO8OaRqBpgPb+WlyQJra29WFxkQXAx12lQ4WBwLBggawIBwr5DEIVFcBetB79toyrTAeegkwa4axQcRPB1BckvmhHUXiosRPfUlOJnR7GLlEQHnp0Y5fcaEYphguC6urrwrne9K+z5pqYmdHZ26uIUQRBEONTGIkW7Tg+dsPd4Xx8wMABUVABVVVa0tFhVBZXphSAABw7kwGJhAXHejalmIyomRGkMPM/aC3v3Y19rEUSsBZe1Fi0bowS8KXbLg2/gA2xrLTbCQwDf4zHaE6MQWA/Rttc5SQKmptR1gQSLxQmCAKBBAnHmzBlMTk6GPT81NYWZmRldnCIIgkg0euiEvT/zPJO88nxqdKeiyAphbNgQR//ximdFEeLgLMTcStTnDkAcmlNlIiGaXT2MJkFMrNiFYUTMBLGyUb0Brq2tRas39FeBV199FbUU4UoQxDJBD52w92dBYJJXQUiN7pTjWCGM48fj6D9e8SzHgSvPAjfdj87pCnBlmapMJESzq4fRJIiJFbswjIiZIFY2qiUQO3bswLe+9S00NzejsbEx4Nxbb72F22+/HV//+td1d5AgCGOSbJmi3v3xPPt22WsTCM1TK++3qcmvE5Zf67URTkest99K9nge2Lr1DAoK2EY8oB+1DgRPSAxf9/M7tqFlywREcODqClVLVeLpVpVRQHlxVdgQujzjQQn0vs2Vx56ICSEIIhjVG+BbbrkF//u//4stW7Zg+/bt2LBhA0wmEzo7O/HCCy/g4osvxi233JJIXwmCMAiJzmmbrP7kRRoiaXzlx4HQY+GKWujtdyR7paUSamvZJjxmB+IVKfM8eJ7XvFFMiDY62uKqQACPfT08a9qTmPtcceyJEosTBOFDtQRi1apVeO6553DnnXdieHgYv/zlL7Fnzx4MDw/jzjvvxHPPPYdVq1Yl0leCIAxCsmWKie4vmsY31jyzevut2R7pSeOaA5o+gli5qN4AA2wT/PWvfx1Hjx7FmTNnMDMzg6NHj+LrX/+6Yn5gNdx///2w2WzIzMzEli1b8Morr0S8fn5+Ht/85jexbt06ZGRk4JxzzsHDDz8cU98EQcRGsmWKie4vmsY31jyzevut2R7pSeOaA5o+gli5qJZAJILHH38cX/nKV3D//ffj4osvxoMPPoj3v//96OjoQGVlpWKba6+9FqdPn8ZDDz2EmpoaOBwOuFyuJHtOEGc3CdFtJrm/YGmskv1wxyP5osZupOvVzoMk+Z9IlpaqaBCkhRXAJ09mqlUIHUvy33DEcfMk+z4nCCJ5qC6EkQje+c53YvPmzXjggQd8x+rr6/GhD30Id911V8j1f/nLX3D99dejt7cXxcXFMfVJhTCMDSUlNy4raW0SpSnWajdWP4LbXXmlhKmpKGsjayRYyrEPH4DoKky8hlvvSUm2AF0HVtJnZ6VBa2NcDFMIQ28WFhZw6NAh/Nu//VvA8csvvzxsurWnnnoKF154IX784x/jN7/5DXJycnD11Vfje9/7HrKyshTbzM/PY35+3vfem8tYkiRIkqTTaAi9kCQJS0tLtDYGZCWtjcMBjIwAGzYAx4+z92GfoibQbqx+BLcbGVlCenqUtZE1chwYxQhmsGFrnq7jV+VsvJOSqMVLICvps7PSoLUxLolek5RtgEdHRyFJElavXh1wfPXq1RAEQbFNb28vXn31VWRmZuJPf/oTRkdHcfPNN2NsbCysDviuu+7CHXfcoWgrEX9REPGxtLSEsbEx9PT0IC1Nk0SdSDAraW0mJsxwuXLw2msWFBS4MDFxBt3d8f+y1Wo3Vj9C203BbI68NuaJCeS4XLC89hqW0tdiCdO6jz9av66CApyZmIDU3R3z9VrtGYGV9NlZadDaGJdIxdf0IGUSiFOnTqGsrAytra246KKLfMfvvPNO/OY3v8Hx48dD2lx++eV45ZVXIAgCCgoKAABPPvkkPvrRj+LMmTOKT4GVngBXVFRgZGSEJBAGRJIk9PT0oKamhr6OMhgrbW0SlcdYb7mrmnalpSrXRtborNEAG4CV9tlZSdDaGBen04nS0lLjSCB27dqleNxkMiEzMxM1NTW45ppromp0S0pKYDabQ572OhyOkKfCXtasWYOysjLf5hdgmmG3243BwUHFSnQZGRnIyMgIOW42m+lmNyhpaWm0PjqQiD2CEdcm1nGWlbFXPDaUfBkfVyhGodIPLcjbSZLKtZE1KoOKfvWaGK2DjHZ9rJOWQmL97Cyzvf6yxIi/1wgkfD00b4CPHDmCw4cPQ5Ik1NXVwe12o7u7G2azGRs2bMD999+Pr371q3j11VexcePGsHbS09OxZcsWPP/88/jwhz/sO/7888/jmmuuUWxz8cUX4w9/+AOmp6eRm5sLADhx4gTS0tJQXl6udSgEsWJZhnFCMaHHOPWaqxU35ytuQMsPWgKCSByaBS/XXHMNtm/fjlOnTuHQoUM4fPgwhoaG8L73vQ8f//jHMTQ0hPe85z2qqsLt2rULv/71r/Hwww+js7MTt9xyC/r7+3HTTTcBAG677Tbs3LnTd/2OHTvAcRw+9alPoaOjAy+//DK+9rWv4cYbbwwbBEcQZyNnSwJ/Pcap11ytuDlfcQNaftASEETi0PwE+O6778bzzz8foMfIz8/Hd77zHVx++eX48pe/jNtvvx2XX355VFvXXXcdRFHEd7/7XQwPD2PTpk149tlnsW7dOgDA8PAw+vv7fdfn5ubi+eefx7/+67/iwgsvBMdxuPbaa/H9739f6zAIYkVztiTw12Oces3VipvzFTeg5QctAUEkDs0b4ImJCTgcjhB5w8jIiC9ir7CwEAsLC6rs3Xzzzbj55psVzz3yyCMhxzZs2IDnn39em9MEcZZhlAT+egR4RWrH80BTE2C3AzZb7OOsqWGvurrwNtT4FM6OIABdXeznSH3E2m9CUCiiIRzogggOXF2Jdl/UBrZJEmA2J27Ay0FU6/GR5zi0tCQxWJEgziI0b4CvueYa3HjjjfjJT36CpqYmmEwm/P3vf8ett96KD33oQwCAv//971i/fr3evhIEoQGeT+1/mHoVeYjUThCAgwfZtaOj2oLPlPqqq4vNp0h2BAHYuxfwpjdvbgZ27NB/LhKC9yYSBAh792NfaxFETIBrtqBlR6F6X9ROYF8fMDAAVFQAVVX6DzjlE6qCIB/5lhbwDQbzkSBWAJo1wA8++CAuu+wyXH/99Vi3bh0qKytx/fXX47LLLsOePXsAsKe0v/71r3V3liCI5UOs+kUt7eLVSKptH+26SOdFERgcBHJz2WtoKDFzkVBEEeLgLMTcStTnDkAcmtPmi9oJ5HlW2ILnEzNgw0xoBJaDjwSxAtD8BDg3Nxe/+tWvcM8996C3txdutxvnnHOOLysDAJx//vl6+kgQxDIkVv2ilnbxaiTVto92XaTzHAeUlwPecIbGxsTMRULhOHDlWeD6+9GJCnCNmdp8UTuBfX3sMb4gsCfAeg/YMBMageXgI0GsAFJWCCNVTE5OoqCgAGNjY1QIw4BQXXbjEsvaKMkt1UgwtchBtdhTuib4XDifo2l429qYFjkvD1i9Wl37qL4LAoSuiai6W/najIyYA216HbPZ2OZSpunVWpwiwBckqLiF0qLrqdtNgQZY82dnOeiUVwj0f45xcTqdKC4uNk4hDAA4ePAg/vCHP6C/vz8k2O3JJ5/UxTGCIJY/wTpktRJM7zG110baI0TrU95e6dpgP5R0wl4tcjgJq5KPUefCcwHvCYZCXQuAyJshQQD+8heZTVsH+Md2M1lBXh77Wj07G7B4fvW7XOq1sDwPnueZB7FoaaMtVLjzeut2Uy2OV8Ny8JEgljmaNcC///3vcfHFF6OjowN/+tOfsLi4iI6ODuzfvz+gQhtBEEQwydT36tWnmvaxSFij2o1hAkKatAvMoS1bgFOn2JPg+nomRB4cTLx4Wg9IE0sQRALQvAH+wQ9+gHvuuQfPPPMM0tPT8dOf/hSdnZ249tprUVlZmQgfCYJYISRT36tXn2rae48Lgl/CGvf4YpiAkCYNPHPo0CFg7Vomg+jsZGWEy8sTL57WA9LEEgSRADRrgHNyctDe3o6qqiqUlJTgxRdfxLnnnovOzk5s27YNw8PDifJVF0gDbGxIj2Vc9FobLfJGvaSQ8fapt25ZtV8qHU+KBjhG33RhmWti6feacaG1MS6G0wAXFxdjamoKAFBWVoZjx47h3HPPxfj4OGZmZnR3kCCI1OLdexQW6mNPi7zRe533W+9Y9z5a+wzWLauJ3eI4oKFBXR+q2yg4Hs2fkCaNjewlv0DpZ62bTDXia503rYIjbdns2QmCMDaaN8CXXHIJnn/+eZx77rm49tpr8eUvfxn79+/H888/j8suuywRPhIEkSLk8UdFRcCGDWbU1qamfz3in/TuPxb/4hlTwuZDb8N62vPYEvrmsG+gEWJFJriqfM0mU30vEQRhLDRrgH/xi1/g+uuvBwDcdtttuPXWW3H69Gl85CMfwUMPPaS7gwRBpI7g+KPx8eR+RZjq+Ce19Ru0+BfPmBI2H3ob1tOep73Ib4TocKGedxo+bo8gCOMTkwTCS1paGr7+9a/j61//uq5OEQRhDILjjwoLpZT2n+z4J7X1G7T4F8+YEjYfehvW056nPdfXAc7aiE6hCFyVseP2CIIwPpo3wGazGcPDw7BarQHHRVGE1WqFJCX3P0iCIPzorXHkefZVsVcDPDWV3M+3vP/gMcUS2BYQoKaikEOk/pXOA0B7u8K1Mmd5no9oM9b5iBmvb01N6qP35O2UFqari2lmamr8iZMVJ0YFnkHzoogWqRSiOT8eM6QBJggCQAwb4HBJI+bn55Genh63QwRBxEaiNI7eWCdJAjzxr0klpiISCtcGFKkoGkcL9oN3DUY1oLZ+Q1ifFE7wPJ+UgL6oxHrThGsnCMDevUBrK7uuuZlthA8ejO/G9AyaR7RSIKrMEARBqN8A/+xnPwMAmEwm/PrXv0Zubq7vnCRJePnll7Fhwwb9PSQIQhVyjWNnp78ww0pEy1jlRSoOHWIPOsXeOYjuWfCX6jdZYX0y8sLE6lu4dqLIimx4/38YHGQp2Iw6foIgzlpUb4DvueceAOwJ8J49ewLy5aWnp6Oqqgp79uzR30OCIFRxNmkctYzVe76vz1+koqosExyydJ2ssD4ZeWFi9S1cO45jRTZOnmTvGxtZ/uHRUWOOnyCIsxbVG2C73Q4AuPTSS/Hkk09SEQmCMBjxahzj0Q9ryZWrV0ELtWP1zktXl//6urpC8NgW1YCWAhhhfdJbfCoIELomIIIDV1cS3pwa52P0TQAPseYacDUi+LoCfzueB3bsYKWXAaCujuXuNTeCq1kE31zN2rYDnOQAbx4xpiA3eO7UVjmhRMMEsWzQrAF+8cUXfT+/9tpruPDCC5GRkaGrUwRBxEasGsdE5qbVS5usZEdt4QkA6OlhbZ1OT1xWlMlS47dqn/QSnwoChL37sa+1CCImwDVb0LKjEKWlMTgfo29+0yXguBK01AXpcmX2hDYH9u3uguhwgbNa0FRowUE7IPZNghvoQktFG/iqTGMl5Q2eu6YmpmEOEJFXJe5GJwgiKWjOAyzn/e9/P4aGhvTyhSCIFJHI3LR65V9Ndv5cNW2SnltWFCEOzkLMrUR97gDEoTnlPhPomBbTon2S5e7dkgPR4YK9fYa15Z0QHS6I/EbjJeUNHqBXw8zzgMPh1zon6kYnCCIpxLUBDpcRgiCI5UUic9PqJYFNdv5cNW2SLu/lOHDlWeCm+9E5XQGuLFO5zwQ6pkl/bcsHZ7Wg89AZcFYLbA3ZrK1QBM5qASd0GE8XHDxAm439Kwh+EXkib3SCIJKCyR3HLjYvLw9vvfUWqqur9fQpoUxOTqKgoABjY2OkYzYgkiShu7sbtbW1AYGWROKJJl+MtDap0AAnQ+OsVQOclG+8FTTAimuTQMc05WBuc0C0T4Kz5YNvtPrbnkUaYPq9ZlxobYyL0+lEcXExJiYmkJ+fr7t9zRpgOQ8++CBWr16tly8EQaSQeGSqanPlxouinaANIcAC3gCm9ZVfPzrKXl5bsfQXvMeJeWzeghFKjkbpkAfAi6cBuBA2M26MjqmNnVNrmrcugTfPA9xSUFur56WDQ4lG7YAp0TBBLBvi2gDv2LFDLz8IgiC0ExQUZtm0CpMowLFj7HRzM0tKAITWZ9ixI7anyLrEOSkVjFBySKlDIPRYSBRcbOgexxWvwVQEllEwG0GcFcSkAT5w4ACuuuoq1NTUoLa2FldffTVeeeUVvX0jCIKITFBQ2KB9EXY7q8OQm8vqMHjjkQYH/ceHhmKLUdItzkmtQ0odGiTALSkGUxFYRsFsBHFWoHkD/Nvf/hbbt29HdnY2vvSlL+GLX/wisrKycNlll2Hv3r2J8JEgCEKZoKCwctsq2GzA9DR7lZf745HKy/3Hy8pii1HSLc5JrUNKHRokwC0pBlMRWEbBbARxVqA5CK6+vh6f+9zncMsttwQc3717N371q1+hs7NTVwf1hoLgjA0FJBgXtWuT7EA1tRpgQWCKg8lJYPNmVqTMezywSEbkvmMdn7ydwwHYDzthk3rQWDOjTQOscEzT5yZKQJeq8WmKgotzwiIFnmm0rfpynXXH9HvNuNDaGBfDBcH19vbiqquuCjl+9dVX49///d91cYogiOVJIgtqhIXnwXsCw2SHFHE6mf2DB1lGK4BJcffvB4aHgTVrgG3bIuuDY4lzko9tZgbo6ACmp4tgtTZhVxPQGMmeUofxVjyJUNQhqmmtCxXvhIXrQ6Mfmi6nYDaCWPFolkBUVFTgr3/9a8jxv/71r6ioqNDFKYIglifJLlYRr29eKa7ZDJhMgMUSuz5Ybd92O9tsb9nieRJs17cvVY5EK+qgxkYiNbJq+tDoB0l7CYKQo/kJ8Fe/+lV86UtfwtGjR9Hc3AyTyYRXX30VjzzyCH76058mwkeCIJYJyS5WoYdv5eXAiROA2w24XLHrg9X2bbMBs7PAoUPsKbTNpm9fqhzp6/MXdaiqMtZCqe1Dox8k7SUIQo7mDfAXvvAF8DyPn/zkJ3jiiScAMF3w448/jmuuuUZ3BwmCSBx6p1jlefbVsle66X3K5n3SmZcHrF6t3J+3rVe/q7ffct+8mx9RBM4/HyguZv7W1ETXAMdCcN/e+bDZ2D60vT1JaW6DF0iurVU7qTwPNDX5B5AIp4MnjOdDCmooXaPJJASgPcX5hQmCSBmaNsAulwt33nknbrzxRrz66quJ8okgiCSQqHSnXhvBmldRZE8+3/lOYNOm8P319LBre3p0kX6G+Obd6ylJYROx+Q3u2/tzY2OKUs6Gq+6h1hFBYCJqUWRVRazWxG2CPXaFNgf27e6C6HCBs1rQsgv+TbCGvn2XU65fgjjr0aQBtlgsuPvuuyFJUqL8IQgiSSRSE6mkea2tBcbGgJyc+GSdevithxRWDwyjS9XiSAqcFu2TEB0u1G/JgehwQbRPxmnQKBNPEESq0BwEt337drz00ksJcIUgiGSSSE1ksOZ1zRqgu5tJDc6ciU/WqYff3naC4JfCpkIXahhdqhZHUuA0Z8sHZ7Wg89AZcFYLOFucKZEMM/EEQaQKzRrg97///bjttttw7NgxbNmyBTk5OQHnr776at2cIwgicWiUUMZlW40GWK1PevgdSQqbTBK5BglzJAVO841WtOxCoAY4LoNGmXiCIFKF5kIYaWnhHxqbTCbDyyOoEIaxoaTkDK1BXnoHsykRvDZ69xnNXvB5tf3H66e8PaDBlg4TpNZEwNqMjMTocJhO9ZzARC5UckzG1AH9XjMutDbGxXCFMJaWlnR3giAIP1rjc1IRz6N3n9HsBZ9vavLHYUXqP14/5e0tnt+WLpcKWzpMUEwmBAH4y19icDhMp2onOt5BJOAmTvjnggLpCGJZo1kDLGdubk4vPwiC8KA1PicV8Tx69xnNXvB5u11d//H6KW8/NMSKZqiypcMExWQiZofDdKp2ouMdRAJu4oR/LiiQjiCWNZo3wJIk4Xvf+x7KysqQm5uL3t5eAMC3v/1tPPTQQ7o7SBBnG1rjc1IRz6N3n9HsBZ+32dT1H6+f8vZlZaxohipbOkxQTCZidjhMp2onOt5BJOAmTvjnggLpCGJZo1kD/N3vfhePPvoovvvd7+Kzn/0sjh07hurqajzxxBO455578PrrryfKV10gDbCxIT0WYyVrgJXaCYK/AIZSLl6l87FIS4EwgW8RjIXTAMt/VuzfSBpg+YABoLUVmJwENm9mCYkjdRrOCbWibW/f0aIN5dc7neyYlsTMwf1xHATwpAEmIkJrY1wMpwF+7LHH8Mtf/hKXXXYZbrrpJt/xxsZGHD9+XFfnCOJsRWN+f83X60EsfSrJJoHAY3V1kdt4z6vtP1rxi5YmB/iD4bWcwf3IbUWUf+qwKDGZUGokFzKfOgW89BIwPw9s3Ah885uBm2ClAWstnBGLltj7fu9etkEHgOZmYMcO9drloMXlW1rANyTwg5GKDx5BELqgWQIxNDSEmpqakONLS0tYXFzUxSmCIFYmSrJJrfrfWKWW3rYhxS/sk5o7WFbyT7mzg4MsIbPbzRIgnzrFdL7x2NRbtD00BOTmstfgoDbtcqormxAEsWzQvAFuaGjAK6+8EnL8D3/4Ay644AJdnCIIYmWiJJvUqv+NVWrpbRtS/MKWr7mDZSX/lDtbXs5K8plMbKO4di3T+cZjU2/RdlkZMD3NXuXl2rTLqa5sQhDEskGzBOI//uM/8MlPfhJDQ0NYWlrCk08+ia6uLjz22GN45plnEuEjQRAppK2NPcCrrASysuKzFa7+gPwYALS3B56vqWEvtZLQcNLMoiImD730UnlBDitgDXJKQfgrSKXocrICDHV16uoohNM7i12j4CCCryuIOKCIElP5ydLSyA2bmtgi2mxsg9jcHF4DrAalhQzuM/i81RpmMmTHeJ5JHrZsgW+iw10bjPcm8S5yIgXxBEEsezRvgK+66io8/vjj+MEPfgCTyYTbb78dmzdvxtNPP433ve99ifCRIIgU0dYG7N7NHhaWlgIf/nA6amvjs6kkmwzW6YaTjgbrg5UIpzOOKC2VO6WQ/FdwZmBv1xa0zuQBmVm+9g0N2v3Yt3ccYmsvOIhoaXaC37FNcaMWUWYbfPLKK8M3lE/i6Cgz9JGPRJ/IaISbM7mzkbTEkdpo0RxTPl6CIGIgpjzAV1xxBQ4cOIDp6WnMzMzg1VdfxeWXX663bwRBpBi7nW1+t2xh/w4NrUpof3qkoQ2nMx4c9EtLh4Yi2ArWzA4NQeQ3Ymg4DbmWOdXS1LB656E51OcOQMythDg4G9ZQRJltpJN6TKJWYhFFa2mjZbyk+yUIQgWaN8A33ngjHn300ZDjk5OTuPHGG3VxiiAIY+D9xvzQIfZvWVliA131SEMbTmdcXu6XlpaVRbAVrJktKwMndKBszRKmXZmqpalh9c5lmeicrgA33Q+uPCusoYgy20gn9ZhErcQiitbSRst4SfdLEIQKNOcBTktLQ1ZWFj796U/j3nvvRVoa20OfPn0aa9euhSRJCXFULygPsLGhnIzGw68BlpCVlfi1UZuGVosN77FIuYbDGgAUNcCxapEToQGWSksDPzd6TKJW9FqoWK5NxvjigH6vGRdaG+OS6DzAMW2A9+/fj89+9rOoqqrCE088gaKiItoAE7pAv4wSSzz7BO/a5OXVYnzcbKi9hh6FQ5K5h4qrL1njNgfv++Mkd7oV1QUFMFut+g0g1iIYSnZU//URYx8Ghn6vGRdaG+NiuEIYALBx40a88cYb+Kd/+ic0NTXh6aefRnFxsd6+EQShI3rECo2MmPHKK6xQl1HijbSOS00xjkSOK651kDVum6nB7o4r4JjOQWnuHD5Z2oHqCieLVtRjAOEcjWXCtRa3oMA2giASjGYNsMlkAgBwHIcXXngB733ve/Gud70LTz31lO7OEQShH3rECo2Pmw0Xb6R1XGGD05I0rrj6kjW297rhECQWoDjswtBQOrBhg34DCOdoLBOuOgIxSt8EQRA6ofkJsFwxYbFY8Otf/xobN27EzTffrKtjBEHoix6xQoWFkuHijbSOK9z1yRpXXOsga2yrroF1zswCFNdYUFa6ABw/zp4A6zGASBOldcLLy4H+fva+sTH2RSIIgtAJzRvgF198MUTusGvXLjQ2NuK1117TzTGCIKKjRSYZrghFJBveADhvIoHxcTMuvBBITwckyf9gTu9vp7WOq6kJOHwYUCMTC54HgP3c1BShfoIgQOiagAgOXF2JuvGGGURwXQpNcydzvlGSsKtxGPZJDpXn5SN3eiNQUMDSdegRJBbuhgl3PJIdheIWEV3S2kc0VpCemCAIfdC8Ad66davi8e3bt2P79u1xO0QQhDpikUmqrUUABBbByM1l367PzuZi/Xrgne8MLFChp0QzFonpCy/4JaZ9fdElpuEKbyj2JQgQ9u7HvtYiiJgA12xBy47C6FkkwhgWhMC6FJpj1rwX79uHRlFEI8dBsl6J7qxSVuY4OJAnHj2tUlGKSMdV2lHlktY+wkF6YoIgFIipEAZBEKlHD5lkJBvyIhiCwDaW1dULCa+tEIvEdGjILzFVU6RCU1+iCHFwFmJuJStgMTQX3X4Ew7rIW7UYMaCeNqkuGXD8BEGkHtoAE8QyRQ+ZZCQb8iIYPA9UVQG9vekJr60Qi8S0rMxf5EJNkQpNfXEcuPIscNP9rIBFWWZ0+xEM6yJv1WLEgHrapLpkwPETBJF6NOcBXu5QHmBjQzkZtaGHtFGh5oPv59ZWYHIS2LwZ4DgJhw+fxObN61BWZtaUS1drGti2NqbplSSgpsbfRsmO95gosvRsU1NAZSXQXOMAbx6JOjnhxh/QJB4NsCSFiIt1yUMcqRBG8CWSirmI5lTYyYliLkzfYcer900tLwSisBbJgH6vGRdaG+NiiDzAH/nIR/DII48gPz8fjz32GK677jpkZGTo7gxBENrQQyappIe1eH4zuFxsr2C1suQCtbULAbFQavTEWtPAejW9+/ezn3ke2LYN2L49UOvb3MyOefW0MzNs02y3A+mmBVxVNYCbznsDfFVmRN2nKj0wz4PneWiaaplWN9ioFi12RPveixQKEAXatKKlxRreZrTkyME3RBQHfeb6JsENdKGloi1kHRTvXT30upFskBaYIAgPqiQQzzzzDM6cOQMA+NSnPoWJiYmEOkUQRPKRSyUHB5muVotsMlLaWC1pYL3XWyyA283+HRxkG9tgO3Itcm8vO2a1AibJBftgOkR+o+oBJEQqqtJoIvrWZFPpYvmxoSE2+Sod9DXlnRAdLvXrkEhhO2mBCYKQoeoJ8IYNG3Dbbbfh0ksvhdvtxhNPPBH2cfTOnTt1dZAgiOQgl0qWl7NjWmSTkdLGakkD673+xAnAZGIPHcvLme747bcD7dhsLJNCZydQXQ2Mj3ueAJstsJUvgBM6gCp1A0iIVFSl0UT0rclmpMXr7GQia0C1gz5zfUXgrBZwQpu6dUiksJ20wARByFClAW5tbcWuXbvw9ttvY2xsDHl5eb6KcAHGTCaMjY0lxFG9IA2wsSE9VmqJJPlUszZ6aYDlul6Oi64B9l7ncPjzAavVAKvxPy5UGo2n73Bro8lmkjXAmvzQSqQbUfcFjg79XjMutDbGJdEaYM1BcGlpaRAEAVarVXdnkgFtgI0N/TIyLkNDLAiuvHwd0tPNEQO4UlV3QDc/ou3YkzlAFZu5kCC4FVz4YTkOjX6vGRdaG+NiiCA4OXa7HaWlpbo7QhCEcREE4NlngSNH8nHmDMuyUFUVGivFcazKWaKKZETzURc/okXtJbOwQqTIQvnxK6+M3mYFsIKHRhBEktGcB3jdunWYmJjAT37yE3zmM5/BZz/7WezevZsC4whiBeONGeI4FxwOtulQipUSxcQWyVDjY9x+iGLkyhrJDKaKJaBrBQd7reChEQSRZDRvgN98802cc845uOeeezA2NobR0VHcc889OOecc3D48OFE+EgQRIrxxgyJogVWK3sS5z0WHFuUyCIZanyM2w+Oi1xZI5nBVLEEdK3gYK8VPDSCIJKMZg3wJZdcgpqaGvzqV7+CxZMb0uVy4TOf+Qx6e3vx8ssvJ8RRvSANsLEhPVby0FqM4WzWAAvgIXaNgoMIvq6AHY8xOEwzKiILpZoadE9NJUYDbDDRrcHcUQX9XjMutDbGxXAa4DfffDNg8wsAFosFX//613HhhRfq6hxBEIkhWt0DJX0lz7NCGLW1rJiWnOCiBnoU6IgF3fyQNRQEYN/ecYitveAgoqXZCX7HNqChITmi1EiD6OlhfZ84AfOGDUBtbfQ2WjCg6DZV9xZBECsLzRKI/Px89HsTccoYGBhAXl6eLk4RBJFYotU9IH2lH1EExKE51OcOQMythDg4a4ziCkF9m8fHE94H3RQEQawUNG+Ar7vuOnz605/G448/joGBAQwODuL3v/89PvOZz+DjH/94InwkCEJnlLSUpK9UhuMAriwTndMV4Kb7wZVnGaO4QlDfUmFhwvugm4IgiJWCZgnEf/7nf8JkMmHnzp1wuVwAgFWrVuELX/gCfvjDH+ruIEEQfvTSP/I8+zY72FZLi1/6miriqL2gT6dBHfE80LJ9AWKxCVx+AfjmC/zXhJtIPXyIttjyvhcWYB4cBNragPR0/SYseHwA0N6uzf5yFO0uR58JgtCE5g1weno6fvrTn+Kuu+7C22+/DbfbjZqaGmRnZyfCP4IgPOgtxwynpfTKSnt6ki/5lI/RG2bgciVYfhptYgUB/MF94EURkDgA1YHt9RClxprE2Hvs6aeRd/gwMDMTmKRZr02wUu5hNfYNqCGOynL0mSAIzWiWQHjJzs7Gueeei8bGRtr8EkQSSIYcM9WST3n/Q0MsBW/CfYk26FRMvJYkxp7zUkkJQpI0J9JHNfZTfUPFwnL0mSAIzWh+AkwQRGpIhhwz1ZJPef9lZexYwn2JNuhUTLzNBoyOquvTc9588iR8SZqrqvT3M5Z5SPUNFQvL0WeCIDSjOQ/wcofyABsbyskYmWRIE8P1kay1MZoGWNX5RPigoU9paAgnDx/GuvJymPXUAEfzMVFtUo3OPtPvNeNCa2NcDJcHmCCI1JGMHKipzrOqlMs3BL03VREGzbriwXF8YuclUhLjSOP1FMQwO53A5s3+R+fJ8DFRbWJEt9si1R8CgiASTswaYL24//77YbPZkJmZiS1btuCVV15R1e61116DxWLB+eefn1gHCYIwFt4gJe9LEFZCV7E5IQjA3r3Affeh8L//G/jd71LkZOoxxFoRBLFsiOkJ8IkTJ/DSSy/B4XBgaWkp4Nztt9+u2s7jjz+Or3zlK7j//vtx8cUX48EHH8T73/9+dHR0oLKyMmy7iYkJ7Ny5E5dddhlOnz4dyxAIgliuyIOUOjvZzwl6WpfErmJzQhRZpGBuLtxuN4scTImTqccQa0UQxLJB8wb4V7/6Fb7whS+gpKQEPM/DZDL5zplMJk0b4N27d+PTn/40PvOZzwAA7r33Xvzf//0fHnjgAdx1111h233+85/Hjh07YDab8ec//1nrEAiCWM4kMUjJEPFQkZzgOKC8HDh5EqYzZ5j84SwN2jLEWhEEsWzQvAH+/ve/jzvvvBPf+MY34up4YWEBhw4dwr/9278FHL/88svR2toatt1//dd/4e2338Zvf/tbfP/734/az/z8PObn533vJycnATDhuyRJMXpPJApJkrC0tERrY0DUro1HkgoAqKtTlypWk26ztBS48kp/o9JSQO39oqEz76WbNwNm5yg4iCiVCiBJSU6M7HPCHDpeSQLOPx9L5eUYm5lB1qZNLBXa8LD/+rPkMWg8t0Wiod9rxoXWxrgkek00b4CdTic+9rGPxd3x6OgoJEnC6tWrA46vXr0aQhjxVnd3N/7t3/4Nr7zyCiwWda7fdddduOOOO0KO9/b2JiSqkIiPpaUljI2NoaenB2lpKZeoEzLUrM3IiBnPPJOHo0ezAJhw/vkz+OAHp1BaqvyLbGTEjAMHcjAxYUFBgQtbt54Je20I6enA1BR7qcA8MoKcAwdgmZiAq6AAZ7ZuhVRaGtWvorRxfAD7sGrpFE63Rm6nJ4r+esccfD4/H86KCmT87/9i1alTsAgCpDVrsLB2bdL8NQoab4ukQL/XjAutjXHxPrBMFJo3wB/72Mfw3HPP4aabbtLFAbmEAgDcbnfIMYD9JbBjxw7ccccdWL9+vWr7t912G3bt2uV7Pzk5iYqKClRXV1MaNAMiSRJ6enpQU1NDKWkMhpq1WVhgL+9Dx4WFHBQUlKK2VtnmwgKr+HbxxcDx40BBAcJeGzcaOgu49IALacgDtzUZTmrwV3be3dkJ1/Q0rBYLTPX1QF8fsGEDqwyXLH+JsNDvNeNCa2NcnE5nQu1r3gDX1NTg29/+Nt544w2ce+65WLVqVcD5L33pS6rslJSUwGw2hzztdTgcIU+FAWBqagpvvvkmjhw5gi9+8YsA2F9ubrcbFosFzz33HLZt2xbSLiMjAxkZGSHHzWYz3ewGJS0tjdbHoERbG6sVqKgABgbY+/POY8fCLaXVyr6qPnGC/Rvp2rjR0FnApRXZsCIb5qQ4qcFf2XmppASu0lKYRkZg7u8HVq8GRkZYQYxk+UtEhH6vGRdaG2OS6PXQXAjDZrOFN2Yyobe3V7Wtd77zndiyZQvuv/9+37GNGzfimmuuCQmCW1paQkdHR8Cx+++/H/v378cf//hH2Gw25OTkRO2TCmEYG0pKnnzUymLVro0mDbAgQOiagAgOXF1J4uWqssEK4NXXvoDCJOmRdDbWAhze45IEmM2QCgvRPTWF2rw8mMfHfcdDqonIf052JRW15/XqS+lciopy0O8140JrY1wMVwjDbrfr1vmuXbvwyU9+EhdeeCEuuugi/PKXv0R/f79PXnHbbbdhaGgIjz32GNLS0rBp06aA9larFZmZmSHHCYJQhzd3qndP0NIS/75AdQ0BT+e8KILnOKCuBUByqnyoGXfgOPjQDWi8E6fdifDtSkuZ6JXnAwthyK/1xk24XPotttYx6XnDRbKldA7Q/2YnCGLZEpfi2+12I55Kytdddx3uvfdefPe738X555+Pl19+Gc8++yzWrVsHABgeHkZ/f388LhIEEQF57lTvz2dD53F3rYfvsdrQ0k5+7dAQyxmcyPmO5pueax7JltK5lN7sBEEYjZg2wI899hjOPfdcZGVlISsrC42NjfjNb34TkwM333wz+vr6MD8/j0OHDuE973mP79wjjzyCl156KWzb73znOzh69GhM/RIEkeLcqSnsPO6u9fA9Vhta2smvLStjOYMTOd/RfNNzzaPlRw4+R4mCCYKQoVkCsXv3bnz729/GF7/4RVx88cVwu9147bXXcNNNN2F0dBS33HJLIvwkCCIB8Dz7JjgFssiUdh5313r4HqsNpXbh8mUGXwskdr6jjUnPNY9kK9y5lN3sBEEYjZiC4O644w7s3Lkz4Pijjz6K73znO7pqhBMBBcEZGwpIkJGigJ1wrjgcEiYmetHcXL081kbN/CVqjoOC1BId8CUNDeHk4cNYt3kzzHINcKp9PpuItD70e82w0NoYF8MFwQ0PD6O5uTnkeHNzM4aHh3VxiiDOehIRnRanKyMjgMuVg+rqwDgrQ6Jm/hI1x167fX0sH1xFBUtHlsiAr2efRe6JE+znq66KPSBPT5/PJgz0eSUIQh2aNcA1NTV44oknQo4//vjjqKVk6wShDwYK2PF2v2EDMDFhWR6xQ2rmL1Fz7LXF86wsMc8nJeBrobo6/oA8PX0+mzDQ55UgCHVofgJ8xx134LrrrsPLL7+Miy++GCaTCa+++ir++te/Km6MCYKIAQMF7Hi7Z8XIXMsjdkjN/CVqjr22+vpYEQpBYE9TExzwlX7iBLB+fXwBeXr6fDZhoM8rQRDq0KwBBoBDhw7hnnvuQWdnJ9xuNzZu3IivfvWruOCCCxLho66QBtjYkB5LRjI0wCr7UK0BVlMEQXWVjDhJlQZYPsaiotRpgNvaALsdsNmAxkb1BSoiaYANpEs3HKQBXpbQ2hgXw2mAAWDLli347W9/q7cvBEHIUV1RIkY06BZ5ntVa6O4Ok21AjT1BAPbuBVpb2fvmZmDHjsSNUc386T3HidSCRvKV57FQW+s/39YG7N7N5AxWK7BzJ9sMR/Ir2lyQzjUyif68EgShK6o0wJOTkwE/R3oRBLFM0Fu3qKYIwuAgkJvLXkNDK08raRQtqN3ONr9btrB/29tTV7iDIAjCgKh6AlxUVITh4WFYrVYUFhbCZDKFXON2u2EymSCFy0dJEISx0Fu3qKYIQnk54K3u2Ni48rSSRtGC2mzsye+hQ+zfhga2KU5F4Q6CIAgDomoDvH//fhQXFwMAXnzxxYQ6RBBEkvAWC/DqVSPANMDAxIQZYZO9qCmCsGMHeyoJ+DTAEWWlqczlGwuR5kDuJxC7z2rG29gI7NqlTQMczX5Kq6YQBEHoi6oN8NatW30/22w2VFRUhDwFdrvdGBgY0Nc7giAST08P29T09CjqOjXlAY6mgww6H1FWmspcvvGgNAdyPy2eX7sul3aftYy3sZG9Ivml1T7pXAmCWCFozgNss9kwMjIScnxsbAw2m00XpwiCSBIqdJ2JzAMcsftU5vLVG7mfg4NM/xyLz4ke73KZT4IgiDjRnAXCq/UNZnp6GpmZmbo4RRBEklCh60xkHuCI3acyl6/eyP0sL2fHYvE50eNdLvNJEAQRJ6o3wLt27QIAmEwmfPvb30Z2drbvnCRJ+Nvf/obzzz9fdwcJgkggKnSd3kuYBviMrt+AR+xejeZ0uehSg/0EYvM50eNdLvNJEAQRJ6o3wEeOHAHAngD/4x//QHp6uu9ceno6zjvvPNx66636e0gQRGKJoOuUx0M1NETJA6yS4BgrHgJ4iIBDAkQzBKkUotnqOa9jLl+9g+ViCSrjuMAJjQU1+XrD+aV0LmRBSOdLEMTKR/UG2Jv94VOf+hR++tOfJqQqB0EQxiE4HurKK/W32dLkAH9wHyvBOzAAoage+5zNECsywVXl6xfTpnewnFp7egW/afHrL39R9kvJZ8B4QYQEQRBJQHMQ3L333guXyxVyfGxsjAphEMQKIhHxUCE27ZPsB54HHA6IOZUQHS7U8059Y7CSXfRD6bqhIRYAl8gAs0h+KZ2joDeCIM5SNG+Ar7/+evz+978POf7EE0/g+uuv18UpgiBSTyLioUJs2vLZD4IAWK3gzvSDs1rQKRTpG4OV7KIfSteVlbEAuEQGmEXyS+kcBb0RBHGWYnK73W4tDYqLi/Haa6+hvr4+4Pjx48dx8cUXQzT4E4TJyUkUFBRgbGwMRUVFqXaHCEKSJHR3d6O2thZmsznV7pzdCAKErgmI4MDVlaC0VIe18djsEksAjmO1MODRoEoSYPZogJ1p4MQT4LlFX8EMPcYTEITmLQASq321hS3CXafUJkadcsDnZmQksi/B4zZSIZEVCv1eMy60NsbF6XSiuLgYExMTCZHdak6DNj8/ryiBWFxcxOzsrC5OEQSRYjx6UV4UwXMcUNcCCaW62ETfHHoGGiFWnIeenny0tPDgG/wbL14QwL/wW6C1lR1obmYV5OLdnHmDuwQB2Ls3fvtye1qKR4RrA+ijx40WxKZU+IQ2vgRBnGVolkA0NTXhl7/8ZcjxPXv2YIu3xClBEMubBAqARX5jZJ2vKDK9bG4uew0O6qtNFUVm02t/aCg++7HMVar0uKT5JQiCABDDE+A777wT27dvx1tvvYXLLrsMAPDXv/4VBw8exHPPPae7gwRBpIAECoC5vg5w1kam861SMM1xTC978iR739iorzaV45gWt79fH/uxzFW4NonW45LmlyAIAkAMG+CLL74Yr7/+Ou6++2488cQTyMrKQmNjIx566CHU1tYmwkeCWLYsW3mlUkEEKYYcwMET0NICXhTRIpVCNOcrzwvPM0mC9xslvTTAWu2rXbxYikeEa9PS4tfoxuJLrP0SBEGcZWjeAAPA+eefj//+7//W2xeCWFHonXo26cSrDQ03ATwPHkBEy4nWpaopJqFl8WLxN1ybYI0uoO+NRJpfgiAI7RpgObOzs5icnAx4EQTBOOvllst5AlLlO+XqJQiCSAqaN8AzMzP44he/CKvVitzcXBQVFQW8CIJgnPVyy+U8AanynXL1EgRBJAXNEoivfe1rePHFF3H//fdj586duO+++zA0NIQHH3wQP/zhDxPhI0EsS8LKLZMoDFbqKtbuBQHo7k7H/9/evQfHcRX4o/+OZvSyZL1aGrf19MhSZFmOTCz7EotkkzXGkIjs5RfubsCAkw3ZWi+BJGh3yQOKBCpgit31GqpIApuQ3VwwZIHwSOwADk5MuA6JsWIUy4osobFlyWqP1BppJNt6TKvvH60Zzfs9mh7p+6lSOerpPn36nJFzPPqec1av1uaohZXKvGksDxkgr+xeBxmloSMbiaqDb5sB2n9v2wYYjfE9z1JL9f2JiEKIegD84osv4rnnnsPNN9+Mu+++GzfeeCPq6upQU1ODH/7wh/jEJz6RjHoSpSW/uOUSBoMTudSsJAGHDwNnz+ZDkoDbbouw2qnIm8bSxgGukSDiUJ+oHeqLsqvi6edI1xdO1v0TIdX3JyIKI+oIxNjYGCwWCwCgoKAAY2NjAIAbbrgBv//97xNbO6LlZgnznImMk7rOra2d1X8MNUHr8sbVVYno53jKSHVuONX3JyIKI+oBcG1tLc6dOwcA2LhxI/73f/8XgPbJcFFRUSLrRrT8LGGeM5FxUte5/f1Z+o+hJmhd3ri6KhH9HE8Zqc4Np/r+RERhGFRVVaO54D//8z9hNBpx33334dVXX0VbWxsURYHT6cT+/ftx//33J6uuCeFwOFBYWIixsTFO2tOhFbEve5pmgIeGFHR0nMeWLTWoqNB53yQiAxxjMXHVIcYyAv7cpDqDm+r768iK+HstTbFv9Mtut6OkpAQTExMoKChIePlRD4B9DQwM4E9/+hPWr1+PzZs3J6peScMBsL7xL6MUCzFoCdU3UqcNstUBwVIA0TwfduDjvo1ig2gcSfkgTVLKIBvNKZusGC9laAjnOzpQs2ULjBHNUKSk83j/KGVl/HtNp/j/HP1K9gA4qklwc3Nz2LVrF7773e/immuuAQBUV1ejuro64RUjoiUW48QlqdOGQ/t7INucEPJn0bbRCnGVI2gZ7tucc0C40IO2qk6I63JSNlFLOjeNQxeaIVflQFhXoFUDaTSJa2GGYv7Zs4huhiIlje/P0oc+lOoaEZGPqDLAmZmZOH36NAwGQ7LqQ0SpEuPEJdnqgGxzorElD7I0C7l/ImQZ7tuIdsg2J2RxY0onasniRq3+on2xGuk0iWuhfrO1tfqv60qRTu8fohUq6klwe/bswTPPPJOMuhBRKsU4cUmwFEAwm9B98jIEMQtCbWHIMty3kYohmE0QpDMpnaglSGe0+kvFi9VIp0lcC/XL6u/Xf11XinR6/xCtUFGvAzw7O4unn34aR44cwdatW5GXl+f1+v79+xNWOaKVSpIAuWcUAmSIDYXhs7SJiKnGuHGF2GxGWzs8MsCbQ5axeJsCCJcEiJOVgMWSuF/bSxLQ06P9d0ND8HIXKiLKMtqUMsjGAo8qp3ATj2i43gBbt2JKFCFs2aLfuq4kvj9LZWXA5GSqa0VEHqIeAJ8+fRpbtmwBAJw9e9brNUYjiOInScChg+OQj/dDgIy2VjvE3TuCZ2kTGVONceMKsdkMsdnsXU6420AC/viG9gCjo4DZHP8DSBJw8CBw/Lj2fWsrsHt36EGwKEIE/Hd6S8UmHtHwfAMUF0PZsEHf9V1pPN8/ipLauhCRn4gHwP39/bBYLHj11VeTWR+iFU+WAXloGo35F9CNDZAHL0KUZb/BjWfMsLtb+++0Gv8k4wFkGRgcBPLzte+HhtKwYSLk2X5dXTCOj6e6RkREaSPiDHB9fT1GRkbc399xxx24dOlSUipFtJIJAiBU5KB7qgrC1ACEytzQWdp0jRkm4wEEAaisBKamtK+KijRsmAj5tJ/CjYiIiCIW8SfAvssFHz58GPv27Ut4hYiWk2gzuq7zt+0sgrGlFgIKg2aAg0V2vdbk9YwlxBsYliRk9fYCq1drA0vX4WD3C6WzE7Batezv9dfHtmGFogBG4+KfrjJ27wZaWrRzQ2WAA5Wp57yvL883QFER0N8PdHUlJkoSjGc7AXG1WTo2OREtH1FngIkoMtFmdP3PL4Uoloa8h29M1WtNXrMJbe1aPjfuwHCQtWaD3i+Uzk5g/37AZtMGa+3t0Q1+Dx0Czp0DLlwAiosBux2oqgLWrVt8rmifLV3W/PXletahIeQdOwaYTNqEq2Q8g2c7mRb+1+F0xtRm6dzkRLQ8RByBMBgMfpPcOOmNKLholwJNxNKhXmvy2pyQrY7EFB5krdmg9wvFatUGvy0t2p9Wa9T1gChq1+blaX+KYhyNtgzWbJVlmCYmgA0bkvcMnu00NKRlrWNss+XQ5ESU3qKKQNx1113Izs4GAExPT2Pv3r1+y6C98MILia0hUZqKNuKaiEis15q8ZhMES0FiCnetNXv2LHDNNe7rg94vFItF++T35EntT4sl6nrg3Dnt2suXtT8lSfsEOKZGS/cwNQBBgLOwEHj3Xe0T4GQ8g2c7uSIwMbbZcmhyIkpvBtU33BvE3//930dU4LPPPhtXhZLN4XCgsLAQY2NjKC4uTnV1yMdy25c91gxwPLnIZGWAlaEhnO/oQM2WLTAmMgPc3BxdRcJlgGOR5oFURVHQf/w4agsLYWQGWHeW299rywn7Rr/sdjtKSkowMTGBgoIIPlyJUsQD4OWCA2B9S9e/jGL+n3kSRwG+48SYbuFRP6WsLC37JizPjTOKixcbC0ibEZpufm5W0qg2CrrpH/LDvtGvZA+AOQmOKE4xT+hJ4kwg37livnPEoirEVb8PfSghddMVz40zpqeBVau0lSNc/ziOcZLXisSZbUSURiKeBEdEgcU8oSeJM4F854rFNEdsJcxU8tw4w2jUBnELqyrEM8lrRVoJ7xciWjY4ACaKU8wTepI4E8hVnCQtzhGL+hYrYaaS58YZiqINfiVJm+RVWbm8nz3RVsL7hYiWDUYgiOIUbEMKTwGjkZFcGEawnK9n0YEywIHq4ze/6fgEhKl8iHXFQGurtrrA5KT3/X0nwMWZAXVffukMxMneyCbJxXNP18YZ69YBDgdQXQ2sWRNfBti3PqnKxSZg45Oo3tQJeD8TES0VDoCJEiDU3gsho5HRbtoQoNxgOd9gRQeqD+Czx8HFi3C+dgrCzDDaNloh1tVpA2DPcnw3wdgzCtH6RswZUHe9Tl+E8OYbaMs9CrEqU9soI9ggOFG5U7t98V8LGzculhFtWb712bYNOHFi6XOxidj4JNT1wV6P4/1MRLSUGIEgSrJkRSNjzfkGqo/nscFBYKj3ChrVbsjmDZAvzgTcrMJvE4wuKa4Hdddh1QDkMUCuvz78RhkJ2T0kgR3kW5bVmppcbLzPFO565n2JKM1xAEyUZMmKRsaa8w1UH89jlZVARf0qdBsaIdjehVCeHXCzCr9NMJrEuB7UXYcr1RBKAKH3j+E3ykjI7iEJ7CDfsiyW1ORiE7TxSdDrmfclojTHdYBJV5brmozJioFGtNZvgJtHlgHugeCwQtxSDjQ3Q1EUHH7uFOYmSlDXvBrNO0qTmwEeeAsoKNDyx6GytAnZPSSOMsJlfmMpO8prAv7cLGEGWILI6G8Iy/XvteWAfaNfXAeYaBlIVjQybLlBspqBrvM9Jt7eAKDB/X3n0VH8v982YvKKgjXCGNofB5p3mL13f4vzQd2XCyWAVQH6+rR8bqgsbSIaN9YyQmVhYy07UbnmeNsl3PULr3P5XyJKR4xAEC1nCcxqWruuYGw8C1s2O2Eby4C160oCK+pDL1nacJKRhU2zfG2aVZeICAAHwETLWwKzmpamVSgpmkXHn00wl8zD0rQqgRX1oZcsbTjJyMKmWb42zapLRASAEQii5S2Ba7M27yjFp+4bxNyEEXXNRWjeUZrAivoIVG+zWX9rzCZj7ds0W083zapLRASAA2BaBmKe6xPJhanaxCABFqsuQmxKTN3rt+ehvr469GSRQJPAenoWjzU0RDY5zDODmugJcImeIJbo90a8ZbraHFhs7yTi8r9ElG44AKa0FvMEnEguTOPZPSmreqCNIF55BTh6VHtNFIEdO4CdOyPfICLYw8T6kMneJCLVJAk4eBA4flz7vrVV2+1OT3UkIkoxZoAprcU8ASeSC9N4dk/Kqh5o8trgoLa9nKpqfw4ORjepLdjDxPqQyd4kItVkWWvj/Hzta2hIf3UkIkoxDoAprcU8ASeSC9N4dk/Kqh5o8lplJeB0AgaD9mdlZXST2oI9TKwPmexNIlJNELQ2nprSvioq9FdHIqIUYwSC0lrME3AiuTCBs3uWOkrsrnrPKATIEFEIIPSN/eoYS6WDTV5rafHPAHtOagOArq7A9wrWD9H0j++zxNOvep/1JYpa5KGlRft+CTLARETphgNgSnsxT8CJ5MIEzO5JVWRUhASxb+HGfaFv7FfHbTaIJ3wqXVYW4Y0j3AjCdTySBgpXRiiRbFYRLb3P+tJ7/YiIUowRCKIk000eN8SN/U61Opau0sluIL1ndomIaMlxAEyUZLrJ44a4sd+ploKlq3SyG0jvmV0iIlpyjEAQJZpP3tQrjyufhdgzByBILtPn2qhiuAsnS0oZZKNZW/83wqyqf6zVDJjbFtfvPX4cKCqCMTMTWL0aGB9P7PrJdXXaVzLyqr4PBwTPG8ci3LOm8VrSRETLFQfARIkUJG8qQoJ4MszarD7XSttuw6ET5siywwvXSuemcehCM+SqHAjrCtDWFvkmGAFjoydPamv4Dg8Da9eiqKZGW8Fhfj4x6yf7ntvQEFFdoxZN3jga4crT+5rBREQrFCMQRIkUas3aoaHFtVkHB/2zqD7XylZH5NHVhRNkcSNkmxONoj3+uKtrPVmjUVvCzGiEaWhIe45ErZ+81PncRN8vXHnMHxMR6RIHwESJFGrN2oqKxbVZKyv9s6g+1wqWgsijqwsnCNIZCGYTuqXi+OOurvVkFUXbxEJR4Kyo0J4jUesnL3U+N9H3C1ce88dERLpkUFVVTXUllpLD4UBhYSHGxsZQXFyc6uqQD0VR0Nvbi/r6ehiNxlRXJzbBMp+SpGVqgeBZ14RmgBPw23ZXnWUZSlER+jMzUVtbC2MiM8BLnZFN9P10kAFeFj83yxj7R7/YN/plt9tRUlKCiYkJFBQUJLx8ZoBpxZMkjw0jGgrjH9TFsmatZ9lNTWEvCViVhf8Q5RFAAWTZ7Hk48G07bZCtDgiWAojN5tB1VhQovb3a9xUVwQuNpK7B7pGwQkNI9Bq54crjmrxERLrDATCtaJIEHDo4Dvl4PwTIaGu1Q9y9I76JXTFVIvKyg54edCJckEF0pw2H9vdAtjkhmE1oa0fgQXAczyOZKnEIt0J2FiWu2TixjIiI4sQMMK1osgzIQ9NozL8AOb8a8uDV+Cd2xVSJyMsOenqUE+Fkq0M7ryUPss2pbX6R4OeRB69q7ZvIZuPEMiIiihMHwLSiCQIgVOSge6oKwtQAhMrc+Cd2xVSJyMsOenqUE+EES4F23snLEMwmbfOLBD+PUJmrtW8im40Ty4iIKE4pj0A88cQT+Ld/+zcMDw+jqakJBw4cwI033hjw3BdeeAFPPvkkTp06hZmZGTQ1NeGxxx7DBz/4wSWuNelJvHHQtt1FkFtqIaAweAbYf6eIxFQ+QNkSRMhdgKDYIBpH/O7ntbEGZIgoBCC6XxBlGW1KGWRjweKlgSbINZvR1g7vDHCYxhwZMWJ2FjCbwzRBcTEwOQmxOgdtdbOQjTE0W7C6xNMXrjIVRVveLR02p+BGGkRECZfSAfDzzz+PBx54AE888QTe97734bvf/S5uueUWnDlzBtXV1X7n//73v8cHPvABfP3rX0dRURGeffZZ3HbbbXjzzTdx3XXXpeAJKNUSEQfV5iiVAiiN5MSY6xpJ2e7nOeeAcKEHbVWdENfl+D2YCAli38KD93k8+MKXCMB9dqhscLN5MfcbpjElCTh2LA8mE1BWFqStJQk4eFDbPEOStLrsOAfRd9OPcMJ1bKwT5w4dAs6dAy5cAKqqgHXr9J0hZt6ZiCgpUhqB2L9/Pz796U/jnnvuQWNjIw4cOICqqio8+eSTAc8/cOAAvvCFL2Dbtm2or6/H17/+ddTX1+PFF19c4pqTXiy3OKj7eUS7lssVN8a/wUKk2eAwZcoyMDFhwoYNIW7p2jzDZNLWDjaZAm/6EXFDJLBjXeWIImCzaX/q/U2z3N7gREQ6kbJPgGdnZ3Hy5Ek89NBDXsd37dqF467tYsOYn5/H5OQkSkpKgp4zMzODmZkZ9/cOhzbRR1EUKIoSQ80pmRRFwfz8fMR9U1Sk/ba9q0v7gKyoSPvtdrpyP8/5QghlGSi62AllXbH/g0Xz4AvnFp07jeKyZnQNF0KoUfwvCVNmUZGCggInurtVlJYGuN5VRnk5cPas9v3cnPZ9tB2TjI51lXn+vPYR9sWL2ifAen7TRNgO0f7c0NJi/+gX+0a/kt0nKRsAj46OQlEUrFmzxuv4mjVrIElSRGX8x3/8By5fvoy/+7u/C3rOvn378JWvfMXveH9/f1IWVqb4zM/PY2xsDH19fcjIiOwXFBs2GDE+bkRRkYLJSQWTk96vG0dGYBwfh1JUBKWsLAm1jo5nfQD41W3DBiPGRSOErdmYMa7Dm0oZ5I4ZFBX1o6xs8S+ErLIyZM7OYq6sDLOTk/B98JERIyatDmB8EhmoRXGDgqZt2Rh3/AUCZFzpz0XvZJlXvbKys4HycsxaLFB8yjRcuoT3Ftkwu3odCtcXBWxrADC+973IMpuRMT6O+aKigGVF1E4bNiy2TQzXez6XqxzjQpvNr18PpbRUG0x2dOjmvRFIJO0Qy88NLR32j36xb/TL9YFlsqR8EpzBYPD6XlVVv2OB/OhHP8Jjjz2GX/7ylzCbg69d+vDDD6O9vd39vcPhQFVVFWpra7kTnA4pioK+vj7U1dVFvCtPfX2IFyUJeP31xQxlbW1qM5Se9TEt/Pg5nV51W3yeGkgS0HU4QPUlCRgZ0QZDIyPAtm1+ed3XX3Lg3GuTGBwuRtVaJ2pursWt107jvX96SStwxqPAcO0kSZg/dgyF585CuGYMGbUfDt6O9fVAa2v8bRWyYyMUrL2zsrRtnv/0J/28N4KJoB1i+bmhpcP+0S/2jX7Z7faklp+yAXBpaSmMRqPfp702m83vU2Ffzz//PD796U/jJz/5CXbu3Bny3OzsbGRnZ/sdNxqNfLPrVEZGRuL6Z3wcsNu13dW6u7Xvo9jFLOE86/Pqq4DBANx8c9C6Ba1+mOcaHwfswzMoN47ibUMz/i9jJ+zDFRgfuIyKQNeFa6eF1511dciw27WtkFPZjpEK1d4DA/p6b8QpoT83lHDsH/1i3+hTsvsjZZ/3Z2VloaWlBUeOHPE6fuTIEbSG+PToRz/6Ee666y4cPHgQbW1tya4mpTu9rRnrWZ/KSm3AFaJu4db8DXata31jSSmDWbVBUsogVORoa/0Gui5cOy0cy+rv10c7RipUe1ss+npvEBHRkklpBKK9vR2f+tSnsHXrVmzfvh3f+973MDAwgL179wLQ4gtDQ0N47rnnAGiD3z179uBb3/oWrr/+evenx7m5uSgsLEzZc5COJXP93kTUBwhZt6DVD/Nci+sbV0GRx2EUiiA0FGmnmQNcF66dRBG49VZMiSKELVtS346RCtfeZrN+3htERLRkUjoAvuOOOyDLMr761a9ieHgYmzZtwuHDh1FTUwMAGB4exsDAgPv87373u3A6nbj33ntx7733uo/feeed+O///u+lrj6li3BrxnpuNACEHRAF3JegsxOwWrVPFZubAUmC1DMBGQKEhlLvonzr43OfiPc9CPBcXtdCglgqAw0CIJaGvC7kcUkCenoARdEm7rkyw6EqqafNG0K1dzLXdo6WntqMiGiZS/kkuM985jP4zGc+E/A130Hta6+9lvwK0criudGA76S0AJsOBNyXwNYJ7N+vrS1rNgN79kA6JeHQ8WLImIDQakLb7qKIxjS+5W/bBpw4Edk+CF7XmsbRhqMQnYPxbaDg2thiYWnC1evXA/n5QEdH8Epx84bosc2IiJYU1/yglc1zo4GhIW3ThhCbDgTcl8Bq1Qa/LS3an11dkAevQs6vRmP+BchD0xHvX+BbvtUa9X4X2rlD05AHr8a/gYJrY4v8fCAvD5mXLoWvFDdviB7bjIhoSaX8E2CilPKcJOVaASDaSWmKRfvk9+RJ7c+mJghzEoSBAXSjCkJzTsTzq3zLt1iA0dHI5ml5XVuRAwG58U/wEgRt8thCFGlu/XqtUnZ72AlznFwWBbYZEdGS4gCYlp2oopQJmJQmoRny3zwM5cIQjFUVEDY2QNwooa1lIQNcPAtR7gLgUWaQSnqVr9ggGkdg3lYG2WjWvpdHvMvxeeZt2wCjERCEIojYEX+mVBSB3bu1T7cVBZOZmShrbgbWrtVywZG0aTr8Kj/R+dtoy0vHNiMiSmMcANOyElOUMsyktFCnu+537lwDLlxoQNUosM4OtLWJEG8SIQaqEBCykqKoTWBznSMKAsQQYeDgz5ygCV6uB1YUKL29i8f7+rSb9vX5N7SeJpeFk+j8bazlpVObERGlOWaAaVlZ6iil6x6iqMV/RdHnvoEqFEklowgDpyQ+upwyq4l+luXUNkREyxQHwLSsLHWU0nUPSdLiv5IUwYYVkVQyUBg4yDUpiY8up8xqop9lObUNEdEyxQgEpT3fuOVSRik976corvytz33r6tCJZliNdbDYirVlgrfdBrnjPISCOQSsYqAHCbJpg28denq0r4ZiLUPsVyHfNYtD8WzcsrLQ9Ysm9+paWxgAGhpS+6v/RL9pmOclItI9DoAprQWLWy7lmCPo/RYq13k6A/vffB9suTkwvwbs2QNYT2VBPq5CwATazh2FuHtH4N3XIszVug67l+ydvoLWvH7svuYkxHU5iw3T6bNmcXt78EGwb+N+6EPB6xdN7tVnbWG0tmoT7VI9CE7k/ZnnJSLSNUYgKK3pOm65UCFr3kbYxjLQUj/pWiYY8tC0tkZwfrW2Xm8CKi7L2lLG+flAvmkGgxcNkMWN3g3ju2ax1Rq2/tEvQhzBua61hfPztUrrquOIiGi54wCY0pqu45YLFbJcPgNzyTxO9q52LRMMoSIH3VNVEKYGIFTmJqTigqAtZTw1BUw5s1FZrkKQzng3jMVnzWKLJWz9o1+EOIJzKysXKjqlVVpXHUdERMsdIxCU1nQdt1yoXPP1MtpvK4F1Mtcdu5U2FkFuqYWAQogNhQmpuOeSvcAqNBTXQjSu9m6Y5mYt9hBJBti3ccvKgMnJyM4N9TzeFU19BpiIiFYcDoAp7cUTt3TN2wo6gS3EBZI9W9vooqE0eAZ4YUDY3FQKz6GmVudSSFIpumRAQIgccRSje69Y7tFRdHVdgtBkgLgwSU3qmUCPXAEIzWgwI/AEvGAFKsri8c5OoKMDKCgA6uoWG6+pKWwd3eUCgaMP0TxzNBP6oi07Ep7lATr9lxgREfniAJhWrMVNLIALF4CqKmDduhDztzwukM46cOjyTZBzKiG0mtC2u8j7mggmhYU9JY4NGqSjZ3DoS29AHgOEEiva2seAgQEcPCriuJQJiLlo3bEqtrlnnZ3Avn1a3MFg0Bpt8+YwjedbwSDPFs0zRzOhL9Q9Y+VZnmnhr1KnMzFlExFRUjEDTCtW2E0sQlwgX5yBbFqjTWQbmva/JoJJYWFPiWOGn9wlQR4DGjdnQx4D5I7zkAevYshUjXx1SpskNxjj3DOrFRge1gadiqJNaAvbeBE+WzTPHM2EvmjLjvYZBge1yXy6nI1JRES+OACmFSvsJhYhLhDKsyE4L2kT2Spy/K+JYFJY2FPimOEnNIkQSoDuP89AKAGELTUQKnNR4RzAlCFfmyRXGePcM4sFWLtWG3QajdqEtrCNF+GzRfPM0Uzoi7bsaJ+hslKbzKfL2ZhEROTLoKqqmupKLCWHw4HCwkKMjY2huLg41dVJW4mOUrrKtNkUTEz0o7W1FkajMab7RpPrDXQu4HEPLN5Qggi5ZxQCZIjFMwEzwF71g3dlA9Xd95jf9502yFYHBEsBxGZzxG0AaDEIuUuC0CRC3LFxMQP8FxMw6UBD1VWIrbXac0XQXoqioLe3F6tX12P8zT4IF05BrMr0zgCHa+hQDx9NJ7sEygCHuj4R9wx2/yAblSwFV9/U19cH/Lmh1GL/6Bf7Rr/sdjtKSkowMTGBgoKChJfPDDBFLdFRSs8yR0YApzMPtbXaB2rR3jfaXK/vBDqve5jG0YajEJ2DkEyVOIRbITtLIQilWnnN3pPI/OsnQmwSQ9Y91F4S27YBJ06YIctmCKNAmzb+jbjtxR0btYGvx8OKAMSTi5tQSJ07cajgYzhnL4qovUZGjHj9dcBub4AgNKCtNYK+j+ThQ3VKKM3N3rnfcG+SQGXH+oaWJODECe260VHtukgnARIRUUoxAkFRS8bmE65yNmwAJiZMAcuM5L5R53pD3WNoWtukorER8uBVbfOKCO4d6Jxo6u46x2r1vybutvfZhEK2OiAPTUfcXuPjxujvn4w3TCLvFWv9lvK5iIgooTgApqglY/MJVznvvgsUFjoDlhnJfaPO9Ya6R0WOtklFdzeEylxt84oI7h3onGjq7jrHYvG/Ju6299mEQrAUQKjIibi9ioqU6O+fjDdMIu8Va/2W8rmIiCihmAGmmCQ6A+xaVjY/X8GaNZFngENFSyNe2zfAPXp6tP9uKLZBNI54ZYAVeRxGoQhC8TxE4wgkpQyy0QxBsQF2u5YLXnjN8+Zh874S0HNcBhwONGzJA8zmxXo0+D9fzG3v9YANEWeAh4YUdHScR2VlDbKyjNHdP5ZcbqzlxlJerHWI5rpkBOcXMMeob+wf/WLf6BczwKRL8Ww+4ctzOdeyMuD//J/gfwmFysyGi5ZGo69PK7dPMKOtzayVKUnAyaM4dLwY8vQqCKumsa3iIk7Y6yAXOyHY/4K2qk40Fc9ohfisCRs27/vKOOTjf4EAGcXnruJEwQ7IziIIgjYADtQGMfEpQERkY7fDh4GzZ/NxzTXAbbdFWYdglY4nUJ7IN0CsjRrpdckIzhMRUcwYgaCU813OdWgoM6LrkhXBDFquLGtZ4PxqNBp7IUuzsOZthGxzonHVAGSbE7K4UVsPdnAwqjWArVYtc9yYfwFyfjWs/WrYzPFSctWhtnY2sfWJpxPTKYObTnUlIloBOACmlPNdzrWiYi6i65IVwQxariBoWeCpAXQr9RDELFgun4FgNqH7SjUEswmCdEZbvqKyMqo1gC0WLXPcPVUFYWoAllpD2MzxUnLVob8/K7H1iacT0ymDm051JSJaAZgBJl1wLadaXa0gNzfyPFayYpVBy11YS9cz5+uZAXbnfoGwFQuYCXatM9xQ6M7mJiEyGhNXBnjLlhpUVCQwK5foDLBeMQO8YrF/9It9o1/MANOK4FrOVVGA3l5trDA+Hn6sEFceNsSmCKIsQ/Q87rHhgXhTM7Awj6wHZjQ0AE0iAGkecP1me6FikgTIXcEnvrk2y5DOGCBPZi1seKEFfkNmc30KCvgoiRxwSRJE2YYNRRMQxZr4yvIVTycmMoyebOlUVyKiZY4DYNKdxc0WkjhfKNikpEDHbbbFWXpmM6Q9X8DBUxtde0mgtRXYvdMG8YT3dRLEABtbeJyyTbtGOj2KQ2+WQs6tgFCVh7Z2uHd9i6Tu0rbbcOiE2ftRkMBJVx67lOQ5nQi4SwkREVEaYQaYdCemzRaiFWxSUqDjPrP05C7Jcy8JDA0BstXhd13YjS0WrpHzqiGPAY31ijaRzuqIqu6y1eH/KImcdOW6fsMGmCYmOIGLiIjSHj8BJt2JabOFaAWblBTouKJ4zdITmkRUzgEDA9olzc2AYCkARr2vE+A/0W101OOUhWuE4QEIJaXo7jVCqDJpx6Oou2ApgDDq+ygJnHTluv7dd+EsLOQELiIiSnscAFNSRBs/dZ1fVASUlSmordXW4o2kcM9vgcjuK0GEXPd/Q6jTJpy5TxZFLS4gy5CUMhw/bobDIWLLhx9Gc+a7Wga4eSN2bwRa1smQBy5DWD0L2BUt4+Cxk4QoSWirm4BcJ0BoKNViCcVTsE4aYSlWIJrzIVm2Q74iwVJRhNKZTFjEaYjm+dAN6VFHCAJE0Yw2s8dpCwFlCSJkczOELTUQxRCRinCd4op0nBnDnwevYjNEVMB/0l7QnUlivWfIDkyjyW9ERKQ7HABTwkW75r/n+cXFwIYNRqxe7bEZRZ9HGSHyr6aFd7PP/hMh7lcKQShFW4M24cxNFCFBxFNPAS+9BMzMABs3NuCLX2xAc/PCKZCAc0dx6GgO+qQ59ImZaNsxDXH3Dq8ssXsyXfE2SK+cxomjOZClOYyKmcDxSZwYEHFOzseFiSxUFU9itHAc5olerRwgeEP6bmYherTPwYOQjp7BoeHrIK/NhzCWhTZzlOPEAO18+C8NOHtWxtAM8N73ujbu6IcAGW2tdog7N/mEnKPMHUf6xuGmEkREFCdmgCnhoo2f+p7vygAH24wiWP41gv0nIq6fK7Orqlr64eJF7XvPE+TBq5BNa9CodkM2rYE8eDV4lthq9Tvf2uuEPDwDsb4ANtkAUbkIOb96sZxYcryyDAwNQTaaIRtKtQ07hqajj+0GaWfXRhi+G3fIg1cDhJyjvGmkz5vIfDMREa1IHABTwkW75r/v+a4McLDNKPzyrwvfRrD/RMT1c2V2DQZt/lt5ufa95wlCZS4E5yV0GxohOC9BqMwNniW2WPzOt9SbIKzNhtTrgFlQIRnLIUwNLJYTy+YJggBUVEBQbBDUUW3Djoqc6GO7QdrZtRGG78YdQmXuwsE4cseRPi83lSAiojhxIwxKikARzVCxzcUMsILJSW1R8pERY+DzE5EBDrYEcKcNcsd5CAVzQF0djveZ4XAAW7YAzWafizo70fnqKKyOUliaVqG5Nd+7Ajabe+1gNDcvbqLhKqKhENKZMchdEpS1FTCWCd552kAVDbX+78KawlAUSH1T6BnIBaqr0dAqLL4WTWbWp2GHXBng978HFRXGxQywfBaiMAc0NETeAdF2TID66CIDrJMsMhfz1zf2j36xb/SLG2FQWvJd8z9cbNN1vqIAk5OBywhWuO95kYxDApUtddpw6PG3IXdfgpA1ibbbOnH73o8EXh9420Km93gxZExjNK8a5rpZiCdehF8geXRUy1GIIkRRXMwbSxJE6xsQr8rAhBW4oQ0QG4JXNNT6v6ZxtOEoROcgJFMlDuFWyM4iCHagwWYDTsSQmXXd25VnHhmBwenEGogAKhY28nACh7qBPo+wdlNT+LKj6ZgAzx73fRKBWWQiorTFCAQtiXSIbcpWB2RpFo3mMciGUsj9HmveBsv05ldrOdihae+1gCMJJMcZlvZa/3doWsvhNjZq9Rqa9ltvOO5sbqB1gJeqY/X4BtJjnYiIKCL8BJiWRDrENgVLAQQxC93dJRCyRiHUeqx5GyjT+5fLEAYG0I0qCM05ECwZi2sBu3ZKCxc0jiMs7bX+b0UOBOQC3d0QKishIMdvveG4s7mB1gFeqo7V4xtIj3UiIqKIMANMSyaSuGSq81ieGWCxtdY/x+GTx5V6JiBjYZ1fEdHnVGNdMDlUBlgQtDWAPYtNwPq8is2G/okJ1La2evfNUuVgdZK39aKTOqX654ZCY//oF/tGv5gBpmXHZot8zJDo8YVfeT4HxGYzxGZz4GuUDMBugtwzAUGYgNhQCPGmBkAC5OM9gMMKcUu5lvd1lakowJtvLk6E860PRMgQIcBnLeJg9Q2Zf178xnUqJAnokhc/oXT9mj7aTStEESgrg9LbG/i1pRj8LdV9oqHHOhERUVgcANOScM0XOncOuHABqKoC1q0LPW8o0XOM/MrbZoMYZnKY+5pzDpjO9gNjY3DaJyEsbHyBnTtx6IUZyC+dgjAzjDbLC9ogeNUq4MoV7dfjk5PaoLi93WsQHO754n5+zwJ8dwnZti2+TSuIiIjSGCfB0ZJwzRESRe0TYFEMP28o0XOM/MqLYHKY+xrRjsGLBgzNr/Xa+EK2OiBbJ7Rj5g2LG0I0Nmp/XrwItLQsLokWxfPF/fyeBQwOahPzPCbxcQIXERGtVPwEmJaE6zfw585pH4ZKkvYJcKh5Q4meY+RXXgSTw9zXnCtGZfkwMDbsvfGFpQCCJRvdXY0QbO9CsOQClnKtTIsFmJ4GTp7UHtprJ43wzxf383sWUFmpHfOYxIfRUU7gIiKiFYkDYFoSoqj9ln1hnwYYjeGjp65renpiu6fvBDFRltG2rQyy0QxBsUE0jmhRgBCVWax3AQSlFrAXe21kAdGMNjMgN78HgqMQ4pYdgNm8ODnur2wQJ3v9M8CSR33sGdoGGCiEZxJYFIE2yxnIVyQIFhGiuDHKZxchbLtNe85Ak/I8s8qR7FYSLZ1MECMiIvLFATAtmVjnC/X1aeMo1z4LkS6W4M7PemwSIQoCxCjzr4v1NgMw+01WE0VAvL0BQMPivftcqzCUoq1to/9OdguVE00mrTynE+jzqUtnJ8Tn9kO02YCTZqC0PeBEupDPLpjR1mZevH+g3UcCXxhfLpibRBARkY4xA0y6FmsO1us6j00iliL/GrbOobK5nidbrVp2OEiGOOb7J/zCJJdFRESUYPwEmHQt1hys13Uem0QsRf41bJ1DZXM9T7ZYtJhCkAxxzPdP+IVJLouIiCjBOAAmXXDFRYuKvI97Zoc99p8IGi2VJEDuGYUij6OuuBh1dQIaGoog2jYB1rzFLK5nThelQKcNstUBwVLgtw6wy9GfyujqmEXTlizs+H+0AZ20cJ2yugjGNaXubHGbxQDZeBmKAsg9FgClixtVKApQV6d9FRcDdrtWVnEjZNmsrQksQqtne7v2ye/q1VpWWZL8N8DwaQN3m/WMBswWB+Xb2ADQ1RVbhjdQxxEREekEB8CUcp5x0eJiYMMGI+rrF1/3jKqGipZKEnDo4DjOHb2AC8NGVK2dxLodRjQUzy5mfkdHtcEvRHdO1/SqA+juh3PyKgSzCW3t8BsEH/2pjC89OIcxRyZKCubwOGRsvEbBof09OHchAxeuzqNqkxPrZnvRVnwc4tBJYCwLh+zbIa+9AmFrLdoKXodo715cCNm1E6HTCclUiUPYDNnp81wLg3UcOgT86U/aLm/bbsOhE+aQ8VoREsS+hYbyzRaH4mrsQA1dVhZdx3KTCCIi0ilmgCnlfOOi4+PBt6MMFS2VZS3vKxpHYDOYIRpHtPxvgPV+PcsZss5i8KIBjS15kG1O7XwfXR2zGHOYsHnDDMYcJnR1zGprANucEOtXwzaWARGXtOvzqoHhYcjzxZANpWg09kK2Tmg5ZM+FkIeGtAxwY6O2pvDQdODIrM9Dy1ZH+HhtvBlcZniJiGgZ4yfAlHK+cdGiIiXicz2jpYKg5X3P9ZTBrNogKWVYV5EDwZLht96vgMVyKixZwLSK7pNXIJhN2vrAPpq2ZKGkYA5/fjcbJQVONG3JgmAxQjCbcK53EuaSHEhYg3VmB4TLA8DatRDG7BDUUXQrmyBYCiEU5ALSucWFkCsqtMK7uyFUVkJATuDIrM9DC5YCCOEizPFmcJnhJSKiZcygqqqa6kosJYfDgcLCQoyNjaHY9StoSrnFDLCCycle1NfXw2gM/ElwpBlgo1AEoaFUOyfARZ6HYEtsBhiKAtjtkORMyMI1Wj08M8CutYcBdyUkiEGfy7f+ES2xG+86vD7XK4qC3t7QfUOpwb7RN/aPfrFv9Mtut6OkpAQTExMoKPD/YCpe/ASYohJuTCVJixtXNDQEH3e5zvvLX4DJSS0SW1enHfvNbwoxPw+UlwMFBdpYccsWLQ7ruv+lS1qsd2wMKCkB8vOBzExtrtiaNaVAcSl6+oA3/gO4ehVYv17ElppMFMMOozAKoXgesNsxKpdiVBAwMmLG8LAZa+eAsj4ZcDjQsCXPPRjuPDqKyeEruGnnKpg3Cotzw5rNPgNm88KXRoTX1hbeg9eFozJECDYbRPsx7VyhAZIkoqdnsa0bGkRAWBgg22wLE+cEQCj16xdgoQ/6ptBgvACx+lJsA+EwGV7uc0FEROmKA2CKWLi9DSQJOHgQOH5c+761Fdi9O/CntAcPAi+/DJw5o30gWlysDYInJoDu7lIoCpCRAaxapb3W3Azs3astiHD6NPD661o9HA7tnIwMYM0awGQCNm3S7nH6NDA8rJWfm6OgsXQONfmzuGZNL4pzp+G4asJpeyZspnycH85GRgYwPzeH6lXTWJM9jdaNl7D7i4BtNAP7vzQG21gG8vPnsPHmLKwyF8S0v4NnG5oWfvqcdgeEnrfRduUnEHPGIW3aiYP4OI7+qdC16AO2btX+MeC0O2Dq6QeuXIYzZwJCqwnbdha55/iZTFqbnP7/7MDZMbRm9mN30WGIN9ZrDZOgDSm4zwUREaUzToKjiIWbFyXL2ryu/Hzta3Aw8NwpWdZeUxRtA7SsLGB2Vlsc4fJlwOk0ICsLmJsDpqeBwkJtwNXVpV2bl6f9mZmpnZORoQ36iou1T4QBrR5XrgCqqn2CPDcDXL5iwEXFDNE5hMEhwDpfg3x1CjNXnHA4tEiuwwHMXFWRb87D4EUDZKsD1q4rsI1loGXzPCQ5E/29SkLmlrnnwIl2yNIsZKMZyM+H3D+BQescTCat/iYT0N+/sF+GaMfQcAYGjTVozL8AeWjaa1+PwUHtHwn5igP5znEMrqrT6rhqVUIns3GOHBERpTMOgCli4eZFCYI2iJya0r4qKwPPnRIE7TWjURvczc5qg+CqKm1wazKpmJ3VBrg5OdqnwqIINDVp116+rP05N6edMz+vfTpqt2txCECrx6pVgMGgDbQzs4G8VSrKjTZIpgpUVgCWjPOYMuQje5UJBQXaALOgAMjONWDKdhmV5SoESwEsTatgLpnHyT9nQBTmUFtvTMjcsooKrR26pWIIYhYExQZMTUGoLUSlJRNOp1Z/pxOordXO75aKUbF2HpXKeXRPVUGoyIHF4r2vhsUCTBkLMGUqQuWVPq2OV64kdDIb58gREVE6YwSCIhZubwNR1CIPLS3a98EywJ7n+WaAR0eB3/xmFPPz5qAZ4OuvB267DRgYCJYB1ga9fX3AG2+4MsDGhQxwFoxCvTsD3CMXAkI2Rka0uMTatZkoy8gBHLNo2CJoGV8A7Y8D1jeGYRGnYd62GrJRi0CIkIBjAULPQQKygfaakOUCCMp1EO052jkNDdiNQrR4ZYA9z60F7HZtE4+GIoiitrCEVwb4r4uBvhI0TAkQV98OrF8fOpTtK0zAd8Xsc8GgMxHRssRVIEhXdDsjN1DoFQgcegb0EZCNNagb5Drd9k2ypFHQecX1TZph/+gX+0a/kr0KBCMQRJEIFHp1hZldoeehIe/XUh2QjbUeeql/qrEdiIiWLUYgiCIRLPRaWallMQAto+E6roeAbKxBXQZ8NWwHIqJliwNgolA8M6CBQq/BQs96CMjGGtTVS8A31fnbQO2Q6joREVFCcABMFEygDGhTk/c5wTaLCLOJxJKJtR6prr9e8ree7aCXOhERUdyYASYKhhnQ1NFj2+uxTkREFBMOgImCYQY0dfTY9nqsExERxYQRCKJg9JKFXYn02PZ6rBMREcWEA2CiUFKdhV3J9Nj2eqwTERFFjREIIiIiIlpROAAmIiIiohWFA2AiIiIiWlE4ACai8CQJ6OqCcWQk1TUhIiKKGyfBEVForg0gRkaQ53QCtbVARUWqa0VERBQzfgJMRKG5Nn3YsAGmiQluAEFERGmPA2AiCs216cO778JZWMgNIIiIKO0xAkFEobk2gLDZcHliguvgEhFR2uMAmIjCE0WgrAxKb2+qa0JERBS3lEcgnnjiCVgsFuTk5KClpQWvv/56yPOPHTuGlpYW5OTkoLa2Fk899dQS1ZSIiIiIloOUDoCff/55PPDAA/jiF7+It99+GzfeeCNuueUWDAwMBDzfarXi1ltvxY033oi3334bjzzyCO677z787Gc/W+KaExEREVG6SukAeP/+/fj0pz+Ne+65B42NjThw4ACqqqrw5JNPBjz/qaeeQnV1NQ4cOIDGxkbcc889uPvuu/Hv//7vS1xzIiIiIkpXKcsAz87O4uTJk3jooYe8ju/atQvHjx8PeM0bb7yBXbt2eR374Ac/iGeeeQZzc3PIzMz0u2ZmZgYzMzPu7ycmJgAA4+PjcT4BJYOiKHA4HLDb7TAajamuDnlg3+gX+0bf2D/6xb7RL9c4TVXVpJSfsgHw6OgoFEXBmjVrvI6vWbMGkiQFvEaSpIDnO51OjI6OYu3atX7X7Nu3D1/5ylf8jtfW1sZReyIiIiJKNlmWUVhYmPByU74KhMFg8PpeVVW/Y+HOD3Tc5eGHH0Z7e7v7+/HxcdTU1GBgYCApDUrxcTgcqKqqwoULF1BQUJDq6pAH9o1+sW/0jf2jX+wb/ZqYmEB1dTVKSkqSUn7KBsClpaUwGo1+n/babDa/T3ldRFEMeL7JZIIQZHH+7OxsZGdn+x0vLCzkm13HCgoK2D86xb7RL/aNvrF/9It9o18ZGcmZrpaySXBZWVloaWnBkSNHvI4fOXIEra2tAa/Zvn273/m//e1vsXXr1oD5XyIiIiIiXyldBaK9vR1PP/00vv/976O7uxuf//znMTAwgL179wLQ4gt79uxxn793716cP38e7e3t6O7uxve//30888wz+Jd/+ZdUPQIRERERpZmUZoDvuOMOyLKMr371qxgeHsamTZtw+PBh1NTUAACGh4e91gS2WCw4fPgwPv/5z+M73/kOysvL8e1vfxsf/ehHI75ndnY2Hn300YCxCEo99o9+sW/0i32jb+wf/WLf6Fey+8agJmt9CSIiIiIiHUr5VshEREREREuJA2AiIiIiWlE4ACYiIiKiFYUDYCIiIiJaUTgAJiIiIqIVZVkMgPft24dt27Zh9erVMJvN+MhHPoKenh6vc1RVxWOPPYby8nLk5ubi5ptvRldXl9c5MzMz+NznPofS0lLk5eXhb/7mbzA4OLiUj7LsPPnkk2hubnbvsrN9+3a8/PLL7tfZL/qxb98+GAwGPPDAA+5j7J/Ueeyxx2AwGLy+RFF0v86+Sa2hoSF88pOfhCAIWLVqFd7znvfg5MmT7tfZP6mzbt06v58dg8GAe++9FwD7JpWcTie+9KUvwWKxIDc3F7W1tfjqV7+K+fl59zlL1j/qMvDBD35QffbZZ9XTp0+rp06dUtva2tTq6mp1amrKfc43vvENdfXq1erPfvYz9Z133lHvuOMOde3atarD4XCfs3fvXrWiokI9cuSI2tHRof71X/+1unnzZtXpdKbisZaFX/3qV+qhQ4fUnp4etaenR33kkUfUzMxM9fTp06qqsl/04q233lLXrVunNjc3q/fff7/7OPsndR599FG1qalJHR4edn/ZbDb36+yb1BkbG1NramrUu+66S33zzTdVq9WqvvLKK2pfX5/7HPZP6thsNq+fmyNHjqgA1FdffVVVVfZNKj3++OOqIAjqSy+9pFqtVvUnP/mJmp+frx44cMB9zlL1z7IYAPuy2WwqAPXYsWOqqqrq/Py8Koqi+o1vfMN9zvT0tFpYWKg+9dRTqqqq6vj4uJqZman++Mc/dp8zNDSkZmRkqL/+9a+X9gGWueLiYvXpp59mv+jE5OSkWl9frx45ckS96aab3ANg9k9qPfroo+rmzZsDvsa+Sa0HH3xQveGGG4K+zv7Rl/vvv19dv369Oj8/z75Jsba2NvXuu+/2Onb77bern/zkJ1VVXdqfnWURgfA1MTEBACgpKQEAWK1WSJKEXbt2uc/Jzs7GTTfdhOPHjwMATp48ibm5Oa9zysvLsWnTJvc5FB9FUfDjH/8Yly9fxvbt29kvOnHvvfeira0NO3fu9DrO/km93t5elJeXw2Kx4GMf+xj6+/sBsG9S7Ve/+hW2bt2Kv/3bv4XZbMZ1112H//qv/3K/zv7Rj9nZWfzgBz/A3XffDYPBwL5JsRtuuAG/+93vcPbsWQDAn//8Z/zhD3/ArbfeCmBpf3ZSuhVyMqiqivb2dtxwww3YtGkTAECSJADAmjVrvM5ds2YNzp8/7z4nKysLxcXFfue4rqfYvPPOO9i+fTump6eRn5+Pn//859i4caP7jcp+SZ0f//jH6OjowIkTJ/xe489Nar33ve/Fc889h2uuuQaXLl3C448/jtbWVnR1dbFvUqy/vx9PPvkk2tvb8cgjj+Ctt97Cfffdh+zsbOzZs4f9oyO/+MUvMD4+jrvuugsA/15LtQcffBATExPYsGEDjEYjFEXB1772NXz84x8HsLT9s+wGwJ/97GfR2dmJP/zhD36vGQwGr+9VVfU75iuScyi0hoYGnDp1CuPj4/jZz36GO++8E8eOHXO/zn5JjQsXLuD+++/Hb3/7W+Tk5AQ9j/2TGrfccov7v6+99lps374d69evx//8z//g+uuvB8C+SZX5+Xls3boVX//61wEA1113Hbq6uvDkk09iz5497vPYP6n3zDPP4JZbbkF5ebnXcfZNajz//PP4wQ9+gIMHD6KpqQmnTp3CAw88gPLyctx5553u85aif5ZVBOJzn/scfvWrX+HVV19FZWWl+7hr5rTvvwxsNpv7XxmiKGJ2dhZ2uz3oORSbrKws1NXVYevWrdi3bx82b96Mb33rW+yXFDt58iRsNhtaWlpgMplgMplw7NgxfPvb34bJZHK3L/tHH/Ly8nDttdeit7eXPzsptnbtWmzcuNHrWGNjIwYGBgDw/zl6cf78ebzyyiu455573MfYN6n1r//6r3jooYfwsY99DNdeey0+9alP4fOf/zz27dsHYGn7Z1kMgFVVxWc/+1m88MILOHr0KCwWi9frFosFoijiyJEj7mOzs7M4duwYWltbAQAtLS3IzMz0Omd4eBinT592n0OJoaoqZmZm2C8p9v73vx/vvPMOTp065f7aunUrPvGJT+DUqVOora1l/+jIzMwMuru7sXbtWv7spNj73vc+v6U2z549i5qaGgD8f45ePPvsszCbzWhra3MfY9+k1pUrV5CR4T30NBqN7mXQlrR/opi8p1v/9E//pBYWFqqvvfaa19InV65ccZ/zjW98Qy0sLFRfeOEF9Z133lE//vGPB1xWo7KyUn3llVfUjo4OdceOHVz2JE4PP/yw+vvf/161Wq1qZ2en+sgjj6gZGRnqb3/7W1VV2S9647kKhKqyf1Lpn//5n9XXXntN7e/vV//4xz+qH/7wh9XVq1er586dU1WVfZNKb731lmoymdSvfe1ram9vr/rDH/5QXbVqlfqDH/zAfQ77J7UURVGrq6vVBx980O819k3q3HnnnWpFRYV7GbQXXnhBLS0tVb/whS+4z1mq/lkWA2AAAb+effZZ9znz8/Pqo48+qoqiqGZnZ6t/9Vd/pb7zzjte5Vy9elX97Gc/q5aUlKi5ubnqhz/8YXVgYGCJn2Z5ufvuu9Wamho1KytLLSsrU9///ve7B7+qyn7RG98BMPsndVxrX2ZmZqrl5eXq7bffrnZ1dblfZ9+k1osvvqhu2rRJzc7OVjds2KB+73vf83qd/ZNav/nNb1QAak9Pj99r7JvUcTgc6v33369WV1erOTk5am1trfrFL35RnZmZcZ+zVP1jUFVVjeVjbCIiIiKidLQsMsBERERERJHiAJiIiIiIVhQOgImIiIhoReEAmIiIiIhWFA6AiYiIiGhF4QCYiIiIiFYUDoCJiIiIaEXhAJiIiIiIVhQOgImIiIhoReEAmIgojd18880wGAwwGAw4depUVNfedddd7mt/8YtfJKV+RER6xAEwEZFO/PrXv3YPSIN9vfzyy37X/cM//AOGh4exadMm97G77roLH/nIR7zO++lPf4qcnBx885vfBAB861vfwvDwcFKfiYhIj0yprgAREWluuukmrwHppk2b8I//+I/43Oc+5z5WWlrqd92qVasgimLIsp9++mnce++9+M53voN77rkHAFBYWIjCwsIE1Z6IKH1wAExEpBO5ubnIzc0FAAwNDUGWZdxwww1hB7fhfPOb38SXv/xlHDx4EB/96EcTUVUiorTGATARkQ69/fbbAICWlpa4ynnooYfwne98By+99BJ27tyZiKoREaU9DoCJiHSoo6MDFRUVMJvNMZfx8ssv45e//CV+97vfYceOHQmsHRFReuMkOCIiHero6MCWLVviKqO5uRnr1q3Dl7/8ZUxOTiaoZkRE6Y8DYCIiHero6Ig7/lBRUYFjx45heHgYH/rQhzgIJiJawAEwEZHOyLKMCxcuxP0JMABUV1fj2LFjsNls2LVrFxwORwJqSESU3jgAJiLSmZMnTwJAQgbAAFBZWYnXXnsNsixj165dmJiYSEi5RETpigNgIiKdefvtt2E2m1FRUZGwMl1xiPHxcXzgAx/A+Ph4wsomIko3BlVV1VRXgoiIYnPzzTfjPe95Dw4cOBBzGQaDAT//+c/9do4jIlqu+AkwEVGae+KJJ5Cfn4933nknquv27t2L/Pz8JNWKiEi/+AkwEVEaGxoawtWrVwFoE96ysrIivtZms7knxa1duxZ5eXlJqSMRkd5wAExEREREKwojEERERES0onAATEREREQrCgfARERERLSicABMRERERCsKB8BEREREtKJwAExEREREKwoHwERERES0onAATEREREQrCgfARERERLSi/P//5AUnxSThJAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 800x550 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 5.5) )\n",
+    "           #, dpi=300)\n",
+    "                         #gridspec_kw={'width_ratios': [1.35, 1]})\n",
+    "i=0\n",
+    "x1, y1, x2, y2, this_ylabel = datasets[i]\n",
+    "this_leftlim, this_rightlim = this_xlim[i]\n",
+    "this_bottomlim, this_toplim = this_ylim[i]\n",
+    "\n",
+    "plt.plot(x1, y1,\n",
+    "        #'rs', ms=2.5, label='Melting')\n",
+    "        color='red', marker='o', linewidth=0, markersize=2,\n",
+    "        label='Melting', alpha=.3)\n",
+    "plt.plot(x2, y2,\n",
+    "        #'bo', ms=2.5, label='Freezing')\n",
+    "        color='blue', marker='o', linewidth=0, markersize=2,\n",
+    "        label='Freezing', alpha=.3)\n",
+    "\n",
+    "#plt.set_title(specific_title, \n",
+    "#             fontsize=15, fontweight='bold', pad=15)\n",
+    "plt.xlabel('$T$ [K]')\n",
+    "           #, fontsize=14, labelpad=8)\n",
+    "#if i==0:\n",
+    "plt.ylabel(this_ylabel)\n",
+    "           #fontsize=14, labelpad=8)\n",
+    "plt.xlim(this_leftlim, this_rightlim)\n",
+    "plt.ylim(this_bottomlim, this_toplim)\n",
+    "#plt.tick_params(axis='both')\n",
+    "#plt.grid(True, color='#E0E0E0', linestyle='-', linewidth=0.8)\n",
+    "plt.grid(True, alpha=0.5)\n",
+    "plt.legend(loc=leg_loc[i])\n",
+    "              #frameon=True, facecolor='white', edgecolor='#D0D0D0', framealpha=1)\n",
+    "#plt.tight_layout()\n",
+    "plt.savefig('NEW_IMGS/0count_CNAP.pdf', dpi=300)\n",
+    "            #, bbox_inches='tight')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "3e586137-7897-45dc-83d5-c13cb50a25fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x550 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 5.5) )\n",
+    "           #, dpi=300)\n",
+    "                         #gridspec_kw={'width_ratios': [1.35, 1]})\n",
+    "i=1\n",
+    "x1, y1, x2, y2, this_ylabel = datasets[i]\n",
+    "this_leftlim, this_rightlim = this_xlim[i]\n",
+    "this_bottomlim, this_toplim = this_ylim[i]\n",
+    "\n",
+    "plt.plot(x1, y1,\n",
+    "        #'rs', ms=2.5, label='Melting')\n",
+    "        color='red', marker='o', linewidth=0, markersize=2,\n",
+    "        label='Melting', alpha=.3)\n",
+    "plt.plot(x2, y2,\n",
+    "        #'bo', ms=2.5, label='Freezing')\n",
+    "        color='blue', marker='o', linewidth=0, markersize=2,\n",
+    "        label='Freezing', alpha=.3)\n",
+    "\n",
+    "#plt.set_title(specific_title, \n",
+    "#             fontsize=15, fontweight='bold', pad=15)\n",
+    "plt.xlabel('$T$ [K]')\n",
+    "           #, fontsize=14, labelpad=8)\n",
+    "#if i==0:\n",
+    "plt.ylabel(this_ylabel)\n",
+    "           #fontsize=14, labelpad=8)\n",
+    "\n",
+    "plt.xlim(this_leftlim, this_rightlim)\n",
+    "plt.ylim(this_bottomlim, this_toplim)\n",
+    "\n",
+    "#plt.tick_params(axis='both')\n",
+    "\n",
+    "#plt.grid(True, color='#E0E0E0', linestyle='-', linewidth=0.8)\n",
+    "plt.grid(True, alpha=0.5)\n",
+    "plt.legend(loc=leg_loc[i])\n",
+    "              #frameon=True, facecolor='white', edgecolor='#D0D0D0', framealpha=1)\n",
+    "\n",
+    "#plt.tight_layout()\n",
+    "\n",
+    "plt.savefig('NEW_IMGS/NEWcount_CNAP.pdf', dpi=300)\n",
+    "            #, bbox_inches='tight')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e91feffe-7bfe-42ea-a9a4-c9dcd27bc2c0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/cnapQuenchMelting.ipynb b/cnapQuenchMelting.ipynb
new file mode 100644
index 00000000..cc3da1f0
--- /dev/null
+++ b/cnapQuenchMelting.ipynb
@@ -0,0 +1,1023 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "58765919",
+   "metadata": {},
+   "source": [
+    "Define pySnow location and allow python to look it up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "48a9927a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "from pathlib import Path\n",
+    "# 1. Define the absolute path to the directory containing the 'snow' folder\n",
+    "# (Replace this with the actual path where your 'snow' directory lives)\n",
+    "local_packages_path = Path(\"/home/tmlkyza/Documents/Universita/Master/nanopants/lcm/pySNOW\")\n",
+    "\n",
+    "# 2. Convert to an absolute string and inject it into Python's search path\n",
+    "sys.path.append(str(local_packages_path.resolve()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0afb3497",
+   "metadata": {},
+   "source": [
+    "Get all the sims dir and look at all the runs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "31b9d741",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "script_dir= Path('.')\n",
+    "\n",
+    "# Find all directories in the current folder ending with 'Sims'\n",
+    "sims_root = [\n",
+    "    p for p in script_dir.iterdir() \n",
+    "    if p.is_dir() and p.name.endswith('quenchMeltingSims')\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "49bf3ba8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Extract the real, absolute path of every subfolder inside the Sims directories\n",
+    "sims_runs = []\n",
+    "for sim in sims_root:\n",
+    "    sims_runs.append([\n",
+    "        run.resolve() for run in sim.iterdir() \n",
+    "        if run.is_dir()\n",
+    "    ])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "78fa3bb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n",
+      "[]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Safely print the true absolute paths of every single item inside those runs\n",
+    "movies = []\n",
+    "outs = []\n",
+    "for root in sims_runs:\n",
+    "    for run in root:\n",
+    "        for item in run.glob('*.xyz'):\n",
+    "            if item.name == \"movie.xyz\":\n",
+    "                movies.append(item)\n",
+    "        # 2. Grab all out[number].xyz files in this folder\n",
+    "        out_files = list(run.glob('out*.xyz'))\n",
+    "        \n",
+    "        if out_files:\n",
+    "            # Find the file with the maximum numeric value\n",
+    "            # item.stem gives us 'out11', [3:] strips 'out' to leave '11', int() makes it 11\n",
+    "            highest_out = max(out_files, key=lambda item: int(item.stem[3:]))\n",
+    "            outs.append(highest_out)\n",
+    "\n",
+    "print(movies)\n",
+    "print(outs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "680746c9",
+   "metadata": {},
+   "source": [
+    "Start by computing the coordination number and appending it to xyz, such that we can read it with ovito. The output dir is cns/[processType]/run_natoms.xyz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ddc8d398",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#define some useful functions, you can ignore this\n",
+    "\n",
+    "def getProcess(file):\n",
+    "    return file.parent.parent.name\n",
+    "\n",
+    "def FileOut(file, subDirName, filename):\n",
+    "    run_folder = file.parent       # This gives 'run_14'\n",
+    "    process_folder = run_folder.parent  # This gives 'growthSims'\n",
+    "\n",
+    "    process_type = process_folder.name  # Extracts the text string 'growthSims'\n",
+    "    run_name = run_folder.name          # Extracts the text string 'run_14'\n",
+    "\n",
+    "    output_dir = script_dir / subDirName / process_type\n",
+    "    output_dir.mkdir(parents=True, exist_ok=True) # Automatically creates the cns/ and [processtype]/ folders if they don't exist\n",
+    "\n",
+    "    #return output_dir / f\"{run_name}.xyz\"\n",
+    "    return output_dir / filename\n",
+    "\n",
+    "def writeDict(data_dict, file_path):\n",
+    "    field_names = list(data_dict.keys())\n",
+    "    columns = [data_dict[key] for key in field_names]\n",
+    "    with open(file_path, \"w\") as outfile:\n",
+    "        header_line = \"# \" + \" \".join(field_names) + \"\\n\"\n",
+    "        outfile.write(header_line)\n",
+    "        for row in zip(*columns):\n",
+    "            row_line = \" \".join(str(val) for val in row) + \"\\n\"\n",
+    "            outfile.write(row_line)\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a7abba6",
+   "metadata": {},
+   "source": [
+    "Generate PDDF RDF in .dat files and append CNS and CNAPS to existing .xyz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "863a210c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from snow.io.xyz import read_xyz, write_xyz\n",
+    "from snow.descriptors.coordination import coordination_number\n",
+    "from snow.descriptors import cna\n",
+    "from snow.descriptors.distributions import pddf_calculator, com_rdf_calculator\n",
+    "from collections import Counter\n",
+    "\n",
+    "alat   = 4.087077141 # a lattice for Ag also checksout with https://reference.wolfram.com/language/ref/ElementData.html\n",
+    "cutoff = 0.85*alat\n",
+    "\n",
+    "for file in outs:\n",
+    "    el, coords = read_xyz(file)\n",
+    "    cns   = coordination_number(coords, cutoff)\n",
+    "    cnaps = cna.cnap_peratom(coords, cutoff)\n",
+    "\n",
+    "    write_xyz(FileOut(file, \"GCN\", f\"{file.parent.name}.xyz\"), el, coords, additional_data={\"cns\":cns,\"cnaps\":cnaps})\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "55a63173",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "\n",
+    "# 1. Your specified directory path\n",
+    "target_dir = Path(\n",
+    "    \"/home/tmlkyza/Documents/Universita/Master/nanopants/lcm/growth_Melting_Silver_ICO/growth_Melting_Silver_ICO_3269/GCN/quenchMeltingSims\"\n",
+    ")\n",
+    "\n",
+    "# Initialize a list to store data from each file\n",
+    "data_summary = []\n",
+    "\n",
+    "# 2. Read and parse all .xyz files\n",
+    "for file_path in target_dir.glob(\"*.xyz\"):\n",
+    "    with open(file_path, \"r\") as f:\n",
+    "        lines = f.readlines()\n",
+    "\n",
+    "    if not lines:\n",
+    "        continue\n",
+    "\n",
+    "    # The 1st line contains the total number of atoms\n",
+    "    num_atoms = int(lines[0].strip())\n",
+    "\n",
+    "    # Collect cnaps values (the last column, index -1)\n",
+    "    # Skip the first two lines (header metadata)\n",
+    "    cnaps_values = []\n",
+    "    for line in lines[2:]:\n",
+    "        parts = line.split()\n",
+    "        if parts:  # Ensure the line isn't empty\n",
+    "            try:\n",
+    "                # Convert float string to int (e.g., 14.000000 -> 14)\n",
+    "                cnaps = int(float(parts[-1]))\n",
+    "                cnaps_values.append(cnaps)\n",
+    "            except (ValueError, IndexError):\n",
+    "                continue\n",
+    "\n",
+    "    if not cnaps_values:\n",
+    "        continue\n",
+    "\n",
+    "    # Count frequencies of each cnaps value\n",
+    "    cnaps_series = pd.Series(cnaps_values)\n",
+    "    cnaps_counts = cnaps_series.value_counts()\n",
+    "\n",
+    "    # Calculate percentages based on total atom count from line 1\n",
+    "    cnaps_percentages = (cnaps_counts / num_atoms)\n",
+    "\n",
+    "    # Store results along with the atom number (X-axis value)\n",
+    "    for cnaps_val, percentage in cnaps_percentages.items():\n",
+    "        data_summary.append(\n",
+    "            {\n",
+    "                \"Atoms_Number\": num_atoms,\n",
+    "                \"CNAPS_Value\": cnaps_val,\n",
+    "                \"Percentage\": percentage,\n",
+    "            }\n",
+    "        )\n",
+    "\n",
+    "# 3. Create a DataFrame\n",
+    "df = pd.DataFrame(data_summary)\n",
+    "\n",
+    "if not df.empty:\n",
+    "    # Pivot the data so:\n",
+    "    # - Rows (Index) = Atoms_Number (X-axis)\n",
+    "    # - Columns = CNAPS_Value (The stacks)\n",
+    "    # - Values = Percentage (Y-axis)\n",
+    "    # aggfunc='mean' handles cases if multiple files share the exact same atom count\n",
+    "    df_pivot = df.pivot_table(\n",
+    "        index=\"Atoms_Number\",\n",
+    "        columns=\"CNAPS_Value\",\n",
+    "        values=\"Percentage\",\n",
+    "        aggfunc=\"mean\",\n",
+    "    ).fillna(0)\n",
+    "# --- CUT DATA UP TO 50 ATOMS ---\n",
+    "    # This filters the index (Atoms_Number) to only include values <= 50\n",
+    "    df_pivot = df_pivot[df_pivot.index <= 60]\n",
+    "    # Sort the index so the X-axis grows sequentially\n",
+    "    df_pivot = df_pivot.sort_index()\n",
+    "\n",
+    "    # 4. Plotting the Stacked Histogram / Bar Chart\n",
+    "    # You can change the color map via `cmap` if you want a different palette (e.g., 'viridis', 'tab10')\n",
+    "    ax = df_pivot.plot(kind=\"bar\", stacked=True, figsize=(14, 7), width=0.8)\n",
+    "\n",
+    "    # Formatting the graph\n",
+    "    plt.xlabel(\"Number of Atoms (System Size)\")\n",
+    "    plt.ylabel(\"CNAPS#/Total Atoms\")\n",
+    "    plt.title(\"Stacked Distribution of CNAPS Percentages by cluster size\")\n",
+    "    plt.grid(axis=\"y\", linestyle=\"--\", alpha=0.5)\n",
+    "\n",
+    "    # Place the legend outside the plot area so it doesn't cover data points\n",
+    "    plt.legend(title=\"CNAPS Type\", bbox_to_anchor=(1.02, 1), loc=\"upper left\")\n",
+    "\n",
+    "    # Optional: If you have too many unique atom numbers, the X-axis text can overlap.\n",
+    "    # This line thins out the labels to show every 5th label (uncomment if needed):\n",
+    "    # ax.set_xticklabels([label.get_text() if i % 5 == 0 else '' for i, label in enumerate(ax.get_xticklabels())])\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "else:\n",
+    "    print(\"No valid data was extracted. Check if the directory contains .xyz files.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "6c3c00b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Starting Stage 1: Processing Simulations ---\n",
+      "Stage 1 complete. Files written to GCN folders.\n",
+      "\n",
+      "--- Starting Stage 2: Aggregating & Averaging Statistical Runs ---\n",
+      "Total unique sizes found: 86\n",
+      "Filtering to display the first 50 sizes (From 14 to 60 atoms)\n",
+      "\n",
+      "--- Summary of Consolidated Statistical Averages (First 50 Sizes) ---\n",
+      "    System Size  CNAPS Type  Average Percentage\n",
+      "0            14          14            0.571429\n",
+      "1            14           0            0.428571\n",
+      "2            15           0            0.866667\n",
+      "3            15           8            0.066667\n",
+      "4            15          14            0.066667\n",
+      "5            16           0            0.750000\n",
+      "6            16          14            0.250000\n",
+      "7            17           0            0.588235\n",
+      "8            17          14            0.352941\n",
+      "9            17           5            0.058824\n",
+      "10           18           0            0.611111\n",
+      "11           18          16            0.055556\n",
+      "12           18           2            0.166667\n",
+      "13           18           8            0.055556\n",
+      "14           18          11            0.055556\n",
+      "15           18          10            0.055556\n",
+      "16           19          14            0.210526\n",
+      "17           19           0            0.789474\n",
+      "18           20           0            0.550000\n",
+      "19           20          15            0.150000\n"
+     ]
+    },
+    {
+     "data": {
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u3759m/Tv3LlzevfunSVLluz0sQLb5uabb87FF19c//N+++2XadOm5fjjj0/UPrRKzz77bCZOnJgJEyakqqoqixYtatJH7UPrMmTIkJx55pk58MADs2bNmsyePTu33XZb/vCHP2T+/PnZe++91T20MgsXLkySjBkzJlVVVbnllltSqVQyZcqUXHzxxamurs6ECRPUPoJb2NWMHTs28+bNy7e+9a0cfPDByZvfOp03X7ib06VLl/o+wK7j9NNPzyGHHJJXX301CxYsyPTp0xvdJkHtQ+tz4YUXpn///vna17622T5qH1qXTa+WGzVqVI499tiMHj06EydOzI9//GN1D61M3W1Runfvnrlz59bX9tlnn53DDjss1157bS666CK1j1slwK7kiiuuyJQpUzJmzJhcdtll9du7deuWJFm/fn2zx61du7a+D7Dr6Nu3b0444YScfvrpufrqq3P77bfn61//eq699tpE7UOr8/Of/zy//e1vc9NNN6Vjx46b7af2ofU755xzsv/++2fGjBmJuodWp2vXrkmSkSNHNgplO3XqlFGjRmXt2rWZP3++2kdwC7uKq666Ktdcc01Gjx6dqVOnplKp1O/bd999k6TZ/yKxbt26vPLKK83+1wpg1zJw4MB84AMfyJQpUxK1D61KbW1tvvKVr+SUU05Jv379smjRoixatKi+vletWpVFixalpqZG7UMbsf/+++fll19OvOdDq1NXr3vvvXeTfXXbli9frvYR3MKu4Oqrr87VV1+dz3zmM7ntttvSrl3j0t1rr73St2/f/OUvf2ly7Lx581IURYYMGbITRwzsKGvXrs3y5csTtQ+typo1a/Lf//439913X6qqqurbMccck7x5NW5VVVVuuukmtQ9tQFEU+de//pW99tor8Z4Prc7QoUOTJIsXL26y77nnnkuS9OnTR+0juIWymzRpUq666qqMGjUqt99+e5PQts7IkSPzn//8J7/+9a8bbf/ud7+bDh065Oyzz95JIwa2VcNvkW5o9uzZeeKJJ+pP9KL2odXo3r177rnnniZt6tSpSZKTTjop99xzT0aMGJGofWg1XnrppWa3//CHP8ySJUvyyU9+sn6buofW47TTTkuPHj3y05/+NDU1NfXbV61alWnTpuVd73pXjjrqqETtt3mVoiiKlh4E0Lwbb7wxF110Ufr165dJkyalffv2jfb36dMnJ554YpKkuro6gwcPztKlSzNu3LhUVVXl3nvvzX333ZcJEyZk0qRJLTQLYGudccYZefHFF/ORj3wk/fv3z7p16/LII4/kF7/4Rbp165Y5c+bk8MMPT9Q+tHqLFi1KVVVVzj///PzoRz+q3672oXUYN25cZs2alVNOOSX9+/fP2rVrM2fOnEyfPj0HHnhgHnjggfTu3TtR99Dq3HrrrfnCF76Qgw46KGPGjEmlUsktt9ySp59+OrfffntGjx6dqP02T3ALJXbuuedm2rRpm91/7LHHZs6cOfU/v/jii7nssssyY8aMrFy5Mu9973tz0UUX5YILLthJIwa2h7vuuivTpk3LY489lpdffjmVSiX9+/fPiSeemEsvvTT9+vVr1F/tQ+u1ueA2ah9ahd/85jeZMmVKnnjiiSxbtiyVSiUDBgzI6aefnksvvTQ9e/Zs1F/dQ+syffr0fOc738lf//rXFEWRI444IuPHj8/HP/7xRv3UftsluAUAAAAAKBn3uAUAAAAAKBnBLQAAAABAyQhuAQAAAABKRnALAAAAAFAyglsAAAAAgJIR3AIAAAAAlIzgFgAAAACgZAS3AAAAAAAlI7gFAAAAACgZwS0AAAAAQMkIbgEA2GkGDhyYSqWSzp07p6ampsn+oijSq1ev7Lnnni0yPgAAKAvBLQAAO8W6devy1FNPJUlqa2szffr0Jn3+/e9/p6amJoMGDWqBEQIAQHkIbgEA2Cn+9re/5fXXX89JJ52Ujh075u67727S55FHHkkSwS0AAG1eh5YeAAAAbcOjjz6aJDnhhBNSFEV+97vf5dVXX81uu+1W36cuuD3iiCNabJwAAFAGrrgFAGCnaHg17fDhw7Nu3brMnDmzUZ+6cNcVtwAAtHWVoiiKlh4EAACt36BBg7JgwYJUV1dn/fr12WeffXLmmWfmzjvvrO+zxx57ZOPGjamurk6lUmnR8QIAQEtyxS0AADtcbW1tnnjiiRxwwAHp1atX+vTpk6OPPjozZszI2rVrkyTPPPNMqqurM2jQIKEtAABtnuAWAIAd7rHHHstrr73W6BYII0aMyOrVq/P73/8+cZsEAABoRHALAMAOVxfKNvzSseHDh6dSqeTuu+9OfDEZAAA0IrgFAGCHa/jFZHX69u2bIUOGZPr06amtrW22DwAAtFWCWwAAdrjN3QZhxIgRqampyaxZs7JgwYLsvvvuOeigg1polAAAUB6CWwAAdqjXXnstjz/+ePr165fevXs32jdixIgkyfXXX59ly5bl8MMP98VkAAAguAUAYEd78skns379+mZvgTBgwIC8//3vz5/+9KfE/W0BAKCe4BYAgB3q7e5dW3fV7Vv1AQCAtqZSFEXR0oMAAAAAAOD/XHELAAAAAFAyglsAAAAAgJIR3AIAAAAAlIzgFgAAAACgZAS3AAAAAAAlI7gFAAAAACgZwS0AAAAAQMkIbgEAAAAASkZwCwAAAABQMoJbAAAAAICSEdwCAAAAAJSM4BYAAAAAoGT+B8VjhhkkGmiVAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "from pathlib import Path\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from collections import Counter\n",
+    "\n",
+    "# ==============================================================================\n",
+    "# 0. DEFINE ENVIRONMENT & PATHS\n",
+    "# ==============================================================================\n",
+    "local_packages_path = Path(\"/home/tmlkyza/Documents/Universita/Master/nanopants/lcm/pySNOW\")\n",
+    "sys.path.append(str(local_packages_path.resolve()))\n",
+    "\n",
+    "from snow.io.xyz import read_xyz, write_xyz\n",
+    "from snow.descriptors.coordination import coordination_number\n",
+    "from snow.descriptors import cna\n",
+    "\n",
+    "alat = 4.087077141  \n",
+    "cutoff = 0.85 * alat\n",
+    "root_dir = Path('.') \n",
+    "\n",
+    "statistical_runs = [\n",
+    "    p for p in root_dir.iterdir()\n",
+    "    if p.is_dir() and any(sub.is_dir() and sub.name == 'quenchMeltingSims' for sub in p.iterdir())\n",
+    "]\n",
+    "\n",
+    "def FileOut(file, subDirName, filename):\n",
+    "    run_folder = file.parent          \n",
+    "    process_folder = run_folder.parent \n",
+    "    irand_folder = process_folder.parent \n",
+    "\n",
+    "    output_dir = irand_folder / subDirName\n",
+    "    output_dir.mkdir(parents=True, exist_ok=True)\n",
+    "    return output_dir / filename\n",
+    "\n",
+    "# ==============================================================================\n",
+    "# STAGE 1: COMPUTE & WRITE CNAPS TO GCN FOLDER\n",
+    "# ==============================================================================\n",
+    "print(\"--- Starting Stage 1: Processing Simulations ---\")\n",
+    "\n",
+    "for irand in statistical_runs:\n",
+    "    quench_dir = irand / \"quenchMeltingSims\"\n",
+    "    if not quench_dir.exists():\n",
+    "        continue\n",
+    "    \n",
+    "    for run_folder in quench_dir.iterdir():\n",
+    "        if not run_folder.is_dir():\n",
+    "            continue\n",
+    "            \n",
+    "        out_files = list(run_folder.glob('out*.xyz'))\n",
+    "        \n",
+    "        if out_files:\n",
+    "            highest_out = max(out_files, key=lambda f: int(f.stem[3:]))\n",
+    "            el, coords = read_xyz(highest_out)\n",
+    "            cns = coordination_number(coords, cutoff)\n",
+    "            cnaps = cna.cnap_peratom(coords, cutoff)\n",
+    "            \n",
+    "            output_file_path = FileOut(highest_out, \"GCN\", f\"{run_folder.name}.xyz\")\n",
+    "            write_xyz(output_file_path, el, coords, additional_data={\"cns\": cns, \"cnaps\": cnaps})\n",
+    "\n",
+    "print(\"Stage 1 complete. Files written to GCN folders.\")\n",
+    "\n",
+    "# ==============================================================================\n",
+    "# STAGE 2: READ GCN FOLDERS, AVERAGE FREQUENCIES, AND COMPUTE STATS\n",
+    "# ==============================================================================\n",
+    "print(\"\\n--- Starting Stage 2: Aggregating & Averaging Statistical Runs ---\")\n",
+    "\n",
+    "master_data = {}\n",
+    "\n",
+    "for irand in statistical_runs:\n",
+    "    gcn_dir = irand / \"GCN\"\n",
+    "    if not gcn_dir.exists():\n",
+    "        continue\n",
+    "        \n",
+    "    irand_name = irand.name\n",
+    "    \n",
+    "    for gcn_file in gcn_dir.glob(\"*.xyz\"):\n",
+    "        with open(gcn_file, 'r') as f:\n",
+    "            lines = f.readlines()\n",
+    "            \n",
+    "        if not lines:\n",
+    "            continue\n",
+    "            \n",
+    "        try:\n",
+    "            num_atoms = int(lines[0].strip())\n",
+    "        except ValueError:\n",
+    "            continue  \n",
+    "            \n",
+    "        cnaps_in_file = []\n",
+    "        for line in lines[2:2 + num_atoms]:\n",
+    "            parts = line.split()\n",
+    "            if parts:\n",
+    "                try:\n",
+    "                    cnap_value = int(float(parts[-1]))\n",
+    "                    cnaps_in_file.append(cnap_value)\n",
+    "                except ValueError:\n",
+    "                    continue\n",
+    "                \n",
+    "        if num_atoms not in master_data:\n",
+    "            master_data[num_atoms] = {}\n",
+    "            \n",
+    "        if irand_name not in master_data[num_atoms]:\n",
+    "            master_data[num_atoms][irand_name] = Counter()\n",
+    "            \n",
+    "        master_data[num_atoms][irand_name].update(cnaps_in_file)\n",
+    "\n",
+    "all_unique_sizes = sorted(master_data.keys())\n",
+    "first_50_sizes = set(all_unique_sizes[:47])\n",
+    "\n",
+    "print(f\"Total unique sizes found: {len(all_unique_sizes)}\")\n",
+    "print(f\"Filtering to display the first 50 sizes (From {min(first_50_sizes)} to {max(first_50_sizes)} atoms)\")\n",
+    "\n",
+    "final_averaged_records = []\n",
+    "\n",
+    "for num_atoms, irand_dict in sorted(master_data.items()):\n",
+    "    if num_atoms not in first_50_sizes:\n",
+    "        continue  \n",
+    "    \n",
+    "    cnap_percentages_per_seed = {}\n",
+    "    \n",
+    "    for irand_name, counts in irand_dict.items():\n",
+    "        total_cnaps_counted = sum(counts.values())\n",
+    "        if total_cnaps_counted == 0:\n",
+    "            continue\n",
+    "            \n",
+    "        for cnap_type, occurencies in counts.items():\n",
+    "            pct = occurencies / total_cnaps_counted\n",
+    "            \n",
+    "            if cnap_type not in cnap_percentages_per_seed:\n",
+    "                cnap_percentages_per_seed[cnap_type] = []\n",
+    "            cnap_percentages_per_seed[cnap_type].append(pct)\n",
+    "            \n",
+    "    for cnap_type, pct_list in cnap_percentages_per_seed.items():\n",
+    "        total_seeds_for_size = len(irand_dict)\n",
+    "        missing_seeds = total_seeds_for_size - len(pct_list)\n",
+    "        full_pct_list = pct_list + [0.0] * missing_seeds\n",
+    "        \n",
+    "        avg_pct = np.mean(full_pct_list)\n",
+    "        \n",
+    "        final_averaged_records.append({\n",
+    "            \"System Size\": num_atoms,\n",
+    "            # --- FIXED: Store purely as an integer to maintain numerical sorting ---\n",
+    "            \"CNAPS Type\": int(cnap_type), \n",
+    "            \"Average Percentage\": avg_pct\n",
+    "        })\n",
+    "\n",
+    "df_results = pd.DataFrame(final_averaged_records)\n",
+    "print(\"\\n--- Summary of Consolidated Statistical Averages (First 50 Sizes) ---\")\n",
+    "print(df_results.head(20))\n",
+    "\n",
+    "# ==============================================================================\n",
+    "# STAGE 3: VISUALIZATION (STAKED BAR CHART)\n",
+    "# ==============================================================================\n",
+    "if not df_results.empty:\n",
+    "    # Pivot the data \n",
+    "    df_pivot = df_results.pivot(index=\"System Size\", columns=\"CNAPS Type\", values=\"Average Percentage\").fillna(0)\n",
+    "    \n",
+    "    # --- FIXED: Explicitly sort columns numerically so 2 comes before 10 ---\n",
+    "    df_pivot = df_pivot.reindex(columns=sorted(df_pivot.columns))\n",
+    "    \n",
+    "    # Render the bar chart\n",
+    "    ax = df_pivot.plot(kind=\"bar\", stacked=True, figsize=(14, 6), width=0.8, cmap=\"tab20\")\n",
+    "\n",
+    "    plt.xlabel(\"$N$\", fontsize=14)\n",
+    "    plt.ylabel(\"Fractions of each CNAP type\", fontsize=14)\n",
+    "    #plt.title('Stacked Distribution of CNAPs', fontsize=12, fontweight='bold', pad=15)\n",
+    "    plt.grid(axis=\"y\", linestyle=\"--\", alpha=0.5)\n",
+    "    plt.tick_params(axis='both', which='major', labelsize=13)\n",
+    "\n",
+    "    # Updated Legend Title to match plain numbers\n",
+    "    #plt.legend(title=\"CNAP types\", bbox_to_anchor=(1.02, 1), loc=\"upper left\", fontsize=13)\n",
+    "    legend=plt.legend(title=\"CNAP types\", loc=\"upper left\", bbox_to_anchor=(1.0,1.02), fontsize=12)\n",
+    "\n",
+    "    plt.setp(plt.gca().get_legend().get_texts(), fontsize='13') #legend 'list' fontsize\n",
+    "    \n",
+    "    # --- Align ticks with actual multiples of 10 ---\n",
+    "    data_index_floats = df_pivot.index.astype(float)\n",
+    "    min_val = int(data_index_floats.min())\n",
+    "    max_val = int(data_index_floats.max())\n",
+    "    \n",
+    "    start_tick = min_val + (10 - min_val % 10) if min_val % 10 != 0 else min_val\n",
+    "    target_values = np.arange(start_tick, max_val + 1, 10)\n",
+    "    \n",
+    "    tick_indices = [np.where(data_index_floats == val)[0][0] for val in target_values if val in data_index_floats]\n",
+    "    tick_labels = [int(data_index_floats[i]) for i in tick_indices]\n",
+    "    \n",
+    "    ax.set_xticks(tick_indices)\n",
+    "    ax.set_xticklabels(tick_labels, rotation=0) # Kept at 0 rotation since 10-step numbers fit comfortably\n",
+    "    # --------------------------------------------------------\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    \n",
+    "    \n",
+    "    output_img = \"CNAPSperSize.png\"\n",
+    "    plt.savefig(output_img, dpi=300)\n",
+    "    plt.show()\n",
+    "else:\n",
+    "    print(\"No structural data resolved for the selected subset.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "676a4d41",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Cluster Configurations Ranked by Custom Structural Purity Score (Strict Max Size 50) ---\n",
+      " System Size Diversity (Entropy) Non-Zero Concentration Custom Score\n",
+      "          14              0.9852                 0.5714       0.5800\n",
+      "          21              0.9587                 0.3810       0.3974\n",
+      "          17              1.2210                 0.4118       0.3372\n",
+      "          16              0.8113                 0.2500       0.3082\n",
+      "          28              0.7496                 0.2143       0.2859\n",
+      "          27              1.0378                 0.2963       0.2855\n",
+      "          19              0.7425                 0.2105       0.2835\n",
+      "          24              1.4136                 0.3750       0.2653\n",
+      "          38              0.9089                 0.2368       0.2606\n",
+      "          37              1.4042                 0.3514       0.2502\n",
+      "          40              0.6098                 0.1500       0.2460\n",
+      "          43              0.5830                 0.1395       0.2393\n",
+      "          22              0.5746                 0.1364       0.2373\n",
+      "          30              1.4256                 0.3333       0.2338\n",
+      "          20              1.9815                 0.4500       0.2271\n",
+      "          50              0.5294                 0.1200       0.2267\n",
+      "          60              0.8853                 0.2000       0.2259\n",
+      "          36              0.7584                 0.1667       0.2198\n",
+      "          29              0.7877                 0.1724       0.2189\n",
+      "          58              0.9509                 0.2069       0.2176\n",
+      "          18              1.7917                 0.3889       0.2171\n",
+      "          61              0.8369                 0.1803       0.2155\n",
+      "          56              0.8343                 0.1786       0.2140\n",
+      "          42              0.9046                 0.1905       0.2106\n",
+      "          57              1.0121                 0.2105       0.2080\n",
+      "          35              0.6948                 0.1429       0.2056\n",
+      "          23              0.4262                 0.0870       0.2040\n",
+      "          63              0.8595                 0.1746       0.2032\n",
+      "          33              1.5280                 0.3030       0.1983\n",
+      "          44              1.5061                 0.2955       0.1962\n",
+      "          47              2.1931                 0.4255       0.1940\n",
+      "          41              0.8948                 0.1707       0.1908\n",
+      "          15              0.6998                 0.1333       0.1905\n",
+      "          45              1.2843                 0.2444       0.1903\n",
+      "          51              0.9276                 0.1765       0.1902\n",
+      "          49              1.7223                 0.3265       0.1896\n",
+      "          62              1.9577                 0.3710       0.1895\n",
+      "          52              1.3200                 0.2500       0.1894\n",
+      "          48              1.5414                 0.2917       0.1892\n",
+      "          25              0.6396                 0.1200       0.1876\n",
+      "          39              0.7283                 0.1282       0.1760\n",
+      "          34              1.0108                 0.1765       0.1746\n",
+      "          59              1.2988                 0.2203       0.1697\n",
+      "          46              0.6614                 0.1087       0.1643\n",
+      "          31              0.4096                 0.0645       0.1575\n",
+      "          32              0.3998                 0.0625       0.1563\n",
+      "          53              0.7267                 0.1132       0.1558\n",
+      "          55              0.3555                 0.0545       0.1534\n",
+      "          54              0.8463                 0.1296       0.1532\n",
+      "          26             -0.0000                 0.0000       0.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ==============================================================================\n",
+    "# CELL: SCORE CLUSTERS (LOW DIVERSITY & HIGH NON-ZERO CNAP FREQUENCIES)\n",
+    "# ==============================================================================\n",
+    "import numpy as np\n",
+    "\n",
+    "# 1. Filter out system sizes above 50\n",
+    "df_filtered = df_results[(df_results[\"System Size\"] >= 14) & (df_results[\"System Size\"] <= 63)].copy()\n",
+    "if not df_filtered.empty:\n",
+    "    scores_record = []\n",
+    "    \n",
+    "    # Group by system size to look at the full distribution of each cluster\n",
+    "    for num_atoms, group in df_filtered.groupby(\"System Size\"):\n",
+    "        # Extract types and percentages\n",
+    "        group = group[group[\"Average Percentage\"] > 0] # drop 0% occurrences for entropy math\n",
+    "        \n",
+    "        percentages = group[\"Average Percentage\"].values\n",
+    "        types = group[\"CNAPS Type\"].values\n",
+    "        \n",
+    "        # Calculate Shannon Entropy (measures diversity/fragmentation across ALL types)\n",
+    "        entropy = -np.sum(percentages * np.log2(percentages))\n",
+    "        \n",
+    "        # Calculate the sum of squares of ONLY non-zero CNAPs\n",
+    "        non_zero_sum_squares = 0.0\n",
+    "        for pct, cnap_type in zip(percentages, types):\n",
+    "            if cnap_type != \"cnap_0\":\n",
+    "                #non_zero_sum_squares += pct **2 \n",
+    "                non_zero_sum_squares += pct \n",
+    "                \n",
+    "        # Calculate the custom score (higher score = better)\n",
+    "        # We add 1e-6 to avoid division by zero if entropy is perfectly 0\n",
+    "        score = non_zero_sum_squares / (entropy + 1e-6)\n",
+    "        \n",
+    "        scores_record.append({\n",
+    "            \"System Size\": num_atoms,\n",
+    "            \"Non-Zero Concentration\": non_zero_sum_squares,\n",
+    "            \"Diversity (Entropy)\": entropy,\n",
+    "            \"Custom Score\": score\n",
+    "        })\n",
+    "        \n",
+    "    df_scores = pd.DataFrame(scores_record)\n",
+    "    \n",
+    "    # 2. Sort by Custom Score in descending order (highest score / best configurations first)\n",
+    "    df_scores_sorted = df_scores.sort_values(by=\"Custom Score\", ascending=False)\n",
+    "    \n",
+    "    print(\"--- Cluster Configurations Ranked by Custom Structural Purity Score (Strict Max Size 50) ---\")\n",
+    "    print(df_scores_sorted[[\"System Size\", \"Diversity (Entropy)\", \"Non-Zero Concentration\", \"Custom Score\"]].to_string(\n",
+    "        index=False, \n",
+    "        formatters={\n",
+    "            \"Diversity (Entropy)\": lambda x: f\"{x:.4f}\",\n",
+    "            \"Non-Zero Concentration\": lambda x: f\"{x:.4f}\",\n",
+    "            \"Custom Score\": lambda x: f\"{x:.4f}\"\n",
+    "        }\n",
+    "    ))\n",
+    "else:\n",
+    "    print(\"No data found. Verify Stage 2 executed successfully.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e17fc955",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Cluster Configurations Ranked by Custom Purity Score (Sizes 30 to 50) ---\n",
+      " System Size Diversity (Entropy) Non-Zero Concentration Custom Score\n",
+      "          14              0.9852                 0.5714       0.5800\n",
+      "          21              0.9587                 0.3810       0.3974\n",
+      "          17              1.2210                 0.4118       0.3372\n",
+      "          16              0.8113                 0.2500       0.3082\n",
+      "          28              0.7496                 0.2143       0.2859\n",
+      "          27              1.0378                 0.2963       0.2855\n",
+      "          19              0.7425                 0.2105       0.2835\n",
+      "          24              1.4136                 0.3750       0.2653\n",
+      "          38              0.9089                 0.2368       0.2606\n",
+      "          37              1.4042                 0.3514       0.2502\n",
+      "          40              0.6098                 0.1500       0.2460\n",
+      "          43              0.5830                 0.1395       0.2393\n",
+      "          22              0.5746                 0.1364       0.2373\n",
+      "          30              1.4256                 0.3333       0.2338\n",
+      "          20              1.9815                 0.4500       0.2271\n",
+      "          50              0.5294                 0.1200       0.2267\n",
+      "          60              0.8853                 0.2000       0.2259\n",
+      "          36              0.7584                 0.1667       0.2198\n",
+      "          29              0.7877                 0.1724       0.2189\n",
+      "          58              0.9509                 0.2069       0.2176\n",
+      "          18              1.7917                 0.3889       0.2171\n",
+      "          61              0.8369                 0.1803       0.2155\n",
+      "          56              0.8343                 0.1786       0.2140\n",
+      "          42              0.9046                 0.1905       0.2106\n",
+      "          57              1.0121                 0.2105       0.2080\n",
+      "          35              0.6948                 0.1429       0.2056\n",
+      "          23              0.4262                 0.0870       0.2040\n",
+      "          63              0.8595                 0.1746       0.2032\n",
+      "          33              1.5280                 0.3030       0.1983\n",
+      "          44              1.5061                 0.2955       0.1962\n",
+      "          47              2.1931                 0.4255       0.1940\n",
+      "          41              0.8948                 0.1707       0.1908\n",
+      "          15              0.6998                 0.1333       0.1905\n",
+      "          45              1.2843                 0.2444       0.1903\n",
+      "          51              0.9276                 0.1765       0.1902\n",
+      "          49              1.7223                 0.3265       0.1896\n",
+      "          62              1.9577                 0.3710       0.1895\n",
+      "          52              1.3200                 0.2500       0.1894\n",
+      "          48              1.5414                 0.2917       0.1892\n",
+      "          25              0.6396                 0.1200       0.1876\n",
+      "          39              0.7283                 0.1282       0.1760\n",
+      "          34              1.0108                 0.1765       0.1746\n",
+      "          59              1.2988                 0.2203       0.1697\n",
+      "          46              0.6614                 0.1087       0.1643\n",
+      "          31              0.4096                 0.0645       0.1575\n",
+      "          32              0.3998                 0.0625       0.1563\n",
+      "          53              0.7267                 0.1132       0.1558\n",
+      "          55              0.3555                 0.0545       0.1534\n",
+      "          54              0.8463                 0.1296       0.1532\n",
+      "          26             -0.0000                 0.0000       0.0000\n",
+      "\n",
+      "================================================================================\n",
+      "CENTER OF THE DISTRIBUTION (Expected System Size): 36.4262 atoms\n",
+      "================================================================================\n",
+      "Interpretation: Crystalline structural order in this range is centered around this cluster size scale.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# --------------------------------------------------------------------------\n",
+    "# NEW: Compute the center of the distribution using: (natoms * score) / sum(natoms * score)\n",
+    "# --------------------------------------------------------------------------\n",
+    "df_scores[\"Weight_Component\"] = df_scores[\"System Size\"] * df_scores[\"Custom Score\"]\n",
+    "total_weight_component = df_scores[\"Weight_Component\"].sum()\n",
+    "total_scores = df_scores[\"Custom Score\"].sum()\n",
+    "\n",
+    "# Calculate the singular center of the distribution for this range\n",
+    "# (i.e. Expected system size weighted by structural purity)\n",
+    "if total_scores > 0:\n",
+    "    center_of_distribution = total_weight_component / total_scores\n",
+    "else:\n",
+    "    center_of_distribution = 0.0\n",
+    "\n",
+    "# 2. Sort by Custom Score in descending order (highest score first)\n",
+    "df_scores_sorted = df_scores.sort_values(by=\"Custom Score\", ascending=False)\n",
+    "\n",
+    "print(\"--- Cluster Configurations Ranked by Custom Purity Score (Sizes 30 to 50) ---\")\n",
+    "print(df_scores_sorted[[\"System Size\", \"Diversity (Entropy)\", \"Non-Zero Concentration\", \"Custom Score\"]].to_string(\n",
+    "    index=False, \n",
+    "    formatters={\n",
+    "        \"Diversity (Entropy)\": lambda x: f\"{x:.4f}\",\n",
+    "        \"Non-Zero Concentration\": lambda x: f\"{x:.4f}\",\n",
+    "        \"Custom Score\": lambda x: f\"{x:.4f}\"\n",
+    "    }\n",
+    "))\n",
+    "\n",
+    "print(\"\\n\" + \"=\"*80)\n",
+    "print(f\"CENTER OF THE DISTRIBUTION (Expected System Size): {center_of_distribution:.4f} atoms\")\n",
+    "print(\"=\"*80)\n",
+    "print(\"Interpretation: Crystalline structural order in this range is centered around this cluster size scale.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "48d8033c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<>:102: SyntaxWarning: \"\\m\" is an invalid escape sequence. Such sequences will not work in the future. Did you mean \"\\\\m\"? A raw string is also an option.\n",
+      "<>:102: SyntaxWarning: \"\\m\" is an invalid escape sequence. Such sequences will not work in the future. Did you mean \"\\\\m\"? A raw string is also an option.\n",
+      "/tmp/ipykernel_424539/216917900.py:102: SyntaxWarning: \"\\m\" is an invalid escape sequence. Such sequences will not work in the future. Did you mean \"\\\\m\"? A raw string is also an option.\n",
+      "  ax1.set_ylabel('$T_\\mathrm{m}$ [K]', fontsize=12)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Cluster Configurations Ranked by Custom Structural Purity Score (Strict Max Size 50) ---\n",
+      " System Size Diversity (Entropy) Non-Zero Concentration Custom Score\n",
+      "          26             -0.0000                 1.0000 1000000.0000\n",
+      "          55              0.3555                 1.0000       2.8130\n",
+      "          32              0.3998                 1.0000       2.5013\n",
+      "          31              0.4096                 1.0000       2.4412\n",
+      "          23              0.4262                 1.0000       2.3462\n",
+      "          50              0.5294                 1.0000       1.8891\n",
+      "          22              0.5746                 1.0000       1.7402\n",
+      "          43              0.5830                 1.0000       1.7152\n",
+      "          40              0.6098                 1.0000       1.6398\n",
+      "          25              0.6396                 1.0000       1.5636\n",
+      "          46              0.6614                 1.0000       1.5120\n",
+      "          35              0.6948                 1.0000       1.4392\n",
+      "          15              0.6998                 1.0000       1.4289\n",
+      "          53              0.7267                 1.0000       1.3761\n",
+      "          39              0.7283                 1.0000       1.3731\n",
+      "          19              0.7425                 1.0000       1.3468\n",
+      "          28              0.7496                 1.0000       1.3341\n",
+      "          36              0.7584                 1.0000       1.3186\n",
+      "          29              0.7877                 1.0000       1.2696\n",
+      "          16              0.8113                 1.0000       1.2326\n",
+      "          56              0.8343                 1.0000       1.1986\n",
+      "          54              0.8463                 1.0000       1.1817\n",
+      "          60              0.8853                 1.0000       1.1296\n",
+      "          41              0.8948                 1.0000       1.1176\n",
+      "          42              0.9046                 1.0000       1.1054\n",
+      "          38              0.9089                 1.0000       1.1002\n",
+      "          51              0.9276                 1.0000       1.0781\n",
+      "          58              0.9509                 1.0000       1.0517\n",
+      "          21              0.9587                 1.0000       1.0431\n",
+      "          14              0.9852                 1.0000       1.0150\n",
+      "          34              1.0108                 1.0000       0.9893\n",
+      "          57              1.0121                 1.0000       0.9880\n",
+      "          27              1.0378                 1.0000       0.9636\n",
+      "          17              1.2210                 1.0000       0.8190\n",
+      "          45              1.2843                 1.0000       0.7786\n",
+      "          59              1.2988                 1.0000       0.7700\n",
+      "          52              1.3200                 1.0000       0.7576\n",
+      "          37              1.4042                 1.0000       0.7121\n",
+      "          24              1.4136                 1.0000       0.7074\n",
+      "          30              1.4256                 1.0000       0.7015\n",
+      "          44              1.5061                 1.0000       0.6639\n",
+      "          33              1.5280                 1.0000       0.6545\n",
+      "          48              1.5414                 1.0000       0.6488\n",
+      "          49              1.7223                 1.0000       0.5806\n",
+      "          18              1.7917                 1.0000       0.5581\n",
+      "          20              1.9815                 1.0000       0.5047\n",
+      "          47              2.1931                 1.0000       0.4560\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x550 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# ==============================================================================\n",
+    "# CELL: SCORE CLUSTERS & GRAPH TEMPERATURE WITH SCORES\n",
+    "# ==============================================================================\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import re\n",
+    "\n",
+    "# 1. Filter out system sizes above 50\n",
+    "df_filtered = df_results[(df_results[\"System Size\"] >= 14) & (df_results[\"System Size\"] <= 60)].copy()\n",
+    "if not df_filtered.empty:\n",
+    "    scores_record = []\n",
+    "    \n",
+    "    # Group by system size to look at the full distribution of each cluster\n",
+    "    for num_atoms, group in df_filtered.groupby(\"System Size\"):\n",
+    "        # Extract types and percentages\n",
+    "        group = group[group[\"Average Percentage\"] > 0] # drop 0% occurrences for entropy math\n",
+    "        \n",
+    "        percentages = group[\"Average Percentage\"].values\n",
+    "        types = group[\"CNAPS Type\"].values\n",
+    "        \n",
+    "        # Calculate Shannon Entropy (measures diversity/fragmentation across ALL types)\n",
+    "\n",
+    "        entropy = -np.sum(percentages * np.log2(percentages))\n",
+    "        \n",
+    "        nonzero_entropy=0\n",
+    "        for pct, cnap_type in zip(percentages, types):\n",
+    "            if cnap_type != \"cnap_0\":\n",
+    "                nonzero_entropy=nonzero_entropy-np.sum(pct*np.log2(pct))\n",
+    "\n",
+    "        # Calculate the sum of squares of ONLY non-zero CNAPs\n",
+    "        non_zero_sum_squares = 0.0\n",
+    "        for pct, cnap_type in zip(percentages, types):\n",
+    "            if cnap_type != \"cnap_0\":\n",
+    "                #non_zero_sum_squares += pct **2 \n",
+    "                non_zero_sum_squares += pct \n",
+    "                \n",
+    "        # Calculate the custom score (higher score = better)\n",
+    "        # We add 1e-6 to avoid division by zero if entropy is perfectly 0\n",
+    "        score = non_zero_sum_squares / (entropy + 1e-6)\n",
+    "        \n",
+    "        scores_record.append({\n",
+    "            \"System Size\": num_atoms,\n",
+    "            \"Non-Zero Concentration\": non_zero_sum_squares,\n",
+    "            \"Diversity (Entropy)\": entropy,\n",
+    "            \"nonzeroentropy\": nonzero_entropy,\n",
+    "            \"Custom Score\": score\n",
+    "        })\n",
+    "        \n",
+    "    df_scores = pd.DataFrame(scores_record)\n",
+    "    \n",
+    "    # 2. Sort by Custom Score in descending order (highest score / best configurations first)\n",
+    "    df_scores_sorted = df_scores.sort_values(by=\"Custom Score\", ascending=False)\n",
+    "    \n",
+    "    print(\"--- Cluster Configurations Ranked by Custom Structural Purity Score (Strict Max Size 50) ---\")\n",
+    "    print(df_scores_sorted[[\"System Size\", \"Diversity (Entropy)\", \"Non-Zero Concentration\", \"Custom Score\"]].to_string(\n",
+    "        index=False, \n",
+    "        formatters={\n",
+    "            \"Diversity (Entropy)\": lambda x: f\"{x:.4f}\",\n",
+    "            \"Non-Zero Concentration\": lambda x: f\"{x:.4f}\",\n",
+    "            \"Custom Score\": lambda x: f\"{x:.4f}\"\n",
+    "        }\n",
+    "    ))\n",
+    "\n",
+    "    # ==============================================================================\n",
+    "    # ADDED: LOAD TEMPERATURE DATA AND GENERATE PLOT\n",
+    "    # ==============================================================================\n",
+    "    try:\n",
+    "        # Load and parse the temperature text file\n",
+    "        with open(\"/home/tmlkyza/Documents/Universita/Master/nanopants/lcm/growth_Melting_Silver_ICO/statistical_tempbysize.txt\", \"r\") as f:\n",
+    "            temp_text = f.read()\n",
+    "        \n",
+    "        # Strip out any rows starting with #\n",
+    "        lines = [line.strip() for line in temp_text.splitlines() if line.strip() and not line.strip().startswith('#')]\n",
+    "        \n",
+    "        # Tokenize whitespace-separated numbers and shape into groups of 5 columns\n",
+    "        tokens = (\" \".join(lines)).split()\n",
+    "        rows = [tokens[i:i+5] for i in range(0, len(tokens), 5)]\n",
+    "        \n",
+    "        # Create a matching DataFrame and convert values to float\n",
+    "        df_temp = pd.DataFrame(rows, columns=['System Size', 'Tmelt', 'TmeltError', 'Tfreeze', 'TfreezeError']).astype(float)\n",
+    "        \n",
+    "        # Merge custom scores with temperature data on System Size\n",
+    "        df_plot = pd.merge(df_scores, df_temp, on='System Size', how='inner').sort_values(by='System Size')\n",
+    "        \n",
+    "        if not df_plot.empty:\n",
+    "            # ------------------------------------------------------------------\n",
+    "            # LAYOUT 1: Dual-Axis Overlaid Graph (Histogram layer behind the line layer)\n",
+    "            # ------------------------------------------------------------------\n",
+    "            fig, ax1 = plt.subplots(figsize=(8,5.5))\n",
+    "            ax2 = ax1.twinx()\n",
+    "            \n",
+    "            # Plot the scores as background bar/histogram layer (plotted first/lower zorder)\n",
+    "            ax2.bar(df_plot['System Size'], df_plot['Diversity (Entropy)'], color='royalblue', alpha=0.35, width=0.8, label='Entropy')\n",
+    "            ax2.set_ylabel('Shannon entropy', fontsize=12, rotation=270, labelpad=18)\n",
+    "            ax2.tick_params(axis='y')\n",
+    "            \n",
+    "            # Plot the melting temperature on top with errorbars\n",
+    "            ax1.errorbar(df_plot['System Size'], df_plot['Tmelt'], yerr=df_plot['TmeltError'], \n",
+    "                         fmt='o-', color='crimson', ecolor='crimson', capsize=3, elinewidth=1.2, markersize=5, label='Melting Temp.', linewidth=0)\n",
+    "            ax1.set_xlabel('$N$', fontsize=12)\n",
+    "            ax1.set_ylabel('$T_\\mathrm{m}$ [K]', fontsize=12)\n",
+    "            ax1.tick_params(axis='y')\n",
+    "            \n",
+    "            #ax1.set_title('Melting Temperature and Entropy by System Size', fontsize=12, fontweight='bold', pad=15)\n",
+    "            ax1.grid(True, linestyle='--', alpha=0.3)\n",
+    "            \n",
+    "            # Combine the legends safely from both axes\n",
+    "            lines1, labels1 = ax1.get_legend_handles_labels()\n",
+    "            lines2, labels2 = ax2.get_legend_handles_labels()\n",
+    "            ax1.legend(lines1 + lines2, labels1 + labels2, loc='upper right')\n",
+    "            \n",
+    "            plt.tight_layout()\n",
+    "            plt.savefig('melting_temp_and_scores_dual_axis.png', dpi=300)\n",
+    "            plt.show()\n",
+    "            plt.close()\n",
+    "        else:\n",
+    "            print(\"\\n[Warning] No matching system sizes found between the cluster scores and the temperature dataset.\")\n",
+    "            \n",
+    "    except FileNotFoundError:\n",
+    "        print(\"\\n[Error] The file 'statistical_tempbysize.txt' was not found in the workspace directory.\")\n",
+    "    except Exception as e:\n",
+    "        print(f\"\\n[Error] Failed to generate visualizations: {e}\")\n",
+    "\n",
+    "else:\n",
+    "    print(\"No data found. Verify Stage 2 executed successfully.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4d5b842",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.14.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/phasecollector.ipynb b/phasecollector.ipynb
new file mode 100644
index 00000000..70643cdc
--- /dev/null
+++ b/phasecollector.ipynb
@@ -0,0 +1,634 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d2e227fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Requirement already satisfied: pandas in /home/tmlkyza/.local/lib/python3.14/site-packages (3.0.3)\n",
+      "Requirement already satisfied: numpy>=2.3.3 in /usr/lib64/python3.14/site-packages (from pandas) (2.4.6)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/lib/python3.14/site-packages (from pandas) (2.9.0.post0)\n",
+      "Requirement already satisfied: six>=1.5 in /usr/lib/python3.14/site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "693b117f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def plot_temperature_vs_size(sizes, T_melt, T_freeze, melt_errors, freeze_errors, filename=\"temperature_vs_size.png\"):\n",
+    "    \"\"\"Plots melting and freezing temperatures with error bars as a function of system size.\"\"\"\n",
+    "    fig, ax = plt.subplots(figsize=(8, 5.5))\n",
+    "    \n",
+    "    sort_idx = np.argsort(sizes)\n",
+    "    x = np.array(sizes, dtype=float)[sort_idx]\n",
+    "    y1 = np.array(T_melt)[sort_idx]\n",
+    "    y2 = np.array(T_freeze)[sort_idx]\n",
+    "    y1_err = np.array(melt_errors)[sort_idx]\n",
+    "    y2_err = np.array(freeze_errors)[sort_idx]\n",
+    "    \n",
+    "    # Melting Series\n",
+    "    ax.errorbar(\n",
+    "        x, y1, yerr=y1_err, fmt='o', color='crimson', \n",
+    "        linewidth=2, markersize=6, capsize=4, elinewidth=1.5, \n",
+    "        label='Melting Temperature ($T_{\\\\text{m}}$)'\n",
+    "    )\n",
+    "    \n",
+    "    # Freezing Series\n",
+    "    ax.errorbar(\n",
+    "        x, y2, yerr=y2_err, fmt='o', color='dodgerblue', \n",
+    "        linewidth=2, markersize=6, capsize=4, elinewidth=1.5, \n",
+    "        label='Freezing Temperature ($T_{\\\\text{f}}$)'\n",
+    "    )\n",
+    "    \n",
+    "    ax.set_xlabel('$N$', fontsize=11, labelpad=8)\n",
+    "    ax.set_ylabel('Temperature [K]', fontsize=11, labelpad=8)\n",
+    "    #ax.set_title('Phase Transition Temperatures vs. Particle Size', fontsize=12, fontweight='bold', pad=15)\n",
+    "    ax.grid(True, linestyle='--', alpha=0.5)\n",
+    "    ax.legend(loc='best', fontsize=10, frameon=True)\n",
+    "    \n",
+    "    fig.tight_layout()\n",
+    "    fig.savefig(filename, dpi=300)\n",
+    "    print(f\"Graph successfully saved to: {filename}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "979b618c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully aggregated data across 3 subdirectories.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 1. Find all 'tempBySize.dat' files inside immediate subdirectories (Fixed Case-Sensitivity)\n",
+    "current_dir = Path.cwd()\n",
+    "data_files = list(current_dir.glob(\"*/tempBySize.dat\"))\n",
+    "\n",
+    "if not data_files:\n",
+    "    print(\"Warning: No 'tempBySize.dat' files found in subdirectories. Check your path structure!\")\n",
+    "\n",
+    "all_data = []\n",
+    "\n",
+    "# 2. Read each file dynamically\n",
+    "for filepath in data_files:\n",
+    "    try:\n",
+    "        # Fixed: Changed delim_whitespace=True to sep=r'\\s+'\n",
+    "        df = pd.read_csv(filepath, comment='#', sep=r'\\s+', \n",
+    "                         names=['size', 'Tmelt', 'TmeltError', 'Tfreeze', 'TfreezeError'])\n",
+    "        \n",
+    "        # Drop rows with incomplete records\n",
+    "        df = df.dropna()\n",
+    "        all_data.append(df)\n",
+    "    except Exception as e:\n",
+    "        print(f\"Skipping unreadable file {filepath}: {e}\")\n",
+    "\n",
+    "# 3. Concatenate everything into a single master dataframe\n",
+    "if all_data:\n",
+    "    master_df = pd.concat(all_data, ignore_index=True)\n",
+    "\n",
+    "    # 4. Statistical aggregation and error propagation\n",
+    "    def propagate_errors(series):\n",
+    "        n_elements = len(series)\n",
+    "        return np.sqrt(np.sum(series**2)) / n_elements\n",
+    "\n",
+    "    aggregated = master_df.groupby('size').agg(\n",
+    "        Tmelt=('Tmelt', 'mean'),\n",
+    "        TmeltError=('TmeltError', propagate_errors),\n",
+    "        Tfreeze=('Tfreeze', 'mean'),\n",
+    "        TfreezeError=('TfreezeError', propagate_errors)\n",
+    "    ).reset_index()\n",
+    "\n",
+    "    print(f\"Successfully aggregated data across {len(data_files)} subdirectories.\")\n",
+    "else:\n",
+    "    print(\"No data collected. Double check that your notebook is running inside the 'growth_Melting_Silver_ICO' directory!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "334bb4c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Aggregated statistics written to: /home/tmlkyza/Documents/Universita/Master/nanopants/lcm/growth_Melting_Silver_ICO/statistical_tempbysize.dat\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Graph successfully saved to: statistical_temperature_vs_size.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x550 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "output_file = Path(\"statistical_tempbysize.dat\")\n",
+    "\n",
+    "# Write out the results with space separations and a matching header format\n",
+    "with output_file.open('w') as f:\n",
+    "    f.write(\"# size Tmelt TmeltError Tfreeze TfreezeError\\n\")\n",
+    "    aggregated.to_csv(f, sep=' ', index=False, header=False)\n",
+    "\n",
+    "print(f\"Aggregated statistics written to: {output_file.resolve()}\")\n",
+    "\n",
+    "# Send the columns straight into your plotting function\n",
+    "plot_temperature_vs_size(\n",
+    "    sizes=aggregated['size'],\n",
+    "    T_melt=aggregated['Tmelt'],\n",
+    "    T_freeze=aggregated['Tfreeze'],\n",
+    "    melt_errors=aggregated['TmeltError'],\n",
+    "    freeze_errors=aggregated['TfreezeError'],\n",
+    "    filename=\"statistical_temperature_vs_size.png\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "c6397f2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimizing localized structural fluctuation model...\n",
+      "\n",
+      "================== FIT CONVERGED SUCCESFULLY ==================\n",
+      "Icosahedron Peak Position : 15.24 atoms (Height: 191.1 K)\n",
+      "Decahedron Region Center  : 37.69 atoms (Height: 300.0 K)\n",
+      "Fluctuation Decay Rate    : 0.0000\n",
+      "Calculated Bulk Limit     : 535.92 K\n",
+      "================================================================\n",
+      "\n",
+      "Plot successfully saved to: melting_point_localized_fluctuations.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.optimize import curve_fit\n",
+    "\n",
+    "# 1. Arbitrary fluctuation model using localized Gaussian features\n",
+    "def arbitrary_fluctuation_model(N, A, C, N_0, H1, P1, W1, H2, P2, W2, alpha, T_bulk):\n",
+    "    # Background macro-envelope\n",
+    "    #envelope = A * ((N - N_0)**3) * np.exp(-C * (N - N_0))\n",
+    "    envelope = A * (N**2) * np.exp(-C * (N - N_0))\n",
+    "    \n",
+    "    # Feature 1: The sharp N=15 peak\n",
+    "    feature_ico = H1 * np.exp(-((N - P1)**2) / (2 * (W1**2)))\n",
+    "    \n",
+    "    # Feature 2: The broader N=30-35 region\n",
+    "    feature_deca = H2 * np.exp(-((N - P2)**2) / (2 * (W2**2)))\n",
+    "    \n",
+    "    # Total localized fluctuations combined and killed off exponentially at large N\n",
+    "    total_fluctuations = (feature_ico + feature_deca) * np.exp(-alpha * N)\n",
+    "    \n",
+    "    return T_bulk + envelope + total_fluctuations\n",
+    "\n",
+    "# 2. Extract your clean data columns\n",
+    "N_data = aggregated['size'].values\n",
+    "T_melt_data = aggregated['Tmelt'].values\n",
+    "T_melt_errors = aggregated['TmeltError'].values\n",
+    "\n",
+    "# 3. Targeted structural initial guesses (p0)\n",
+    "# P1 is centered at 15 (width of ~2 atoms), P2 is centered at 33 (width of ~5 atoms)\n",
+    "p0 = [\n",
+    "    0.005, 0.08, 10.0,   # Background Envelope: A, C, N_0\n",
+    "    150.0, 15.0, 2.0,    # Feature 1: Height, Position, Width\n",
+    "    80.0,  40.0, 5.0,    # Feature 2: Height, Position, Width\n",
+    "    0.05,                # alpha (Dampening rate of fluctuations)\n",
+    "    550.0                # T_bulk baseline\n",
+    "]\n",
+    "\n",
+    "# Set search boundaries to keep the peaks locked to their respective physical regions\n",
+    "bounds = (\n",
+    "    [0.0,  0.01, 0.0,   0.0,   12.0, 0.5,   0.0,   25.0, 1.0,   0.00, 450.0], # Lower bounds\n",
+    "    [1.0,  0.5,  25.0,  400.0, 18.0, 6.0,   300.0, 42.0, 15.0,  0.25, 650.0]  # Upper bounds\n",
+    ")\n",
+    "\n",
+    "print(\"Optimizing localized structural fluctuation model...\")\n",
+    "\n",
+    "try:\n",
+    "    # 4. Fit unweighted to prevent individual data sinks from stealing the fit\n",
+    "    popt, pcov = curve_fit(\n",
+    "        arbitrary_fluctuation_model, \n",
+    "        N_data, \n",
+    "        T_melt_data, \n",
+    "        p0=p0, \n",
+    "        bounds=bounds,\n",
+    "        maxfev=20000\n",
+    "    )\n",
+    "    \n",
+    "    # Extract optimized positions for labeling\n",
+    "    P1_fit, P2_fit, T_bulk_fit = popt[4], popt[7], popt[-1]\n",
+    "    \n",
+    "    # 5. Generate continuous grid for smooth plotting\n",
+    "    N_fit = np.linspace(N_data.min(), N_data.max(), 1000)\n",
+    "    T_fit_line = arbitrary_fluctuation_model(N_fit, *popt)\n",
+    "    \n",
+    "    # 6. Plotting\n",
+    "    fig, ax = plt.subplots(figsize=(9, 6))\n",
+    "    \n",
+    "    # Plot experimental data\n",
+    "    ax.errorbar(\n",
+    "        N_data, T_melt_data, yerr=T_melt_errors, fmt='o', color='crimson', \n",
+    "        linewidth=1.5, markersize=5, capsize=3, elinewidth=1.2, zorder=2,\n",
+    "        label='Cleaned Experimental Data ($T_{\\\\text{melt}}$)'\n",
+    "    )\n",
+    "    \n",
+    "    # Plot the customized localized fit line\n",
+    "    ax.plot(\n",
+    "        N_fit, T_fit_line, '-', color='darkred', linewidth=2.5, alpha=0.9, zorder=3,\n",
+    "        label=f'Localized Fluctuation Fit ($T_{{\\\\text{{bulk}}}}$ = {T_bulk_fit:.1f} K)'\n",
+    "    )\n",
+    "    \n",
+    "    # Visual cues showing where the optimizer placed the two structural centers\n",
+    "    ax.axvspan(P1_fit - popt[5], P1_fit + popt[5], color='blue', alpha=0.05, label=f'Ico Cluster Window (~{P1_fit:.1f})')\n",
+    "    ax.axvspan(P2_fit - popt[8], P2_fit + popt[8], color='purple', alpha=0.05, label=f'Magic number Cluster Window (~{P2_fit:.1f})')\n",
+    "    \n",
+    "    ax.set_xlabel('System Size (Number of Atoms)', fontsize=11, labelpad=8)\n",
+    "    ax.set_ylabel('Temperature (K)', fontsize=11, labelpad=8)\n",
+    "    ax.set_title('Melting Point Fit: Localized Structural Fluctuation Model', fontsize=12, fontweight='bold', pad=15)\n",
+    "    ax.grid(True, linestyle='--', alpha=0.3)\n",
+    "    ax.legend(loc='lower right', fontsize=9, frameon=True)\n",
+    "    \n",
+    "    fig.tight_layout()\n",
+    "    output_img = \"melting_point_localized_fluctuations.png\"\n",
+    "    fig.savefig(output_img, dpi=300)\n",
+    "    \n",
+    "    print(\"\\n================== FIT CONVERGED SUCCESFULLY ==================\")\n",
+    "    print(f\"Icosahedron Peak Position : {P1_fit:.2f} atoms (Height: {popt[3]:.1f} K)\")\n",
+    "    print(f\"Decahedron Region Center  : {P2_fit:.2f} atoms (Height: {popt[6]:.1f} K)\")\n",
+    "    print(f\"Fluctuation Decay Rate    : {popt[9]:.4f}\")\n",
+    "    print(f\"Calculated Bulk Limit     : {T_bulk_fit:.2f} K\")\n",
+    "    print(\"================================================================\\n\")\n",
+    "    print(f\"Plot successfully saved to: {output_img}\")\n",
+    "\n",
+    "except Exception as e:\n",
+    "    print(f\"\\nOptimization failed: {e}\")\n",
+    "    print(\"Tip: If it fails, widen the bounds slightly or adjust the peak initial guesses to match your filtered data.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "a30787e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting corrected bulk sphere model using data from N = 60 onward...\n",
+      "\n",
+      "================== CORRECTED BULK FIT COMPLETE ==================\n",
+      "Optimized C factor       : 1.9272 ± 0.0475\n",
+      "Optimized T_correction   : 200.00 ± 22.4425 K\n",
+      "Effective Potential Bulk : 1034.93 K\n",
+      "=================================================================\n",
+      "\n",
+      "Clean plot saved to: melting_point_corrected_bulk_fit.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x550 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.optimize import curve_fit\n",
+    "\n",
+    "# True physical constant for Silver's bulk melting point\n",
+    "T_BULK_SILVER = 1234.93\n",
+    "\n",
+    "# 1. Slice out everything below N = 60\n",
+    "flat_mask = aggregated['size'] >= 60\n",
+    "N_flat = aggregated['size'][flat_mask].values\n",
+    "T_flat = aggregated['Tmelt'][flat_mask].values\n",
+    "T_flat_errors = aggregated['TmeltError'][flat_mask].values\n",
+    "\n",
+    "# 2. Define the corrected bulk sphere volume envelope model\n",
+    "def corrected_bulk_envelope(N, C, T_correction):\n",
+    "    effective_bulk = T_BULK_SILVER - T_correction\n",
+    "    return effective_bulk * (1.0 - (C / (N**(1/3))))\n",
+    "\n",
+    "print(f\"Fitting corrected bulk sphere model using data from N = {N_flat.min()} onward...\")\n",
+    "\n",
+    "try:\n",
+    "    # 3. Fit the model using error weights\n",
+    "    # p0 = [C, T_correction] -> start C at 0.6 and correction at 20 K\n",
+    "    popt, pcov = curve_fit(\n",
+    "        corrected_bulk_envelope, \n",
+    "        N_flat, \n",
+    "        T_flat, \n",
+    "        p0=[0.6, 20.0],\n",
+    "        sigma=T_flat_errors,\n",
+    "        absolute_sigma=True,\n",
+    "        bounds=([0.1, -100.0], [3.0, 200.0])\n",
+    "    )\n",
+    "    C_fit, T_corr_fit = popt\n",
+    "    perr = np.sqrt(np.diag(pcov))\n",
+    "    \n",
+    "    # 4. Generate smooth lines for plotting\n",
+    "    N_fit = np.linspace(N_flat.min(), N_flat.max(), 1000)\n",
+    "    T_fit_line = corrected_bulk_envelope(N_fit, C_fit, T_corr_fit)\n",
+    "    \n",
+    "    # 5. Fluff-free Minimal Visualization\n",
+    "    fig, ax = plt.subplots(figsize=(8, 5.5))\n",
+    "    \n",
+    "    # Experimental data points\n",
+    "    ax.errorbar(\n",
+    "        N_flat, T_flat, yerr=T_flat_errors, fmt='o', color='crimson', \n",
+    "        linewidth=1.5, markersize=5, capsize=3, elinewidth=1.2, zorder=1,\n",
+    "        label='Data'\n",
+    "    )\n",
+    "    \n",
+    "    # Fit line\n",
+    "    effective_bulk_display = T_BULK_SILVER - T_corr_fit\n",
+    "    ax.plot(\n",
+    "        N_fit, T_fit_line, '-', color='darkred', linewidth=2.5, zorder=2,\n",
+    "        label=f'Fit ($T_{{\\\\text{{bulk, eff}}}}$ = {effective_bulk_display:.1f} K, C = {C_fit:.3f})'\n",
+    "    )\n",
+    "    \n",
+    "    ax.set_xlabel('System Size (Number of Atoms)', fontsize=11)\n",
+    "    ax.set_ylabel('Temperature (K)', fontsize=11)\n",
+    "    ax.grid(True, linestyle='--', alpha=0.3)\n",
+    "    ax.legend(loc='lower right', fontsize=10, frameon=True)\n",
+    "    \n",
+    "    fig.tight_layout()\n",
+    "    output_img = \"melting_point_corrected_bulk_fit.png\"\n",
+    "    fig.savefig(output_img, dpi=300)\n",
+    "    \n",
+    "    print(\"\\n================== CORRECTED BULK FIT COMPLETE ==================\")\n",
+    "    print(f\"Optimized C factor       : {C_fit:.4f} ± {perr[0]:.4f}\")\n",
+    "    print(f\"Optimized T_correction   : {T_corr_fit:.2f} ± {perr[1]:.4f} K\")\n",
+    "    print(f\"Effective Potential Bulk : {effective_bulk_display:.2f} K\")\n",
+    "    print(\"=================================================================\\n\")\n",
+    "    print(f\"Clean plot saved to: {output_img}\")\n",
+    "\n",
+    "except Exception as e:\n",
+    "    print(f\"\\nFitting failed: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "acd94f25",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mask applied: Dropped 3 data points near 1200.0 K.\n",
+      "Fitting unweighted model over remaining 82 robust data points...\n",
+      "\n",
+      "======================= FIT CONVERGED SUCCESSFULLY =======================\n",
+      "  Parameter                      |  Optimized Value  \n",
+      "---------------------------------------------------- \n",
+      "  1st Magic Peak Height (H1)     |          722.846   \n",
+      "  1st Magic Peak Center (P1)     |           12.250   \n",
+      "  1st Magic Peak Width  (W1)     |            6.000   \n",
+      "----------------------------------------------------- \n",
+      "  2nd Magic Peak Height (H2)     |          594.891   \n",
+      "  2nd Magic Peak Center (P2)     |           35.466   \n",
+      "  2nd Magic Peak Width  (W2)     |            9.155   \n",
+      "==========================================================================\n",
+      "\n",
+      "Plot successfully rendered to: melting_point_final_no_decay.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.optimize import curve_fit\n",
+    "\n",
+    "# 1. Fluctuations model without global amplitude (B) and without decay rate (alpha)\n",
+    "def arbitrary_fluctuation_model(N, H1, P1, W1, H2, P2, W2):\n",
+    "    # --- PHYSICAL SPHERE ENVELOPE BASELINE ---\n",
+    "    # Locked to your notebook variables from the macro calibration cell\n",
+    "    T_bulk_silver = 1234.93\n",
+    "    envelope = (T_bulk_silver - T_corr_fit) * (1.0 - (C_fit / (N**(1/3))))\n",
+    "    \n",
+    "    # --- STRUCTURAL FLUCTUATIONS ---\n",
+    "    # Feature 1: The sharp N=15 magic number\n",
+    "    feature_ico = H1 * np.exp(-((N - P1)**2) / (2 * (W1**2)))\n",
+    "    \n",
+    "    # Feature 2: The sharper N=40 magic number\n",
+    "    feature_deca = H2 * np.exp(-((N - P2)**2) / (2 * (W2**2)))\n",
+    "    \n",
+    "    return envelope + feature_ico + feature_deca\n",
+    "\n",
+    "# 2. Extract full data columns from active environment\n",
+    "N_raw = aggregated['size'].values\n",
+    "T_raw = aggregated['Tmelt'].values\n",
+    "T_errors_raw = aggregated['TmeltError'].values\n",
+    "\n",
+    "# --- FILTER MASK DEFINITION ---\n",
+    "# Mask out unphysical data points sitting right around 1200 K\n",
+    "T_TARGET = 1200.0\n",
+    "T_TOLERANCE = 20.0  # Captures everything from 1180 K to 1220 K\n",
+    "\n",
+    "valid_mask = np.abs(T_raw - T_TARGET) > T_TOLERANCE\n",
+    "\n",
+    "# Apply mask to create the clean dataset for fitting\n",
+    "N_data = N_raw[valid_mask]\n",
+    "T_melt_data = T_raw[valid_mask]\n",
+    "T_melt_errors = T_errors_raw[valid_mask]  # Retained for plotting purposes only\n",
+    "\n",
+    "print(f\"Mask applied: Dropped {np.sum(~valid_mask)} data points near {T_TARGET} K.\")\n",
+    "print(f\"Fitting unweighted model over remaining {len(N_data)} robust data points...\")\n",
+    "\n",
+    "# 3. Structural initial guesses (alpha completely removed)\n",
+    "p0 = [\n",
+    "    150.0, 15.0, 2.0,    # Feature 1: Height, Position, Width\n",
+    "    300.0, 40.0, 1.0     # Feature 2: Height, Position, Width\n",
+    "]\n",
+    "\n",
+    "# Set boundaries to lock the features safely into place\n",
+    "bounds = (\n",
+    "    # [H1,   P1,   W1,   H2,    P2,   W2]\n",
+    "    [0.0,  12.0, 0.5,  0.0, 30.0, 1.0], # Lower bounds\n",
+    "    [1200.0, 18.0, 6.0,  1200.0, 50.0, 10.0]  # Upper bounds\n",
+    ")\n",
+    "\n",
+    "try:\n",
+    "    # 4. Perform unweighted optimization\n",
+    "    popt, pcov = curve_fit(\n",
+    "        arbitrary_fluctuation_model, \n",
+    "        N_data, \n",
+    "        T_melt_data, \n",
+    "        p0=p0, \n",
+    "        bounds=bounds,\n",
+    "        maxfev=30000\n",
+    "    )\n",
+    "    \n",
+    "    # Extract optimized parameters and compute standard errors from the covariance matrix\n",
+    "    H1_fit, P1_fit, W1_fit, H2_fit, P2_fit, W2_fit = popt\n",
+    "    perr = np.sqrt(np.diag(pcov))  # Standard deviations of parameters\n",
+    "    \n",
+    "    # 5. Generate continuous grid for smooth plotting\n",
+    "    N_fit = np.linspace(N_raw.min(), N_raw.max(), 1000)\n",
+    "    T_fit_line = arbitrary_fluctuation_model(N_fit, *popt)\n",
+    "    \n",
+    "    # Pure underlying calibrated background envelope line\n",
+    "    pure_envelope = (1234.93 - T_corr_fit) * (1.0 - (C_fit / (N_fit**(1/3))))\n",
+    "    \n",
+    "    # 6. Clean Visualization\n",
+    "    fig, ax = plt.subplots(figsize=(9, 6))\n",
+    "    \n",
+    "    # Plot robust data points WITH error bars intact on the plot layout\n",
+    "    ax.errorbar(\n",
+    "        N_data, T_melt_data, yerr=T_melt_errors, fmt='o', color='crimson', \n",
+    "        linewidth=1.5, markersize=5, capsize=3, elinewidth=1.2, zorder=2,\n",
+    "        label='Valid Data'\n",
+    "    )\n",
+    "    \n",
+    "    # Plot excluded data points faintly for tracking integrity\n",
+    "    N_excluded = N_raw[~valid_mask]\n",
+    "    T_excluded = T_raw[~valid_mask]\n",
+    "    if len(N_excluded) > 0:\n",
+    "        ax.plot(N_excluded, T_excluded, 'x', color='gray', alpha=0.4, label='Masked Out (~1200 K)')\n",
+    "    \n",
+    "    # Combined Fit Line\n",
+    "    ax.plot(N_fit, T_fit_line, '-', color='darkred', linewidth=2.5, alpha=0.9, zorder=4, label='Full Model Fit')\n",
+    "    \n",
+    "    # Base Envelope Line running beneath\n",
+    "    ax.plot(\n",
+    "        N_fit, pure_envelope, '--', color='gray', linewidth=1.5, alpha=0.7, zorder=3,\n",
+    "        label=f'Background Envelope (C_fit = {C_fit:.4f})'\n",
+    "    )\n",
+    "    \n",
+    "    # Visual cues for magic number windows\n",
+    "    ax.axvspan(P1_fit - W1_fit, P1_fit + W1_fit, color='blue', alpha=0.04, label=f'1st peak (~{P1_fit:.1f})')\n",
+    "    ax.axvspan(P2_fit - W2_fit, P2_fit + W2_fit, color='purple', alpha=0.04, label=f'2nd peak (~{P2_fit:.1f})')\n",
+    "    \n",
+    "    ax.set_xlabel('$N$', fontsize=11)\n",
+    "    ax.set_ylabel('Temperature [K]', fontsize=11)\n",
+    "    #ax.set_title('Melting point fit', fontsize=12, fontweight='bold', pad=15)\n",
+    "    ax.grid(True, linestyle='--', alpha=0.3)\n",
+    "    ax.legend(loc='upper right', fontsize=9, frameon=True)\n",
+    "    \n",
+    "    fig.tight_layout()\n",
+    "    output_img = \"melting_point_final_no_decay.png\"\n",
+    "    fig.savefig(output_img, dpi=300)\n",
+    "    \n",
+    "    # --- DETAILED FIT PARAMETER REPORT ---\n",
+    "    print(\"\\n======================= FIT CONVERGED SUCCESSFULLY =======================\")\n",
+    "    print(\"  Parameter                      |  Optimized Value  \")\n",
+    "    print(\"---------------------------------------------------- \")\n",
+    "    print(f\"  1st Magic Peak Height (H1)     |  {H1_fit:15.3f}   \")\n",
+    "    print(f\"  1st Magic Peak Center (P1)     |  {P1_fit:15.3f}   \")\n",
+    "    print(f\"  1st Magic Peak Width  (W1)     |  {W1_fit:15.3f}   \")\n",
+    "    print(\"----------------------------------------------------- \")\n",
+    "    print(f\"  2nd Magic Peak Height (H2)     |  {H2_fit:15.3f}   \")\n",
+    "    print(f\"  2nd Magic Peak Center (P2)     |  {P2_fit:15.3f}   \")\n",
+    "    print(f\"  2nd Magic Peak Width  (W2)     |  {W2_fit:15.3f}   \")\n",
+    "    print(\"==========================================================================\\n\")\n",
+    "    print(f\"Plot successfully rendered to: {output_img}\")\n",
+    "\n",
+    "except Exception as e:\n",
+    "    print(f\"\\nOptimization failed: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "efe1b0cc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.14.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/statistical_tempbysize.dat b/statistical_tempbysize.dat
new file mode 100644
index 00000000..95bd8386
--- /dev/null
+++ b/statistical_tempbysize.dat
@@ -0,0 +1,86 @@
+# size Tmelt TmeltError Tfreeze TfreezeError
+14 900.7748737469808 97.45233965930312 855.6348901267759 129.0346682019275
+15 974.1528831131022 66.66414196476967 778.6396004120111 77.98757016967518
+16 905.2969392978578 203.20386188939815 721.469306008536 78.20394718593558
+17 950.4924543112354 213.939358906572 628.119070686575 117.36696363719504
+19 638.7820147635161 18.178088791437627 400.0000000093112 106.298343304914
+20 662.7365626744015 92.58455728807819 537.2420853603012 136.34577482663988
+21 696.4984061086525 92.03131133534207 609.6307364999616 36.26977842187556
+22 838.9289298477717 57.3844421300829 698.8079223517849 71.46435257509366
+23 879.4523915722779 176.45479929669295 649.7260210912964 129.53402450194721
+24 1199.999999952201 287.270475765932 826.6533483220547 113.0623205571904
+26 556.9287780721454 199.2751081963987 532.2858755649208 127.25034107791387
+27 846.6084489202598 150.32116297559753 742.0889615415967 71.2702719474852
+28 1059.677355071996 147.92906037715454 659.3835436467847 142.49110534144367
+29 854.7348058281087 175.17991659593508 542.0164155969276 149.64000472689773
+30 801.4087618910102 138.13006990573746 661.7203149371124 131.45751139932614
+31 1199.9999999997608 295.4873298195664 565.9076663418361 91.0113332089446
+32 916.5118164366936 147.02569591378898 769.9582520392836 224.4123329850768
+33 1177.7232764295386 170.51497398267642 969.3811348417878 120.53077774995204
+34 973.7959585043475 214.64082161371937 554.9164785609655 135.47326995696065
+35 747.8216364283605 0.9762084190846214 547.4978449420702 355.5766476099125
+36 1068.9797229112776 144.02472309993746 737.6940401317923 207.03144154049184
+37 926.6812714435334 182.1597619140599 671.4248211331077 111.38617817305233
+38 1199.9361540345274 319.8708876033288 966.2560573691424 523.3078729531811
+39 901.1807303398339 223.20588749489022 400.0000847956587 102.50740179466561
+40 838.3528289810141 242.1152781681487 414.64936372919516 168.62827098667384
+41 1150.6727313195142 64.44181686424162 784.7824875807723 186.91691077214415
+42 1142.0386371296745 317.6509849721653 617.5687848960363 250.69094970194968
+43 1073.369261460254 66.72984537469293 914.1179303049946 246.59299822982464
+44 927.613876660589 202.43016235504007 769.0542990169847 223.81915925082274
+45 809.9172788973544 244.13056813668723 508.96830584572115 243.27827140342427
+46 672.8116089620314 69.75708492675932 539.5807288431706 228.73283175477215
+47 864.056066102761 542.0054121996911 400.00000000002575 52.84617725567983
+48 725.8070952989258 238.4345747432255 446.0438766576625 23.738802499436098
+49 539.7164255715552 13.79324969450304 434.3075751802569 29.517635164998303
+50 588.9329526418135 217.59362307315988 459.1790584995989 23.57261825160339
+51 503.877464597577 13.531949848529566 462.0867243762544 181.85525633929737
+52 556.8496914160373 182.63565001803372 509.1672987062439 13.935096266942828
+53 552.7910761756169 15.165387946219951 503.5845717359459 14.831229936029077
+54 556.9852505941293 11.141769378565547 496.2181343231478 18.831412566491334
+55 607.8415362476524 11.357493342225883 537.7344992188957 17.313713957135597
+56 583.3990884784454 22.294607459848496 457.36078078038446 50.828024166148516
+57 546.8564073473663 18.100924678375893 466.8411194843188 49.21528739813491
+58 556.1429162078487 0.7646493482472997 436.26543286755384 36.878215625360475
+59 460.5899529083609 39.95627495883555 456.8787660188319 29.149948534145498
+60 613.2116305863424 454.2867198264129 415.2971798585238 61.63048601136232
+61 533.1855552070014 16.3923006407561 486.1977101149569 49.43796534097344
+62 598.0300312267419 163.03546960638963 431.2705562782325 46.42306571680509
+63 535.6481223827255 1.4578390035838795 419.88231081026703 72.01414764461761
+64 754.3760088260423 226.39071400305326 400.0000000000002 59.1554750742622
+65 453.95054508458054 78.44685391081457 436.9277639862904 65.68007529136231
+66 526.0006339000046 28.17688122860214 400.0000000912316 61.14214299995907
+67 557.4978677711617 142.45377165442443 438.2276532861363 51.60105490779999
+68 723.2031751164233 109.51541419462554 426.64016270827193 39.380639201714594
+69 561.58968427498 422.571439980534 501.3515736851528 13.625510765386576
+70 485.42460293612305 38.54257851065934 412.6183168501121 42.09824278063907
+71 549.5385515775812 14.26679109776623 418.8496048080212 45.686236725427925
+72 497.5573842649135 84.50053286572917 457.81251297045776 42.64025343470198
+73 553.8617251258928 10.990541821820358 491.32052307104254 21.150516639279015
+74 522.7181887184071 20.48970156099301 476.08201952523626 32.93604728674881
+75 758.1305200831154 164.2439482796584 484.55496405864005 24.38634178228756
+76 539.8275837618086 92.15498231667647 488.41591019542443 17.173984080373316
+77 526.3506843684079 31.816886982994816 459.3894932247004 29.564414226207187
+78 547.9702160817741 20.19009633146281 462.3784179501855 31.225703081026385
+79 537.5889861403612 13.14255272313297 487.7597959203481 30.75341047575257
+80 522.1053260352005 25.080572688523954 487.7069617733658 36.811490170435
+81 543.6429216481187 14.608916644307124 500.7406217254047 19.813554933739297
+82 581.511281188444 190.74905921957804 468.40272874703993 40.07073619000021
+83 558.5317502208371 44.56985488800752 486.0713886728442 50.56461286875305
+84 520.5997547042248 28.31583904771024 466.0692787441135 65.05417193074358
+85 579.9453481442997 167.17276312909493 492.78819572358907 21.029847520078206
+86 627.6031806718629 3.3819194977943905 531.5141912047267 30.371427907433105
+87 533.0028048616897 13.72414284866638 441.17737205086866 43.14118632728792
+88 535.9197059599214 21.605888042289287 484.0773784892983 27.078513291198025
+89 565.4448025148375 24.44912789376772 445.7922824676974 41.932414044798634
+90 561.0447489766794 12.693703245533172 466.8335947481012 36.53526773947728
+91 553.6001823079489 11.17106874852238 424.8569996753172 46.406815271856395
+92 545.5778102330818 19.56205723354854 465.6767988612675 30.03250217228586
+93 549.7212164570085 13.783529711048468 508.0342723210856 15.922390603056009
+94 530.9671119638589 25.873517187285454 464.88126055580705 33.309091106217274
+95 595.412583403267 20.37462542350361 504.8944585275516 31.814421458693353
+96 572.2083832666367 10.494740839216325 523.0282182324983 18.257494212955198
+97 554.3202435319222 4.73019600818921 508.49661332115096 38.98046424066705
+98 562.1007747568615 10.309684025095189 515.3486290254995 21.928455944344478
+99 572.9741196856567 30.807678854234997 533.1936591048252 22.25847356968082
+100 548.1738878954877 18.151497089212427 503.33829897411135 27.307237664141606
diff --git a/statistical_tempbysize.txt b/statistical_tempbysize.txt
new file mode 100644
index 00000000..95bd8386
--- /dev/null
+++ b/statistical_tempbysize.txt
@@ -0,0 +1,86 @@
+# size Tmelt TmeltError Tfreeze TfreezeError
+14 900.7748737469808 97.45233965930312 855.6348901267759 129.0346682019275
+15 974.1528831131022 66.66414196476967 778.6396004120111 77.98757016967518
+16 905.2969392978578 203.20386188939815 721.469306008536 78.20394718593558
+17 950.4924543112354 213.939358906572 628.119070686575 117.36696363719504
+19 638.7820147635161 18.178088791437627 400.0000000093112 106.298343304914
+20 662.7365626744015 92.58455728807819 537.2420853603012 136.34577482663988
+21 696.4984061086525 92.03131133534207 609.6307364999616 36.26977842187556
+22 838.9289298477717 57.3844421300829 698.8079223517849 71.46435257509366
+23 879.4523915722779 176.45479929669295 649.7260210912964 129.53402450194721
+24 1199.999999952201 287.270475765932 826.6533483220547 113.0623205571904
+26 556.9287780721454 199.2751081963987 532.2858755649208 127.25034107791387
+27 846.6084489202598 150.32116297559753 742.0889615415967 71.2702719474852
+28 1059.677355071996 147.92906037715454 659.3835436467847 142.49110534144367
+29 854.7348058281087 175.17991659593508 542.0164155969276 149.64000472689773
+30 801.4087618910102 138.13006990573746 661.7203149371124 131.45751139932614
+31 1199.9999999997608 295.4873298195664 565.9076663418361 91.0113332089446
+32 916.5118164366936 147.02569591378898 769.9582520392836 224.4123329850768
+33 1177.7232764295386 170.51497398267642 969.3811348417878 120.53077774995204
+34 973.7959585043475 214.64082161371937 554.9164785609655 135.47326995696065
+35 747.8216364283605 0.9762084190846214 547.4978449420702 355.5766476099125
+36 1068.9797229112776 144.02472309993746 737.6940401317923 207.03144154049184
+37 926.6812714435334 182.1597619140599 671.4248211331077 111.38617817305233
+38 1199.9361540345274 319.8708876033288 966.2560573691424 523.3078729531811
+39 901.1807303398339 223.20588749489022 400.0000847956587 102.50740179466561
+40 838.3528289810141 242.1152781681487 414.64936372919516 168.62827098667384
+41 1150.6727313195142 64.44181686424162 784.7824875807723 186.91691077214415
+42 1142.0386371296745 317.6509849721653 617.5687848960363 250.69094970194968
+43 1073.369261460254 66.72984537469293 914.1179303049946 246.59299822982464
+44 927.613876660589 202.43016235504007 769.0542990169847 223.81915925082274
+45 809.9172788973544 244.13056813668723 508.96830584572115 243.27827140342427
+46 672.8116089620314 69.75708492675932 539.5807288431706 228.73283175477215
+47 864.056066102761 542.0054121996911 400.00000000002575 52.84617725567983
+48 725.8070952989258 238.4345747432255 446.0438766576625 23.738802499436098
+49 539.7164255715552 13.79324969450304 434.3075751802569 29.517635164998303
+50 588.9329526418135 217.59362307315988 459.1790584995989 23.57261825160339
+51 503.877464597577 13.531949848529566 462.0867243762544 181.85525633929737
+52 556.8496914160373 182.63565001803372 509.1672987062439 13.935096266942828
+53 552.7910761756169 15.165387946219951 503.5845717359459 14.831229936029077
+54 556.9852505941293 11.141769378565547 496.2181343231478 18.831412566491334
+55 607.8415362476524 11.357493342225883 537.7344992188957 17.313713957135597
+56 583.3990884784454 22.294607459848496 457.36078078038446 50.828024166148516
+57 546.8564073473663 18.100924678375893 466.8411194843188 49.21528739813491
+58 556.1429162078487 0.7646493482472997 436.26543286755384 36.878215625360475
+59 460.5899529083609 39.95627495883555 456.8787660188319 29.149948534145498
+60 613.2116305863424 454.2867198264129 415.2971798585238 61.63048601136232
+61 533.1855552070014 16.3923006407561 486.1977101149569 49.43796534097344
+62 598.0300312267419 163.03546960638963 431.2705562782325 46.42306571680509
+63 535.6481223827255 1.4578390035838795 419.88231081026703 72.01414764461761
+64 754.3760088260423 226.39071400305326 400.0000000000002 59.1554750742622
+65 453.95054508458054 78.44685391081457 436.9277639862904 65.68007529136231
+66 526.0006339000046 28.17688122860214 400.0000000912316 61.14214299995907
+67 557.4978677711617 142.45377165442443 438.2276532861363 51.60105490779999
+68 723.2031751164233 109.51541419462554 426.64016270827193 39.380639201714594
+69 561.58968427498 422.571439980534 501.3515736851528 13.625510765386576
+70 485.42460293612305 38.54257851065934 412.6183168501121 42.09824278063907
+71 549.5385515775812 14.26679109776623 418.8496048080212 45.686236725427925
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