diff --git a/ContributedExamples/MLD_plot.ipynb b/ContributedExamples/MLD_plot.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "68d709b7-86bb-4a90-aada-5f8e392a8a4e",
+   "metadata": {},
+   "source": [
+    "<font size=\"5\"> **Mixed layer depth in the Southern Ocean** </font>  \n",
+    "  \n",
+    "This script loads model-derived MLD data and plots the Mixed Layer Depth (MLD) and plots de Mixed Layer Depth for different sectors of the Southern Ocean using the IAF cycle 3 output from ACCESS-OM2-01. \n",
+    "  \n",
+    "**Requirements:** It is suggested you use the `conda/analysis3-24.01` (or later). This can be defined using the drop down list on the left hand corner, or type `!module load conda/analysis3` in a code cell.\n",
+    "\n",
+    "This script is a simplified version of this code https://github.com/lidefi87/ACCESS_scripts_DFA/blob/main/Scripts/Stephy/MLD_calc_Denisse.ipynb\n",
+    "made by Denisse Fierro Arcos that was posted as a issue on ACCESS-hive. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c058c71f-effd-4133-8c17-51c63bb5bd7e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "229f7f76-e1c9-4a31-ab34-13d567c5b47f",
+   "metadata": {},
+   "source": [
+    "# Loading relevant modules\n",
+    "These modules are used to access relevant outputs and to manipulate data. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "fd343ff4-438d-4563-8330-17f52860efbe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cosima_cookbook as cc\n",
+    "import xarray as xr\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "from glob import glob\n",
+    "import re\n",
+    "from collections import OrderedDict\n",
+    "import calendar\n",
+    "\n",
+    "# Packages for plotting\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.ticker as mticker\n",
+    "import matplotlib.dates as mdates\n",
+    "import matplotlib.colors as mcolors\n",
+    "from matplotlib.ticker import AutoMinorLocator\n",
+    "import matplotlib.cm as mcm\n",
+    "import matplotlib.gridspec as gridspec\n",
+    "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cft\n",
+    "import cmocean as cm\n",
+    "import geopandas\n",
+    "\n",
+    "# Dask for parallel computing\n",
+    "from dask.distributed import Client, progress\n",
+    "import dask.array\n",
+    "\n",
+    "# Scipy for statistical functions\n",
+    "import scipy.stats\n",
+    "import scipy.signal\n",
+    "\n",
+    "# Prevents cell to print warnings when called\n",
+    "import warnings\n",
+    "\n",
+    "# This line will show plots produced by matplotlib inside this Jupyter notebook\n",
+    "%matplotlib inline\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2031af28-3746-478d-82d8-72f38025b11d",
+   "metadata": {},
+   "source": [
+    "# Accessing ACCESS-OM2-01 outputs\n",
+    "Start a cluster that has multiple cores to work with. Remember that the number of cores cannot exceed the number of CPUs requested when accessing GADI.  \n",
+    "If the line below does not run, skip it. The result is that the job will not be parallelised, but the script will still run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "790b0e6c-e3c5-484f-b93d-3b8d64277b96",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "client = Client(n_workers = 8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6dfca856-3758-4761-9ac9-94599faff71d",
+   "metadata": {},
+   "source": [
+    "Access the default database of experiments from where data will be loaded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d9bedf19-f26f-4d25-bd04-09669160583f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "master_session = cc.database.create_session('/g/data/ik11/databases/cosima_master.db')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0ec4491-247f-4f8c-8d58-7652187e370a",
+   "metadata": {},
+   "source": [
+    "This notebook uses outputs for the third cycle of ACCESS-OM2-01, which includes wind forcing. A list of ACCESS-OM2-01 experiments can be accessed using `cc.querying.get_experiments(session)`. A detailed list of experiments can be obtained by adding the following argument: `all = True`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ce4abb6c-ba16-44d7-af97-44b4c015f01c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Monthly data and temporal variability\n",
+    "\n",
+    "#model\n",
+    "expt    = '01deg_jra55v140_iaf_cycle3'\n",
+    "model   = 'access-om2-01'\n",
+    "var1 = 'pot_rho_0'\n",
+    "\n",
+    "#time domain\n",
+    "yrst    = 1958 \n",
+    "yren    = 2018\n",
+    "#start and end times\n",
+    "st = str(yrst)+'-01-02'   #ACCESS OM dates are at 00:00Z, so a start date of 1st Jan would give you December of prev year\n",
+    "en = str(yren)+'-12-31'\n",
+    "\n",
+    "#Common latitude and depth range\n",
+    "# Latitude between 85 S and 55 S [85 S range for Weddell and Ross Gyre]\n",
+    "latmin  = -75. \n",
+    "latmax  = -50.\n",
+    "\n",
+    "# Depth [Upper 300m]\n",
+    "zmin    = 0.\n",
+    "zmax    = 500      \n",
+    "\n",
+    "# Set the longitude range for each sector in a list to read it in easier\n",
+    "kh   =  [-15, 70, -55]         # 345,  70  # King Haakon\n",
+    "ea   =  [70, -210, -57]        #  70 ,150  # East Antarctic\n",
+    "rs   =  [-210, -135, -62]      # 150, 250  # Ross Sea\n",
+    "am   =  [-135, -90, -63]       # 250, 290  # Amundsen sea\n",
+    "bs   =  [-90, -60, -60]                    # Bellingshausen sea\n",
+    "wl   =  [-60, -15, -58]        # 290, 345  # Weddell sea\n",
+    "\n",
+    "sectors = [kh, ea, rs, am, bs, wl]\n",
+    "\n",
+    "# winter ice extent and northern latitude bound for kh, ea, ra, ab, wl\n",
+    "winter_sie = [55, 57, 62, 63, 58]\n",
+    "\n",
+    "# Put sector names in a list for plot titles\n",
+    "\n",
+    "sNam    = ['King Haakon', 'East Antarctic', 'Ross Sea', 'Amundsen Sea', 'Bellingshausen Sea', 'Weddell Sea']\n",
+    "sNam2   = ['KH', 'EA', 'RS', 'AM', 'BS', 'WL']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "afbf7174-14e0-4704-8362-f633526d56b9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e34a242b-c214-405c-adef-76214cfd7481",
+   "metadata": {},
+   "source": [
+    "## Loading data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3bd6206-2025-480e-a25d-06b99ac2cb9b",
+   "metadata": {},
+   "source": [
+    "MLD & pot_rho"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ba7ddbc9-3fdc-4b31-83a6-cd7ebdb57c15",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 2 µs, sys: 5 µs, total: 7 µs\n",
+      "Wall time: 14.8 µs\n"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'session' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[5], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39mrun_line_magic(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m OM_mld \u001b[38;5;241m=\u001b[39m cc\u001b[38;5;241m.\u001b[39mquerying\u001b[38;5;241m.\u001b[39mgetvar(expt,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmld\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[43msession\u001b[49m,start_time\u001b[38;5;241m=\u001b[39mst,end_time\u001b[38;5;241m=\u001b[39men, ncfile\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mocean-2d-mld-1-monthly-mean-ym_\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m.nc\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m.\u001b[39msel(yt_ocean\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mslice\u001b[39m(latmin, latmax))\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m#Density\u001b[39;00m\n\u001b[1;32m      6\u001b[0m OM_rho \u001b[38;5;241m=\u001b[39m cc\u001b[38;5;241m.\u001b[39mquerying\u001b[38;5;241m.\u001b[39mgetvar(expt,var1,session,start_time\u001b[38;5;241m=\u001b[39mst,end_time\u001b[38;5;241m=\u001b[39men)\u001b[38;5;241m.\u001b[39msel(yt_ocean\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mslice\u001b[39m(latmin, latmax), st_ocean\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mslice\u001b[39m(zmin, zmax))\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'session' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "%time \n",
+    "\n",
+    "OM_mld = cc.querying.getvar(expt,'mld',session,start_time=st,end_time=en, ncfile='ocean-2d-mld-1-monthly-mean-ym_%.nc').sel(yt_ocean= slice(latmin, latmax))\n",
+    "\n",
+    "#Density\n",
+    "OM_rho = cc.querying.getvar(expt,var1,session,start_time=st,end_time=en).sel(yt_ocean= slice(latmin, latmax), st_ocean= slice(zmin, zmax))\n",
+    "\n",
+    "# Area\n",
+    "A = cc.querying.getvar(expt,'area_t',session, n=1)\n",
+    "\n",
+    "#OM_mld.coords   # Print the loaded file to check for year ranges etc\n",
+    "#OM_mld # Print the array structure\n",
+    "#OM_mld.shape # print the shape of the array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "67d37438-fe07-4b17-9be5-0c139871f5d0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ba4affc-469f-4be5-a046-2bdac01b49e6",
+   "metadata": {},
+   "source": [
+    "### Functions to calculate WEIGHTED sector average from mld data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "02ff7fb6-4bd6-4838-b074-f034531bb249",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def sector_mean(data, area, region):\n",
+    "    \"\"\"\n",
+    "    Create spatially averaged dataarray for the\n",
+    "    data: pot_rho, MLD dataset\n",
+    "    region: Sea ice sectors\n",
+    "            kh, ea, ra, ab, wl\"\"\"\n",
+    "    \n",
+    "    # Step one: slice to latitude ranges\n",
+    "    \n",
+    "    region_slice = data.sel(yt_ocean=slice(latmin, region[2]))\n",
+    "    area_slice   = area.sel(yt_ocean=slice(latmin, region[2]))\n",
+    "    \n",
+    "    # Step two: create the weighted dataset\n",
+    "    region_weighted = region_slice.weighted(area_slice.fillna(0)).mean('yt_ocean')\n",
+    "    \n",
+    "    # Step three: do the longitudinal selection and average    \n",
+    "    \n",
+    "    if region[0]>region[1]:\n",
+    "        region_slice=region_weighted.isel(xt_ocean = ( data.xt_ocean > region[0] ) | (data.xt_ocean < region[1]) )\n",
+    "    else:\n",
+    "        region_slice=region_weighted.sel(xt_ocean=slice(region[0],region[1]))\n",
+    "             \n",
+    "        \n",
+    "    spatial_mean=region_slice.mean('xt_ocean')\n",
+    "   \n",
+    "    \n",
+    "    return spatial_mean\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd2a2341-6304-4ad3-9aa0-7ef38601ce80",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "519d0822-3caa-43d7-b5ef-92d350cb5d5e",
+   "metadata": {},
+   "source": [
+    "## Calculating sector wise averages (here, I am just using East Antarctica)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad2a7621-27da-427b-9a16-ffa903ce7a56",
+   "metadata": {},
+   "source": [
+    "### Calculating weight mean for 75S"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3af10977-a202-418c-a60f-4185bfa9e205",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "rho_monthly = OM_rho.groupby('time.month').mean('time')\n",
+    "#EA\n",
+    "print ('Calculating EA rho Clim')\n",
+    "ea_rho = sector_mean(rho_monthly, A, ea)\n",
+    "ea_rho.to_netcdf('/g/data/v45/ns3783/data_for_code/AreaWeighted_EA_rho_75S_01072024.nc')\n",
+    "print ('Saved to AreaWeighted_EA_rho_75S_01072024.nc')\n",
+    "\n",
+    "mld_monthly = OM_mld.groupby('time.month').mean('time')\n",
+    "#EA\n",
+    "print ('Calculating EA mld Clim with 70S lat min')\n",
+    "ea_mld = mld_sector_mean(mld_monthly, A, ea)\n",
+    "ea_mld.to_netcdf('/g/data/v45/ns3783/data_for_code/AreaWeighted_EA_mld_75S_01072024.nc')\n",
+    "print ('Saved to AreaWeighted_EA_mld_75S_01072024.nc')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "900538cd-df53-4c01-87ba-d7f94621b185",
+   "metadata": {},
+   "source": [
+    "### Load the calculated data from Gadi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "13679f1e-6008-44c9-8ad4-0ee77d36b2f1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# loading the data back\n",
+    "da = xr.open_dataset('/g/data/v45/ns3783/data_for_code/AreaWeighted_EA_rho_75S_01072024.nc') \n",
+    "ea_rho = da.pot_rho_0\n",
+    "\n",
+    "da = xr.open_dataset('/g/data/v45/ns3783/data_for_code/AreaWeighted_EA_mld_75S_01072024.nc') \n",
+    "ea_mld = da.mld\n",
+    "\n",
+    "ea_drhodz = np.gradient(ea_rho)/np.gradient(ea_rho.st_ocean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "3280beb1-a81c-47ab-bdf8-2cdf50a8e5dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'ea_rho' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[66], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mea_rho\u001b[49m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'ea_rho' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "ea_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4069e9ae-e7d6-4585-a2df-78cc8656c446",
+   "metadata": {},
+   "source": [
+    "## Simple Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "2b92aa67-15c5-445a-a506-f45b10682279",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ahR6vUaMGERERLFy4kNjYWACys7NZvHgx48aNMyOyiIiIiPwLh8NBTk6O2THEg3l5eeHt7X3J2x65dJG6//77mTp1KnPmzCEoKKjgviebzUa5cuWwWCyMGjWKMWPGUKdOHerUqcOYMWMICAhg0KBBJqcXERERkb9LTU3l8OHDuNGi0VJGBQQEEBkZia+v70W/hksvf36+ljhp0iSGDh0KULAh7yeffFJoQ978BSmKIjk5GZvNht1u16p9IiIiIqXA4XCwe/duAgICqFSp0iWPBohcDMMwyM7O5tixYzgcDurUqXPWprtF7QYuXaQuFxUpERERkdKVmZlJXFwcMTExlCtXzuw44uHS09M5cOAANWrUwN/fv9BjRe0GbrNqn4iIiIi4P41EiSv45yjURb1GCeQQERERERHxKCpSIiIiIiIixaQiJSIiIiLiBmJiYnjnnXfMjlHAYrEwe/Zss2OYRkVKREREROQ8hg4disVi4d577z3rsREjRmCxWApWk86/vl+/fud9vZiYGCwWCxaLhXLlyhETE8PNN9/M77//XuLZJ0+eTIUKFUr8dfMlJCTQvXv3i37+/v37C74W/zy+++67guv+/jXLP5588slCrzVjxgzatGmDzWYjKCiIRo0a8eijj150tqJQkRIRERER+RfR0dFMnz6djIyMgnOZmZlMmzaNatWqFfv1Xn75ZRISEti5cydffvklFSpUoEuXLrz66qslGbvURURE4Ofnd9HPj46OJiEhodDx0ksvERgYeFZBy/+a5R/PPvtswWO//vort9xyCzfeeCOrVq1i7dq1vPrqq2RnZ190tqJQkRIRERGRy84wDNKzc005irv7T4sWLahWrRozZ84sODdz5kyio6OJjY0t9uceFBREREQE1apVo0OHDnz66ac899xzPP/88+zcubPIr/PWW2/RpEkTAgMDiY6OZsSIEaSmpgKwaNEihg0bht1uLxjFefHFF8/5Oi+++CLNmzdn4sSJVKtWjfLly3PffffhcDgYP348ERERVK5c+ayi9/epffmjSzNnzqRz584EBATQrFkzVqxYcd78Xl5eREREFDpmzZrFwIEDKV++/Dm/ZvnH3x+fN28eV111FaNHj6ZevXrUrVuXfv368f777xf5a3kxvEv11UVEREREziEjx0HD5xeY8t7bXu5GgG/xfg0eNmwYkyZNYvDgwQBMnDiRO+64g0WLFpVIpoceeohXXnmFOXPm8PjjjxfpOVarlffee4+YmBji4uIYMWIEjz/+OB9++CHt2rXjnXfeKVTO/llO/m7v3r389NNP/Pzzz+zdu5cbb7yRuLg46taty+LFi1m+fDl33HEH1157LW3btj3v6zzzzDO88cYb1KlTh2eeeYb//Oc/7NmzB2/vC3+9165dy4YNG/jggw/OemzcuHG88sorREdHc9NNNzF69Gh8fX2BvJGxqVOnsmXLFho3bnzB9ykpKlIiIiIiIhdw66238tRTTxWMvPz5559Mnz69xIpUSEgIlStXZv/+/UV+zqhRowr+vUaNGrzyyivcd999fPjhh/j6+mKz2bBYLERERFzwtZxOJxMnTiQoKIiGDRvSuXNndu7cyY8//ojVaqVevXqMGzeORYsW/WuReuyxx+jZsycAL730Eo0aNWLPnj3Ur1//ghk+//xzGjRoQLt27Qqdf+ihh2jRogUVK1Zk1apVPPXUU8TFxfHZZ58BMHLkSJYuXUqTJk2oXr06bdu2pWvXrgwePPiSph5eiIqUi/l5SwLB/j5cWStUG9aJiIhImVXOx4ttL3cz7b2LKywsjJ49e/LFF19gGAY9e/YkLCysRHMZhlGs3//++OMPxowZw7Zt20hOTiY3N5fMzEzS0tIIDAws1nvHxMQQFBRU8HF4eDheXl6FNq4NDw8nKSnpX1+nadOmBf8eGRkJQFJS0gWLVEZGBlOnTuW5554767GHH3640OtXrFiRG2+8kXHjxhEaGkpgYCDz589n7969/PHHH6xcuZJHH32Ud999lxUrVhAQEPDvn/xFUpFyIdm5Tl6Yu5WjyVk0qWJjeMeaXN8oAm8v3comIiIiZYvFYin29Dqz3XHHHTzwwAMA55x+dilOnDjBsWPHqFGjRpGuP3DgAD169ODee+/llVdeISQkhGXLlnHnnXeSk5NT7Pf38fEp9LHFYjnnOafTWeTXyS+FF3oOwPfff096ejq33XbbBa/NHxHbs2cPoaGhBedr1apFrVq1uOuuu3jmmWeoW7cu33zzDcOGDbvga14M/YbuQjJyHHRrFIG/j5XNR+w8MHU917y5mK9W7Ccj22F2PBERERGPdv3115OdnU12djbdupXsaNq7776L1Wr916XT/27NmjXk5uby5ptv0rZtW+rWrUt8fHyha3x9fXE43ON3yM8//5w+ffpQqVKlC167fv164P9HvM4lJiaGgIAA0tLSSizjP7nXnwHKOFs5H17u25iHrq3DlysO8OWK/Rw8mc5zc7by9q+7ue3K6tx2ZQwhgb5mRxURERHxOF5eXmzfvr3g38/HbrezYcOGQudCQkIKlkpPSUkhMTGRnJwc4uLi+Prrr/nss88YO3YstWvXLlKWWrVqkZuby/vvv0/v3r35888/+fjjjwtdExMTQ2pqKr/99hvNmjUjICCg1Ka5XYo9e/awZMkSfvzxx7MeW7FiBStXrqRz587YbDZWr17Nww8/TJ8+fQq+ni+++CLp6en06NGD6tWrc/r0ad577z1ycnK47rrrSi23RqRcUGh5Px6+ri7Ln7yWl/s2IjqkHCfTsnnn1920e+03XpizhUMn082OKSIiIuJxgoODCQ4O/tdrFi1aRGxsbKHj+eefL3j8+eefJzIyktq1a3Prrbdit9v57bffeOKJJ4qco3nz5rz11luMGzeOxo0bM2XKFMaOHVvomnbt2nHvvfcycOBAKlWqxPjx44v3yV4mEydOpEqVKnTt2vWsx/z8/Pjmm2/o1KkTDRs25Pnnn+fuu+9m2rRpBdd07NiRffv2cdttt1G/fn26d+9OYmIiv/zyC/Xq1Su13BajuAvpl0HJycnYbDbsdvsF/4dhhlyHk5+2JPLJkr1sOZIMgNUCPZpEMrxDLZpUtZmcUEREROTfZWZmEhcXR40aNfD39zc7jni4f/t5LGo30NQ+N+DtZaV3syh6NY1k+d4TfLJkH0t2HWPepgTmbUqgfe1Q7ulQiw51wrTSn4iIiIjIZaAi5UYsFgvta4fRvnYY2+KT+XTJXn7YlMCfe07w554TNIgMZniHmvRsGomPVvoTERERESk1+m3bTTWMCuadW2JZPLoTw9rHUM7Hi+0JyYz6ZgOdXl/ExGVxpGXlmh1TRERERKRMUpFyc1UrBvBC70aseOoaHutal7Dyvhw5ncHL87bR7rXfeWPBTo6lZJkdU0RERESkTFGRKiMqBPjywDV1WPbENbzavzExoQHYM3KY8Mce2o/7nadnbSbueOmtoy8iIiJSFFrnTFxBSfwcqkiVMf4+XgxuU53fHu3Ex0Na0Cy6Atm5Tqb+dZBr3lzEfV+vZf3BU2bHFBEREQ+Tv+9Sdna2yUlEID09byshHx+fi34NLX+O6y9/fikMw2BV3Ek+WbKP33ckFZxvXSOEezvWpFPdylitWulPRERESpdhGBw8eJCcnByioqKwWvX3fLn8DMMgPT2dpKQkKlSoQGRk5FnXFLUbqEhRtovU3+06msKnS/YxZ8MRchx53/a64eW5p0Mt+jSLwtdb/4cmIiIipSc7O5u4uDicTqfZUcTDVahQgYiIiHNuHaQiVQyeUqTyJdgzmPTnfqb+dZDUMyv7RQT7c8dVMfyndTWC/C9+iFNERETk3zidTk3vE1P5+PgUTDU9FxWpYvC0IpXPnpHD1L8OMunPOJLOrOwX5OfN4LbVGdY+hvBg7TouIiIiIp5FRaoYPLVI5cvKdTBnfTyfLNnL3mN5K/v5eFnoH1uFezrUpHblIJMTioiIiIhcHipSxeDpRSqf02nw244kPlm8lzUH/n9lvy4Nwrm3Y01axYSYmE5EREREpPSpSBWDitTZ1h44ySeL97Fw+1Hyf0JaVKvA8I61uK5BuFb6ExEREZEySUWqGFSkzm9PUiqfLd3HzHVHyHbkrbBTs1Ig91xdk36xVfD3Of+NeiIiIiIi7kZFqhhUpC4sKTmTycv389XKA6Rk5q30VynIj6HtYhjSpjq2AK30JyIiIiLuT0WqGFSkii41K5fpqw7y+bI4EuyZAAT6evGf1tW446oaRFUoZ3JCEREREZGLpyJVDCpSxZed62Tepng+WbyPnUdTAPC2WujTPIp7OtSkfoS+jiIiIiLiflSkikFF6uIZhsGiXcf4ZPFeVu47WXC+U71KDO9Qi7Y1Q865Y7SIiIiIiCtSkSoGFamSseHQaT5dspeftyTiPPNT1ayqjeEda9GtUQReWulPRERERP4hKSWTF+ZspW/zKnRrFG76H+FVpIpBRapk7T+exmfL9vHdmsNk5eat9Fc9NIC7rq7JTS2raqU/ERERESnw+bI4Xpm3jebRFZh9f3uz4xS5G1gvYybxEDFhgfy3XxP+fPIaHrymNrZyPhw4kc5zs7fQ/rXfee+33ZxKyzY7poiIiIi4gDkbjgDQP7aKyUmKRyNSaESqtKVl5fLtmkN8tjSOI6czACjn48XAK6K586oaRIcEmJxQRERERMyw91gq1765GC+rhb+evpaw8n5mR9KIlLiOQD9vhrWvweLRnXj3luY0jAwmI8fB5OX76fTGIh6ctp4tR+xmxxQRERGRy2zO+rzRqKvrhLlEiSoOb7MDiOfw9rLSt3kV+jSLYtme43yyeB/L9hxn7sZ45m6M5+o6YdzToSZX1Q4z/SZDERERESldhmEwe0M84H7T+kBFSkxgsVi4uk4lrq5TiS1H7Hy6ZB/zNyewdPdxlu4+TtOqNl6/sRn1IoLMjioiIiIipWTdwdMcPJlOgK8X1zUMNztOsWlqn5iqcRUb7/0nlkWPdWJouxjK+Xix6bCdPhOWMeWvA+gWPhEREZGyKX+Ria4Nwwnwdb/xHRUpcQnRIQG82KcRix/vRIe6lcjKdfLMrC3cP3Ud9owcs+OJiIiISAnKcTiZtykBgH5uOK0PVKTExVQO8mfy0Ct4ukd9vK0WftycSI93l7L2wCmzo4mIiIhICVm6+xgn07IJK+/LVbXDzI5zUVSkxOVYrRbu6VCL7+9rR7WQAI6czuDmT1bwwR97cDo11U9ERETE3c1en7fIRK+mUXh7uWclcc/U4hGaR1dg3oNX0atpJA6nwesLdnLbxFUkpWSaHU1ERERELlJqVi6/bEsE3HdaH6hIiYsL9vfh/f/EMu6GJvj7WFm25zg93l3K4l3HzI4mIiIiIhfhl62JZOY4iQkNoFlVm9lxLpqKlLg8i8XCwCuqMW/kVdSPCOJ4aja3T1zF2B+3k53rNDueiIiIiBRD/t5RfZtXceu9Q1WkxG3UrhzE7Pvbc2vb6gB8smQfN328nIMn0k1OJiIiIiJFcSwli2W782YWufO0PlCREjfj7+PFK/0a8/GQFgT7e7PxsJ2e7y1l7sZ4s6OJiIiIyAX8sDEepwHNoitQIyzQ7DiXREVK3NL1jSP58aGraVm9IilZuTw4bT1PfL+J9Oxcs6OJiIiIyHnkb8Lbr3mUyUkunYqUuK2qFQP45p62PNC5NhYLfLPmEH0m/MmOxGSzo4mIiIjIP+w7lsrGw3a8rBZ6NVWREjGVt5eVx7rVY8qdbagc5MeepFT6TPiTr1YewDC055SIiIiIq8hfZOKq2mFUCvIzOc2lU5GSMqFd7TB+euhqOterRHauk+dmb+Her9diT88xO5qIiIiIxzMM4/+n9cW6/2gUqEhJGRJa3o/Pb7+CZ3s2wMfLwoKtR+nx3lLW7D9pdjQRERERj7bh0GkOnEinnI8XXRtGmB2nRLhVkRo7diwWi4VRo0YVnDMMgxdffJGoqCjKlStHp06d2Lp1q3khxVRWq4W7rq7JjPvaUT00gCOnMxj46Uom/L4bh1NT/URERETMMHt93mhU10bhBPp5m5ymZLhNkVq9ejWffvopTZs2LXR+/PjxvPXWW0yYMIHVq1cTERHBddddR0pKiklJxRU0rVqBeSOvom/zKBxOgzd+2cWQz/7iaHKm2dFEREREPEqOw8m8TQkA9Gvu3ntH/Z1bFKnU1FQGDx7M//73PypWrFhw3jAM3nnnHZ555hkGDBhA48aN+eKLL0hPT2fq1KkmJhZXEOTvwzsDm/P6jU0p5+PFin0n6P7uUv7YkWR2NBERERGPsWzPcU6kZRMa6MtVdcLMjlNi3KJI3X///fTs2ZMuXboUOh8XF0diYiJdu3YtOOfn50fHjh1Zvnz5eV8vKyuL5OTkQoeUTRaLhZtaRfPDyKtoEBnMybRshk1ezX/nbSM712l2PBEREZEyL39aX6+mkfh4uUX9KBKX/0ymT5/OunXrGDt27FmPJSYmAhAeHl7ofHh4eMFj5zJ27FhsNlvBER0dXbKhxeXUrlyeWSPaMbRdDACfLYvjxo+Xs/94mrnBRERERMqwtKxcftl6FIC+sWVnWh+4eJE6dOgQDz30EF9//TX+/v7nvc5isRT62DCMs8793VNPPYXdbi84Dh06VGKZxXX5+3jxYp9GfHprSyoE+LDpsJ2e7y0tWIpTRERERErWwm1HychxUD00gNjoCmbHKVEuXaTWrl1LUlISLVu2xNvbG29vbxYvXsx7772Ht7d3wUjUP0efkpKSzhql+js/Pz+Cg4MLHeI5ujaK4McHr6Z1TAhp2Q4emr6Bx77bSFpWrtnRRERERMqUWWem9fVtXuVfBzrckUsXqWuvvZbNmzezYcOGgqNVq1YMHjyYDRs2ULNmTSIiIli4cGHBc7Kzs1m8eDHt2rUzMbm4uqgK5Zh6dxsevLYOFgt8v/YwvScsY2u83exoIiIiImXC8dQslu05DkC/5mVjE96/c+lF3IOCgmjcuHGhc4GBgYSGhhacHzVqFGPGjKFOnTrUqVOHMWPGEBAQwKBBg8yILG7E28vKI9fV5cqaoYz6Zj37jqXR/8PlPNOjAbddWb3M/dVERERE5HKatzEeh9OgWVUbNSuVNztOiXPpEamiePzxxxk1ahQjRoygVatWHDlyhF9++YWgoCCzo4mbuLJWKD891IFr61cmO9fJC3O3cs9Xazmdnm12NBERERG3NWtDPJA3ra8sshiGYZgdwmzJycnYbDbsdrvul/JghmEw6c/9vPbTDrIdTiJt/rx7Syyta4SYHU1ERETErcQdT6PzG4uwWmDl09dSOej8C8e5mqJ2A7cfkRIpKRaLhTuuqsHMEe2oERZIgj2TWz5dwbu/7sbh9Pi/N4iIiIgUWf6qyFfVqeRWJao4VKRE/qFxFRs/jLyKAbFVcBrw9q+7GPS/lSTaM82OJiIiIuLyDMMo2IS3LC4ykU9FSuQcyvt589bA5rx5UzMCfL34K+4k3d9dwm/bj5odTURERMSlbTxsZ/+JdPx9rHRtFGF2nFKjIiXyL25oWZV5I6+iUVQwp9JzuPOLNbz0w1aych1mRxMRERFxSfmjUV0bRlDez6UXCb8kKlIiF1CzUnlmjmjHsPYxAEz6cz8DPlxO3PE0c4OJiIiIuJhch5N5m/JW6+sXW3an9YGKlEiR+Hl78ULvRnx2WysqBviwNT6ZXu8tZea6w2ZHExEREXEZy/Yc53hqNiGBvlxdp5LZcUqVipRIMXRpGM5PD3WgTY0Q0rIdPPLtRh75dgNpWblmRxMREREx3Zwze0f1ahqJj1fZrhpl+7MTKQURNn+m3t2Wh7vUxWqBmeuO0Ov9ZWw5Yjc7moiIiIhp0rNzWbA1ESi7m/D+nYqUyEXwslp4qEsdpt3dlkibP3HH0xjw4XImLotDe1yLiIiIJ1q47Sjp2Q6qhQTQoloFs+OUOhUpkUvQpmYoPz54Ndc1DCfb4eTledu4+8s1nEzLNjuaiIiIyGX1972jLBaLyWlKn4qUyCWqGOjLp7e25KU+jfD1svLr9iR6vLuUlftOmB1NRERE5LI4nprFkt3HAegbW/an9YGKlEiJsFgs3N4uhln3t6NmpUASkzMZ9L+VvL1wF7kOp9nxRERERErV/E0JOJwGTarYqFWpvNlxLgsVKZES1CjKxg8PXMWNLaviNODd33Yz6LO/SLBnmB1NREREpNTM3nBmWp+HjEaBipRIiQv08+aNm5rxzsDmBPp6sSruJN3fXcrCbUfNjiYiIiJS4vYfT2P9wdNYLdC7WaTZcS4bFSmRUtIvtgrzH7yaJlVsnE7P4e4v1/Di3K1k5jjMjiYiIiJSYvL3jmpfO4zKQf4mp7l8VKRESlFMWCAz7mvHXVfVAGDy8v0M+HA5e4+lmpxMRERE5NIZhsGc/Gl9HrB31N+pSImUMl9vK8/2asikoVcQEujLtoRker+/jO/XHtaeUyIiIuLWNh22s+94Gv4+Vro1jjA7zmWlIiVymXSuX5mfHrqaK2uGkp7t4LHvNvLItxtJzco1O5qIiIjIRclfZKJLg3DK+3mbnObyUpESuYzCg/35+q42PNa1Ll5WC7PWH6HXe0vZfNhudjQRERGRYsl1OPlhYwIA/T1otb58KlIil5mX1cID19Thm3vaEmXzZ/+JdAZ89CefLd2nqX4iIiLiNv7ce4LjqVlUDPChQ91KZse57FSkREzSKiaEHx+6mm6NwslxGPx3/nbumLyaE6lZZkcTERERuaA56/Om9fVsGomPl+fVCs/7jEVcSIUAXz4e0pJX+jXG19vKHzuP0eO9pew+mmJ2NBEREZHzSs/OZcHWRMAzp/WBipSI6SwWC7e2rc6c+9tTq1IgR5OzGPL5Xxw8kW52NBEREZFzWrjtKGnZDqJDytGiWkWz45hCRUrERTSIDOb7e9tRN7w8R5OzGPz5ShLtmWbHEhERETlL/ia8/ZpXwWKxmJzGHCpSIi6kYqAvX9/ZhuqhARw6mcGQz//SPVMiIiLiUk6kZrFk1zEA+nrYJrx/pyIl4mIqB/vz9Z1tiAj2Z09SKrdPWkVyZo7ZsUREREQAmL85gVynQeMqwdSuXN7sOKZRkRJxQdEhAXx9VxtCA33ZciSZOyatJj1bG/eKiIiI+WafWa2vnwePRoGKlIjLql25PF/c0Zogf2/WHDjF8K/WkpXrMDuWiIiIeLCDJ9JZd/A0Vgv0aRZldhxTqUiJuLDGVWxMHnYF5Xy8WLr7OA9OW0+uw2l2LBEREfFQszfkjUa1qxVG5WB/k9OYS0VKxMW1rB7C/25rha+XlQVbj/L495twOg2zY4mIiIiHMQyjoEj189C9o/5ORUrEDVxVJ4wJg2LxslqYuf4IL/6wFcNQmRIREZHLZ8uRZPYdS8PP20q3RuFmxzGdipSIm+jaKII3b2qGxQJfrjjA6wt2mh1JREREPMisM4tMdGkYTpC/j8lpzKciJeJG+sVW4ZW+jQH4cNFePvhjj8mJRERExBM4nAY/bMrbhLe/h6/Wl09FSsTNDGlbnae61wfg9QU7+XLFfnMDiYiISJm3fO9xjqVkUSHAhw51K5kdxyWoSIm4oeEdazHymtoAPD9nKzPWHjY5kYiIiJRl+dP6ejaJxNdbFQJUpETc1iPX1WVouxgARn+/kZ+3JJgbSERERMqkjGwHC7YkAtBfq/UVUJEScVMWi4XnezXkppZVcRowctp6Fu86ZnYsERERKWN+3X6UtGwHVSuWo2X1imbHcRkqUiJuzGq18NoNTenRJIIch8Hwr9awev9Js2OJiIhIGTL7zLS+vs2jsFgsJqdxHSpSIm7Oy2rhnYGxdKxbicwcJ3dMWs3mw3azY4mIiEgZcDItu2DGSz+t1leIipRIGeDrbeXjIS1pXSOElKxcbpv4F7uPppgdS0RERNzc/M0J5DoNGkUFUyc8yOw4LkVFSqSMKOfrxee3t6JpVRun0nMY8vlfHDyRbnYsERERcWP50/o0GnU2FSmRMiTI34cvhrWmbnh5jiZnMfjzlSTaM82OJSIiIm7o0Ml01h44hcUCfZpHmR3H5ahIiZQxFQN9+frONlQPDeDQyQyGfP4XJ1KzzI4lIiIibmbOhrzRqHa1QgkP9jc5jetRkRIpgyoH+/P1nW2ItPmzJymV2yauIjkzx+xYIiIi4iYMwyjYhLevpvWdk4qUSBkVHRLAV3e2ITTQl63xydwxaTXp2blmxxIRERE3sDU+mb3H0vDztnJ94wiz47gkFSmRMqx25fJ8eWdrgvy9WXPgFMO/WktWrsPsWCIiIuLi8heZ6NIgnGB/H5PTuCYVKZEyrlGUjcnDWhPg68XS3cd5cNp6ch1Os2OJiIiIi3I4DeZujAfyNuGVc1OREvEALatX5H+3tcLXy8qCrUd5/PtNOJ2G2bFERETEBa3Ye4KklCwqBPjQqV5ls+O4LBUpEQ/RvnYYEwbF4mW1MHP9EV6YuxXDUJkSERGRwmafWa2vR5NIfL1VF85HXxkRD9K1UQRv3tQMiwW+WnmA8Qt2mh1JREREXEhmjoOftyQC2oT3QlSkRDxMv9gq/LdfYwA+WrSXD/7YY3IiERERcRW/bj9KalYuVSqUo1X1imbHcWkqUiIeaHCb6jzdoz4Ary/YyZcr9psbSERERFzC7PX/v8iE1WoxOY1rU5ES8VD3dKjFg9fUBuD5OVv5fu1hkxOJiIiImU6lZbNoZxKQN4NF/p2KlIgHe/i6ugxrHwPA499v5KfNCeYGEhEREdPM35xArtOgYWQwdcODzI7j8ly+SB05coQhQ4YQGhpKQEAAzZs3Z+3atQWPG4bBiy++SFRUFOXKlaNTp05s3brVxMQi7sNisfBcz4bc1LIqTgMenL6exbuOmR1LRERETJC/CW+/WO0dVRQuXaROnTpF+/bt8fHx4aeffmLbtm28+eabVKhQoeCa8ePH89ZbbzFhwgRWr15NREQE1113HSkpKeYFF3EjVquF125oSs8mkeQ4DIZ/tYbV+0+aHUtEREQuo0Mn01lz4BQWC/Rppml9ReHSRWrcuHFER0czadIkWrduTUxMDNdeey21atUC8kaj3nnnHZ555hkGDBhA48aN+eKLL0hPT2fq1KkmpxdxH15WC28PbE6nepXIzHFyx6TVbD5sNzuWiIiIXCZzN+YtMnFlzVAibP4mp3EPLl2k5s6dS6tWrbjpppuoXLkysbGx/O9//yt4PC4ujsTERLp27Vpwzs/Pj44dO7J8+fLzvm5WVhbJycmFDhFP5+tt5eMhLWlTI4SUrFxum/gXu49qZFdERKSsMwyDWfnT+rR3VJG5dJHat28fH330EXXq1GHBggXce++9PPjgg3z55ZcAJCbmbRYWHh5e6Hnh4eEFj53L2LFjsdlsBUd0dHTpfRIibsTfx4vPbm9Fs6o2TqXnMPizvzh4It3sWCIiIlKKtsYnsycpFV9vK9c3iTA7jttw6SLldDpp0aIFY8aMITY2luHDh3P33Xfz0UcfFbrOYim8xr1hGGed+7unnnoKu91ecBw6dKhU8ou4oyB/HyYPa0298CCSUrIY9NlKEu2ZZscSERGRUjJnQ95oVJcGlQn29zE5jftw6SIVGRlJw4YNC51r0KABBw8eBCAiIq8x/3P0KSkp6axRqr/z8/MjODi40CEi/69ioC9f3dma6qEBHD6VwZDP/+JEapbZsURERKSEOZwGczbkb8KraX3F4dJFqn379uzcubPQuV27dlG9enUAatSoQUREBAsXLix4PDs7m8WLF9OuXbvLmlWkrKkc7M/Xd7Yh0ubPnqRUbpu4iuTMHLNjiYiISAlaue8ESSlZBPt706leJbPjuBWXLlIPP/wwK1euZMyYMezZs4epU6fy6aefcv/99wN5U/pGjRrFmDFjmDVrFlu2bGHo0KEEBAQwaNAgk9OLuL/okAC+vqsNoYG+bI1P5o5Jq0nPzjU7loiIiJSQ/L2jejaNws/by+Q07sWli9QVV1zBrFmzmDZtGo0bN+aVV17hnXfeYfDgwQXXPP7444waNYoRI0bQqlUrjhw5wi+//EJQkHZjFikJtSqV56s72xDs782aA6cY/tVasnIdZscSERGRS5SZ4+CnLXm3yPRrrk14i8tiGIZhdgizJScnY7PZsNvtul9K5DzWHjjFrZ//RXq2g26NwvlgUAu8vVz6bzEiIiLyL+ZvSuD+qeuIsvmz7IlrsFrPv1ibJylqN9BvQSJSJC2rV+R/t7XC18vKgq1Hefz7TTidHv93GBEREbc1+8xqfX1jq6hEXQQVKREpsva1w/hgcAu8rBZmrj/CC3O3okFtERER93M6PZtFO5MAbcJ7sVSkRKRYrmsYzls3N8Niga9WHmD8gp0XfpKIiIi4lPmbE8hxGNSPCKJehNYWuBgqUiJSbH2bV+HVfk0A+GjRXj74Y4/JiURERKQ45qzP2zuqf6xGoy6WipSIXJRBbarxTI8GALy+YCdfLN9vbiAREREpksOn0lm1/yQWC/TRan0XTUVKRC7a3R1q8uA1tQF4Ye5Wvl972OREIiIiciFzNuSNRrWpEUKkrZzJadyXipSIXJKHr6vLsPYxADz+/UZ+2pxgbiARERE5L8MwCjbh1bS+S6MiJSKXxGKx8FzPhtzcqipOAx6cvp7Fu46ZHUtERETOYVtCMruTUvH1snJ940iz47g1FSkRuWRWq4WxA5rSs0kkOQ6D4V+tYVXcSbNjiYiIyD/kT+u7pn5lbOV8TE7j3lSkRKREeFktvD2wOZ3rVSIzx8kdk1ez+bDd7FgiIiJyhsNpMPdMkeqnaX2XTEVKREqMr7eVj4a0pE2NEFKzcrlt4l/sPppidiwREREB/tp3gsTkTIL9velcv5LZcdyeipSIlCh/Hy8+H3oFzaraOJWew+DP/uLgiXSzY4mIiHi82RvyFpno0SQSP28vk9O4PxUpESlx5f28mTysNfXCg0hKyWLQZytJtGeaHUtERMRjZeY4+GlzIqBpfSVFRUpESkXFQF++urM1MaEBHD6VweDPVnIiNcvsWCIiIh7p9x1JpGTlEmXzp3VMiNlxygQVKREpNZWD/fn6rjZE2vzZeyyN2yauwp6RY3YsERERj5O/d1Tv5lFYrRaT05QNKlIiUqqqVgxgyl1tCCvvy9b4ZO6cvJr07FyzY4mIiHiM0+nZLNqZt8ejNuEtOSpSIlLqalYqz5d3tCHY35s1B04x/Ku1ZOU6zI4lIiLiEX7cnEi2w0n9iCDqRwSbHafMUJESkcuiYVQwk4a1JsDXi6W7jzNy6npyHU6zY4mIiJR5+av19W2u0aiSpCIlIpdNy+oV+d9trfD1tvLLtqOM/n4TTqdhdiwREZEy68jpDFbFnQSgb/Mok9OULSpSInJZta8dxoeDWuBltTBr/RGen7sFw1CZEhERKQ1zzoxGtakRQlSFcianKVtUpETksuvSMJy3bm6GxQJfrzzI+AU7zY4kIiJSJs1ZHw9o76jSoCIlIqbo27wKY/o3AeCjRXv54I89JicSEREpW7YnJLPzaAq+XlZ6NI40O06ZoyIlIqb5T+tqPNOjAQCvL9jJF8v3mxtIRESkDMnfO6pz/UrYAnxMTlP2qEiJiKnu7lCTB6+tA8ALc7fy/drDJicSERFxf06nwdyNZ6b1abW+UqEiJSKme7hLHe5oXwOAx7/fyJJdx0xOJCIi4t7+ijtJgj2TIH9vOtevbHacMklFSkRMZ7FYeK5XA25oURWnAaO+2UCCPcPsWCIiIm4rf1pfj8aR+Pt4mZymbFKREhGXYLFYeLV/YxpFBXMyLZsHpq4nRxv2ioiIFFtmjoMftyQA0DdWe0eVFhUpEXEZ/j5efDi4BUH+3qw9cIpxP+0wO5KIiIjbWbQziZTMXCJt/rStEWp2nDJLRUpEXEr10EBev7EZAJ8ti+PnLYkmJxIREXEvs85M6+vTLAqr1WJymrJLRUpEXM71jSO466q8xSdGf7eRAyfSTE4kIiLiHuzpOfyxI2/Rpr5ara9UqUiJiEt6ont9WlavSEpWLiOmrCMzx2F2JBEREZf305YEsh1O6oUH0SAyyOw4ZZqKlIi4JB8vKxMGxVIxwIet8cm8PG+b2ZFERERcXv60vr6xUVgsmtZXmlSkRMRlRdrK8c4tsVgsMPWvgwVLuYqIiMjZ4k9n8FfcSSDv/igpXSpSIuLSOtatxMjOtQF4auZmdh9NMTmRiIiIa5q7MR6A1jVCqFoxwOQ0ZZ+KlIi4vIe61KV97VAychzcN2UdaVm5ZkcSERFxOfkzN/ppkYnLQkVKRFyel9XCOwNjqRzkx56kVJ6ZtRnDMMyOJSIi4jJ2JCazIzEFHy8LPZpEmB3HI6hIiYhbqBTkx4RBLfCyWpi9IZ6pqw6aHUlERMRlzF6fN62vc73KVAjwNTmNZ1CREhG30bpGCKO71QPgpbnb2HLEbnIiERER8zmdBnM3nJnWF6tpfZeLipSIuJV7rq5JlwaVyXY4uW/KWuwZOWZHEhERMdWq/SeJt2cS5OfNNfUrmx3HY6hIiYhbsVotvHlTc6pWLMehkxmM/m6j7pcSERGPNufMaFT3JhH4+3iZnMZzqEiJiNuxBfjwwaAW+HpZ+WXbUT5fFmd2JBEREVNk5TqYvykB0Gp9l5uKlIi4pWbRFXi2VwMAXvtpB2sPnDQ5kYiIyOX3x45jJGfmEh7sR5uaoWbH8SgqUiLitm5tW53ezaLIdRrcP2U9J1KzzI4kIiJyWeVP6+vbvApeVovJaTyLipSIuC2LxcLYAU2oWSmQxORMRn2zAYdT90uJiIhnsGfk8Nv2JAD6No8yOY3nUZESEbdW3s+bjwa3xN/HytLdx5nw+x6zI4mIiFwWP29JINvhpE7l8jSMDDY7jsdRkRIRt1cvIoj/9msCwDu/7WLZ7uMmJxIRESl9+Zvw9outgsWiaX2Xm4qUiJQJN7asysBW0RgGPDR9PUeTM82OJCIiUmoS7BmsjDsBaFqfWVSkRKTMeKlvI+pHBHEiLZuRU9eT63CaHUlERKRUzN0Qj2HAFTEVqVoxwOw4HklFSkTKDH8fLz4a0pLyft6s2n+S13/ZaXYkERGRUjF7w/9P6xNzqEiJSJlSIyyQ8Tc2BeCTxftYuO2oyYlERERK1s7EFLYnJOPjZaFnk0iz43gsFSkRKXN6NIlkaLsYAB79dgOHTqabG0hERKQEzT6zd1THupWpEOBrchrPpSIlImXS0z0a0Dy6AsmZudw/dR1ZuQ6zI4mIiFwyp9Ng7plpff01rc9UKlIiUib5elv5YHALKgT4sOmwnf/O2252JBERkUu2ev9JjpzOoLyfN9c2qGx2HI+mIiUiZVaVCuV4e2BzAL5aeYC5G+PNDSQiInKJ8heZ6N44An8fL5PTeDaXLlK5ubk8++yz1KhRg3LlylGzZk1efvllnM7/X9LYMAxefPFFoqKiKFeuHJ06dWLr1q0mphYRV9K5XmXu71wLgCdnbGJPUqrJiURERC5OVq6DHzcnAFqtzxW4dJEaN24cH3/8MRMmTGD79u2MHz+e119/nffff7/gmvHjx/PWW28xYcIEVq9eTUREBNdddx0pKSkmJhcRV/Jwl7q0rRlCeraD+6esIyNb90uJiIj7WbTzGPaMHCoH+dG2ZqjZcTyeSxepFStW0LdvX3r27ElMTAw33ngjXbt2Zc2aNUDeaNQ777zDM888w4ABA2jcuDFffPEF6enpTJ061eT0IuIqvL2svHdLLGHl/dh5NIVnZ2/BMAyzY4mIiBTLnDOr9fVtHoWX1WJyGnHpInXVVVfx22+/sWvXLgA2btzIsmXL6NGjBwBxcXEkJibStWvXguf4+fnRsWNHli9fft7XzcrKIjk5udAhImVb5WB/3v9PLFYLzFh3mG/XHDI7koiISJElZ+bw6/YkAPo217Q+V+BdnIt37tzJtGnTWLp0Kfv37yc9PZ1KlSoRGxtLt27duOGGG/Dz8yuxcE888QR2u5369evj5eWFw+Hg1Vdf5T//+Q8AiYmJAISHhxd6Xnh4OAcOHDjv644dO5aXXnqpxHKKiHu4slYoj3atx+sLdvL8nK00qVKBhlHBZscSERG5oJ83J5Kd66R25fI00n+7XEKRRqTWr1/PddddR7NmzViyZAlXXHEFo0aN4pVXXmHIkCEYhsEzzzxDVFQU48aNIysrq0TCffPNN3z99ddMnTqVdevW8cUXX/DGG2/wxRdfFLrOYik8tGkYxlnn/u6pp57CbrcXHIcO6S/TIp7ivo616FyvElm5TkZMWUtyZo7ZkURERC4ofxPe/rFV/vX3XLl8ijQi1a9fP0aPHs0333xDSEjIea9bsWIFb7/9Nm+++SZPP/30JYcbPXo0Tz75JLfccgsATZo04cCBA4wdO5bbb7+diIgIIG9kKjIysuB5SUlJZ41S/Z2fn1+JjpyJiPuwWi28dXNzer2/jP0n0nni+018OLiF/qMkIiIuK9GeyYp9JwDo0yzK5DSSr0hFavfu3fj6+l7wuiuvvJIrr7yS7OzsSw4GkJ6ejtVaeNDMy8urYPnzGjVqEBERwcKFC4mNjQUgOzubxYsXM27cuBLJICJlT8VAXyYMiuXmT1bw05ZEJv25nzuuqmF2LBERkXOau/EIhgGtqlckOiTA7DhyRpGm9hWlRF3K9efTu3dvXn31VebPn8/+/fuZNWsWb731Fv379wfypvSNGjWKMWPGMGvWLLZs2cLQoUMJCAhg0KBBJZJBRMqm2GoVebpHAwDG/LiddQdPmZxIRETk3Gavz9uEV3tHuZZiLTaRb9WqVSxatIikpKRCm+MCvPXWWyUSDOD999/nueeeY8SIESQlJREVFcXw4cN5/vnnC655/PHHycjIYMSIEZw6dYo2bdrwyy+/EBQUVGI5RKRsGtouhjX7TzF/cwIPTFnH/AevpmJgyfwhSEREpCTsOprCtoRkvK0WejaJvPAT5LKxGMXcTGXMmDE8++yz1KtXj/Dw8EL3FVgsFn7//fcSD1nakpOTsdls2O12goO1CoqIJ0nJzKH3mfulOtWrxMTbr8CqvTlERMRFjP95Bx8u2kuXBpX57PYrzI7jEYraDYo9IvXuu+8yceJEhg4dein5RERcQpC/Dx8Obkn/D/9k0c5jfLR4L/d3rm12LBEREZxOgzkbNK3PVRV7Q16r1Ur79u1LI4uIiCkaRgXzct9GALz5y06W7z1uciIRERFYe/AUR05nUN7Pmy4Nzr8itZij2EXq4Ycf5oMPPiiNLCIiprm5VTQ3tKiK04AHp20gKTnT7EgiIuLhZq3P2zuqW6MI/H28TE4j/1TsqX2PPfYYPXv2pFatWjRs2BAfH59Cj8+cObPEwomIXC4Wi4X/9mvMliN2dh5NYeS09Uy5qw3eXsX+e5OIiMgly8518uPmBCBvE15xPcX+DWHkyJH88ccf1K1bl9DQUGw2W6FDRMRdlfP14sMhLQj09eKvuJO8tXCX2ZFERMRDLd51jNPpOVQO8uPKWqFmx5FzKPaI1JdffsmMGTPo2bNnaeQRETFVrUrlee2Gpoyctp4PF+3lipgQOtevbHYsERHxMLPPTOvr3SwKL60m65KKPSIVEhJCrVq1SiOLiIhL6N0situurA7Aw99u4MjpDJMTiYiIJ0nJzOHX7UcBTetzZcUuUi+++CIvvPAC6enppZFHRMQlPNOzAU2r2jidnsP9U9aRneu88JNERERKwM9bEsnKdVKrUiCNorTHqasq9tS+9957j7179xIeHk5MTMxZi02sW7euxMKJiJjFz9uLDwa1oOd7S9lw6DRjftzOi30amR1LREQ8wOwNedP6+jWvgsWiaX2uqthFql+/fqUQQ0TE9USHBPDWzc2568s1TF6+nytiQujZNNLsWCIiUoYdTc5k+d4TAPRtrml9rqzYReqFF14ojRwiIi6pS8NwhnesySeL9/HEjE00iAyiZqXyZscSEZEyau6GeAwDWlavSLXQALPjyL8olQ1SDMMojZcVETHF6K71aB0TQmpWLiOmrCMzx2F2JBERKaP+f1pflMlJ5EKKVKQaNGjA1KlTyc7O/tfrdu/ezX333ce4ceNKJJyIiCvw9rLy/qBYwsr7siMxhefnbDE7koiIlEG7j6awNT4Zb6uFnk1VpFxdkab2ffDBBzzxxBPcf//9dO3alVatWhEVFYW/vz+nTp1i27ZtLFu2jG3btvHAAw8wYsSI0s4tInJZhQf78+4tsQz5/C++XXOYK2JCuKlVtNmxRESkDMkfjepYtxIhgb4mp5ELKVKRuuaaa1i9ejXLly/nm2++YerUqezfv5+MjAzCwsKIjY3ltttuY8iQIVSoUKGUI4uImKN97TAe7lKXtxbu4rk5W2hS1Ub9CC1LKyIil84wDOZsiAegr/aOcgvFWmyiXbt2tGvXrrSyiIi4vAc612bNgVMs2XWMEVPWMfeBqyjvV+x1e0RERApZe+AUh09lEOjrxXUNws2OI0VQKotNiIiUVVarhbdvbkZEsD/7jqXx5IxNWmBHREQu2az1edP6ujWOoJyvl8lppChUpEREiim0vB8fDI7F22ph3qYEvlp5wOxIIiLixrJznczfnADkbcIr7kFFSkTkIrSsHsKT3esD8Mq8bWw8dNrcQCIi4raW7DrG6fQcKgX50a5WqNlxpIhUpERELtKdV9WgW6NwchwGI6as43T6v28RISIici750/p6N43C20u/nrsLfadERC6SxWJh/I3NqBYSwJHTGTz67UacTt0vJSIiRWdPz2Hh9qMADGihaX3u5KKWmnI6nezZs4ekpCScTmehxzp06FAiwURE3IGtnA8fDm7BgI+W89uOJD5duo97O9YyO5aIiLiJ+ZsTyM51Ui88iEZR2lLDnRS7SK1cuZJBgwZx4MCBs1aqslgsOByOEgsnIuIOGlex8WLvRjw9azOvL9hJbHQF2tTUHHcREbmwmesOA3mjURaLxeQ0UhzFntp377330qpVK7Zs2cLJkyc5depUwXHy5MnSyCgi4vL+0zqa/rFVcDgNRk5bz7GULLMjiYiIiztwIo01B05htUA/bcLrdoo9IrV7926+//57ateuXRp5RETcksVi4b/9GrP5iJ09Sak8NH09X93ZBi+r/rooIiLnNnNd3iIT7WuHER7sb3IaKa5ij0i1adOGPXv2lEYWERG3FujnzUeDW1DOx4vle0/w7q+7zI4kIiIuyjCMgtX6bmhR1eQ0cjGKNCK1adOmgn8fOXIkjz76KImJiTRp0gQfH59C1zZt2rRkE4qIuJE64UGMHdCEUd9s4P0/9tAyJoSOdSuZHUtERFzM2gOnOHgynUBfL7o2Cjc7jlyEIhWp5s2bY7FYCi0ucccddxT8e/5jWmxCRCRvnvuq/SeZ+tdBRk1fz/wHryaqQjmzY4mIiAuZcWZa3/WNIwnwvaiFtMVkRfquxcXFlXYOEZEy5fleDdl0+DRbjiTzwNR1fDP8Sny0yaKIiACZOQ7mbYoH4AbtHeW2ivRf9erVqxccBw4coEqVKoXOVa9enSpVqnDgwIHSzisi4hb8fbz4cFBLgvy9WXfwNON+2mF2JBERcRG/bU8iJTOXKJs/bbVdhtsq9p9HO3fufM5lzu12O507dy6RUCIiZUG10ADeuKkZAJ8ti+PnLYkmJxIREVeQv3dUv9gqWLW6q9sqdpHKvxfqn06cOEFgYGCJhBIRKSu6NYrg7qtrADD6u40cOJFmciIRETHT8dQsFu06BuRtwivuq8h3tg0YMADIW1hi6NCh+Pn5FTzmcDjYtGkT7dq1K/mEIiJu7vHr67Pu4GnWHjjFiCnrmHFfO/x9vMyOJSIiJvhhYzwOp0GzqjZqVw4yO45cgiKPSNlsNmw2G4ZhEBQUVPCxzWYjIiKCe+65h6+//ro0s4qIuCUfLysTBsUSEujL1vhkXvphm9mRRETEJPmb8A7Q3lFur8gjUpMmTQIgJiaGxx57TNP4RESKIdJWjncGNuf2SauYtuogrWtUpH+s/iMqIuJJdh1NYfMRO95WC72bRZkdRy5Rse+ReuGFFwgMDCQpKYmlS5eybNkykpKSSiObiEiZ0qFuJUZeUweAp2duYdfRFJMTiYjI5ZQ/GtWpXmVCAn1NTiOXqthFKjk5mVtvvZUqVarQsWNHOnToQJUqVRgyZAh2u700MoqIlBkPXVuHq2qHkZHjYMSUdaRl5ZodSURELgOH02D2+rwipb2jyoZiF6m77rqLv/76i3nz5nH69Gnsdjvz5s1jzZo13H333aWRUUSkzPCyWnjnluaEB/uxJymVp2dtxjAMs2OJiEgpW7H3BInJmQT7e3NNg8pmx5ESUOwiNX/+fCZOnEi3bt0IDg4mKCiIbt268b///Y/58+eXRkYRkTIlrLwfEwa1wMtqYc6GeKauOmh2JBERKWX5e0f1bhaFn7dWbi0Lil2kQkNDsdlsZ5232WxUrFixREKJiJR1V8SE8Hi3egC8NHcbW45oarSISFmVlpXLz1vzNmXXan1lR7GL1LPPPssjjzxCQkJCwbnExERGjx7Nc889V6LhRETKsruvrkmXBpXJdji5b8pa7Bk5ZkcSEZFSsGBrIunZDmJCA2hRrYLZcaSEWIxiTs6PjY1lz549ZGVlUa1aNQAOHjyIn58fderUKXTtunXrSi5pKUpOTsZms2G32wkODjY7joh4EHt6Dj3fX8rhUxl0bRjOJ7e2xGKxmB1LRERK0JDP/mLZnuM83KUuD3Wpc+EniKmK2g2KvI9Uvn79+l1KLhER+RtbgA8fDm7BjR+t4JdtR/lsaRx3d6hpdiwRESkhCfYM/tx7HID+sVqtrywpdpF64YUXSiOHiIjHalq1As/1asBzc7by2s87iK1WgVYxIWbHEhGREjB7fTyGAa1jQqgWGmB2HClBxb5HCuD06dN89tlnPPXUU5w8eRLIm8Z35MiREg0nIuIphrStTu9mUTicBg9MXc/x1CyzI4mIyCUyDKNgtb4B2juqzCl2kdq0aRN169Zl3LhxvPHGG5w+fRqAWbNm8dRTT5V0PhERj2CxWBg7oAm1KgWSmJzJqOkbcDi1v5SIiDvbGp/M7qRUfL2t9GgaaXYcKWHFLlKPPPIIQ4cOZffu3fj7+xec7969O0uWLCnRcCIinqS8nzcfDWlJOR8vlu05znu/7TY7koiIXIIZZ0ajujYMJ9jfx+Q0UtKKXaRWr17N8OHDzzpfpUoVEhMTSySUiIinqhsexKv9GwPw3u+7WbzrmMmJRETkYuQ4nMzdEA/ADdo7qkwqdpHy9/cnOTn5rPM7d+6kUqVKJRJKRMSTDWhRlUFtqmEYMGr6euJPZ5gdSUREimnJrmOcSMsmrLwvV9cJMzuOlIJiF6m+ffvy8ssvk5OTt3GkxWLh4MGDPPnkk9xwww0lHlBExBM936shjasEcyo9hwemriM712l2JBERKYaZ6/IWYevTrAreXhe1vpu4uGJ/V9944w2OHTtG5cqVycjIoGPHjtSuXZugoCBeffXV0sgoIuJx/H28+HBQS4L8vVl38DSv/bTD7EgiIlJE9vQcFm4/Cmi1vrKs2PtIBQcHs2zZMn7//XfWrVuH0+mkRYsWdOnSpTTyiYh4rGqhAbx1c3Pu/nINE/+Mo1VMRXo00apPIiKubv7mBLJzndQLD6JRVLDZcaSUFLtI5bvmmmu45pprSjKLiIj8w3UNwxneoSafLNnH499vokFkMDXCAs2OJSIi/2LW+v/fO8pisZicRkpLsab2OZ1OJk6cSK9evWjcuDFNmjShT58+fPnllxhG8fc7WbJkCb179yYqKgqLxcLs2bMLPW4YBi+++CJRUVGUK1eOTp06sXXr1kLXZGVlMXLkSMLCwggMDKRPnz4cPny42FlERFzVY93q0TomhNSsXO77ei0Z2Q6zI4mIyHkcOJHG6v2nsFqgX6ym9ZVlRS5ShmHQp08f7rrrLo4cOUKTJk1o1KgRBw4cYOjQofTv37/Yb56WlkazZs2YMGHCOR8fP348b731FhMmTGD16tVERERw3XXXkZKSUnDNqFGjmDVrFtOnT2fZsmWkpqbSq1cvHA79oiEiZYOPl5X3B8USVt6XHYkpPD9ni9mRRETkPGatz1tkon3tMMKD/S9wtbizIk/tmzx5MkuWLOG3336jc+fOhR77/fff6devH19++SW33XZbkd+8e/fudO/e/ZyPGYbBO++8wzPPPMOAAQMA+OKLLwgPD2fq1KkMHz4cu93O559/zldffVVwj9bXX39NdHQ0v/76K926dStyFhERVxYe7M97t8Qy5PO/+G7tYa6ICeHmK6LNjiUiIn9jGEbBan1aZKLsK/KI1LRp03j66afPKlGQd7/Uk08+yZQpU0osWFxcHImJiXTt2rXgnJ+fHx07dmT58uUArF27lpycnELXREVF0bhx44JrziUrK4vk5ORCh4iIq2tXO4xHrqsLwHNztrAtXv/fJSLiStYeOMXBk+kE+HrRrVGE2XGklBW5SG3atInrr7/+vI93796djRs3lkgogMTERADCw8MLnQ8PDy94LDExEV9fXypWrHjea85l7Nix2Gy2giM6Wn/VFRH3MKJTbTrVq0RWrpMRU9aSnJljdiQRETljxpnRqO6NIwnwveg13cRNFLlInTx58qxS83fh4eGcOnWqREL93T9XOjEM44Krn1zomqeeegq73V5wHDp0qESyioiUNqvVwts3N6dKhXLsP5HOE99vuqjFfkREpGRl5jiYtykegBs0rc8jFLlIORwOvL3P36y9vLzIzc0tkVAAERF5w6H/HFlKSkoqKHQRERFkZ2efVeD+fs25+Pn5ERwcXOgQEXEXFQN9mTAoFh8vCz9tSWTin/vNjiQi4vF+35FESmYuUTZ/2tYMNTuOXAZFHnM0DIOhQ4fi5+d3zsezsrJKLBRAjRo1iIiIYOHChcTGxgKQnZ3N4sWLGTduHAAtW7bEx8eHhQsXcvPNNwOQkJDAli1bGD9+fInmERFxJbHVKvJsz4a8MHcrY3/cTvPoCrSsXvHCTxQRkVIxc13e9jv9YqtgtWrvKE9Q5CJ1++23X/Ca4qzYB5CamsqePXsKPo6Li2PDhg2EhIRQrVo1Ro0axZgxY6hTpw516tRhzJgxBAQEMGjQIABsNht33nknjz76KKGhoYSEhPDYY4/RpEmTglX8RETKqtuurM6q/SeZvymBB6auY97Iqwgtf+4/domISOk5nprFop3HAK3W50mKXKQmTZpU4m++Zs2aQqsAPvLII0BeaZs8eTKPP/44GRkZjBgxglOnTtGmTRt++eUXgoKCCp7z9ttv4+3tzc0330xGRgbXXnstkydPxsvLq8Tzioi4EovFwrgbmrI9IZl9x9IY9c0GJg9rjZf+Eioicln9sDGeXKdB06o2alcOuvATpEywGLpLmeTkZGw2G3a7XfdLiYjb2ZmYQt8PlpGZ4+ThLnV5qEsdsyOJiHiU3u8vY/MROy/2bsjQ9jXMjiOXqKjdoMiLTYiIiGuqFxHEq/2aAPDOb7tYuvuYyYlERDzH7qMpbD5ix9tqoXezKLPjyGWkIiUiUgbc0LIq/2kdjWHAQ9M3kGDPMDuSiIhHmLk+b++oTvUq6z5VD6MiJSJSRrzQuxGNooI5mZbNA1PXk+Nwmh1JRKRMczgNZp8pUto7yvOoSImIlBH+Pl58OLgFQf7erD1winE/7TA7kohImbZy3wkS7JkE+3tzTYPKZseRy0xFSkSkDKkeGsgbNzUD4LNlcfy8JcHkRCIiZdeMM3tH9WoWhZ+3Voz2NCpSIiJlTLdGEdzToSYAo7/bxP7jaSYnEhEpe9Kycvl5SyKgaX2eSkVKRKQMGt2tHlfEVCQlK5f7pqwjM8dhdiQRkTJlwdZE0rMdVA8NoEW1imbHEROoSImIlEE+Xlbe/08LQgN92Z6QzAtztpodSUSkTJm5Lm+RiQGxVbFYtBG6J1KREhEpoyJs/rz3n1gsFvhmzSG+W3PI7EgiImVCoj2TP/ceB6B/rKb1eSoVKRGRMqx97TAe7lIXgOfmbGF7QrLJiURE3N/sDUcwDGgdE0K10ACz44hJVKRERMq4BzrXpkPdSmTmOBkxZR0pmTlmRxIRcVuGYTBjbd5qfQO0yIRHU5ESESnjrFYL7wxsTqTNn7jjaTw5YzOGYZgdS0TELW2NT2Z3Uiq+3la6N4k0O46YSEVKRMQDhAT68sHgFnhbLczfnMDk5fvNjiQi4pby9466rmE4tnI+JqcRM6lIiYh4iBbVKvJMzwYAjPlxO+sOnjI5kYiIe8lxOJm7IR7Q3lGiIiUi4lGGtouhZ5NIchwGD0xZx8m0bLMjiYi4jSW7jnEiLZuw8r5cXaeS2XHEZCpSIiIexGKx8NoNTagRFki8PZNR32zA6dT9UiIiRTFzfd7eUX2aVcHHS79Gezr9BIiIeJggfx8+GtICfx8rS3Yd44M/9pgdSUTE5dkzcli47Sig1fokj4qUiIgHqh8RzCt9GwPw1q+7WLb7uMmJRERc24+bE8jOdVIvPIhGUcFmxxEXoCIlIuKhbmoVzcBW0RgGPDR9PYn2TLMjiYi4rJlnVuvr36IKFovF5DTiClSkREQ82Et9G9EgMpgTadmMnLaOHIfT7EgiIi7nwIk0Vu8/hcUC/ZprWp/kUZESEfFg/j5efDS4BUF+3qzef4rXF+w0O5KIiMuZdWaRiatqhxFh8zc5jbgKFSkREQ8XExbI6zc1BeDTJftYsDXR5EQiIq7DMIyCIqVFJuTvVKRERITrG0dy11U1AHjsu40cOJFmciIREdew7uApDpxIJ8DXi26NIsyOIy5ERUpERAB4ont9WlavSEpmLvd9vY7MHIfZkURETDdjXd5oVPfGkQT4epucRlyJipSIiADg42VlwqBYQgJ92ZaQzEs/bDU7koiIqTJzHMzbGA/ADZrWJ/+gIiUiIgUibeV495bmWCwwbdUhZqw9bHYkERHT/L4jieTMXCJt/rStGWp2HHExKlIiIlLI1XUq8dC1dQB4ZvZmdiQmm5xIRMQc+XtH9YutgtWqvaOkMBUpERE5y8hr6nB1nTAyc5yMmLKO1KxcsyOJiFxWJ1KzWLTzGAADYjWtT86mIiUiImfxslp4Z2BzIm3+7DuWxhMzNmEYhtmxREQumx82xpPrNGha1Uad8CCz44gLUpESEZFzCi3vx4RBLfC2Wpi/KYEvVxwwO5KIyGUzM3/vKI1GyXmoSImIyHm1rF6Rp3o0AOC/87ex4dBpcwOJiFwGu4+msOmwHW+rhd7NosyOIy5KRUpERP7VHe1j6N44ghyHwf1T1nEqLdvsSCIipSp/NKpTvcqElvczOY24KhUpERH5VxaLhXE3NiUmNIAjpzN4+NsNOJ26X0pEyiaH02B2/rQ+7R0l/0JFSkRELijY34cPB7fEz9vKop3H+GjxXrMjiYiUipX7TpBgzyTY35tr6lc2O464MBUpEREpkoZRwbzSrzEAb/6yk+V7jpucSESk5M04s3dUr2ZR+Pt4mZxGXJmKlIiIFNnNraK5qWVVnAY8OH09R5MzzY4kIlJi0rNz+XlLIgA3aFqfXICKlIiIFMvLfRtTPyKI46nZjJy6nlyH0+xIIiIlYsHWRNKzHVQPDaBFtYpmxxEXpyIlIiLFUs7Xi4+GtKS8nzer9p/k9V92mh1JRKREzFyXv3dUVSwWi8lpxNWpSImISLHVCAvk9RubAvDJ4n0s3HbU5EQiIpcm0Z7JsjP3fvbXJrxSBCpSIiJyUbo3ieSO9jUAePTbDRw8kW5yIhGRizd7wxEMA66IqUi10ACz44gbUJESEZGL9mT3+rSoVoHkzFxGTF1LZo7D7EgiIsVmGAYzz6zWN6BFVZPTiLtQkRIRkYvm621lwqAWVAzwYcuRZF6et83sSCIixbY1PpldR1Px9bbSo0mk2XHETahIiYjIJYmqUI53bonFYoGpfx1k1vrDZkcSESmW/EUmrmsYjq2cj8lpxF2oSImIyCXrWLcSD15TB4CnZ25h19EUkxOJiBRNjsPJ3I15RUp7R0lxqEiJiEiJePDaOlxVO4yMHAf3fb2WtKxcsyOJiFzQ0t3HOJ6aTVh5X66uU8nsOOJGVKRERKREeFktvHtLcyKC/dl7LI0nZ27GMAyzY4mI/KsZZ6b19WlWBR8v/WosRaefFhERKTGh5f2YMCgWL6uFHzbG8/XKA2ZHEhE5L3tGTsE+eAM0rU+KSUVKRERKVKuYEJ7qXh+Al+dtY+Oh0+YGEhE5jx83J5Cd66RueHkaRQWbHUfcjIqUiIiUuDuvqkG3RuHkOAxGTFnH6fRssyOJiJxl1plpfQNaVMVisZicRtyNipSIiJQ4i8XC6zc1o3poAEdOZ/DItxtxOnW/lIi4joMn0lm1/yQWC/Rrrml9UnwqUiIiUiqC/X34cHALfL2t/L4jiY+X7DU7kohIgVnr80ajrqodRoTN3+Q04o5UpEREpNQ0irLxSt9GALyxYCcr9p4wOZGICBiGwcwzm4drkQm5WCpSIiJSqm5uFc0NLariNGDktPUkJWeaHUlEPNy6g6c4cCKdAF8vujWKMDuOuCkVKRERKVUWi4X/9mtM/YggjqdmMXLaenIdTrNjiYgHy9876vrGEQT4epucRtyVqUVqyZIl9O7dm6ioKCwWC7Nnzy54LCcnhyeeeIImTZoQGBhIVFQUt912G/Hx8YVeIysri5EjRxIWFkZgYCB9+vTh8OHDl/kzERGRf1PO14sPB7egvJ83f8Wd5M2Fu8yOJCIeKivXwbyNeb9P3tCiqslpxJ2ZWqTS0tJo1qwZEyZMOOux9PR01q1bx3PPPce6deuYOXMmu3btok+fPoWuGzVqFLNmzWL69OksW7aM1NRUevXqhcPhuFyfhoiIFEHNSuUZd0NTAD5atJffth81OZGIeKLftyeRnJlLpM2ftjVDzY4jbsxiGIZLrEdrsViYNWsW/fr1O+81q1evpnXr1hw4cIBq1apht9upVKkSX331FQMHDgQgPj6e6OhofvzxR7p161ak905OTsZms2G32wkO1mZsIiKl6cW5W5m8fD/B/t7Mf/BqokMCzI4kIh7kri/W8Ov2o9zXqRZPXF/f7DjigoraDdzqHim73Y7FYqFChQoArF27lpycHLp27VpwTVRUFI0bN2b58uXnfZ2srCySk5MLHSIicnk83aMBzaMrkJyZy4gp68jK1QwCEbk8TqRmsWhnEgADYrVan1watylSmZmZPPnkkwwaNKigGSYmJuLr60vFihULXRseHk5iYuJ5X2vs2LHYbLaCIzo6ulSzi4jI//P1tvLB4BZUCPBh8xE7r8zbZnYkEfEQP2yMJ9dp0LSqjTrhQWbHETfnFkUqJyeHW265BafTyYcffnjB6w3DwGKxnPfxp556CrvdXnAcOnSoJOOKiMgFVKlQjncGNsdiga9XHmTOhiNmRxIRDzDzzCa8Go2SkuDyRSonJ4ebb76ZuLg4Fi5cWGieYkREBNnZ2Zw6darQc5KSkggPDz/va/r5+REcHFzoEBGRy6tTvcqM7FwbgKdmbmb30RSTE4lIWbYnKYVNh+14Wy30bhZldhwpA1y6SOWXqN27d/Prr78SGlp4ZZWWLVvi4+PDwoULC84lJCSwZcsW2rVrd7njiohIMT3UpS7ta4eSnu3gvinrSMvKNTuSiJRRM8/sHdWpXiVCy/uZnEbKAlOLVGpqKhs2bGDDhg0AxMXFsWHDBg4ePEhubi433ngja9asYcqUKTgcDhITE0lMTCQ7OxsAm83GnXfeyaOPPspvv/3G+vXrGTJkCE2aNKFLly4mfmYiIlIUXlYL794SS3iwH3uSUnlm1mZcZDFZESlDnE6DWfnT+rR3lJQQU4vUmjVriI2NJTY2FoBHHnmE2NhYnn/+eQ4fPszcuXM5fPgwzZs3JzIysuD4+4p8b7/9Nv369ePmm2+mffv2BAQE8MMPP+Dl5WXWpyUiIsUQVt6PCYNa4GW1MHtDPG8v3KUyJSIlauW+EyTYMwn29+aa+pXNjiNlhMvsI2Um7SMlImK+yX/G8eIPeSv43dG+Bs/1avCvCweJiBTVo99uZMa6wwxqU40x/ZuYHUdcXJncR0pERMquoe1r8HLfRgBM/DOOp2ZuxuH0+L/1icglSs/O5actCQDc0EKr9UnJUZESERGXcduVMbxxUzOsFpi++hAPTV9Pdq7T7Fgi4sYWbE0kPdtB9dAAWlSreOEniBSRipSIiLiUG1tW5YNBLfDxsjBvUwL3fr2WzByH2bFExE3lr9bXP7aKpgtLiVKREhERl9O9SST/u60V/j5Wft+RxLBJq0nV0ugiUkyJ9kz+3HMcgAGxWq1PSpaKlIiIuKRO9Srz5R1tKO/nzYp9Jxjy2V+cTs82O5aIuJE5G47gNOCKmIpUCw0wO46UMSpSIiLislrXCGHq3W2oEODDhkOnueXTlRxLyTI7loi4AcMwmLHuMKC9o6R0qEiJiIhLa1q1At/ccyWVgvzYkZjCzZ+s4MjpDLNjiYiL2xqfzK6jqfh6W+nRJNLsOFIGqUiJiIjLqxcRxHfDr6RKhXLEHU/j5o9XEHc8zexYIuLC8heZuK5hOLZyPiankbJIRUpERNxCTFgg3993JTUrBXLkdAY3fbyCHYnJZscSEReU63Ayd2NekdLeUVJaVKRERMRtRNrK8e3wK2kQGczx1CwGfrKSDYdOmx1LRFzM0t3HOZ6aTWigL1fXqWR2HCmjVKRERMSthJX3Y/rdbWlRrQL2jBwG/28lK/edMDuWiLiQ/EUm+jSPwsdLv+5K6dBPloiIuB1bgA9f3dmG9rVDSct2cPvEVfyxI8nsWCLiAuwZOfyy7SgAN2i1PilFKlIiIuKWAv28+fz2K+jSoDJZuU7u/nIN8zclmB1LREz20+YEsnOd1A0vT6OoYLPjSBmmIiUiIm7L38eLj4a0pE+zKHKdBiOnrePbNYfMjiUiJspfrW9Ai6pYLBaT00hZpiIlIiJuzcfLytsDm/Of1tE4DXj8+01M+jPO7FgiYoKDJ9JZtf8kFgv0a67V+qR0qUiJiIjb87JaGNO/CXdfXQOAl37YxoTfd2MYhsnJRORymrU+bzTqqtphRNj8TU4jZZ2KlIiIlAkWi4WnezTg4S51AXjjl1289tMOlSkRD2EYBrPW563W1z9Wo1FS+lSkRESkzLBYLDzUpQ7P9mwAwCdL9vHs7C04nSpTImXduoOn2X8inQBfL7o1ijA7jngAFSkRESlz7rq6Jq8NaILFAlP+Osij320k1+E0O5aIlKKZZ/aOur5xBIF+3ianEU+gIiUiImXSLa2r8d4tsXhbLcxaf4QRU9aRleswO5aIlIKsXAc/bIwHtHeUXD4qUiIiUmb1bhbFJ7e2xNfbyi/bjnLXF2tIz841O5aIlLDftyeRnJlLpM2ftjVDzY4jHkJFSkREyrRrG4QzedgVBPh6sXT3cW77fBX2jByzY4lICZpxZu+ofrFV8LJq7yi5PFSkRESkzGtXK4yv72pDsL83aw6cYtD/VnIiNcvsWCJSAk6kZrFoZxIAA7Ran1xGKlIiIuIRWlSryPR7riSsvC9b45O5+ZMVJNozzY4lIpdo3qYEcp0GTarYqBMeZHYc8SAqUiIi4jEaRgXzzfAribT5s/dYGjd9spyDJ9LNjiUilyB/tb4BLTQaJZeXipSIiHiUWpXK8929VxITGsChkxnc9Mlydh9NMTuWiFyEPUkpbDxsx9tqoXezKLPjiIdRkRIREY9TtWIA3w6/knrhQRxNzmLgpyvZcsRudiwRKaaZZxaZ6FSvEmHl/UxOI55GRUpERDxS5WB/pt/TlmZVbZxMy+Y/n65kzf6TZscSkSJyOg1mrc8rUgO0d5SYQEVKREQ8VsVAX76+qw2ta4SQkpXLrZ+vYunuY2bHEpEiWLnvBAn2TIL9vbmmfmWz44gHUpESERGPFuTvwxfDWtOpXiUychzcOXkNP29JNDuWiFzAzDOjUb2aReHv42VyGvFEKlIiIuLxyvl68emtrejRJIJsh5P7p65j1vrDZscSkfNIz87lp80JgPaOEvOoSImIiAC+3lbeuyWWG1tWxeE0ePibjXy18oDZsUTkHH7ZepS0bAfVQgJoWb2i2XHEQ6lIiYiInOHtZWX8DU0Z2i4GgOdmb+HjxXvNDSUiZ5nxt72jLBaLyWnEU6lIiYiI/I3VauGF3g15oHNtAF77aQdvLNiJYRgmJxMRgER7Jn/uOQ7AgFit1ifmUZESERH5B4vFwmPd6vFk9/oATPhjDy/9sA2nU2VKxGxzNhzBacAVMRWpFhpgdhzxYCpSIiIi53Fvx1q80q8xFgtMXr6fJ2ZswqEyJWIawzAKNuHV3lFiNhUpERGRf3Fr2+q8dXMzvKwWvlt7mAenrSc712l2LBGPtC0hmZ1HU/D1ttKjSaTZccTDqUiJiIhcQP/YqnwwqAW+Xlbmb07gnq/WkJHtMDuWiMfJH426rkE4tnI+JqcRT6ciJSIiUgTXN47gs9tb4e9jZdHOY9w+aRUpmTlmxxLxGLkOJ3M25E/r095RYj4VKRERkSLqULcSX93ZhiA/b1bFnWTIZ39xKi3b7FgiHmHp7uMcT80mNNCXDnUrmR1HREVKRESkOK6ICWHaPW2pGODDxsN2bvl0JUnJmWbHEinz8veO6tM8Ch8v/Qor5tNPoYiISDE1rmLj2+FXEh7sx86jKdz8yQoOn0o3O5ZImWXPyOGXbUcBuEGr9YmLUJESERG5CHXCg/hueDuiQ8qx/0Q6N3+8gn3HUs2OJVIm/bQ5gexcJ3XDy9MoKtjsOCKAipSIiMhFqxYawHfD21GrUiDx9kxu/mQF2+KTzY4lUubMXP//e0dZLBaT04jkUZESERG5BBE2f74dfiWNooI5nprNLZ+uYN3BU2bHEikzDp1MZ1XcSSwW6Ns8yuw4IgVUpERERC5RaHk/pt7dllbVK5KcmcuQz/5i+Z7jZscSKRNmnRmNal8rjEhbOZPTiPw/FSkREZESYCvnw5d3tuaq2mGkZzsYOnk1v20/anYsEbdmGAYzz6zWp72jxNWoSImIiJSQAF9vPru9Fdc1DCc718nwr9byw8Z4s2OJuK11B0+z/0Q6Ab5edGsUYXYckUJUpEREREqQv48XHw5uQb/mUeQ6DR6cvp7pqw6aHUvELeWPRl3fOIJAP2+T04gUpiIlIiJSwny8rLx1c3MGt6mGYcCTMzfz+bI4s2OJuJWsXAfzNiUA2jtKXJOKlIiISCmwWi38t19jhneoCcAr87bx7q+7MQzD5GQi7uGPHUnYM3KItPnTtmao2XFEzqIiJSIiUkosFgtPdq/PY13rAvD2r7sY8+N2lSmRIpixLm+1vr7Nq+Bl1d5R4npUpEREREqRxWLhgWvq8ELvhgD8b2kcT8/ajMOpMiVyPifTsvljRxKg1frEdalIiYiIXAbD2tdg/A1NsVpg2qpDPPzNBnIcTrNjibikHzbGk+s0aFLFRt3wILPjiJyTqUVqyZIl9O7dm6ioKCwWC7Nnzz7vtcOHD8disfDOO+8UOp+VlcXIkSMJCwsjMDCQPn36cPjw4dINLiIichFuviKa9/4Ti7fVwtyN8dz39VoycxxmxxJxOdo7StyBqUUqLS2NZs2aMWHChH+9bvbs2fz1119ERUWd9dioUaOYNWsW06dPZ9myZaSmptKrVy8cDv2HSUREXE+vplH877ZW+Hlb+XV7End+sZq0rFyzY4m4jD1JqWw8bMfbaqF3s7N/9xNxFaYWqe7du/Pf//6XAQMGnPeaI0eO8MADDzBlyhR8fHwKPWa32/n8889588036dKlC7GxsXz99dds3ryZX3/9tbTji4iIXJTO9SvzxR2tCfT14s89J7j187+wZ+SYHUvEJcxanzca1aleJcLK+5mcRuT8XPoeKafTya233sro0aNp1KjRWY+vXbuWnJwcunbtWnAuKiqKxo0bs3z58vO+blZWFsnJyYUOERGRy6ltzVCm3N0WWzkf1h08zS2fruR4apbZsURM5XQazDqzWt8A7R0lLs6li9S4cePw9vbmwQcfPOfjiYmJ+Pr6UrFixULnw8PDSUxMPO/rjh07FpvNVnBER0eXaG4REZGiaB5dgW+GtyWsvB/bE5K5+ZMV7ExMMTuWiGlWxp0g3p5JkL8319SvbHYckX/lskVq7dq1vPvuu0yePBmLpXh7BxiG8a/Peeqpp7Db7QXHoUOHLjWuiIjIRakfEcx3915JlQrl2Hcsje7vLuGpmZtISsk0O5rIZTfzzGhUr6ZR+Pt4mZxG5N+5bJFaunQpSUlJVKtWDW9vb7y9vTlw4ACPPvooMTExAERERJCdnc2pU6cKPTcpKYnw8PDzvrafnx/BwcGFDhEREbPUCAtkxn3t6N44AqeRtzx6p9cX8d5vu8nI1uJJ4hnSs3P5aXMCADdotT5xAy5bpG699VY2bdrEhg0bCo6oqChGjx7NggULAGjZsiU+Pj4sXLiw4HkJCQls2bKFdu3amRVdRESk2CJs/nw0pCXf33slzaMrkJ7t4K2Fu+j0xh98t+aQNvCVMu+XrUdJy3ZQLSSAltUrXvgJIibzNvPNU1NT2bNnT8HHcXFxbNiwgZCQEKpVq0ZoaGih6318fIiIiKBevXoA2Gw27rzzTh599FFCQ0MJCQnhscceo0mTJnTp0uWyfi4iIiIloVVMCLNGtGPepgTG/byDw6cyGP39Jib9uZ9nejagfe0wsyOKlIoZf9s7qri3dYiYwdQitWbNGjp37lzw8SOPPALA7bffzuTJk4v0Gm+//Tbe3t7cfPPNZGRkcO211zJ58mS8vDSvVkRE3JPFkrd/znUNw/lyxX7e/30P2xKSGfzZX1xTvzJP96hP7cpBZscUKTFHkzP5c89xAAbEarU+cQ8WwzA8fq5AcnIyNpsNu92u+6VERMTlnEzL5r3fdvP1ygPkOg28rBZuuSKaUV3qUilI++yIe9sab2f0d5vYlpDMFTEV+e5e3Z4h5ipqN1CRQkVKRETcw75jqYz7eQcLth4FoLyfN/d1qsWdV9XQCmfidrJyHUz4fQ8fLdpLrtOgQoAPn93WilYxIWZHEw+nIlUMKlIiIuJO/tp3gld/3M6mw3YAomz+PNatHv2aV8Fq1b0l4vrWHzzF499vYndSKgA9m0TyYp9GGmEVl6AiVQwqUiIi4m6cToMfNsUz/uedHDmdAUDjKsE806MhV9YKvcCzRcyRke3grYU7+XxZHE4Dwsr78UrfRnRvEml2NJECKlLFoCIlIiLuKjPHwcQ/4/jwj72kZuUC0KVBOE/1qE+tSuVNTify//7ad4InZmxi/4l0AAbEVuG5Xg2pGOhrcjKRwlSkikFFSkRE3N3x1Cze/XU3U1cdxHFmQYrBbarx0LV1CC2v6VJintSsXMb/vIMvVxwAICLYnzEDGnNN/XCTk4mcm4pUMahIiYhIWbEnKZXXftrOr9uTAAjy82ZE59oMax+jBSnkslu6+xhPzthcMP30P62jeapHA4L9fUxOJnJ+KlLFoCIlIiJlzfK9x3l1/na2xicDUKVCOR6/vh69m0ZpQQopdfaMHMbM3843aw4BULViOcbd0FQbSotbUJEqBhUpEREpi5xOg1nrj/D6gp0kJmcC0KyqjWd6NqR1DS0xLaXj121HeWb2Zo4mZ2GxwO1XxjC6Wz0C/bzNjiZSJCpSxaAiJSIiZVlGtoPPl+3jo0V7Sct2ANCtUThPdm9AjbBAk9NJWXEyLZuXftjKnA3xANQMC2TcjU25QvtCiZtRkSoGFSkREfEEx1KyePvXXUxfdRCnAd5WC7deWZ0Hr6mjldPkkszflMDzc7ZwIi0bqwXu7lCTh7vU1X154pZUpIpBRUpERDzJrqMpjP1xO3/sPAZAsL83I6+pw23tquPnrV98peiSUjJ5fvZWft6aCEDd8PK8fmMzmkVXMDeYyCVQkSoGFSkREfFEy3Yf57/zt7EjMQWA6JByPHF9fXo2icRi0YIUcn6GkXf/3Us/bMOekYO31cKIzrW5v3MtlXFxeypSxaAiJSIinsrhNJix7jBvLNhJUkoWALHVKvBszwa0rK57W+RsCfYMnp65uWBEs3GVYMbf0IyGUfodSsoGFaliUJESERFPl56dy/+WxPHJkr2kn1mQomeTSB6/vh7VQ7UgheSNQk1bdYixP24nJSsXXy8ro66rwz1X18Tby2p2PJESoyJVDCpSIiIieZKSM3lr4S6+XXMIpwE+XhZuvzKGkdfUwRagTVQ91cET6Tw5cxPL954AoEW1Coy/sSm1KweZnEyk5KlIFYOKlIiISGHbE5IZ8+N2lu4+DoCtnA8PXluHW9tWx9dbow+ewuk0+HLFfsb9vJOMHAf+PlZGd6vP0HYxeGljZymjVKSKQUVKRETk3BbvOsaY+dvZeTRvQYrqoQE8eX19rm8coQUpyri9x1J54vtNrDlwCoC2NUMYd0NTTfWUMk9FqhhUpERERM7P4TT4bs0h3ly4i2NnFqRoVb0iz/RsQGy1iiank5KW63Dy2bI43lq4i+xcJ+X9vHmye30Gta6GVaNQ4gFUpIpBRUpEROTC0rJy+WTJPj5dspfMHCcAvZtF8Xi3ekSHBJicTkrCjsRkHv9+E5sO2wHoULcSYwc0oUqFciYnE7l8VKSKQUVKRESk6BLtmbz5y06+X3cYwwBfLyvD2scwonNtbOW0IIU7ys518tGivUz4Yzc5DoNgf2+e792IG1pU0RRO8TgqUsWgIiUiIlJ8W+PtjPlxO3/uyVvJrWKADw9dW4fBbavjo+Ww3cbmw3ZGf7+xYGPm6xqG82q/xlQO9jc5mYg5VKSKQUVKRETk4hiGwaKdx3j1x+3sSUoFoEZYIE92r0/XhuEazXBhmTkO3vttN58s2YfDaRAS6MtLfRrRq2mkvm/i0VSkikFFSkRE5NLkOpxMX32Itxfu4kRaNgBtaoTwTM8GNK1awdxwcpa1B07y+Peb2HssDci71+3F3g0JLe9ncjIR86lIFYOKlIiISMlIyczh48V7+WxpHFm5eQtS9Gsexejr62vBAheQnp3LGwt2MWl5HIYBlYL8+G+/xnRrFGF2NBGXoSJVDCpSIiIiJSv+dAZvLNjJzPVHAPD1tnLnVTUY0akWQf5akMIMy/ce58kZmzl4Mh2Am1pW5dmeDbEF6Psh8ncqUsWgIiUiIlI6thyx89/521i57yQAoYG+jLquLv+5IhpvLUhxWaRk5vDaTzuY8tdBAKJs/oy9oSkd61YyOZmIa1KRKgYVKRERkdJjGAa/bk9i7E/b2XfmnpxalQJ5ukcDrqlfWQsblKJFO5N4euZm4u2ZAAxpW40nrq+vUUGRf6EiVQwqUiIiIqUvx+Fk2qqDvPPrbk6eWZCiXa1Qnu7RgMZVbCanK1tOp2fzyrztzFh3GIBqIQGMu6EpV9YKNTmZiOtTkSoGFSkREZHLJzkzhw//2MvEP+PIznViscCA2Ko81q0ukTYtSHGpFmxN5NnZWziWkoXFAne0r8GjXesS4OttdjQRt6AiVQwqUiIiIpffoZPpvPHLTuZsiAfyFqRoWsVGg8hg6kcG0SAymHrhQQT6qQAUxYnULF6Yu5V5mxKAvOmT429sRsvqFU1OJuJeVKSKQUVKRETEPBsPnebV+dtZtf/kOR+vHhpAg4j/L1cNIoKpWrEcVqvurYK8e9B+2JTAi3O3cjItGy+rhXs71mTkNXXw9/EyO56I21GRKgYVKREREXMZhsHupFS2xSezPTGZ7Qkp7EhIJikl65zXl/fzpl5EEPUjzpSryCDqRQRT3sNGr44mZ/Ls7C0s3HYUgPoRQbxxUzPdcyZyCVSkikFFSkRExDWdSM1iR2IK2xPOlKvEZHYfTSXb4Tzn9dVCAgqVqwaRwURXDChzo1eGYfD92sO8Mm8byZm5+HhZGHlNHe7tWAtfby0rL3IpVKSKQUVKRETEfeQ4nMQdTytUrrYnJHM0+dyjVwG+XtTLL1dn/lkvIshtlwA/fCqdp2dtYcmuYwA0q2pj/I3NqBcRZHIykbJBRaoYVKRERETc38m07DOlKm9a4I7EFHYeTSE799yjV9Eh5agf8f/lqn5kMNVDXHf0yuk0mLLqIK/9uJ20bAe+3lYeva4ud15VQ5sbi5QgFaliUJESEREpm3IdTvafSGPb38rV9oRkEs5sUPtP5XzyR6/OlKszi1wEmzx6tf94Gk/M2MRfcXkLcrSqXpFxNzalVqXypuYSKYtUpIpBRUpERMSznE7PLjQtcEdiCjsTU8g6z+hVlQrlCu67qh+R98/qoYF4lfLolcNpMOnPON74ZSeZOU4CfL144vr63Nq2usuOnIm4OxWpYlCREhEREYfTIO54GjsSk9mRkFJQsI6czjjn9eV8vKgbEUSDv60eWD8iGFtAyYxe7UlKYfT3m1h/8DQA7WqFMu6GpkSHBJTI64vIualIFYOKlIiIiJyPPT0nr1zlrx6YmMLOxGQyc84/elU/Iqhg36v6EcHUCCv66FWOw8mnS/bx7q+7yXY4CfLz5umeDbjlimgsFo1CiZQ2FaliUJESERGR4nA4DQ6cSPvb9MC8knW+0Ss/b2vevVdn7rnKnx5YIcC30HVb4+08/v0mtsYnA9C5XiXGDGhCpK1cqX9OIpJHRaoYVKRERESkJNgzctiZWLhc7UxMISPHcc7rI23+Z0atgshxOJn0535ynQa2cj682Kch/ZpX0SiUyGWmIlUMKlIiIiJSWpxOgwMn09lxZlpg3r1XyRw6ee7Rq+6NI3ipbyMqB/lf5qQiAkXvBt6XMZOIiIiIx7FaLdQIC6RGWCDdm0QWnE/JzBu9yi9XScmZDGhRlR5/u0ZEXJeKlIiIiIgJgvx9aBUTQquYELOjiMhF0DbYIiIiIiIixaQiJSIiIiIiUkwqUiIiIiIiIsWkIiUiIiIiIlJMKlIiIiIiIiLFpCIlIiIiIiJSTCpSIiIiIiIixaQiJSIiIiIiUkwqUiIiIiIiIsWkIiUiIiIiIlJMphapJUuW0Lt3b6KiorBYLMyePfusa7Zv306fPn2w2WwEBQXRtm1bDh48WPB4VlYWI0eOJCwsjMDAQPr06cPhw4cv42chIiIiIiKextQilZaWRrNmzZgwYcI5H9+7dy9XXXUV9evXZ9GiRWzcuJHnnnsOf3//gmtGjRrFrFmzmD59OsuWLSM1NZVevXrhcDgu16chIiIiIiIexmIYhmF2CACLxcKsWbPo169fwblbbrkFHx8fvvrqq3M+x263U6lSJb766isGDhwIQHx8PNHR0fz4449069atSO+dnJyMzWbDbrcTHBx8yZ+LiIiIiIi4p6J2A5e9R8rpdDJ//nzq1q1Lt27dqFy5Mm3atCk0/W/t2rXk5OTQtWvXgnNRUVE0btyY5cuXn/e1s7KySE5OLnSIiIiIiIgUlcsWqaSkJFJTU3nttde4/vrr+eWXX+jfvz8DBgxg8eLFACQmJuLr60vFihULPTc8PJzExMTzvvbYsWOx2WwFR3R0dKl+LiIiIiIiUra4bJFyOp0A9O3bl4cffpjmzZvz5JNP0qtXLz7++ON/fa5hGFgslvM+/tRTT2G32wuOQ4cOlWh2EREREREp21y2SIWFheHt7U3Dhg0LnW/QoEHBqn0RERFkZ2dz6tSpQtckJSURHh5+3tf28/MjODi40CEiIiIiIlJULlukfH19ueKKK9i5c2eh87t27aJ69eoAtGzZEh8fHxYuXFjweEJCAlu2bKFdu3aXNa+IiIiIiHgObzPfPDU1lT179hR8HBcXx4YNGwgJCaFatWqMHj2agQMH0qFDBzp37szPP//MDz/8wKJFiwCw2WzceeedPProo4SGhhISEsJjjz1GkyZN6NKlS5Fz5C9cqEUnREREREQ8W34nuODi5oaJ/vjjDwM467j99tsLrvn888+N2rVrG/7+/kazZs2M2bNnF3qNjIwM44EHHjBCQkKMcuXKGb169TIOHjxYrByHDh06Zw4dOnTo0KFDhw4dOnR45nHo0KF/7RAus4+UmZxOJ/Hx8QQFBf3rIhWXQ3JyMtHR0Rw6dEj3bnkgff89m77/nk3ff9HPgGfT9991GIZBSkoKUVFRWK3nvxPK1Kl9rsJqtVK1alWzYxSiRTA8m77/nk3ff8+m77/oZ8Cz6fvvGmw22wWvcdnFJkRERERERFyVipSIiIiIiEgxqUi5GD8/P1544QX8/PzMjiIm0Pffs+n779n0/Rf9DHg2ff/djxabEBERERERKSaNSImIiIiIiBSTipSIiIiIiEgxqUiJiIiIiIgUk4qUiIiIiIhIMalIuZgPP/yQGjVq4O/v/3/t3W1QVOX/BvBrWV3ZFVB52MVNCQrGADGBDROfR0JRSYrSEMmVRsMw3SjyKU2aXBKTsFBqDdHJB3yhqGNjZTqwolMiiqYwKoZgpG2OO46yAgL7f+G4/zYQ3Z+wh+T6zJwZ93uf+5xreXW+e59zRGhoKI4cOSJ0JLKD9PR0vPDCC3B2doZcLkdMTAzOnz8vdCwSSHp6OkQiETQajdBRyE5qa2sxc+ZMuLm5QSaTYejQoSgtLRU6FtlBU1MTPvroI/j4+EAqleKZZ57BJ598gpaWFqGjUSfQ6/WIjo6GUqmESCTCnj17rMbNZjNWrlwJpVIJqVSKsWPH4ty5c8KEpYdiI9WF7Ny5ExqNBsuWLcOpU6cwatQoREVFoaamRuho1MmKioqQnJyMX375BQcPHkRTUxMiIyNRV1cndDSys5KSEuh0OgwZMkToKGQnRqMRI0aMQM+ePXHgwAGUl5dj7dq16Nu3r9DRyA5Wr16Nr7/+GtnZ2aioqEBGRgbWrFmDr776Suho1Anq6urw/PPPIzs7u83xjIwMZGZmIjs7GyUlJfD09MRLL72EW7du2TkpPQq+/rwLGTZsGEJCQpCTk2Op+fv7IyYmBunp6QImI3v7+++/IZfLUVRUhNGjRwsdh+zk9u3bCAkJwYYNG/Dpp59i6NChyMrKEjoWdbLFixfj6NGjvAOhm5oyZQoUCgVyc3MttdjYWMhkMnz33XcCJqPOJhKJUFBQgJiYGAD3VqOUSiU0Gg0WLVoEAGhoaIBCocDq1avx9ttvC5iW2sIVqS6isbERpaWliIyMtKpHRkbi2LFjAqUiody8eRMA4OrqKnASsqfk5GRMnjwZERERQkchO9q3bx9UKhVef/11yOVyBAcHY+PGjULHIjsZOXIkDh06hAsXLgAATp8+jeLiYkyaNEngZGRvVVVVuHbtmtW1YK9evTBmzBheC3ZRPYQOQPdcv34dzc3NUCgUVnWFQoFr164JlIqEYDabkZKSgpEjR2Lw4MFCxyE7yc/Px8mTJ1FSUiJ0FLKz33//HTk5OUhJScHSpUtx/PhxLFiwAL169cKbb74pdDzqZIsWLcLNmzfx3HPPQSwWo7m5GatWrUJcXJzQ0cjO7l/vtXUtWF1dLUQkegg2Ul2MSCSy+mw2m1vV6Mk2f/58nDlzBsXFxUJHITu5cuUKFi5ciJ9++gmOjo5CxyE7a2lpgUqlglarBQAEBwfj3LlzyMnJYSPVDezcuRNbt27F9u3bERgYiLKyMmg0GiiVSsyaNUvoeCQAXgv+d7CR6iLc3d0hFotbrT4ZDIZWv0zQk+vdd9/Fvn37oNfrMWDAAKHjkJ2UlpbCYDAgNDTUUmtuboZer0d2djYaGhogFosFTEidqX///ggICLCq+fv7Y9euXQIlIntKTU3F4sWL8cYbbwAAgoKCUF1djfT0dDZS3YynpyeAeytT/fv3t9R5Ldh18RmpLkIikSA0NBQHDx60qh88eBDh4eECpSJ7MZvNmD9/Pnbv3o3Dhw/Dx8dH6EhkR+PHj8dvv/2GsrIyy6ZSqRAfH4+ysjI2UU+4ESNGtPrvDi5cuICnn35aoERkTyaTCQ4O1pdjYrGYrz/vhnx8fODp6Wl1LdjY2IiioiJeC3ZRXJHqQlJSUpCQkACVSoXhw4dDp9OhpqYGSUlJQkejTpacnIzt27dj7969cHZ2tqxM9unTB1KpVOB01NmcnZ1bPQ/Xu3dvuLm58Tm5buC9995DeHg4tFotpk2bhuPHj0On00Gn0wkdjewgOjoaq1atgpeXFwIDA3Hq1ClkZmYiMTFR6GjUCW7fvo3KykrL56qqKpSVlcHV1RVeXl7QaDTQarXw8/ODn58ftFotZDIZZsyYIWBqehC+/ryL2bBhAzIyMnD16lUMHjwYX3zxBV9/3Q086N7nvLw8qNVq+4ahLmHs2LF8/Xk3sn//fixZsgQXL16Ej48PUlJSMGfOHKFjkR3cunULy5cvR0FBAQwGA5RKJeLi4rBixQpIJBKh41EHKywsxLhx41rVZ82ahc2bN8NsNiMtLQ3ffPMNjEYjhg0bhvXr1/NHtS6KjRQREREREZGN+IwUERERERGRjdhIERERERER2YiNFBERERERkY3YSBEREREREdmIjRQREREREZGN2EgRERERERHZiI0UERERERGRjdhIERERERER2YiNFBER2cXKlSsxdOhQwc6/fPlyzJ07V7Dzt0ckEmHPnj3/01yDwQAPDw/U1tZ2bCgiImoXGykiInpsIpGo3U2tVuODDz7AoUOHBMn3119/Yd26dVi6dKmlplarIRKJkJSU1Gr/d955x5K7I3VGMymXy5GQkICPP/64Q49LRETtYyNFRESP7erVq5YtKysLLi4uVrV169bByckJbm5uguTLzc3F8OHD4e3tbVUfOHAg8vPzcefOHUutvr4eO3bsgJeXl51T/u9mz56Nbdu2wWg0Ch2FiKjbYCNFRESPzdPT07L16dMHIpGoVe3fqzFqtRoxMTHQarVQKBTo27cv0tLS0NTUhNTUVLi6umLAgAHYtGmT1blqa2sxffp09OvXD25ubpg6dSouX77cbr78/Hy8/PLLreohISHw8vLC7t27LbXdu3dj4MCBCA4Ottq3oaEBCxYsgFwuh6OjI0aOHImSkhLLeGFhIUQiEQ4dOgSVSgWZTIbw8HCcP38eALB582akpaXh9OnTlpW6zZs3W+Zfv34dr7zyCmQyGfz8/LBv3z7LmNFoRHx8PDw8PCCVSuHn54e8vDzLeFBQEDw9PVFQUNDu34GIiDoOGykiIhLM4cOH8eeff0Kv1yMzMxMrV67ElClT0K9fP/z6669ISkpCUlISrly5AgAwmUwYN24cnJycoNfrUVxcDCcnJ0ycOBGNjY1tnsNoNOLs2bNQqVRtjs+ePduqKdm0aRMSExNb7ffhhx9i165d2LJlC06ePAlfX19MmDABN27csNpv2bJlWLt2LU6cOIEePXpYjjV9+nS8//77CAwMtKzUTZ8+3TIvLS0N06ZNw5kzZzBp0iTEx8dbjr18+XKUl5fjwIEDqKioQE5ODtzd3a3OGxYWhiNHjjzsT05ERB2EjRQREQnG1dUVX375JQYNGoTExEQMGjQIJpMJS5cuhZ+fH5YsWQKJRIKjR48CuLey5ODggG+//RZBQUHw9/dHXl4eampqUFhY2OY5qqurYTaboVQq2xxPSEhAcXExLl++jOrqahw9ehQzZ8602qeurg45OTlYs2YNoqKiEBAQgI0bN0IqlSI3N9dq31WrVmHMmDEICAjA4sWLcezYMdTX10MqlcLJyQk9evSwrNRJpVLLPLVajbi4OPj6+kKr1aKurg7Hjx8HANTU1CA4OBgqlQre3t6IiIhAdHS01Xmfeuqph67MERFRx+khdAAiIuq+AgMD4eDw/7/pKRQKDB482PJZLBbDzc0NBoMBAFBaWorKyko4OztbHae+vh6XLl1q8xz3n39ydHRsc9zd3R2TJ0/Gli1bYDabMXny5FarPZcuXcLdu3cxYsQIS61nz54ICwtDRUWF1b5Dhgyx/Lt///4A7r1Z72HPXP1zXu/eveHs7Gz53vPmzUNsbCxOnjyJyMhIxMTEIDw83Gq+VCqFyWRq9xxERNRx2EgREZFgevbsafVZJBK1WWtpaQEAtLS0IDQ0FNu2bWt1LA8PjzbPcb8pMhqND9wnMTER8+fPBwCsX7++1bjZbLZk+Xf937V/5r8/dj9/e9r73lFRUaiursb333+Pn3/+GePHj0dycjI+//xzy/43btx44PcjIqKOx1v7iIjoPyMkJAQXL16EXC6Hr6+v1danT5825zz77LNwcXFBeXn5A497/xmrxsZGTJgwodW4r68vJBIJiouLLbW7d+/ixIkT8Pf3f+T8EokEzc3Nj7z/P3l4eECtVmPr1q3IysqCTqezGj979myrF2QQEVHnYSNFRET/GfHx8XB3d8fUqVNx5MgRVFVVoaioCAsXLsQff/zR5hwHBwdERERYNUH/JhaLUVFRgYqKCojF4lbjvXv3xrx585CamooffvgB5eXlmDNnDkwmE956661Hzu/t7Y2qqiqUlZXh+vXraGhoeKR5K1aswN69e1FZWYlz585h//79Vg2cyWRCaWkpIiMjHzkLERE9HjZSRET0nyGTyaDX6+Hl5YVXX30V/v7+SExMxJ07d+Di4vLAeXPnzkV+fn67t9i5uLi0e4zPPvsMsbGxSEhIQEhICCorK/Hjjz+iX79+j5w/NjYWEydOxLhx4+Dh4YEdO3Y80jyJRIIlS5ZgyJAhGD16NMRiMfLz8y3je/fuhZeXF0aNGvXIWYiI6PGIzPdv/CYiInpCmc1mvPjii9BoNIiLixM6TocLCwuDRqPBjBkzhI5CRNRtcEWKiIieeCKRCDqdDk1NTUJH6XAGgwGvvfbaE9kgEhF1ZVyRIiIiIiIishFXpIiIiIiIiGzERoqIiIiIiMhGbKSIiIiIiIhsxEaKiIiIiIjIRmykiIiIiIiIbMRGioiIiIiIyEZspIiIiIiIiGzERoqIiIiIiMhGbKSIiIiIiIhs9H/IIz8QTOW+YwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create the plot\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# Plot data1\n",
+    "plt.plot(ea_mld, label='MLD lat min 75S')\n",
+    "\n",
+    "# Invert y-axis to display depth correctly\n",
+    "plt.gca().invert_yaxis()\n",
+    "\n",
+    "# Add labels and title\n",
+    "plt.xlabel('Time (Months)')\n",
+    "plt.ylabel('Depth (m)')\n",
+    "plt.title('MLD Model-derived')\n",
+    "plt.legend()\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8ae36ec-104f-4daf-944d-471f5dad98b0",
+   "metadata": {},
+   "source": [
+    "### to do for next commit\n",
+    "- compare with calculated MLD using density output\n",
+    "- compare summer vs winter\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e21d99e8-9192-4bf7-8e65-e4baabaeafe3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:analysis3-24.01] *",
+   "language": "python",
+   "name": "conda-env-analysis3-24.01-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}