90-analysis-shorten.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# DIVAnd full analysis\n",
    "\n",
    "This notebook presents the different steps necessary for the creation of a climatology:\n",
    "1. ODV data reading.\n",
    "2. Extraction of bathymetry and creation of mask\n",
    "3. Data download from other sources and duplicate removal.\n",
    "4. Quality control.\n",
    "5. Parameter optimisation.\n",
    "6. Spatio-temporal interpolation with `DIVAnd`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import packages\n",
    "We assume that the Julia packages listed below are installed.    \n",
    "If that is not the case, run the commands:\n",
    "```julia\n",
    "import Pkg\n",
    "Pkg.add(NameOfThePackage)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "using NCDatasets\n",
    "#using PhysOcean\n",
    "#using DataStructures\n",
    "using DIVAnd\n",
    "using Dates\n",
    "using Statistics\n",
    "using Printf\n",
    "using PyPlot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Configuration\n",
    "* Define the horizontal, vertical (depth levels; variable `depthr`) and temporal resolutions (variables `yearlist` and `monthlist`).\n",
    "* Select the variable of interest (variable `varname`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "dx, dy = 0.125, 0.125\n",
    "lonr = 19:dx:30\n",
    "latr = 32:dy:38\n",
    "\n",
    "depthr = [0.,5., 10., 15., 20., 25., 30., 40., 50., 66, \n",
    "    75, 85, 100, 112, 125, 135, 150, 175, 200, 225, 250, \n",
    "    275, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, \n",
    "    800, 850, 900, 950, 1000, 1050, 1100, 1150, 1200, 1250, \n",
    "    1300, 1350, 1400, 1450, 1500, 1600, 1750, 1850, 2000];\n",
    "\n",
    "varname = \"Phosphate\"\n",
    "yearlist = [1900:2020];\n",
    "monthlist = [[1,2,3], [4,5,6], [7,8,9], [10,11,12]];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create here the variable TS (for \"_time selector_\"), which allows us to work with the observations corresponding to each period of interest.    \n",
    "Here it means we have 4 time periods (January-March, April-June, July-September and October-December) containg all the years from 1900 to 2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mTimeSelectorYearListMonthList{Vector{UnitRange{Int64}}, Vector{Vector{Int64}}}(UnitRange{Int64}[1900:2020], [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])\n",
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39m4\n"
     ]
    }
   ],
   "source": [
    "TS = DIVAnd.TimeSelectorYearListMonthList(yearlist,monthlist);\n",
    "@info TS;\n",
    "@info length(TS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 1. Read your ODV file\n",
    "Adapt the `datadir` and `datafile` values.     \n",
    "The example is based on a sub-setting of the [Mediterranean Sea](https://www.seadatanet.org/Products#/metadata/cd552057-b604-4004-b838-a4f73cc98fcf) aggregated dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[91m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[91m\u001b[1mError: \u001b[22m\u001b[39mNo data file present\n",
      "\u001b[91m\u001b[1m└ \u001b[22m\u001b[39m\u001b[90m@ Main In[10]:3\u001b[39m\n"
     ]
    }
   ],
   "source": [
    "datadir = \"../data\"\n",
    "datafile = joinpath(datadir, \"1_data_from_Eutrophication_Med_profiles_2022_unrestricted.nc\")\n",
    "isfile(datafile) ? @info(\"Data file is there\") : @error(\"No data file present\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the netCDF file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7360 out of 17361 - 42.393871320776455 %\n",
      "14950 out of 17361 - 86.11255112032717 %\n",
      "  5.976107 seconds (3.23 M allocations: 404.242 MiB, 2.31% gc time)\n"
     ]
    }
   ],
   "source": [
    "@time obsval,obslon,obslat,obsdepth,obstime,obsid = NCODV.load(Float64, datafile, \n",
    "    \"Water body phosphate\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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0UGdnJ02bNo0A0NWrV2XPz8zMpNzcXMrMzHS5LGL2upaWFtn935ycHLpy5Qrl5OSo7ttRnxBmZMGZv0YozmQ0Kioqovb2dioqKjJTxnfddRcBoNzcXCIiio+Pp+zsbIqPjyci+XSg2dnZlJ+fT9nZ2a65MMbvSU1NpTlz5lB8fLxVxVtdXU0ajYaqq6vN3lfjuGVNSaanp1NOTg5FRkaaKX8xa921a9fspo6tqqoiQRCoqKiIpk2bRnq9npqbm+3KxDBErJgZB5CzmMVXe3u7dJ7o6S2+xxaz/2Cat7y3t5fWrFlDMTEx1NbW5tZxW1paaOzYsXTrrbfS6tWrrXorh4aG0oYNGyg0NNTsfZ1OR8uXLyedTmd3LIPBQA8//DAZDAaz961ZzABo5syZVnPUmyIIAi1cuFD6XYSFhVF4eLjddgxDxIqZUYGpYs3LyyMAlJeXZzVkZGhoiD777DMKDg6mK1euOF1QgPEMAwMDlJmZSZcvX6acnBxavXo1AaDp06eT0Wh069iRkZGUlZVFkZGRw451d3fTkiVLqLu7mxobGyk0NJQaGxvNznGFxWxJQUEBAZAmoefPn7fbtxqLmfNxM5awYmaG0draSmPHjqU77rjDrErUggULSKPR0IIFC8ysZPElPiTFIhuWL2cLCjCeIScnh86ePSsVKxk3bhxFRkaafcfuwnSv2ZIlS5bQ/v37acmSJW6VwRIAFBERQQBo69atwyqqOQtXsGIsYcXMDCMqKopiYmJo1KhRlJ6eLr2v0+lIr9cTAHrttddkle+HH34o+75Go2GL2U8YGBig1NRUevvttykpKYkiIiJIq9VKkytvYWoxexLRYv7ss8+kimq2qrSphS1mxhJWzD7E4OAg5ebm0uDgoFflsGYx19bWEgC65ZZbSBAEIvp37WfTl8FgMPtbq9XS+PHjqaOjY9hY/FDyHSoqKggABQUFSd9dTEwMrVy5kgBQVFSUov1VR5FTvM3NzRQeHq7YcUquJKpcVjqif2ftunLlitm4cjXFiYiKi4sJAF26dIni4+PprbfeolWrVnH2LsblsGL2AURHm7///e/00ksv0XvvvedtkYYhPsROnDhBgiDQ7t27CQAdOnRI1kL+6KOPKDg4mEpLSyk+Pp5eeuklSk5OHtYvL+P5BgMDA7Lfo1arpfT0dLp69SoJgkBVVVVuk0FuqTo8PJy+853vKHacAkDp6elmE4jU1FTavn07paamDjt3zpw5BMBsXNErW1wtEq1jABQXFyetDMXHx1NPT49bQ7eYkQkrZi/T19dHS5YsoYsXL9KGDRsoJyeHXnnlFZ+agVdVVQ2LUTb9+9y5c9L/S0tLpXai9/bJkycJAJ06dWpY32wx+wZ//OMfZRWzJ9MXmFrMTU1NFBYWRteuXVNlMYtWbXFxsfSesxazGOa3dOlSyWI2vWfZYmZcDStmL5OVlUWffPIJLVmyhHp7e2nHjh00c+ZMevfdd702AzcNYbJUynfddRcRkWQpHzp0yKyt3NKjafsLFy548EoYpSxfvlxy8AoJCZG+r/DwcJfso6olLCzMIWdBS2vXFVjuJ5uGCzKMO2DF7GUsLcbs7Gz64osvKDU11Wsz8OzsbDpy5AiFhoYSALr55psJAM2YMcOu4014eDhlZGRIlgUR0eXLlwkABQcHe9QCY5TzwAMPSMu64eHhNGnSJNJoNLRnzx6KjIz0uMOVaDGrdRa0tj/sDJbx+KYJdtzRP8OwYvYx+vv76bnnnqP169d7TTH39/dTaGgo/fCHP6SbbrpJ0d7ixYsXCQAdO3ZMdhn0woULbDH7MD09PaTT6ejb3/42AaDk5GRKTU2lyMhI2rNnj8dDlFxFa2ur5Ekt97cSLDPYudIjW65/hmHF7IP4gjOJkgeY6X6e3N6k+KDp6Oig5ORkM69sOSuB9+q8ixhDXF1dLVmd3gpRchQxTaxozUZFRdEDDzxAAOi9994jADR16lRVWbgs71W1v0+5+9+UtrY2MhqN1NbWJo3V1tZG+fn5snvjTODDitnDKFE+vq6gxKxLpl6qpq8f//jHpNVqpQdRcnLyMK9sOSvBFyYkzL/xR8c8McRLXK0Rnx9jxowx205ZsWIFERGdPXuWANDZs2cVjyGGCJaVlQ079tFHH0lRCSJy978ppkvj4u/CaDTSrFmz6Omnnx7mTc4EPqyYPUwgKB8xT7GoiIuLi6WEIy+//PIwS5gtZv/EH0PZLC1mIqJ3332XANArr7xCAOgrX/mKtAytxvtcznPdErlj9ixmU2cytpgZIlbMHkOMVe7q6vJL5WO63GYtT7Ec7Njiv3jbYpZLFuIIAMhoNEqhUUT/dhJTqpg7OjoUhZJZWszO3P+20pMygY0avacB4xBDQ0P4//6//w933nkn/vznP+PVV1/FqFGjvC2WKlJSUpCZmYmUlBRERESgo6MDERERdtvNmjULGzduxKxZs9wvJONS9Ho9tm3bBr1e75Xxo6OjkZ6ejujoaKf6KS0tRXl5OUJDQ3HzzTcDADIyMrBx40ZERUUBAM6dO2ezj7S0NKvHuru7MXfuXAiCgEWLFqGgoAB33XUXAOfu/4SEBKxfvx4JCQmq2zIjCA9MFIYRCBbz+++/T3v37qUf/OAHNmu4+jKixVxZWUlGo1Hywp47dy4BkDKB4V/hNvjXXjNkrIxf/epXkhWjtkwkM3JwxGK+du0aATdqJpty5coVWYtZLqzq0qVLBNzIICY6vXV0dEj71OLr+eefl861fImotZjFZe3BwUE6efIkW8wjFF7KdjODg4P0/PPP0xtvvEHvv/++t8VxGjFfsitfdXV1RDTcIUzMuLRq1SpvXvKIQs3ytbeXuuUAbpSndMaOAEA6nY4A0JIlSyQlfuedd1JOTg7p9XrpPpW7nwsKChweu6ioiNra2uiVV16htWvXcnjhCIWXst1MXl4epk6dipKSEmRkZHhbHKdpaWlBUFCQy/pbsmSJtFSZn5+PnTt3Ij8/HwCwd+9evPbaa9i7d6/LxmNss3XrVqxcuRJbt2516bme4tq1aygqKsK1a9eGHevo6EBCQgI6Ojps9nHp0iUMDAwgPT0dBw8elJa9e3p6sHPnTlRWVkr3qXivarVadHZ2goiQnJzssPwJCQn4/PPPceTIEaSkpODFF190uC9mhOD+ecJw/N1ifv/992n//v0BYS0T/Xup8NixY6TRaOiTTz4h4EZJPFhYDn/7299kLYqf//znBICee+45Av6dIYzxPr5mMTuznSHGYH/++efS1okjiTwsl71FR86BgQGrqzrOZCAbGhqiCxcu0IYNG/zOSZRxDW5dyn7jjTcoMjKSiIj2799P8+bNo7vvvls2/s8VAvoivlLK0VWI1X4AUHZ2NgGgDRs2SHHN4kuMJbV8ifWcJ06cSABo7dq1BEDKLmat4ADjvzjjmaxkO0MsRlFeXm72vlityvT+M5XDXhiTNXJycujKlSuUk5MjO3EQ5fnpT3+qOmc3+1UwRG5UzIODg7Ry5Uq6/fbbqa+vj9LS0qi3t5c+/fRT2rRpk1sEZNyPWKSivLyc4uPjqbS0VAqdEgvK//Wvf5ViSS2Vcl1dnWRlv/322xQcHEyCINDChQtJEASrJfoY/8WZlJNKFBXw7/KNpogWs+k9aIq9xB/WMLWYrckjFgRRazF/9atfldpeuHAhoCb1jHLcppj37NlDb7zxBs2ePZsKCgpow4YN0rF58+ZZbdfT00Otra3SS5x9smL2Pt3d3ZSamkoA6ODBgwRA+vvtt98eZh2LqRDtvURHGwC0efNmAkCffPKJ7EOZk5D4H+6IZc/PzycAlJ+fb2Yxm74v8uGHHxJwo4ayKadPnyYAdPr0aZfIJFZl+4//+A8CQI8++qjZccvVIHE1raGhgcLCwujq1avDfhsZGRn0zjvvuEQ+xn9wi2IeGBig5cuX0+DgIM2ePZuOHj1K3/3ud6Xjc+bMsdr2mWeekX14s2L2PpbWh5je0F0vuWXMQMicxjgPAAoKCpKsYFH5W75vr4/Jkyc75cFtSnZ2NuXn59Odd95ptlooyqfT6eiXv/yltBqUm5tL1dXVpNPp6LbbbiMA9OCDD0r3//jx4wkArVy50iXyMf6DWxTz7t27ac+ePURENHv2bLp48SJbzAGAUovZYDDYVLibNm0i4N/xzkajkXbs2EEAKCsriwDQyZMn2WJmrGJpGYvL5REREcMsZmsUFhYSAJoyZYrqfWY5RIu5q6uLUlNT6eOPP5bSmcbHx9Nzzz1HOp2OOjs7qaenh1auXEkvvfSS9LtYuHAhBQUF0X//939TXV0dGQwGuv/++3m/eQTiFsX8wx/+kDIyMmjp0qUUFhZG3/ve92ju3LnSHvM3vvENtwjI+A61tbWk0+lo1KhRFB4eLlXzqa6uJo1GQ9XV1V6WkPF3qqqqJKdB0WK+cuWKqvvL0X1me4ge69evXycAdOrUKdJqtVRTU0MDAwOUmpoqu/2TlZUlOZYxIxe3JxiZPXs2ERHt27eP5s2bR4sWLRpRXtkjHdMc20REGo2GFi9eTBqNRnqYXrt2TXIMY4uYUYqp06CI6f2lBLWe2ZZRFqYFKOQAQPPmzSPgRo3rpKQkysnJoc8//5y0Wq1ZmOHevXupr6/PpmMZMzLgzF+M2zF1/jG1mMXlR3EfTa/X0/r162nDhg20fv16b4vN+DimFrOI5YqMkrriIkpqT4v7wrm5uURkXrLRlKeeemqYNazVamnhwoU0MDBA4eHh9OMf/5iMRqMjl84EOJz5i1HMmTNnIAgCzpw5g9LSUgiCgNmzZ0MQBBQWFuLw4cMQBAE333wzDhw4AEEQ8Pe//x0xMTG4evUqYmJicPToUQwNDeHQoUOoqanB1q1b8Ytf/AJvvfUW+vv7ceLECTQ3N2PatGmqZOvu7sb8+fPR3d3tpqtnvEF+fj4EQZAybJkyadIkDA0NYdKkSVbbJyYmYsOGDUhMTBx2rKamBlqtFjU1NQCAFStWIDMzEytWrLDa3+LFi3H+/HksXrwYwI1MXdXV1ZgyZQq2bNmC/v5+9PX14YUXXhjWdnBwEG1tbRgYGMDChQvxxz/+EQUFBfY+AoaxjQcmCsNgi9kcZzIKOUNnZ6dbPbBNX9nZ2dTf369KvvT0dMrJyVGd0IHxbQDQqFGjFHtOWy5l27KYLc8VLeaOjg7Vy8mmtaszMzOlBDqmr6lTp9KyZcs4BzxjF17K9jPcrYDk9ni7urqkjF3AjbjP733ve2YPncuXL9MvfvGLYZ7Zv/71r6X3fv3rX9Ozzz477IF14MAB2rdvHwGgffv2OSS3tyYsjHs5c+YMAaAzZ87YPbezs1MKO1Li/GXNETEnJ4cKCwspKytLcXKPvr4+qcCLVqulBx54gNauXUvt7e2UmZlJNTU1FBYWRk1NTZzdi7ELK2Y/w90KSC5OWK6gvPgABEBjxowhIhqmcOXes3xZJp9wNhlFV1cXzZs3j1555RV2oPEhHE1/qQZXZY0bGBigrKwsqqiooEOHDtGlS5do165d1Nvbazfjl/gKCwszU76c0Y5Rw4hUzPZS6o1krFnMiYmJZDAYqL6+nohuWCcJCQlmy4QnT56UHkxi3mLTbEYajYbOnz8v/X316tVh6RqdSd9IdGMSkZmZSampqRxy4iXklLC7wpJMcWWeddH7+vLly7R9+3bKy8ujzMxMm6FMr7/+umQxf/nLX5aWqwcHB+mvf/0r54BnFDPiFHN/fz997Wtfo+9+97u0Y8cOl/TpK7S0tHi8sHp9fb2ZwpbDlhXsKou5q6uL0tLSKDU1lWbPnk0vv/wy7dy5kydfXkBOCbvDYrZ3r7hCUQ8NDSm2mE0xXa629ORmGHuMOMWcnZ1NmzZtosjISHr66add0qevEBkZSVlZWVJFL2cQsyIVFhZK74nFJz755BMiImpoaBi2dP3Pf/6TANA///lPqd1bb71FAOitt94aNs6f//xnAkB//vOfhx27cOECAVBULD49PZ1iY2Mli+WrX/0qXb58ma1mL+CJZWsi+dUV0/tWXD6Ojo6WknuYIibBqa2tVTSetSpWclRUVBAAqqiooMHBQXrzzTfN4vkZxhYjTjH39/dTUlISrV69mr72ta+5pE9fwZUWMzA8j7ClErbMlS13jrX35I5Zyg6Axo0bp8gjt6urS5JHr9eTRqOhr33ta9TW1sZLiH6MrcmZnMVset+KFrNWq6V7772XtFqtWXudTkfLly8nnU43rG85By1AvoqVHABo9OjR0rlGo5GeeeYZMhqNiuKlmZHNiFPMRFzzVAlqLebi4mLq7e2ladOmOWQxAxhm7auxmImIZsyYQWPGjJGU84YNG0iv19P//u//stONn6JmckYkf9/W1NSotpjlQprUWMxLly4lALR06VIiMs+Al5GRQS+99BJlZGQouiZm5DEiFTPjHpyp/GS69OconZ2dFBISQmFhYTRu3DgaNWoUPf/886TT6eixxx6jpKQkty+vMq5F7eTMVdgroGJvGdxW/PT58+fp7rvvpvPnz7tNfsa/YcXMKEJJakPxwSXGbqrJd+2sN7ZIT08P3X///TR79my65ZZbaOrUqfSlL32JBEGgefPmudUrmAlsTCeetpbBiW6USN2/fz8tWbJk2DF7+bUZhhWzD+OOAvNERM3NzRQeHk7Nzc2K20RFRUnlGm15YBMRPfjggxQeHk4PPvig4v5dda1i6b3bbruNXnrpJRIEgaZOnSotb7PFzDiKnMVcUlIim1fAl/aRuTCM/8GK2Ydx1Iq0rOhkSXh4OH3nO9+RSjGKmFa6+fvf/252TPwexJdlKIpYT1mn05Fer6ekpCSKiooiIqKHHnqIANBDDz00TJbf//73BIB+//vfS+OIlvnTTz9NAGS95999910CQO+++67Z+2Kx+ueee470ej395Cc/kfYptVot/fjHPyaNRkMPPfQQP6i8jJLJmJrMX9aorKwkAFRZWelwH0Tye8ymmfhMl7dLSkoIAJWUlEjnWrvehoYGCg4OpoaGBqfks0ZmZia9++67lJqaqjrVLeMdWDH7MI5akaYeoJZ0dnbSzJkzyWAwDLOY5TJ3WTs+c+ZM6V/LY8HBwWbL3kr7JCK66aabCID0r9J2IqLF3N/fTx0dHZSYmEgvvPACAaC77rpLysokCIJDe+GM64iPj6eVK1cSAProo49kzwHU5cq21oeYUvb48eNO9WPplW2aic90eRsA3XrrrdK1ifeq3EQ7ODiY1q5dS8HBwQ7LZove3l5KTU2lL774grKzs90yBuNaWDH7KbZiRW1ZzLZSA9qymIn+ndno9ddfJ0EQSKPRSLVwTS1my5m/LQVraTGLfYiWslqL2ZKcnBzasGEDLVq0iKZNm0a33367ZL2zxexd2tvbbd4bRK61mG2NowR7XtnWLGbTsSdMmOBxi5nIfMLK+D6smP0UR1Mcuipt4bx580ij0dC8efPsnmstXEUOMYXn1atXnZJPZGBggHbs2EGbN2+mRYsW0be//W3q6+tzSd+M84jW5EcffeTWzHXHjx932mJ2FPEa77zzTmklSEnIFTNyUaP3BCIieJi2tjaMHTsWra2tCAsL8/TwPktnZyfS0tJw4sQJhIaGenz87u5uZGRkIC8vD8HBwS7rd/bs2di0aRN27NiBU6dOuazfF154AcuWLcN7772Hp556ymX9Mq5jwoQJWL9+Pf70pz+hvr7e2+K4lLa2NiQmJqKmpgZz5szBF198AS88Thk/QY3eY8XMuJ2uri4sWLAAR44cQUhIiMv6HRgYwLZt27BlyxbodDqX9TuSGBwcxJ49e7Bu3TpotVqX99/a2oqEhAQUFxdjcHAQsbGxKCsrQ0REhMvHchXl5eWSnEaj0e75FRUVMBqNKC8vR0xMjAckZPwRNXpP4yGZmBFMSEgITp065VKlDAA6nQ4/+MEP8MQTT6Cvr8+lfY8EKisrodPpsGHDBvzsZz9zyxhjx45FfX09xo4di9jYWDz00EOIjY11y1iuIjY2FqmpqYrljImJARGxUmZcBitmxozBwUHs3r0bg4OD3hZFEZs3b8bq1auxefNmb4viV7S3t5spkmeffRYzZsxAV1eXy8dqbm7G2LFjcfbsWRw4cABlZWVO9dfZ2YmUlBR0dnY6LVt3dzfmz5+P7u5u6b2ysjKcPn3aaTkZxlFYMTNm7NmzB/Pnz8eePXu8LYoitm/fjv3792P79u3eFsWvSElJGfbehQsXMG/ePJePFRcXh0ceeQSpqano6Ohwehk7LS0NmzZtQlpamtOyZWRkYOPGjcjIyJDeMxqNICIYjUZ0dXVh9uzZbpmwMIxV3OmFZg32yvZdBgYGFNenZfwP8fvdsGHDsBh3OBl6ZI2mpiYKCwujpqYml/TnSAlKa6F4pjHLctgKRWQYNbBXNsMwsuzevRvz58/HokWLQESoqqrChAkT0NLSgv7+fowZMwY1NTVucdbzFgMDA9Dr9dLflo+8vr4+bN68Gdu3b8eoUaPMjrnLcZEZebDzF8OYMDAwgBdeeAEDAwPeFsXrrFu3Dp9++ikuXbqE0NBQGI1GHDt2DP39/QCAs2fPYsGCBdi0aRMWLFjgZWldw7Zt22wet+Wn4C7HRYaxBStmJqDp6+vD3LlzsXjxYrsP6JGAVqvFY489hjFjxmDixIn4yU9+gqSkJAiCgKCgIMyePRtHjhzBjh07cOTIEW+L6xDt7e2IjY1Fe3s7AGDLli341re+BQA4efLksPM96afQ19eHxx9/nKMIGJvwUjYT0Dz++ON46KGH8KMf/QjHjx/neGcTxIQyr7/+OqZNmwa9Xo/a2lqMGzfO26I5RWxsLDIzM7Fr1y67ntW2lrHdweOPP47Vq1dj//79ePXVV90+HuM78FI2w/yL7du348CBA/jss89YKVsQHByMTz/9FEuXLgURYWhoCAaDwWvyuCoMqqCgALt27UJBQYHdc02XseVCp0xpa2vDpEmT0NbW5rBs9qxzU4uat2BGLqyYmYCio6MDCQkJ6OjoAACMGjUKr776qkesIX+lqKgIwI29+IULF3otREhNGNSVK1cgCALOnj07TNYxY8agrKwMY8aMsduPqaKUC50yJTExERs2bEBiYqLyi7LA3v1oOlHYtm0bli1bxlswIxF3uodbg8OlGFfT3NxM4eHhNHXqVNX1rnt6euiuu+4ig8FA9fX1sueIVbo+++wzV4nsMzz44INmpT+9FSKkJgwKAMXExBAAioyMpOnTpzs9vr3QKdO64u6it7eXMjMzqbe3l6tHBRhcXcpPqaioIABUUVGhqt3JkycJAJ08eVL1mEofhj09PbRq1Srq6ekhIqK3336bANDbb7+tarzc3FwCQLm5uapl/eKLLwgAffHFF8OOhYeH03e+8x3SaDSSgr1+/ToBoOvXr1vts6+vj6ZOnWoWxxsdHU01NTWUnJxMNTU1FB8fb3ZcSUUtf6K3t5fWrFlDKSkptGjRImpsbFRVray7u5uWLFlC3d3dZu+LkyXLGuHWsFYqsa6ujvR6PdXV1UnvFRUVEQAyGo0UGRlpdyJmWYrUlCtXrkjfbWlpqV057SlwhpGDFbOfAoDmzZunOsmDqdKwxN6sW2mpyVWrVtHBgwdp1apVdsc0ZXBwkHJzc2lwcFBVO7n2lm3FmryVlZWSErj77rtp//79tGTJEgJASUlJVj+XZ555hoxGI40ePdpqsg1rr0Cjr6+PjEYjvf7667RkyRJVbRctWkQAaNGiRWbvi5Ol8PBwRf0EBwfT6tWrCYA0ASQi0uv1tHLlStLr9cPatLe3U3x8/LB6yJZY++4s7ysl3216ejrl5ORQenq6outiGCJWzH6LoxazrYdKdnY25efnU3Z2tmxbRy1mpQ+y3Nxcqq6ulixktRazaXtLixkApaenm8lgar3Zspizs7MpNTVVtUIONMXc19dHTz75JN1+++106NAhMhqNwyxfewCgcePGDftcHLGYAdCuXbukCSCRvMWsFmsWc25urrQkzhYz405YMY8wbKU8dGafqq2tjYxGI7W1tQ071tjYSKGhodTY2GizD0uLWS1y7cUJzIkTJySLWS39/f0OK+WwsDCHrsXXaGlpIa1WS6NHj6aQkBC67bbbqK+vT3U/VVVVJAgCVVVVOS2T5QTQVVibgIqTB51ORy0tLS4dk2FMUaP32Cvbh3A0HCM8PBytra0IDw8fdkyn0+Gpp55yKFQoJSUFmZmZsgUPIiIiFBUk0Gg0yMjIgEbj2K1m2v769esQBAExMTGYN28e0tLSQESYPHmy6n4tPw/xswsNDQVwI+OTNZnb2trQ2NioekxPYi/0BwASEhIwevRodHR0gIgQFBRklrpSKZMmTcLQ0BAmTZrkjMgAAIPBgL/85S8uC9sSw4/mzJkj6/GdlJSEcePGYcyYMXj88cddMiYAj3i2K/mOGf+EFbMP4YpwDFeiJh7UE0ybNg3Tp08HAHz++eeoqKhwqr+goCAAN5R0SUkJkpOTUVtbCyJCZ2cnBgcHMXr0aNm2RqPRqbHdyeDgIJKTk3HzzTfjy1/+MgYHB/Hqq6/i1VdfNSvnWVxcjO7ubkRGRkKv1yMvL8+LUrsHMfzotttuw44dO/DRRx+ZKczCwkJ0d3fjS1/6Evbu3euycT2R1tReeBfjv3DmLx+ira0NiYmJKCws5M/FBPFzef/99zFr1ixcu3YNU6dOdbrfmJgYNDU1ISIiwqqSb2lpwaRJk6DVanHhwgXMmDEDfX19qKqqwvjx452WwR3s3r0b5eXl+OMf/4j//u//xqhRo1BTUwMAiIqKwmOPPSad+8ILL6CgoACjR49GaGhowMXMWmb2mj17NjZt2oQdO3bg1KlTbhvXFcUvhoaGcPjwYSxevFh29UbM3JaXl4fg4GBnRWbcjCq9595VdXl4j9l92NoX9leioqJo69atFBUV5dJ+lXr0+hsDAwO0c+dO2rlzJw0MDNDAwAD94Q9/oK997WvU0dFh5nPQ399Pzz33HE2fPp1qa2tVl1N0JUrC22xRXFxMAGj+/PlWHdg6OztVhYKZ0tLSQpGRkdTS0kKnT5+WfA5qa2tttjt27BgBoGPHjqkaz9JxkvFv2PlrBGM0GqUwIF9EqdOYKZ5I7BDoiN75S5YsoZMnT1J6ejr19fVRb28vxcfH09///ncKCwujl156iZKSkig5OZnq6uocVmKOABvhbUrbGwwGAqA65MseXV1dpNFopIQmsHAItPTQNo0OMD1PiXObeE5XV5dTjpOMb8GKeQTjaovZVUpR9IoNDQ2lDRs2UGhoqNOy1dfX28zWxfwb0Tu/u7ub0tPT6eOPP6asrCzKzMykd999l+Lj46mpqYmSk5NpypQp9NJLL5Fer/doBjBXWcwA6PLlyy6VLT09nYKDg6UENJMnT5bG+vWvfz0spnnJkiVSPL2pxWyZD0AOJecw/gcrZsZluGoZWUxkEhUVpdpitobBYKCHH36YDAaD032NJPr6+igrK0uymMUUkEQ3lsF/97vfecRiNk0Q4yoA0K233uryWPOuri668847aerUqdTR0SHFMjc2NsrGNFvLhqbGYnZ1yBjjXdToPXb+YmziKoe0zs5OpKWl4cSJE1JIkrM0NDQgJiYGFRUViIyMdEmfI52dO3fi8uXLuOWWW7Bx40a3lkUUBAHp6ek4evQo5B5DHR0dmDVrFvLz8616x4uIjlA7d+5EUlISSkpKMGXKFNX9MIy74LKPfoK3qvioISwsDNXV1RgcHMTYsWPR3NzsUD+hoaEoKChwmVIGgMjISPT09LBSdiF1dXUYPXo06urqAPw73Gj16tUQBAGnT5/Ggw8+iN7eXqfHqqysxNGjR1FZWSl7fNasWdi4cSNmzZpltY++vj78x3/8ByZMmID169dj48aNICJJKSvth2F8Cjdb77LwUvYNvFXFxxHCwsLoiSeeCJisV4w8lpnixKVumKTd9NT+pxKv+czMTEpOTqaZM2dSaGiobJrMQPW+Z/wLXsr2E1wR6+gpmpubERcXh5KSEtkMY0xgU1BQgFtvvRX33HMPCgsLUVBQoKjesbvp6+vDY489htLSUnzwwQeK4nndsa3CMPbgpWw/ISQkBKdOnfJ5pQzYTvvJBDZVVVW49dZbYTQa8cEHH2Dy5Mn43ve+57HxbW35jBo1Cm+88QaOHj06TCl3dHQgISEBHR0dZu+npaWZpee0dh7DeAtWzIzf0tPTg6VLl6KnpwcAQES4cuWKrCMR4zjR0dGIiopCeXk5AOD48ePYvn27dHxwcBC7d+82S/fpShxNb2ltb/nEiRPYsWMHTpw4YfM8hvEWrJgZn8eaRbNs2TLk5uZi2bJlAG48cG+//XbpgQsAjY2NCAkJGVZ0ggsAKKeyshI1NTVYuXIlAODcuXPQaDR44YUXMDAwgD179mD+/PnYs2ePS8br7+/Hli1b0N/fDwA4cuQIduzYgSNHjqjqJz8/Hzt37kR+fr7Z+5aOiNbOcydicY2+vj6Pjcn4EW7e75Yl0J2/Ll26RADo0qVLqtqJCRKKi4tVtTtz5gwBoDNnzqhqR+RYbdkrV65IyRU++eQTVeM54ogTHx9P2dnZNGbMGNLpdFRbW0u9vb0EgG666SYSBIGIiEJDQ+lrX/uaWfKS4OBgWrt2LQUHB5v1ycXuncO0zrdlClBnycrKoqNHj1JWVpYLJPVNMjMzKTc3lzIzM70tCuMhOMGIlwFAEydOVJ3kAADFxcU51G7UqFF228klPXBEQYlKWXypQVSy8fHxituIylyn09Hy5ctJp9NRZmYm7du3jwRBoIaGBiKST/fZ0NBAwcHB0jkiaickjkxgAhlT7+3BwUHKysqiwsJCysnJcbpv0wQotvDnvPCWiV2YwIcVs5fxVYvZNE2giD9YzCK1tbVmFrMnH2y+amG7I3uWWnJzc6miooKysrJcYjErxd154R39PTKMHKyYvYwjhRo8gbU0gf6MM9WC1OCrFjMASk9Pd3kKSjUMDg56pdiCuy1mR1ewPAWn7vQv1Og9dv5yA7GxsXjooYcQGxvrbVHMCAoKwqFDhxAUFORtUVyGJwrSA0BwcDA+/fRTn6t7ay97lifQaDTIyMiQrRnsTsaMGYOysjK3xVMXFxejpKQExcXFbunfWdasWYNHH30Ua9as8bYojIthxewGysrKcODAAZSVlXlblIDHUY9dX6StrQ2TJk1CW1ub4jaTJ08GEWHy5MnDjhUVFUEQBBQVFblSTEU0NTVh9OjRaGpq8vjYcjjy2cbHx4OIEB8f70bJHGfv3r147bXXsHfvXm+LwrgYVsxuICIiAh0dHYiIiPC2KAGPvSQt7o6xdQRrCTMSExOxYcMGJCYmumScxMRETJ482WX9qcHWqpFl/LknUPrZ+lOyEYPBgL/85S8wGAzeFoVxMayYmYDG1TG2tqiuroZGo0F1dbXN86wtvxcWFiInJweFhYXSe+3t7YiNjUV7e7v0nlLlUVhYiKqqKrP+PIWtVaMVK1YgMzMTK1as8Jg8cp+tHJxshPEJ3L7jLUOgO38xvsPAwADl5OS4xFvYWhhPf38//exnPyMAdMcdd5AgCDY9epU4rInhSJMmTTLzPBZ/O3PmzFEUctbU1ERhYWHU1NSk8mrdh+iE2Nzc7DFnxMLCQgJAhYWFNs/jgheMu2CvbIb5F1VVVSQIAlVVVTndl7XEF9nZ2bR27Vpat24dAaCqqiozj17xQX/58mUCQBs3bpSqN1lDTODx7LPPmnkeR0VFUVpaGgGg//qv/7Irsy9XBZML33MGW0oVAE2ePNlnPayZwIcVM8PQjTAeADRhwgSXPJDtWczf/e53pWOixRwbGyslVIFJUpbs7GybY1mWXxRpbW2lqKgo2rp1K/X395tZ3+Ix09+VPYvZmyF0rh5bLnmNGFJ18uRJRRYzw7gLVsyMYqxlxlKKnDJQS0tLC0VGRlJLS4vDfciRm5tL999/PwGghx9+2Km+LGNmrSlOS06fPk0A6PTp06osZqWY1vQWFXZUVJTi9q62Wr2JnMWsJgmJ0nvZU7HzTGDBiplRjLVc0kpxRBlYEhkZSVlZWRQZGelwH3IMDg7Sk08+SStXrqRXXnlFdXtTC9nyAW+aK9oWACgmJsZtS6jWLGZR9oqKCtLpdARAduLjC0ln3JkoQ00SEnv3sphkJikpSZoMMYxSWDEziglki5nIOecv0z1lRy3moqIiAkBFRUUOye8oouymy+e2Jj7Nzc0UHh5Ozc3NnhPyX6xatYoOHjxIq1atcqqfgwcPEgA6ePCgQ+3t3ctiWtY777yTLWZGNWr0nkDk+eK1bW1tGDt2LFpbWxEWFubp4RlGEV1dXViwYAGOHDliNU7aVxFl/7//+z8pQUZYWBhaW1tlz4+IiMDatWvx+uuvezwpSG9vL1atWoW+vj787W9/czgznSAI0v/d8Vjr7u5GRkYG8vLyfC4DHOP7qNF7quKYL1y4gPT0dNx1112477770NHRgd/97ndIS0vD3Llz8c477zglOONeBgYGpBq6jH2efvpp/Pa3v8XTTz/tbVFUI8r+wx/+EEVFRYiMjERFRYXV869du4bXX38d165d85iMnZ2dSElJwcDAAPr7+/H44487Fdt88OBBs39djTfTsnL95hGGGlPc1Bv12Wefpddee42SkpKov7+fWltbad68eS436RnXoXRf1FdxdtndHpZL6h0dHZScnEwdHR1uGc+diHvMPT09biswIe5vV1dXO7SdkZycTC+99BIlJyf7xF63L8P1m/0fj+wx//CHP6QTJ07Q8uXLqa2tjSoqKui+++6TPbenp4daW1ulV3l5uc8rZjH+9cKFC6orRX344YcEgD788ENVY5aWlhIAKi0tVdVu9+7dBIB2795t8zy5fVFHx9yzZw8BoD179qhq54xHK0z2S69cuaK6vT0sndAeeughioqKooceesjlY/kz165dG1aTGwCFhISo6sfZiY+nvaM//vhjAkAff/yx2ftqruPixYsEgC5evKgqmQnXb/Z/3KqYc3NzadasWTRnzhxqbGyk3//+9xQTE0OTJk2iQ4cOybZ55plnZH/IvqyYBUGghQsXEgDasGEDhYaGKm5reo1qAEAzZ8602s6ak5Sj4ykZk0j+oeDomKbhPY7I6sy12sPy842Pj6eZM2cqyrDlLwwMDNBPfvITAkDXr19X3V6MDbf2cga1yseZe0ktTU1NVq/T1PK3B/7lhId/JZ6xjLtmAhePWMy/+MUv6LnnnqOZM2dSd3c3tba20uzZs2loaGjYuSPNYnb0QWXPerUWVqTUYnZkTCL5ZTRBEAgACYKgajzRyiktLSWDwUD19fWK2/72t791icWs1JPc0qIJhHSNOTk5ZsrBFqZhTAMDA/Tkk0/aVMoA6Fvf+pYiOeRWbzIzM+mtt96i0NBQm5+xuOzd2NjoMYs5LCyMvvKVr3jNYmb8H7cpZtM4wz/84Q/0m9/8hubOnUtDQ0PU399Pt956q6K9rEDfY3ZX+I87w4psIWfJ1NTUkFarpZqaGof6NBgM9PDDD5PBYHCVmBL2FK+jsdeBYOHIWcwDAwP06quv0nvvvSf9fjs7O8lgMFBaWhrdf//9kkI3fX3yySeyyvrll1+WnaCbIufv0NvbS6GhofTTn/7U5mcsJkW55557PKaYfTHnOONfuE0xv/POO7Rw4UJatGgRff3rX6fOzk765S9/SXPnzqUvfelLtH37dpcLyAQm9fX1qi1mpdhTvI7GXgeqhZOTk0M7d+6khx56iBYvXky9vb106623Soo2Li6OoqKizJSvGO9szXL+4osvbI5pLQ7c2mds+p2JFnNcXBwZjUa65ZZbXPp5KMHePeTM/jk7wgUmnGAkwKisrCQAVFlZ6W1R/AJXJD0ZSXR3d1NycjKlpqbSD37wA3rkkUfsLlkreanh/PnzBIDOnz8ve1xusnX77bdTVFQU3XbbbR5XZPYmf5b7zmoykAVSmlTm36jRe1yP2Q+Ijo5Geno6oqOjvS1KwGMt1juQY8B//OMf4w9/+APGjx+PxsZGlJaWelyGGTNmmP1riVw95U8//RSTJ09GWFiYz9V3PnHiBHbs2IETJ06gr68PN910Ex599FGkpKSgqKgIgiCgqKhItu3Bgwexa9cut8VjM36AByYKwxgpFrOrcgCzxawOZ/J3W+59dnR00NSpU8loNNJnn33mtzHgtrCsmtXV1UWzZs0iABQWFkb5+fkEgCZOnGjXStZoNATcqKp13333Kb734aClTWTuDHb77bfTsmXL3JJ321EyMzOlfX3xxSUoRx68lO0juCoHsLMEcjWc6upq0mg0VF1dLb3nzFK2WMJxx44d1NLSQgBIp9NRcHAwGY1Gl1WF8nXuuOMOGj9+vOQHEBUVRd/61rfMlMvSpUuH7T1bvmbMmKFoPFvRAZb3r7X7OTU1lWbMmEHx8fFe/82Z0tvbK/vZcAnKkQUrZh/BnVVz1ODJeE9Po9FoaPHixaTRaFzWZ05ODl25coUiIyOlylChoaHU1dXlsjF8nejoaLvW8XPPPUdE1h3AlFjASvZeLe9fy79FRV1fX++TFjOR+Wfk6agKxjdgxcyYMdIsZmcRK1I1NzfTxIkTad68eSNKKRMRJSUlDVOwWq2WFixYQADo1ltvpZ/+9KdERHTq1CmHvbOV1EuWK22ZkJAg3c/+MPF0NryQ8X9YMTMuwVcsfk8i1tzt6uqioqIiam9vp6KiIrMQlpGQHrGzs5OmTZtGAOijjz6Skuz09/fTT3/6U/rZz34mLetbu0+OHz9uFnLV2NgofbYiSr2Vxc8/KiqKvvvd75JGo7G7tM0wvgQrZuKEAK7AdI98pHyeYs3d9PR0GhoaoqKiIhoaGjILYRkpBQVef/11SbHm5ORQfX299PfJkyeJ6IZSFN+bMmWKzT3n0NBQ6bNVi/j5z5s3jzQaDb344osetZBN46vVhD4xjAgrZrqRQu+JJ56gsLAwl/c9UpSUqSXkzs/TlzC1mE0ZaRYz0fC9Y4PBYPZ3d3e3qrhmOYtZKaafvzuT01jDNOubkuV3hrFEjd4TiNxQUdwOagpGO0pzczPi4uJQUlKC8PBwl/Y9duxYPPLII9i3b5/VwvOBhjs/T8Y3EQRB+n9YWBiuXr2KCRMmAAAeeeQRNDY2Ijc3Vzpn4sSJqKurk/5ubm7G8uXL0dnZiU8//RQhISFWx+rq6sKcOXMwefJkvPPOOwgKCrJ67uzZs7Fp0ybs2LEDp06dcuYSFdPR0YFZs2YhPz8fRISUlBQUFBRgzJgxHhmf8X/U6L2ATTASHh6O1tZWtyiRkpIS7Nu3DyUlJS7v21dx5+fpq7S2tmLChAnS5Ku2thY6nQ7nz5+HXq+XlFBHRwcSEhLQ0dHhTXFdzkcffST9v7y8HJGRkejv70d2djZee+21YQkwjEYjcnJykJ6eDiLCuHHjcOTIEZw+fdqmUgaABQsWgIhw+fJlq4lCurq6MHv2bBw6dAh//OMfQUTo7u622W93dzfmz5+P7u5uDA4OYvfu3RgcHLR77W1tbZg0aRLa2toAAENDQ+js7ERraytSUlIQExMDvV4vnV9TUwOtVouamhq7fTOMPQJWMbuTkaikRiIJCQlYv349EhISANzIwJaRkYHbbrsNy5YtkzKxzZo1Cxs3bsSsWbO8KK1tLBWNEu666y7Qje0uaYav0+nw1FNPQafTISgoSFLU/f39OHLkCHbu3Im8vDyb/ZoqS+CGwhWV7C233GI149WCBQuwadMmLF26FCEhIXjyySeRkZFhc6yMjAxs3LgRGRkZ2LNnD+bPn489e/bYvfbExERs2LABiYmJZn/HxMQAAIqKirBixQppUjZp0iTcfffdPpOdr6+vD48//jj6+vq8LQrjCO5bUbeOO/eY/SUBvBiSMzAw4G1RHMK0fF2gYlnNSwx5OXfuHOl0OqqtrSUi/yhu4Uw2NFdj6mBHdCPn9Zw5c+j222+32c7U+9qaL4Alpuep+c1ZJqkR/66oqCCj0Uh33HEHdXd3S3vPN910k82wPTnPcXd6k48UB0V/YkQ7f1kmgL927RoBoGvXrqnqx5nqMKdPnyYAdPr0aavniEkscnJynB7z3LlzBIDOnTtn9RxrDmuOTmTgRApFRzl27BgBoGPHjnlszEDBUtEouWfchaVSXb16NcXHx9Pq1as9LouzKJ2UycVauzP+eqQ4KPoTI1oxWyoaADR9+nTVCsSyOowa8K80jrbGlJu9OzomABo9erTN8ax5VTtaycYbitkbYwYqSu4ZT2FNiVizKJVay3J4Kzbf0xYz43uMaMVsia9azK4c0xsWs6PX6AxsMbsOb1rMSrFmUVouhavB2fz11iYRtbW1ZtsbDGMJK+YAYCRm3fIGXLvZc6hdXvVFi9na3q1Op6Ply5eTTqdT1I+/+MIwroPrMXuZoaEh5OXlYWhoyOE+1qxZg0cffRRr1qxxoWSMJZbet4z72Lx5M1avXo3NmzcrOj8kJASnTp0aFmoVHByMTz/9FMHBwaplMBgM+Mtf/gKDwaC6LQBs374d+/fvx/bt283er6ysxHvvvYfKykpF/axYscLjNaQZP8IDE4VhBLrFnJubS9XV1ZSbm+twH2wxewZPWsymKT79EUf3REULt6WlhR2S/gVbzCMPtpi9zOLFi3H+/HksXrzY4T6cndkzyggLC0N1dbXbMtCZcuHCBWzevBkXLlxw+1juQIwjXrBggap2Yizxfffdh1dffRWjRo1yk4S+jxhfrNFocOjQIZsZzpiRCytmN6DRaJCRkQGNhj9e5t9s2bIF9913H7Zs2eJtURziyJEj2LFjB44cOWLzPDFDV1dXFwAgLy9PUeKRQObKlSsQBAH/8R//oWo5nxmZsOZgGA9x8OBBvP/++1YzW/k61vZ8LVmwYAEefvhhjBkzBm1tbYr3hA8fPgxBEHD48GFXim0T0YLt6urCCy+8gOPHj0MQBFU5uC0zmckxffp0xMTE4M0338T+/fvx/PPPY/To0WhqanLFZSiCs4H5ER5YWh9GoO8xM87jjOct4106OztJEAQCQDfddJPidvBCnLroZZ2amkr5+fkOyaAkfKuoqIgAUFFRERERhYaG0oYNGyg0NNTpa1AKZwPzLrzHzLidnp4eLF26FD09PW7p3zTHMeNfhISEgIig1+tRW1trdsxWzu4PPvjA7F9n6O3txYMPPoje3l6b54le1keOHMF7772Hzz//HABw8uRJq20sLWQlS/U333wziAg333wzAKCsrAwHDhxAWVmZ2ktzGGse5YwP4vZpggxsMfs/X/rSlwgAfelLX3K6r+bmZgoPD6fm5mbpPbaY/Rsx4ca1a9fMkuZ4Kme3mkQiau81tQlO5PrnlJkjD04wwrgdWFnyc+SBEx4eTt/5zncoPDzc1WIyXsYyzawYnpaQkODWVJRqwg1doWjV9q90WZknqIEDK2bG7Vy9epUA0NWrV83ed2QfS85iZgIDuTSz7ize4AjuVn7OWMzOpB9lfAtWzB6mtLSUAFBpaam3RfEY1h4svETH2MPZ4g3+YEW6SkZ/uFZGGez85WGmTJmCmTNnYsqUKd4WxWNYS684atSoEZ9EgpFHDEU6f/68orAry3hoEX9wDLQlY2dnJ1JSUtDZ2Wm3H2fSjzJ+jAcmCsNgi9n/8UXLmCv8+DZQGYpkbcnbH6xIWzI6U1KW8V94KduEjz/+mADQxx9/rKpdVVUVCYJAVVVVqsf88MMPCQB9+OGHqtrV1NSQVqulmpoaVe2OHj0qPfAaGhpUtXVEmdXW1pJGoyEAFB0d7ZF83g0NDQSAtFot1dXVyZ5jrcLP8ePHCQAdP35c1ZhvvPEGAaA33njDYbkdpa2tjYxGI7W1tXl8bEexV7b0888/JwD0+eefK+ovkOoVm342zpSUZfwXVswmqJ2liwiCQAsXLiRBEDw2plarpXvvvZe0Wq3D4wUHB6tqq7ZcndjGdMwlS5aoGtMRgoODpfH0er3sOdYmGY5+H462cwVGo5GeeeYZMhqNHh/bURITEwkAJSYmelsUn4OtZIb3mE34+OOPzf5VSmVlJY4cOaK4jJspH374odm/asbMy8tTPebRo0el/5eXl6seU025OrGNaR7wS5cuqRrTEcTr0mq1VmWdOHEi+vv7MXHiRLP3jx8/bvavUt544w2zf51FTTnQgoIC7Nq1CwUFBS4Z2xMUFhYiODgYhYWF3hbF5zhx4gR27NiBEydOeFsUxg8QiIg8PWhbWxvGjh2L1tZWj1T1YdxDe3s7UlJSUFBQgDFjxnhbHJ8nLy8PM2bMwPnz533acclRqqurER0djcrKSkyaNMnb4riFnp4erFixAgcPHnSqMlRHRwdmzZqF/Px8jB492oUSMr6KGr0X8BbzSKCjowMJCQno6Ojw6LhjxoxBWVkZK2WFuKIcqC8zadIkDA0N2VTK3rpXldDf348tW7agv7/f6jkrVqxAZmYmVqxY4dRYs2bNwsaNGzFr1iyn+mECE1bMAQD/yP0DLgfq2/fq1q1bsXLlSmzdutXqOQcPHsSuXbucrhCWn5+PnTt3Ij8/36l+XMXg4CB2796NwcFBb4vCgBVzQOBrP3IiwpUrV+CFXRKnsFVggXENvnavmpKdnY233noL2dnZVs8JCgrCoUOHnFrGBoDRo0ejuLgYo0ePVmSpu5s9e/Zg/vz52LNnj8v6HBgYwI9+9CMIgoDq6mqX9TsSYMUcAJj+yH2B4uJiTJo0CcXFxd4WRRWJiYnYsGEDEhMTPTKeJ6wUNQ5nnsDX7lVT9Ho9tm3bBr1e79FxlVjqrsZyS2HdunX49NNPsW7dOpeNsWXLFmRnZ0Ov12Py5MkYGBhwWd+BDitmxuUkJCSguroaCQkJ3hZFFYWFhcjJyfGYV7E7rBTLcoeHDx/GjBkzcPjwYZeNwbgW0VL/r//6L9n9dzEDWnt7u8smcpZbClqtFo899hi0Wq3TfYv8+te/hlarlVYCbrnlFnz/+99HRUUFdDodamtrfWK1wCdxc+iWLIGW+YthHGFgYIBycnJoYGDAZX1aljscHByk3NxcGhwcdNkYImfPniUAdPbsWVXtOEObPPHx8ZSdnU3x8fHSe21tbaTVaunFF18ko9FIV65cod///vdOJ15pb2+n+Ph4am9vd4Xospw/f94sFwAAuvfee6V/tVotZWVl0f/93/8RAKqsrHSbLL4AJxhhmBGKmnKHjiI+1AFQSEjIsAQs9jJbOZLUxhdTwLoaOWVpNBrp//2//0darZba2tooJyfHY9W5+vv7KTs7m/r7+53qx1Qxf+9736Py8nLp78LCQgJAd9xxh1cS+XgSTjDCBDx1dXXQ6/Woq6vztig+hcFgwF/+8hcYDAa3jSEugwI3llnPnj1rdjwtLQ2bNm1CWlqabHtHktpYK5piSk9PD5YuXYqenh7F/foScvvvBQUF2L9/P5qbmzFmzBg89thjOHLkCHbs2IEjR464VZ5t27Zh2bJl2LZtm1P9XL58GQBw/vx5aLVa3HTTTQAAnU6HxMREREVF4bPPPkNlZSX+8Y9/QBAECIKAqqoqqY/8/Hzp/WPHjqGxsREhISFobGx0SjafxQMThWGwxcw4Ql9fH2VlZVFfX59DVpda/KFYgjewtwxqz2J2JAe2Eot5yZIltH//fo+kiPU0nlh6tsRVFrOYXjY0NJSOHj1KWVlZdPPNN0tW84wZM+j222+nvXv3Dlv6FrF8X0zRqzYFsTfhpWwmIMnKypJ+2OI+5bp166ivr88t43GRevfgrqXY7u5uWrJkCXV3d7u0XyW4e6ldbv/Z07S2tlJUVJTq57ZYkKWxsVGaWIuTtylTphAASk5OHqZ8Tfecz5w5I70vFu0RBMGvlr9ZMTMBianFTGSuqG3hqAMUW8zuwR+rRtmT+dFHH6XJkyfTo48+6pbxXW0xy12PPf+EiIgIuu222ygiIsJqv93d3ZSRkUHf+ta3pN+prc/OVBGLFvPevXut9v/3v/+dANDu3bvJYDBQfX290kv2OqyYmRGBpaK2Rm5uLlVXV1Nubq6HJJNnaGiIioqKaGhoyKtyMOqxZ+UnJCRQWloaJSQkeFQuR8uDyl2PpUe/JevWraPRo0fTunXrrPa7ZMkSuvfeeyktLY2ysrKop6eHQkNDKSwsjGbNmkVE5r/bkpISAkBf//rXFa02yC1z+wusmBnGBFeEDImepAcOHCAAdOzYMdV9FBUVUXt7OxUVFTksh7fwRyvXldi7fm/VWHa0PKgjFrOS5XpLi3nVqlUUFhZGer2eJk2aRETmK12dnZ0UHh5O77zzDq1bt87mUnl3d/cwxXzp0iUCQJcuXVJ1/d6AFbMXqaurI71eT3V1dR4bs76+3qPLOg0NDRQcHEwNDQ0eGc8XAEBz5sxxasbuzxazI/vCju5JMsqRs5i9MUno7Oyk22+/nW6//XazrR9xQnvTTTdRe3s79fT00Ne+9jX69re/TX19fZSamkq//vWvKTw8nCIiImjr1q0UFRUlO8aSJUvoxRdfJAB05coVIrrxu5w4caJfWNCsmE24fPkyAaDLly+7fSwiIr1eTxkZGQSAysrKPDKmwWAgAGQwGDwyXnBwMN15550EgE6cOOGRMb2NKyxmEXcm/XAXjljMUVFRsg9aR61vpQpHzvLzpmOYK1FyHcnJyfTSSy9RcnKy9J5afwmln3V3dzfdc889JAiC5CkNgJqamojoxvNw+vTppNfriejGcvm+ffuk81asWCHdC/YmcnLXzhazC/GkYgZAUVFRHptR1dXVEQAyGo0eG9PT+y4NDQ1+vdfjbXxlz9vdWHvQpqam0ksvvUQ6nU6VM5OcwpFDbq/Ul0KpnJmYKbkOOaWqNsLA9LMWJzoNDQ0UHx9P06dPlyZVS5Ysofj4eEpMTDR7JoSGhpJer6d169aRTqejH/zgB0R0Y9Kk1+tH5PODFbMJlhazJ5YTy8rKPGoxV1dXk0ajoerqao+MR0R04sSJEWUxuxJvWMw1NTWk1WqppqaGiIgKCgqkB6PSJU9XLZF2dnaSTqej//7v/x4W/mO6NGup2D1pMTc1NVFYWJhk+TmDpUe13MRMqde1o5a/MxbzrFmzpHtFzPR2++23U3d3Ny1cuJAWLVpEo0aNkpaUxdfKlStJr9cPi4UWn//iq7q62uMx2t6AFbMN/NkBRw3e3s9U8qBx5cOPkUecBGi1Wik/MZH5KktSUpKivpRarEqwdn+YOjNZWwq3hisnPGFhYfTEE09QWFiY031ZxiDLyekLccqWiMrZMr44PDycbrnlFlqyZAn96U9/osjISGppaaHg4GBKSkqS9oBt+dq8/fbbBIBSUlIIAD3zzDM+de3ugBWzDbytsDyF5QTE1ddtrxCBkgeNKx9+3sJ0v9QX7y3ROtuzZ4+ZxWz6oFWyvNnR0UFJSUk0ZcoUuxarM/Hf4l5+eXm5aucxR7cI5OStrKwcluTCUZRMUl0Zp+yqfOniRMz0XklLS6OkpCTq7Oyk7u5uioyMpL/85S+0atUqWWu+sbGRQkNDqbGx0axv0z6zsrJIo9GwxWzCiFPMjuCLD1x7WMrs6pUCeykxR4rFbOqt7IrPePfu3VICBVdgzYq8fv06AaDp06fbXRatqakhAPTjH/9YkbUcERFBAGwmorBGZGQkZWVlUWRkpOq2jlrMcvuv/pT1zXKSbC8eWWlK1draWkpOTpYcrADQH//4R7PPxNokQJzsGAwG2rBhA4WGhpodN1XMoixKJkOuShPqDVgxu5hAWP72tMU8Erhy5Yr0cKmpqXHJZ+xqp5ji4mICQMXFxQ73odVqadGiRYr3o525hpaWFmlp1B3I7VPLWcz+lPXNcpJsz2K2t5olt2WRnp5OL7/8MoWGhir6TMSJzZw5c+xazKbvpaen27xvsrOzKT8/n7Kzs+3K4GuwYnYxzjxw/dHaZqxjapWZPlzUJniwhlKLWbR6SkpKbMaUA6C4uDinFL2l45iItaXmDz/8kADQhx9+6PCY7sLWPrm//lYtJ8lykxvRWRMA1dbWSlaq3ARE6eTFFvbO//nPf04A6Oc//7n0nhKL2d4qmy9b1KyYXYwzezaBYG3L4Y1qN+5E6UPZdB/T1GKWS4no6kQspvehaPXILQma4gqL2dpnY805y97e8PHjxwkAHT9+3GGZ1FJaWkoA6OLFi1Y9u/39typuT4j7tqbbAab3iUajodbWVurq6qLQ0NBhy9OewNaqii2lLtdO9POoq6ujyMhIOnbsmE9a1KyYXYy9PRtb+PIs3JnwF1/0InUGpQ9lNfuYwcHBtHbtWpeVpjO9D8WJkemDyl3fh7XPxpoCtudN7Y0YVgA0c+ZMm2P68m9VCaKHPQCbFvOWLVsoKipK9fK0q2WVuwdaW1tJq9XSyy+/LDtZsLzf+/v7JT8P02Ovvfaa0zK6yolOhBWzi3H1F+QrqAl/sbT+RqrFrAZ3WswiopV0/vx5t30faj8bUWFXVlaaySRaNn/9618JgEcTrIgWc2lpqcfG9DTivXD9+nWr57S2tlJwcDBFRkZSdXW11/bRTZWoaBi0t7cTANq4cSNptVpZubZt2yZ5h4t7zeJ9ZRnW5SzOGGRysGJmhtHc3Ezh4eHU3NwsvafGYna19RdIKK1y5Wra2tooJiaGAHg0uYwcctaz5aqKaNkIguCw5zWR4xWVfAFrsrv6msTvo7q6WgrnE4tQOOP57ioaGxsJAP30pz+VDIP4+Hh6+umnbeoGcf+8oaFh2F6y6VL+G2+8YXXsoqIi6byjR49aPY8tZsbthIeH03e+8x0KDw93qP1ILFyhFKV1oZ3FMk5UTPsqpjj0ZuUnueXr2tpa6QF45coVybKpqqpyyvPa0YpKSjF1nhIVXFlZmUu8xa3JruSalCgKUV7x+9BoNLR9+3aaNWsWGQwG+utf/0qrVq1yq+e7UiwNAyWrcK6YVNiyrN1ZRY0VMzMMOYuZcQ2espgt8ySbWszPPPOMqspPSqmoqCAAVFFRYfM8OYvZcnnRVbjDYhaVgrjkvWHDBoqMjJQUnJxDlSM4YzErWVoV5Q0JCTGzmMPCwigrK4sMBoOiuse+irPhdP39/bRmzRqrFrMjVdSUwoqZYQIQ0WKuqqoyCxlx5yzfGcXa2dlJkyZNkixmX8b0OletWkUA3GIxO4MSi9naREpu4mR63/hKmJG1TGGuIjs7m86cOUNZWVmyPhN+aTGfP3+e7rzzTlq4cCF95Stfofb2diovL6fly5fTXXfdRc8++6zLBWS8izcKZDC2kUtlai3WWAm2lhAtFbP4gG9pafFrD2ZLLJc33aUY3I21JfGBgQHKycmRCuyIjnmhoaEUHx9Pzz33HMXGxtJzzz3nFrmUboWFhobKZgpzBdXV1QSAfvSjH3llAuI2xWy6VPfss8/Sa6+9RqtXr7a7zOWMgIx30Wg0tHjxYtJoNN4WhfkXckkWLItUqMFW6JvlxExcKo2MjPTrmF9Lbr/9dkkp+/N2j7Ul8ZycHCnuXsyuFRkZSWFhYRQZGUnTp0+n++67j6ZPn+4WuZQ6j7rTYtZoNBQREeG1Z5lHlrJ/+MMf0okTJ2jevHn08MMP0913323Vw62np4daW1ull5io3hOK+fDhwwSADh8+rKqdM5aikrAFV475q1/9igDQr371K1XtlIxp7fiPfvQjafapBmf2upUkyygpKSEAVFJS4vSYVVVVJAgCVVVV2TxPLpzI0THPnz8vhT9Z0tjYSIIg0G233SYttYnZksT73JGC8VevXiUAdPXqVSKyHVpkajF/97vfJQD0/PPP213mPXPmDAGgM2fO0D/+8Q8CQP/4xz/Mzvn8888JAH3++eeyfRw8eJAA0MGDB83eV7I/u2fPHgJAe/bskT1urZzi+++/TwDo/fffN3v/iy++kBT52bNnh/Un3qvAjcQvanIF1NfXk8FgoPr6elUOc2JOcwB06tQps2NyFrO4X1tRUUHjxo0jANK4rsa0hvtTTz1ltjohjtfc3Gz2/q9//eth/SxYsIAA0IIFC4YdO3fuHAGgjz76SHEaUBHx+8rPz6f4+HiqrKwkQRAcXoWSw62KOTc3l2bNmkVz5syhiooKCgkJofLycqqurqY5c+bItnnmmWeGLRV5SjE7ukemxFKU248Qb8Bp06a5ZUw5nNkH9PSYzniHA/bTSwKgW2+91ewcR8cUBIEWLlxIgiDYPE8uAYejYwKgsLAw2WsMDQ2l8PBwAkCpqalS3KeYOMPRSl2WuZZN+7Qnq/iy5xgFgEaNGmXTI9bePWXtuBKPZkfvVyWyyvVpeXzs2LGKxzQYDPTwww+TwWBQFWKm1WoVXaflXrWlnAaDwaGaz/awdg8AoIaGBgoPDyeNRqP4c5U7Nnr0aAIwbDncUunLtRWfLZYZ9RxZhZLDIxbzL37xC/r5z39Os2bNkt674447ZNfuA9VilvPgCw4OlhL+B4LFbA22mP+Ntyzm2NhY6eExfvx4hyt1WeZaVpqM43e/+13AWMzWcIXFbGo5K0kA4w6L2ZRZs2YRAOnZbSqjOBkw9f63h7XVBksuX74sq5TvvfdeCg4OHqY8/+u//mtYH45azOKE1ppi9muL2dQb8A9/+AP95je/oUWLFlFLSwt1dHTQ7NmzXS6gL2PNYuZ4X8YTBAcHmz1snM137KowJH+qzGSKq0MKRQ9pABQUFOQzKWwtFdTHH39MAOjjjz8mIuWKVsQyjM8eUVFRNHHiRLrpppskOfLz86Xj9krKOoIvhIu6TTG/8847tHDhQlq0aBF9/etfp87OTjp69CjNnz+f5s6dS++++67LBWSYkYiSsI2mpibpwZacnGxTEdpKqylOJqOjo12SuENtLeO6ujrS6/VUV1fn1LhqsXSiczYJjy18KYWtKwqbmGKpyMX97IGBAdnzRT8Fa9sBgVpSluOYGcVwOJRvYi3RgekypxpsFekQPWYBeMVi1uv1tHLlStLr9U6NqxbLsDNfsKqsoaZ4ir3qXu5G9ADPycmxeZ6YlhMAlZeXe0Y4L8KKmVEMh0P5JtYsZlPHIDUosZiVbL/Ys/wcyczkCYtZTunaq+3rCZTmqzctN2oN8bsZPXq0bHWvrq4uuvPOOyUHLLGYyEcffUQAqKyszObET+mES7SYq6urvbIS4quwYv4XrvrheeoH7I0HBVvM/oWjFrOrxh07dqy0Vyq3XKk2l7GnrDtby9TNzc2k1Wq9YmUqrfCmxGI2jUeXu5b09HSKjIwc5pAGQEqfamvip3aLwlsrIb4KK+Z/IZchyZv9+Mo4DKMW0wd2fHw8/fa3v6WjR4+SIAhme4FqLeaoqCj6wQ9+QADcUi3qD3/4AwGgF154YZjFLHqfAzBzpLM3OXblZMKZmuiWiBZzZWWlrHzWLObc3Fxpz1mcgFVVVVFWVhY1NzdL8qndovCW74Al7k7zqRRWzP+CLWb/x1d+VL6O5UPQNDzJHmfPnrUa9iNSX18vG+py3333kU6nU+XYJC6dfvTRR9Kz4Pvf/75bqkVZynvo0CG6cOGC9LcYry3GAIuVumxNjk0raVlTqs6kSJUL+VOCmNkLgOxStjUyMzMpNzeXMjMzpffEimnh4eE2LXqlytpyu2TXrl0EgHbt2jXsXFvbJeIkQk0tbzHNp/jZHD9+nIhcOylSAitmJmBwZ+7cQEJUKOKyoa2YTUsAUEhIiN1z5RSzTqejMWPGqAoFspTNXbWIW1tbJYvZ1kuM125vb6eIiAjFFnNMTIxVpWUtRaqSrQhgeJIcJQCQYtvVWPRinWbTqlNixTRTi1kOe8vborFhmZLT1v1pK0WsmvtaRJzcW7ZVuo3gKlgxBzC2nHgCEbaYlWH60BF/X0ofYEosZssxxNfly5dVhwKZWszOIrd0Llcb+tChQ5LF7MjDXQ5bFpc1i9l0S8Dakrg9i3lgYIBefPFFCgsLo6tXr1JYWBhdu3ZNuia1lbzUxi2bYs9iFrfnxO0CJRazeC3Xrl0bdswRi1nk+PHjVi1meyFeroAVcwBjK+zFX1G6hO8rpelcjdolNbnJWUFBAQGggoKCYTGijvQnh7XMTRMmTPDYcqAlcs5m9vaA1Xzerow/bm9vl9Ktiq9vf/vb0gTCUi7TsU2P5eTkDFt2B0CPPPIIAZCNfe/r66O1a9eSVqul4uJis2tasmQJ7dy5kwwGg+x1dnR0UFJSEj388MPDajnbU+rWfttyClb8fYsrMXL3rrsUqNIQL2dgxRzA+IrF7MoMZ0qd3rKzsyk/P5+ys7OdHtOXULOk1tnZScnJyVRbW2s2OTP1gG1tbSW9Xq/4+1Ez2evt7R3m2QvAY8uBlphazO5I4mFrWdUWcpak6AFt+frNb35DkZGRdMstt5jdB6Zjm94jly5dGtaH6Lz2q1/9aljsO9GNPWONRkOTJ08mAGbX1N3dTQaDgX7yk59I77W3t9Po0aNJq9VSYmIipaamktFoNNuHJlKW9Uvus5CbOIq/b4PBQAAoIiJi2PfpLgXKFjOxYg4ElJZxUwJbzMotuNTUVHr55ZcpOTnZbHLmqAdsf3+/lPdcTcicpXI4d+6cqnHdgaNK1BaOKntx7zUtLU3aimlvb5eK21i+srKyKDg42K7FfObMGbNlawCUmJhIwI0kHdayxdmymFtbW2n06NFm71lOIhy1mE0/C9N9aFsWc0NDA0VGRlJkZOSw71OpAvVWWKEtWDE7gCdmTIEE5wR3HkeUqZJUnWoQlw5vvvlm1VV0xOpW48ePJwBeT6HoS2kvRStRo9HIOi+ahmo9//zzw4qliOUZz58/b7aHPjQ0RDqdTrJ8LYtrOCKj3J68qcUs7pM7+ox0NHe6M9+no4l43AkrZhnsfcme2GNgGFPcmYBBaYWo/v5++q//+i8C4FBoj1jLee7cuaqKDgTq6oclls6L4tK7uC8s7qWK6VdFz3BLa9p0D90V8cFiedpt27bRjBkzFHlx+9Mz0p7F7A1DjBWzCeISkL1lLraYGU/jzgQMgLKayq7AkaIDgeovYA/RWU2r1dKYMWMIAFVUVEirIPHx8ZJTlxj+pDbFqRKCg4Np9erVBECxJRtIz0hvTDJYMZsg7r9ER0d7bJnL0QQBjiYlsFXD1104WufanygsLCQAVFhYqLqtkjAvd4aCiRbz+fPn7VpDn332GQGgzz77zG6/rppMOGIxV1ZWEgCqrKxUPZ6tFQRPJpqwDO8SV/KqqqrIaDTSpEmTaNy4cZJiFvf9xfNqamoclrWnp4dWrVpFPT09ZltR4qrH1atXVfdpyzK1da88/PDDBIAefvjhYcdsbdeIqztytZrtrYreeeedBIDuvPNOtpjl8KRiNvVY9BSAYwkCrCUlUDKeuDTmKVwVC+rLAJD28tRimRhF7kHgieQpcvuHplgWp7eFuL/5ta99zS3L7/YelsCNmtOOfB+2VhA8nWjCFHElTxAEioiIoPDwcAoLC6OQkBBKTEyUvjfxPK1W67Csq1atooMHD9KqVavM3gf+nTNb7STF1l6ura0aW/ectcpq9trZWxX19jOLFbMJ3ijSwBazdXp7e2n9+vX0ne98h/r6+twknWtwpcUst3TmieQp9mJ6w8PDac6cOYos5qKiIrp27RrpdDq3LL/bW14MBIvZEkuL+eabb6ampiZasWIFhYeHS9+bKy3mhoYGM8vS1GJWO0mxZTEXFRURANkwvAceeIAA0AMPPDDsmK3v6v777ycAdP/99w87psZi9gasmBmfJTMzk1auXEn33XcfZWVleVscj+HupTNHJ6BqahC7O4Y+kPYwfRlblqUrJym2KnrZSuVpywr353uEFTPjVWyVqPMni9mfcHddbXFP8tq1a14pj+hKrl+/TgDo+vXr3hbFJajdr/dUWJncpE98r6qqymoIlS/GILsCNXpPA4ZxMYcPH8aMGTNw+PDhYcdGjRqF3bt34ze/+Q30er3iPvv6+vDYY4/hgQceQG9vryvFdRoiwpUrV0BEXuursrIS//znP1FZWam6L3vn/e1vf0NkZCS6u7sxbdo0bNiwAYmJiark8yWmTp2KpKQkTJ061duiyFJbWwudTofr169j9uzZKCsrQ1BQEBoaGmTP37ZtG5YtW4Zt27bZ7LepqQmjR49GX18fiouLMXr0aKvn1tTUQKvVoqamBgDQ3t6O2NhYzJw5E93d3TbHaW1txYQJEyAIApqamjBu3Djp2LRp07B27VpMmzbNavtjx46ht7cXx44dszmOWkpKSiAIAkpKSlzar1tw4wTBKmwxBzZKirqrJTMzk+644w665557hjmveBtX5i/3Rl/2zoOJ04xOp2OL2QHUFIoQnUABSPv/cku7ood3Q0ODIotZjbOh5QqM0WikqKgoCg8Pt1pJSkQuf7mIaDHPnTvX6lK26f3mSvCvDGZeUnu8lM0EHuIS+PLly6mnp8fb4pjhyr1Xd/clt7xob8yDBw8SANJoNF7P7uWv2MspLToavvrqq2aKSaPRSP9/+umn6ZNPPpEyfgmCQJs3b6bIyEhFy9lqnA0tfRbE0py33Xab3bhnuYpflphmA7NMyWur8pQzeHsLgxUzw/gpapyxTFHq/CXnkONIysT29naKi4sjAFRWVqZK1pGIPWUFk9A8pa9f/epXJAgCtbS0+GTCFlvxyKaRJGIRmzFjxsjWbjYlPz+fAFB+fr7NseU8wpXm5HcXrJgZt+Pq4vbMDWx5stpCqfOXnOK3V+xeLuRKjHu1Vp6PMcdaDLGINYs5PDxcVim///77ZkrPF1Oc2otHFnMviAoTAK1cuVK6RtM8/OLEBgAFBQXZvecAUExMjNl5SqvYuQtWzIxbaWtrM3tIuKqgAuN+i1kOexaztSIHI9liVrvKYJp1Syli3Lbp68qVK3bbubrQiaMotZhFmpqaCAB95StfGZaURNy3Fj8HtpjdACtm/8ZoNJo9LORmxEzgYGox+0o9cG9jb5XBUUyTfcAk09nChQspKSnJrCTk1KlTSaPRDFM0ppaq+N1VVVV5LYmKHNYmoNbSeIrVtvx5EsiKmXErphbzhAkTvD4zZ9yPmJXuyJEjLvMa92ccLWVojStXrki/KXGbwDTTmWVGLvEccV/WFFNLVVztEATBa2lH5VC7ZWMwGEiv16su4+iJ7HpKYcXMMIxLMc3jbmoxO7JEywwHuFFJSlTOlgUlLDNy2bKYTfE3i9ka4h602v1hT+SjVwonGGEY/DspQnt7u7dF8WkaGxsREhKCxsZGq+dUVlYiLy8PlZWVuPnmmyEIAgBgzZo1ePTRR7FmzRrF43V2diIlJQWdnZ1Oyx4oXLlyBWVlZVKiF1sJOABgYGAALS0taGxsRHh4uNXzwsLCUF1djUmTJqGgoAChoaFobm7G2LFj0dzc7OrLMKOtrQ2TJk1CW1vbsGPjxo1DU1MTQkJC8Pjjj6Ovr89mXyUlJQgLCzNLDlJfX49Ro0ahvr5+2PniPX3mzBkcOHAAZWVlTl+PJ2HFzAQsKSkpyMzMREpKikfG6+3txYMPPuhzmcnsYTQasWrVKhiNRqvn3HTTTRgYGMBNN90kvdfU1IT3338f//u//4u9e/cqHi8tLQ2bNm1CWlqaU3IHEpMmTUJycjImTZoke1z8zObMmYOEhARMnDhRyqBlS0HJERcXh0ceeQRxcXEuvILhJCYm2s0St3nzZqxevRqbN2+22Vd4eDhaW1vNJiHR0dFYvnw5oqOjh50v3tMzZ85ER0cHIiIiHL8Qb+B+A344vJTt33gq166zeDqky15IjK9iWptXDY4uE3qzmpOvYq+qk/iZRUVFUXZ2Nk2bNk1aCrZVXlEOV3on24rPtlfZjOhG4qDMzEzq7e1VNJ7pvWqr3rOj97Q74T1mE86ePUsA6OzZs6ra1dbWkk6nU53pqLa2Vtonunz5sqq2jpZ9zM3NJQCUm5urqh2RY9c5depU6RoLCgpUj+kong4DsVV+Tg5X7LcquUY5z2hnnJEc/Vw3btxIAGjjxo3SeyMpvt2VscOmk5XPP/+cANDnn38uHRcnw+fOnSMAdO3aNemYqKBWrVpFAOib3/ymqrEdfe4Q2U6faZma11SR/vSnPyUA9P3vf3+Yc5at+1GMm9fpdKplPXDgAAGgAwcOyB53tzJnxWwCAAoJCVGdBEGn09Hy5ctV3wDijWPtZrWFqYONGhwdj8ix6zQdz5OLLlOmTCGdTkdTpkzxyHgAaObMmR69RltJGUTkcls7Gr7T2NhIAOj55583G1OJtSN3DxiNRnrmmWfIaDSqksMfcVe2LbnPVSzViH8l4ZCzjj393LE3Zm5uLlVXV0sGg6llb9rOctXFXmISR5879trayjjmClgxm8AWs20cuc6GhgavWMzBwcE0atQot/1wLFFrMbsCJdar3FKkoxZzaGgorV27dliiGLmkIpb88Ic/JAD0wx/+UHqPLWblWPvObFnMYkrK4uLiYf1985vf9LjFbCu+2B0Wc0FBgcPPHbaY7cB7zIwj+OK+kTdwNG2nHNbiPK1ZzI5mJmOGY22V4+LFiwSAPvvss2H7t0omTN6E7w/rqNF7ApELisiqpK2tDWPHjkVrayvCwsI8PTzDyFJZWYmYmBhUVFTIenr6Ci0tLZg2bRquXbtmVuvWE0RERGDt2rV4/fXX0dTU5NGxA4nOzk7MmTMHBoMBx44dQ3BwsHRMDEUDgKysLPzpT3+SPK7b2tqQmJiIwsJCn3x28v1hHTV6T+chmRjG54mJicG8efMQExMDL8xXFSPGgHqDa9euSZMCxnHS0tLwjW98Azt27DBTypb86U9/QnFxsfS3GJfsq/D94Ro4jplh/kVFRQU+//xzVFRUeFsUn0WcFHjaUneWjo4OJCQkoKOjw9uiAABOnDiBHTt24MSJEzbPq6+vx9ixY1FdXQ2NRjNMKVu7roGBAbzwwgsYGBhwWtampiaMHj1a0WTQX+8PX4MVM8P8i+joaBCRTy9jM44xa9YsbNy4EbNmzfK2KACA0NBQKROXHJZWdHR0NBYsWDDs3rR2Xdu2bcOyZcuwbds2p2WNjY3FQw89hNjYWKf7coSWlhZERESgpaXFK+N7A1bMDMP4HUrSiJqSn5+PnTt3Ij8/372CqUTOGq2trUV/fz9qa2ul9yorK3HkyBFUVlaatbd2XVu2bMF7772HLVu2OC1jWVmZy9NaKkkLKp4THh4uZTkbKbDzF8MwfkdISAhWrVqFN998E11dXd4WZxhNTU2IjY1FWVmZzXSQo0ePxkMPPYQDBw74zDI74H4ns7Fjx+KRRx7Bvn370NraanasubkZcXFxaGtrg1arxeDgIMLDw13q7Kj0+3ElavQeW8yM39HT04OlS5eip6fH26JYxVOFAkYCcsUQysvL8eabb6K8vNyLkllH6fKvO6xRV6Akz7UzlJSUYN++fWZFKUTEXN4AMDg4iNDQUJfvW3t7ed4e7JXN+B0rVqxAZmYmVqxYgUOHDnlbHFlMCwVYWgSMOkyVhOj8NH78eJ+0lEXKysoki8wWERERLrOUXRlGV1hYKFnM7kAsSiFHSUkJ4uLiUFxcjJkzZyqatHR3d+Oee+7BuHHj8NZbb8FgMNg8X+n34y3YYmZ8AjUVcg4ePIhdu3bh4MGDTo+rxuNUDbYsAmY4XV1dmD17tqyyLSwsRE5OjtuUhOjZXFRUBEEQUFVVZVcme4gK13SZ1HQVRa2XuK0SiiLTpk1DZ2enS/ZixbAs0yXX1tZWTJgwwS0TTfH6br75ZhgMBrS2tiI+Pn7YZ2jqCDY0NIS8vDwMDQ0hIyMDXV1duH79uqISpHLfjy/BipnxCWyVcLMkKCgIhw4dQlBQkNPjumtJS65MHWOdBQsWYNOmTViwYMGwY3JKwpWIns2JiYlIT0+X7kFbMjmC6SqKWi9xJUvLwcHB0Gq1NuOinSEhIQHr169HQkKCy/tOTExET08P6urqbH7e06ZNkxzBDh8+jBkzZuDw4cPIy8tDSEgIpk6dqqoEqc/i1hxkVuCUnIwltkq4uRNrKSkZz+LpymGmiHmoCwsLCQBVVla6RSbTHOdqS6cqKSri7nKatko8Oot4fQkJCTY/b9OUn5a5uH0dTsnJMAzjRcSl2KamJjQ2NvrlykljYyOMRiPKy8sxfvx4b4szDF9PT2oJe2V7mf7+fmzZsgX9/f3eFoVhGC+QkJCAxsZG6HQ6xMXFeVschzAajVi1ahWMRqPL+3aFb4e7Pce9ScArZm9kjcnMzMRLL72EzMxMj43JMHV1ddDr9airq/O2KMPo7OxESkoKOjs7vS2KRyguLsb48eMxMDDg8w6A1hwv3RmSZsu3Q6nTnVqnQGec+TxNwCtmU2cBT7Fv3z5ERUVh3759qto582D19EO5trYWOp3OLDsR4zxlZWUQBEF1GEdNTQ1uuukm3H777apSioresBMnTlTkIWxq6ZSWlkIQBJSWltptl5aWhk2bNiEtLQ3Xrl2DIAiKCx20t7cjNjYWubm5EAQBn332maJ2ppSXl0MQBNVKxtH7/Pjx42hsbMT7778/bBnbXoz7yZMnIQgCTp48OeyYNUOjt7cXycnJEAQBFy9eVCXrxIkT0d/fj4kTJ5q9L4ak2VrGdtTwefnll5GTk4OXX3552DFbTnem36MSp8C+vj48/vjj6Ovrw7Rp03D69GmbukD0PC8tLfVuGlC373jL4EnnL2/UB3W08LhOp6Ply5eTTqdT1a6trY0A0NKlS1W37enpoVWrVlFPT4+qdlqtlu69917SarWq2jG2AUAzZ84ktT9NjUZD6enpBIBqa2sVt4uKiqJRo0ZRaGgoxcfH2z0/NDSUNmzYQKGhoQSAbr31VkWymjomAaD4+HjF12g0GumZZ54hANJLLY62dfQ+txzv4MGDBIAOHjxIYWFh9MQTT1BYWJgqWRsaGggAPf7448Nqca9atcrha3Tmc3W0NritMW053QGg1NRUxbJmZmZSbm4uZWZmKrrOyMhIysrKIgAuq3kuokbvBbxi9idqa2tJp9OperAS3Xhwbd68WfVDmejGD/rgwYO0atUqVe0cnXwwtiktLSUAVFpaqqpddXU1aTQaqq6uVtVO9IadMGGCIg9hUy/2kpISAkAlJSWqxrx69SoBoKtXryo6v62tjYxGIx06dIgA0LFjx1SNR+S48nH0PhdlPXTo0LDxTb2z5fjiiy8IAH3xxRdm7wcHB9P9999PAIYZGj09PZSUlEQAqKCgQJWsL774IgGgF198UVU7IscNn3feeYcA0DvvvDPsWENDAwUHB1NDQ8OwY5cvXyYAdPnyZUXj9Pb2UmZmJvX29lJFRQUBoIqKCqvni57nJSUlLjfoWDGPMMQHV1tbm+q2jlrMjuLp8RiGyDsrZ6aYWsyOYkthBRLBwcG0du1aCg4OHnYsNTWVtm/fTqmpqX73ebBiZtyOozGTjlrojOMoiYH1BfztQWuL69evEwC6fv26t0VxGk9/L7bGM13mtqXAfRE1ei/gnb8Y92DqzKOGvXv34rXXXnMoO093dzfmz5+P7u5uj7QLFPwlrMSd4TmeZurUqUhKSsLUqVOl99yV/lVErSOWaUpLW3j6e7HldBYSEoJTp04hJCTE5wuZOIUHJgrDYIvZ/3F3liE50tPTKScnh9LT0z3Szt/p6+ujrKwsamhoYIvZw8hZzKaOc+7AmiOWeB/09fWZvZ+bm0vV1dWUm5trs99A+l68CS9lMwFJV1cXpaenU1dXl0famTqOqMXRMV1JVlYWHT16lLKysrwmgytxxpfCF3B3+ldr++jW7gN/S2np77BiZhgXsH79errjjjto/fr1qtv6gpVuzVLyV8SwKaPR6Pax3JkXmohU58oWJyXXr19Xbb329fXRN7/5TUpKSvLoCpejyE1gvO285wp4j5lhXEBTU5NUpF0teXl52LlzJ/Ly8lS1U1LeTw65fXS9Xo9t27ZBr9er6stXKSgowK5du1BQUGDzPFf4FLizkhIA1dWlUlJSkJmZialTp6re79Xr9fjkk0/wjW98Q7VPiDeQywrmjURRXsUDE4VhsMXM+APeCO2KioqirVu3UlRUlKp2vmCh+wqJiYmk1WopMTHR4T4CyWIm8o5PiKOYWszujCv2NFxdimH8FEcr5nR3dyMjIwN5eXmq6/E2NTUhNjYWZWVlqgrHOzOmOwkKCoJWq8Xg4CB6enpc3n9zczPi4uJQUlLil1Wj/IkJEyZg/fr1+NOf/jQsl7e/wdWlGMZPUZL/V47g4GB8+umnDilIWwUFbJGRkYGNGzciIyND9ZjupKKiAoODg6ioqHBL/3FxcXjkkUcUV43q6enB0qVL3TJJCHSKi4vxpz/9CcXFxd4WxaOwYg4Aent78eCDD6K3t9fbojB+SFlZGQ4cOKC6cIaj++gdHR1ISEhQVDTDFKX3eWRkJHp6ehAZGamqf6WUlJRg3759iqtGrVixApmZmVixYoVb5Alkxo4di/r6eowdO9bbongUVsx+Tl9fH26++WZUV1cjKChItdMQw0RERKCjo0PVMjbguJWu1vFJZM2aNXj00UexZs0aVe0cxVrCjvDwcLS2tipexj548CB27dqFgwcPukFKRimNjY0ICQlBY2Ojt0WxiyrFfOHCBaSnp+Ouu+7CfffdJ814y8rKYDAYcOHCBbcIyVhn8+bNiIuLw7Fjx5CUlOTz2Z2Yf9PS0oJRo0YhPj5e1nr05frKzpCfn4+dO3ciPz9fVTtnssY5sqrkKk/goKAgHDp0CEFBQU71M5JQEp1gr3ymJX6VWU6NV5lpPOSzzz5Lr732GhER/ed//ifdfffddP78eZd7p40kHKku1dvbS+PHj6dZs2YRAM7O40eEh4dTWFiYVAbREkfLgLoTdyfJcBeO5GgPhNhZf0VJdIK98pmWeDuDmdvimE3jIbu6unDLLbfg+vXrEATBpuNIb28v2trazF6eIj8/H4Ig2J2dd3V1Yfbs2ejq6gIAVFdXQ6PRoLq6WvWYp0+fhiAIOH36tKp20dHRWLZsmapC96NGjcI777yD/Px8pKenIysrC6TC0d4Rq6yxsRGCICAoKAgNDQ2K2zmLp5ei3D3etWvX0N3djWnTpkn3Z2dnJ1JSUtDZ2YnKykq89957qKysdMv4Imr2fE0dxRz9fBxdCaivr8eoUaMc8s796le/ihUrVuCrX/2q4jbjxo1DcXExYmJi7MayW16T+AyYNWuWFE9dVVUFQRBQVVVls6+///3vEAQBf//7383et2dFnj17FoIg4OzZs0ovEQDQ2tqKCRMmoLW11eZ54r1ZVlYmfe+2xrR8pppy/PhxCIKA48ePS+8NDg5i9+7dGBwcRGFhIXJyclBYWGjWrqSkBIIgoKSkRPVe/7lz59Dd3Y1z584pOt+rqNX6ubm5NGvWLJozZw41NjbSE088QdevX6f169dbtZgti5yLL09YzABo1KhRduuwmpYTIyISBIEWLlxIgiA4NKZGo1Fd+9XResymn+mPfvQjKioqUtzWEassODhYGs9gMKiS1Rk8XU3GG9VrkpOT6aWXXqLk5GSPjRkfH0/Z2dmyVrslphazo5+PoysBer2eVq5cSXq9XlU7IsfrMSvNb215TabjibHl4v/tyWBNVntWJAAKCgpSfY2RkZGUlZVFkZGRNs8T700A0vdua0zLZ6qlrJbXmJOTQ1euXKGcnByrMgCQ6k6rxXRMezWx3YFHUnL+4he/oJ/97Gf0xBNPEBHZVMw9PT3U2toqvcrLyz2mmM+cOUMA6MyZMzbPMy0nRkRUVVVFgiBQVVWV6jFPnTpFAOjUqVOOiKwa8Ro3bNhAFy9epKGhIcVtHZkMNDQ0SEq5vr7eEZEdwpfKz7kLbySCUJvsQsTRz8fRCWhdXR3p9Xqqq6tT1Y7IccVcWlpKAKi0tNTmeZbXJD4DZs6cKeVLr6ysJABUWVlps6933nmHANA777xj9r698p35+fkEgPLz85VeHhEpT6Yi3pulpaXS925rTMtnqily38fAwADl5OTQwMCAVRmcKaf54YcfEgD68MMPKSwsjB588EGPbv+5LcFIb28vDAYDAOCVV15BTU0NPvjgA4SGhuL8+fOYOnUq/vnPf9pNAcgJRhiG8SSOJgVJSEjAxo0bsXPnTlWxtB0dHZg1axby8/MxevRoR0Q2w9EkML5KV1cXFixYgCNHjiAkJMTj4zc3NyMiIgIPPvgg/vGPf8gut7saNXpPp6bjvLw8/PKXv4RGo8GECROwe/duPPPMMwCAxx57DFu2bAmYvLwMwwQOYoiTWvLz8yUFqwbTkDBLhe6I0jbd21cb/+2LiHWVvUV4eDgaGhpgNBp9sp4zp+RkGIZxMbaUrz0rXK5toFnMIxFOyckwAYjauE21NDU1YfTo0Q5V02LMGT16NIqLi2UtYntx3HIJWBxNAuNuON2oe2DFzAQMtbW10Ol0qK2t9bYobkFtjma1OJozm1GHLaUNOJ6AxdFUp87A6Ubdg6o9ZobxZaKjo5GRkYHo6GgMDAx4WxyXU1JSIjkwuYOysjJpuZTxHqLiVoutfW13cfDgQaxYsYLTjboYtpgDgIGBAbzwwgsBqYzUcPHiRbz//vu4ePGit0VxC2pzNKvFV5dLGWU4amkDtpOB2ILTjboHVswBwLZt27Bs2TJs27bN26J4FTHzWXp6urdFYRi3YCvbmr0lclssWLAAmzZtwoIFC1S188by+UiAFXMAsGXLFrz33nvYsmWLx8Y0TR3pK4zU2q3MyMFdhRiOHDmCHTt24MiRI6raOVopjLENK+YAQKfT4amnnoJO5zmXgbS0NGzatAlpaWkeG9PeZGCk1m4NJNgz3Dbl5eV48803XR57K8YVq0324UpHtYaGBo/n3/dVWDEzDnHixAns2LEDJ06c8NiYnp4MOLrvxjgOe4bbZvz48ejq6sL48eO9LQoAx5fP5SztmJgYfPWrX0VMTIzVdv5UU9kZ2CubcYjQ0FAUFBR4dMwTJ04gLS3NY5MB0303b2Qp8nbaQm/AnuGO409JSOQyqlVUVCAmJgYVFRVW25ku5QfyhJktZsZvECcDoaGhHhnP0X03V+GoQ447cfdSs7s8w5WWNvRn/GW1ob6+HhEREfjss8/MLO3IyEj09PQgMjLSaltnlvJ90S/GGmwxM4wVvJ3P98iRI5LF7Cv4a87mhIQErF+/HgkJCQ7Vc/YH/GW1ITo6GsuXL0d0dDT6+vpUtRWX8h3BdCvM06t9amGLmWF8FEcdctxJWVkZDhw44NTD39G9e2f2/M+dO4cXX3wR586d88iY9fX1GDVqlEOTgEuXLkEQBFy6dElVu46ODnR2diqeMBUWFkIQBPz1r391aDwAKCgogCAIqhRdZWUl3nrrLfT398tGUIhyFRYWDjtWXFwMQRAcirzYs2cPvv/972PPnj3DjlVVVUEQBFRVVanu1y24uQSlLGrqUjIME1ikpqbS9u3bKTU11SPtvDGmXq+nlStXkl6vV9WO6Eat4sjISNW1o/Gv+s9K2wGgqKgoh8cT+xg3bpxDssbGxsq2M5VLTTslY9rqNz093aF+laJG77FiZhjGo3R2dlJqaip1dnZ6pJ07xmxoaKDg4GBqaGiQbVdXV0d6vZ7q6upUy3rx4kUCQBcvXpTea2trI6PRSG1tbVbblZaWEgAqLS1VNM7ly5cJAL399tvDxlPKhQsXCABduHBBVbsrV64QALpy5YpVuS5fvqyqnT1s9VtZWUkAqLKyUnW/SlGj97jsI8MwjEpCQkKwatUqvPnmm+jq6nK7R3RsbCwyMzOxa9cun99DZuRRo/fY+YthGEYl5eXlMBqNknewu53iCgoKkJKS4vNOS4xrYMXMMAyjEkvvYHd7RI8ZM0Zx352dnVK8v6dCCxnXwl7ZAUZNTQ20Wi1qamq8LQrjJ/hTfKc7EGOzq6qqsHTpUvT09KjuQ0n8tafSjborQ15LSwsiIiLQ0tLi0n5F3O0Z7W75XQkr5gAjOjoad999N6Kjo70tCuMneCPvuS8hLkNHR0fjn//8J+bPn+/WcdydAMRd6XKnTZuGtWvXYtq0aS7tVyQ6Ohrp6elue3a5W35XwkvZAUZlZSWio6NRWVnpbVEYP8HTqU59DXEZGriR+9ld9bw9lQDEXelyr127hmnTpuHatWsu7xtw/7PL3fK7EraYA4yoqCgMDg4iKirK26IwfoKnU536GuIydENDA3p6elxeuUlEr9cjIiICer3eLf27m3HjxqGpqQnjxo1zS/+TJ08GEWHy5MlWz1G67SKXGMbd8rsSVswMwzBwf+WmlJQUZGZmIiUlxS39jwSUbrv4Yp55NfBSNsMwjAcY6SFPjY2NUoiZo5MfpdsuvphnXg1sMTMM43XcXfvaF6pLiSFPY8aM8ZoM3sS0ZKOjKN128cU882pgxcwwjNdx99KjaXUpxjs4U7JxpMFL2QzDeB13Lz0WFxcjISHBoapEnsDdKT19AWdKNo40WDEzDON13F37euzYsT5dh9lf61wz7oGXshmG8Wn8KWOTo5jWue7u7sb8+fPR3d3tbbEYL8GKmWEYn8afMjY5imlKz4yMDGzcuBEZGRnDzvNUWk9v0NzcjLFjx6K5udnbonidgFfMPT09Due/dRRPz3i9kevYG7N6d3vuens8Rp5r167h9ddf94uMTa4gJycHGzZswK9+9athSthWWs+GhgYEBQWhoaFB1XjV1dXQaDSorq6W3lM6AThz5gwEQcCZM2dkj1vzhi8oKIAgCGahY3FxcXjkkUcQFxdndbzKykoIgoDKykoMDg5i9+7dGBwcBGD+HOzo6EBCQoLZtsC5c+cgCALOnTs3rF+fyxfvtqrQNlBTMNpZlixZQvv376clS5a4fSyR9PR0ysnJofT0dI+Ml5ycTC+99BIlJyd7ZDwiz18jEVFqaipt376dUlNTA3I8xnepq6sjvV5PdXV1bh8LAM2ZM4cA0IYNGyg0NFQ61tjYSKGhodTY2DisncFgoIcffpgMBoOq8QRBoIULF5IgCNJ7oaGhw8a2JqtOpyNrqiQyMpKysrIoMjJyWLtx48aZtSsuLiYAVFxcbHO8efPmEQDKycmhK1euUE5ODhGZPwfj4+MpOzub4uPjzdqGhITIyuqJZ6gavRfwirm7u5uWLFlC3d3dbh9LpKuri9LT06mrq8sj43V0dFBycjJ1dHR4ZDwiz18jEVFnZyelpqZSZ2dnQI7H+C56vZ5WrlxJer3e7WOVl5cTADp79qykhNva2shoNFJbW5vVdvX19WQwGKi+vl7VeFVVVSQIAlVVVUnv2ZoAmHL69GkCQKdPn5Y93tLSQpGRkdTS0mL2/oULFwgAXbhwQXpPyWSgoqKCAFBFRQUNDAxQTk4ODQwMEJH5c7C9vZ3i4+Opvb1danv27Fnpc7XEE89QNXpPICLyrI0OtLW1YezYsWhtbUVYWJinh2cYhlFFfX29VGBhwoQJHh8/NjYWmZmZ2LVrl9uLYHgLMWTs7NmzSE1NRUlJCcLDw+22a2lpkYpT+HIebDV6j8OlGIZh7DBhwgT09fV5bfyRkM5TdIAbO3astNesJFObqXNgoDjFsWJmGIbxccR0niOBkpISxMXFoaSkRNH5/lTOUSkB75XNMAzD/Jve3l48+OCD6O3t9bYosoSHh6O1tdVsGdtWLLs/lXNUCitmhmGYEcSaNWvw6KOPYs2aNd4WRTEjIZbdFF7KZhiGGUHs3bsXa9aswd69e70timICcbnaFuyVzTAMwzBuRo3e46VshmEYhvEhWDEzDMMwjA/BiplhGI8yUqsnuTsfc3t7O2JjY9He3u6W/uXwl6IajY2NCAkJQVxcnF/kvmfFzDCMR7FVPSmQSUtLw6ZNm5CWluaW/lNSUpCZmYmUlBS39C+HraIavoTRaMTg4CAqKyuxYMECb4tjF/bKZhjGo+Tl5SEjIwN5eXneFsWjnDhxAmlpaThx4oRb+vdGdrCysjLExsb6fPKT8vJyGI1GTJo0CUeOHPG2OHZhr2yGYRjGL2hra0NiYiIKCwtdqjsaGxthNBpRXl6O8ePHu6xfU9grm2EYhgk4EhMTsWHDBiQmJrq0X6PRiFWrVsFoNLq0X0dhxcwwDONDlJWVQRAE1cvD9fX1GDVqFOrr61W1q6yshCAIqKysVNUOACoqKiAIAioqKlS1Ky8vhyAIKC8vV9UuLy8P2dnZDm2D2Ppcy8vL8eabb6qWx12wYmYYhrGgoaEBQUFBaGho8PjYU6ZMwcyZMzFlyhRV7aKjo7F8+XJER0erahcTE4N58+YhJiZGVTvghqU5Z84c1ZZmbGwsUlNTVTuNzZgxAzNnzsSMGTNUtQNsf67jx49HV1eX25ax1cLOXwzDMBbExMTgq1/9KmJiYtDT0+PRsUtLSzFlyhSUlpaqaldZWSnVjFZDRUUFYmJiVFu9wL+dqtRamo46jTn62Tjb1tOw8xfDMIwFDQ0NkrKKjIz0tjgBQVNTk6SMIyIi3DpWa2srEhISUFxcjLFjx7p1LKWw8xfDMIwTREZGoqenx2eUsq2yh/6CJ2OeExISsH79eiQkJLh9LHfAS9kMwzA+jmnZQ1/PsmUNT8Y8FxcXSxazP8KKmWEYxscJhLKHERER6OjocHm/LS0t0mczbtw4AMDYsWNVe6f7EryUzTAM4+OMGzcOTU1NkuJh/o3pakKgwBYzwzAM47cEwmqCJWwxMwzDMF5HrADV2Nioql0griawYmYYhmG8jq+lxfQmvJTNMAzDeB1Hk5UEImwxMwzDAOjs7ERKSgo6Ozu9LcqIxNfSYnoTVswMwzAAkpKSUFVVhaSkJG+LwoxwWDEzDMP8i4GBAW+LwDCsmBmGYQCgoKAA4eHhKCgo8LYozAhHlfPXhQsX8MQTT0Cn02H06NF444038PWvfx09PT3QarXIyclBXFycm0RlGIZxH2PGjPFIukiGsYeq6lL9/f3Q6/UAgOeeew5xcXG45557EB0djdzcXBw8eBC///3v7fbD1aUYhmGYkYTbqkuJShkAurq6kJycLBXl1uv10OnkDfDe3l60tbWZvRiGYZjhlJeXQxAE1WFDFy9ehCAIuHjxoqp21dXV0Gg0qK6uVtUOuFEeMygoCA0NDaralZSUQBAElJSUSO/V1NRAq9WipqbGaruysjIIguDQyoajn6tXIJXk5ubSrFmzaM6cOdTY2EhERH19fbRo0SIqLi6WbfPMM88QgGGv1tZWtcMzDMMENAAoNTWV1D6eAVBERITqdoIg0MKFC0kQBFXtiIgMBgM9/PDDZDAYVLUDQElJSWayajQaWrx4MWk0GpvtZs6cqfoaxbaOfK6uorW1VbHeU7WUbcr//M//YGhoCE899RQ2bNiAe++9Fw8//LDsub29vejt7ZX+bmtrg9Fo5KVshmEYC8rLy6XyiGqyYF28eBEpKSkoKChAcnKy4nbV1dWIjo5GZWUlJk2apErWhoYGxMTEoKKiQlXt6pKSEkydOhXXr1+X/JJqamokOaKiomTblZWVYcqUKSgtLVVd19nRz9VVqFnKVqWYe3t7YTAYAACvvPIK+vr60NraiqGhITz99NNuEZBhGIZh/B01ek+VV3ZeXh5++ctfQqPRYMKECfjf//1fTJkyBenp6fjwww9xxx13IDs72ynhGYZhGGYk4/BStjOwxcwwDMOMJNzmlc0wDMMwjHthxcwwDMMEPIODg9i9ezcGBwe9LYpdWDEzDMMwAc+ePXswf/587Nmzx9ui2IUVM8MwDBPwrFu3Dp9++inWrVvnbVHsosorm2EYhmH8Ea1Wi8cee8zbYiiCLWaGYRiG8SFYMTMMwzCMD8GKmWEYhmF8CFbMDMMwDONDsGJmGIZhGB+CFTPDMAzD+BCsmBmGYRjGh2DFzDAMwzA+BCtmhmEYhvEhWDEzDMMwjA/BiplhGIZhfAhWzAzDMAzjQ7BiZhiGYRgfwivVpYgIANDW1uaN4RmGYRjGo4j6TtR/tvCKYm5vbwcAGI1GbwzPMAzDMF6hvb0dY8eOtXmOQErUt4sZGhpCVVUVxowZA0EQ3DpWW1sbjEYjysvLERYW5taxvMVIuEZgZFwnX2NgMBKuERgZ1+mqayQitLe3Y/LkydBobO8ie8Vi1mg0iImJ8eiYYWFhAXvjiIyEawRGxnXyNQYGI+EagZFxna64RnuWsgg7fzEMwzCMD8GKmWEYhmF8iIBXzAaDAc888wwMBoO3RXEbI+EagZFxnXyNgcFIuEZgZFynN67RK85fDMMwDMPIE/AWM8MwDMP4E6yYGYZhGMaHYMXMMAzDMD5EQCrm9vZ2zJ07F6NHj8aFCxcAABUVFXjggQewaNEiPPfcc16W0HnkrvF3v/sd0tLSMHfuXLzzzjteltB5Tp06hQULFuCuu+7CQw89hP7+fvz5z3/GHXfcgS9/+csoLy/3tohOY3mNra2tWLJkCRYuXIi7774bJSUl3hbRJch9lwBQVlYGg8Eg3cP+jNw1BtpzR+4aA+25c+HCBaSnp+Ouu+7Cfffdh46ODs8/dygA6e/vp7q6Olq/fj2dP3+eiIhWr15NFRUVXpbMdchdY1JSEvX391NrayvNmzfPyxI6T3V1NXV2dhIR0datW+nAgQOUlpZGvb299Omnn9KmTZu8LKHzWF7j/v37pfv00KFD9J//+Z/eFM9lyH2XRET/+Z//SXfffbd0D/szctcYaM8duWsMtOdOX1+f9P9nn32WXnvtNY8/dwLSYtbpdJgwYYL0d39/P0pKSpCVlYUvf/nLOHbsmBelcw2W1wgACQkJ6O7uRnt7O8aPH+8lyVxHVFQUQkJCAAB6vR5FRUVISUnBqFGjkJ6ejvPnz3tZQuexvMZRo0YhOjpa+lun80pyPpdjeZ06nQ7Xr1+HIAiIjY31snSuwfIaAQTcc0fuewy054743QFAV1cXYmNjPf7cCUjFbElDQwPOnTuHbdu24Y033sD3vvc9b4vkFu69914kJydjzpw5ePLJJ70tjssoKyvDBx98gPnz55ulxBscHPSiVK5FvMb7778fwI3J5E9+8pOA+h4B8+v8xS9+gS1btnhbJJcjXuO8efMC9rlj+j0G4nMnLy8Pt99+O/75z39Cr9d7/LkTGNNxO4wbNw7Tp0+X8nPrdDoMDAwEjDUC3Ei0/sc//hFXrlxBX18fvvzlLyMjI8PtRULcTVtbG9atW4ecnBwMDg6alQrVarVelMx1mF6jOFv/xje+gW9+85uIj4/3snSuw/Q6y8rKAABxcXHeFcrFmF5jZGRkQD53TK+xu7s7IJ87GRkZOHPmDP7nf/4HH3/8scefO/59hygkODgY48aNQ2trK3Q6Hfr6+vz+x2GJRqNBUFAQDAYDdDodent7QUR+/QMZHBzEmjVr8PTTT2P69Ono7+/HxYsX0dfXhy+++AK33Xabt0V0GstrBICf/exnmDp1Kh5++GEvS+c6LK/zrbfeQkFBAe69916cP38excXFknXir8h9l4H23LG8xo6OjoB77vT29kpZvsaOHYu+vj7PP3fcvovtJZYtW0aTJk2iefPmUU5ODh09epTmz59Pc+fOpXfffdfb4rkEy2v85S9/SXPnzqUvfelLtH37dm+L5zRvvPEGRURE0F133UV33XUX7d+/n/bt20fz5s2jRYsWUVlZmbdFdBrLa9y9ezfpdDrp76eeesrbIroEue9SxNSB0Z+Ru8ZAe+7IXWOgPXfeeecdWrhwIS1atIi+/vWvU2dnp8efO5ySk2EYhmF8iBHh/MUwDMMw/gIrZoZhGIbxIVgxMwzDMIwPwYqZYRiGYXwIVswMwzAM40OwYmYYhmEYH4IVM8MwDMP4EKyYGYZhGMaHYMXMMAzDMD4EK2aGYRiG8SFYMTMMwzCMD/H/AzmvUXGBffVUAAAAAElFTkSuQmCC",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(\"Mediterranean-Data\")\n",
    "ax = subplot(1,1,1)\n",
    "plot(obslon, obslat, \"ko\", markersize=.1, markerfacecolor=\"k\")\n",
    "aspectratio = 1/cos(mean(latr) * pi/180)\n",
    "ax.tick_params(\"both\",labelsize=6)\n",
    "gca().set_aspect(aspectratio)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Check the extremal values of the observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mChecking ranges for dimensions and observations\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              minimum and maximum of obs. dimension 1: (15.770000457763672, 29.924999237060547)\n",
      "              minimum and maximum of obs. dimension 2: (31.783000946044922, 41.39500045776367)\n",
      "              minimum and maximum of obs. dimension 3: (0.0, 4512.64892578125)\n",
      "              minimum and maximum of obs. dimension 4: (DateTime(\"1930-05-13T07:00:00\"), DateTime(\"2017-10-10T00:00:00\"))\n",
      "                          minimum and maximum of data: (0.0005000000237487257, 15.260000228881836)\n"
     ]
    }
   ],
   "source": [
    "checkobs((obslon,obslat,obsdepth,obstime),obsval,obsid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 2. Extract the bathymetry\n",
    "It is used to delimit the domain where the interpolation is performed.\n",
    "\n",
    "### 2.1 Choice of bathymetry\n",
    "Modify `bathname` according to the resolution required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"../data/gebco_30sec_8.nc\""
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bathname = \"../data/gebco_30sec_8.nc\"\n",
    "isfile(bathname) ? @info(\"Bathymetry file already downloaded\") : download(\"https://dox.ulg.ac.be/index.php/s/U0pqyXhcQrXjEUX/download\", bathname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  3.442543 seconds (3.91 M allocations: 252.442 MiB, 5.23% gc time, 99.50% compilation time)\n"
     ]
    }
   ],
   "source": [
    "@time bx,by,b = load_bath(bathname,true,lonr,latr);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(\"Mediterranean-Bathymetry\")\n",
    "ax = subplot(1,1,1)\n",
    "pcolor(bx, by, permutedims(b, [2,1]));\n",
    "colorbar(orientation=\"vertical\", shrink=0.8).ax.tick_params(labelsize=8)\n",
    "contour(bx, by, permutedims(b, [2,1]), [0, 0.1], colors=\"k\", linewidths=.5)\n",
    "gca().set_aspect(aspectratio)\n",
    "ax.tick_params(\"both\",labelsize=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### 2.2 Create mask\n",
    "* False for sea        \n",
    "* True for land"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size(mask) = (89, 49, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(89, 49, 3)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = falses(size(b,1),size(b,2),length(depthr))\n",
    "for k = 1:length(depthr)\n",
    "    for j = 1:size(b,2)\n",
    "        for i = 1:size(b,1)\n",
    "            mask[i,j,k] = b[i,j] >= depthr[k]\n",
    "        end\n",
    "    end\n",
    "end\n",
    "@show size(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject Text(0.5, 1.0, 'Land-sea mask')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "figure(\"Mediterranean-Mask\")\n",
    "ax = subplot(1,1,1)\n",
    "gca().set_aspect(aspectratio)\n",
    "ax.tick_params(\"both\",labelsize=6)\n",
    "ax.pcolor(bx, by, transpose(mask[:,:,1]), cmap=PyPlot.cm.binary_r)\n",
    "ax.set_title(\"Land-sea mask\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 4. Quality control\n",
    "We check the salinity value.     \n",
    "Adapt the criteria to your region and variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "sel = (obsval .<= 40) .& (obsval .>= 0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "obsval = obsval[sel]\n",
    "obslon = obslon[sel]\n",
    "obslat = obslat[sel]\n",
    "obsdepth = obsdepth[sel]\n",
    "obstime = obstime[sel]\n",
    "obsid = obsid[sel];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 5. Analysis parameters\n",
    "\n",
    "### Correlation lengths and noise-to-signal ratio\n",
    "We will use the function `diva3D` for the calculations.     \n",
    "With this function, the correlation length has to be defined in meters, not in degrees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "sz = (length(lonr),length(latr),length(depthr));\n",
    "lenx = fill(100_000.,sz)   # 100 km\n",
    "leny = fill(100_000.,sz)   # 100 km\n",
    "lenz = fill(25.,sz);      # 25 m \n",
    "len = (lenx, leny, lenz);\n",
    "epsilon2 = 0.1;\n",
    "#epsilon2 = epsilon2 * rdiag;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Output file name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"../outputs/Water_body_Phosphate_Mediterranean.nc\""
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outputdir = \"../outputs/\"\n",
    "isdir(outputdir) ? @debug(\"Output directory already exists\") : mkpath(outputdir)\n",
    "filename = joinpath(outputdir, \"Water_body_$(replace(varname,\" \"=>\"_\"))_Mediterranean.nc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 7. Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Remove the result file before running the analysis, otherwise you'll get the message     \n",
    "```julia\n",
    "NCDatasets.NetCDFError(13, \"Permission denied\")\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "if isfile(filename)\n",
    "    rm(filename) # delete the previous analysis\n",
    "    @info \"Removing file $filename\"\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### 7.1 Plotting function\n",
    "Define a plotting function that will be applied for each time index and depth level.     \n",
    "All the figures will be saved in a selected directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"/outputs/\""
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "figdir = \"/outputs/\"\n",
    "if ~(isdir(figdir))\n",
    "    mkdir(figdir)\n",
    "else\n",
    "    @info(\"Figure directory already exists\")\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "plotres (generic function with 1 method)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function plotres(timeindex,sel,fit,erri)\n",
    "    tmp = copy(fit)\n",
    "    nx,ny,nz = size(tmp)\n",
    "    for i in 1:nz\n",
    "        figure(\"Mediterranean-Additional-Data\")\n",
    "        ax = subplot(1,1,1)\n",
    "        ax.tick_params(\"both\",labelsize=6)\n",
    "        ylim(39.0, 46.0);\n",
    "        xlim(11.5, 20.0);\n",
    "        title(\"Depth: $(depthr[i]) \\n Time index: $(timeindex)\", fontsize=6)\n",
    "        pcolor(lonr.-dx/2.,latr.-dy/2, permutedims(tmp[:,:,i], [2,1]);\n",
    "               vmin = 33, vmax = 40)\n",
    "        colorbar(extend=\"both\", orientation=\"vertical\", shrink=0.8).ax.tick_params(labelsize=8)\n",
    "\n",
    "        contourf(bx,by,permutedims(b,[2,1]), levels = [-1e5,0],colors = [[.5,.5,.5]])\n",
    "        aspectratio = 1/cos(mean(latr) * pi/180)\n",
    "        gca().set_aspect(aspectratio)\n",
    "        \n",
    "        figname = varname * @sprintf(\"_%02d\",i) * @sprintf(\"_%03d.png\",timeindex)\n",
    "        PyPlot.savefig(joinpath(figdir, figname), dpi=600, bbox_inches=\"tight\");\n",
    "        PyPlot.close_figs()\n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### 7.2 Create the gridded fields using `diva3d`\n",
    "Here only the noise-to-signal ratio is estimated.     \n",
    "Set `fitcorrlen` to `true` to also optimise the correlation length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mCreating netCDF file ../outputs/Water_body_Phosphate_Mediterranean.nc\n",
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mTime step 1 / 4\n",
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mscaled correlation length (min,max) in dimension 1: (100000.0, 100000.0)\n",
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mscaled correlation length (min,max) in dimension 2: (100000.0, 100000.0)\n",
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mscaled correlation length (min,max) in dimension 3: (25.0, 25.0)\n",
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mnumber of windows: 1\n",
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mnumber of windows: 1\n",
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mTime step 2 / 4\n",
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mscaled correlation length (min,max) in dimension 1: (100000.0, 100000.0)\n",
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mscaled correlation length (min,max) in dimension 2: (100000.0, 100000.0)\n",
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mscaled correlation length (min,max) in dimension 3: (25.0, 25.0)\n",
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mnumber of windows: 1\n",
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mnumber of windows: 1\n",
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mTime step 3 / 4\n",
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mscaled correlation length (min,max) in dimension 1: (100000.0, 100000.0)\n",
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mscaled correlation length (min,max) in dimension 2: (100000.0, 100000.0)\n",
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mscaled correlation length (min,max) in dimension 3: (25.0, 25.0)\n",
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mnumber of windows: 1\n",
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mnumber of windows: 1\n"
     ]
    }
   ],
   "source": [
    "@time dbinfo = diva3d((lonr,latr,depthr,TS),\n",
    "    (obslon,obslat,obsdepth,obstime), obsval,\n",
    "    len, epsilon2,\n",
    "    filename,varname,\n",
    "    bathname=bathname,\n",
    "    plotres = plotres,\n",
    "    fitcorrlen = false,\n",
    "    niter_e = 2,\n",
    "    surfextend = true\n",
    "    );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Save the observation metadata in the NetCDF file.      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "DIVAnd.saveobs(filename,(obslon,obslat,obsdepth,obstime),obsid);"
   ]
  }
 ],
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