{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook shows a few examples of how to use the `poligrain` functions to visualize CML meta data. \n",
    "\n",
    "*To add: plotting SML & PWS meta data*\n",
    "\n",
    "All the functions rely on the [OpenSense](https://github.com/OpenSenseAction/OS_data_format_conventions) naming convention so that we can easily pass an `xarray.Dataset` or `DataArray` to the functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.dates as mdates\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "\n",
    "import poligrain as plg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting length vs. frequency"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the distribution of frequency against the corresponding length for the entire CML dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load example dataset\n",
    "ds_cmls = xr.open_dataset(\"../../tests/test_data/openMRG_CML_180minutes.nc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Link length vs. frequency of the OpenMRG-CML dataset')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 4))\n",
    "scatter = plg.plot_metadata.plot_len_vs_freq(\n",
    "    ds_cmls.length, ds_cmls.frequency, marker_size=30, grid=True, ax=ax\n",
    ")\n",
    "\n",
    "# optionally customize output plot\n",
    "ax.set_title(\"Link length vs. frequency of the OpenMRG-CML dataset\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Frequency vs. length hexbin\n",
    "Alternatively, plot the same as above but as a scatter density plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x2220f69a320>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 4))\n",
    "hexbin = plg.plot_metadata.plot_len_vs_freq_hexbin(\n",
    "    ds_cmls.length, ds_cmls.frequency, gridsize=50, ax=ax\n",
    ")\n",
    "\n",
    "plt.colorbar(hexbin, label=\"density\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Frequency vs. length with margin plots\n",
    "\n",
    "It's also possible to visualize the density of the path length vs. frequency scatter plot by adding histograms of the distribution of frequency and length in the margins. <br>\n",
    "*This is similar Seaborn's jointplot, but relies only on Matplotlib.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IiMh+1atXVwkM5rJ74oknVDcSVEQg4Rg/fryqmPj888/VPEM33XSTSnzwmBkzZvifA7VC6BOLfq+ouMBUJEiGYvXyyy/LvHnzVM2R1ZMIJ2UeHYyJR0df87ByMzRjYXg5xut7ETJmZMl2zUODZNGYg8aOeXSQoKI2zs55dNxeBnRix1xQyXgf4XOky/DLVIrfC2Vwe/xeKQO5Q0w1OocPH47YXoehv+ZZKomIiIhck+hg1t5IbXaYmRFDzYiIiIhcl+hgmmm04YVqkjh37pzqvHTHHXdYGR8RERFRcoaXP/PMMzJz5ky1NskjjzziX1gOfXOMXt9PP/10/NEQEREROZXoYPGvH374Qa0j9eSTT/onF0KPbmMcv7FAGJEboZ8Zl4AgIkrRpitj5NU333wjx44dU4t6rVixQv0ft2GtKSI3w+gPJDmYWAsjx+y4YPkKIkotqAjAHDMY2dmsWTM1l43d0MoybNgwdW4uWLCg1KhRQ55//vmAGZDxfyyrhP612AZDzzGPjhWWLFmi1qzE8k34XjWvjG7AyDustIARu5jsEEPbzSO30VUGC5CWLl1aLUravXt3NTDK1kTHgPH4CAgTFOH/RF6Coev4gNlx4dIVRKkF89VgxW/0Y8V8N9dee61qBbF7jajRo0ereXSwbBMSClwfM2aMWkfSgOuYWHDChAmq8gLJBmKzYnoQrEOJsiLJC2Xnzp1qfUosSIpV1DGgCYmZea1KrKH55ZdfyieffKJWXj948KB069bN/iUgiIiIKDqYSBeT7d53333qOpKKr7/+Wt59913561//att+0dWkc+fO0rFjR3UdNUpYjNuoTUJtDiYCRv9bbAeYGRldUFD70rNnz4T236FDB3UJB316McgJyZYBtU6GkydPyqRJk2T69Olq5maYPHmymnsJrUk33HCDvTU6RBR/PyC7xTAPKBHZ6OLFi7J27dqAtSCxgCauL1++3NZ9t2jRQs2OvG3bNnX9p59+kmXLlvmTj127dqmlGsyxoQkJTWt2x4aFQJHsYXATapCuuOIKtV9z8xaOGxYWNceH2p/KlSvHFB9rdIiSzJgFFn118CVoNcwabSzcR0TOQh9W9JUJHqiD64muOJ4T1BadOnVKJQf43kEcI0aMUGteAZIcI5bg2Iz77IJmuzNnzsioUaPkhRdeUM1qWHkBzVILFy5U63Ahhnz58kmJEiUSis8ziQ7aAF966SVVeLQJog0S/YeIdIW+Onb01/HCyDG3x0+kg48//limTZummn6w6DZWI3/88cdV5+C+ffs6GhtqdABNZuiHA9ddd51qbkPTHhIdq+TxUkcvHBxUfaHNEVVhW7duVdVhRKk6csyORKpQoULqL2ukiHJWpkwZ9ZkMHimE6+XLl7d130OGDFG1OkZfG6xgvmfPHhk5cqRKdIz9Ixbzqga4jqTD7uOCH2Xp6ekBt6P/DZrXAPHhO+bEiRMBtTqxHrtcXuvohYOGhAdfxujoRZSq7Bo5lpmZaevzc1SaPtjXK3FoemnUqJHqK2OuzcD15s2b27rv33//XfUHMkPSZdSmYNg5EgZzbGjqwugru2PDccHIbVRImKE/EaaxARw3rK9pjg/bY/h5LPHl8UpHL0xgGE1HL3xw8RjzBxi/fvEYvPiRbkf7Ji64DQc/1JcAHhPvlwMehy96vBFDdVgN99zR3m6UwarnDwfHF8fIvI9EYzczXgO7nh+vQfDzhxLvPo3XwdiHVbGbnz/aMsSzT3P8sX4Oorkdz4klZYyaKezLzPjiNr6sc7odz4PnNt+O/+N58V41N5FF+10Qze24hIrRijIBngO1aub7rHwv4b2DuUuCyxoq9njKhE6meIzxnRfqdTKfmON5PYwm0Jy2j7dMxndpTs2saHFADUrjxo1Vlwq0OmDotTEKyy6Yw2bEiBGq8y6artatW6cqBvr166fuR9xoykIfmVq1aqnEB8O70bTVpUuXhPePPjiYO8yAzs9oPitVqpSKCTVOPXr0kJtuukluueUW1UcHQ8kx1NzoGN2/f391/PCYYsWKycCBA1WSE+2IK08kOrF29MKv0c8//zyJERKRroJ/TVLyuf016Nq1q0rOI8HJ/OjRo2piPvQjRbMQTup2rySAvqrDhg2Thx9+WHX+RQLz4IMPqjgMGRkZKul64IEHVBMR5rVBbOa5bOK1Zs0alcAYkLAAkr4pU6aoY4cWGDSlPfroo2pZqc8++0zFYHj11VdVgomJAlHbi24pb731VkxxpPlcXjeJyYOwqjo6MJmrsvDiYXIhVMGZobhG1TsREVEioqnRIWflSbWOXnhD5pR9ExERkTe4vjOykx29iIiISG+ur9FxsqMXERER6c0TiY5THb2IiIhIb67vjExERETkiT46WBPDGPdvwBwMAwYMUPM9FClSRA1BC+6YTERERKnJNU1Xq1evlokTJ0qDBg0CbscaGVgB9ZNPPlGTCz3yyCNqUbDvv/8+5POgozKSIw4JJCKieBlTlWC+meDZhyNtz3NP8rki0cHsilht9Z///KeawdFw8uRJmTRpklqw7NZbb1W3TZ48Wa2VsWLFipAzJyLJ+eqrr5IaPxERedMdd9zhX/8tEiQ5s2bNUotYYj03p2VnZ6u+rWXLlo0qUXNzLK5IdNA01bFjR7WsgznRwdIPmEYctxuwHD2mlsbyD6ESHWNK/A4dOsQ8nw5mYMZ01jVr1lRz97iN2+MHlsF5bo/fC2Vwe/xeKAPOPd9++23My6wYy484zefzSdGiRS9bFkP3WOJ5r2if6Hz00Ufy448/qqarYBhhhXl0zKuaAkZb4b5QjCrDX375Ja4DhsdgvQ63cnv8wDI4z+3xe6EMbo/f7WUwkpVYm6GwaKUbEztd1K9f31uJzr59++Sxxx6TefPmWbLuhhnW1IinRgdv0tq1a7vyjer2+IFlcJ7b4/dCGdwevxfKgBqdeNbpQg2W1ecztx//LJtj0TrRQdMUFiJr2LBhwAFZsmSJjB07VubOnatWH8ZCZOZanXDLP5gZq+XGI5HH6sDt8QPL4Dy3x++FMrg9fjeXIdSK8m4sb26N4rErFq0TndatW8vGjRsDbsNsx+iHM3ToULnqqqtUrQyWe8CwckCGvXfvXi7/QERERHonOuicFNweV7hwYTVnjnF7//791RIQpUqVkmLFisnAgQNVkhOqIzIRERGlFq0TnWi8+uqrajgaanQuXLgg7dq1k7feesvpsIiIiC7j9FBucxyYikWHeOyOxXWJzqJFiwKuo1PXuHHj1IWIiIii71CdX4M5feyOxflUjoiIKEXE24nZjjh27NihRTx2x8JEh4iIiDzLdU1XZL2VK1f65zBo1qyZ0+EQEZEL+Xw+LdfxYqJjQYKAuXwwQ/NLL72kruOFxpod1apVk5YtW6p5ff71r3/J2bNn1egwjBhLT09Xw+QxNB7D4QsWLKjW9Dp16pR6sxw/flz9xRxBWJ+rZMmS8uKLL8rSpUtl1apV6rmbNGniX84CsO8tW7bIzz//LMeOHVMdu7DQKZ4fMdSoUUPatm2r1lw5ePCgegyeyzyEv3379vL777+r/2MfxqylKAe2wz5QNgz9x/B+lBvlx+SO+/fvV/MeYR4ErP+CEXHGccJU6UasRjIVfPwiJVrGc6As6JfVq1cvadGihaVJHBM+IrIbvnt1aC7y/d9SC1glwIrkBP1rcE6Il52dotN8Ti9y4UCHJyys1rVr17hmRt68ebPqHf7UU0/JmDFjbIvTC6pXr66SkQ8++CDg9oyMDPU31PHDfaNHjw64DXMmhdoWyQ4St3CJSfDjQj13PNsmyvw+0mWirlSK3wtlcHv8XihDrOcSY/trr71W/d9rChQooGZ91hH76MQBv/yZ5OQMvxSCkxzAsQt3/HA7jm80xxrP3adPHzVnEhIVs1CPC37ueLYlIkpEitUtRH1MTp8+bduxYaITh+3btzsdgqeh+SjU/yMJTkzCPS7U7bFsS0RE1kJT3p49ezjqSie1atVyOgRPQ1NUqP/nxJyYhHtcqNtj2ZaIiNyFiU4c0B+kd+/eTofhWmhmMvrphLrP3N8G/w+3baTEJNTjgp87nm2JiMhdOOoqTlOnTlXtieY+KOiMhRFSkWCV9TJlyqjJkaKBTnrotBesYsWK6q8xeiq4xqlcuXL+UVfz5s0LGxdGYu3cufOy26+77jrVac4YdYWRUStWrPDff+ONN6oFVtG57uuvv/bfbnRGM5cPSSE6JgePuurWrVtUo67QKRjbmkddYXTatGnTIiYmxuOiGUkVy7ZERGQtO2doZqKTgPfff18eeeSRgJNjqCHTEHwCNW+HodnmYeLBQ7GDRwQhcUCiBZMmTZLVq1erhU4xXD3cSRrbYZ9XXnmltGnTRiU3xrbBz4/ro0aNimr4NYaQh7o9mqHauD3ahMLY1jxSAwu42rEPIiI7T+h58njv1Js/gUQFP+jt7BLC4eUuGQ5pxRwvkeJ3yxwybh+S6oUyuD1+L5TB7fGn8vDyzp07a7G+VHZ2tpqnDa0MVs1hE++EgXbEYua9tNKj7K5tYG0GEZH9MHmrDrMH+3w+1QUAE9VaEQ+SvfLly8cdC7phoKuFHZjoEBERJQlmnsfs9Lo4deqUJc+DvpO6YqKTgOD+OBMmTJANGzaoajj0mQleAgJvcCzlEM8SEEWKFFGdZQ8dOqQ6CFu1BIQBfXfwPJs2bfL3/9FxCQjsB9uinxKXgCAiopywj04MuASEXktAINm57bbbuAREkrk9fi+Uwe3xp3IfnQYNGmhVo6PDEhCoHMAP8sqVK9vSR4fz6MSBS0DosQQERr1xCQgichMd+ufoBslN1apVbVvYk4lOHLgEhL24BAQReVWKNaJEXaOD7hV2LQHBPjpx4BIQ+i4BYTRhcQkIItIR+hjqMI+Oz+eTCxcuqKHuVtQyJTJkHrEcPXpUTaZrB+ePtgvhZNqrV6+QzTKUMzQz4Y0dqkkq3BIQ0TQVhloCIngixEhLQESzLRFRIjCrvQ4jlLL+r48U+lJa1Ucq3nl07MZEJ07oH4IXFH9jEesSEEYnN7RdIvs24M2J6wcOHLjsMegQhvkMjFFX8+fPl3PnzoV8fnSMw5sTI6rMI7Dw/OZRV9EuAYGRXTgudi0BYYy6On36NJeAICLSSJqGSQ4w0UkAhmEPGDBAnRyxnpQ56Wnfvr3cfffdliwBEWn4sxVLQIR77mBcAoKIyBvJQFpampruRId47I6Fw8stHA6p+zwsbh/OCSyD89wevxfK4Pb4U3l4eTznHkoMa3QsxBoBIiKKxK6RRfHEcejQIalQoYJtw7p1iYXDy4mIiJJEl0YUn2kGfq/HwkSHiIiIPIuJDhEREXkWEx0iIqIk0WGUkxEHFmjWIR67Y2FnZCIioiRxuuOvOY5y5cpJKsSixxEnIiJKATqNutq9e7cW8dgdCxMdIiKiJNFhlJMRx5kzZ7SIx+5YmOgQERGRZ2mf6IwfP16tx1SsWDF1ad68uX+JBGjVqpXqwGS+PPTQQ47GTERERHrQvjMy1mYaNWqU1KpVS1Vrvffee9K5c2dZt26dXH311Wqb+++/X5577jn/YwoVKuRgxERERKHpMMrJiAMrqesQj92xaJ/odOrUKeD6iBEjVC0PVtI2Eh0kNlitm4iISGc6jboqVaqUpEIsehzxGBaB++ijj+Ts2bOqCcswbdo0KVOmjNSvX1+efPJJ+f333x2Nk4iIKNx5TJc4tm/frkU8dseifY0ObNy4USU258+flyJFiqgVYNPT09V9d999t1SpUkVVe23YsEGGDh0qW7dulZkzZ0Z8ThzQWDNr40XQ4Y0RD7fHDyyD89wevxfK4Pb4vVCGeOPG43Qoc1ZWlly4cMF1scSz0n2aT4exZTm4ePGi7N27V06ePCmffvqpvPPOO7J48WJ/smP23XffSevWrWXHjh1So0aNy+6/dOmSSpTq1KkT1wEjIiLCSRk/qrt27Sp58+bNcXuee6yBlhtP1ujky5dPatasqf7fqFEjWb16tbz++usyceLEy7Zt1qyZ+hsu0THgzRbNmzP4jb1t2zapXbu2K9+obo8fWAbnuT1+L5TB7fF7oQxIXJDoxArnsgIFCojTsjQ6/nbH4opEJxhmT0Q1Vyjr169XfytUqBDxOXAw4z2giTxWB26PH1gG57k9fi+Uwe3xu7kM8c7iix/YOpQ3V65cqtsH4nF65JXdsWif6KBzcYcOHaRy5cpy+vRpmT59uixatEjmzp0rO3fuVNdvv/12KV26tOqjM2jQILnpppvU3DtEREQ6cTqpMMdRtGhRSYVYtB91deTIEenTp49qakLfGzRbIcm57bbbVJPW/PnzpW3btlK3bl154oknpHv37vLll186HTYREdFldOj8a8Tx888/axGP3bFoX6MzadKksPddddVVqlMyERERxSZbgwU9kxGL9jU6RERERPFiokNERESexUSHiIgoBZeAqFmzphbx2B2L8yUkIiKiiOyY2zdvjHPJ2cnOWLTvjExEROQV+/fvj7njbf78+dXgGytlZ2fL5s2bpV69eo7P62N3LEx0iIiIkrikUWZmptNhpBQmOhpbuXKlmhYbHwzMGYTpscGYKttY7iLeYftfffWVmregYcOGalLGUM9nxBBqf5Hui1ao57DieYmIiICJjg2sOFH37t1bPvjgg4jbZGRkqH1gEsUmTZpI//79I8aDtb+KFSsmLVq0kFWrVvnvxwSLf//739XzjR49OmwMHTt2lGHDhqkyYRLH999/PyAW47Hhyh98O1aaHzNmTMBzHDp0KOB5zfu08pibt4eckjlzshntaxouiduyZYtqj0Y1LRER2cyXYi5evOibMWOG+hurzMxM38aNG9XfcDIyMtBjzH/B9Vj17t074DmivTRt2jTHeG688caIz7FixYocY0hPTw/72HDlD7491jIazxPqNYj1mAdvH+6x4baL5jUNFVPwbUOGDPG5UTSfA925vQxuj98LZYj1XGJsv3nzZlXuWC7bt2+3PP7s7Gx17PHXaXbHwkTHwg8mTvSRkodohHuOaC/vvPNOQs81derUuGMYPnx42JgSKZP5OAa/BrEe82jKhm1y2i7SaxrL8YvlvaELt5+gvFAGt8fvhTJ4IdE5d+6cNomOnbFweLmF0EwRy+2JbhsKmrESeS40syQaQ6SYEhEqrliPeTRlwzY5bRfp/mS+3kTkLmgCL1CgQEwXjLqyY6TTjh07tFgGwu5Y2EfHQkZ/j2hvT3TbUNBXJ6fnSk9PVwuoBUOfmUT6FKFDM/r6hIpp4sSJl93eq1evHPshmYUqT6zHPJrjm+g2yXy9ichdKlasqJKXWKEFRpeVz13Hl2KS3Udn6NChMe8nUh8S86V8+fIB15s1a5bjc/Xv31/Fj+akLl26+Dp16qSanIKbUCLFgP0E97fJqfzhbsd+jeaySPs0to+mj05Oxzya/UTaLprXNFRMVvTf0oHbmxy8UAa3x5/KTVd79uzx7d27N6bLoUOHPH38M22OJQ3/SAq5dOmSzJo1S7p27RrzTIwYih3NpEZWDbv+9ttv1eRSR44cUfu74447pH79+gHPjWHisY66Mscf7fBxMG9nxAfBQ9OjHXUVqdyACbKCRzqFew3cOOqqR48ejk/UFY9oPwc6c3sZ3B6/F8oQ67nE2L5BgwYxz6ODGiAskWD18d+6davUqVPH8eNvdyxsutLUuHHjAoZZw+zZs6V9+/ZSpUoVdeJNdB6dUaNGqXZRQ/Dwcti5c6e6GElHqPhCDU2PVnAyMHbs2JBD2omIvECX5qfcuXOrbgwpEYsvxbhheHmvXr2iaroqVKhQzMPL+/Xrp7bLaRRQpOYdK4aXhxpuHe55zc/D4eXO06nKO1XL4Pb4vVAGL4y6OnXqlDajruyMhYlODDi8PPKFw8tjP+4cXu4Mt5fB7fF7oQxuT3QyNTr+dsfC4eUW4vDynGNKBIeXExFRrJjopODw8khDxNFPJp4YjOHlOcWUiGQOL89pOw4vJyJyByY6FkKSgE65icxNE+o5wilcuPBljzWPvAr1XLi/adOmIROVqVOnRhVDcHmMx4YrP/YZ6vbg2yIdp3DHMdZjnlPZjMdG2i6n1zRcTMG34ToXLSVKLbpMGAh2Pa9usXB4ucbDy41hzfv27VO3ocZk06ZNAcPJ4x1ePmXKFLV6eenSpeX+++8Pu3J5uKHe8axqHs1K5TkN++bwcue5fViwF8rg9vhTeXh5POce4ISB8WOik0IfTLfHDyyD89wevxfK4Pb4UznR6dy5sxY1KdnZ2XLixAkpUaKE5MqVy9OxsOmKiIgoSXSpW/D5fHLw4EEt4rE7FiY6RERE5FlMdIiIiMizmOgQEREliS4ditPS0qRIkSJaxGN3LFzrioiIKEmc7vhrjqNq1aqiA7tj0eOIExERpQCMMNIljsOHD2sRj92xMNEhIiJKEh1GORlxHD16VIt47I6FiQ4RERF5FhMdIiIi8iwmOkREREmiwygnI46SJUtqEY/dsXDUFRERUQqOuqpUqZKkQix6HPEIxo8fLw0aNFCLUeLSvHlz/0KTcP78eRkwYIBanBLj8Lt37656bxMREelGh1FORhwHDhzQIh67Y9E+0bnyyitl1KhRsnbtWlmzZo3ceuutalG0f//73+r+QYMGyZdffimffPKJLF68WK2X0a1bN6fDJiIiuowOo5yMOI4fP65FPHbHon3TVadOnQKujxgxQtXyrFixQiVBkyZNkunTp6sECCZPnqxWw8X9N9xwg0NRExERkQ60r9Exy8rKko8++kjOnj2rmrBQy3Pp0iVp06aNf5u6detK5cqVZfny5Y7GSkRERM7TvkYHNm7cqBIb9MdBP5xZs2ZJenq6rF+/XvLlyyclSpQI2L5cuXLy66+/5pg0xdopDI8x/3Ubt8cPLIPz3B6/F8rg9vi9UIZ440Y/FB3KnJ2drfq26tJHJ9pYcufO7c1Ep06dOiqpOXnypHz66afSt29f1R8nEVu3bo3rgMG2bdvEzdweP7AMznN7/F4og9vjd3MZ4k1Wtm/fHve5xw7/+c9/xE2x1K9f35uJDmptatasqf7fqFEjWb16tbz++uvSo0cPuXjxopw4cSKgVgejrsqXL59j8pQ3b96Y39j4UNauXVurN2qqxA8sg/PcHr8XyuD2+L1QBnSbwA/mWKFVQodaFJ/Pp8qA86DTc+mEiiV//vyWDTl3RaITDG+SCxcuqKQHB2bBggVqWDngjbd3717V1BUJPljxfrgSeawO3B4/sAzOc3v8XiiD2+N3cxniTVZwQs/MzBRdXLx4UXSMBQmPVe8L7ROdJ598Ujp06KA6GJ8+fVqNsFq0aJHMnTtXihcvLv3795fBgwdLqVKl1Dw7AwcOVEmO0yOuVq5cqeb72b9/vxodhjI0a9bMfz9Gi6FmCv2O0CSHXzcNGzaUq666StVg4QU3/92yZYv8/PPParsrrrhCPaexLe5DlR/aONEZG7+QAPv/8ccf1ZvljjvuUB/MefPmyW233Sb333+/P5ann35aPvvsMylYsKA88sgjqmrQ+KVljjk49iZNmqjjj8ej8zeOO0bFoez//Oc/VRUtXiNAGfF/o4z79u1Tt+O4GLGar2P/ocp+3XXXqTJifx988IHatk+fPuqvOSbj+IfbB8oVvE2oskbzOkc6VqG2rVGjhnqvUmqI5T1CRNZL81k0cP2NN96I+TH33XefFC1aNOI2OGmhxubQoUPqRInJA4cOHapO1oBE4YknnpAPP/xQ1fK0a9dO3nrrrbBNV8im0Zm5a9eucTVdbd68WQ1fx5w+wV9exokTycQPP/xw2eOvueYaqVq1qsyfP1/OnTsnTqpYsaLMnDlT2rZtK6dOnQq7Xe/evWXq1Kn+5OZf//qX7Nq1K+z2RnLmJLxPkFhFUqFCBfWeMuvYsaMMGzYs6pMRjo2RbEGvXr3k/fffj2rbfv36ydtvv23LL1m7T6zmz0G88Tt98reiDNHAd9WYMWP81zMyMmT06NGuid9Obi9DrOcSY3ucw3Sq0dFVgQIF/F1WtEl0MIIJtQzRvmHxix5fdNWrV5dksiLRwYn/pZdeCvjyAvMXmpegpkinDmt2i+ZkhFqkUEmNkRhGs+33338vLVq0EDecWK08QSUjRh1OskjmQtUsY46vRJM7tycJXigDEx33JDqWzqODWg782o/mgmYSN9qwYUNAkgP40vZqkgOplOQAXkucpMLBfeFqbnC7+bGRtkXTnpWwr+D3YU5lSTY3xGj3aCK3jjIiazjd8TcVWdZHZ/jw4ao3ebSeeuop1a/Gbfbs2eN0CJQEOBmF+9Wd04nK/NhI29aqVSvBKKOLK1JZks0NMVrF6CsX7e2UGlD7o0MNls/n8/eDdDr5ChULRl1pmejE2snYjapUqeJ0CJQEkU5GOZ2ozPeH2xZLm1h9YnfDidUNMVoFry+a5cw1WGi281pCR7H3j0SzjC5Nh9WrV3c88QoXi9GzJtFEzJZRV88995y0bNnSv/6UAUs3vPzyy/Lss8+KW6F9dciQIQHNV/jywguia/MVMuNx48aphU8xWi1WoTruJqJ9+/aqCRALsOYEb/xffvklx+0wagrP99NPP/lvQ/+X3bt3R7Ufs5xORqFOYOEeG2pbdFrGdql4YnVDjFZC3yMsMsxRV2Q4cuSI4zUo5iQCq4Y7HU+4WJD0IDHUpjNycMdkVM+NHDlSDf02T+SHoJ2c/joZo66ME++mTZvUSCV05sW+MNQcb3J0xMbwYvOwaGNoOKrvPv/8c/9zY+g2GM9rHn6OIdboP4Oh4xiddu+996qyGTVPZcqU8Q/5NuKfMWOGGq6N18iIEZ1nsWRGoUKF1NxExtBzc/nMQ8qN4edGDEuWLFEjt5AQmYenYz/ffPON/Pbbb2p6gNatWwcM48ZzfvXVVwFD63FsgofkG8PVsbYZkmW8+ZHE4IJjEfwaBL8u5tjNxxvvRzwnXgsk5sbQ/GhPRjlNIRC8rRFX48aNbe2EyVFX3u8I6/b4vVAGdkZ2T4dk2xIdDPceMGCAqqKfOHGiOiF5KdFx4wfT7fEDy+A8t8fvhTK4PX4vlIGJjnsSHdsmDLzlllvUrzYkOq1atZLZs2dLKjB+qZprZsqWLauWnMCvfrzZly1bppqCUANSrVq1kBMGBk8CiLmEsLgpal3wXAbjObEt5hQy5rjBBwnVgPgAooYl0oSBmD8Ga4ehieuhhx6SO++8M+KvbfMEgdgWNUbBNVCogVm6dKkqG8qDcqBswRMGmmtxzIInRMSEgSgbvhBR64TaGSsnDDT2FWsNQ/DkiZHeF8a+MHeRnRMGOl1bQoH4epCZ081EqciWGh2cjHAixwy+aNL405/+JP/+979lwoQJ6sTo1aarcPOl6MiYMBDNP5GmMjcmwTNO6KipizTBoA7inTAw1nldrr76apWAma+jKTCneWPsnDDQDfPo6HDy54SBznN7GVij454aHUvn0TGYcyf8ckU/DbwZunTpIl6AEWOYCAyJDf7iywwTxbklyQF00EXsOa3XgloT9PX585//rJogdU9yIKckByJ1ro5mXhfUCpmTHEAyb9QeRZo3Bt59913L545xyxw1+LwEf368yC2vB5HX2ZLoTJ482b/GkdpJrlxqiQj8gjWaGdwq3ISB5un9vSbVJgzMaf4bnKjmzJkT8j7cbj6RRXoeqycMdMMEdal08nfD60GUCmzpo9O3b9+wa1vh4macMDA1RJrXJZYJAyM9j9UTBrphjhpOGKjX60HJh76AefI4v562z+dT60Oib6bT/YbCxWLVpIGWHu1oFvZEIbDCuFtxwkDvy2lel1gmDAw35w46Llt9YnfDHDWpdPJ3w+tByYeBFkh2nOZzQaJj3KfVhIGvvvpqwHWMpkGnT3P26vZEJ9oJAzEpHjqoGaOu0G8Eo6YMJUuWVJ3Tzpw5E3F/OHbRdFzDcQ3uV44PE+avMUZdWTVhIDoyo/OgMXLKygkD0R/o2LFjlkwYeOONN6qRWtFOGIh9TZ8+PccTUSwTBponjUvGqCvdJ6hLtZO/7q8Hpba8MQ7IcSIWK5IwSxMdY2izoWjRomrYcrJXKLcbJkLs3r37ZV9eOX2hRZrMLnjCQAwNR0KAodvmx4Hxf2NCQmNoM4Z2f/bZZ2rBVEzcFzzcGRMKhpow0DypIRgdasNNGGg8rzFkGkPfjx49GjC83ByjeVLA4AkDzWU39hV8nIwJA9FXCAkChqKjZg2JTLQTBqIDOWI0J2uIxZgwEH3HIg0Pj3QCM9ZoiXQiw+3GfcZoE7uY96WjVDv56/56UHLlNAAkmXFs1mTUm92x2DK83Jzo4Be2TokOJwx0b/yJliHaOW/s5vbXwe3xe6EMbo8/lYeXY4oVnda6qqfB8bc7Fud7RBElCZIbJxMcIiLyyPByIiIiIh1YWqMTPJkcOhGhs23w7XZOf09ERKQr9I/UJY569eppEY/dsVia6JQoUSKghzS6/1x//fUB13G/k0tAEBERkah+Q1bNVaNzLJYmOgsXLrTy6YiIiDxFp1FXO3bs0KIzst2xWJro3HzzzVY+HREREZG+o66wyKG5mQqZGlZ4JiIiIkoGS3v+LF26VM1RYsDKxOijc91116kLZhWeP3++lbskIiKiOOTSoCNyMmKx9Jnfeust6d2792X9djBjMqbxf+yxx2T8+PFW7pKIiMg1nO4PY44jPT1di3jsjsXSRGfNmjVy6623XraAGabrr1q1qkqCli9fbuUuiYiIXMPGxQhijuP06dNaxGN3LJYmOliDqHjx4v7r7733npQvX95/vVSpUmq9IiIiolSk06irPXv2aBGP3bFYmuhgbaudO3f6r2PhvkKFCvmvowmLkwUSERFRslia6GCF3qlTp4a9f8qUKVzFl4iIiNw5vHzw4MHSpk0bKV26tAwZMkSuuOIKdfuRI0dk9OjR8sEHH8i//vUvK3dJREREccivyazIdsdiaaJzyy23yJtvvimDBg2SV155RTVTYcmHkydPSp48eeS11167rLMyERFRqtBhlJMRR61atSQVYrF8wsCHH35YOnXqJJ9++qls375d3YYC/OEPf5CrrrrK6t0RERG5hg6df404Tpw4odaodHo+HbtjsWVmZCQ0qNUhIiKi/9JhOLcRx8GDBwNGSns1FstSpy+++EKtPhqtb775Rs6dO2fV7omIiIjsS3S6du2qqp6i1bNnTzl06FCO240cOVItK4Gh6+jc3KVLF9m6dWvANq1atVJ9gcyXhx56KK5yEBERkXfksbLq6d5774265/T58+ej2m7x4sUyYMAAlexkZmbKU089JW3btpWff/5ZChcu7N/u/vvvl+eee85/3Tx/DxERkQ7wQ1yXOIoUKaJFPHbHYlmi07dv35i2v+eee6KaPHDOnDmXzcWDmp21a9fKTTfdFJDYmGdhJiIi0o3THX/NcWBpplSIxbJEZ/LkyZIMGKpuLCdhNm3aNDVPD5IdjPoaNmxYxFqdrKysmN9weIz5r9u4PX5gGZzn9vi9UAa3x++FMsQbN/qy6pDsZGdny7Fjx6RMmTKOxxNLLPEMz0/z6dIFPMqDceedd6q+QMuWLfPf/vbbb6uFQytWrCgbNmyQoUOHStOmTWXmzJkh32SzZs2SOnXqaDOfARERuS/RQX9R9E/Nmzdvjtvz3GON+vXr6zG83C7oq7Np06aAJAceeOAB//+vueYaqVChgrRu3Vqtu1WjRo2Qz4U3WzRvzuA39rZt26R27dqufKO6PX5gGZzn9vi9UAa3x++FMiBxCR4YE42aNWtKgQIFxGlZGh1/u2NxTaLzyCOPyFdffSVLliyRK6+8MuK2xnpaO3bsCJvo4GDGe0ATeawO3B4/sAzOc3v8XiiD2+N3cxninfhPt/Lm1igeu2LRPtFBy9rAgQNVld+iRYukWrVqOT5m/fr16i9qdoiIiHShwygnI46SJUtqEY/dsdiS6Pzyyy9SvXp1y5qrpk+fLp9//rmaS+fXX39Vt2MGxYIFC6rmKdx/++23q8VE0UcHszJjRFaDBg0siYGIiMgKTnf8NcdRqVIlSYVYbDniaIPEAp8YBRXtfDnhjB8/Xo20wqSAqKExLjNmzFD358uXT+bPn6/m1qlbt6488cQT0r17d/nyyy8tKg0REZH31ro6cOCAFvHYHYstNTo//vijGm4+ePBg1bemR48e0r9/fzUSKlY5DQrDulqYVJCIiEh3ugx09vl8cvz4cS3mn7M7FltqdK677jp5/fXX1SJd7777rlrqoWXLlmpY2CuvvCJHjx61Y7dEREREAWxtLMyTJ49069ZNPvnkExk9erQaBfW///u/qhamT58+Ua11RURERKRlorNmzRp5+OGHVZ8a1OQgyUHn4Xnz5qnans6dO9u5eyIiIgoBI5zKli2rzagrO2OxpY8Okhr00cFkShgNNXXqVPXX6G2OIeJYs0qXdTaIiIiSQZc5a3LlyiXlypWTVIjFlkQHI6X69eunVjMPN5cNFuacNGmSHbsnIiLSkg6jnIw49u7dK5UrV3Z8yLvdsdiS6Gzfvj3HbTAsPNYVz4mIiNxMp1FXZ86c0SIeu2OxJY1DsxU6IAfDbe+9954duyQiIiJKTqIzcuRItdx6qOaqF1980Y5dEmlp5cqV8v7776u/RETkkUQHbW2h1qSqUqWKuo8oFQwdOlRuuOEGNZUC/uI6EaU2HUY5GXFUrFhRi3jsjsWWRAc1N1hzKthPP/2k1qMi8jrU4IwZMybgNlxnzQ5RanO64685jlKlSmkRj92x2PKsd911lzz66KOycOFCycrKUpfvvvtOHnvsMenZs6cduyTSyrZt22K6nYhSA86HusSxfft2LeKxOxZbRl09//zzsnv3bmndurWaHdkYPoYqfPbRoVRQu3btmG4notSgwygnw4ULFyQVYrGlRgdDx7G6+JYtW2TatGkyc+ZMNSMy1r3CfURe16xZM8nIyAi4DX10cDsRpS4d+sSkGltqdMy/XvkLllIV1nfDWm9orsLngEkOEZFHEh20s2GJhwULFsiRI0cumwkS/XWIUgGSGyY4RGTQofOvEQdGQusQj92x2JLooNMxEp2OHTtK/fr1WVVHRESkUdNVWlqaFC1aVFIhFlsSnY8++kg+/vhjtZAnUSrDcHI2XRGRucUjb968WsSxdetWqVOnjuMLjdodi22dkWvWrGnHUxO5BicMJCKdZWuywKjdsdiS6DzxxBPy+uuvazWMjiiZOGEgEZEebGm6WrZsmZos8Ntvv5Wrr776smo6DDcnStUJAxs3bpz0eIiIUpUtiU6JEiWka9eudjw1kStwwkAi0rkzcq5cuVQXE11GXdkZiy2JzuTJk+14WiLXTRhobr4yJgzUYcp1IkrtRAd06BSdjFhsS+UyMzNl/vz5MnHiRDl9+rS67eDBg3LmzBm7dkmk3YSBK1askKlTp6q/o0aNcjokInKYLh2As7OzZfPmzVrEY3csttTo7NmzR9q3by979+5V61fcdtttaow8vvhxfcKECXbslkg7nDCQiMhZueyaMBAdLo8fPy4FCxb0345+O5gtmYiIiCgZbKnRWbp0qfzwww+XLeBZtWpVOXDggB27JCIiIkpOjQ7a2UJ1uNy/f782U04TERElmw6jnIw46tWrp0U8dsdiy7O2bdtWXnvttYBe5uiEPHz4cC4LQUREpIFLly5JKsRiS6Lz8ssvy/fffy/p6ely/vx5ufvuu/3NVuiQTERElIp0GOVkxLFjxw4t4rE7Flv66Fx55ZXy008/qcU9N2zYoGpz+vfvL/fcc09A52QiIiIiO+Wx7Ynz5JFevXrZ9fREREREziQ6mCAtEqzmTERERM7JpUFH5GTEkseueXSCOxn9/vvvarh5oUKFmOgQEVFKyp07t+gSR3p6uqRCLLakUJgo0HxBH52tW7dKy5Yt5cMPP4zpuUaOHClNmjRRw9KvuOIK6dKli3ouM3R4HjBggJQuXVqKFCki3bt3l8OHD1tcKiIiosT4fD7RJY7Tp09rEY/dsSSt3qpWrVpqrZ/g2p6cLF68WCUxWCto3rx5qnYIw9fPnj3r32bQoEHy5ZdfyieffKK2x5pa3bp1s6EURERE8dNhlJMRB5Zr0iEeu2OxrTNyyJ3lyaOSkFjMmTMn4PqUKVNUzc7atWvlpptukpMnT8qkSZNk+vTpcuutt/pXT8fkQ0iObrjhBkvLQERERO5hS6LzxRdfBFxHddShQ4dk7NixcuONNyb03EhsoFSpUuovEh7U8rRp08a/Td26daVy5cqyfPnysIkOZm6OtfOTMdtzqFmf3cDt8QPL4Dy3x++FMrg9fi+UId648Tgdypyl0fGPJZZ4+jjZkuigH40ZZkYuW7asqnHBZILxQrXW448/rpKl+vXrq9t+/fVX1cm5RIkSAduWK1dO3RcO+vnE2yls27Zt4mZujx9YBue5PX4vlMHt8bu5DPEmCJgYT5cOybod/2hiMc79jic6drWzoa/Opk2bZNmyZQk/V506dSRv3rwxv7HxQtSuXVurN2qqxA8sg/PcHr8XyuD2+L1QBrQkBA+MiQYG1kRKkvLnzy+VKlVKMDpyrI9OIh555BH56quvZMmSJWrmZUP58uXl4sWLcuLEiYBaHYy6wn3h4IMV74crkcfqwO3xA8vgvEjxr1y50n8Sa9asmejKy6+BW7i1DPH+oMf5KjMzM+z9aAFJxvHIzs72nzednk/H7lhsSXQGDx4c9bavvPJKxPvRv2fgwIEya9YsWbRokVSrVi3g/kaNGqmamQULFqhh5YAse+/evdK8efM4S0BkzUk73scGPy7SdfSJQ380vN9HjBgRRwljjxdLu6B/HPrDBW83dOhQGTNmjP86Zkh///33RScoJ0Zonjp1Slq0aCFe4ZYEk5zn8/nU4KDixYt7PhZbEp1169apC6r20EQE+PAhS23YsGFA5hpNcxVGVH3++eeqys/od4MDgnWz8BfraCG5QgflYsWKqcQIX/occUWJnhSCT9oZGRlRL0wb/NiOHTvKsGHDpHHjxv7979y587L9Bz+uadOmsmrVqrDXDQsXLpR3331XdfyPR6h4u3btqvrAbdmyRf7zn/+oOa3wQ+Kll14KeUxQJvNzwAcffKA+6znNmJ4sibymOotULiZAlNJ8Nnj55Zd9nTp18v3222/+2/D/zp07+/7xj3/E9FwIMdRl8uTJ/m3OnTvne/jhh30lS5b0FSpUyNe1a1ffoUOHQj7fxYsXfTNmzFB/Y5WZmenbuHGj+utGbo/fyjJkZGQEvJ9wPdiKFStCvvdwe07CPRaXIUOG+Pr16xdy/5EeF+3lqaeeivl4JLpf45hMnTo1x22clMhrqvPnIFK5onmvO8Ht30exnkuM7Tdv3qzKHe6yfft2X6od/0ybY7GlYQ4jqzCjccmSJf234f8vvPBCzKOukOuEutx7773+bQoUKCDjxo2T3377TU0kOHPmzIj9cyi1hap1wHXcHs0IgGhGBkTaBrUhqHkJtX8rRkCgGStWie7XeDxqDOzahxUSeU11Fi7+b7/9Nqr3OqWetLQ0tZJANC0rbo/FlkQH7d5Hjx697Hbchmmeidxwsgt30o50Mo9lm1D7j+dxweLpm5bofo3Ho1kEfXLs2IcVEnlNdRZr/G5P7NxMh8QC0Om3atWqjndETkYstjwr2vXvu+8+VbOyf/9+dfnss89UXxouzUBuOdnhpI1+DsH9IKLp4xDqsdHEFepxwfuLtP8KFSrE1SE5nnjDHRN0PO7du3fEbZySyGuqs3Dl6tChgycTOzfD4Bm0QoS7YHh5skY6HT58WJslIGyNxY72sLNnz/r+8pe/+PLnz+/LlSuXuuTLl0/ddubMGZ+T2EfHvfHb2Udn6NChYbdFPwf0PYmnHwce07Fjx8v6SAT30Qnef/A+I11Hn5xbbrklrr450cQbfGnWrJlv2rRpvilTpkQ8JokcN7t9//33vhdffFH99dLnINQxj+W9nkyp2kcHfUpzkp2d7Uul459pcyxp+MeeFEpUfxmMKoEaNWpI4cKFxWkYCYah6qh1imfCwM2bN6t1tNw474Pb47e6DMkciWLeF0ZdoQxo4g016koHRryY8yN41BX6x/F95K74dRx15fbXINZzibE9RgNHar7CcyWjj2mWRsff7lhsnTAQw1xxweKbGAqOnEqX9kkifOEn60vfvC9jVlRc13UOl0jHJpqp73U8saayZL7XKbLff/894oSBaL4iFyQ6+OX3pz/9Sc3rgcRm+/btUr16ddVHB6OvElnvisiNEwYaNSNGjY7uEwaa4w1VZk4YqCcmmBSttLQ0dT7WofLB9ljsaA/r3bu3r127dr59+/b5ihQp4tu5c6e6fc6cOb709HSfk9hHx73xW12GaPqQJDIHSfBjzfPooAzoHxJq/8GPa9q0acTr5kv58uXjPh7h4kWfHfT/efDBB33vvPOOij/cMQk3nwu+E3Sh67wyiX4OIpVLx/5SqdpHZ8uWLVrMo5NKbEl0ypUr51u/fr36vznRwd/ChQv7nMREx73xe2XCQFzuuOMOThjoAE4YqE9i5/bvI7dPGJiVleXbv3+/+us0u2PJZVcn5EKFCl12Oyb0S9bQOSJdJwwELFAbav+cMNBenDCQEwbS/4eKjuPHj6u/Xo/FlkTnf/7nfwLWtUG7G8bH4wN2yy232LFLoqhxwsDLccJA52NLBCcMJEpyooOE5u2331aTVaFTIyayql+/vixZssQTi+eRuzk9YWCkRIATBtqLEwZ6I7FzM3Ty12HCwFRiy6grJDX4xTB27Fi14viZM2fUjMhYiRxfxEQ6nBTMVfrhTnZIzPHejWcki/mxwaOu8H/zelfm/YfaZzJGXQXHizk/vv7668u2w/4fffRRNS9IqFFXgBpdfN51HAGEcnbu3FmNurr55ps9M+oq3Hs12vc6JUelSpVyTGaSMRVLWlqalC1bVptRV3bGYvmEgfjya9++vUyYMEFq1aoluuGEge6N380TBoYqg84TBgYfI04YqBdOGOjeCQPjOfeQZjU6eAExxwaR7pyeRE3nCQMjHaNoJgwkvTj9Xqf/0mFtKSOOvXv3SuXKlR1f2NPuWGwpHfogTJo0yY6nJiIici0dRjkZcaBbiQ7x2B2LLX10ML01+h/Mnz9fGjVqdNkaV6+88ooduyUiIiKyP9HZtGmTNGzYMOQwRh06PhEREVFqsDTR+eWXX6RatWpqjSsiIiISLX/sp6WlScWKFbWIx+5YLO2jg1FWR48e9V/v0aOHHD582MpdEBERuZbTHX/NcZQqVUqLeOyOxdJnDe5I9M0336jlIIiIiEifUYtZWVmyfft2LeKxOxbnUzkiIiJKugsXLkgqxGJpooP2teA2Nh3a/4iIiCg15bG66QqzphrTW58/f14eeuihy4aXz5w508rdEhEREdmf6PTt2zeqxQuJiIhSkQ6df404qlSpokU8dsdiaaIzefJkK5+OiIjIU3TpzpGWlqYW3U6FWJxP5YiIiFKEDqOcjDh+/vlnLeKxOxYmOkRERCkoW5MFRu2OhYkOEREReRYTHSIioiTSYcXwVMJEh4iIKEn+85//aNEhOVeuXFKzZk1tRl3ZGYvzJSQiIkoRly5dEl3kzZtXUiEWJjpEREQpJjs7WzZv3qxFh2S7Y9E+0VmyZIl06tTJv4T77NmzA+7HTMzG0hPGpX379o7FS0RERPqwdMJAO2D182uvvVb69esn3bp1C7kNEhvzZIXGEhRERPRfK1eulG3btknt2rWlWbNmTodDlBTaJzodOnRQl0iQ2JQvXz5pMVHqieUEgW2//fZb9X+8d+M5ocR6QjJvD06dzBI9kfJEbJ+hQ4fKmDFj/NczMjJk9OjRjsZElBQ+F0G4s2bNCritb9++vuLFi/vKli3rq127tu+hhx7yHTt2LOxzXLx40Tdjxgz1N1aZmZm+jRs3qr9u5Pb4nSpDRkaGeu8ZF1yPdttQ2+dUhlj2F26f0T42HuHijzXuYIk+PpU+C7HGv2LFipDvD9zuFLe/BrGeS4ztd+3a5dNBdna2Ovb46/VYtK/RyQmardCkVa1aNdm5c6c89dRT6lf08uXLJXfu3GEfh6mmYx3KZkxPrcOU2fFwe/xOlAE1DOZfwYDrnTt3vqzGIdS2obaPVIZY9hdpn9E8Nl6h4o817mCJPj7VPguxxr9ly5awtzdu3Fic4JXXIFY4L+lQZp/PJxcvXpR8+fI5Ptw9llgindfDcX2i07NnT///r7nmGmnQoIHUqFFDFi1aJK1btw77uK1bt8Z1wABV627m9viTWYbFixeHvb1YsWJRbRtu+1BliGV/Oe0zp8cmyhx/rHGH2i6Rx6fqZyHa+MMN3cXtGO3iJLe+BvEmK7/99pucPHnS8nhSRf369VMv0QlWvXp1KVOmjOzYsSNiolOnTp2Yx+3jjW30H4g3SXKS2+N3ogynTp0KefvNN98s9erVi2rb4O0jlSGW/eW0z5weG69Q8ccad7BEH59qn4VY48cxXL9+vbz00ksBfXR69OghTnH7a4D5cPCDOVYlSpRQw6gzMzP9fUwrVaokqXz8s2yOxXOJzv79+9XMkxUqVIi4HQ5mvAc0kcfqwO3xJ7MMLVq0kKZNm8qqVav8t6EpBbeHiimWeEPdFsv+wm1vFumxiTLHH67s0b5OsZbbKm7/LMQSf6gmAR3K7tbXIN45X86dO+dPcozXxcny59bo+NsVi/bz6Jw5c0b9EsEFdu3apf6/d+9edd+QIUNkxYoVsnv3blmwYIFq08dU0u3atXM6dPIA9B0JTiJwGy6xVMFHWz0fy/7CbR/tY60Urnx2lZtiE64PFI9vasulwfIPyYhFn1KGsWbNGrn++uvVBQYPHqz+/+yzz6rMb8OGDXLnnXeqKq/+/ftLo0aNZOnSpZxLh5J+AjeGdocS6b549xfp9li3SVS48tlVbooNj68+nO74a8D5Mz09XYvaHLtj0b7pqlWrVhFXep07d25S46HUEssJHE0t6PcQ/MsZ85dEO3Io1oQhmkQi2mQjEaHKbme5KTY8vvrQZeVyn8+nWkWKFCniePJldyza1+gQOck4gZtFOoFjAjY0pQ4fPlxd8P9Ro0bZtr9Q20f7WKsZZZ86dart5abY8PhSqD5Ge/bs0WatKztj0b5Gh8hpOIFjrqZoZ+zF/YmcQGLdX/D24NTswomUPdZyU2x4fClVMdEhikKiyYvd+wve3q0nsWQf51TD4+s8TIqXJ89/T73sT2o/JjpERERJUrFiRSlQoMBlfVSc6CeTX6Mky85YmOgQERElSaiRRU4kOblz55ZatWqJDuyOhZ2RiYiIkkSHzr9GHFiOQod47I6FiQ4REVEKDi8/ePCgFvHYHQsTHSIiIvIsJjpERETkWUx0iIiIksTpWYjNcegwK3IyYuGoKyIiohRbSDNXrlxStWpVSYVY9DjiREREKUCHUU5GHIcPH9YiHrtjYaJDRESUJDqMcjLiOHr0qBbx2B0LEx0iIiLyLCY6RERE5FlMdIiIiJJEh1FORhwlS5bUIh67Y+GoKyIiohQcdVWpUiVJhVj0OOJEREQpQIdRTkYcBw4c0CIeu2NhokNERJQkOoxyMuI4fvy4FvHYHQsTHSIiIvIsJjpERETkWUx0iIiIkkSHUU5GHGXLltUiHrtj4agrIiKiFBx1Va5cOUmFWPQ44kRERClAh1FORhy7d+/WIh67Y2GiQ0RElCQ6jHIy4jhz5owW8dgdCxMdIiIi8iwmOkRERORZTHSIiIiSRIdRTkYcFStW1CIeu2PhqCsiIqIUHHVVqlQpSYVY9DjiREREKSArK0t0iWP79u1axGN3LEx0iIiIUtCFCxckFWJhokNERESexUSHiIiIPEv7RGfJkiXSqVMnf4/s2bNnB9yPCYaeffZZqVChghQsWFDatGmj2vqIiIh0o1Nn5CpVqmgRj92xOF/CHJw9e1auvfZaGTduXMj7x4wZI2+88YZMmDBBVq5cKYULF5Z27drJ+fPnkx4rERFRJDoM5zbiKFq0qBbx2B2L9sPLO3TooC6hoDbntddek2eeeUY6d+6sbps6dapaHAw1Pz179kxytEREROFhZFHevHm1iGPr1q1Sp04dyZ07t6dj0b5GJ5Jdu3bJr7/+qpqrDMWLF5dmzZrJ8uXLHY2NiIhIZ9kaLOiZjFi0r9GJBEkOBC/vjuvGfZEyyFjbA40x/jrMOxAPt8cPLIPz3B6/F8rg9vi9UIZ448bjdChzlkbHP5ZY4qnxcXWikwhUk8VbRbZt2zZxM7fHDyyD89wevxfK4Pb43VyGeBOEHTt2ON5UpOvxjyaW+vXrp1aiU758efX38OHDatSVAdevu+66iI9FW2Cs7aR4Y+OFqF27tlZv1FSJH1gG57k9fi+Uwe3xe6EMly5dUj+YY4Xy5suXT5zm8/nk4sWLKhanOyTbHYurE51q1aqpZGfBggX+xObUqVNq9NVf/vKXiI/FByveD1cij9WB2+MHlsF5bo/fC2Vwe/xuLkO8fUp0Ka/P55P8+fOrLhw6JDp2xqJ9Z+QzZ87I+vXr1cXogIz/7927Vx2Qxx9/XF544QX54osvZOPGjdKnTx81506XLl2cDp2IiEjLDsDZ2dmyefNmLeKxOxbta3TWrFkjt9xyi//64MGD1d++ffvKlClTJCMjQ82188ADD8iJEyekZcuWMmfOHClQoICDURMREZEOtE90WrVqpaq1wkGtznPPPacuRERERK5quiIiIiKKFxMdIiKiJNFhbSkjjnr16mkRj92xOF9CIiIicmSIfCrEwkSHiIgoSXQY5WTEgckLdYjH7liY6BAREZFnMdEhIiIiz2KiQ0RElIJyadARORmxaD+PDhERkVfosPyDEUd6erqkQiz6pHNEREQeF2kC3GTHcfr0aS3isTsWJjpERERJosMoJyOOPXv2aBGP3bEw0SEiIiLPYqJDRESURDo0F6USdkYmIiJXW7lypWzbtk1q164tzZo1s2xbOxw7dkyuvPJK0UH+/PlFF3bGwhodIiJyraFDh8oNN9wgffr0UX9x3Ypt7ZKVlSW6jHSqVauWFqPA7I6FiQ4REbkSamfGjBkTcBuu4/ZEtk2FZqvs7Gz57bfftOmMbGcsTHSIiMiVvv3226hvj2XbVODz+eTgwYNaJF52x8JEh4iIiDyLiQ4REblShw4dor49lm3JW5joEBGRK2HUVEZGRsBt6GAcajRVLNumgrS0NClSpIj66/VYOLyciIhca/To0dKtW7eohozHsq1d8uXLJ7osolm1alVJhViY6BARkashYYk2aYllWzuULl1adbp1uiYlOztbjh49KmXLlnV8FXO7Y2HTFRERUZLokOQYcSC50GXUlZ2xMNEhIiIiz2KiQ0RERJ7FRIeIiChJdGi2MuIoWbKkFvHYHQs7IxMRESWJ0x1/zXFUqlRJUiEWPY44ERFRCtBhbSkjjgMHDmgRj92xMNEhIiJKEh1GORlxHD9+XIt47I6FiQ4RERF5Vsr10TEyxkuXLsX82KysLHXBY3Wo7ku1+IFlcJ7b4/dCGdwevxfKYJxDdKgRocjSfCn2Kv3+++/y1VdfOR0GERF5wB133CGFChWKKjGaNWuWdO3aVfLmzZuU2ChFEx38cjh//rzkyZNHi2F1RETkPjh1ZmZmSoECBaIaSWVsz3NP8qVcokNERESpg52RiYiIyLOY6BAREZFnMdGJ0rhx46Rq1aqqPbZZs2ayatUqcYuRI0dKkyZNpGjRonLFFVdIly5dZOvWreJWo0aNUm3cjz/+uLgJJsTq1auXlC5dWgoWLCjXXHONrFmzRtwCI2SGDRsm1apVU/HXqFFDnn/+ea1HnSxZskQ6deokFStWVO+Z2bNnB9yP2J999lmpUKGCKlObNm1k+/bt4ob40bl16NCh6n1UuHBhtU2fPn3k4MGD4qbXwOyhhx5S27z22mtJjZG8jYlOFGbMmCGDBw+W4cOHy48//ijXXnuttGvXTo4cOSJusHjxYhkwYICsWLFC5s2bp74g27ZtK2fPnhW3Wb16tUycOFEaNGggboLJsG688UY12uLbb7+Vn3/+WV5++WW1votbjB49WsaPHy9jx46VzZs3q+tjxoyRN998U3SF9zg+r/ihEgrif+ONN2TChAmycuVKlTDgs40BC7rHjxGk+D5C8om/M2fOVD9g7rzzTnHTa2DAiCR8RyEhIrIUOiNTZE2bNvUNGDDAfz0rK8tXsWJF38iRI31udOTIEfwE9y1evNjnJqdPn/bVqlXLN2/ePN/NN9/se+yxx3xuMXToUF/Lli19btaxY0dfv379Am7r1q2b75577vG5Ad7zs2bN8l/Pzs72lS9f3vfSSy/5bztx4oQvf/78vg8//NCne/yhrFq1Sm23Z88en47ClWH//v2+SpUq+TZt2uSrUqWK79VXX3UkPvIm1ujk4OLFi7J27VpVpW3AUEJcX758ubjRyZMn1d9SpUqJm6BWqmPHjgGvhVt88cUX0rhxY/njH/+omg+vv/56+ec//ylu0qJFC1mwYIFs27ZNXf/pp59k2bJl0qFDB3GjXbt2ya+//hrwfipevLhqmnbzZxtNPyVKlBA3TfnRu3dvGTJkiFx99dVOh0MelHIzI8fq2LFjqm9CuXLlAm7H9S1btojb4EsFfVvQjFK/fn1xi48++khVz6Ppyo1++eUX1eyDJtCnnnpKlePRRx+VfPnySd++fcUN/vrXv8qpU6ekbt26kjt3bvW5GDFihNxzzz3iRkhyINRn27jPTdDchj47d911lxQrVkzcAk2gmFsGnwciOzDRSTGoFdm0aZP6Je4W+/btk8cee0z1L0JncDdCgokanRdffFFdR40OXgf0DXFLovPxxx/LtGnTZPr06eqX9/r161XSjD4VbimDV6Hf3Z/+9CfVuRoJtVugtvz1119XP2I4iR7ZhU1XOShTpoz69Xr48OGA23G9fPny4iaPPPKIWv5i4cKFcuWVV4qbvgzR8bthw4bqlx8u6GCNTqT4P2oWdIdRPenp6QG31atXT/bu3StugaYF1Or07NlTjfRBc8OgQYPUqD43Mj6/bv9sG0nOnj171I8BN9XmLF26VH22K1eu7P9soxxPPPGEGuVKZAUmOjlA00KjRo1U3wTzr3Ncb968ubgBfuUhycGohu+++04ND3aT1q1by8aNG1UNgnFB7QiaTPB/JKK6Q1Nh8JB+9HWpUqWKuAVG+QRPdY9j78YFGQGfAyQ05s82muYw+sotn20jycGQ+Pnz56upC9wEyfKGDRsCPtuoIURSPXfuXKfDI49g01UU0K8CVfM4uTZt2lTN8YAhk/fdd5+4pbkKzQ2ff/65mkvH6H+AjpeYO0R3iDm4PxGGAeNL3S39jFDzgc68aLrCiQnzML399tvq4haYCwV9cvDrG01X69atk1deeUX69esnujpz5ozs2LEjoAMyTqboiI9yoOnthRdekFq1aqnEB0O1caLFXFO6x49awj/84Q+q2Qc1tajZND7buB8/0tzwGgQnZ5iCAQlonTp1HIiWPMnpYV9u8eabb/oqV67sy5cvnxpuvmLFCp9b4GUOdZk8ebLPrdw2vBy+/PJLX/369dXw5bp16/refvttn5ucOnVKHXN8DgoUKOCrXr267+mnn/ZduHDBp6uFCxeGfO/37dvXP8R82LBhvnLlyqnXpXXr1r6tW7f63BD/rl27wn628Ti3vAbBOLycrMZFPYmIiMiz2EeHiIiIPIuJDhEREXkWEx0iIiLyLCY6RERE5FlMdIiIiMizmOgQERGRZzHRISIiIs9iokNERESexUSHiPzuvffemJc/+Nvf/qZWnsYFy6MYcH327NmWxrdo0SL/vnRZpoGI9MZEh8gFyYTVdu/erZIFrDlkBax9dejQIXnggQfETlgvDPvBemFERNHgop5ElLA8efKohRjthoUqsR8sRnvhwgXb90dE7scaHSLNbNq0STp06CBFihSRcuXKSe/eveXYsWP++1u1aiWPPvqoZGRkqBWgceJH85HZli1bpGXLllKgQAFJT0+X+fPnBzQlYaVuuP7669XteE6zf/zjH2p1bKwsPWDAALl06VLC5Ro+fLh6zg0bNqjrVatWVSuH9+nTR5W1SpUq8sUXX8jRo0elc+fO6rYGDRrImjVrEt43EaUuJjpEGjlx4oTceuutKgHBCX7OnDly+PDhy5pq3nvvPSlcuLCsXLlSxowZI88995zMmzdP3ZeVlaWaxgoVKqTuf/vtt+Xpp58OePyqVavUXyRAaAqaOXOm/76FCxfKzp071V/sZ8qUKeoSL6wbPHDgQJk6daosXbpUJS+GV199VW688UZZt26ddOzYUSV1SHx69eolP/74o9SoUUNd59rDRBQvNl0RaWTs2LEqyXnxxRf9t7377rty1VVXybZt26R27drqNiQLqCGBWrVqqcctWLBAbrvtNpXwIFFBx12jOWnEiBHqPkPZsmXVX9TYBDc5lSxZUj1f7ty5pW7duioBwXPff//9MZcnMzNTJS1IZJYtWyaVKlUKuP/222+XBx98UP3/2WeflfHjx0uTJk3kj3/8o7pt6NCh0rx5c5XsJaNpjIi8h4kOkUZ++uknVZOCZptgSF7MiY4ZmoSOHDmi/r9161aVGJkTg6ZNm8bUsRhJjvm5N27cGFd5Bg0aJPnz55cVK1ZImTJlLrvfXA4008E111xz2W0oGxMdIooHm66INHLmzBnp1KmTGg1lvmzfvl1uuukm/3Z58+YNeBz62WRnZ1sSg5XPjVqkAwcOyNy5c3PcF/YT7jarykZEqYc1OkQaadiwoXz22Weqoy5GMsWjTp06sm/fPtXcY9SIrF69+rLRS0Z/HjvdeeedKnG7++67VS1Rz549bd0fEVEw1ugQOeDkyZOX1dogOcEIp99++03uuusulZyguQq1Iffdd1/USQlqUdCJt2/fvmqE0/fffy/PPPNMQA3JFVdcoYZoG52dEY9dunbtKu+//74qw6effmrbfoiIQmGiQ+QAdBRGp2Pz5e9//7tUrFhRJSZIatq2bav6qzz++ONSokQJyZUruo8rak4wjBzNYOjY++c//9k/6grDzQG1RW+88YZMnDhR7RPDue30hz/8QY3gwqgq8wgvIiK7pfk4bpPI85A8YV6dHTt2qNoeK2EOHyRWVs2yHO3s0hiKb/USE0TkPazRIfKgWbNmqWHmWOoBc+VgaQbMV2N1kmPAqCyMFHvrrbdseX4D5uHBfqZNm2brfojIO1ijQ+RBmJwPsw7v3btXDetu06aNvPzyy2reHKuhTxEuxvw8xYsXF7ucO3dOjeICJDwcck5EOWGiQ0RERJ7FpisiIiLyLCY6RERE5FlMdIiIiMizmOgQERGRZzHRISIiIs9iokNERESexUSHiIiIPIuJDhEREXkWEx0iIiISr/p/g/xRsuJYnn4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(\n",
    "    2,\n",
    "    2,\n",
    "    gridspec_kw={\n",
    "        \"hspace\": 0.05,\n",
    "        \"wspace\": 0.05,\n",
    "        \"width_ratios\": [5, 1],\n",
    "        \"height_ratios\": [1, 5],\n",
    "    },\n",
    ")\n",
    "_, _, _, scatter, _, _, _ = plg.plot_metadata.plot_len_vs_freq_jointplot(\n",
    "    ds_cmls.length, ds_cmls.frequency, axes=axes\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting distributions of frequency, length, and polarization. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 4))\n",
    "len_bars = plg.plot_metadata.plot_distribution(\n",
    "    length=ds_cmls.length, frequency=ds_cmls.frequency, variable=\"length\", ax=ax\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot the distribution as a percentage. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 4))\n",
    "len_bars = plg.plot_metadata.plot_distribution(\n",
    "    length=ds_cmls.length,\n",
    "    frequency=ds_cmls.frequency,\n",
    "    variable=\"length\",\n",
    "    percentage=True,\n",
    "    ax=ax,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And customize the plot a bit using keyword arguments. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PpUeXqdMmWfeRmJh4vW8RABDCrrvGOGbMGPniiy+kUqVKnnPafPr666+bqRR//vmnTJw4MVNTN1wulwwZMkRiY2Nl1apVUqFCBa/H69evLzlz5pTly5d7rqfTOTR8GzVqlOE8Sz0AALgpwZiUlCSXLl266ryec/f7RUZGyqlTpzLVfKojTr/66isz3cP9+zryNU+ePOZ2wIABZhk6HZBTsGBBE6Qaigy8AQDYoin1/vvvN0vCbdq0yXNOf37iiSc8Uyq2bt16Ve0vPdOnTzfNnc2aNZNSpUp5jk8//dSrT/Mf//iHqTHqoB5tQtUFywEAsEWNUecV9unTx9PM6a4ttmjRwjymdBCOLgSQmabUv6MjXadNm2YOAABsF4xaY9MJ/tu3bzeDblS1atXMYa1VAgAQUhP8daK9HgAASKgH4/79++Xrr782o0N1OoXVm2++6auyAQBg/2DUqRO6w4ZO4tfmVF2lZu/evaa/UNdMBQAgpEal6gT6Z555xow81YExOqdRJ9HrLhvdunXzTykBALBrMP7+++/St29f83OOHDnM1lM6ClX3aHzttdf8UUYAAOwbjPny5fP0K+qcw927d3seO3r0qG9LBwCA3fsYdcUZXehb90Rs166dPP3006ZZVSfdsxoNACDkglFHnermxEp3sdCfdaWaKlWqMCIVABB6waijUa3Nqu+9956vywQAgHP6GDUYdaupK+nWUNbQBAAgJIJR5yxevnz5qvOpqaly4MABX5ULAAB7N6XqSjduS5YsMVtCuWlQ6sT/8uXL+76EAADYMRg7d+5sbsPCwqRfv35ej+kuGxqKmdlRA8FPlwr0xdSd4sWLS9myZX1SJgDweTCmpaWZW91ncf369eZ/WkB6oaiLy+vCD1mlm1XrsoOEIwBbj0rds2ePf0qCoKA1RQ3FLl26ZOnLk15H58bqLcEIwPa7a2h/oh5Hjhzx1CTdPv74Y1+VDQ6moRgZGRnoYgCA/4NRJ/Xruqh33nmnWRJO+xwBAAjZYNQJ/bNmzZI+ffr4p0QAADhpHqMuIN64cWP/lAYAAKcF46OPPirz5s3zT2kAAHBaU+r58+dlxowZsmzZMqlTp46Zw2jFQuIAgJAKxi1btsjtt99uft62bZvXYwzEAQCEXDCuXLnSPyUBAMCJfYxuu3btMmumulc4cblcviwXAADOCEbdcqpFixZStWpVadeunSQlJZnzAwYMkKefftofZQQAwL7BOHz4cDPgRtfEzJs3r+d89+7dZfHixb4uHwAA9u5j/O6770wTaunSpb3OV6lSRfbt2+fLsgEAYP8a45kzZ7xqim7Hjx+X8PBwX5ULAABnBOO9994rc+bM8ZqioQuJT5w4Ue6//35flw8AAHs3pWoA6uCbDRs2mOXhnnvuOfn1119NjXHNmjX+KSUAAHatMdaqVUv++OMPadKkiXTq1Mk0reree5s2bZJKlSpd17VWr14tHTp0MNsTac3zyy+/9Hq8f//+5rz1eOCBB663yAAA+Hc/xkKFCsno0aMlqzRU69atK4888ogJ1/RoEM6cOdNzn35MAICtglFDKn/+/NKtWzev8wsWLJCzZ89Kv379Mn2ttm3bmuNaNAgjIiKut5gAANycptTo6GizO/uVSpQoIa+++qr42qpVq8y1q1WrJk888YRZYAAAANvUGHVif4UKFa46X65cOfOYL2kzqjax6uvt3r1bRo0aZWqYa9eulezZs6f7O6mpqeZwS0lJ8WmZAADB7bqDUWtvusNG+fLlvc7/8ssvUqxYMV+WTR5++GHPz7Vr1zbbXOkAH61F6sjYjGq0Y8eO9Wk5AACh47qbUnv06CFPPfWU2WXj8uXL5lixYoUMHTrUK8j8oWLFiqYZVxcwz0hUVJQkJyd7jsTERL+WCQAQ4jXGl19+Wfbu3WtqbDly/O/XdYJ/3759/dLHaLV//37Tx1iqVKlrDtZh5CoA4KYEo24tdejQIZk1a5a88sorsnnzZsmTJ49p5tQ+xut1+vRpr9rfnj17zDWLFi1qDm0S7dq1qxmVqn2MuphA5cqVpU2bNtf9WgAA+CUYNZh0pRtdNFyPrNDVc6zLyI0YMcLc6pSP6dOnm77M2bNny8mTJ80iAK1btzY1VmqEAABbBGO2bNlMGGpzZlZDUTVr1uyaGxzrLh4AANh68M2ECRPk2WeflW3btvmnRAAAOGnwjQ6y0RVudCm3XLlymT5GK11MHACAkAnGKVOm+KckAAA4MRivZy1UAACCvo9R6dSJMWPGmMn+R44cMecWLVpkRqsCABBSwRgXF2fmLa5bt05iYmLMXET3knAvvviiP8oIAIB9g/Hf//63mdy/dOlSM/jGrXnz5hIfH+/r8gEAYO9g3Lp1qzz44IPpLi5+9OhRX5ULAABnBGPhwoUlKSnpqvObNm2SW2+91VflAgDAGcGoO2iMHDnSrJkaFhZmFhBfs2aNPPPMM2aOIwAAIRWMuoNG9erVpUyZMmbgTc2aNaVp06bSuHFjM1IVAICQmseoA24++OADeeGFF0x/o4ZjvXr1fLJ2KgAAjglGbTKdNGmSfP3113LhwgWzH6NOz7hySTgAAEKiKXX8+PEyatQoyZ8/vxlkM3XqVBk8eLB/SwcAgF2Dcc6cOfLuu++araC+/PJLWbhwocydO9fUJAEACLlgTEhIkHbt2nnut2zZ0oxKPXjwoL/KBgCAfYPx0qVLkjt3bq9zOXPmlIsXL/qjXAAA2Hvwjcvlkv79+0t4eLjn3Pnz52XQoEGSL18+zzldPxUAgKAPxvS2m+rdu7evywMAgDOCcebMmf4tCQAATt2PEQCAYEUwAgBgQTACAGBBMAIAYEEwAgBgQTACAGBBMAIAYEEwAgBgQTACAGBBMAIAYEEwAgBgl2BcvXq1dOjQQSIjI83ejroB8pU7erzwwgtSqlQpyZMnj9kDcufOnQErLwAg+AU0GM+cOSN169aVadOmpfv4xIkT5a233pL33ntP1q1bZ7a3atOmjdnuCgCAgO6u4Q9t27Y1R3q0tjhlyhQZM2aMdOrUyZybM2eOlCxZ0tQsH3744ZtcWgBAKLBtH+OePXvk0KFDpvnUrVChQtKwYUNZu3ZtQMsGAAheAa0xXouGotIaopXedz+WntTUVHO4paSk+LGUAIBgY9sa442Kjo42NUv3UaZMmUAXCQDgILYNxoiICHN7+PBhr/N63/1YeqKioiQ5OdlzJCYm+r2sAIDgYdtgrFChggnA5cuXezWL6ujURo0aZfh74eHhUrBgQa8DAABH9DGePn1adu3a5TXgZvPmzVK0aFEpW7asDBs2TF555RWpUqWKCcrnn3/ezHns3LlzIIsNAAhiAQ3GDRs2yP333++5P2LECHPbr18/mTVrljz33HNmruNjjz0mJ0+elCZNmsjixYsld+7cASw1ACCYBTQYmzVrZuYrZkRXwxk3bpw5AAAI6T5GAAACgWAEAMCCYAQAwAkr3wC+lJCQIEePHs3ydYoXL25GTAMIXgQjQiIUq1evLufOncvytXT7s+3btxOOQBAjGBH0tKaoodilSxdT48vKdWJiYswtwQgEL4IRIUNDUReIAIBrYfANAAAWBCMAABYEIwAAFgQjAAAWBCMAABYEIwAAFgQjAAAWBCMAABYEIwAAFgQjAAAWBCMAABYEIwAAFgQjAAAWBCMAABYEIwAAFgQjAAAWBCMAABYEIwAAFgQjAAAWBCMAABYEIwAAFgQjAAAWBCMAABYEIwAATgnGl156ScLCwryO6tWrB7pYAIAglkNs7rbbbpNly5Z57ufIYfsiAwAczPYpo0EYERER6GIAAEKErZtS1c6dOyUyMlIqVqwovXr1koSEhEAXCQAQxGxdY2zYsKHMmjVLqlWrJklJSTJ27Fi59957Zdu2bVKgQIF0fyc1NdUcbikpKTexxAAAp7N1MLZt29bzc506dUxQlitXTj777DMZMGBAur8THR1tAhQAgKBsSrUqXLiwVK1aVXbt2pXhc6KioiQ5OdlzJCYm3tQyAgCczVHBePr0adm9e7eUKlUqw+eEh4dLwYIFvQ4AAIIiGJ955hmJi4uTvXv3yo8//igPPvigZM+eXXr06BHoogEAgpSt+xj3799vQvDYsWNyyy23SJMmTSQ+Pt78DABAyAXj/PnzA10EAECIsXVTKgAANxvBCACABcEIAIAFwQgAgAXBCACABcEIAIAFwQgAgAXBCACABcEIAIBTVr4Bgpluun306NEsX6d48eJStmxZn5QJAMEIBCwUq1evLufOncvytfLkySPbt28nHAEfIRiBANCaooZily5dTI0vK9eJiYkxtwQj4BsEIxBAGoqRkZFiFzTvAgQjgP9D8y7wPwQjAIPmXeB/CEYAtm7eBW425jECAGBBMAIAYEEwAgBgQTACAGBBMAIAYEEwAgBgQTACAGBBMAIAYEEwAgBgQTACAGBBMAIAYEEwAgBgwSLiABAEfLWXpgr1/TQJRgBwOF/upalCfT9NghEAHM5Xe2n6ej/NBIfWYglGAAgSdtpLM8HBtVhHBOO0adNk0qRJcujQIalbt668/fbbctdddwW6WABuAjvWOuxYJrs5atNabFAE46effiojRoyQ9957Txo2bChTpkyRNm3ayI4dO6REiRKBLh6AEKt12LFMdlbcRrXYoAnGN998UwYOHCj/+te/zH0NyG+//VY+/vhj+fe//x3o4gEIsVqHHcuEEArGCxcuyMaNGyUqKspzLlu2bNKyZUtZu3Ztur+TmppqDrfk5GRzm5KSkqWynD592twmJSWZct2oY8eOea5HmSgTZcpcmS5evJilMrmv4Yty2blMWf3b+fLvd9pmZXL/nsvl+vsnu2zswIED+g5cP/74o9f5Z5991nXXXXel+zsvvvii+R0ODg4ODg654khMTPzb7LF1jfFGaO1S+yTd0tLS5Pjx41KsWDEJCwvzfHMoU6aMJCYmSsGCBcWJeA/2wHuwj2B4H7wH/9Ga4qlTpzLV32nrYNT2++zZs8vhw4e9zuv9iIiIdH8nPDzcHFaFCxdO97n6R7PTH+5G8B7sgfdgH8HwPngP/lGoUCHnr5WaK1cuqV+/vixfvtyrBqj3GzVqFNCyAQCCk61rjEqbRfv16yd33nmnmbuo0zXOnDnjGaUKAEBIBWP37t3lr7/+khdeeMFM8L/99ttl8eLFUrJkyRu+pja1vvjii1c1uToJ78EeeA/2EQzvg/dgD2E6AifQhQAAwC5s3ccIAMDNRjACAGBBMAIAYEEwAgAQysGoW1iVL19ecufObXbr+Omnn8RJoqOjpUGDBlKgQAGzu0jnzp3NTiNONWHCBLMi0bBhw8RpDhw4IL179zarKukOCbVr15YNGzaIU1y+fFmef/55qVChgil/pUqV5OWXX87cWpIBsnr1aunQoYNZvUT/3Xz55Zdej2vZdQR7qVKlzHvSdZV37twpTnkPunbqyJEjzb+lfPnymef07dtXDh48KE77W1gNGjTIPEen2zlBSAWjewsrHUr8888/m70ddQurI0eOiFPExcXJ4MGDJT4+XpYuXWo+SK1btzZzO51m/fr18v7770udOnXEaU6cOCH33HOP5MyZUxYtWiS//fabvPHGG1KkSBFxitdee02mT58u77zzjvz+++/m/sSJE81+p3al/871c6tfcNOj5X/rrbfMLjzr1q0z4aKf8fPnz4sT3sPZs2fN/5v0C4ve6s4b+sW3Y8eO4rS/hVtsbKz5/5Wjtp5yhRBdeHzw4MGe+5cvX3ZFRka6oqOjXU515MgRszBuXFycy0lOnTrlqlKlimvp0qWu++67zzV06FCXk4wcOdLVpEkTl5O1b9/e9cgjj3id69Kli6tXr14uJ9B/97GxsZ77aWlproiICNekSZM8506ePOkKDw93ffLJJy4nvIf0/PTTT+Z5+/btc9mVZPA+9u/f77r11ltd27Ztc5UrV841efJklxOETI3RvYWVNq1kdgsrJ3Bvq1W0aFFxEq31tm/f3uvv4SRff/21WY2pW7dupkm7Xr168sEHH4iTNG7c2Cyv+Mcff5j7v/zyi/zwww/Stm1bcaI9e/aYRUCs/6Z0bUztMnH6Z1ybITNa89mu0tLSpE+fPvLss8/KbbfdJk5i+5VvfEU3AtU+lStXzNH7unu2E+k/PO2b0ya9WrVqiVPMnz/fNBNpU6pT/fnnn6YZUpvmR40aZd7LU089Zdb31SUMnUA3+tadEHQ3el2sXz8f48ePl169eokTaSiq9D7j7secRpuAtc+xR48etluQ++9o03yOHDnM58JpQiYYg5HWurZt22a+5TuFbkUzdOhQ0z+qA6CcSr+UaI3x1VdfNfe1xqh/C+3bckowfvbZZzJ37lyZN2+e+Ua/efNm80VL+4Kc8h6CmY4f+Oc//2kGFOmXMCfZuHGjTJ061XwBdm/35yQh05R6I1tY2dmTTz4p33zzjaxcuVJKly4tTvrA6GCnO+64w3yb1EMHFOmACf1Zay1OoKMea9as6XWuRo0akpCQIE6hTVxaa3z44YfNKEht9ho+fLgZ+exE7s9xMHzG3aG4b98+8yXSabXF77//3nzOy5Yt6/mc63t5+umnzawAuwuZYAyWLaz026OGoo70WrFihRlq7yQtWrSQrVu3mtqJ+9Calzbf6c/65cUJtPn6ymky2ldXrlw5cQodAan97Fb6318/F06knwUNQOtnXJuKdXSqkz7j7lDUaSbLli0z04Gcpk+fPrJlyxavz7m2ROiXsSVLlojdhVRTajBsYaXNp9r09dVXX5m5jO6+Ex1koPO27E7LfGV/qA6p1w+/k/pJtWalg1e0KVX/J6bzYWfMmGEOp9A5aNqnqN/qtSl106ZN8uabb8ojjzwidnX69GnZtWuX14Ab/Z+uDj7T96FNwa+88opUqVLFBKVOe9D/Iet8Xye8B22JeOihh0wTpLYIaQuK+zOuj+sXfKf8LYpdEeg6tUm/uFSrVk1szxVi3n77bVfZsmVduXLlMtM34uPjXU6if7L0jpkzZ7qcyonTNdTChQtdtWrVMtMBqlev7poxY4bLSVJSUsx/d/085M6d21WxYkXX6NGjXampqS67WrlyZbr//vv16+eZsvH888+7SpYsaf4uLVq0cO3YscPllPewZ8+eDD/j+ntO+ltcyUnTNdh2CgCAUOxjBAAgMwhGAAAsCEYAACwIRgAALAhGAAAsCEYAACwIRgAALAhGIIj179//hld9adq0qVllye3vdmm/EYsXL5bbb7/dscvQITgRjEAAw8dX9u7da4JLl+Ty1X6Tuvi2LjDuTw888IBZKkx3+QDsgmAEcBXd7UTXEL5ykXF/fbHQ1wPsgmAE/Ez3aWzbtq3kz5/fbJqrOw/oxtluzZo1M5u5Pvfcc2YBZl1o+aWXXvK6hm6m3aRJE7OHpW53pbsuWJs23bus6L6Qel6vafX666+bBap1YWddiF53cMjIX3/9ZXZu0UXGr+XFF18019RdFJRuJ6QLePft29e8V91pRGueer1OnTqZc3Xq1JENGzZ4XUdfR8/t3r070/9NAX8iGAE/OnnypDRv3twElv7PX/vUtIlSd+Swmj17ttllRLdImjhxoowbN87sw6d0hwVtqs2bN695XHfwGD16tNfv6+4eSgMzKSlJYmJiPI/pnp0aOnqrrzNr1ixzZEQ3vtbX0v0l06PLKw8ZMkTmzJlj9t3TsHObPHmy2ZJLd+po3769+RKgQdm7d2+zY0SlSpXMfesSzboTg35h0GsBthDoVcwBp9PdBDp16pTuYy+//LKrdevWXucSExPNLgTuXR90d5EmTZp4PadBgwaukSNHmp8XLVrkypEjhyspKcnz+NKlS801YmNjzX33rgybNm26qmy6q8GlS5c857p16+bq3r17hu9Hd0DQnTaupNdfsGCBq2fPnq4aNWq49u/f7/W4vk7v3r0997W8+ju624Xb2rVrzTnre1H16tVzvfTSSxmWCbiZQmo/RuBm++WXX0xNTZsRr6S1uKpVq5qfrbUupU2UugO60g2Ry5Qp47ULve4nmlm616J1A2i9tm4WnZFz586ZJtuM9qEMDw+X+Ph4KV68+FWPW9+H1gJV7dq1rzqn7836fnQvUd04GbADmlIBP2/mqn1o1p3M9dDd2XU6hJuOzLTSfkJfTWG43mtr4J04cSLdx1q1aiUHDhzIcBd262vp62R07srXP378uNxyyy2Zej+Av1FjBPzojjvukC+++MIMTMmR48Y+brrjeWJioumbdNe41q9f7/Uc987u2h+ZVdofqrvGazgWKVLE67GOHTuaoO/Zs6ephfpiOsf58+dN7VlfF7ADaoyADyQnJ19VK9Qw0xGgWhvq0aOHCTMNAK1t6VSIzIaY1tJ00Eq/fv3MCNA1a9bImDFjvGpgJUqUMM2R7sE9Wp4bpQGltUZ9nfQ8+OCD8p///Me8h88//1yySptltXm2UaNGWb4W4AsEI+ADq1atMoFiPcaOHSuRkZEmYDQEW7dubfrbhg0bJoULF870HEGtmem0DG2WbdCggTz66KOeUanuvkCtjepcwPfff9+8pk6PuFH6ehp615p0/9BDD5kRrjrq1DoC9kZ88skn0qtXLzMSFrCDMB2BE+hCALg+GrY6r3HXrl2mNulr2pSqg3Z0ioXOR/QXnc+pTcU6lcU9FxMINIIRcIDY2FgzsrVKlSomDIcOHWr6/3TOob9oLVUXBLj33nv99hruif3du3f322sA14tgBBxAJ9PrqjIJCQmm/69ly5byxhtvmOAC4FsEIwAAFgy+AQDAgmAEAMCCYAQAwIJgBADAgmAEAMCCYAQAwIJgBADAgmAEAMCCYAQAQP6//wfGWu7xNgwxRAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 4))\n",
    "len_bars = plg.plot_metadata.plot_distribution(\n",
    "    length=ds_cmls.length,\n",
    "    frequency=ds_cmls.frequency,\n",
    "    variable=\"length\",\n",
    "    percentage=True,\n",
    "    ax=ax,\n",
    "    rwidth=0.5,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot the polarization of CMLs.<br> \n",
    "In this dataset all CMLs have two sub-links, both with the same polarization, of which the vast majority is vertically polarized.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 4))\n",
    "pol_bar = plg.plot_metadata.plot_polarization(\n",
    "    ds_cmls.polarization, colors=[\"blue\", \"orange\", \"green\"], ax=ax\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting data availability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot data availability distribution\n",
    "Finally we can also take a look at the time series. Do all the CMLs have a data record for all the time periods? We can view this by plotting a histogram with the distribution of availability. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.05, 0.95, 'Mean = 98.90%')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# take the mean over all sublinks to get the availability per link path\n",
    "ds_cml_paths = ds_cmls.mean(dim=\"sublink_id\")\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "hist, _, _ = plg.plot_metadata.plot_availability_distribution(ds_cml_paths, ax=ax)\n",
    "\n",
    "# adapt the x-label if we choose to plot the availability of the link paths like above\n",
    "ax.set_xlabel(\"Data availability of link paths (%)\")\n",
    "\n",
    "# we can also add some statistics to the plot, like the mean availability\n",
    "availability = (\n",
    "    ds_cml_paths[\"rsl\"].count(dim=\"time\") / ds_cml_paths.sizes[\"time\"]\n",
    ") * 100\n",
    "mean_availability = np.nanmean(availability.values)\n",
    "ax.annotate(\n",
    "    f\"Mean = {mean_availability:.2f}%\",\n",
    "    xy=(0.05, 0.95),\n",
    "    xycoords=\"axes fraction\",\n",
    "    ha=\"left\",\n",
    "    va=\"top\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot data availability time series\n",
    "\n",
    "It's also possible to plot the availability as a time series, where we can see the number of sublinks and link paths available per time step. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2220fa5e860>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "\n",
    "scatter_sublinks, scatter_cmls = plg.plot_metadata.plot_availability_time_series(\n",
    "    ds_cmls, variable=\"rsl\", ax=ax\n",
    ")\n",
    "\n",
    "# finetuning the plotting result\n",
    "ax.legend(loc=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the dataset is very large we can resample the availability to a coarser time resolution with the argument ```resample_to```. *Note that this does not guarantee that the data for a link is available ALL day.*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hh1FszLEGAAAAQL45Ud4Va1OnTs33LQAAAABQ7+Q9xxoAAAAAUM2KtYEDB8Zll10WrVq1yp5/myuvvLKm2gYAAAAAxR2svfHGG7Fo0aLK5982xxoAAAAANAR5L15QH1m8AAAAAIB8cyJzrAEAAABAAQRrAAAAAFAAwRoAAAAAFECwBgAAAAAFEKwBAAAAwMoK1u6+++7Yeeedo1OnTvHRRx9l+66++up46KGHCrkcAAAAANT/YO3GG2+MgQMHRu/evWP27NlRXl6e7W/Tpk0WrgEAAABAQ5B3sHbdddfFLbfcEhdccEE0bty4cv8OO+wQEydOrOn2AQAAAED9CNamTp0a22233TL7mzdvHl9//XVNtQsAAAAA6lewtuGGG8abb765zP4nnngittxyy5pqFwAAAACs0prk+4Y0v1r//v1j/vz5kcvl4uWXX4777rsvhgwZErfeemvttBIAAAAAij1Y+/nPfx4tWrSICy+8MObNmxdHHnlktjroNddcE4cffnjttBIAAAAAVjEluVR2VqAUrM2dOzfat28fxaysrCxat24dc+bMidLS0rpuDgAAAABFkBPlXbG2tJYtW2YbAAAAADQ0eQdr//73v+Piiy+Op59+OmbNmhVLliypcvyLL76oyfYBAAAAQP0I1vr27Rvvv/9+HHfccdGhQ4coKSmpnZYBAAAAQH0K1p577rl4/vnn43vf+17ttAgAAAAAikCjfN+wxRZbxH/+85/aaQ0AAAAA1Ndg7YYbbogLLrggxo4dm823llZKWHoDAAAAgIYg76Ggbdq0yQK03Xffvcr+XC6XzbdWXl5ek+0DAAAAgPoRrB111FHRtGnTGDZsmMULAAAAAGiw8g7W3nrrrXjjjTdi8803r50WAQAAAEB9nGNthx12iI8//rh2WgMAAAAA9bVi7dRTT43TTz89zjrrrOjatWs2LHRp3bp1q8n2AQAAAMAqqSSXVh3IQ6NGyxa5pXnWinnxgrQYQ+vWrWPOnDlRWlpa180BAAAAoAhyorwr1qZOnfpd2gYAAAAA9ULewVqXLl1qpyUAAAAAUN+CtYcffjj22WefbD619Pzb7L///jXVNgAAAAAo7jnW0rxqM2bMiPbt2y93jrXKi5ljDQAAAIAiVuNzrC1ZsmS5zwEAAACgoVpx+dkK3HXXXbFgwYJl9i9cuDA7lq9PP/00jj766GjXrl20aNEiunbtGq+++mrl8VRQd/HFF8c666yTHd9zzz1jypQpVa7xxRdfxFFHHZWliG3atInjjjsu5s6dm3dbAAAAAKDWgrWf/vSnWSncN3311VfZsXx8+eWXsfPOO2dztz3++OPx9ttvxx/+8IdYc801K8+54oor4tprr42bbropXnrppWjVqlX06tUr5s+fX3lOCtUmTZoUo0aNikcffTSeffbZOOGEE/L9agAAAABQs3OsLS3NsTZz5sxYe+21q+z/xz/+ET/84Q+z6rHqOvfcc+OFF16I5557brnHU9M6deoUZ555Zvzyl7/M9qVQr0OHDjF06NA4/PDD45133omtttoqXnnlldhhhx2yc5544ono3bt3fPLJJ9n7/xtzrAEAAACQb05U7Yq17bbbLrbffvtsgYI99tgje16xfe9734tddtklG6aZj7TCaArDDjnkkGxhhPQZt9xyS+XxqVOnZosmLH3d9MV69OgR48ePz16nxzT8syJUS9L5KQBMFW7Lk4aypv9IS28AAAAAkI9qLV6QHHjggdnjm2++mQ3FXH311SuPNWvWLDbYYIPo06dPXh/+4Ycfxo033hgDBw6M888/P6s6O+2007LrHXPMMVmolqQKtaWl1xXHKlYrrfKlmjSJtm3bVp7zTUOGDIlLL700r7YCAAAAQEHB2iWXXJI9pgDtsMMOi9VWWy2+q7TCaKo0Gzx4cPY6Vay99dZb2XxqKVirLeedd14W5lVIFWudO3eutc8DAAAAoP7Je/GCFHjVRKiWpJU+0/xoS9tyyy1j2rRp2fOOHTtmj2lOt6Wl1xXH0uOsWbOqHF+8eHE211vFOd/UvHnzbIzs0hsAAAAA1GqwVpPSiqCTJ0+usu+9996LLl26ZM833HDDLBwbM2ZMleqyNHdaz549s9fpcfbs2fHaa69VnvPUU09l1XBpLjYAAAAAqNOhoLVhwIABsdNOO2VDQQ899NB4+eWX4+abb862JC2UcMYZZ8Svf/3r2HTTTbOg7aKLLspW+qyY8y1VuP3oRz+K448/PhtCumjRojjllFOyFUOrsyIoAAAAABSiJJfL5aIOPfroo9mcZ1OmTMmCszT3WQrJKqTmpfndUtiWKtP+93//N2644YbYbLPNKs9Jwz5TmPbII49kq4GmRRSuvfbaKgss1NQyqgAAAADUX/nkRHkFa6kabIsttsjCsFQpVl8I1gAAAADINyfKa461pk2bxvz58/N5CwAAAADUS3kvXtC/f//47W9/m628CQAAAAANVd6LF7zyyivZKp1PPvlkdO3aNVq1alXl+MiRI2uyfQAAAABQP4K1Nm3aZIsDAAAAAEBDlnewdscdd9ROSwAAAACgPs+xlqT51UaPHh1/+tOf4quvvsr2ffbZZzF37tyabh8AAAAA1I+KtY8++ih+9KMfxbRp02LBggWx1157xRprrJEtaJBe33TTTbXTUgAAAAAo5oq1008/PXbYYYf48ssvo0WLFpX7DzrooGxRAwAAAABoCPKuWHvuuedi3Lhx0axZsyr7N9hgg/j0009rsm0AAAAAUH8q1pYsWRLl5eXL7P/kk0+yIaEAAAAA0BDkHaztvffecfXVV1e+LikpyRYtuOSSS6J379413T4AAAAAWCWV5HK5XD5vSJVpvXr1ivS2KVOmZPOtpce11lornn322Wjfvn0Um7KysmjdunXMmTMnSktL67o5AAAAABRBTpR3sJYsXrw4hg8fHhMmTMiq1bbffvs46qijqixmUEwEawAAAADkmxPlvXhB9qYmTeLoo48u5K0AAAAAUC8UFKxNnjw5rrvuunjnnXey11tuuWWccsopscUWW9R0+wAAAACgfixe8MADD8Q222wTr732Wnzve9/Lttdffz26du2aHQMAAACAhiDvOdY23njjbD61QYMGVdmfVgW955574oMPPohiY441AAAAAPLNifKuWJs+fXr069dvmf1pzrV0DAAAAAAagryDtd122y2ee+65ZfY///zzscsuu9RUuwAAAACgfi1esP/++8c555yTzbG24447ZvtefPHFuP/+++PSSy+Nhx9+uMq5AAAAAFAf5T3HWqNG1StyKykpifLy8igG5lgDAAAAIN+cKO+KtSVLluT7FgAAAACod/KeYw0AAAAAEKwBAAAAQEEEawAAAABQAMEaAAAAABRAsAYAAAAAKyNYe/3112PixImVrx966KE48MAD4/zzz4+FCxcW0gYAAAAAqP/B2i9+8Yt47733sucffvhhHH744dGyZcu4//774+yzz66NNgIAAABA8QdrKVTbdttts+cpTNt1111j2LBhMXTo0HjggQdqo40AAAAAUPzBWi6XiyVLlmTPR48eHb17986ed+7cOT7//POabyEAAAAA1IdgbYcddohf//rXcffdd8fYsWNj3333zfZPnTo1OnToUBttBAAAAIDiD9auvvrqbAGDU045JS644ILYZJNNsv1/+ctfYqeddqqNNgIAAADAKqckl8Z21oD58+dH48aNo2nTplFsysrKonXr1jFnzpwoLS2t6+YAAAAAUAQ5Ud4Va7/73e+Wuz8Fav369cv3cgAAAABQlAoK1m677bYq+8rLy+Pwww+PN998sybbBgAAAACrrCb5vuGxxx6LvffeOyuJ+8lPfhKLFy+OQw89NN599914+umna6eVAAAAAFDswdr//M//xAMPPBAHHnhgNGvWLKtee//997NQzaqgAAAAADQUeQ8FTXbfffe46667ok+fPjF16tQYO3ZsQaHar371qygpKamybbHFFpXHd9ttt2WOn3jiiVWuMW3atNh3332jZcuW0b59+zjrrLOyKjoAAAAAqPOKtYMPPni5+9dee+1o06ZNnHDCCZX7Ro4cmVcDtt566xg9evT/36AmVZt0/PHHx6BBgypfpwBt6bndUqjWsWPHGDduXEyfPj1bQCEtpDB48OC82gEAAAAANR6spfnUlqdXr17fvQFNmmTB2IqkIG1Fx5988sl4++23s2AuVcxtu+22cdlll8U555yTVcOloaoAAAAAUGfB2h133BG1ZcqUKdGpU6dYbbXVomfPnjFkyJBYf/31K4/fe++9cc8992Th2n777RcXXXRRZdXa+PHjo2vXrlWGoaaw76STTopJkybFdtttt9zPXLBgQbZVKCsrq7XvBwAAAED9lPfiBTWpR48eMXTo0Nh8882zYZyXXnpp7LLLLvHWW2/FGmusEUceeWR06dIlC94mTJiQVaJNnjy5crjpjBkzlpnbreJ1OrYiKbxLnwUAAAAAKy1YmzlzZvzyl7+MMWPGxKxZsyKXy1U5nuY9q6599tmn8nm3bt2yoC0FaSNGjIjjjjuuytxtqTJtnXXWiT322CM++OCD2HjjjaNQ5513XgwcOLBKxVrnzp0Lvh4AAAAADU/ewdqxxx6brcSZhmSmoCut1FlT0kIIm222Wbz//vvLPZ6CtyQdT8FaGh768ssvLxP8Jd82b1vz5s2zDQAAAABWWrD2/PPPx3PPPZctFFDT5s6dm1Wj9e3bd7nH33zzzewxBXpJmpPt8ssvzyrn2rdvn+0bNWpUlJaWxlZbbVXj7QMAAACACo0iT2nI5DeHfxYqDSkdO3Zs/POf/4xx48bFQQcdFI0bN44jjjgiC9jSCp+vvfZadvzhhx+Ofv36xa677poNG0323nvvLEBLQdw//vGP+Pvf/x4XXnhh9O/fX0UaAAAAAKtWsHb11VfHueeem4Vd39Unn3yShWhp8YJDDz002rVrFy+++GKsvfba0axZsxg9enQWnm2xxRZx5plnRp8+feKRRx6pfH8K4R599NHsMVWvHX300Vn4NmjQoO/cNgAAAAD4NiW5PMvP1lxzzZg3b14sXrw4WrZsGU2bNq1y/IsvvohikxYvaN26dcyZMycbRgoAAABAw1SWR07UpJCKNQAAAABo6PIO1o455pjaaQkAAAAA1LdgLZXAVZS+peffxlBKAAAAABqCJtWdV2369OnRvn37aNOmTZSUlCxzTpqqLe0vLy+vjXYCAAAAQPEFa0899VS0bds2e/7000/XdpsAAAAAoP6tClofWRUUAAAAgFpZFXTChAlRXd26dav2uQAAAABQrKoVrG277bbZ/Gn/rbjNHGsAAAAANBTVCtamTp1a+y0BAAAAgPoWrHXp0qX2WwIAAAAARaRRXTcAAAAAAIqRYA0AAAAACiBYAwAAAIACCNYAAAAAYGUFa7Nnz45bb701zjvvvPjiiy+yfa+//np8+umnhVwOAAAAAOrnqqBLmzBhQuy5557RunXr+Oc//xnHH398tG3bNkaOHBnTpk2Lu+66q3ZaCgAAAADFXLE2cODAOPbYY2PKlCmx2mqrVe7v3bt3PPvsszXdPgAAAACoH8HaK6+8Er/4xS+W2b/uuuvGjBkzaqpdAAAAAFC/grXmzZtHWVnZMvvfe++9WHvttWuqXQAAAABQv4K1/fffPwYNGhSLFi3KXpeUlGRzq51zzjnRp0+f2mgjAAAAABR/sPaHP/wh5s6dG+3bt4///Oc/8YMf/CA22WSTWGONNeLyyy+vnVYCAAAAQLGvCppWAx01alQ8//zz2QqhKWTbfvvts5VCAQAAAKChKMnlcrlo4NKccSkwnDNnTpSWltZ1cwAAAAAogpyoWhVr1157bbU//LTTTqv2uQAAAABQryvWNtxww+pdrKQkPvzwwyg2KtYAAAAAqJWKtalTp1bnNAAAAABoMPJeFXRpqdjNFG0AAAAANEQFBWu33XZbbLPNNrHaaqtlW3p+66231nzrAAAAAGAVVa2hoEu7+OKL48orr4xTTz01evbsme0bP358DBgwIKZNmxaDBg2qjXYCAAAAQPEtXrC0tddeO1sl9Igjjqiy/7777svCts8//zyKjcULAAAAAMg3J8p7KOiiRYtihx12WGZ/9+7dY/HixfleDgAAAACKUt7BWt++fePGG29cZv/NN98cRx11VE21CwAAAACKf461gQMHVj4vKSnJFip48sknY8cdd8z2vfTSS9n8av369au9lgIAAABAsQVrb7zxxjLDPpMPPvgge1xrrbWybdKkSbXRRgAAAAAozmDt6aefrv2WAAAAAEB9nmMNAAAAAKhmxdo3vfrqqzFixIhsXrWFCxdWOTZy5MiaahsAAAAA1J+KteHDh8dOO+0U77zzTjz44IOxaNGibG61p556Klq3bl07rQQAAACAYg/WBg8eHFdddVU88sgj0axZs7jmmmvi3XffjUMPPTTWX3/92mklAAAAABR7sJZWAt13332z5ylY+/rrr6OkpCQGDBgQN998c17X+tWvfpW9d+ltiy22qDw+f/786N+/f7Rr1y5WX3316NOnT8ycObPKNdJw1NSeli1bRvv27eOss86KxYsX5/u1AAAAAKB2g7U111wzvvrqq+z5uuuuG2+99Vb2fPbs2TFv3rx8Lxdbb711TJ8+vXJ7/vnnK4+lsC5Vxt1///0xduzY+Oyzz+Lggw+uPF5eXp6Fammet3HjxsWdd94ZQ4cOjYsvvjjvdgAAAABArS5esOuuu8aoUaOia9euccghh8Tpp5+eza+W9u2xxx75Xi6aNGkSHTt2XGb/nDlz4rbbbothw4bF7rvvnu274447Ysstt4wXX3wxdtxxx3jyySfj7bffjtGjR0eHDh1i2223jcsuuyzOOeecrBouVdQBAAAAwCpRsfbHP/4xDj/88Oz5BRdcEAMHDsyGZ6ZhmikIy9eUKVOiU6dOsdFGG8VRRx2VDe1MXnvttWxhhD333LPy3DRMNM3jNn78+Ox1ekwBXwrVKvTq1SvKysqyBRVWZMGCBdk5S28AAAAAUKsVa23btq183qhRozj33HOjUD169MiGbm6++ebZMNBLL700dtlll2x46YwZM7KKszZt2lR5TwrR0rEkPS4dqlUcrzi2IkOGDMk+CwAAAABqNVjLp6KrtLS02ufus88+lc+7deuWBW1dunSJESNGRIsWLaK2nHfeeVml3dLfr3PnzrX2eQAAAAA00GAtVY2lFTu/TS6Xy85JCwoUKn3OZpttFu+//37stdde2aIEaVGEpavW0rDTijnZ0uPLL79c5RoVq4Yub962Cs2bN882AAAAAKjVYO3pp5+OlWHu3LnxwQcfRN++faN79+7RtGnTGDNmTDZ/WzJ58uRsDraePXtmr9Pj5ZdfHrNmzYr27dtn+9IiCqlqbquttlopbQYAAACgYSrJpVKzOvLLX/4y9ttvv2z452effRaXXHJJvPnmm9lKn2uvvXacdNJJ8be//S2bhy2FZaeeemr2vnHjxmWPqTourQSaFj+44oorsnnVUij385//PAYPHlztdqShoK1bt85WIs1nKCsAAAAA9Us+OVG1KtYmTJgQ22yzTbZYQXr+bdJcadX1ySefxBFHHBH//ve/syDtf//3f+PFF1/MnidXXXVV9pmpYi2t5JlW/Lzhhhsq39+4ceN49NFHswAuVa+1atUqjjnmmBg0aFC12wAAAAAAtVaxlsKtVA2Whlum52kuteW97bvOsVZXVKwBAAAAUCsVa1OnTq2sIkvPAQAAAKChq1awluZAW95zAAAAAGioqhWsLU9aYCCt0Llw4cIq+/fff/+aaBcAAAAA1K9g7cMPP4yDDjooJk6cWGWutfQ8KcY51gAAAAAgX43yfcPpp58eG264YcyaNStatmwZkyZNimeffTZ22GGHeOaZZ/JuAAAAAAA0iIq18ePHx1NPPRVrrbVWtkJo2v73f/83hgwZEqeddlq88cYbtdNSAAAAACjmirU01HONNdbInqdw7bPPPqtc1GDy5Mk130IAAAAAqA8Va9tss0384x//yIaD9ujRI6644opo1qxZ3HzzzbHRRhvVTisBAAAAoNiDtQsvvDC+/vrr7PmgQYPixz/+ceyyyy7Rrl27+POf/1wbbQQAAACAVU5JrmJZz+/giy++iDXXXLNyZdBiU1ZWFq1bt445c+ZEaWlpXTcHAAAAgCLIifKeY+2ee+6prFir0LZt26IN1QAAAACgEHkHawMGDIgOHTrEkUceGX/729+yxQwAAAAAoKHJO1ibPn16DB8+PKtQO/TQQ2OdddaJ/v37x7hx42qnhQAAAABQ3+ZYmzdvXjz44IMxbNiwGD16dKy33nrxwQcfRLExxxoAAAAA+eZEea8KurSWLVtGr1694ssvv4yPPvoo3nnnne9yOQAAAACov0NBKyrV7r333ujdu3esu+66cfXVV8dBBx0UkyZNqvkWAgAAAMAqKO+KtcMPPzweffTRrFotzbF20UUXRc+ePWundQAAAABQX4K1xo0bx4gRI7IhoOk5AAAAADREeQdraQgoAAAAADR0BS1e8PXXX8fYsWNj2rRpsXDhwirHTjvttJpqGwAAAADUn2DtjTfeyBYtSAsYpICtbdu28fnnn2dzrrVv316wBgAAAECDkPeqoAMGDIj99tsvvvzyy2jRokW8+OKL8dFHH0X37t3j97//fe20EgAAAACKPVh7880348wzz4xGjRplixcsWLAgOnfuHFdccUWcf/75tdNKAAAAACj2YK1p06ZZqJakoZ9pnrWkdevW8fHHH9d8CwEAAACgPsyxtt1228Urr7wSm266afzgBz+Iiy++OJtj7e67745tttmmdloJAAAAAMVesTZ48OBYZ511sueXX355rLnmmnHSSSfFv/71r7j55ptro40AAAAAsMopyeVyuWjgysrKsqGsc+bMidLS0rpuDgAAAABFkBPlXbEGAAAAAAjWAAAAAKAggjUAAAAAKIBgDQAAAABqK1hr27ZtfP7559nzn/3sZ/HVV18V8lkAAAAA0LCCtYULF2YrIiR33nlnzJ8/v7bbBQAAAACrtCbVOalnz55x4IEHRvfu3SOXy8Vpp50WLVq0WO65t99+e023EQAAAACKM1i755574qqrrooPPvggSkpKYs6cOarWAAAAAGjQSnKpBC0PG264Ybz66qvRrl27qC/SMNfWrVtngWFpaWldNwcAAACAIsiJqlWxtrSpU6d+l7YBAAAAQMNZvOCbxo4dG/vtt19ssskm2bb//vvHc889V/OtAwAAAID6Eqyl+db23HPPaNmyZbaIQcVCBnvssUcMGzas4Ib85je/yeZvO+OMMyr37bbbbtm+pbcTTzyxyvumTZsW++67b9ae9u3bx1lnnRWLFy8uuB0AAAAAUB15DwW9/PLL44orrogBAwZU7kvh2pVXXhmXXXZZHHnkkfleMl555ZX405/+FN26dVvm2PHHHx+DBg2qfJ0CtArl5eVZqNaxY8cYN25cTJ8+Pfr16xdNmzaNwYMH590OAAAAAKi1irUPP/wwGwb6TWk4aCHzr82dOzeOOuqouOWWW2LNNddc5ngK0lJwVrEtPWnck08+GW+//XZWRbftttvGPvvsk4V7119/fSxcuDDvtgAAAABArQVrnTt3jjFjxiyzf/To0dmxfPXv3z+rOkvDS5fn3nvvjbXWWiu22WabOO+882LevHmVx8aPHx9du3aNDh06VO7r1atXtnrDpEmTVviZCxYsyM5ZegMAAACAWh0KeuaZZ2ZDP998883Yaaedsn0vvPBCDB06NK655pq8rjV8+PB4/fXXs6Ggy5OGlXbp0iU6deoUEyZMiHPOOScmT54cI0eOzI7PmDGjSqiWVLxOx1ZkyJAhcemll+bVVgAAAAD4TsHaSSedlA3J/MMf/hAjRozI9m255Zbx5z//OQ444IBqX+fjjz+O008/PUaNGhWrrbbacs854YQTKp+nyrR11lknWyThgw8+iI033jgKlSrfBg4cWPk6VawVUm0HAAAAQMOVd7CWHHTQQdn2Xbz22msxa9as2H777assRvDss8/GH//4x2y4ZuPGjau8p0ePHtnj+++/nwVrKeB7+eWXq5wzc+bM7DEdW5HmzZtnGwAAAACstDnWakqqPJs4cWI2pLRi22GHHbKFDNLzb4ZqSdqfpMq1pGfPntk1UkBXIVXApQUOttpqq5X4bQAAAABoaAqqWKsJa6yxRrYgwdJatWoV7dq1y/an4Z7Dhg2L3r17Z/vSHGsDBgyIXXfdNbp165adv/fee2cBWt++feOKK67I5lW78MILswURVKQBAAAAUC+Dtf+mWbNm2UqjV199dXz99dfZHGh9+vTJgrMKqart0UcfzeZ9S9VrKZg75phjYtCgQXXadgAAAADqv5JcLpeLBi4tXtC6deuYM2dONowUAAAAgIapLI+cqM7mWAMAAACAYlZjwdpDDz0Ud911V01dDgAAAAAaxlDQLbbYIqZMmRLl5eVRbAwFBQAAAKBWh4IuWrQo9thjjyxA+6Z33323KEM1AAAAAChEXsFa06ZNY8KECQV9EAAAAAA06DnWjj766LjttttqpzUAAAAAUCSa5PuGxYsXx+233x6jR4+O7t27R6tWraocv/LKK2uyfQAAAABQP4K1t956K7bffvvs+XvvvVflWElJSc21DAAAAADqU7D29NNP105LAAAAAKA+z7EGAAAAABRQsfb111/Hb37zmxgzZkzMmjUrlixZUuX4hx9+WJPtAwAAAID6Eaz9/Oc/j7Fjx0bfvn1jnXXWMa8aAAAAAA1S3sHa448/Ho899ljsvPPOtdMiAAAAAKiPc6ytueaa0bZt29ppDQAAAADU12Dtsssui4svvjjmzZtXOy0CAAAAgPo4FPQPf/hDfPDBB9GhQ4fYYIMNomnTplWOv/766zXZPgAAAACoH8HagQceWDstAQAAAIAiUpLL5XLRwJWVlUXr1q1jzpw5UVpaWtfNAQAAAKAIcqK851gDAAAAAARrAAAAAFAQwRoAAAAAFECwBgAAAAAFEKwBAAAAQAGa5PuG8vLyGDp0aIwZMyZmzZoVS5YsqXL8qaeeKqQdAAAAAFC/g7XTTz89C9b23Xff2GabbaKkpKR2WgYAAAAA9SlYGz58eIwYMSJ69+5dOy0CAAAAgPo4x1qzZs1ik002qZ3WAAAAAEB9DdbOPPPMuOaaayKXy9VOiwAAAACgPg4Fff755+Ppp5+Oxx9/PLbeeuto2rRpleMjR46syfYBAAAAQP0I1tq0aRMHHXRQ7bQGAAAAAOprsHbHHXfUTksAAAAAoD7PsQYAAAAAFFCxlvzlL3+JESNGxLRp02LhwoVVjr3++us11TYAAAAAqD8Va9dee2389Kc/jQ4dOsQbb7wR3//+96Ndu3bx4Ycfxj777FM7rQQAAACAYg/Wbrjhhrj55pvjuuuui2bNmsXZZ58do0aNitNOOy3mzJlTO60EAAAAgGIP1tLwz5122il73qJFi/jqq6+y53379o377ruv5lsIAAAAAPUhWOvYsWN88cUX2fP1118/Xnzxxez51KlTI5fL1XwLAQAAAKA+BGu77757PPzww9nzNNfagAEDYq+99orDDjssDjrooNpoIwAAAAAUf7CW5le74IILsuf9+/eP22+/PbbccssYNGhQ3HjjjQU35De/+U2UlJTEGWecUblv/vz52WekxRFWX3316NOnT8ycOXOZoan77rtvtGzZMtq3bx9nnXVWLF68uOB2AAAAAEB1NIk8NWrUKNsqHH744dn2Xbzyyivxpz/9Kbp161Zlf6qGe+yxx+L++++P1q1bxymnnBIHH3xwvPDCC9nx8vLyLFRLw1PHjRsX06dPj379+kXTpk1j8ODB36lNAAAAAFCjFWvJc889F0cffXT07NkzPv3002zf3XffHc8//3ze15o7d24cddRRccstt8Saa65ZuT+tMHrbbbfFlVdemQ0/7d69e9xxxx1ZgFYxr9uTTz4Zb7/9dtxzzz2x7bbbxj777BOXXXZZXH/99bFw4cJCvhoAAAAA1E6w9sADD0SvXr2yFUHfeOONWLBgQWUQVkiVWBrqmarO9txzzyr7X3vttVi0aFGV/VtssUW2YML48eOz1+mxa9eu0aFDh8pzUtvKyspi0qRJK/zM1OZ0ztIbAAAAANRqsPbrX/86brrppqzCLA25rLDzzjvH66+/nte1hg8fnr1nyJAhyxybMWNGNGvWLNq0aVNlfwrR0rGKc5YO1SqOVxxbkfR5aWhpxda5c+e82g0AAAAAeQdrkydPjl133XWZ/Smgmj17drWv8/HHH8fpp58e9957b6y22mqxMp133nlZhV3FltoCAAAAALUarKWFAt5///1l9qf51TbaaKNqXycN9Zw1a1Zsv/320aRJk2wbO3ZsXHvttdnzVHmW5kn7ZliXVgVNbahoyzdXCa14XXHO8jRv3jxKS0urbAAAAABQq8Ha8ccfn1WavfTSS1FSUhKfffZZVnX2y1/+Mk466aRqX2ePPfaIiRMnxptvvlm57bDDDtlCBhXP01DTMWPGVKmWmzZtWrZoQpIe0zVSQFdh1KhRWVC21VZb5fvVAAAAAKDamkSezj333FiyZEkWjM2bNy8bFpoqwFKwduqpp1b7OmussUZss802Vfa1atUq2rVrV7n/uOOOi4EDB0bbtm2zsCxdP4VpO+64Y3Z87733zgK0vn37xhVXXJHNq3bhhRdmCyKkNgEAAADAKhOspSq1Cy64IM4666xsSOjcuXOzcGv11Vev8cZdddVV0ahRo+jTp0+2kmda8fOGG26oPN64ceN49NFHs0q5FLilYO6YY46JQYMG1XhbAAAAAGBpJblcLhcNXFlZWbb4QlrIwHxrAAAAAA1XPjlRtSvWfvazn1XrvNtvv726lwQAAACAolXtYG3o0KHRpUuX2G677UKRGwAAAAANXbWDtTSP2X333RdTp06Nn/70p3H00UdniwoAAAAAQEPUqLonXn/99TF9+vQ4++yz45FHHonOnTvHoYceGn//+99VsAEAAADQ4BS8eMFHH32UDQ+96667YvHixTFp0qRaWRl0ZbB4AQAAAAD55kTVrlhb5o2NGkVJSUlWrVZeXl7oZQAAAACgKOUVrC1YsCCbZ22vvfaKzTbbLCZOnBh//OMfY9q0aUVbrQYAAAAAtbp4wcknnxzDhw/P5lb72c9+lgVsa621VkEfCgAAAAANZo61NPRz/fXXj+222y4bAroiI0eOjGJjjjUAAAAA8s2Jql2x1q9fv28N1AAAAACgIal2sJZWAAUAAAAAvuOqoAAAAADQkAnWAAAAAKAAgjUAAAAAKIBgDQAAAAAKIFgDAAAAgAII1gAAAACgAII1AAAAACiAYA0AAAAACiBYAwAAAIACCNYAAAAAoACCNQAAAAAogGANAAAAAAogWAMAAACAAgjWAAAAAKAAgjUAAAAAKIBgDQAAAAAKIFgDAAAAgAII1gAAAACgAII1AAAAAChAk0LexKrtk08+iX//+9/Rrl27WG+99eq6OQANinswQN1w/wWoG5808PuvYK2eGTVqVIwbN67y9U477RR77bVXnbYJoKFwDwaoG+6/AHVjlPuvoaD1LSVeukMn6XXaD0Dtcg8GqBvuvwB1w/33/wjW6pFUepnPfgBqjnswQN1w/wWoG+6//0ewVo+k8cz57Aeg5rgHA9QN91+AuuH++38Ea/VImiQwjWde2s4779wgJw8EWNncgwHqhvsvQN1w//0/JblcLhcNXFlZWbRu3TrmzJkTpaWlUewa+oocAHXJPRigbrj/AtSNT+rh/TefnEiwVg+DNQAAAABqPycyFBQAAAAAClCnwdqNN94Y3bp1y9K/tPXs2TMef/zxyuO77bZblJSUVNlOPPHEKteYNm1a7LvvvtGyZcto3759nHXWWbF48eI6+DYAAAAANCRN6vLD09jb3/zmN7HppptGGpF65513xgEHHBBvvPFGbL311tk5xx9/fAwaNKjyPSlAq1BeXp6Fah07doxx48bF9OnTo1+/ftG0adMYPHhwnXwnAAAAABqGVW6OtbZt28bvfve7OO6447KKtW233Tauvvrq5Z6bqtt+/OMfx2effRYdOnTI9t10001xzjnnxL/+9a9o1qxZtT7THGsAAAAAFO0ca6n6bPjw4fH1119nQ0Ir3HvvvbHWWmvFNttsE+edd17Mmzev8tj48eOja9eulaFa0qtXr+w/wKRJk1b4WQsWLMjOWXoDAAAAgKIZCppMnDgxC9Lmz58fq6++ejz44IOx1VZbZceOPPLI6NKlS3Tq1CkmTJiQVaJNnjw5Ro4cmR2fMWNGlVAtqXidjq3IkCFD4tJLL63V7wUAAABA/Vbnwdrmm28eb775ZlZe95e//CWOOeaYGDt2bBaunXDCCZXnpcq0ddZZJ/bYY4/44IMPYuONNy74M1Pl28CBAytfp4q1zp07f+fvAgAAAEDDUedDQdM8aJtsskl07949qyT73ve+F9dcc81yz+3Ro0f2+P7772ePadGCmTNnVjmn4nU6tiLNmzevXIm0YgMAAACAogrWvmnJkiXZHGjLkyrbklS5lqQhpGko6axZsyrPGTVqVBaUVQwnBQAAAIB6NxQ0DcncZ599Yv3114+vvvoqhg0bFs8880z8/e9/z4Z7pte9e/eOdu3aZXOsDRgwIHbdddfo1q1b9v699947C9D69u0bV1xxRTav2oUXXhj9+/fPqtIAAAAAoF4Ga6nSrF+/fjF9+vRsGdMUmKVQba+99oqPP/44Ro8eHVdffXW2UmiaA61Pnz5ZcFahcePG8eijj8ZJJ52UVa+1atUqm6Nt0KBBdfm1AAAAAGgASnK5XC4auLR4QQr20gIK5lsDAAAAaLjK8siJ6nxV0FVBRbaY/sMBAAAA0HCV/b98qDq1aIK1iGx+tyQNNwUAAACAr776Kqtc+zaGgv6/lUg/++yzWGONNaKkpCTqQ7KaQsI0T52hrRQjfZhipv9SzPRfipn+SzHTfylmZfWw/6aoLIVqnTp1ikaNGn3ruSrWIrL/SOutt17UN6lD15dOTcOkD1PM9F+Kmf5LMdN/KWb6L8WstJ713/9WqVbh22M3AAAAAGC5BGsAAAAAUADBWj3UvHnzuOSSS7JHKEb6MMVM/6WY6b8UM/2XYqb/UsyaN/D+a/ECAAAAACiAijUAAAAAKIBgDQAAAAAKIFgDAAAAgAII1gAAAACgAIK1VdD1118fG2ywQay22mrRo0ePePnllyuPzZgxI/r27RsdO3aMVq1axfbbbx8PPPDAt17vH//4RxxxxBHRuXPnaNGiRWy55ZZxzTXXVDnn2GOPjZKSkmW2rbfeuta+J/WXPkwx038pZvovxUz/pZjpvxQz/fc7SquCsuoYPnx4rlmzZrnbb789N2nSpNzxxx+fa9OmTW7mzJnZ8b322iv3P//zP7mXXnop98EHH+Quu+yyXKNGjXKvv/76Cq9522235U477bTcM888k73n7rvvzrVo0SJ33XXXVZ4ze/bs3PTp0yu3jz/+ONe2bdvcJZdcslK+N/WHPkwx038pZvovxUz/pZjpvxQz/fe7E6ytYr7//e/n+vfvX/m6vLw816lTp9yQIUOy161atcrdddddVd6TOt8tt9yS1+ecfPLJuR/+8IcrPP7ggw/mSkpKcv/85z/z/g40bPowxUz/pZjpvxQz/Zdipv9SzPTf785Q0FXIwoUL47XXXos999yzcl+jRo2y1+PHj89e77TTTvHnP/85vvjii1iyZEkMHz485s+fH7vttltenzVnzpxo27btCo/fdttt2ed26dLlO3wjGhp9mGKm/1LM9F+Kmf5LMdN/KWb6b81oUkPXoQZ8/vnnUV5eHh06dKiyP71+9913s+cjRoyIww47LNq1axdNmjSJli1bxoMPPhibbLJJtT9n3Lhx2f8wHnvsseUe/+yzz+Lxxx+PYcOGfcdvREOjD1PM9F+Kmf5LMdN/KWb6L8VM/60ZKtaKzEUXXRSzZ8+O0aNHx6uvvhoDBw6MQw89NCZOnJgd32effWL11VfPtuVN+vfWW2/FAQccEJdccknsvffey/2MO++8M9q0aRMHHnhgrX8fGh59mGKm/1LM9F+Kmf5LMdN/KWb6bzXUwHBSasiCBQtyjRs3zsYWL61fv365/fffP/f+++/n0v/L3nrrrSrH99hjj9wvfvGL7Pknn3ySmzJlSrZ9c2xymoiwffv2ufPPP3+FbViyZEluk002yZ1xxhk1+t1oGPRhipn+SzHTfylm+i/FTP+lmOm/NUPF2iqkWbNm0b179xgzZkzlvjSGOb3u2bNnzJs3r3LM89IaN26cnZesu+66WUlm2pYemzxp0qT44Q9/GMccc0xcfvnlK2zD2LFj4/3334/jjjuuFr4h9Z0+TDHTfylm+i/FTP+lmOm/FDP9t4bUUEBHDS5127x589zQoUNzb7/9du6EE07IlrqdMWNGbuHChVmSu8suu2RL3ab0+Pe//322csZjjz22wmtOnDgxt/baa+eOPvroKsvZzpo1a5lz0zk9evSo5W9JfaYPU8z0X4qZ/ksx038pZvovxUz//e4Ea6ug6667Lrf++uvnmjVrli19++KLL1Yee++993IHH3xwVk7ZsmXLXLdu3ZZZ+vabLrnkkqx885tbly5dqpw3e/bsXIsWLXI333xzrX03GgZ9mGKm/1LM9F+Kmf5LMdN/KWb673dTkv5PTVW/AQAAAEBDYY41AAAAACiAYA0AAAAACiBYAwAAAIACCNYAAAAAoACCNQAAAAAogGBtFXX99dfHBhtsEKuttlr06NEjXn755cpj8+fPj/79+0e7du1i9dVXjz59+sTMmTP/6zXvv//+2GKLLbJrdu3aNf72t79VOZ4WiL344otjnXXWiRYtWsSee+4ZU6ZMqZXvR/1W0/135MiRscMOO0SbNm2iVatWse2228bdd99d5Rz9l5XRf2+++ebYbbfdorS0NEpKSmL27NnVuuYzzzwT22+/fTRv3jw22WSTGDp0aF6fC3XVf59//vnYeeeds3t2urem3xFXXXVVXp8L1bWifvTFF1/EqaeeGptvvnnWD9dff/047bTTYs6cOf/1mn7/Uqz91+9fVqZv+3P8F7/4RWy88cZZH1t77bXjgAMOiHffffe/XvP+hnT/zbHKGT58eK5Zs2a522+/PTdp0qTc8ccfn2vTpk1u5syZ2fETTzwx17lz59yYMWNyr776am7HHXfM7bTTTt96zRdeeCHXuHHj3BVXXJF7++23cxdeeGGuadOmuYkTJ1ae85vf/CbXunXr3F//+tfcP/7xj9z++++f23DDDXP/+c9/av07U3/URv99+umncyNHjsz67vvvv5+7+uqrs/78xBNPVJ6j/1IT/lv/veqqq3JDhgzJtvRH6Jdffvlfr/nhhx/mWrZsmRs4cGDWh6+77rpl+u9/+1yoq/77+uuv54YNG5Z76623clOnTs3dfffdWX/+05/+VO3Pher4tn6Ufq8efPDBuYcffjj7HZB+Q2y66aa5Pn36fOs1/f6lmPuv37+sLP/tz/H0Z/7YsWOz3wGvvfZabr/99sv+Prd48eIVXvOFBnb/Faytgr7//e/n+vfvX/m6vLw816lTp+yH8OzZs7MOef/991cef+edd7IfyOPHj1/hNQ899NDcvvvuW2Vfjx49cr/4xS+y50uWLMl17Ngx97vf/a7yePqs5s2b5+67774a/obUZ7XRf5dnu+22y27Qif7Lyui/3/yxW91g4uyzz85tvfXWVfYddthhuV69euX9ubCy++/yHHTQQbmjjz4678+Fb5NvPxoxYkT2F8FFixat8Jp+/1LM/Xd5/P5lVei/KQRLvyNS4Lsihzaw+6+hoKuYhQsXxmuvvZaVQVZo1KhR9nr8+PHZsUWLFlU5nsorU0lxOl4hlXH+6le/qnydji39nqRXr16V75k6dWrMmDGjyjmtW7fOykCXvi7URf9dWvoHgTFjxsTkyZNj1113zfbpv6yM/ltdaajdscceW+37b019Lg1bbfXfb3rjjTdi3Lhx8YMf/KBGP5eGrZB+lIbRpWHNTZo0qdzn9y/1qf8uze9fVpX++/XXX8cdd9wRG264YXTu3Lly/wYN/P4rWFvFfP7551FeXh4dOnSosj+9Th0vbc2aNcvG2i/veIU0BnqttdaqfJ2OreiaFccr9n3bdaEu+m/FD5A0J1t6/7777hvXXXdd7LXXXtkx/ZeV0X+rKwXFaa6I/3b/LSsri//85z819rk0bLXVfyust9562RyBab6fNE/mz3/+8xr9XBq2fPtROv+yyy6LE044ocp+v3+pT/038fuXVaX/3nDDDVlfTNvjjz8eo0aNyvplhY0b+P33/4/IqVfSv2hAfeq/a6yxRrz55psxd+7c7PjAgQNjo402yqorYFVy11131XUToMb773PPPZfdf1988cU499xzs0U4jjjiiJXePkj/KJEChq222mqZ6h6/f6lv/dfvX1YVRx11VBbqTp8+PX7/+9/HoYceGi+88EK2MEHS0O+/grVVTEp5GzduvMwqiel1x44dsy2Va6aVvJau+qk4viLp2IquWXG8Yt/S/1KdXqcVaKAu+29FSXL6i1yS+uQ777wTQ4YMyX5Y6L+sjP5bqBXdf9MQkLQCUvrM2vhcGpba6r8V0pCPJK3qla6Z/kKYgrXa/lwahur2o6+++ip+9KMfZWHDgw8+GE2bNv3W6/r9SzH338TvX1aV/puGaaZt0003jR133DHWXHPNrB+v6B/ZOjaw+6+hoKuYVE7ZvXv3KonvkiVLstc9e/bMjqWb8NLH01j7adOmZcdXJB37Zoqcyjcr3pN+MKfOvfQ56V9UXnrppW+9LqyM/rs86boLFizInuu/rIz+W6j/dv+trc+lYVmZ/Wjp+6/+S02oTj9Kf67vvffe2bkPP/xwZZXEt/H7l2Luv8vj9y81rZA/x//fIpiVfXF5Gtz9t65XT2D5y92m1TCGDh2aLU17wgknZMvdzpgxIzt+4okn5tZff/3cU089lXv11VdzPXv2zLal7b777rnrrruuynK3TZo0yf3+97/PVmG85JJLlrvcbfqchx56KDdhwoTcAQccULTL3VK/+u/gwYNzTz75ZO6DDz7Irpn6cerPt9xyS+U5+i8ro/9Onz4998Ybb2R9L/0R+uyzz2av//3vf1deo2/fvrlzzz238vWHH36Ya9myZe6ss87K7r/XX399tvz4E088Ue3Phbrqv3/84x9zDz/8cO69997LtltvvTW3xhpr5C644IJqfy5Ux7f1ozlz5mSryXXt2jVbhS715Ypt8eLFldfw+5f61H/9/mVV6L+p/6W+mP7e9tFHH2X31f322y/Xtm3b3MyZMyuvsXsDv/8K1lZRqVOm8CEtw5yWv33xxRcrj6WOdvLJJ+fWXHPN7C9radn7dGNeWpcuXbLO+81lnTfbbLPsmltvvXXuscceq3I8LXl70UUX5Tp06JD9D2uPPfbITZ48uZa/KfVRTfff9Be4TTbZJLfaaqtl70tBXPoDYGn6Lyuj/6Z+mQKJb2533HFH5Tk/+MEPcsccc0yVaz799NO5bbfdNrvmRhttVOX86nwu1FX/vfbaa7PfDOl+XVpamttuu+1yN9xwQ668vLzanwvVtaJ+lO6hy+u7aZs6dWrl+/3+pT71X79/WRX676effprbZ599cu3bt8+CsfXWWy935JFH5t59990q7+/SwO+/Jen/1HXVHAAAAAAUG3OsAQAAAEABBGsAAAAAUADBGgAAAAAUQLAGAAAAAAUQrAEAAABAAQRrAAAAAFAAwRoAAAAAFECwBgAAAAAFEKwBANQzxx57bJSUlGRb06ZNo0OHDrHXXnvF7bffHkuWLKn2dYYOHRpt2rSp1bYCABQzwRoAQD30ox/9KKZPnx7//Oc/4/HHH48f/vCHcfrpp8ePf/zjWLx4cV03DwCgXhCsAQDUQ82bN4+OHTvGuuuuG9tvv32cf/758dBDD2UhW6pES6688sro2rVrtGrVKjp37hwnn3xyzJ07Nzv2zDPPxE9/+tOYM2dOZfXbr371q+zYggUL4pe//GV27fTeHj16ZOcDADQ0gjUAgAZi9913j+9973sxcuTI7HWjRo3i2muvjUmTJsWdd94ZTz31VJx99tnZsZ122imuvvrqKC0tzSrf0pbCtOSUU06J8ePHx/Dhw2PChAlxyCGHZBVyU6ZMqdPvBwCwspXkcrncSv9UAABqdY612bNnx1//+tdljh1++OFZGPb2228vc+wvf/lLnHjiifH5559nr1Nl2xlnnJFdq8K0adNio402yh47depUuX/PPfeM73//+zF48OBa+14AAKuaJnXdAAAAVp70b6ppWGcyevToGDJkSLz77rtRVlaWzb02f/78mDdvXrRs2XK57584cWKUl5fHZpttVmV/Gh7arl27lfIdAABWFYI1AIAG5J133okNN9wwW9QgLWRw0kknxeWXXx5t27aN559/Po477rhYuHDhCoO1NAdb48aN47XXXssel7b66quvpG8BALBqEKwBADQQaQ61VHE2YMCALBhbsmRJ/OEPf8jmWktGjBhR5fxmzZpl1WlL22677bJ9s2bNil122WWlth8AYFUjWAMAqIfS0MwZM2ZkIdjMmTPjiSeeyIZ9piq1fv36xVtvvRWLFi2K6667Lvbbb7944YUX4qabbqpyjQ022CCrUBszZky26EGqYktDQI866qjsGimUS0Hbv/71r+ycbt26xb777ltn3xkAYGWzKigAQD2UgrR11lknC8fSip1PP/10tgLoQw89lA3hTEHZlVdeGb/97W9jm222iXvvvTcL3paWVgZNixkcdthhsfbaa8cVV1yR7b/jjjuyYO3MM8+MzTffPA488MB45ZVXYv3116+jbwsAUDesCgoAAAAABVCxBgAAAAAFEKwBAAAAQAEEawAAAABQAMEaAAAAABRAsAYAAAAABRCsAQAAAEABBGsAAAAAUADBGgAAAAAUQLAGAAAAAAUQrAEAAABAAQRrAAAAAFAAwRoAAAAARP7+P1Y+pwLwcn8oAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "\n",
    "scatter_sublinks, scatter_cmls = plg.plot_metadata.plot_availability_time_series(\n",
    "    ds_cmls, variable=\"rsl\", resample_to=\"h\", ax=ax\n",
    ")\n",
    "# finetuning the plotting result\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%m-%d\\n%H:%M\"))\n",
    "\n",
    "# In this case the resulting plot won't look very nice\n",
    "# because the example data set only has a few hours of data available."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the data set is very large and we resampled the availability, we may also want to investigate the variation in mean data availability within the resampled period by using the argument ```mean_over```. \n",
    "\n",
    "If we are only interested in the number of sublinks we can use the argument ```show_links``` (\"both\" by default)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "\n",
    "scatter_sublinks, scatter_cmls = plg.plot_metadata.plot_availability_time_series(\n",
    "    ds_cmls, variable=\"rsl\", mean_over=\"hour\", show_links=\"sublinks\", ax=ax\n",
    ")\n",
    "\n",
    "ax.set_xlabel(\"Hour of day\")\n",
    "ax.set_ylabel(\"Mean nr. sublinks\")\n",
    "\n",
    "# finetuning the plotting result - force integer values only as hours on the x-axis\n",
    "ax.xaxis.set_major_locator(plt.MaxNLocator(integer=True))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "poetry_env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}