notebooks/lead_lag_example_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import lead_lag\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_time_series(bitflyer: pd.Series, btcbox: pd.Series) -> None:\n",
    "    d = pd.DataFrame(data={'bitflyer': bitflyer['last'], 'btcbox': btcbox['last']}).ffill().dropna()\n",
    "    slice_d = d['2018-08-12 12:05:30':'2018-08-12 12:08:25']\n",
    "    slice_d['bitflyer'].plot(color='blue', linewidth=2, x_compat=True, legend='b')\n",
    "    slice_d['btcbox'].plot(color='limegreen', linewidth=2, x_compat=True, legend='b')\n",
    "    plt.grid(True)\n",
    "    plt.title('Bitflyer vs Btcbox (2018, on 1s data)')\n",
    "    plt.xlabel('Time')\n",
    "    plt.ylabel('Bitcoin price (Yen)')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "bitflyer = pd.read_csv('../data/bitflyerJPY_2018-08-12_small.csv', parse_dates=True, index_col=0)\n",
    "btcbox = pd.read_csv('../data/btcboxJPY_2018-08-12_small.csv', parse_dates=True, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_time_series(bitflyer, btcbox)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision = 100.0 ms.\n"
     ]
    }
   ],
   "source": [
    "ll = lead_lag.LeadLag(\n",
    "    ts1=bitflyer['last'],\n",
    "    ts2=btcbox['last'],\n",
    "    max_lag=30,  # [-X seconds, +X seconds]\n",
    "    verbose=True,\n",
    "    min_precision=0.1  # in seconds.\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running inference...\n",
      "Running slow_inference() on (-300:300) with 1 thread.\n",
      "Lag=-300, contrast=0.01639, elapsed=15.22ms.\n",
      "Lag=-299, contrast=0.01817, elapsed=15.44ms.\n",
      "Lag=-298, contrast=0.01817, elapsed=15.12ms.\n",
      "Lag=-297, contrast=0.01817, elapsed=14.85ms.\n",
      "Lag=-296, contrast=0.01817, elapsed=14.96ms.\n",
      "Lag=-295, contrast=0.01817, elapsed=14.14ms.\n",
      "Lag=-294, contrast=0.01817, elapsed=14.18ms.\n",
      "Lag=-293, contrast=0.01817, elapsed=14.44ms.\n",
      "Lag=-292, contrast=0.01817, elapsed=14.42ms.\n",
      "Lag=-291, contrast=0.01817, elapsed=14.12ms.\n",
      "Lag=-290, contrast=0.01873, elapsed=14.11ms.\n",
      "Lag=-289, contrast=0.01600, elapsed=14.07ms.\n",
      "Lag=-288, contrast=0.01600, elapsed=14.31ms.\n",
      "Lag=-287, contrast=0.01600, elapsed=14.52ms.\n",
      "Lag=-286, contrast=0.01600, elapsed=14.14ms.\n",
      "Lag=-285, contrast=0.01600, elapsed=13.91ms.\n",
      "Lag=-284, contrast=0.01600, elapsed=14.08ms.\n",
      "Lag=-283, contrast=0.01600, elapsed=14.68ms.\n",
      "Lag=-282, contrast=0.01600, elapsed=14.76ms.\n",
      "Lag=-281, contrast=0.01600, elapsed=14.18ms.\n",
      "Lag=-280, contrast=0.00234, elapsed=14.21ms.\n",
      "Lag=-279, contrast=0.01730, elapsed=14.24ms.\n",
      "Lag=-278, contrast=0.01730, elapsed=14.34ms.\n",
      "Lag=-277, contrast=0.01730, elapsed=14.30ms.\n",
      "Lag=-276, contrast=0.01730, elapsed=14.48ms.\n",
      "Lag=-275, contrast=0.01730, elapsed=14.46ms.\n",
      "Lag=-274, contrast=0.01730, elapsed=14.12ms.\n",
      "Lag=-273, contrast=0.01730, elapsed=14.09ms.\n",
      "Lag=-272, contrast=0.01730, elapsed=14.15ms.\n",
      "Lag=-271, contrast=0.01730, elapsed=14.16ms.\n",
      "Lag=-270, contrast=0.03848, elapsed=14.03ms.\n",
      "Lag=-269, contrast=0.03162, elapsed=15.64ms.\n",
      "Lag=-268, contrast=0.03162, elapsed=15.84ms.\n",
      "Lag=-267, contrast=0.03162, elapsed=16.10ms.\n",
      "Lag=-266, contrast=0.03162, elapsed=16.32ms.\n",
      "Lag=-265, contrast=0.03162, elapsed=15.86ms.\n",
      "Lag=-264, contrast=0.03162, elapsed=15.36ms.\n",
      "Lag=-263, contrast=0.03162, elapsed=15.52ms.\n",
      "Lag=-262, contrast=0.03162, elapsed=14.48ms.\n",
      "Lag=-261, contrast=0.03162, elapsed=15.52ms.\n",
      "Lag=-260, contrast=0.01696, elapsed=14.25ms.\n",
      "Lag=-259, contrast=0.00137, elapsed=14.16ms.\n",
      "Lag=-258, contrast=0.00137, elapsed=14.09ms.\n",
      "Lag=-257, contrast=0.00137, elapsed=14.17ms.\n",
      "Lag=-256, contrast=0.00137, elapsed=14.32ms.\n",
      "Lag=-255, contrast=0.00137, elapsed=14.21ms.\n",
      "Lag=-254, contrast=0.00137, elapsed=14.18ms.\n",
      "Lag=-253, contrast=0.00137, elapsed=14.10ms.\n",
      "Lag=-252, contrast=0.00137, elapsed=14.52ms.\n",
      "Lag=-251, contrast=0.00137, elapsed=15.52ms.\n",
      "Lag=-250, contrast=0.01132, elapsed=14.65ms.\n",
      "Lag=-249, contrast=0.00209, elapsed=14.49ms.\n",
      "Lag=-248, contrast=0.00209, elapsed=14.34ms.\n",
      "Lag=-247, contrast=0.00209, elapsed=14.41ms.\n",
      "Lag=-246, contrast=0.00209, elapsed=14.93ms.\n",
      "Lag=-245, contrast=0.00209, elapsed=14.83ms.\n",
      "Lag=-244, contrast=0.00209, elapsed=14.86ms.\n",
      "Lag=-243, contrast=0.00209, elapsed=15.25ms.\n",
      "Lag=-242, contrast=0.00209, elapsed=14.36ms.\n",
      "Lag=-241, contrast=0.00209, elapsed=14.22ms.\n",
      "Lag=-240, contrast=0.01038, elapsed=14.62ms.\n",
      "Lag=-239, contrast=0.01194, elapsed=14.26ms.\n",
      "Lag=-238, contrast=0.01194, elapsed=14.31ms.\n",
      "Lag=-237, contrast=0.01194, elapsed=14.33ms.\n",
      "Lag=-236, contrast=0.01194, elapsed=14.17ms.\n",
      "Lag=-235, contrast=0.01194, elapsed=13.93ms.\n",
      "Lag=-234, contrast=0.01194, elapsed=14.83ms.\n",
      "Lag=-233, contrast=0.01194, elapsed=14.34ms.\n",
      "Lag=-232, contrast=0.01194, elapsed=14.19ms.\n",
      "Lag=-231, contrast=0.01194, elapsed=14.55ms.\n",
      "Lag=-230, contrast=0.00113, elapsed=15.17ms.\n",
      "Lag=-229, contrast=0.00393, elapsed=15.72ms.\n",
      "Lag=-228, contrast=0.00393, elapsed=14.85ms.\n",
      "Lag=-227, contrast=0.00393, elapsed=15.39ms.\n",
      "Lag=-226, contrast=0.00393, elapsed=14.46ms.\n",
      "Lag=-225, contrast=0.00393, elapsed=14.43ms.\n",
      "Lag=-224, contrast=0.00393, elapsed=14.28ms.\n",
      "Lag=-223, contrast=0.00393, elapsed=14.47ms.\n",
      "Lag=-222, contrast=0.00393, elapsed=14.33ms.\n",
      "Lag=-221, contrast=0.00393, elapsed=14.25ms.\n",
      "Lag=-220, contrast=0.01678, elapsed=14.13ms.\n",
      "Lag=-219, contrast=0.01392, elapsed=14.46ms.\n",
      "Lag=-218, contrast=0.01392, elapsed=14.63ms.\n",
      "Lag=-217, contrast=0.01392, elapsed=14.34ms.\n",
      "Lag=-216, contrast=0.01392, elapsed=14.22ms.\n",
      "Lag=-215, contrast=0.01392, elapsed=15.46ms.\n",
      "Lag=-214, contrast=0.01392, elapsed=14.30ms.\n",
      "Lag=-213, contrast=0.01392, elapsed=14.34ms.\n",
      "Lag=-212, contrast=0.01392, elapsed=16.23ms.\n",
      "Lag=-211, contrast=0.01392, elapsed=17.15ms.\n",
      "Lag=-210, contrast=0.04392, elapsed=14.77ms.\n",
      "Lag=-209, contrast=0.02683, elapsed=14.27ms.\n",
      "Lag=-208, contrast=0.02683, elapsed=14.45ms.\n",
      "Lag=-207, contrast=0.02683, elapsed=14.90ms.\n",
      "Lag=-206, contrast=0.02683, elapsed=14.50ms.\n",
      "Lag=-205, contrast=0.02683, elapsed=14.28ms.\n",
      "Lag=-204, contrast=0.02683, elapsed=14.25ms.\n",
      "Lag=-203, contrast=0.02683, elapsed=14.22ms.\n",
      "Lag=-202, contrast=0.02683, elapsed=14.25ms.\n",
      "Lag=-201, contrast=0.02683, elapsed=14.27ms.\n",
      "Lag=-200, contrast=0.00720, elapsed=14.14ms.\n",
      "Lag=-199, contrast=0.01117, elapsed=14.14ms.\n",
      "Lag=-198, contrast=0.01117, elapsed=14.34ms.\n",
      "Lag=-197, contrast=0.01117, elapsed=14.59ms.\n",
      "Lag=-196, contrast=0.01117, elapsed=15.39ms.\n",
      "Lag=-195, contrast=0.01117, elapsed=15.23ms.\n",
      "Lag=-194, contrast=0.01117, elapsed=15.69ms.\n",
      "Lag=-193, contrast=0.01117, elapsed=14.93ms.\n",
      "Lag=-192, contrast=0.01117, elapsed=14.54ms.\n",
      "Lag=-191, contrast=0.01117, elapsed=15.46ms.\n",
      "Lag=-190, contrast=0.02096, elapsed=14.33ms.\n",
      "Lag=-189, contrast=0.02334, elapsed=14.63ms.\n",
      "Lag=-188, contrast=0.02334, elapsed=14.23ms.\n",
      "Lag=-187, contrast=0.02334, elapsed=14.17ms.\n",
      "Lag=-186, contrast=0.02334, elapsed=14.40ms.\n",
      "Lag=-185, contrast=0.02334, elapsed=15.30ms.\n",
      "Lag=-184, contrast=0.02334, elapsed=14.32ms.\n",
      "Lag=-183, contrast=0.02334, elapsed=14.19ms.\n",
      "Lag=-182, contrast=0.02334, elapsed=14.53ms.\n",
      "Lag=-181, contrast=0.02334, elapsed=14.47ms.\n",
      "Lag=-180, contrast=0.00901, elapsed=14.37ms.\n",
      "Lag=-179, contrast=0.01946, elapsed=14.11ms.\n",
      "Lag=-178, contrast=0.01946, elapsed=14.25ms.\n",
      "Lag=-177, contrast=0.01946, elapsed=14.41ms.\n",
      "Lag=-176, contrast=0.01946, elapsed=14.27ms.\n",
      "Lag=-175, contrast=0.01946, elapsed=14.27ms.\n",
      "Lag=-174, contrast=0.01946, elapsed=15.32ms.\n",
      "Lag=-173, contrast=0.01946, elapsed=14.86ms.\n",
      "Lag=-172, contrast=0.01946, elapsed=14.31ms.\n",
      "Lag=-171, contrast=0.01946, elapsed=15.00ms.\n",
      "Lag=-170, contrast=0.01335, elapsed=14.73ms.\n",
      "Lag=-169, contrast=0.00778, elapsed=14.26ms.\n",
      "Lag=-168, contrast=0.00778, elapsed=14.19ms.\n",
      "Lag=-167, contrast=0.00778, elapsed=14.22ms.\n",
      "Lag=-166, contrast=0.00778, elapsed=14.25ms.\n",
      "Lag=-165, contrast=0.00778, elapsed=14.19ms.\n",
      "Lag=-164, contrast=0.00778, elapsed=14.21ms.\n",
      "Lag=-163, contrast=0.00778, elapsed=14.23ms.\n",
      "Lag=-162, contrast=0.00778, elapsed=14.18ms.\n",
      "Lag=-161, contrast=0.00778, elapsed=14.29ms.\n",
      "Lag=-160, contrast=0.00359, elapsed=14.35ms.\n",
      "Lag=-159, contrast=0.00147, elapsed=14.51ms.\n",
      "Lag=-158, contrast=0.00147, elapsed=14.56ms.\n",
      "Lag=-157, contrast=0.00147, elapsed=14.18ms.\n",
      "Lag=-156, contrast=0.00147, elapsed=15.35ms.\n",
      "Lag=-155, contrast=0.00147, elapsed=15.16ms.\n",
      "Lag=-154, contrast=0.00147, elapsed=14.47ms.\n",
      "Lag=-153, contrast=0.00147, elapsed=14.60ms.\n",
      "Lag=-152, contrast=0.00147, elapsed=14.57ms.\n",
      "Lag=-151, contrast=0.00147, elapsed=14.48ms.\n",
      "Lag=-150, contrast=0.00110, elapsed=14.59ms.\n",
      "Lag=-149, contrast=0.00377, elapsed=14.40ms.\n",
      "Lag=-148, contrast=0.00377, elapsed=14.39ms.\n",
      "Lag=-147, contrast=0.00377, elapsed=14.20ms.\n",
      "Lag=-146, contrast=0.00377, elapsed=14.30ms.\n",
      "Lag=-145, contrast=0.00377, elapsed=14.25ms.\n",
      "Lag=-144, contrast=0.00377, elapsed=14.34ms.\n",
      "Lag=-143, contrast=0.00377, elapsed=14.38ms.\n",
      "Lag=-142, contrast=0.00377, elapsed=15.16ms.\n",
      "Lag=-141, contrast=0.00377, elapsed=14.27ms.\n",
      "Lag=-140, contrast=0.01432, elapsed=13.98ms.\n",
      "Lag=-139, contrast=0.02476, elapsed=14.17ms.\n",
      "Lag=-138, contrast=0.02476, elapsed=14.09ms.\n",
      "Lag=-137, contrast=0.02476, elapsed=15.29ms.\n",
      "Lag=-136, contrast=0.02476, elapsed=14.91ms.\n",
      "Lag=-135, contrast=0.02476, elapsed=14.59ms.\n",
      "Lag=-134, contrast=0.02476, elapsed=14.28ms.\n",
      "Lag=-133, contrast=0.02476, elapsed=14.20ms.\n",
      "Lag=-132, contrast=0.02476, elapsed=14.20ms.\n",
      "Lag=-131, contrast=0.02476, elapsed=14.33ms.\n",
      "Lag=-130, contrast=0.02374, elapsed=14.23ms.\n",
      "Lag=-129, contrast=0.01434, elapsed=14.21ms.\n",
      "Lag=-128, contrast=0.01434, elapsed=14.18ms.\n",
      "Lag=-127, contrast=0.01434, elapsed=14.28ms.\n",
      "Lag=-126, contrast=0.01434, elapsed=14.24ms.\n",
      "Lag=-125, contrast=0.01434, elapsed=14.23ms.\n",
      "Lag=-124, contrast=0.01434, elapsed=14.25ms.\n",
      "Lag=-123, contrast=0.01434, elapsed=14.40ms.\n",
      "Lag=-122, contrast=0.01434, elapsed=15.07ms.\n",
      "Lag=-121, contrast=0.01434, elapsed=14.71ms.\n",
      "Lag=-120, contrast=0.00949, elapsed=14.55ms.\n",
      "Lag=-119, contrast=0.00858, elapsed=15.57ms.\n",
      "Lag=-118, contrast=0.00858, elapsed=14.45ms.\n",
      "Lag=-117, contrast=0.00858, elapsed=14.43ms.\n",
      "Lag=-116, contrast=0.00858, elapsed=15.46ms.\n",
      "Lag=-115, contrast=0.00858, elapsed=14.48ms.\n",
      "Lag=-114, contrast=0.00858, elapsed=14.47ms.\n",
      "Lag=-113, contrast=0.00858, elapsed=14.25ms.\n",
      "Lag=-112, contrast=0.00858, elapsed=14.23ms.\n",
      "Lag=-111, contrast=0.00858, elapsed=14.17ms.\n",
      "Lag=-110, contrast=0.02002, elapsed=14.16ms.\n",
      "Lag=-109, contrast=0.01622, elapsed=14.32ms.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lag=-108, contrast=0.01622, elapsed=14.44ms.\n",
      "Lag=-107, contrast=0.01622, elapsed=14.21ms.\n",
      "Lag=-106, contrast=0.01622, elapsed=15.47ms.\n",
      "Lag=-105, contrast=0.01622, elapsed=15.29ms.\n",
      "Lag=-104, contrast=0.01622, elapsed=14.35ms.\n",
      "Lag=-103, contrast=0.01622, elapsed=14.25ms.\n",
      "Lag=-102, contrast=0.01622, elapsed=14.38ms.\n",
      "Lag=-101, contrast=0.01622, elapsed=14.29ms.\n",
      "Lag=-100, contrast=0.01506, elapsed=14.37ms.\n",
      "Lag=-99, contrast=0.02313, elapsed=15.03ms.\n",
      "Lag=-98, contrast=0.02313, elapsed=14.98ms.\n",
      "Lag=-97, contrast=0.02313, elapsed=15.30ms.\n",
      "Lag=-96, contrast=0.02313, elapsed=14.40ms.\n",
      "Lag=-95, contrast=0.02313, elapsed=14.25ms.\n",
      "Lag=-94, contrast=0.02313, elapsed=14.32ms.\n",
      "Lag=-93, contrast=0.02313, elapsed=14.20ms.\n",
      "Lag=-92, contrast=0.02313, elapsed=15.55ms.\n",
      "Lag=-91, contrast=0.02313, elapsed=14.89ms.\n",
      "Lag=-90, contrast=0.02579, elapsed=14.64ms.\n",
      "Lag=-89, contrast=0.02779, elapsed=14.37ms.\n",
      "Lag=-88, contrast=0.02779, elapsed=14.27ms.\n",
      "Lag=-87, contrast=0.02779, elapsed=14.22ms.\n",
      "Lag=-86, contrast=0.02779, elapsed=14.18ms.\n",
      "Lag=-85, contrast=0.02779, elapsed=14.16ms.\n",
      "Lag=-84, contrast=0.02779, elapsed=14.17ms.\n",
      "Lag=-83, contrast=0.02779, elapsed=14.23ms.\n",
      "Lag=-82, contrast=0.02779, elapsed=14.37ms.\n",
      "Lag=-81, contrast=0.02779, elapsed=14.28ms.\n",
      "Lag=-80, contrast=0.02420, elapsed=14.16ms.\n",
      "Lag=-79, contrast=0.03995, elapsed=14.23ms.\n",
      "Lag=-78, contrast=0.03995, elapsed=14.31ms.\n",
      "Lag=-77, contrast=0.03995, elapsed=14.43ms.\n",
      "Lag=-76, contrast=0.03995, elapsed=14.22ms.\n",
      "Lag=-75, contrast=0.03995, elapsed=14.04ms.\n",
      "Lag=-74, contrast=0.03995, elapsed=14.24ms.\n",
      "Lag=-73, contrast=0.03995, elapsed=15.22ms.\n",
      "Lag=-72, contrast=0.03995, elapsed=15.90ms.\n",
      "Lag=-71, contrast=0.03995, elapsed=15.38ms.\n",
      "Lag=-70, contrast=0.04241, elapsed=15.22ms.\n",
      "Lag=-69, contrast=0.03364, elapsed=15.16ms.\n",
      "Lag=-68, contrast=0.03364, elapsed=15.09ms.\n",
      "Lag=-67, contrast=0.03364, elapsed=15.10ms.\n",
      "Lag=-66, contrast=0.03364, elapsed=15.30ms.\n",
      "Lag=-65, contrast=0.03364, elapsed=15.22ms.\n",
      "Lag=-64, contrast=0.03364, elapsed=14.62ms.\n",
      "Lag=-63, contrast=0.03364, elapsed=14.42ms.\n",
      "Lag=-62, contrast=0.03364, elapsed=14.25ms.\n",
      "Lag=-61, contrast=0.03364, elapsed=14.21ms.\n",
      "Lag=-60, contrast=0.02032, elapsed=14.58ms.\n",
      "Lag=-59, contrast=0.02188, elapsed=14.48ms.\n",
      "Lag=-58, contrast=0.02188, elapsed=14.00ms.\n",
      "Lag=-57, contrast=0.02188, elapsed=14.99ms.\n",
      "Lag=-56, contrast=0.02188, elapsed=14.72ms.\n",
      "Lag=-55, contrast=0.02188, elapsed=15.55ms.\n",
      "Lag=-54, contrast=0.02188, elapsed=14.41ms.\n",
      "Lag=-53, contrast=0.02188, elapsed=14.17ms.\n",
      "Lag=-52, contrast=0.02188, elapsed=14.16ms.\n",
      "Lag=-51, contrast=0.02188, elapsed=14.16ms.\n",
      "Lag=-50, contrast=0.01791, elapsed=14.11ms.\n",
      "Lag=-49, contrast=0.03213, elapsed=14.16ms.\n",
      "Lag=-48, contrast=0.03213, elapsed=14.14ms.\n",
      "Lag=-47, contrast=0.03213, elapsed=14.14ms.\n",
      "Lag=-46, contrast=0.03213, elapsed=14.18ms.\n",
      "Lag=-45, contrast=0.03213, elapsed=14.19ms.\n",
      "Lag=-44, contrast=0.03213, elapsed=14.19ms.\n",
      "Lag=-43, contrast=0.03213, elapsed=14.47ms.\n",
      "Lag=-42, contrast=0.03213, elapsed=14.19ms.\n",
      "Lag=-41, contrast=0.03213, elapsed=13.93ms.\n",
      "Lag=-40, contrast=0.04115, elapsed=14.05ms.\n",
      "Lag=-39, contrast=0.04715, elapsed=14.58ms.\n",
      "Lag=-38, contrast=0.04715, elapsed=14.96ms.\n",
      "Lag=-37, contrast=0.04715, elapsed=14.60ms.\n",
      "Lag=-36, contrast=0.04715, elapsed=14.57ms.\n",
      "Lag=-35, contrast=0.04715, elapsed=14.25ms.\n",
      "Lag=-34, contrast=0.04715, elapsed=14.23ms.\n",
      "Lag=-33, contrast=0.04715, elapsed=14.26ms.\n",
      "Lag=-32, contrast=0.04715, elapsed=14.54ms.\n",
      "Lag=-31, contrast=0.04715, elapsed=14.50ms.\n",
      "Lag=-30, contrast=0.01799, elapsed=14.50ms.\n",
      "Lag=-29, contrast=0.03414, elapsed=14.35ms.\n",
      "Lag=-28, contrast=0.03414, elapsed=14.23ms.\n",
      "Lag=-27, contrast=0.03414, elapsed=14.37ms.\n",
      "Lag=-26, contrast=0.03414, elapsed=14.32ms.\n",
      "Lag=-25, contrast=0.03414, elapsed=15.61ms.\n",
      "Lag=-24, contrast=0.03414, elapsed=15.07ms.\n",
      "Lag=-23, contrast=0.03414, elapsed=14.27ms.\n",
      "Lag=-22, contrast=0.03414, elapsed=14.14ms.\n",
      "Lag=-21, contrast=0.03414, elapsed=15.21ms.\n",
      "Lag=-20, contrast=0.04666, elapsed=14.55ms.\n",
      "Lag=-19, contrast=0.04730, elapsed=14.96ms.\n",
      "Lag=-18, contrast=0.04730, elapsed=14.32ms.\n",
      "Lag=-17, contrast=0.04730, elapsed=14.22ms.\n",
      "Lag=-16, contrast=0.04730, elapsed=14.19ms.\n",
      "Lag=-15, contrast=0.04730, elapsed=14.19ms.\n",
      "Lag=-14, contrast=0.04730, elapsed=14.20ms.\n",
      "Lag=-13, contrast=0.04730, elapsed=14.50ms.\n",
      "Lag=-12, contrast=0.04730, elapsed=14.27ms.\n",
      "Lag=-11, contrast=0.04730, elapsed=14.17ms.\n",
      "Lag=-10, contrast=0.03438, elapsed=14.12ms.\n",
      "Lag=-9, contrast=0.03633, elapsed=14.16ms.\n",
      "Lag=-8, contrast=0.03633, elapsed=14.16ms.\n",
      "Lag=-7, contrast=0.03633, elapsed=14.18ms.\n",
      "Lag=-6, contrast=0.03633, elapsed=14.32ms.\n",
      "Lag=-5, contrast=0.03633, elapsed=14.33ms.\n",
      "Lag=-4, contrast=0.03633, elapsed=14.12ms.\n",
      "Lag=-3, contrast=0.03633, elapsed=14.08ms.\n",
      "Lag=-2, contrast=0.03633, elapsed=14.67ms.\n",
      "Lag=-1, contrast=0.03633, elapsed=15.28ms.\n",
      "Lag=0, contrast=0.02917, elapsed=14.89ms.\n",
      "Lag=1, contrast=0.02998, elapsed=14.62ms.\n",
      "Lag=2, contrast=0.02998, elapsed=14.25ms.\n",
      "Lag=3, contrast=0.02998, elapsed=14.24ms.\n",
      "Lag=4, contrast=0.02998, elapsed=14.22ms.\n",
      "Lag=5, contrast=0.02998, elapsed=14.24ms.\n",
      "Lag=6, contrast=0.02998, elapsed=14.18ms.\n",
      "Lag=7, contrast=0.02998, elapsed=14.18ms.\n",
      "Lag=8, contrast=0.02998, elapsed=14.14ms.\n",
      "Lag=9, contrast=0.02998, elapsed=14.16ms.\n",
      "Lag=10, contrast=0.03762, elapsed=14.10ms.\n",
      "Lag=11, contrast=0.07104, elapsed=14.16ms.\n",
      "Lag=12, contrast=0.07104, elapsed=14.30ms.\n",
      "Lag=13, contrast=0.07104, elapsed=15.19ms.\n",
      "Lag=14, contrast=0.07104, elapsed=15.38ms.\n",
      "Lag=15, contrast=0.07104, elapsed=14.53ms.\n",
      "Lag=16, contrast=0.07104, elapsed=15.04ms.\n",
      "Lag=17, contrast=0.07104, elapsed=14.36ms.\n",
      "Lag=18, contrast=0.07104, elapsed=14.21ms.\n",
      "Lag=19, contrast=0.07104, elapsed=14.19ms.\n",
      "Lag=20, contrast=0.07868, elapsed=14.19ms.\n",
      "Lag=21, contrast=0.08531, elapsed=14.91ms.\n",
      "Lag=22, contrast=0.08531, elapsed=15.85ms.\n",
      "Lag=23, contrast=0.08531, elapsed=14.45ms.\n",
      "Lag=24, contrast=0.08531, elapsed=14.26ms.\n",
      "Lag=25, contrast=0.08531, elapsed=14.33ms.\n",
      "Lag=26, contrast=0.08531, elapsed=14.29ms.\n",
      "Lag=27, contrast=0.08531, elapsed=14.47ms.\n",
      "Lag=28, contrast=0.08531, elapsed=14.17ms.\n",
      "Lag=29, contrast=0.08531, elapsed=13.99ms.\n",
      "Lag=30, contrast=0.06094, elapsed=14.09ms.\n",
      "Lag=31, contrast=0.08530, elapsed=14.13ms.\n",
      "Lag=32, contrast=0.08530, elapsed=14.12ms.\n",
      "Lag=33, contrast=0.08530, elapsed=14.10ms.\n",
      "Lag=34, contrast=0.08530, elapsed=15.48ms.\n",
      "Lag=35, contrast=0.08530, elapsed=14.56ms.\n",
      "Lag=36, contrast=0.08530, elapsed=14.23ms.\n",
      "Lag=37, contrast=0.08530, elapsed=14.25ms.\n",
      "Lag=38, contrast=0.08530, elapsed=14.20ms.\n",
      "Lag=39, contrast=0.08530, elapsed=14.17ms.\n",
      "Lag=40, contrast=0.10071, elapsed=14.15ms.\n",
      "Lag=41, contrast=0.08855, elapsed=14.18ms.\n",
      "Lag=42, contrast=0.08855, elapsed=14.21ms.\n",
      "Lag=43, contrast=0.08855, elapsed=14.22ms.\n",
      "Lag=44, contrast=0.08855, elapsed=14.32ms.\n",
      "Lag=45, contrast=0.08855, elapsed=14.21ms.\n",
      "Lag=46, contrast=0.08855, elapsed=14.20ms.\n",
      "Lag=47, contrast=0.08855, elapsed=14.19ms.\n",
      "Lag=48, contrast=0.08855, elapsed=14.34ms.\n",
      "Lag=49, contrast=0.08855, elapsed=14.14ms.\n",
      "Lag=50, contrast=0.05466, elapsed=13.90ms.\n",
      "Lag=51, contrast=0.07074, elapsed=14.15ms.\n",
      "Lag=52, contrast=0.07074, elapsed=14.20ms.\n",
      "Lag=53, contrast=0.07074, elapsed=14.73ms.\n",
      "Lag=54, contrast=0.07074, elapsed=15.71ms.\n",
      "Lag=55, contrast=0.07074, elapsed=14.81ms.\n",
      "Lag=56, contrast=0.07074, elapsed=14.24ms.\n",
      "Lag=57, contrast=0.07074, elapsed=14.43ms.\n",
      "Lag=58, contrast=0.07074, elapsed=14.59ms.\n",
      "Lag=59, contrast=0.07074, elapsed=14.44ms.\n",
      "Lag=60, contrast=0.06846, elapsed=14.26ms.\n",
      "Lag=61, contrast=0.09047, elapsed=14.40ms.\n",
      "Lag=62, contrast=0.09047, elapsed=14.21ms.\n",
      "Lag=63, contrast=0.09047, elapsed=14.19ms.\n",
      "Lag=64, contrast=0.09047, elapsed=14.15ms.\n",
      "Lag=65, contrast=0.09047, elapsed=14.22ms.\n",
      "Lag=66, contrast=0.09047, elapsed=14.20ms.\n",
      "Lag=67, contrast=0.09047, elapsed=15.13ms.\n",
      "Lag=68, contrast=0.09047, elapsed=14.36ms.\n",
      "Lag=69, contrast=0.09047, elapsed=14.13ms.\n",
      "Lag=70, contrast=0.08765, elapsed=15.11ms.\n",
      "Lag=71, contrast=0.09166, elapsed=14.93ms.\n",
      "Lag=72, contrast=0.09166, elapsed=14.68ms.\n",
      "Lag=73, contrast=0.09166, elapsed=14.56ms.\n",
      "Lag=74, contrast=0.09166, elapsed=14.38ms.\n",
      "Lag=75, contrast=0.09166, elapsed=14.25ms.\n",
      "Lag=76, contrast=0.09166, elapsed=14.17ms.\n",
      "Lag=77, contrast=0.09166, elapsed=14.21ms.\n",
      "Lag=78, contrast=0.09166, elapsed=14.18ms.\n",
      "Lag=79, contrast=0.09166, elapsed=14.16ms.\n",
      "Lag=80, contrast=0.09065, elapsed=14.14ms.\n",
      "Lag=81, contrast=0.11544, elapsed=14.23ms.\n",
      "Lag=82, contrast=0.11544, elapsed=14.17ms.\n",
      "Lag=83, contrast=0.11544, elapsed=14.20ms.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lag=84, contrast=0.11544, elapsed=14.36ms.\n",
      "Lag=85, contrast=0.11544, elapsed=14.13ms.\n",
      "Lag=86, contrast=0.11544, elapsed=14.38ms.\n",
      "Lag=87, contrast=0.11544, elapsed=14.70ms.\n",
      "Lag=88, contrast=0.11544, elapsed=14.21ms.\n",
      "Lag=89, contrast=0.11544, elapsed=14.53ms.\n",
      "Lag=90, contrast=0.10205, elapsed=14.28ms.\n",
      "Lag=91, contrast=0.11301, elapsed=14.44ms.\n",
      "Lag=92, contrast=0.11301, elapsed=14.39ms.\n",
      "Lag=93, contrast=0.11301, elapsed=14.23ms.\n",
      "Lag=94, contrast=0.11301, elapsed=14.29ms.\n",
      "Lag=95, contrast=0.11301, elapsed=14.27ms.\n",
      "Lag=96, contrast=0.11301, elapsed=14.28ms.\n",
      "Lag=97, contrast=0.11301, elapsed=14.28ms.\n",
      "Lag=98, contrast=0.11301, elapsed=14.29ms.\n",
      "Lag=99, contrast=0.11301, elapsed=14.21ms.\n",
      "Lag=100, contrast=0.10519, elapsed=14.27ms.\n",
      "Lag=101, contrast=0.13084, elapsed=14.56ms.\n",
      "Lag=102, contrast=0.13084, elapsed=15.14ms.\n",
      "Lag=103, contrast=0.13084, elapsed=14.42ms.\n",
      "Lag=104, contrast=0.13084, elapsed=15.65ms.\n",
      "Lag=105, contrast=0.13084, elapsed=14.54ms.\n",
      "Lag=106, contrast=0.13084, elapsed=14.32ms.\n",
      "Lag=107, contrast=0.13084, elapsed=14.40ms.\n",
      "Lag=108, contrast=0.13084, elapsed=14.30ms.\n",
      "Lag=109, contrast=0.13084, elapsed=14.19ms.\n",
      "Lag=110, contrast=0.12119, elapsed=14.16ms.\n",
      "Lag=111, contrast=0.13513, elapsed=14.16ms.\n",
      "Lag=112, contrast=0.13513, elapsed=14.18ms.\n",
      "Lag=113, contrast=0.13513, elapsed=14.16ms.\n",
      "Lag=114, contrast=0.13513, elapsed=14.20ms.\n",
      "Lag=115, contrast=0.13513, elapsed=14.18ms.\n",
      "Lag=116, contrast=0.13513, elapsed=14.33ms.\n",
      "Lag=117, contrast=0.13513, elapsed=15.00ms.\n",
      "Lag=118, contrast=0.13513, elapsed=14.36ms.\n",
      "Lag=119, contrast=0.13513, elapsed=14.02ms.\n",
      "Lag=120, contrast=0.11965, elapsed=13.90ms.\n",
      "Lag=121, contrast=0.12722, elapsed=14.15ms.\n",
      "Lag=122, contrast=0.12722, elapsed=14.72ms.\n",
      "Lag=123, contrast=0.12722, elapsed=14.37ms.\n",
      "Lag=124, contrast=0.12722, elapsed=14.19ms.\n",
      "Lag=125, contrast=0.12722, elapsed=14.21ms.\n",
      "Lag=126, contrast=0.12722, elapsed=14.20ms.\n",
      "Lag=127, contrast=0.12722, elapsed=14.23ms.\n",
      "Lag=128, contrast=0.12722, elapsed=14.17ms.\n",
      "Lag=129, contrast=0.12722, elapsed=14.17ms.\n",
      "Lag=130, contrast=0.10766, elapsed=14.10ms.\n",
      "Lag=131, contrast=0.11787, elapsed=14.38ms.\n",
      "Lag=132, contrast=0.11787, elapsed=14.14ms.\n",
      "Lag=133, contrast=0.11787, elapsed=13.94ms.\n",
      "Lag=134, contrast=0.11787, elapsed=14.44ms.\n",
      "Lag=135, contrast=0.11787, elapsed=15.07ms.\n",
      "Lag=136, contrast=0.11787, elapsed=14.47ms.\n",
      "Lag=137, contrast=0.11787, elapsed=14.24ms.\n",
      "Lag=138, contrast=0.11787, elapsed=14.91ms.\n",
      "Lag=139, contrast=0.11787, elapsed=14.75ms.\n",
      "Lag=140, contrast=0.12630, elapsed=14.31ms.\n",
      "Lag=141, contrast=0.15079, elapsed=14.31ms.\n",
      "Lag=142, contrast=0.15079, elapsed=14.27ms.\n",
      "Lag=143, contrast=0.15079, elapsed=14.22ms.\n",
      "Lag=144, contrast=0.15079, elapsed=14.20ms.\n",
      "Lag=145, contrast=0.15079, elapsed=14.18ms.\n",
      "Lag=146, contrast=0.15079, elapsed=14.11ms.\n",
      "Lag=147, contrast=0.15079, elapsed=14.17ms.\n",
      "Lag=148, contrast=0.15079, elapsed=14.25ms.\n",
      "Lag=149, contrast=0.15079, elapsed=14.15ms.\n",
      "Lag=150, contrast=0.14867, elapsed=14.64ms.\n",
      "Lag=151, contrast=0.13476, elapsed=14.38ms.\n",
      "Lag=152, contrast=0.13476, elapsed=14.32ms.\n",
      "Lag=153, contrast=0.13476, elapsed=15.04ms.\n",
      "Lag=154, contrast=0.13476, elapsed=14.47ms.\n",
      "Lag=155, contrast=0.13476, elapsed=14.61ms.\n",
      "Lag=156, contrast=0.13476, elapsed=14.30ms.\n",
      "Lag=157, contrast=0.13476, elapsed=14.57ms.\n",
      "Lag=158, contrast=0.13476, elapsed=15.27ms.\n",
      "Lag=159, contrast=0.13476, elapsed=14.54ms.\n",
      "Lag=160, contrast=0.11529, elapsed=15.06ms.\n",
      "Lag=161, contrast=0.13766, elapsed=14.70ms.\n",
      "Lag=162, contrast=0.13766, elapsed=14.96ms.\n",
      "Lag=163, contrast=0.13766, elapsed=14.62ms.\n",
      "Lag=164, contrast=0.13766, elapsed=14.62ms.\n",
      "Lag=165, contrast=0.13766, elapsed=14.68ms.\n",
      "Lag=166, contrast=0.13766, elapsed=15.30ms.\n",
      "Lag=167, contrast=0.13766, elapsed=14.43ms.\n",
      "Lag=168, contrast=0.13766, elapsed=14.83ms.\n",
      "Lag=169, contrast=0.13766, elapsed=14.41ms.\n",
      "Lag=170, contrast=0.12619, elapsed=14.13ms.\n",
      "Lag=171, contrast=0.15298, elapsed=15.18ms.\n",
      "Lag=172, contrast=0.15298, elapsed=14.46ms.\n",
      "Lag=173, contrast=0.15298, elapsed=14.17ms.\n",
      "Lag=174, contrast=0.15298, elapsed=14.15ms.\n",
      "Lag=175, contrast=0.15298, elapsed=14.11ms.\n",
      "Lag=176, contrast=0.15298, elapsed=14.10ms.\n",
      "Lag=177, contrast=0.15298, elapsed=14.12ms.\n",
      "Lag=178, contrast=0.15298, elapsed=14.05ms.\n",
      "Lag=179, contrast=0.15298, elapsed=14.07ms.\n",
      "Lag=180, contrast=0.14159, elapsed=14.15ms.\n",
      "Lag=181, contrast=0.13716, elapsed=14.31ms.\n",
      "Lag=182, contrast=0.13716, elapsed=14.28ms.\n",
      "Lag=183, contrast=0.13716, elapsed=14.36ms.\n",
      "Lag=184, contrast=0.13716, elapsed=15.22ms.\n",
      "Lag=185, contrast=0.13716, elapsed=14.75ms.\n",
      "Lag=186, contrast=0.13716, elapsed=14.84ms.\n",
      "Lag=187, contrast=0.13716, elapsed=15.22ms.\n",
      "Lag=188, contrast=0.13716, elapsed=14.36ms.\n",
      "Lag=189, contrast=0.13716, elapsed=14.32ms.\n",
      "Lag=190, contrast=0.11791, elapsed=14.20ms.\n",
      "Lag=191, contrast=0.13848, elapsed=14.23ms.\n",
      "Lag=192, contrast=0.13848, elapsed=14.17ms.\n",
      "Lag=193, contrast=0.13848, elapsed=14.21ms.\n",
      "Lag=194, contrast=0.13848, elapsed=14.21ms.\n",
      "Lag=195, contrast=0.13848, elapsed=14.19ms.\n",
      "Lag=196, contrast=0.13848, elapsed=14.17ms.\n",
      "Lag=197, contrast=0.13848, elapsed=14.54ms.\n",
      "Lag=198, contrast=0.13848, elapsed=14.60ms.\n",
      "Lag=199, contrast=0.13848, elapsed=14.86ms.\n",
      "Lag=200, contrast=0.13486, elapsed=14.39ms.\n",
      "Lag=201, contrast=0.12562, elapsed=14.15ms.\n",
      "Lag=202, contrast=0.12562, elapsed=14.01ms.\n",
      "Lag=203, contrast=0.12562, elapsed=14.21ms.\n",
      "Lag=204, contrast=0.12562, elapsed=14.77ms.\n",
      "Lag=205, contrast=0.12562, elapsed=14.66ms.\n",
      "Lag=206, contrast=0.12562, elapsed=14.65ms.\n",
      "Lag=207, contrast=0.12562, elapsed=14.30ms.\n",
      "Lag=208, contrast=0.12562, elapsed=14.15ms.\n",
      "Lag=209, contrast=0.12562, elapsed=14.25ms.\n",
      "Lag=210, contrast=0.11333, elapsed=14.24ms.\n",
      "Lag=211, contrast=0.12336, elapsed=14.15ms.\n",
      "Lag=212, contrast=0.12336, elapsed=14.16ms.\n",
      "Lag=213, contrast=0.12336, elapsed=14.23ms.\n",
      "Lag=214, contrast=0.12336, elapsed=14.30ms.\n",
      "Lag=215, contrast=0.12336, elapsed=14.18ms.\n",
      "Lag=216, contrast=0.12336, elapsed=15.08ms.\n",
      "Lag=217, contrast=0.12336, elapsed=14.34ms.\n",
      "Lag=218, contrast=0.12336, elapsed=14.32ms.\n",
      "Lag=219, contrast=0.12336, elapsed=14.83ms.\n",
      "Lag=220, contrast=0.11845, elapsed=14.43ms.\n",
      "Lag=221, contrast=0.12126, elapsed=14.26ms.\n",
      "Lag=222, contrast=0.12126, elapsed=14.14ms.\n",
      "Lag=223, contrast=0.12126, elapsed=14.22ms.\n",
      "Lag=224, contrast=0.12126, elapsed=14.14ms.\n",
      "Lag=225, contrast=0.12126, elapsed=14.19ms.\n",
      "Lag=226, contrast=0.12126, elapsed=14.10ms.\n",
      "Lag=227, contrast=0.12126, elapsed=14.18ms.\n",
      "Lag=228, contrast=0.12126, elapsed=14.19ms.\n",
      "Lag=229, contrast=0.12126, elapsed=14.05ms.\n",
      "Lag=230, contrast=0.10215, elapsed=14.49ms.\n",
      "Lag=231, contrast=0.10435, elapsed=14.45ms.\n",
      "Lag=232, contrast=0.10435, elapsed=14.45ms.\n",
      "Lag=233, contrast=0.10435, elapsed=16.92ms.\n",
      "Lag=234, contrast=0.10435, elapsed=14.80ms.\n",
      "Lag=235, contrast=0.10435, elapsed=14.27ms.\n",
      "Lag=236, contrast=0.10435, elapsed=14.08ms.\n",
      "Lag=237, contrast=0.10435, elapsed=14.75ms.\n",
      "Lag=238, contrast=0.10435, elapsed=15.12ms.\n",
      "Lag=239, contrast=0.10435, elapsed=14.31ms.\n",
      "Lag=240, contrast=0.08905, elapsed=14.08ms.\n",
      "Lag=241, contrast=0.09779, elapsed=14.34ms.\n",
      "Lag=242, contrast=0.09779, elapsed=14.28ms.\n",
      "Lag=243, contrast=0.09779, elapsed=14.66ms.\n",
      "Lag=244, contrast=0.09779, elapsed=14.25ms.\n",
      "Lag=245, contrast=0.09779, elapsed=14.30ms.\n",
      "Lag=246, contrast=0.09779, elapsed=14.33ms.\n",
      "Lag=247, contrast=0.09779, elapsed=14.45ms.\n",
      "Lag=248, contrast=0.09779, elapsed=15.28ms.\n",
      "Lag=249, contrast=0.09779, elapsed=14.97ms.\n",
      "Lag=250, contrast=0.09845, elapsed=14.52ms.\n",
      "Lag=251, contrast=0.07868, elapsed=14.37ms.\n",
      "Lag=252, contrast=0.07868, elapsed=14.13ms.\n",
      "Lag=253, contrast=0.07868, elapsed=14.05ms.\n",
      "Lag=254, contrast=0.07868, elapsed=14.30ms.\n",
      "Lag=255, contrast=0.07868, elapsed=14.73ms.\n",
      "Lag=256, contrast=0.07868, elapsed=14.66ms.\n",
      "Lag=257, contrast=0.07868, elapsed=14.18ms.\n",
      "Lag=258, contrast=0.07868, elapsed=14.13ms.\n",
      "Lag=259, contrast=0.07868, elapsed=14.37ms.\n",
      "Lag=260, contrast=0.04636, elapsed=14.17ms.\n",
      "Lag=261, contrast=0.07602, elapsed=14.19ms.\n",
      "Lag=262, contrast=0.07602, elapsed=14.20ms.\n",
      "Lag=263, contrast=0.07602, elapsed=14.15ms.\n",
      "Lag=264, contrast=0.07602, elapsed=14.39ms.\n",
      "Lag=265, contrast=0.07602, elapsed=14.92ms.\n",
      "Lag=266, contrast=0.07602, elapsed=14.71ms.\n",
      "Lag=267, contrast=0.07602, elapsed=14.14ms.\n",
      "Lag=268, contrast=0.07602, elapsed=14.10ms.\n",
      "Lag=269, contrast=0.07602, elapsed=14.29ms.\n",
      "Lag=270, contrast=0.08465, elapsed=14.44ms.\n",
      "Lag=271, contrast=0.06588, elapsed=14.68ms.\n",
      "Lag=272, contrast=0.06588, elapsed=14.84ms.\n",
      "Lag=273, contrast=0.06588, elapsed=14.33ms.\n",
      "Lag=274, contrast=0.06588, elapsed=14.12ms.\n",
      "Lag=275, contrast=0.06588, elapsed=14.35ms.\n",
      "Lag=276, contrast=0.06588, elapsed=14.21ms.\n",
      "Lag=277, contrast=0.06588, elapsed=14.29ms.\n",
      "Lag=278, contrast=0.06588, elapsed=14.16ms.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lag=279, contrast=0.06588, elapsed=14.32ms.\n",
      "Lag=280, contrast=0.04953, elapsed=14.04ms.\n",
      "Lag=281, contrast=0.06859, elapsed=15.01ms.\n",
      "Lag=282, contrast=0.06859, elapsed=14.23ms.\n",
      "Lag=283, contrast=0.06859, elapsed=14.46ms.\n",
      "Lag=284, contrast=0.06859, elapsed=14.82ms.\n",
      "Lag=285, contrast=0.06859, elapsed=14.24ms.\n",
      "Lag=286, contrast=0.06859, elapsed=14.49ms.\n",
      "Lag=287, contrast=0.06859, elapsed=14.32ms.\n",
      "Lag=288, contrast=0.06859, elapsed=13.92ms.\n",
      "Lag=289, contrast=0.06859, elapsed=14.17ms.\n",
      "Lag=290, contrast=0.05739, elapsed=14.83ms.\n",
      "Lag=291, contrast=0.04296, elapsed=15.50ms.\n",
      "Lag=292, contrast=0.04296, elapsed=14.69ms.\n",
      "Lag=293, contrast=0.04296, elapsed=14.14ms.\n",
      "Lag=294, contrast=0.04296, elapsed=14.14ms.\n",
      "Lag=295, contrast=0.04296, elapsed=14.27ms.\n",
      "Lag=296, contrast=0.04296, elapsed=14.31ms.\n",
      "Lag=297, contrast=0.04296, elapsed=14.08ms.\n",
      "Lag=298, contrast=0.04296, elapsed=14.19ms.\n",
      "Lag=299, contrast=0.04296, elapsed=14.97ms.\n",
      "Lag=300, contrast=0.03044, elapsed=14.94ms.\n"
     ]
    }
   ],
   "source": [
    "print('Running inference...')\n",
    "ll.run_inference()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated lag: 17.1 seconds (bitflyer leading).\n"
     ]
    }
   ],
   "source": [
    "print(f'Estimated lag: {ll.lead_lag} seconds (bitflyer leading).')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive lag means TS1 is leading. LLR: 5.02 (cf. paper for the definition of LLR).\n"
     ]
    }
   ],
   "source": [
    "print(f'Positive lag means TS1 is leading. LLR: {ll.llr:.2f} (cf. paper for the definition of LLR).')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkAAAAG1CAYAAAARLUsBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAA9hAAAPYQGoP6dpAACMxUlEQVR4nO3dd3hUZdoG8HtmkkmvhCSUQEBq6BBKgoIsJQiKKAuofIKsi2sBwSCrWEBsoAKCyoLoihVhFUXXVRSjgEIACUU6SEsoCSAkIYWUmfP9MTknU860k6nJ/buuXJBzzpx5M4TMk+d93udVCYIggIiIiKgBUXt7AERERESexgCIiIiIGhwGQERERNTgMAAiIiKiBocBEBERETU4DICIiIiowWEARERERA0OAyAiIiJqcBgAERERUYPDAIiIiIgaHK8HQMuWLUNycjKCg4PRt29f7Ny50+q1Bw8exJgxY5CcnAyVSoUlS5bIXnfu3Dn83//9Hxo1aoSQkBB06dIFu3btctNXQERERP7GqwHQ2rVrkZmZiblz52L37t3o1q0bMjIycPHiRdnry8rK0Lp1ayxYsACJiYmy11y9ehX9+/dHYGAgvvvuOxw6dAiLFi1CTEyMO78UIiIi8iMqb26G2rdvX/Tu3RtvvfUWAECv1yMpKQnTpk3Dk08+afOxycnJmDFjBmbMmGFy/Mknn8TWrVvxyy+/KB6XXq/H+fPnERERAZVKpfg+RERE5DmCIODatWto2rQp1GrbOZ4AD43JQmVlJXJycjB79mzpmFqtxpAhQ5Cdna34vl9//TUyMjIwduxYbN68Gc2aNcPDDz+MKVOmWH1MRUUFKioqpM/PnTuHlJQUxWMgIiIi78nLy0Pz5s1tXuO1AOjy5cvQ6XRISEgwOZ6QkIAjR44ovu/JkyexfPlyZGZm4qmnnsJvv/2GRx99FFqtFpMmTZJ9zPz58zFv3jyL43l5eYiMjFQ8FiIiIvKc4uJiJCUlISIiwu61XguA3EWv1yM1NRUvv/wyAKBHjx44cOAAVqxYYTUAmj17NjIzM6XPxRcwMjKSARAREZGfcaR8xWtF0HFxcdBoNCgoKDA5XlBQYLXA2RFNmjSxmL7q2LEjcnNzrT4mKChICnYY9BAREdV/XguAtFotevXqhaysLOmYXq9HVlYW0tLSFN+3f//+OHr0qMmxY8eOoWXLlorvSURERPWLV6fAMjMzMWnSJKSmpqJPnz5YsmQJSktLMXnyZADAxIkT0axZM8yfPx+AoXD60KFD0t/PnTuHvXv3Ijw8HG3atAEAPPbYY0hPT8fLL7+McePGYefOnVi5ciVWrlzpnS+SiIiIfI5Xl8EDwFtvvYXXXnsN+fn56N69O9544w307dsXAHDzzTcjOTkZ77//PgDg9OnTaNWqlcU9Bg4ciE2bNkmff/PNN5g9ezaOHz+OVq1aITMz0+YqMHPFxcWIiopCUVGRzekwnU6Hqqoqh+9L3hEYGAiNRuPtYRARkZs5+v4N+EAA5IvsvYCCICA/Px+FhYWeHxwpEh0djcTERPZ1IiKqx5wJgOrdKjBPEIOf+Ph4hIaG8k3VhwmCgLKyMqm7eJMmTbw8IiIi8gUMgJyk0+mk4KdRo0beHg45ICQkBABw8eJFxMfHczqMiIi8vxmqvxFrfkJDQ708EnKG+O/Fmi0iIgIYACnGaS//wn8vIiIyxgCIiIiIGhwGQERERNTgMABqYPLz8zFt2jS0bt0aQUFBSEpKwm233WbSkbuubr75ZsyYMcNl9/OV5yIiovqDq8AakNOnT6N///6Ijo7Ga6+9hi5duqCqqgrff/89HnnkERw5csRjYxEEATqdDgEB/BYkIs8qKq/CteuWCyICNWokRAZ7YUTkDcwANSAPP/wwVCoVdu7ciTFjxqBdu3bo1KkTMjMzsX37dgBAbm4ubr/9doSHhyMyMhLjxo0z2bD2ueeeQ/fu3fHRRx8hOTkZUVFRuOuuu3Dt2jUAwH333YfNmzdj6dKlUKlUUKlUOH36NDZt2gSVSoXvvvsOvXr1QlBQEH799VecOHECt99+OxISEhAeHo7evXvjxx9/NBn3v/71L7Rt2xbBwcFISEjAX//6V5vPRURkze9nC5H64kbc+MrPFh99X87Coh+O2r8J1Qv89dsFBEFAeZXO488bEqhxeHXTlStXsGHDBrz00ksICwuzOB8dHQ29Xi8FP5s3b0Z1dTUeeeQRjB8/3mSrkRMnTmD9+vX45ptvcPXqVYwbNw4LFizASy+9hKVLl+LYsWPo3Lkznn/+eQBA48aNpcDkySefxMKFC9G6dWvExMQgLy8PI0aMwEsvvYSgoCB8+OGHuO2223D06FG0aNECu3btwqOPPoqPPvoI6enpuHLlCn755RcAsPpcRETWHDpfjCqdAJUK0GpqcwA6vYBqvYA9uYXeGxx5FAMgFyiv0iFlzvcef95Dz2cgVOvYP+Eff/wBQRDQoUMHq9dkZWVh//79OHXqFJKSkgAAH374ITp16oTffvsNvXv3BgDo9Xq8//77iIiIAADce++9yMrKwksvvYSoqChotVqEhoYiMTHR4jmef/55DB06VPo8NjYW3bp1kz5/4YUX8OWXX+Lrr7/G1KlTkZubi7CwMNx6662IiIhAy5Yt0aNHDwCw+1xEROZ0Nbs/De2YgJUTU6Xj3+2/gIc+2Y2Kas//MkvewSmwBsKRLd8OHz6MpKQkKfgBgJSUFERHR+Pw4cPSseTkZCn4AQzbS4hbTdiTmppq8nlJSQkef/xxdOzYEdHR0QgPD8fhw4eRm5sLABg6dChatmyJ1q1b495778Unn3yCsrIyh56LiMicXm/4WahRm2bPgwINb4cV1XqPj4m8gxkgFwgJ1ODQ8xleeV5HtW3bFiqVyiWFzoGBgSafq1Qq6PWO/dAwn357/PHHsXHjRixcuBBt2rRBSEgI/vrXv6KyshIAEBERgd27d2PTpk344YcfMGfOHDz33HP47bffEB0dXeevhYh8U2FZJfbkFcqeaxkbitaNwxXdt7omAFKbB0ABhp+nlQyAGgwGQC6gUqkcnoryltjYWGRkZGDZsmV49NFHLQKRwsJCdOzYEXl5ecjLy5OyQIcOHUJhYSFSUlIcfi6tVgudzrE08tatW3HffffhjjvuAGDICJkXMgcEBGDIkCEYMmQI5s6di+joaPz000+48847nXouIvIf97yzA4cuFMue02rU+OWJQYpWbOlqAqAAswBIG8AMUEPj2+/a5FLLli1D//790adPHzz//PPo2rUrqqursXHjRixfvhyHDh1Cly5dMGHCBCxZsgTV1dV4+OGHMXDgQIupK1uSk5OxY8cOnD59GuHh4YiNjbV6bdu2bfHFF1/gtttug0qlwrPPPmuSTfrmm29w8uRJDBgwADExMfj222+h1+vRvn17q8+lVnNml8jf5V01THW3SwiXsjMAcPJSCUorddh/tggJKc4HQPqacgCNyjwDZPi5wQxQw8F3igakdevW2L17NwYNGoSZM2eic+fOGDp0KLKysrB8+XKoVCp89dVXiImJwYABAzBkyBC0bt0aa9eudep5Hn/8cWg0GqSkpKBx48ZSPY+cxYsXIyYmBunp6bjtttuQkZGBnj17Suejo6PxxRdf4C9/+Qs6duyIFStW4NNPP0WnTp2cfi4i8h9ipubdib3x32k3Sh+DOyYAAP64VKLwvoY/rU2BsQi64VAJjlTHNjDFxcWIiopCUVERIiMjTc5dv34dp06dQqtWrRAczIZZ/oL/bkT+pd0z36GyWo9fnxiE5jGh0vE3so5j8cZj+Guv5lg4tpuNO8h766fjWPjDMYxPTcIrf+0qHT91uRSDFm5CRFAA9s/zfE0nuYat929zzAAREZHP0Uu1OqZvUzfUFD8fv1i3DJBGIz8FVqHjFFhDwQCIiIh8Tu1qLdPjreIMCzjyrihrh6FzoAaIEyMNAwMgIiLyKWL2B7DMAIVq67Zc3VofIHEVGMCVYA0FAyAiIvIp1UYBkLVARWkAJGWWLDJAtSvNKjkN1iAwAFKIKVL/wn8vIv+hF6wHQIE1+3dV6pRNVYn3DtCY31cFMSaqqGIA1BAwAHKS2AWZ2zH4F/Hfy7yLNRH5nmqTKTCzDJDRBqbG1zlKZyUDpFKppHtzKXzDwEaITtJoNIiOjpb2vgoNDXV4R3byPEEQUFZWhosXLyI6OhoajePbhxCRd+iMAhvzQCUwoPbzKp1eygg5e2+5hwUFqFFRrWczxAaCAZAC4s7jjm4ASt4XHR3NHeOJ/ITOwQxQZbUeoVpl9zZfBQYA2gANgGoWQTcQDIAUUKlUaNKkCeLj41FVVeXt4ZAdgYGBzPwQ+RGTDJBZAKRRG2p1BEFZsbK0DF5myxxuh9GwMACqA41GwzdWIiIXs7ZhKWD4BTRQo0ZltR5VOgVF0LamwAK5IWpDwiJoIiLyKdU1GyKbZ39E4jRYlYJARSqClrk3i6AbFgZARETkU2riH9kMEGDUC0jJFJiNGqCgwLo1WST/wgCIiIh8ipgBMu8BJAqs6eGjJFCprQGSCYACOAXWkDAAIiIin6K3EaQAtc0Qq+qSAbIZAHEKrCFgAERERD6l2kYRNGBUA6SkCNqBDBCnwBoGBkBERORTrHVrFtVlP7BqnfV7i/uBcQqsYfCJAGjZsmVITk5GcHAw+vbti507d1q99uDBgxgzZgySk5OhUqmwZMkSm/desGABVCoVZsyY4dpBExGRW9haBg/UbQpM2gtMbhUYM0ANitcDoLVr1yIzMxNz587F7t270a1bN2RkZFjtslxWVobWrVtjwYIFdjv7/vbbb3j77bfRtWtXdwydiIjcwNZSdcCoCLoONUBy9w7VGjJA165XO31f8j9eD4AWL16MKVOmYPLkyUhJScGKFSsQGhqK9957T/b63r1747XXXsNdd92FoKAgq/ctKSnBhAkT8M477yAmJsZdwyciIhdzZwZILBuSWwYfU7OvRmFZpdP3Jf/j1QCosrISOTk5GDJkiHRMrVZjyJAhyM7OrtO9H3nkEYwcOdLk3kRE5PvsZYDqMlWls7HEPibMEABdKeMWRw2BV7fCuHz5MnQ6HRISEkyOJyQk4MiRI4rvu2bNGuzevRu//fabQ9dXVFSgoqJC+ry4uFjxcxMRUd3YywBpXbAMXi64igkNBMAMUEPh9SkwV8vLy8P06dPxySefIDg42KHHzJ8/H1FRUdJHUlKSm0dJRETWVOutb1gK1E6BVSraC8zwp1xwJWWAShkANQReDYDi4uKg0WhQUFBgcrygoMBugbM1OTk5uHjxInr27ImAgAAEBARg8+bNeOONNxAQEACdzrLB1ezZs1FUVCR95OXlKXpuIiKqu9puzfLnAwPqsBeYYH0ZfG0NkPUpsGqdHmWV1RYf16vYPNHfeHUKTKvVolevXsjKysLo0aMBAHq9HllZWZg6daqiew4ePBj79+83OTZ58mR06NABTzzxhOzu7UFBQTYLqomIyHN0OtsZIK1G+V5g1TY6QceG2s4AnbxUgtHLtqLYyiqxx4a0w/QhbZ0eE3mHVwMgAMjMzMSkSZOQmpqKPn36YMmSJSgtLcXkyZMBABMnTkSzZs0wf/58AIbC6UOHDkl/P3fuHPbu3Yvw8HC0adMGERER6Ny5s8lzhIWFoVGjRhbHiYjI90gZIPkSIGgDDCesZYDKKqux/2wR5CbIzl0tM9xbJraKDjPUAJVX6XC9SofgQNNfmPedLbQa/ADAj4cLGAD5Ea8HQOPHj8elS5cwZ84c5Ofno3v37tiwYYNUGJ2bmwu10W8B58+fR48ePaTPFy5ciIULF2LgwIHYtGmTp4dPREQuVlsEbbsGyFoR9MR/78SuM1dtPodcdikiKAABahWq9QIOXyhG4wjTmQGxP9CAdo2x4v96SscPni/G2BXZuHStAuQ/vB4AAcDUqVOtTnmZBzXJyckQBOcK3xgYERH5j9qVWvLn7RVBn/7TkOVJig2RtrcAgDN/lkr7h8n1AVKpVIgJ0+LStQrc8a9tVscXFKBGqLb27bN5TAgA4HJJBfR6weryffItPhEAERERiexlgOz1ARJ7/ay6rzfaxEdIx4cs3ow/LpYAsB5c3dmzGT7cdgaC2QTa9ara5zJfQdYozJApqtYLKCyvQmzNajLybQyAiIjIp9jfCsP2FJi1ZfTibu+AfAYIAGbf0hGzb+locXzY65txrKCk5r6mj9UGqBETGoirZVW4dK2CAZCfqHd9gIiIyL/Zb4RYUwRtJQCy9njjACjAWoW1FcbBlNy4xHoh1gH5D2aAiIhIkcslFXgz67jsyqigADWmDGiNGxqHO33f2j5AtjNA1pbBW1vqrjUKgOT6ANkSaBQwyRVQN44IwrGCElwque7Ufcl7GAAREZEi6/ecwwfZZ6yeV6lUmH9nF6fvKwUwVoIUMZDZeKgAw5dssTgn1gZZZoBqC6KtBVfWGF8fKJM9ahxuyABdLGYGyF8wACIiIkXEZeG9Wsbgls613ft3nLqCjYcKUFphvWeOLXoxALIyTdWyUaj0/Efyr1m9j3kNUZ0yQEZZH7ngKSokUBoT+QeXBkCTJk1CXl4efvrpJ1feloiIfJBYg9OteTT+flNr6bg2QI2NhwoUbVYK2M8ADWofj2+m3YirZpuW/nL8MlZuOSl97toaoNrr5WqAgrWG7FI5t8TwGy4NgJo1a2bStJCIiOovMcAJDDANCGpXaTm/WSlQu4zdWhG0SqVC52ZRFscvl5hOP9mqAbIWXFkTYKcGKLhmeo17gvkPlwZAL7/8sitvR0REPkwMcLRm+0rYW6Zuj/gwZxsKmgcm5n2EjGuAnL13gJ0aoBCtGAAp+5rJ85iuISIiRSpqio0tAyBDgFCtVxoA2c4AWRNodr15BsiRPkDWBGhs1wAF19ybGSD/4XQGSK/XY/Pmzfjll19w5swZlJWVoXHjxujRoweGDBmCpKQkd4yTiIh8TO0UmJUMULXSKTDDn85ngEyvt1UD5OwqsAA7NUC1GSAGQP7C4QxQeXk5XnzxRSQlJWHEiBH47rvvUFhYCI1Ggz/++ANz585Fq1atMGLECGzfvt2dYyYiIh8gBUAa86knQ4BgrU+PPYozQEbjUKksA6g6BUAmGSCZGqBAFkH7G4czQO3atUNaWhreeecdDB06FIGBgRbXnDlzBqtXr8Zdd92Fp59+GlOmTHHpYImIyLMqqnXIu1Ime66ovApAbWdmkZgRUjwFZqcRojX2VmppXZUBkqkBEgMgZoD8h8MB0A8//ICOHS33RzHWsmVLzJ49G48//jhyc3PrPDgiIvKuO5Ztw6ELxTav0ZpNgWnrOAVmbxm8NcaBiVyfn7r0AbI3BVYbALEI2l84HADZC36MBQYG4oYbblA0ICIi8h1i8BMZHGAypVRYViX93doUWJXCDJC9RojWBNjZr8t4nM5Or7EIuv5RvAy+sLAQO3fuxMWLF6E3+yafOHFinQdGRETeJQYiALB51iDEGO1y3uP5H3C1JggyD4DEKTB3NUK0xrRXj+VjjYOeuiyDZxF0/aAoAPrvf/+LCRMmoKSkBJGRkVAZfZOqVCoGQERE9UC1UQBkHjAYpnzkA6C6ToHp7ewGb41pnY5lobLGznYWNu9tHFzJ3JtF0P5HUR+gmTNn4m9/+xtKSkpQWFiIq1evSh9Xrlxx9RiJiMgL9EJtAGMejIhv+IDp6iqgNlhQWgQtBl51WQYvmwEyDmLqUANk3m8IAEJYA+R3FAVA586dw6OPPorQ0FBXj4eIiHyEcQbIVlNBiymwms/FXdmdpVOYAbJX4xNgJ0CyxV4NUFBgTQ1QtQ6CoCzzRZ6lKADKyMjArl27XD0WIiLyITobAZBxBsh8awhx53TjAErJ87o6A2TvvC32lsGLGSBBqO2QTb5NUQ3QyJEjMWvWLBw6dAhdunSx6Ak0atQolwyOiIi8x7gI2nzKKDjQKANk3gm6ZnPU8iodzvxZKnvv+IhgqXDYnOIMkJ1VYMarxJy8tcljbTVCBICKKr3J5+SbFAVAYoPD559/3uKcSqWCTsciMCIif2e/CNrA2maoggAMfG2T7L3jwrX45Z9/kQ2CahshOjdJYbxsXi57ZJy5UdVhhZlcDVCgRo0AtQrVegHlVTpEwbJZMPkWRVNger3e6geDHyKi+kEsgpZt/Ge0s7p5I8RAs8AlVKtBeFCA9AEAl0sqcaGoXPZ5a5fBOzde48DEXg2QsxypH2I3aP+iuA8QERHVb7ZWY5lMgVn0ATK9fsdTgxERXJsR6fnCRlwprbRaIyQ1QqxDDZBcp2dn72ftsXI1QIDhNSmpMBRCk+9THABt3rwZCxcuxOHDhwEAKSkpmDVrFm666SaXDY6IiLxHb6Mhoc0iaI15Bsj0rUYMJqp18gGQWEQcFOBcHY1c7x9j3ZOia+7r/ORHoJ3NUIHa1+T1jccQGxZkcq55TAgeGniD04Xd5D6KAqCPP/4YkydPxp133olHH30UALB161YMHjwY77//Pu655x6XDpKIiDzPVjGyrRog8+vNMy/iVJW1PkHllYYMSrCVImlr7E1xRYdqseuZIdKKLWfY22gVAOLCg3D2ajm+P1gge75vq1ikJsc6/dzkHooCoJdeegmvvvoqHnvsMenYo48+isWLF+OFF15gAEREVA/YmgIzzvqY1wDZKzDWSI0S5TNAYjflUCcDFWtTU8biwoPsXiPH+Ou1FgC99teu+OFQgcnqOQD4ZEcu8ouvo/h6lezjyDsUBUAnT57EbbfdZnF81KhReOqpp+o8KCIi8j5bRdDGQZH5lJc9Up8gK1NgYgbI2jJ5a4yXqru6F6HxtJe1QKttQgTaJkRYHN907BLyi68rbgxJ7qFoFVhSUhKysrIsjv/4449ISkqq86CIiMj7xABFLgNkXBdkKwCy1ZDQ6hRYTQbI2V46dSlytifQZKNV5946xSlCNkj0LYoyQDNnzsSjjz6KvXv3Ij09HYChBuj999/H0qVLXTpAIiLyDjEDJFcErTYJgKwHHnLn7BVBS1NgTmaAjAlwbQrIkRoga8QpwoU/HMX2k5b7ZWrUwNheSehWU6RNnqEoAHrooYeQmJiIRYsW4T//+Q8AoGPHjli7di1uv/12lw6QiIi8Q2djObpxVshWzY9cdkg8prNWAyROgdWhm7Krp8ACHJgCs0YMgPKulOPTnbmy1xwrKMF//pGmfIDkNMXL4O+44w7ccccdrhwLERH5kGobAZCju6mbrxAzvp9cEbQgCFIGyNkaIJP7KH6kvAAXZIAAoGWjUPy1Z3Pp8/NF5fh0Zx4uXauo+yDJKWyESEREsqQpMNk6HsfuIZ8BEqfALGtiqnSClBmqSwDkagF1qAEy7jvUNj4c0wa3lT4/mn8Nn+7MQ1E5V4h5msP/irGxsbh8+TIAICYmBrGxsVY/nLVs2TIkJycjODgYffv2xc6dO61ee/DgQYwZMwbJyclQqVRYsmSJxTXz589H7969ERERgfj4eIwePRpHjx51elxERA2ZWKMjFwA5upeW3HSRrQyQOP0F1HUKzLU5oAA7G63aYhwABZl9TdGhhg7ZhWWVFsvnyb0czgC9/vrriIiIkP7u7EZy1qxduxaZmZlYsWIF+vbtiyVLliAjIwNHjx5FfHy8xfVlZWVo3bo1xo4da9KHyNjmzZvxyCOPoHfv3qiursZTTz2FYcOG4dChQwgLC3PJuImI6jtbRdCOrriSmwITs0Jyq8DE6a8Atcrp5fXuZBzIOV0DZPR1mHehjgoxBEB6AWj91LeyQV+T6GB89o80NFLYw4jkORwATZo0Sfr7fffd57IBLF68GFOmTMHkyZMBACtWrMD//vc/vPfee3jyySctru/duzd69+4NALLnAWDDhg0mn7///vuIj49HTk4OBgwY4LKxExHVZ7aKoAe1j8eC744gMtj224hcEGNrFZgr6n8A19cAmS6DV14DZL60PzhQg6AAtbREvlxmI9WTl0qxJ7cQQ1ISnHpesk1RDZBGo8GFCxcsMjR//vkn4uPjHd4RvrKyEjk5OZg9e7Z0TK1WY8iQIcjOzlYyNFlFRUUAoGh6joioobIVALVPjMCPmQPROMJ2VsJ8Y1SgdjpJbgqsrLIaQN2mv9zBeNl/gLN9gIwDIJn9zSKCA1FRYiiC/uLhdDQ2yvQ8/Mlu7D9XZLVrNimnKACyNrdaUVEBrVbr8H0uX74MnU6HhATTqDYhIQFHjhxRMjQLer0eM2bMQP/+/dG5c2fZayoqKlBRUVuBX1xc7JLnJiLyZzobW2EAQJv4cLv3kMsABdioAbruogyQOzmdAdLUfi1BgZavR6XR7vHdmkeb3F98Haw1jSTlnAqA3njjDQCG4rd3330X4eG13/w6nQ5btmxBhw4dXDvCOnrkkUdw4MAB/Prrr1avmT9/PubNm+fBURER+b5qG5uhOqpDYqTFMbGGRiezCqy80nCszhkgNyZM6rIMXi4DVGZU+G2xcaz4WjED5HJOBUCvv/46AEMGaMWKFdAYRbVarRbJyclYsWKFw/eLi4uDRqNBQYHpzrkFBQVITEx0Zmiypk6dim+++QZbtmxB8+bNrV43e/ZsZGZmSp8XFxdzSw8iavBsFUHbs+6hNHy55xxmZVj+UmwrAyRNgflYDZDxwh+ljRABIFgmA2Rrekucbquy0jWblHMqADp16hQAYNCgQfjiiy8QExNTpyfXarXo1asXsrKyMHr0aACGKausrCxMnTpV8X0FQcC0adPw5ZdfYtOmTWjVqpXN64OCghAUxOp6IiJjtVNgzj+2V8tY9GopX3epsVEDJBVB1zED5Opl8MbqUgNkvgrM/nOJGSBOgbmaohqgn3/+2WUDyMzMxKRJk5Camoo+ffpgyZIlKC0tlVaFTZw4Ec2aNcP8+fMBGAqnDx06JP393Llz2Lt3L8LDw9GmTRsAhmmv1atX46uvvkJERATy8/MBAFFRUQgJCXHZ2ImI6jOdNAXm2uXo4rTOmp25FtmlvWcLAdRtHzB3MB6lszOCQRrrq8AAYHL/ZKzaehr332j5y7qYbWIGyPUUd4I+e/Ysvv76a+Tm5qKystLk3OLFix2+z/jx43Hp0iXMmTMH+fn56N69OzZs2CAVRufm5kJt9J/v/Pnz6NGjh/T5woULsXDhQgwcOBCbNm0CACxfvhwAcPPNN5s816pVq1y6hJ+IqD6zVwStlFjncvrPMrz07WHZayKDA+v0HK4OF4xXuznbB8/WMngAmH1LR9zSuQm6y2yGKq2Yk6mXorpRFABlZWVh1KhRaN26NY4cOYLOnTvj9OnTEAQBPXv2dPp+U6dOtTrlJQY1ouTkZLupTXemPomIGgqdC4qg5RivDEtuFIqeLUzLKbQBatlsiDc1jQ7B8gk9ERnifGBmbwpMG6BGn1by04ViBojL4F1PUQA0e/ZsPP7445g3bx4iIiKwbt06xMfHY8KECRg+fLirx0hERF6gq/llUu2izv8i45VOA9o1xvO3y7coqQt3/B58S5cmih6ntTMFZoutbUOobhRN7B4+fBgTJ04EAAQEBKC8vBzh4eF4/vnn8corr7h0gERE5B21jRBde1/jVVTOFgX7I5MMkMwqMFsCa6bAuAze9RRlgMLCwqS6nyZNmuDEiRPo1KkTAEgbphIRkW/Q6wVcKauUPRcdEogAKxGOu4qgjafUnM2IOEpwZyMgJ9mrAbJFIxVBswbI1RQFQP369cOvv/6Kjh07YsSIEZg5cyb279+PL774Av369XP1GImISCFBEHDn8m3Ym1coe75NfDg2TL9JNghyVxG0cUDlrgyQL5WC1mUZfKCajRDdRVEAtHjxYpSUlAAA5s2bh5KSEqxduxZt27Z1agUYERG5V6VObzX4AYA/LpbgalmV7J5e0hSYa+Mft2aABrZrjM3HLmFSWrJL71sXQXXIAImBKZfBu56iAKh169bS38PCwpzq/kxERJ5j3D/v4LwMhAXV/thv/8x3qKjWo9LK9IpYBK1x8RSYxo01QG/f2wsHzxejh8yScm+pUwDERohuU/+rz4iIGjDjTTTN95kSVydVVlsJgNxUBB1oPAXm4gxQcKAGvVrGuHzari6Ml/073QmajRDdxuEMUExMjMPNn65cuaJ4QERE5DrGiQOLjTYD1ECF9QLb2gDIxRkgdcNaBWZcj+T8MniuAnMXhwOgJUuWuHEYRETkDiYZIJV8BujBj3PQMjbU4rE/H71keJyrM0Aa968C8yXG23ooLYKu5hSYyzkcAE2aNMmd4yAiIjcQ63hUKsvVXIEBhs9PXirFyUulVu+hZDd4WzQeWAXmS+Ijg/HMyI4I0WpMpsMcIdZLVXMKzOUU7wV24sQJrFq1CidOnMDSpUsRHx+P7777Di1atJB6AhERkXfZ2s7CuEPxwHaNcWvX2k7Hvxy/jK/3nQfg+imwgAaWAQKAv9/U2v5FMsR6KXaCdj1F39WbN29Gly5dsGPHDnzxxRfSkvh9+/Zh7ty5Lh0gEREpJ/XykcniGGcjujSLwtjUJOmjX+tG0jmXd4JuYDVAdSHWS7ERousp+s578skn8eKLL2Ljxo3QarXS8b/85S/Yvn27ywZHRER1YysDFGSjQZ9x3YrLGyHWYW+shkasl2IRtOspCoD279+PO+64w+J4fHw8t8IgIvIhtro5myzPNtujKsQoAHL1bvCe2AqjvmAjRPdRFABFR0fjwoULFsf37NmDZs2a1XlQRETkGjZrgEwyQKaBiHEGyNVF0JwCc5yGjRDdRtF33l133YUnnngC+fn5UKlU0Ov12Lp1Kx5//HFpl3giIvK+2m7OdjJANqbAXF0EbdxTjhkg28QpMBZBu56i7+qXX34ZHTp0QFJSEkpKSpCSkoIBAwYgPT0dzzzzjKvHSEREConLp+UCIJMMkPkUWGDtImFXF0Eb17MwA2SbGHxyGbzrOb0MXhAE5Ofn44033sCcOXOwf/9+lJSUoEePHmjbtq07xkhERArpxQyQzDSWVuPYFJiri6AZADmOjRDdR1EA1KZNGxw8eBBt27ZFUlKSO8ZFREQuIE6daGS2dNc6uArM1TVAxm/mAa5OL9UzGjWnwNzF6e88tVqNtm3b4s8//3THeIiIyIX0eusZoECTXdlNM0DGq8Bc/ebLJd2OE+u0OAXmeopC7wULFmDWrFk4cOCAq8dDREQuJGWAnKwBCtXWThBUWNktvq5jIvuYAXIfRVthTJw4EWVlZejWrRu0Wi1CQkJMznM3eCIi36CzEQDZWgVmfH1Ftc6lYxrQtjEAoFl0iJ0rKUDaC4w1QK6mKADizvBERP6hNgCyTPgbF0FrbRQjV1S59s03MSoYOc8MQXiw4u0oGwwxSOW0oes5/d1XVVWFzZs349lnn0WrVq3cMSYiInKR2gDI8pytRojGXD0FBgCNwoNcfs/6SNoLjKvAXM7pGqDAwECsW7fOHWMhIiIXs5UBsjUFZqzSDQEQOSaQfYDcRlER9OjRo7F+/XoXD4WIiFxNKoKWWckeoHFsSwpX1wCR41gE7T6KJmDbtm2L559/Hlu3bkWvXr0QFhZmcv7RRx91yeCIiKhuxEaIATIZIMHoPTXIxpYULRuFunxc5JhAFkG7jaIA6N///jeio6ORk5ODnJwck3MqlYoBEBGRj6iWdoO3fZ1cBujzB9Pw3YF8PHxzG3cMjRwgZoCullXhpf8dsjivVqkwqntTdGoa5emh+T1FAdCpU6dcPQ4iInIDvd56BkhvNK0it1t8anIsUpNj3Tc4ssu4TuudX+Tfe3fnXsVnD6Z7akj1Rp3XIAo1OVSVi1ulExFR3dVmgCx/RhtXlfBnuG8y7sfUITECA9s1lj4/V1iOb36/gKLyKm8Mze8p3oTlww8/RJcuXRASEoKQkBB07doVH330kSvHRkREdVSbAbIMcPQCC2t9nXGh+vDOiZg9oqP0MSk9GQBXiCmlKAO0ePFiPPvss5g6dSr69+8PAPj111/x4IMP4vLly3jsscdcOkgiIlJGygDJZHgY//i+QKOpy1ZxpguOxOmxShZIK6IoAHrzzTexfPlyTJw4UTo2atQodOrUCc899xwDICIiH6ETrGeABEZAPk9jlAFqEWu6Gk/8N61iAKSIoimwCxcuID3dsuAqPT0dFy5cqPOgiIjINXQ1b45ye4G1ahxmcYx8S7BRh+428eEm58RO3pwCU0ZRANSmTRv85z//sTi+du1atG3b1un7LVu2DMnJyQgODkbfvn2xc+dOq9cePHgQY8aMQXJyMlQqldV9yZy5JxFRfSW+N8oFQLd3a4bHh7XD2gf6eXhU5ChtgBpfT+2P/069ERHBgSbnOAVWN4qmwObNm4fx48djy5YtUg3Q1q1bkZWVJRsY2bJ27VpkZmZixYoV6Nu3L5YsWYKMjAwcPXoU8fHxFteXlZWhdevWGDt2rNWpNmfvSUTkbn9cvIaD54sxqltTj6640umtZ4DUahWm/sX5X1rJs7o2j5Y9zimwulGUARozZgx27NiBuLg4rF+/HuvXr0dcXBx27tyJO+64w6l7LV68GFOmTMHkyZORkpKCFStWIDQ0FO+9957s9b1798Zrr72Gu+66C0FB8pvpOXtPIiJ3G7J4C6av2Yuswxc9+rzie6NcAET+jVNgdaO4D1CvXr3w8ccf1+nJKysrkZOTg9mzZ0vH1Go1hgwZguzsbI/ds6KiAhUVFdLnxcXFip6biMiefWcLMSQlwWPPJ2WA2Oen3hGnwKr1AvR6QbbXE1mnKAP07bff4vvvv7c4/v333+O7775z+D6XL1+GTqdDQoLpD4OEhATk5+crGZqie86fPx9RUVHSR1JSkqLnJiKyx9MLr6QMkNxuqOTXjHsEVek5DeYsRQHQk08+CZ3OcndgQRDw5JNP1nlQnjZ79mwUFRVJH3l5ed4eEhHVU55uPsgMUP2lNdomo4rTYE5TNAV2/PhxpKSkWBzv0KED/vjjD4fvExcXB41Gg4KCApPjBQUFSExMVDI0RfcMCgqyWk9ERORKnn6bEvsAsQao/jHeJ8zabvG7Tl9BZbUe6W3iPDUsv6EoAxQVFYWTJ09aHP/jjz8QFuZ4XwmtVotevXohKytLOqbX65GVlYW0tDQlQ3PLPYmIXMXTGSCxEzQDoPpHo1ZBTOyVVFTjepXO5OOPi9fw1xXZuOfdHSirrPbuYH2QogzQ7bffjhkzZuDLL7/EDTfcAMAQ/MycOROjRo1y6l6ZmZmYNGkSUlNT0adPHyxZsgSlpaWYPHkyAGDixIlo1qwZ5s+fD8BQ5Hzo0CHp7+fOncPevXsRHh6ONm3aOHRPIiJv8XQNkK29wMj/BWrUqKzW48ZXfrZ5XWmFDqHaOu9/Xq8oejVeffVVDB8+HB06dEDz5s0BAGfPnsVNN92EhQsXOnWv8ePH49KlS5gzZw7y8/PRvXt3bNiwQSpizs3NhdpoL5Tz58+jR48e0ucLFy7EwoULMXDgQGzatMmhexIReYunt5+wtRs8+T9tTQBkj07PGiFzigKgqKgobNu2DRs3bsS+ffuk3eAHDBigaBBTp07F1KlTZc+JQY0oOTnZoR8gtu5JRORJeqM3H0+/DzEDVL8FGq0Ee+++VPROjjU53/35jdDpBTZLlKE4H6ZSqTBs2DAMGzbMleMhIqp3jJcoe3oKzNZu8OT/AowKoWPDgiy2ywgJ1KCkopoZIBmKiqCJiMhxxkuUBQ+vA9Pb2A2e/J/xUviQQI3FebH4vZp9giwwACIicrOqai9mgHSsAarPjKfA5AKgACkAYgbIHAMgIiI3M54C8/Rv4jpmgOo14ymwYK3lW7rYLZr7hVliAERE5GbGU2AVVR4OgNgHqF4zXhQknwGq3S+MTCkKgHbv3o39+/dLn3/11VcYPXo0nnrqKVRWVrpscERE9YHxFFilh1fjsBFi/WZc3BwsFwDVZIB0rAGyoCgA+sc//oFjx44BAE6ePIm77roLoaGh+Oyzz/DPf/7TpQMkIvJ3xtNejvRscSUug6/fjLOLxltjiMTAl3uFWVIUAB07dgzdu3cHAHz22WcYMGAAVq9ejffffx/r1q1z5fiIiPxeZbVg9HfvZIBYBF0/2VveHlgzBcZl8JYUBUCCIEBf8xvNjz/+iBEjRgAAkpKScPnyZdeNjoioHjBuQufpKTBmgOo3e7U9Gq4Cs0pRAJSamooXX3wRH330ETZv3oyRI0cCAE6dOsXtJoiIzBhPgVV4KwPERoj1kr3antpVYKwBMqeoE/SSJUswYcIErF+/Hk8//bS0Cennn3+O9PR0lw6QiMjfuXsKrPh6FdblnEVZpc7i3OZjlwDUvhFS/WJveTv7AFmnKADq2rWrySow0WuvvQaNxrIKnYioITOeAnNHBuj9raexeOMxm9cwA1Q/2QtspGXwLIK2oCgAysvLg0qlknaC37lzJ1avXo2UlBQ88MADLh0gEZG/M10FZpmlqau8K2UAgG5J0eiYGCEdLyqvwncH8l3+fOQ77BU3S1NgXAZvQVEAdM899+CBBx7Avffei/z8fAwdOhSdOnXCJ598gvz8fMyZM8fV4yQi8lsmU2AKazHmfnUAv/4hv8jkxKVSAMDdvZNwV58W0vFqnR5tnv4OAFB8vVrR85Jvq7IT2IhF0FwFZklRAHTgwAH06dMHAPCf//wHnTt3xtatW/HDDz/gwQcfZABERGTEZBWYgimwkopqfJB9xu51sWFak88DNGrMv7MLvt57HiO7NHH6ecn32dtbTqoB4hSYBUUBUFVVFYKCggAYlsGPGjUKANChQwdcuHDBdaMjIqoH6toI0XgFz+opfaExqud5ZPUeXC6pAAA0Cg+yeOzdfVrgbqOsEDUs4l5hLIK2pCgA6tSpE1asWIGRI0di48aNeOGFFwAA58+fR6NGjVw6QCIif1dVx1Vgxm9eaa0bQWUUALVsFCoFQHHhWovHUsNWuwqMNUDmFAVAr7zyCu644w689tprmDRpErp16wYA+Prrr6WpMSIiMjCu+ymv0uHZ9Qcsrkm/oRFusTJNZbyhqcpsNZfxtJdcBojqt7t6J2HNb3m4rVtT2fNSBohTYBYUBUA333wzLl++jOLiYsTExEjHH3jgAYSGhrpscERE9YHxFJZeAD7ablnPs3ZXHoamJEhvWCaPt7GhqfEO4GFatiFpaJ4b1QkZnRLRr7X87EsAi6CtUhQAAYBGo0F1dTV+/fVXAED79u2RnJzsqnEREdUb4kaUMaGBmJiWbHKuUqfH8k0nUFmth04QZH8o29rOIjiwNmAyzw5R/RccqMGgDvFWz0uboXIKzIKiAKi0tBTTpk3Dhx9+KO0JptFoMHHiRLz55pvMAhERGRHffIZ0TMBjQ9uZnCutqMbyTScAANbeo2xlgG5s2xj/2XXWhaOl+iSwpg+QjlNgFhTtBZaZmYnNmzfjv//9LwoLC1FYWIivvvoKmzdvxsyZM109RiIivyYWQQcGWP7INe7QrLOyplnc70kuA3Rb1yZYNLYbfswc6IqhUj1TmwFiAGROUQZo3bp1+Pzzz3HzzTdLx0aMGIGQkBCMGzcOy5cvd9X4iIj8ntgHKFAmgFEbxUR6KwFQbQbIMoBSqVQY06u5C0ZJ9ZG4FYa9TVMbIkUZoLKyMtld3+Pj41FWVlbnQRER1SfiFFigTIGzcQZIb+W3dHEFj1wGiMgWboZqnaIMUFpaGubOnYsPP/wQwcHBAIDy8nLMmzcPaWlpLh0gEZEv2XnqCg6dL5I917lZFPKulqG80vS37QPnDNfLTYEZNzW09h6ls1EDRGQLl8FbpygAWrJkCYYPH47mzZtLPYD27duH4OBgfP/99y4dIBGRrygqq8KEd7dLq7qcFRpouUxdbRTUWFuqLP72Lm5sSeQoLoO3TlEA1KVLFxw/fhyffPIJjhw5AgC4++67MWHCBISEhLh0gEREvuJyaQWqdAICNSoM65QoHa+s1mPjoQLp8zbx4WgVF2by2IjgANxppVZHrTJkfwSrRdDMAJEyUhG0gk14M9fuxYWi6/jk731NAvX6wukAqKqqCh06dMA333yDKVOmuGNMREQ+qaxCBwCICw/Csnt6Ssevllaix6GN0udjejbHQzff4PB9NWoV9DrB6iqwahurwIhskZbBO5kB0usFfLHnHADgSP41pDSNdPnYvM3pIujAwEBcv37dHWMhIvJppZXVAIBQs47LGrOpKWcDFbGBobU3KZ2NVWBEtojfM84WQVcY7VlXXzOPiv43PfLII3jllVdQXV3t6vEQEfms0grDz7ywINPkucasA7Oz0wXi460kgGprgOrpGxG5j5gBqnZyCqy8Sif9vb7WnimqAfrtt9+QlZWFH374AV26dEFYmOlc9xdffOGSwRER+ZLSSsObgkUGSF23DJB4udUMkI41QKSMRuEyeOMAyFp7Bn+nKACKjo7GmDFjXD0WIiKfViZmgLSmPzrNAx5nM0Di9fYaITIDRM6S+gA5uXKxvLI2AKqvPYQUBUCrVq1y9TiIiHyemAGymAKrYwZIYycA4iowUkrqA+RkEHPdKANUX3sIKaoBOnXqFI4fP25x/Pjx4zh9+rTT91u2bBmSk5MRHByMvn37YufOnTav/+yzz9ChQwcEBwejS5cu+Pbbb03Ol5SUYOrUqWjevDlCQkKQkpKCFStWOD0uIiJjUgYoyHQKTKVSwTg2cTZQEbtBW3uPEleBMQAiZ2mkPkDKa4Dq607yigKg++67D9u2bbM4vmPHDtx3331O3Wvt2rXIzMzE3LlzsXv3bnTr1g0ZGRm4ePGi7PXbtm3D3Xffjfvvvx979uzB6NGjMXr0aBw4cEC6JjMzExs2bMDHH3+Mw4cPY8aMGZg6dSq+/vprp8ZGRGSsRFoFZpk8Nw5OzIui7VE7vAqMARA5RyqCdjIDVGY0BVZfmygqCoD27NmD/v37Wxzv168f9u7d69S9Fi9ejClTpmDy5MlSpiY0NBTvvfee7PVLly7F8OHDMWvWLHTs2BEvvPACevbsibfeeku6Ztu2bZg0aRJuvvlmJCcn44EHHkC3bt3sZpaIiGwR+wCFaS07OhsHJ86umhEfyhogcjVpGXwdaoCUNFH0B4oCIJVKhWvXrlkcLyoqgk6nk3mEvMrKSuTk5GDIkCG1A1KrMWTIEGRnZ8s+Jjs72+R6AMjIyDC5Pj09HV9//TXOnTsHQRDw888/49ixYxg2bJjsPSsqKlBcXGzyQURkTuoDFCSTATLK+qidzABJNUBW3mf07ANECgVKq8CcC2JYA2TFgAEDMH/+fJNgR6fTYf78+bjxxhsdvs/ly5eh0+ksdpZPSEhAfn6+7GPy8/PtXv/mm28iJSUFzZs3h1arxfDhw7Fs2TIMGDBA9p7z589HVFSU9JGUlOTw10BEDYeUAZILgIwzQAprgKx3gmYGiJRxxTL4+joFpmgV2CuvvIIBAwagffv2uOmmmwAAv/zyC4qLi/HTTz+5dIBKvPnmm9i+fTu+/vprtGzZElu2bMEjjzyCpk2bWmSPAGD27NnIzMyUPi8uLmYQREQWxAyQ3BSYuNoGUFAEXfNQu6vA6mlDOnKfAIVbYTSEKTBFAVBKSgp+//13vPXWW9i3bx9CQkIwceJETJ06FbGxsQ7fJy4uDhqNBgUFBSbHCwoKkJiYKPuYxMREm9eXl5fjqaeewpdffomRI0cCALp27Yq9e/di4cKFsgFQUFAQgoKCHB43ETVMYidouSJo42kvZwMgcfrMWsM5ZoBIqYCa6PqPiyUYt8K0tESlAib0a4lR3ZpaPM44A8Q+QGaaNm2Kl19+uU5PrtVq0atXL2RlZWH06NEAAL1ej6ysLEydOlX2MWlpacjKysKMGTOkYxs3bkRaWhoAw2atVVVVUJvNlWs0Gujr6VI+IvKMMqkPkEwGSK08ALK3DF7HZfCkUNPoYACG792dp69YnC8sq5INgK4zAHK/zMxMTJo0CampqejTpw+WLFmC0tJSTJ48GQAwceJENGvWDPPnzwcATJ8+HQMHDsSiRYswcuRIrFmzBrt27cLKlSsBAJGRkRg4cCBmzZqFkJAQtGzZEps3b8aHH36IxYsXe+3rJCL/V+roMniFnaCtTVMwA0RKtYmPwH+n3oizV8tMjv9xsQSLNh6zOr1l0gmaU2DuMX78eFy6dAlz5sxBfn4+unfvjg0bNkiFzrm5uSbZnPT0dKxevRrPPPMMnnrqKbRt2xbr169H586dpWvWrFmD2bNnY8KECbhy5QpatmyJl156CQ8++KDHvz4iqj/EIuhwO0XQzmeADH8K1mqAdFwFRsp1aR6FLs2jTI7lnLmCRRutF95zCsxDpk6danXKa9OmTRbHxo4di7Fjx1q9X2JiIrfrICKXq80A2e4DpLgRIleBkYfYa75ZzmXwREQEGN4orlcZpgLsLoN3uhGivRogdoIm1xK/l6zE3GY1QJwCM1FdXY1NmzbhxIkTuOeeexAREYHz588jMjIS4eHhrhwjEZHXidkfQD4DZFoE7dzvlrWNEJkBIs8Qg+5zheX4y6JNFpmgi8UV0t/rawZIUQB05swZDB8+HLm5uaioqMDQoUMRERGBV155BRUVFdx4lIjqHbH+R6NWISjAMsAxWQbv7BSYnSJoaRUY+wCRixh/v568VGrzWmaAjEyfPh2pqanYt28fGjVqJB2/4447MGXKFJcNjojIVxg3QVTJBDjG015Ki6C5Fxh5ivn36KyM9ujX2rSP32vfH8X2k1dQxQxQrV9++QXbtm2DVqs1OZ6cnIxz5865ZGBERL7E1jYYgIsaIdrrBM1VYOQi5t+ibeLD0aulaQDUunE4tp+8Um+3wlD0v0mv18tuenr27FlERETUeVBERL7G1gowwL2NEJkBIldTm30vyU3bit9v9bUPkKIAaNiwYViyZIn0uUqlQklJCebOnYsRI0a4amxERD5D3AbDagaoTo0QDX9arQHScRUYuZZ5wCP3vSVuo1FVTzNAiqbAFi1ahIyMDKSkpOD69eu45557cPz4ccTFxeHTTz919RiJiLyutKYzriMZIKW7wdurAWIARK6iNguAzDNCABCocCNVf6EoAGrevDn27duHtWvXYt++fSgpKcH999+PCRMmICQkxNVjJCLyurKaDJBcF2igbp2gpWXwVmuADFMQnAIjVzEvJ5P71hK/L7kbvJEtW7YgPT0dEyZMwIQJE6Tj1dXV2LJlCwYMGOCyARIR+YLaDJDrA6Darrzy55kBIlcz/16SrQHSGKKk+toHSFEN0KBBg3DliuWuskVFRRg0aFCdB0VE5GvKpBog+SkwTR1WgdlbBi8eZwaIXMWhKTCxCLqeToEpCoAEQZDtg/Hnn38iLCyszoMiIvI1JTZ2ggcA4x+JzjZCtNsJmpuhkouZB0ByQbvYeLO+rgJzagrszjvvBGBY9XXfffchKChIOqfT6fD7778jPT3dtSMkIvKgbX9cxgv/O4yKKtNWH5dLDFsDhFkpgjbmbMdm8RfKU5dLceh8scX5k5cNnXqZASJXMQ94zAMiAAisCbhZBA0gKioKgCEDFBERYVLwrNVq0a9fP3aCJiK/9sWeczh8wTIIEbVJsN/rzOkMUM31b285ibe3nLR+HQMgchFHlsFLRdAMgIBVq1YBMHR8fvzxxzndRUT1jvjb7sS0lri1a1OTc5EhAWjvSACksA8QYFhmb9xrqLxSh5Ka+iNnd5knskZlNpsqF7QHcgrM0ty5c109DiIinyAGQC0bhaFPq1g7Vxurex8gAHhw4A14dHBb6fMNBy7gwY93A2AGiFzHPOCRKy+TVoExA1SrVatWskXQopMnradwiYh8mbjiyvlkS+2bhNI+QAAQqDF9J2oVF157nZNTa0TWWCyDtzEFxgyQkRkzZph8XlVVhT179mDDhg2YNWuWK8ZFROQVUgBUh2yLrV8Q5RhngALNIq+WjUKlvxeVVykeE5Ex829Rm1NgVjJAl65V4IdD+bJ9ghqFazG8U6KURfJFigKg6dOnyx5ftmwZdu3aVacBERF5kzgF5mwQUxemAZDpG0ZwYO2qMwZA5CqWU2DW9wKz1gjxpf8dwvq9560+x8p7e2FYp8Q6jNK9XBqa3XLLLVi3bp0rb0lE5FHiL7uerLcxfiq5Qud/Dm+P9gkR+Guv5h4bE9VvDnWClhohyk+BXappDdE9KRojuzaRPppEBQMACq5VuHLILqcoA2TN559/jthYZ4oGiYh8i9iM0Pn4R3nAZFIDJFON+vDNbfDwzW0U35/InHmGU3Y3+JpsZJWVDJCYGfr7Ta1MVkxOX7MHX+09j8pq364dUhQA9ejRw+TFEwQB+fn5uHTpEv71r3+5bHBERJ6mE8QAyHMZIOOfp4EBLHQmz9CoVdKUr+wUmJ3d4MXaoACzoD0owPB5RbXO4jG+RFEANHr0aJPP1Wo1GjdujJtvvhkdOnRwxbiIiLzCG1NgxmU/5m8mRO6iUamgg/WMpzgFZm03eHF1mHnbB60YAFXVwwwQ+wARUX1VOwXmwQDIRhE0kbvY279ODMYvl1Ti37+eMn0sgOMXSwzXmdWtBQUYCvcr6ssUWHGx9dbw5iIjIxUNhojI28Rl8HJTAu6isrEMnshdjIN8ue/3sCBDIHO5pAIvfHPI6n2sTYHVmxqg6Ohou8tCxV3idTrfnvcjIrJGp7gIWnm3XFuNEIk8QS4D1LlpFB79SxucuVJmcrywrAqbj12SPreeAfLtWMDhAOjnn3925ziIiHxCbSdo31kGT+RuchkgtVqFzGHtLY4fvlBsGgBZqwGqLxmggQMHunMcREQ+QSyCdn4KTHngomYGiLzMmaJ/82la827PQfUtADJXWFiIf//73zh8+DAAoFOnTvjb3/6GqKgolw2OiMjTdF4ogrbVCZrIE5zJeJrX/JhngIICxRog354CU/Q/bdeuXbjhhhvw+uuv48qVK7hy5QoWL16MG264Abt373b1GImIPEaQ9gLz3HMav/k4u5M8kSs4030hMMAsAKrvq8CMPfbYYxg1ahTeeecdBAQYblFdXY2///3vmDFjBrZs2eLSQRIReYrYCNGje4EZBT3aAGaAyPOcyQAFmgXp5hmhet0HaNeuXSbBDwAEBATgn//8J1JTU102OCIiTxN7vnmtCJoZIPIQwWjlojM1QOY1PxZTYH7SCVrRrxqRkZHIzc21OJ6Xl4eIiIg6D4qIyFtqp8CcDUTqsAyeNUDkZc5kPC2LoOUDoEorHaR9haL/aePHj8f999+PtWvXIi8vD3l5eVizZg3+/ve/4+6773b6fsuWLUNycjKCg4PRt29f7Ny50+b1n332GTp06IDg4GB06dIF3377rcU1hw8fxqhRoxAVFYWwsDD07t1bNmgjIjImFkF7MAHEVWDkV8y/Ry0bIdbUANXHKbCFCxdCpVJh4sSJqK6uBgAEBgbioYcewoIFC5y619q1a5GZmYkVK1agb9++WLJkCTIyMnD06FHEx8dbXL9t2zbcfffdmD9/Pm699VasXr0ao0ePxu7du9G5c2cAwIkTJ3DjjTfi/vvvx7x58xAZGYmDBw8iODhYyZdLRA2I8j5AyiMm46diHyDydeZTXubfs/7SB0jRrxparRZLly7F1atXsXfvXuzduxdXrlzB66+/jqCgIKfutXjxYkyZMgWTJ09GSkoKVqxYgdDQULz33nuy1y9duhTDhw/HrFmz0LFjR7zwwgvo2bMn3nrrLemap59+GiNGjMCrr76KHj164IYbbsCoUaNkAyoiImNK+wB1T1LeAkQwmj1jBog8RVA4a2s+PRzop7vB1+l/WmhoKLp06YKWLVvihx9+kHoCOaqyshI5OTkYMmRI7YDUagwZMgTZ2dmyj8nOzja5HgAyMjKk6/V6Pf73v/+hXbt2yMjIQHx8PPr27Yv169c798URUYOktA/QlAGtMfuWDvh+xgDFzwlwLzDyfeb1Qhqz79lgqQ9QPcwAjRs3Tsq4lJeXIzU1FePGjUPXrl2xbt06h+9z+fJl6HQ6JCQkmBxPSEhAfn6+7GPy8/NtXn/x4kWUlJRgwYIFGD58OH744QfccccduPPOO7F582bZe1ZUVKC4uNjkg4gaJmkzVCfjkKAADf4x8Aa0T3R+IYhxAGReT0Hk6yxXgflHHyBF/9O2bNmCm266CQDw5ZdfQhAEFBYW4o033sCLL77o0gE6S683vOC33347HnvsMXTv3h1PPvkkbr31VqxYsUL2MfPnz0dUVJT0kZSU5MkhE5EP0euVrgJTThCYASLPU75u0ZS/7gWmKAAqKipCbGwsAGDDhg0YM2YMQkNDMXLkSBw/ftzh+8TFxUGj0aCgoMDkeEFBARITE2Ufk5iYaPP6uLg4BAQEICUlxeSajh07Wl0FNnv2bBQVFUkfeXl5Dn8NRFS/6ATPb4WhMwqAPNmAkcgVzH9ZEGuAdHoB1T68FF5RAJSUlITs7GyUlpZiw4YNGDZsGADg6tWrTq200mq16NWrF7KysqRjer0eWVlZSEtLk31MWlqayfUAsHHjRul6rVaL3r174+jRoybXHDt2DC1btpS9Z1BQECIjI00+iKhhkoqgPRkA+e57BJFd5kG7OAUG+HYvIEXL4GfMmIEJEyYgPDwcLVu2xM033wzAMDXWpUsXp+6VmZmJSZMmITU1FX369MGSJUtQWlqKyZMnAwAmTpyIZs2aYf78+QCA6dOnY+DAgVi0aBFGjhyJNWvWYNeuXVi5cqV0z1mzZmH8+PEYMGAABg0ahA0bNuC///0vNm3apOTLJaIGxBtTYHqly3GI6qIO33YqlfVVZMbbuVRU6RGqVf487qQoAHr44YfRp08f5OXlYejQoVDXFO21bt3a6Rqg8ePH49KlS5gzZw7y8/PRvXt3bNiwQSp0zs3Nle4PAOnp6Vi9ejWeeeYZPPXUU2jbti3Wr18v9QACgDvuuAMrVqzA/Pnz8eijj6J9+/ZYt24dbrzxRiVfLhE1IEqLoOvCuAiayN9p1CoEqFWo1gs+XQekKAACgNTUVKSmpkIQBAiCAJVKhZEjRyq619SpUzF16lTZc3JZm7Fjx2Ls2LE27/m3v/0Nf/vb3xSNh4gaLmkZPDNARFapYDuBFBSgRnWlzqd7ASleb/nvf/8bnTt3RnBwMIKDg9G5c2e8++67rhwbEZHHickYT26GqmcGiPyMvWL9wJppsCqd735vK8oAzZkzB4sXL8a0adOk4uPs7Gw89thjyM3NxfPPP+/SQRIReYrey6vAiPyBvf8dYkfzqvpWBL18+XK88847Jhufjho1Cl27dsW0adMYABGR36qdAvPkc3ruuYhcwd7vB1o/CIAU/RevqqpCamqqxfFevXpJm6MSEfkjwQvL4DkFRt4guKwVoiWxoWe9C4DuvfdeLF++3OL4ypUrMWHChDoPiojIW8TpKE8ug+cUGPkblZ1JMHEKrLLad7+3HZ4Cy8zMlP6uUqnw7rvv4ocffkC/fv0AADt27EBubi4mTpzo+lESEXmI0s1Q66JPq1h8nnPWY89HVGd2/nvUqxqgPXv2mHzeq1cvAMCJEycAGLagiIuLw8GDB104PCIizzHek8uTfYD+2rM5ggLU6JEU47knpQavLolHu0XQAfUoAPr555/dOQ4iIq8zbkjoySkwtVqF27s389jzEbmbtr7WABER1UfGtcjclJTIOnv/PaQaoPrWBwgAdu3ahf/85z/Izc1FZWWlybkvvviizgMjIvI0447MnswAEXlDXUITx4ug61kGaM2aNUhPT8fhw4fx5ZdfoqqqCgcPHsRPP/2EqKgoV4+RiMgjTKbAmAEissrRDFC9mwJ7+eWX8frrr+O///0vtFotli5diiNHjmDcuHFo0aKFq8dIROQRxhkgxj9E1tn776ENqKc1QCdOnJA2PtVqtSgtLYVKpcJjjz2GlStXunSARESeojf6Wc0pMCLl6u0UWExMDK5duwYAaNasGQ4cOAAAKCwsRFlZmetGR0TkQTqTZfAMgKh+E+qwDt7uZqiaeroZ6oABA7Bx40Z06dIFY8eOxfTp0/HTTz9h48aNGDx4sKvHSETkEXov9QEi8jcNdjPUt956C9evXwcAPP300wgMDMS2bdswZswYPPPMMy4dIBGRp+ilLtBcBk9kk93NUH2/BkhRABQbGyv9Xa1W48knn3TZgIiIvEXvhY1QibylLpNTzaJDcCT/mtXztX2AfDcAcrgGqLS01KkbO3s9EZG3iTVAas5/Edn0rwk9MaBdY6x9oJ/seWkrDB/eDNXhAKhNmzZYsGABLly4YPUaQRCwceNG3HLLLXjjjTdcMkAiIk8xngIjqu/q8m3eunE4PvxbH/Rt3Uj2fL2qAdq0aROeeuopPPfcc+jWrRtSU1PRtGlTBAcH4+rVqzh06BCys7MREBCA2bNn4x//+Ic7x01E5HJiETSbIFJD4M7cTL2qAWrfvj3WrVuH3NxcfPbZZ/jll1+wbds2lJeXIy4uDj169MA777yDW265BRqNxp1jJiLCwfNFyLsi33ajZ8sYxEcEO31PnZQBYgBEVBf+UAPkdBF0ixYtMHPmTMycOdMd4yGiBqK0ohqj3voVJy5Z1guqVMCEvi3w4uguso89cK4Ity/barJ1hbH2CRH4/rEBTo9JKoLmHBhRnfhDHyDuBk9EXnEkv1g2+AEAQQC+P1hg9bGLNx6DTi+geUwIUlvGSB9dmhn2IjxXWK5oTNIUGAMgagDq0AfRrtoi6HqUASIicoXi69UAgA6JEfjo/r7S8ROXSnDXyu1SQbKcnDNXAQDLJ/RCl+a1GzCfLyxH+oKfFLff17EImhqQplHBOF903S339ocaIGaAiMgrSmoCoOjQQDSOCJI+4sK1AEy3pTBXUa2THmtMG1Bbd6Ckzb+YAWINEDUEqyb3QfoNjfD5g2kuv3e9rAEiInKFazUBUHiQaRAjBh/W6nuA2g0WgwJMf4fTGn1eqdMjKMC5BRniZqgMgKghaJ8YgdVT5Pv41JU/LINXlAHKzc2V/e1KEATk5ubWeVBEVP+VVFQBACKDTX8PE+tvrE2BVev0UrGy+ENWpDX6XEnxJWuAiFyj3hZBt2rVCpcuXbI4fuXKFbRq1arOgyKi+k/KAJkFQFIGyMoUlnFKXWueATIKgJTUAdV2gnb6oURkRBtQT2uABEGQ3SiwpKQEwcHO996ghmnDgXzcvXI78t1UhEe+TQyAIqxmgOQfZxzYmAdAarUKATWPVxIA6dkHiMglpBqg+rIKLDMzE4Bhl+Rnn30WoaGh0jmdTocdO3age/fuLh0g1V8PfpwDAJj334NY/n+9vDwa8jRrNUBiAGQ1A1TzA1WlghTsGNMGqFFdqVMWANU8JTtBE9WNP9QAORUA7dmzB4AhA7R//35otVrpnFarRbdu3fD444+7doRU7/1ZUuntIZAXiDVA5hkge0XQFTWBjVajls1EG37w6hStPhGfk/EPUd2IAdDZq+WY8O52k3MqqHBHj2YY06u5N4YmcSoA+vnnnwEAkydPxtKlSxEZGemWQVHDondnNy6qk2vXq/DV3vMY3jkRceFBLr637SkwwDAlZd6VWQxszKe/RNJSeEUZIBZBE7lCk6hgqFSGX1i2/vGnxfkTl0r8KwASrVq1yuTz4uJi/PTTT+jQoQM6dOjgkoFRw2Gr3wt51xPrfse3+/Px/cF8k2aFrlBSYSUAMkq/6AQBarM9q8WUuvkSeJG2Dv1H2AeIyDWaRodg/cP9cfpP027vF4sr8NK3h1FWqfPSyGopKoIeN24c3nrrLQBAeXk5UlNTMW7cOHTp0gXr1q1z+n7Lli1DcnIygoOD0bdvX+zcudPm9Z999hk6dOiA4OBgdOnSBd9++63Vax988EGoVCosWbLE6XGRZ9ho90Je9u3+fADAL8cvu/zeVvsAGf1UkpsGqzSaApMTVIcMEDdDJXKdbknRuL17M5OP4Z0TAfhGcbSiAGjLli246aabAABffvklBEFAYWEh3njjDbz44otO3Wvt2rXIzMzE3LlzsXv3bnTr1g0ZGRm4ePGi7PXbtm3D3Xffjfvvvx979uzB6NGjMXr0aBw4cMDi2i+//BLbt29H06ZNnf8iyWOUdOwlz3LHjNC16/I1QCZTYDLfG1IAZCUDVJfiS/HpOAVG5B7G3dq9TVEAVFRUhNjYWADAhg0bMGbMGISGhmLkyJE4fvy4U/davHgxpkyZgsmTJyMlJQUrVqxAaGgo3nvvPdnrly5diuHDh2PWrFno2LEjXnjhBfTs2VPKSInOnTuHadOm4ZNPPkFgYKDsvcg3sAbI9yVEur69hTgFFh4kXwQN2MkAuaEGiHuBEbmXmLnV6QWb3d49QVEAlJSUhOzsbJSWlmLDhg0YNmwYAODq1atO9QGqrKxETk4OhgwZUjsgtRpDhgxBdna27GOys7NNrgeAjIwMk+v1ej3uvfdezJo1C506dXLmSyMvsNbvhTynSqdHUXmVyceFotod1eMjXFsALQgCrlcZ/uGDA023qzAtgrZ8bIWDRdAVdWqEyAiIyB1Mtqvx8jSYoiLoGTNmYMKECQgPD0fLli1x8803AzBMjXXp0sXh+1y+fBk6nQ4JCQkmxxMSEnDkyBHZx+Tn58ten5+fL33+yiuvICAgAI8++qhD46ioqEBFRYX0eXFxsaNfArkAM0DedelaBTKWbMGVUuvtCFwdEBgHJyFaswDIrAjanL0aoLoUQYvTsewDROQe5gGQ+f9/T1IUAD388MPo27cvcnNzMXToUKhrqhZbt27tdA2Qq+Xk5GDp0qXYvXu3bI8QOfPnz8e8efPcPDKyhvGPdx2+UGwz+AGAchev2DC+X7BMN2eRkimwwJrjVYqmwGrGwACIyC0C1CqoVIaf+xU6HQDvlago3g2+V69e6NXLtHvvyJEjnbpHXFwcNBoNCgoKTI4XFBQgMTFR9jGJiYk2r//ll19w8eJFtGjRQjqv0+kwc+ZMLFmyBKdPn7a45+zZs6Uu14AhA5SUlOTU10LKcRm8d1XXzDN1aRaFLx5ONzm36/RV3P3Odlyvcm0AdL3acL9AjQoBMpkcjVoFnV6wWQRtvhGqyCXL4LkXGJFbqFQqaDVqVFTr/XMKDADOnj2Lr7/+Grm5uaisNP3tcfHixQ7dQ6vVolevXsjKysLo0aMBGOp3srKyMHXqVNnHpKWlISsrCzNmzJCObdy4EWlpaQCAe++9V7ZG6N5778XkyZNl7xkUFISgINfWOHjaV3vP4dv9F/D6+O4I1Sr+Z/UKToF5V2W14fXXBqgtggpxhVa5iwMgMQNkXv8j0qhU0EG+SLLSTh+guiyDZx8gIvfz6wAoKysLo0aNQuvWrXHkyBF07twZp0+fhiAI6Nmzp1P3yszMxKRJk5Camoo+ffpgyZIlKC0tlYKViRMnolmzZpg/fz4AYPr06Rg4cCAWLVqEkSNHYs2aNdi1axdWrlwJAGjUqBEaNWpk8hyBgYFITExE+/btlXy5fmH6mr0AgLc3n8RjQ9t5dzBOYvzjXWIGKFBj+aYvzs+7fAqsynYApDbsZuG1VWBcBk/kPtoANVDh/aXwihK9s2fPxuOPP479+/cjODgY69atQ15eHgYOHIixY8c6da/x48dj4cKFmDNnDrp37469e/diw4YNUqFzbm4uLly4IF2fnp6O1atXY+XKlejWrRs+//xzrF+/Hp07d1bypdQ7xit3/IW3l0I2dGK/HLkppZCaAEVcseUq4v1CbGSAADt9gKxMgYmBnLIpMMOfjtYPEpHz6vJLiispygAdPnwYn376qeEGAQEoLy9HeHg4nn/+edx+++146KGHnLrf1KlTrU55bdq0yeLY2LFjnQq05Op+6islS3+9jVNg3lWlM7z+tgKgSp0e1Tq9bL2OEmJNkbUASCyEtjUF5o4MkBgMamWyYUTkGr4SACn6aRYWFibV/TRp0gQnTpyQzl2+7PqW+eQ4VxeregLjH+8S3/QDZKZ9jJeoXnfhD6vaGiD5H0HiFJRccFxhbwpMYxhz8fUqlFRUW3xUVFv/P1Ja05wxxM/q6Ij8ibRQwR8zQP369cOvv/6Kjh07YsSIEZg5cyb279+PL774Av369XP1GMkJrp6q8ARmgLxLXC4eKBNQGBcal1fqLLo2KyWuArNVBA3ULks3VjsFJv9YMTBatfU0Vm09bXE+JFCDj+7vg9TkWItzYmAW5sXeJET1na9sh6Hop9nixYtRUlICAJg3bx5KSkqwdu1atG3b1uEVYOQetn679VUMgLyrumaaKVAmA6RSqRASqEF5lc6l2UV7q8BsToHZzQDZnr4qr9Lht9NXZQOgMnFqjgEQkdv4yhSY0wGQTqfD2bNn0bVrVwCG6bAVK1a4fGCkjH9mgLw9goat0kYRNGAIBsqrdC5dCm+vBshmEbTO8Fh7NUAAsP6R/uiQGCF9/sI3h/DJjlyUVVbLPrasZgosjFNgRG5Tl15druR0DZBGo8GwYcNw9epVd4yH6sg/a4AYAXlTdU0RtLUCZzFIceVSeGkVmJVMi8aBDJC1PkBXSqukv3dsEoHgQI30ERli6DorbsRqrqySGSAid/PbDBAAdO7cGSdPnkSrVq1cPR6qI29/QynBDJB32Vv5JBYquzIDVNsHSD6IETsx/362UJqiE+UXG/bts7YM/tTlEunvQQGmgYxY21NWIf+1iFNgrAEicp+6NCt1JUUB0IsvvojHH38cL7zwAnr16oWwsDCT85GRkS4ZHDnPHzNArAHyrip7GaCaYOBCUTnyi66bnIsIDkCYgsJoe40QxSmwZ786aPUeQVaCp1Hdm+Lno5fQOznG4pw41lI7U2D+1k2dyJ/4dRH0iBEjAACjRo0yaRgmCAJUKhV0Ov97E64v/LEPEBshepetRohA7RTYY2v3WZwLClDjq6n90SHRuV96HO0DBACNwrQIDzb9URUTqsWg9vGyj729WzMkxYSiYxPLMYm1PWVWpvM4BUbkfn69DP7nn3929TjIRfwyA8QAyKuqpQBIfgpseOcm2H+uyCJQrdIJqKjW4+C5YsUBkL0MEAA8PbIj7uzZ3OF7q9Uq2RVeABAaZHi+Uis1QGJmKiyIARCRu4gZIG//wq4oAGrVqhWSkpIs2sULgoC8vDyXDIwcZ1xE7O1vKCUY/3hXpY1O0ABw/42tcP+NlvV+f//gN/x4+KKUQXKGWFBtdRWYUQbIVd2nAfsZIKkRYiCnwIjcxa+LoFu1aoULFy4gPt40BX3lyhW0atWKU2AeZhxAmBeM+gPWAHmXmAEKcHL7h0AHlrL+cvwS/vXzCWnDVdEfFw2FylaLoI1+uZLrT6RUaM3UlrUaIKkRIjNARG7jyM8OT1AUAIm1PuZKSkoQHBxc50GRc8zfXGas2WNxTb/WjXBXnxaeGpJdxtNejH+8q3YVmHOZFkd+i3vv11PIPvmn1fNJsaGyx40zQNYyU0qIRdD2VoGFsgaIyG38MgOUmZkJwNAd9tlnn0VoaO0PL51Ohx07dqB79+4uHSDZZ16bsX7veYtrvt53Hrd3b+YzxZ06o6iHGSDvqqr5/pHbC8wWR36LE/v9/P3GVkg1W5XVKDwIqS0tV2oBpkXQzmambJFWgVnrA1QhFkFzCozIXYL8sQh6zx5DZkEQBOzfvx9arVY6p9Vq0a1bNzz++OOuHSHZZTzt9cTwDhbFrC/+7zD0gmGbDJ8JgPQMgHyFrb3AbBF/i6uqtv7vJ2aXereKRUanRIfvbRyLOZuZsiXMaArMPJNdrdNLwRz7ABG5j19mgMTVX5MnT8bSpUvZ78dH6HS1b0D/GNDa5LdnAHjp28MQBO/PtxrTm2SAvDgQql0Gr3YyAJIyQNZr/pROrxmvAnNlEXRoTQZILxhWfAUbNUosNZoW85VfFIjqI+mXJ3+sAVq1apWrx0F1UFVTA6RSwSL4AQxTFZXVeqnhnS9g7x/fIW2GGuDcVFPtDzHr/5biqkRn63jcNQUWarTqLGXO97LXBKhVLs06EZEp8f9XhT8GQKWlpViwYAGysrJw8eJF6M2KcE+ePOmSwZFjdHZqOLQ1AVC1L2WAzIZirbCe3E9MQwc4mQESp1ptpbGr7PQYssY4A+TKYETuFwRzaTc04vcikRvd1acFxqYmWd3Q2FMUBUB///vfsXnzZtx7771o0qQJf1h4mbiZpcbKD3fxzcfb6UZj5nU/Or3g0t/0yXFSBsjZVWAaQzbF1tSqmB1ytr5I46YMkLF7+7XEY0PbWRyPCQ10y/MRkYG1BqiepigA+u677/C///0P/fv3d/V4SAExA2SthiNAqrj3nWknnVkAVK0XEOAb/ycaHKVZGkcKGZXWAKndtAweAJ4Z2RG7c69i9ogO3POLqAFT9L8/JiYGsbHyrebJ88Tf4DVW3sDENx+fygCZ1QBV6vQ+81tBfaTXCzh5uVR2xZ24wamzgYYjmUV7+4xZY/yt7Gxxtj1/v6m1S+9HRP5JUQD0wgsvYM6cOfjggw9MegGRd9irARLfqMwbJnqTRQbIhwq066N/rvsdn+ectXmNs1NNQQ5kgCqrFdYAeWAKjIgaNkUB0KJFi3DixAkkJCQgOTkZgYGmc+a7d+92yeDIMWJgY70GyAenwMwyQEXlVRYFcSGBGqtfEzlny7FLAICokECT17TkerVUw+PsNFWgA5lFsQbI2WJHk60wuCKLiNxAUQA0evRoFw+D6qI2A2S7Bsi3psBMPx+0cJPFNa3iwrBhxk0IYnFQnRSWVeLitQoAwK9PDEJEcO0vLLM+24fPajJDzvbbcWRHZ6XBlXF47Gz2iIjIEYoCoLlz57p6HFQHUg2Q1WXwvrcKzHwKTM6py6U4d7UcrRuHe2BE9dexAsPGo82iQ0yCHwCIjwyS/u5soGEvA6TTC7UF+s4GQEbfH65shEhEJKrTEoicnBwcPnwYANCpUyf06NHDJYMi59ivAbLfsM7TxDFHhQRix1ODLc6nzc/C1bIqNkx0gaMF1wAA7RIsA8nG4cYBkGs3QzUOjJxdBm/8z84MEBG5g6IA6OLFi7jrrruwadMmREdHAwAKCwsxaNAgrFmzBo0bN3blGMkO8Y3GXg2QL2WAxNVIGrVKdvVXgA8Gbf7qxEVDBqhtQoTFucYRwdLfne8DZPvfyCQAcjKIMQ58Xb0KjIgIUBgATZs2DdeuXcPBgwfRsWNHAMChQ4cwadIkPProo/j0009dOkiyTWdnCizAB6fAxABIbaWJppjN8rcMkE4v4Ot953CppubGmFqlwvDOiWge49mVk2evlgMAkmItn9d4CszZ3eDtZ4CUBzHGy/Ud6d5MROQsRQHQhg0b8OOPP0rBDwCkpKRg2bJlGDZsmMsGR44Ra4CsLRf2xT5AYmBj7b1NCtp8aOm+I7Ycv4TH1u6zev77g/n47MF0D44IOHu1DADQPCbE4pzxFJizK7XsZRbF4wFqldNBjAMlYkREdaIoANLr9RZL3wEgMDDQYl8wcj9xN3hrq8B8sQZI/DaxmrWq+Vr8LQMkZn6aRAUjrXUj6bgAYP3ec/jt9FWcLyxH02jLYMQdBEGozQDJZJ7iIoKMrnXu3vZWgVUq3AgV8L9/dyLyP4oCoL/85S+YPn06Pv30UzRt2hQAcO7cOTz22GMYPNiyoJXcq9peEXSAD2aAHJwCszXm/KLr2J17VfZc68Zh6JAYWcdROq+iSgcA6NEiGovHdzc5d66wHDtPXcHGQwWYlJ7skfEUllWhpKIagHwGKDwoAJPSWuJaRTUSjKbDHGGvE7TSLTYAy73iiIhcTVEA9NZbb2HUqFFITk5GUlISACAvLw+dO3fGxx9/7NIBkn32aoACHQgmPM3emDUO1ADd8+52nLxUKnsuQK3C9qcGIy7cuTf1uhKzIXK9izo1jcTOU1dw8dp1lz9v8fUqPPhRDi4Umd5bzMI0jgiyutXIvNs7K3pOqRO0le8rqQeQgh2fGQARkbspCoCSkpKwe/du/Pjjjzhy5AgAoGPHjhgyZIhLB0eOETtBW6sB8skpMMGxwu1qKwHQtetVUvDTJzkWMLrN3txCVOr0uFhc4cUAyPJNXwxAyitdH4juOHkF2078afV8j6Rolz+nuBt8lbUi6JrO4842QQRMl8ETEbmD4j5AKpUKQ4cOxdChQ105HlKgNptipQYowHczQFaLoGu+Fmt7hJ3501DYGxeuxX8eTDM513/BTzhXWG41M+FO12umwGQDoJqs0PVqncuft6zSMM3VpVkU5t6WYnJOpVKhczPXTweK31ellTr8WWK56q2guGaTVWaAiMgHORUA/fTTT5g6dSq2b9+OyEjTH6hFRUVIT0/HihUrcNNNN7l0kGSb3RogH1wFpre3dF+aApMf8+k/Ddmflo3CLM5pvVjzJGaA5KabggMN4xKDJFcqrzTcMyEyCKnJsS6/vxzjzE6vF3+0ep2SImg9U0BE5GZO/WRasmQJpkyZYhH8AEBUVBT+8Y9/YPHixU4PYtmyZUhOTkZwcDD69u2LnTt32rz+s88+Q4cOHRAcHIwuXbrg22+/lc5VVVXhiSeeQJcuXRAWFoamTZti4sSJOH/+vNPj8hdilsR+I0TfeVOxWwQtFdjKj/n0ZUMAlCwTAEnFuTb2qHKXChsZoBCtpuYa14+rrCYACtHWqbm7UxzN7CgKgHznW5WI6imnfjLt27cPw4cPt3p+2LBhyMnJcWoAa9euRWZmJubOnYvdu3ejW7duyMjIwMWLF2Wv37ZtG+6++27cf//92LNnD0aPHo3Ro0fjwIEDAICysjLs3r0bzz77LHbv3o0vvvgCR48exahRo5walz8RsyTWM0CG49Ya1nmD+AandBn86ZopsORGlku7xTdc70yB1dQAyWWAaqbAyt2RAaq5Z0ig57omG2eA7u7TAqfmj5A+dj5duxpUyVZenAIjIndz6kdTQUGBbP8fUUBAAC5duuTUABYvXowpU6Zg8uTJSElJwYoVKxAaGor33ntP9vqlS5di+PDhmDVrFjp27IgXXngBPXv2xFtvvQXAkInauHEjxo0bh/bt26Nfv3546623kJOTg9zcXKfG5i/sbYYqBgTVPtSjSa93NAMkP+arpZUATDsZi7yZ8aqotp4BCnLjFJhYAxTqwQyQcQDUpVkUVCqV9BEbqpXOlVY4//WyDxARuZtTAVCzZs2kTIuc33//HU2aNHH4fpWVlcjJyTFZPaZWqzFkyBBkZ2fLPiY7O9titVlGRobV6wFDfZJKpZL2LTNXUVGB4uJikw9/4vBmqNW+86YiFUHbrQGSH7NYSCxXa+PNztfSKjDZGqCaImgFAdDmY5dw4ys/Ydsfl2XPiyvLxGk2TzD+t7upbZzJOeMd3IvLq5y+NxNARORuTv26OGLECDz77LMYPnw4goODTc6Vl5dj7ty5uPXWWx2+3+XLl6HT6ZCQkGByPCEhQVpeby4/P1/2+vz8fNnrr1+/jieeeAJ33323bO0SAMyfPx/z5s1zeNy+pnYrDGudoH1wFZi4DN7KKjAxm2VtGbw01STTb0csgn74k90m3ZhFgQFqPHLzDegrc66ubK0CC5ECIOf/HSa9Z6iLu+fdHTi9YKTF+fKqapPn8JT/Tr0RZZXVsvuMiYqvOx8A6RgBEZGbORUAPfPMM/jiiy/Qrl07TJ06Fe3btwcAHDlyBMuWLYNOp8PTTz/tloEqUVVVhXHjxkEQBCxfvtzqdbNnz0ZmZqb0eXFxsdTg0ZN0egFVOr3VhnW2HgfYzwB5oybGGrurwMRpOytjlgINmZoX487D2Sfle+MEBajdEgA50gfIPVNghnuGejADBABdmkfZvUbJVCRrgIjI3ZwKgBISErBt2zY89NBDmD17NoSaH1IqlQoZGRlYtmyZRXbGlri4OGg0GhQUFJgcLygoQGJiouxjEhMTHbpeDH7OnDmDn376yWr2BwCCgoIQFOTZhnnmBEHAAx/uQvbJP5E1cyCaRDm+V5Sjq8Cs9dTxBke3wrCeAaqZApPJABmvOuqTHIv/S2spfb43txDvbT2FojLnsxKOcNcy+ECNymYgUbsKzLMBkLsw/iEid3O6YrJly5b49ttvcfXqVfzxxx8QBAFt27ZFTEyM00+u1WrRq1cvZGVlYfTo0QAMG61mZWVh6tSpso9JS0tDVlYWZsyYIR3buHEj0tJqm+GJwc/x48fx888/o1Ej1/+mr9SWY5cw9+uDFm+COr2AizUbaf52+ipGdXM8ALK3CsyXd4O3twrMWgBUG2jIZICMsi83xIdhVLem0udRIYF4b+spRdMyjrBVBC1lgBSsxosIDsSVmsJvOeL3k6czQO7CImgicjfFS0ZiYmLQu3fvOg8gMzMTkyZNQmpqKvr06YMlS5agtLQUkydPBgBMnDgRzZo1w/z58wEA06dPx8CBA7Fo0SKMHDkSa9aswa5du7By5UoAhuDnr3/9K3bv3o1vvvkGOp1Oqg+KjY2FVquVH4iHfLLjDE5dlt+/SnShsNype1bb6QQtrqjyqSkwe1th2CuCrrKeaTFenWReIxQRbPiWFzcIdTVbtUkhdZgCiwwOsBkASRkgD9cA2dKxSSQOX1C2oKB3cixyr5T51NdDRPWL59bMWjF+/HhcunQJc+bMQX5+Prp3744NGzZIU2m5ublQG72xp6enY/Xq1XjmmWfw1FNPoW3btli/fj06dzZs6Hju3Dl8/fXXAIDu3bubPNfPP/+Mm2++2SNflzUHzxveEF4Z0wWdmprWT7z10x/YcDAfZ6/KB0Cbj13CjDV7pDc7UW0RtP90ghaHYm8ZvLVpO7HhoFwAZFwDZF4jFFkTAF277p4ASMoAyWSmxGPlVToIggCVla9dTkRwbfuJymq9xQaj3miEaM/b/9cLL/zvEB4ceIPTj507KgXJjUJxm1H2jojIlXzip+XUqVOtTnlt2rTJ4tjYsWMxduxY2euTk5Ol2iRfU1ReJQU3wzs1QVSoaU+lge0b1wRAZbKP/+lwAa5aqV1RqYCuVgpSvVUDVFmtx/Q1e5DeJg739mtpcs7RrTCs9S6qXQYvVwRtKwNkeM1LKqqdDkIcIXZ5lqtNEoM1QTBk4+SyRNYYZ0KulFYiMcpsFabUB8h3MiYtGoXinYmpih4bGRyIaYPbunhERES1fCIAaigO1WR/mkWHWAQ/ANA8xlD3c+ZKmezmkmLdy99vbIXJN7YyORcSqEFsmPz0ntZLm6FuOJiP7w4YPiwCIKkIWv6xGhs1QIbVcobjckGEaQBkGiCJU2A6vYCySh3Cglz7X8DW6jTjoOh6lXMBkPH05SsbjiAm1PTfWuyMzSkjIiLHMADyoCP5hgCoU1P5FWnNYwy9VE5eKrW5uWRsuBbNoh0vkhYLiis9nAEyjm0qqnUmb/j2VoEFSlNglkGbcQ2NXAbIeHrIfIosJFADjVoFnV7AtevVLg+AbC2DD9SopOeuqNIBIda7qpsz3sbkyz3nrF5XX1aBERG5GwMgD7ovPRmDOyRYLUZuERuKXi1jkHPmqs37aJ3cXMlbNUDhRsFFftF1k53b7U2B2WqEaBIAyWaAjGqAzAIRlUqFiOAAFJZV4dr1KouppLoQBMHmMniVSoXgADVKK3VO7wcm1hb1To5Bb7Pd3vfkFkr9jnxpCoyIyJcxAPIglUqFFjKbd4o0ahXWPZRucbyiWof2z2yQPnd2d21xCsxaU0F3MQ70zhWWmwRAdrfCsFG3JC4j12rUso+3NQUGGAKzwrIqFLu4ELrCKEsj97yAITAqrdQ53Q1afC1nj+iIni1MW05s/eNybQAUyP/SRESO4E9LPxBotrzd2QDIW5uDGk/bnDNb2SYORaOgEaKtOhvA9hQYIBZCl+Oai3sBmQZA8pkYcTxf7jmHZjGm05jNo0MwqEO8/L2rrE+t9W0Vi45NIqFCbY0TERHZxp+WfkCtViFArZKCgUBrG2hZUVsD5OEMkFFA8PW+81KjRwD47fQVALa2whD7AFmvAbK2ZYjWTgYowk1L4cVpKpXK+r+R+NwrNp+QPf/NtBvRuZnlaj5btUUBGjW+ffRG6AXrGTUiIjLFAMhPaAPUqK7p9WLeA8b+Y72zCsz4+X45fhm/HLfcydxazYqUAZLJWtkKBgCzKTCZICnSTc0QjZfAW1te/8QtHfD5rrMWe13tOHUFV0orcfZqmWwAVCl9zfKvl0qlsrqxLBERWWIA5CcMb+qGACjASsdn24/1Qh8gowDort6Wm8sGB2owuX+y7GNtLYO3lwGyVwMk9gJy/RSY7ak5ABjUPh6D2ltOc/39g9/w4+GLKLTS58nWFhtEROQ8BkB+wvhN3dkpMG/tBi9mLUZ3b4oFY7o69VhpGbzMFJiUabESaBi/PvI1QO6ZArtuo07HnqgQQ1+fwnLLAKhap4cYBzqb/SMiInkMgPyE8ZtqoJNvgmI9TZVOr7j78bXrVbIrl7QBakRZ6WcjTlUpedPW2JgCs7UTvPnzebYGyPoSeHuiaxpjymWAHCmuJiIi5zAA8hPGWQ1n+wCJ1wuCYfm5tT3DrNlwIB8Pf5IDuX1JVSrglTFdMS7VcopLrAFydtUaULvyTXYKrNo1U2Cu3hFe3J9MSQYouiaILCq33PDUOABiBoiIyDUYAPkJ0ykwZTVAgCGgME8ibD52Cd/tv4A5t6UgVGYzzW/3X5CCH+PkkSAYPnJOX5UNgCpdkAH66chFfLoz1+ScuILM+hSYvWXw7s0AKcnS2MoAia9jgFplddUcERE5hwGQnzAOIpTWAAGGOiDzoGDSezsBAPERQcgc1t7i8XvyDJ2pP76/L25sGycdf/eXk3jxf4elAl1zdQmAjLNUs7/YL3uNtW0sAtTWO0EDtR2q3VYEraQGqGZvL/kpMBZAExG5GgMgP1G3DFBtQFBVbb0QOs+sWSEAXLpWgbwr5Ybd5pNMl2eLS8wrrNxTLLoOUjAFZrzSrWWjULRLiDA5rw1QY8pNrWUfK6B22kx+Gby4Csw9RdCKaoBqpsDkiqDrUktFRETyGAD5CdMMkHNvhCpVbSNFZ7tBH794DQDQMjZUChxEYkbCWgBUlxog4wzQmJ7N8ejgtg4/1njhWLCNImiX9wGqQ6ZGnAIrKrOsAbLXA4iIiJzHXyn9hLYOy+ANj7G/IaogWAZHZRWGN3W5lV61AZD8FFhdMhfG01iNwrVOPda4yWCATPAV4aYMkFQDZKMPkDXRNpbBi68vM0BERK7Dn6h+wjjoUbSqSmO/G7RcbkjctTxEpmOzmJGosLKxpyuKoAGgUViQU4+VW61mrLYIuko26FOqdr8u5zM1jcK1UKuAskodTl8uNb2vnc7XRETkPE6B+QnjIEJJQKF0Q9Tymu035FaHiZkOqzVAdQiAjIO8OCczQK0bh9k8LwZAVToBFdWWReFK1Xaodv7rDQsKwI1tG2PLsUuY+uluJMWESuculxj2UGMGiIjIdRgA+QnjomBlGSBHpsAsj0kZIJkgwd4UWF1qgEwyQOHOZYDaJUTg35NSkRgVLHs+TBsAlcrw9RZfr3JZAFSXZfAAMD41CVuOXcKBc8U4cK7Y4nxCpPzXQ0REzmMA5C+Myn6cbWQIAIFWNkSttrM9RlmlA1Ng9laBKchc6IzmsZytAQKAwR0TrJ5Tq1UIDwrAtevVuGXJLxa9dVo3DsP7k/s4HRjVdbn6iC6JWD6hJy6XWhZCa1QqDO5ouYcYEREpwwDITxi/RTvbCRqwPgV23Sh4sVkDZCsDZK8GSMF4i4yKgSOs9Pupiy7NorDtxJ/4UybYuHitAgfOFSE1Odape0p7gSnMKKlUKtzSpYmixxIRkXMYAPmhumwtYZ4BEutWANPVU6LySsNKqVCZDFBwoO0psLrUALVsVFsDo2TvMns++Fsf/HGxxGLab+Zn+3D4QrFsYGQPGxYSEfkPBkB+wjgIULIdgjgFZr4jvPH0lVwmp7zK+r5b4hSY3CaphucyRBdKArZOTaPw7sRUJMWG2r9YgUCNGh2bRFocbxoVjMMXinFFUQDE1VpERP6CAZCfqGsORAxCzHdXN84AGf9dVCatArNdBC23y3xlHfvXDEmxXsfjLrFhhnojJQGQ+PopnQIjIiLP4a+qDYS1VWD2AqDrDvQB0gvyu7aL2SZ/Wr4dG648ABIzQHLdp4mIyLfwJ7WfqGsZjLVGiMbTV9dlanmkVWByU2BG/W7kVoLVpQjaWxrVIQNUUcciaCIi8hz/eWeiOhEzQJXV5jVAtUGP2PTQWLmNZfDGgU2FTPZIXHHmTxmgmJpd2VkETURUv7EGyE+4rAbIbKrKuPBZrphZLIKWqwFSq1XQatSo1OmxekcumkSHmJwvq1lB5lcZIGkKrMLmdXIr38rrsBs8ERF5FgMgP1HXpeBahTVAYgbI2pt6oEaFSh2waOMxq88tFzz5qtiafceullpuSiqa/cV+fLoz1+p5ZoCIiHwfA6AGQuwebT4FZlz3Y3sVmPy3SqnRtNnN7RtbnO+eFI14P9rCQawBunStAjq9INty4Jvfz1t9fFx4ENonRLhtfERE5BoMgBoIa52gTabAqvUWy9mv2+gEbe79yX1cMVSvahodAm2AGpXVeuRdKUNynOnGqkVlVbh23TC1t/PpwRaZsdBADQL8aMqPiKihYgDkJ1zXB8j6FJhOL+D7gwUIMMp6lFRY7wRdH2nUKrSOC8OR/Gv442KJRQCUd7UMgGGH+vgI/8lsERGRKQZAfmJCvxb4Ys859Gvt3P5UIq21ZfBmU2IPfpwj+3h7AVCcgg1LfVWb+HBDAHSpBENg2ozxbE0A1DzGPR2qiYjIM3wiAFq2bBlee+015Ofno1u3bnjzzTfRp4/16ZTPPvsMzz77LE6fPo22bdvilVdewYgRI6TzgiBg7ty5eOedd1BYWIj+/ftj+fLlaNu2rSe+HLfo1TIW2bP/gsbhQYoeL07LrN97Hr+fK5KOn/mzTPp7t6Ro2cf2SY5BIzvPG6dwXL6oTXw4AGDxxmNYvumEyTlxk9bmMSEWjyMiIv/h9QBo7dq1yMzMxIoVK9C3b18sWbIEGRkZOHr0KOLj4y2u37ZtG+6++27Mnz8ft956K1avXo3Ro0dj9+7d6Ny5MwDg1VdfxRtvvIEPPvgArVq1wrPPPouMjAwcOnQIwcH+O23RJEr5m26TKMPXnXulDLlXyizOz8poj0cGtVF8/1ZmU0X+7MY2cViadRyV1XqLonFRoh8VdhMRkSWVIMhsAe5Bffv2Re/evfHWW28BAPR6PZKSkjBt2jQ8+eSTFtePHz8epaWl+Oabb6Rj/fr1Q/fu3bFixQoIgoCmTZti5syZePzxxwEARUVFSEhIwPvvv4+77rrL7piKi4sRFRWFoqIiREZabpjpjyqqdfj5yEWUVFiu9ArVavCXDvGK+tf857c8fLT9DN6ZmIrEqPoTFFy6ViFle4x9ve88vtxzFm/e3RPdrWTMiIjIO5x5//ZqBqiyshI5OTmYPXu2dEytVmPIkCHIzs6WfUx2djYyMzNNjmVkZGD9+vUAgFOnTiE/Px9DhgyRzkdFRaFv377Izs6WDYAqKipQUVHb+K64uLguX5ZPCgrQYHjnJi6/77jeSRjXO8nl9/W2xhFBaBxhOa2XObQdMoe288KIiIjIlby6Xvfy5cvQ6XRISDAtNE1ISEB+fr7sY/Lz821eL/7pzD3nz5+PqKgo6SMpqf69oRMREVEtNiwBMHv2bBQVFUkfeXl53h4SERERuZFXA6C4uDhoNBoUFBSYHC8oKEBiYqLsYxITE21eL/7pzD2DgoIQGRlp8kFERET1l1cDIK1Wi169eiErK0s6ptfrkZWVhbS0NNnHpKWlmVwPABs3bpSub9WqFRITE02uKS4uxo4dO6zek4iIiBoWry+Dz8zMxKRJk5Camoo+ffpgyZIlKC0txeTJkwEAEydORLNmzTB//nwAwPTp0zFw4EAsWrQII0eOxJo1a7Br1y6sXLkSgGHT0BkzZuDFF19E27ZtpWXwTZs2xejRo731ZRIREZEP8XoANH78eFy6dAlz5sxBfn4+unfvjg0bNkhFzLm5uVCraxNV6enpWL16NZ555hk89dRTaNu2LdavXy/1AAKAf/7znygtLcUDDzyAwsJC3HjjjdiwYYNf9wAiIiIi1/F6HyBfVB/7ABEREdV3zrx/cxUYERERNTgMgIiIiKjBYQBEREREDQ4DICIiImpwGAARERFRg8MAiIiIiBocBkBERETU4Hi9EaIvElsjFRcXe3kkRERE5CjxfduRFocMgGRcu3YNAJCUlOTlkRAREZGzrl27hqioKJvXsBO0DL1ej/PnzyMiIgIqlcql9y4uLkZSUhLy8vLYZdoOvlaO42vlOL5WjuNr5Ti+Vs5x1+slCAKuXbuGpk2bmmyjJYcZIBlqtRrNmzd363NERkbyP4mD+Fo5jq+V4/haOY6vleP4WjnHHa+XvcyPiEXQRERE1OAwACIiIqIGhwGQhwUFBWHu3LkICgry9lB8Hl8rx/G1chxfK8fxtXIcXyvn+MLrxSJoIiIianCYASIiIqIGhwEQERERNTgMgIiIiKjBYQBEREREDQ4DIA8aNWoUWrRogeDgYDRp0gT33nsvzp8/b3LN77//jptuugnBwcFISkrCq6++6qXRes/p06dx//33o1WrVggJCcENN9yAuXPnorKy0uQ6vlYGL730EtLT0xEaGoro6GjZa3JzczFy5EiEhoYiPj4es2bNQnV1tWcH6iOWLVuG5ORkBAcHo2/fvti5c6e3h+R1W7ZswW233YamTZtCpVJh/fr1JucFQcCcOXPQpEkThISEYMiQITh+/Lh3Butl8+fPR+/evREREYH4+HiMHj0aR48eNbnm+vXreOSRR9CoUSOEh4djzJgxKCgo8NKIvWf58uXo2rWr1OwwLS0N3333nXTe268TAyAPGjRoEP7zn//g6NGjWLduHU6cOIG//vWv0vni4mIMGzYMLVu2RE5ODl577TU899xzWLlypRdH7XlHjhyBXq/H22+/jYMHD+L111/HihUr8NRTT0nX8LWqVVlZibFjx+Khhx6SPa/T6TBy5EhUVlZi27Zt+OCDD/D+++9jzpw5Hh6p961duxaZmZmYO3cudu/ejW7duiEjIwMXL1709tC8qrS0FN26dcOyZctkz7/66qt44403sGLFCuzYsQNhYWHIyMjA9evXPTxS79u8eTMeeeQRbN++HRs3bkRVVRWGDRuG0tJS6ZrHHnsM//3vf/HZZ59h8+bNOH/+PO68804vjto7mjdvjgULFiAnJwe7du3CX/7yF9x+++04ePAgAB94nQTymq+++kpQqVRCZWWlIAiC8K9//UuIiYkRKioqpGueeOIJoX379t4aos949dVXhVatWkmf87WytGrVKiEqKsri+Lfffiuo1WohPz9fOrZ8+XIhMjLS5PVrCPr06SM88sgj0uc6nU5o2rSpMH/+fC+OyrcAEL788kvpc71eLyQmJgqvvfaadKywsFAICgoSPv30Uy+M0LdcvHhRACBs3rxZEATDaxMYGCh89tln0jWHDx8WAAjZ2dneGqbPiImJEd59912feJ2YAfKSK1eu4JNPPkF6ejoCAwMBANnZ2RgwYAC0Wq10XUZGBo4ePYqrV696a6g+oaioCLGxsdLnfK0cl52djS5duiAhIUE6lpGRgeLiYuk3sYagsrISOTk5GDJkiHRMrVZjyJAhyM7O9uLIfNupU6eQn59v8rpFRUWhb9++fN1g+NkEQPr5lJOTg6qqKpPXq0OHDmjRokWDfr10Oh3WrFmD0tJSpKWl+cTrxADIw5544gmEhYWhUaNGyM3NxVdffSWdy8/PN3mTAiB9np+f79Fx+pI//vgDb775Jv7xj39Ix/haOY6vlcHly5eh0+lkX4uG9Do4S3xt+LpZ0uv1mDFjBvr374/OnTsDMLxeWq3Woh6vob5e+/fvR3h4OIKCgvDggw/iyy+/REpKik+8TgyA6ujJJ5+ESqWy+XHkyBHp+lmzZmHPnj344YcfoNFoMHHiRAgNpBm3s68VAJw7dw7Dhw/H2LFjMWXKFC+N3POUvFZE5FmPPPIIDhw4gDVr1nh7KD6rffv22Lt3L3bs2IGHHnoIkyZNwqFDh7w9LABAgLcH4O9mzpyJ++67z+Y1rVu3lv4eFxeHuLg4tGvXDh07dkRSUhK2b9+OtLQ0JCYmWlTAi58nJia6fOye5uxrdf78eQwaNAjp6ekWxc18rUxfK1sSExMtVjrVp9fKUXFxcdBoNLLfNw3pdXCW+NoUFBSgSZMm0vGCggJ0797dS6PyvqlTp+Kbb77Bli1b0Lx5c+l4YmIiKisrUVhYaJLdaKjfZ1qtFm3atAEA9OrVC7/99huWLl2K8ePHe/11YgBUR40bN0bjxo0VPVav1wMAKioqAABpaWl4+umnUVVVJdUFbdy4Ee3bt0dMTIxrBuxFzrxW586dw6BBg9CrVy+sWrUKarVpspKvlePS0tLw0ksv4eLFi4iPjwdgeK0iIyORkpLikufwB1qtFr169UJWVhZGjx4NwPB/MCsrC1OnTvXu4HxYq1atkJiYiKysLCngKS4uln6jb2gEQcC0adPw5ZdfYtOmTWjVqpXJ+V69eiEwMBBZWVkYM2YMAODo0aPIzc1FWlqaN4bsU/R6PSoqKnzjdfJIqTUJ27dvF958801hz549wunTp4WsrCwhPT1duOGGG4Tr168LgmBYPZCQkCDce++9woEDB4Q1a9YIoaGhwttvv+3l0XvW2bNnhTZt2giDBw8Wzp49K1y4cEH6EPG1qnXmzBlhz549wrx584Tw8HBhz549wp49e4Rr164JgiAI1dXVQufOnYVhw4YJe/fuFTZs2CA0btxYmD17tpdH7nlr1qwRgoKChPfff184dOiQ8MADDwjR0dEmK+QaomvXrknfNwCExYsXC3v27BHOnDkjCIIgLFiwQIiOjha++uor4ffffxduv/12oVWrVkJ5ebmXR+55Dz30kBAVFSVs2rTJ5GdTWVmZdM2DDz4otGjRQvjpp5+EXbt2CWlpaUJaWpoXR+0dTz75pLB582bh1KlTwu+//y48+eSTgkqlEn744QdBELz/OjEA8pDff/9dGDRokBAbGysEBQUJycnJwoMPPiicPXvW5Lp9+/YJN954oxAUFCQ0a9ZMWLBggZdG7D2rVq0SAMh+GONrZTBp0iTZ1+rnn3+Wrjl9+rRwyy23CCEhIUJcXJwwc+ZMoaqqynuD9qI333xTaNGihaDVaoU+ffoI27dv9/aQvO7nn3+W/R6aNGmSIAiGpfDPPvuskJCQIAQFBQmDBw8Wjh496t1Be4m1n02rVq2SrikvLxcefvhhISYmRggNDRXuuOMOk1/gGoq//e1vQsuWLQWtVis0btxYGDx4sBT8CIL3XyeVIDSQClwiIiKiGlwFRkRERA0OAyAiIiJqcBgAERERUYPDAIiIiIgaHAZARERE1OAwACIiIqIGhwEQERERNTgMgIjI7/z73//GsGHDvD2MOjl9+jRUKhX27t1r99rLly8jPj4eZ8+edf/AiBoIBkBEpMh9990n7anlSdevX8ezzz6LuXPnevy5vSUuLg4TJ05sUF8zkbsxACIiv/L5558jMjIS/fv39/ZQPGry5Mn45JNPcOXKFW8PhaheYABERG6xePFidOnSBWFhYUhKSsLDDz+MkpISk2veeecdJCUlITQ0FHfccQcWL16M6Ohom/dds2YNbrvtNpNjmzZtQp8+fRAWFobo6Gj0798fZ86ckc5/9dVX6NmzJ4KDg9G6dWvMmzcP1dXV0vnCwkL84x//QEJCAoKDg9G5c2d888030vl169ahU6dOCAoKQnJyMhYtWmTy/MnJyXj55Zfxt7/9DREREWjRogVWrlxpcs3OnTvRo0cPBAcHIzU1FXv27DE5f/XqVUyYMAGNGzdGSEgI2rZti1WrVknnO3XqhKZNm+LLL7+0+foQkWMYABGRW6jVarzxxhs4ePAgPvjgA/z000/45z//KZ3funUrHnzwQUyfPh179+7F0KFD8dJLL9m976+//orU1FTp8+rqaowePRoDBw7E77//juzsbDzwwANQqVQAgF9++QUTJ07E9OnTcejQIbz99tt4//33pefS6/W45ZZbsHXrVnz88cc4dOgQFixYAI1GAwDIycnBuHHjcNddd2H//v147rnn8Oyzz+L99983GdeiRYukwObhhx/GQw89hKNHjwIASkpKcOuttyIlJQU5OTl47rnn8Pjjj5s8/tlnn8WhQ4fw3Xff4fDhw1i+fDni4uJMrunTpw9++eUXB/8FiMgmj227SkT1yqRJk4Tbb7/d4es/++wzoVGjRtLn48ePF0aOHGlyzYQJE4SoqCir97h69aoAQNiyZYt07M8//xQACJs2bZJ9zODBg4WXX37Z5NhHH30kNGnSRBAEQfj+++8FtVptdXfze+65Rxg6dKjJsVmzZgkpKSnS5y1bthT+7//+T/pcr9cL8fHxwvLlywVBEIS3335baNSokVBeXi5ds3z5cgGAsGfPHkEQBOG2224TJk+ebPVrFwRBeOyxx4Sbb77Z5jVE5BhmgIjILX788UcMHjwYzZo1Q0REBO699178+eefKCsrAwAcPXoUffr0MXmM+efmysvLAQDBwcHSsdjYWNx3333IyMjAbbfdhqVLl+LChQvS+X379uH5559HeHi49DFlyhRcuHABZWVl2Lt3L5o3b4527drJPufhw4ct6o369++P48ePQ6fTSce6du0q/V2lUiExMREXL16U7tG1a1eTcaelpZnc86GHHsKaNWvQvXt3/POf/8S2bdssxhISEiK9fkRUNwyAiMjlTp8+jVtvvRVdu3bFunXrkJOTg2XLlgEAKisrFd+3UaNGUKlUuHr1qsnxVatWITs7G+np6Vi7di3atWuH7du3AzBMP82bNw979+6VPvbv34/jx48jODgYISEhyr9QI4GBgSafq1Qq6PV6hx9/yy234MyZM3jsscdw/vx5DB482GKa7MqVK2jcuLFLxkvU0DEAIiKXy8nJgV6vx6JFi9CvXz+0a9cO58+fN7mmffv2+O2330yOmX9uTqvVIiUlBYcOHbI416NHD8yePRvbtm1D586dsXr1agBAz549cfToUbRp08biQ61Wo2vXrjh79iyOHTsm+5wdO3bE1q1bTY5t3boV7dq1k+qE7OnYsSN+//13XL9+XTomBmjGGjdujEmTJuHjjz/GkiVLLAqpDxw4gB49ejj0nERkGwMgIlKsqKjIJLOyd+9e5OXloU2bNqiqqsKbb76JkydP4qOPPsKKFStMHjtt2jR8++23WLx4MY4fP463334b3333nVS8bE1GRgZ+/fVX6fNTp05h9uzZyM7OxpkzZ/DDDz/g+PHj6NixIwBgzpw5+PDDDzFv3jwcPHgQhw8fxpo1a/DMM88AAAYOHIgBAwZgzJgx2LhxI06dOoXvvvsOGzZsAADMnDkTWVlZeOGFF3Ds2DF88MEHeOuttyyyM7bcc889UKlUmDJlCg4dOoRvv/0WCxcuNLlmzpw5+Oqrr/DHH3/g4MGD+Oabb6SvAQDKysqQk5Pj9w0giXyGt4uQiMg/TZo0SQBg8XH//fcLgiAIixcvFpo0aSKEhIQIGRkZwocffigAEK5evSrdY+XKlUKzZs2EkJAQYfTo0cKLL74oJCYm2nzegwcPCiEhIUJhYaEgCIKQn58vjB49WmjSpImg1WqFli1bCnPmzBF0Op30mA0bNgjp6elCSEiIEBkZKfTp00dYuXKldP7PP/8UJk+eLDRq1EgIDg4WOnfuLHzzzTfS+c8//1xISUkRAgMDhRYtWgivvfaayZhatmwpvP766ybHunXrJsydO1f6PDs7W+jWrZug1WqF7t27C+vWrTMpgn7hhReEjh07CiEhIUJsbKxw++23CydPnpQev3r1aqF9+/Y2XxsicpxKEATBi/EXEZFkypQpOHLkiN2l3mPHjkXPnj0xe/ZsD43M+/r164dHH30U99xzj7eHQlQvcAqMiLxm4cKF2LdvH/744w+8+eab+OCDDzBp0iS7j3vttdcQHh7ugRH6hsuXL+POO+/E3Xff7e2hENUbzAARkdeMGzcOmzZtwrVr19C6dWtMmzYNDz74oLeHRUQNAAMgIiIianA4BUZEREQNDgMgIiIianAYABEREVGDwwCIiIiIGhwGQERERNTgMAAiIiKiBocBEBERETU4DICIiIiowWEARERERA3O/wPCdIqgS+4VRwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ll.plot_results()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}