Genernerate_Music_From_Scratch.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1aca5bc8",
   "metadata": {},
   "source": [
    "# Generating Music Using Python From Scratch\n",
    "Have you ever wondered how to generate music by a computer? When I was a kid, I got a digital watch. One of the fascinating features is its music library which includes the classical one, *Für Elise*. It seems that I can still hear this digital sound many years later. Today I am going to share the process to build a music generator from the basic using Python."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a17d47d",
   "metadata": {},
   "source": [
    "![](https://cdn.jsdelivr.net/gh/HuangJiaLian/DataBase0@master/uPic/2022_04_15_19_The_sound_of_music.png)\n",
    "\"When you know the notes to sing, you can sing most anything.\"  -- The sound of music"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "574cba6f",
   "metadata": {},
   "source": [
    "## Preperations\n",
    "To play sounds, I install simpleaudio by `pip install simpleaudio`. Another model is `Numpy` which is used for data processing. If you don't have it installed, you can also install it through `pip install numpy`\n",
    "\n",
    "To start with, we import all the libraries we need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8472f4bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import simpleaudio as sa\n",
    "import numpy as np\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18227d4c",
   "metadata": {},
   "source": [
    "## Store A Sine Wave\n",
    "Tone is a kind of vibration with a fixed frequency. Different frequencies correspond different tones. Let's look at this sine wave $y = \\sin (2\\pi f t)$, where $f=440$ is frenquency in hertz (Hz). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c5e1ea26",
   "metadata": {},
   "outputs": [],
   "source": [
    "fs = 8000\n",
    "ts = np.linspace(0, 1, fs, False)\n",
    "freq = 440\n",
    "amplitudes = np.sin(2 * np.pi * freq * ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b577bdf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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irKAXfDa5vrYP1RUIBKr0dcXFxVXGFcGXarJxxMWX/1xcYGLHJfsM2AnJ4EtRgovObOwcXMhghShWyG0Ix/qVeDyeKn1icDHx4tmkLI8JHt8Fj+uAynaLtd8sh2EnxxQKBbRaLT8WYe0Ou3yTzUCOjY1tcPs8uM9kbQwrqATv64vzQ3a8B1+OzIohQNXxSPDxzsbQwSfG4+Li+KsY2MkVpVIp8J6pOYFAgG87WN/ocDig0+nQv3//Wn3tOlkY2bx5M2w2G4YOHXrFbTiOw48//ogjR46gQ4cOuOOOO67ry3f33Xfj9OnTOHToUE2ETAghhBBCCCGEkBqyatUqOBwOTJgwodZfq06douU4Dm+//TZuueUWLFu27IrblZaWom/fvrj77ruxYMECjBgxAv369av2egRA5XTg6kyPJoQQQgghhBBCSGgtW7YMr7/+ekheq04VRj7++GO88MIL11xAb8aMGcjNzcW+fftw6tQpHDlyBCdPnsTbb79d7ddSKBTVWt+DEEIIIYQQQgghoRXKnL1OFUZGjBiBvXv3olOnTldcMbikpAQrVqzAu+++i44dOwIAWrdujRkzZmDOnDnVXuBMLpdXeyVwQgghhJCGpg5eTU0IIYTwQpmz16nVoBo3bozGjRvDYrFAo9Fcdptff/0VERERuOuuu6o83r17d/52RbGxsfzjx44dw/Hjx/mFIXU6Hbp06QKZTHbN2/4RQgghhNQ3HMeh3OFFXrkTRRUuFFa4UGhxocDiQrHVhaIKF0qsbpidXjTWK5HaJBq9mseiV4tYxGtr51aThBBCyPUKZc5epwojTGlpKYxG42V/l5ubC6PReMmlNnq9HgBgMpmqFEaWL1+OV155pcq2WVlZUCqV1brlEqm7AgEOVpcPpXY3LE4vyh0eWF0+VLh8sLq8KLd7kG9xwezwoMLpQ4XLC4vTC6fHj2axKtzSKg792xiQ2iQaEZI6NXmKEEIIuaoKlxfnSx3ILrEht9yJAosT+WYX8s1O5JmdsLouP/P2YudKHThX6sA3e/MAAK2MatzSKg79WhvQvWk05FLJNZ6BEEIIqR2hzNnrXWFEo9HAbrdf8jhbeFWlUlV5/HL3N3c4HPxODgQCDeI2oQ3FuVI7Ci0uWJxemGweFFZUFjYsTi8qnF4UVrhhdnhgc/lg9/gQuMFZwMcLrTheaMWcP85AJZMgs3kM+rU24JZWcUjWN5xbXhFCCKmfOI6DyeZBntmJc6V2XChz4KzJgdxyBy6UOZBvufo11xq5FInRkYjXKmDUKGDUKpCoU8CgUcAQJYdBo4A2MgIni6zYcaYUW0+bsPNsGU4W2XCyyIZ5m88iMqKyfxzY1ohb2xpgjKLZJIQQQkInlDl7nSuMeL1euFwuREdHX/b3RqMRZrOZL24w2dnZ0Gq1SExMrLJ927ZtMXz4cPj9frjdbpSXl0MsFvP/1+VyNah7P9dHdrcPPx7Mx7JdF7D3vPm6/q9aLoVeJYNOGQGdUoYohRQaRQSiFFLolDIkaBXQq2SIiqx8TBsZAXmEBAcumPH7iWJsPF6M7BI7NhwrxoZjxQCAFgY1PwjsmqyDlGaTEEIICRGHx4eFW3Mwf/MZlDuufPc8uVSMJjFKpMSqkayPRCNdJBK0kUjURaKRrrLvE4lE13y9DoladEjUYlqfFHh8Aew9X47fThRj04kSHC+0YuPxyr4S3wIdEqMwpH08bm1rRJt4TbWenxBCCLlRoczZRVwdW3nL7/dDKpVi+fLlGDly5CW/LykpQXx8PFauXIm7776bf3z48OFwOBxYv359tV5n5syZePTRR1FYWHjF2Smk9nAch93nyrFqTy5+OJAPu6dyZo9KJkHreA10Shli1TLERymgU1YWPjSKCBij5NCrZNDII6CSS2qkaFFgceKPkyXYeLwY206Xwur+a/pxtDIC/dsYMKhdPPq2ioVSVudqiYQQQhoAh8eHr7IuYPambJRYKxeai1JIkRStRJMYJRrrlWgSo0KyPhJJ0UokR0fWeuG+qMKF308UY92RImzNNsHlDfC/a6xXYkiHeNzZqRE6JEZRkYQQQkiNC2XOXqeyvEAggAsXLkCj0eDIkSMYMGAAYmJiqmwTFxeHoUOH4t///jfatWuHVq1a4f3338fq1auxYMGCar+WWq0GUHkJDhVGQifHZMd3+/Pw7b48nCt18I93bxKNMemNcXvH+JAXHxK0kRid1hij0xrD6w8g62wZfj1WjI3Hi5Dz53XX3+zNg1wqRp+WcRjSIR63tTVCq7zyLaUJIYSQ6rC6vFiy4xzm/fHXDJHOSVo8O7gNereMvcb/rl3GKAXfP7q8fmw5ZcIvRwrx24linC9zYO4fZzD3jzNI1kdiSPt4DOmQgK7JOojFVCQhhBByc9w+f0hz9jo1Y+S1117DSy+9xP/7tttuw7p167B9+3b07dsXhw8fRuvWrXHhwgWMHTsW27ZtQ2RkJFwuF2bMmIGPPvqo2tceLV++HKNHj8ahQ4fQoUOH2npLBEChxYUfD+bjhwP5OJBr4R+Pj1JgRNdE3JeahBYGtYARXh7HccgusWHd0SKsO1KE/RfM/O+kYhEym8fg9o4JGNw+HnqVTLhACSGE1DsVLi8+23IWn205i4o/F0rt2liHR/q1wMC2hjo9A8Mf4LDnXDl+OpiPnw4VwmT761aKCVoFBrUz4vaOCejeVA8JFUkIIYRcB4fHhyXbz0GnlEF0dnvIcvY6VRhxu93Iy8tDIFA5VTMuLg5arRZHjx5F//79sWXLFrRs2RJA5eyS9evXo6ioCBkZGWjduvV1vdYPP/yAu+66C1lZWUhLS6vx9xLuiq0u/HyoED8dKsCunDKwo0wlk2Bw+3iM6JqIXi1i69WAqajChXVHCrH2SCF2nCmD/8+VXyViEXq3iMU93RIxuH08FBG0gj8hhJDLK65wYcHWs/hyx3n+0s2MZno8NqAFereIrdMFkcvxBzjsPV+Onw8V4ufDBSgIWhQ2Vi1D/9YGjEpLRlpTvYBREkIIqescHh++2HEOc/84A5PNg3/d0Rbx5iMhy9nrVGEklDZs2IDbbrsNmzZtQt++fYUOp0HINzux9nAhfjlSiF05ZfwdY2RSMQa0NuDOzo0woI0BkbL6Xzgot3uw/mgRfjxUgG2nTfD9+WbVcimGdIjHPd0S0aNZDE0nJoQQAgA4X+rA7D+ysXJPLjy+yhNAmSkxeHJgS/RIibnG/64fAgEOh/IsWHOoAGsOF+BC2V+3WByb3hgvDmtLa3URQgipwunxY+nOc5i9KRsmmwdA5SWlH43tilP7tocsZw/b3ond1vdyt/4l1RMIcDicb8HG48XYcKwIh/Mq+N/JJGL0bRWLYZ0a4da2BmgUDWs9jmiVDKPSkjEqLRlldg9+OpiPlXvzcOCCGSv35GLlnlw0iVHi1eEdcEurOKHDJYQQIgCOq5xNMX/zWfxypJA/YTC4vREz+rVAl2SdoPHVNLFYhM7JOnRO1uEfQ9vgZJEN3+/Pw/zNZ/FV1nnszinD/43qjE5JOqFDJYQQIjCHx4elO85jzh9n+EsyOyfr8NTAlujXKg4ikQj5IczZw7YwEhUVBQCwWq0CR1K/ODw+/HHShF+PFeH3kyX8yvkAoIgQo39rA4Z0iEf/NgZENbBiyJXoVTJMyGyKCZlNcabEhm/2/rW47KTPsjCqexJevrM9VPKw/boRQkhY8fgC+PlwAT7bmoMDf65PJRWLcHfXRMzol4IWBo2wAYaASCRC63gNnhvSBnd2boTHv9qHU8U23D1zGx4f0AKP9GsBmbR276pDCCGk7qlwebFk+zks2HIWZfbKGSIdE7V45rZW6Nc6rsolpaHM2cM2U6MZI9dnx5lSfPTrKew5Vw6376/b9TXSKnBLawMGtTMis3lM2K+vkRKnxt8Ht8aTA1tiwZazeG/9SSzfnYtdOeX4aExXdEzSCh0iIYSQWmKyufF11nks2XEORRWVJw50ygiMy2iMiZlNYYxSCByhMNomRGH1Y73wf+tOYsGWs/hgwymsPVyI90d3QduEKKHDI4QQEgLFVhcWbc3Bkh3nYP1z0fEuyTo8cWsL9G99+UXHQ5mzh21hhN36hwojV5db7sDrPx3Dz4cL+cc6J+swqJ0RA9oY0CZeU+8WiguFCIkY029pjgFtDHj8y304UWTFPbO24pnbWuOhvin1atFZQgghV1Z5uYwZS7bnYM2hQnj8lScPWhrUmNyrKe7pmtQg1ta6WUqZFC8Oa4db2xrwz28O4XihFXd+vAWPD2iJR/s3h1RCs0cIIaQhOlNiw7zNZ7Fq719rbPVI0eOx/i3Rq0XMVXPJUObsYVsY0el0EIvFKC4uFjqUOsnq8uKjX0/h823n4PEHEBkhwfRbmmNiZhNE061pq62VUYPvH+uFN9Ycw+Lt5/D22uPYcroE747sjARtpNDhEUIIuUE2tw/f78/DFzvO41hB5RpbIhFwaxsDJvdqWi/vMBMKPZvHYs0TffDmz8fwxY7zeH/DSWw6WYx3R3ZGSpxa6PAIIYTUgECAwx+nSrBk+zn8erwy3xaJgNvaGTH9luZIbRJdrecJZc4etnelAQCj0Yi7774bs2fPFjqUOiMQ4LD6QD5eX3OMXz/krs6N8M/b21Aif5N+O16Mv684gFK7BzplBN4f1QX92xiEDosQQsh1sLq8+N8vJ7ByTy4cHj+AyrWmRnVPxriMxkjWKwWOsP7YetqEvy0/gMIKFxQRYvzrjnYYn9GYCkqEEFJP2dw+rNqTi4VbzyKn1AGg8g6l93RNxLQ+KWhhuP4CeKhy9rAujDRv3hw9evTA0qVLhQ6lTthzrgyvrD6KQ3kWAEDXxjq8NrwDOiTSuhg1pdjqwnMrD+L3EyUQiYBH+7XAUwNb0hRiQgipBzafKsHzKw8i3+ICAKQ1jca4jCYY2jEecildLnMjzA4P/vPDUXy7Lw9A5S2M37q3I5rEqASOjBBCSHUdL6zAlzvP49t9efz6IYm6SNyf0Rij05IRq5bf8HOHKmcP68JIp06dkJKSgu+++07oUARVaHHhrZ+P4bv9+QAAg0aOvw9ujfu6JUFMa2HUuECAw8zfT+O99ScR4ICezWPw/uguYbsoHyGE1HUWhxev/XQUK/fkAgA6J2nx9n2d0CaeFg6tKT8dLMCL3x9Gmd0DuVSMJ25tiQf7pNCdawghpI7iOA7rjxZh4dYcbD9Tyj+e3lSPST2bYnB7Y42c/A1Vzh7WhZHMzExoNBqsW7dO6FAE4fMHsGhbDt5ffxJ2jx8yqRgP9mmGR/u3gFIWtsvPhMy2bBOe+GofTDYPYtVyzBrfDWlN9UKHRQgh5E8cV3l56Ws/HoXJ5oFMKsaTt7bEw31TaKZfLSize/Daj3/NHmllVOO/IzoivRn1jYQQUpeU2T3417eH+Bt0qGQS3NMtCWPTG6Ndo5o9aRCqnD2sCyODBg2C1WrF9u3bhQ4l5LZnl+K1H4/i6J8Lxg1qZ8SLw9rRtdEhVlzhwhNf78OOM2WQiEX459A2mNq7GV1fTQghAsstd+DF7w7jtxMlACrPgL11b0daIDQEtpwy4d/fHeKvT7+nWyL+ObQt4jQ3PhWbEEJIzdh4vAjPrTwIk80DtVyKpwa2xKi0ZEQpImrl9UKVs4d1YWTkyJE4cuQIjh49KnQoIZNvduL1n47hp0MFACqv/XptRHsMaGMUOLLw5fUH8O4vJzDnjzMAgAFtDHjr3o4waOjSGkIICbWLZ1NqFFL8+462GNU9mYrWIeTy+jHr92zM+j0bHn8AGrkUT93WChMzmyCCZusQQkjImR0evLnmOJbtvgAAyGimx7sjO9f6ifVQ5exhXRiZOnUq1q5di7y8PKFDqXVsgDHnj2y4vJW3353Rrzmm9WlGl83UEWsPF+C5lQdR4fIhVi3Hh2O6oFeLWKHDIoSQsHEk34J/fnMIB3MrFyEf2iEe/xnengrVAsox2fGfH47wM3daGdV45c726En9IyGEhATHcfjxYAFeWX0EpXYPZBIx/j64Fab1TgnJepShytnDOiOOjo6G2WwWOoxaxQ7kt34+jjyzEwBwR6cEvHB7WyTq6Pa7dcmQDgnonKzDM8sOYPuZUoxfsJPuWkMIISFgd/vw0cZTmL/5LPwBDo20Cvz37g40m7IOaBqrwmeT0/DrsWK8+uNRnCyy4f75OzGkfTz+dUdbugSYEEJqUaHFhdd+PMpfbZDeTI837u6AFgZNyGIIVc4e1oURtVoNh8OBQCAAsbjhJZ45Jjv+9d0hbD1duUpwa6MGrw5vj4yUGIEjI1eSoI3EF9My8PHGU/jo11P45LfTyDpbhvdGd0ZSNA3+CCGkJgUCHFbuycX/1p1AidUNkQiY3LMp/j64NdTysB4i1SkikQgD2xnRu2Us5m8+g09/y8baI4XYeKIYD/dNwfRbmkNFnxchhNSYQIDDV7vO4801x2Fz+6CSSfCvO9phbHroLysNVc4e1r2IQlE5NdblckGpbDhJp9cfwNw/zuCjX0/B7QsgWhmBZwe3wei0ZEjo9rt1nkQswlMDWyGjWQye/HofsnLKcPuHm/HWvZ1we8cEocMjhJAG4XSxDf/85iB25ZQDADon6/DKne3QtXG0wJGRK1FESPDYgJa4NzUJb/18HN/vz8fHG0/j610X8H8jO6NvqzihQySEkHrvdLEV//zmEN8/3tbOiJcEvElHqHL2sC6MqNWVK8vb7fYGUxjZe74cL3xzCMcLrQCAe7om4t/D2kGvkgkcGblemc1jsPapvnh+1UGsP1qER5buxYgujfDKXe2hU9LnSQghN6Lc7sH7G07iy53n4QtwiNPI8a/b2+Kuzo1Ccq00uXkJ2kh8OKYrJmY2was/HsOBC2ZMXpiF54a0wcN9U2iRXEIIuQFefwCzf8/GRxtPwevnEKuW45W72uGOjgmCtquhytnDujASE1N5SUlJSQni4ur3WQab24f/rT2OxTvOgeOAxnol3ri7I3q3pMXJ6jO9Soa5E1LxxY5zeH3NMXy3Px9bs0vx3xEdMLh9vNDhEUJIvcFxHJbvvoA3fz4Os8MLsQgYm56MfwxpC62ydm4xSGpXahM9vp3REx9sOImPNp7GWz8fx/7zZvxvZCdoaum2kYQQ0hDtOVeOf33718n1MWnJ+OfQutE/hipnp8IIgPLycoEjuTmbTpbgn6sOIt/iglQswoO3pOCJAS0RKZMIHRqpASKRCBMym6JPyzg8u/IAduWU4+ElezCsUwJeH9GxTjRYhBBSl50ssuKFbw5h97nK/r5n8xi8fGd7tI4P3eJxpHaIxSI8M6g1OiRq8bflB7D2SCGOF1Zg5rhUtGsUJXR4hBBSp3l8AXyw4SRmb8pGgAOS9ZF4655OderOmKHK2cO6MMKm5dhsNoEjuTEcx2H2pjN4e+1xAEDHRC3evrcTDQQaqKaxKix7KBOfb8/BO2tP4MeDBcgusePzB9LoVpKEEHIZTo8fM38/jdmbsvlpwS8Oq7xshi63aFgGtY/H94+p8eiX+3CsoAL3zNqKV+/qgJHdk+izJoSQyzhdbMWTX+/HkfwKiETAjH7N8eStLaGIqFsn10OVs4d1YUSjqTxTZLVaBY7k+vkDHF5ZfQRLdpyDSAQ8M7AVZvRrTrd1beDEYhGm9GqGW9sYMXlhVuXgb+Y2LJqSjhYGtdDhEUJInbHuSCFeWX0E+RYXAGBsemP88/Y2iKJLLBqslDg1vn2kJ1787jBW7MnFc6sOYvNpE968pyPdZYgQQv4UCHBYsuMc3lhzDG5fAMn6SLw3qgvSmuqFDu2yQpWzh3UvoddXfvgmk0ngSK6Py+vHE1/tw7qjRZBJxfhgdBe6W0mYaRyjxIrpmXjg8904cMGMe2dtw9wJqXQrZkJI2Cu2uvDqD0fx48ECAEC7hCi8Orw9utfRAR+pWYoICf43sjN6pMTgxe8P44cD+TiYa8an93dDh0St0OERQoig8s1OPLvyALaeLgUA3JeahJfvbFen12UKVc4e1tML2OItJSUlAkdSfRUuLyZ+loV1R4sQpZDii6kZVBQJUzFqOb5+sAdua2eExenFhAVZ+H5/ntBhEUKIYH48mI9B7/+BHw8WIDJCgpeGtcMPj/emokgYujc1Casf6412CVE4V+rAPTO3YfH2HHAcJ3RohBASchzHYeWeXAx+/w9sPV0KvUqG2eO74d2Rnet0UQQIXc4e1oURmUwGtVqNsrIyoUOpllKbG/fP24Gss2UwRsmxYnpPpDejwV44i5RJMHt8KiZlNoHHH8CTX+/HBxtO0sCPEBJWAgEOb689jse+3Aezw4s+LWOx7um+eKB3M0joFrxhq4VBjW8e6YlxGY3h8Qfw0vdH8PSy/XB4fEKHRgghIVNqc2P6F3vw9xUHYHX7cFs7I355qi+GdKgfJ9dDlbOH9aU0QOViLvVh8dViqwvj5u3EqWIbmsQo8cXUDCTra+8+zqT+kIhFeOWu9mgSo8J/fzqKDzacwqkiG965rxNUdE01IaSBc3r8+NuK/VhzqBASsQgvDWuHiZlNaMFNAqDy0prX7+6IHikxeH7VQXy3Px+H8iz4dFw3tImnxeoJIQ3bppMl+PuKAyixuqGRS/HSne1wX2r9W5Q6FDl7ncmaOI7Dpk2bcPz4cXTt2hUZGRlX3Pbo0aP8VBq32w2O4+DxeNC+fXukpKRc1+vKZDJ4PJ6bir22XShzYPyCnThX6kBLgxpLp2XAEEV3ISF/EYlEeKB3MzSLVeGJr/bhp0MFOF1sw9yJqWgSoxI6PEIIqRXFFS48uHg3DuRaoJFL8em4bujbKk7osEgddGfnRmgTr8GjX+7FySIbRny6Ff8d0RH3pSYJHRohhNQ4rz+Ad385gTl/nAEApDfT471RnZEUXT9PrIciZxdxdWDOfVlZGcaOHYt169YhNjYWJpMJI0eOxBdffAGZTHbJ9rfddhs2bNhQ5TGxWIx3330XTz/99HW9duvWrdGlSxcsW7bspt5DbTleWIGJC7JQbHWjQ2IUPp+Sjhi1XOiwSB12utiGh5bsxpkSOzQKKT4c0wUD2hiFDosQQmrU8cIKTFm4CwUWF5KiI7FwchpaGjVCh0XqOKfHj39/dxir9uYCAO7plojXhnegGZaEkAajxOrGQ0t2Y995MyRiEZ4e2BIz+rWo15eWhiJnrxNrjEyfPh3Hjx9HVlYWSkpKsG3bNqxbtw6ffvrpZbdPSkpC7969UVpaCqvVioqKCtjt9usuigCAQqGAy+W62bdQK/aeL8eo2dtRbHWjR4oeXz3Yg4oi5JpaGNT47tFeGNTOCKvLh6mf78YnG0/RuiOEkAZjx5lSjJy1HQUWF1KbROO7R3tRUYRUS6RMgndHdsI793aCIkKMb/bmYdjHW5B1tn6sN0cIIVeTZ3Zi1Jzt2HfejAStAsse6oHHBrSs10URIDQ5u+CFkfz8fKxYsQIffPAB0tLSAACZmZl44IEHrlgYKSsrQ7NmzWC1WrFhwwZs2LDhhq+TqquX0uzKKcP4+TtR4apcIGfRlPQ6v2IwqTuiFBGYMyEVz9zWCgDw7rqTePTLvbC5acE5Qkj9tuZQASYuyILV7cPtHeOxdFoGYumkAbkOIpEIo9KS8cNjvdEmXoOzJjtGz92ODzachD9AJxEIIfXTmRIbRs7ahrMmO9olRDWou7KFxaU0ixcvxowZM2CxWCCV/jWNcfny5Rg9ejQcDgciIyOr/J/09HTk5eWhsLAQEokEPp8PSUlJ+O2339C8efMq2x47dgzHjx+HWCyGXC6HTqeDUqlEcnIyoqOjkZmZCbVajfXr14fk/VbHnnNlmLggC3aPH/d0TcQ793WCVCJ4DYvUUxuPF+GJr/bD5vahhUGNuRNSkRKnFjosQgi5bl/uPI9/fXcIHAdMymyCl+5sX+/PghFhuX1+fPTrKcz8PRscB6Q1jcb/jeyCxjH18zp8Qkh4Ol5YgfHzs2CyuZHaJBqfTU6DNrLhnFQPRc4ueLadk5ODxMTEKkURAIiNjQVw+fsVFxQUwGQyYfHixXA4HCgsLIRKpcKrr756ybbLly/HPffcgxEjRmDo0KHIzMxE586d+fshSyQS+P3+WnhnN2Z3ThkmfbYLdo8fI7o0wv9GdqaiCLkpA9oYsfqxXmhpUON0sQ3DP9mKDUeLhA6LEEKuy9w/svHCt5VFkb/d1gqv3EVFEXLz5FIJnh3cBkseyECcRo5dOeW44+PN+OVIodChEUJItRzOs2Ds3B0w2dzo3SIWS6amN6iiCBCanF3wjFutVl/2eiG73Q4Al8wWAYD+/fvj66+/xrhx4yCVSmEwGPDggw9i48aNl2x7pR3IHpdIJAgEAjfzFmrMtmwTJn6WBZvbh7s6N8K7IzvToI/UiJQ4Nb59tBeGdoiH1e3DtMW78caaY/D668axTwghV8JxHN5ffxJvrDkOkQh4bUQHPH5ry3p3q0FSt/VuGYt1T/Xl1+d6eMke/OvbQ3B5687JM0IIudjhPAvGzd+JcocXA9oYMH9SdyhlDW8x6VDk7ILvNYPBgOLiYvh8viqzRnJychAbG8vPHAm2ePHiSx7TarUwmUyXPN62bVsMHz4cfr8fbrcb5eXlsNlsuHDhQs2+kZu080wpHli0Cy5vAPd2S8I793WiogipUWq5FDPHdcPsTWfw7roTmPvHGWzPLsUHY7qgOV1aQwipgziOw9trT2D2pmyIRcD/7uuMe+n2qqSWRKtkmDMhFQu2nMU7a09g6c7z2JVTho/HdkPreFrclxBSt+y/YMbEBZVrUg5sa8TMcd0gkwo+76HeEnyNkbNnzyIlJQW//vorBgwYwD9+xx13QCQS4ccff6zW8zz00EPYuXMnDhw4cF2v36dPH0ilUvz222/X9f9q0sFcM8bO3QG7x4+RqUl4+95OEFNRhNSiPefK8MRX+5FndkIpk+Bfd7TF/emN6QwsIaTO4DgOb6w5hnmbz0IqFuHjsV0xtGOC0GGRMHEk34LHv9qHMyV2KCLE+M9d7TGqezL1k4SQOmH/BTMmzN8Jq9uHwe2N+Hhswy6KhCJnF3zvNWvWDJmZmfj3v/+NgoICcByHmTNnYs2aNbjvvvsu2T4QCGDAgAFVLptZs2YNFi5ciAkTJlz36/v9fkgkkpt6DzfjRKEVkz6rXGh1eJdGeIuKIiQEUpvosfapPhjepREcHj/+9e1hTP9iD8rsde8OTYSQ8MNxHF76/gjmbT6LCIkIn9xPRRESWu0bafHj471xX2oSXN4Anl91CM+tPEiX1hBCBLfvfDlfFLmjYwI+ub9hF0WA0OTsdWIPzp8/HxaLBSkpKUhMTMSjjz6KBx54ABMnTgQAbNiwAUqlEmfPnoVYLEZycjIGDRqE/v37IzU1FcOGDcOAAQPw+OOPX/drC1kYOZpfgTFzt6Pc4UX/1nH43320pggJHY0iAh+O6YqPxnaFRi7FL0eKMOyjzdhzrlzo0AghYYzjOPznh6NYsuMcZFIx5kxIxZAOVBQhoaeUSfHuyM54d2RnKCLEWLEnFxMXZMHi8AodGiEkTB24YMaEoFvWfzCmCyLC4EYdYVMYadeuHfbt24cFCxbg+eefx969e7FgwQKIxZXhBQIBSKVS/t+LFi3C2rVr0bZtW/Ts2RPr16/H2rVrIZfLr/u1A4EA/7yhdDjPgvvn7+CLIrPGpzb4Sh+pm+7q3AhrnuyDro11yLe4MHL2Nry//iT8AUGvsiOEhKFAgMOL3x/Gom05kEkqiyID2hiFDouEuftSk/DNjF6Ij1IgK6cM987ehjyzU+iwCCFh5kShFZMWVt6oY1inBHw0pmtYFEWA0OTsgq8xIrQuXbqgcePGWL16dcheM7vEhlGzt6PU7sGtbQyYOb4b5FLhLuchBADcPj/eW3cSczefAccBfVvF4aMxXaBTyoQOjRASBjiusijyxY7z/EyR/q0NQodFCC/f7MTkhVk4WWRDfJQCi6emo5WRFmUlhNS+08U2jPnzlry3tjFg9oTUsCmKAKHJ2cNnb16B1+tFRETo7vOcW+7A/fN2oNTuQd9WlTNFqChC6gK5VIJ/3t4WX0zNgF4lwx8nS3DnJ1twJN8idGiEkDDwzi8n+KLIvIndqShC6pxGukiseLgn0pvqUVjhwqg527HvPF1+SgipXflmJ8bP3wmTzY3eLWLx6bhuYVUUAUKTs4fXHr0Mn88XssLIhTIHxs7bgaIKN9Kb6TGHLp8hdVCvFrFY/VgvdEzU4kKZE/fO2oYNR4uEDosQ0kBxHIf31p/ErN+zIRGLMHt8N9zSKk7osAi5LK0yAounpmNgWwPMDi/Gzd+JzadKhA6LENJAlVjdGL9gJworXEhvpse8id2hiAi/k+qhyNnDPit3Op1QKBS1/jrnSu0YPWc7LpQ50TlJi/mTuiNSFn4HNakfkqKVWDE9E/d2q1yN/+Ev9uCrrPNCh0UIaWA4jsM7v5zAR7+eglgEvDeqM60pQuo8RYQEs8an4p6uiXB4/Ji6aDfW0wkEQkgNszi8GD9/J86U2NE2IQrzJoRv/hiKnJ0KI04nIiMja/U1zpRUXhOWb3EhtUk0lkzLQJQidJfvEHIjFBESvDuyEx7r3wL+AId/fnMIH/96CmG+LBEhpIZwHId3153ArN+zIRWL8PHYbhjeJVHosAiplgiJGO+O7IxJmU3g8Qcw/Ys9+PFgvtBhEUIaCIfHhwc+34UTRVa0MKixZGo6tMrwzR9DkbOHfWHE4/FAJqu9xSXPlNgwas4OFFhc6N4kGp8/kE5FEVJviEQi/H1wa/x3RAeIRMD/rT+Jl74/QnesIYTcFFYU+fS3ystnPh7bFXd0olvykvpFLBbhlbvaY/otzeEPcHjiq334Zm+u0GERQuo5h8eHBxbtwp5z5WikVWDxA+mIVV//3VcbktrO2YEwL4xwHAe73Q61Wl0rz19ocWHCgiyYbG70bB6DxVPToZZLa+W1CKlN43s0waxx3SCTirFkxzk8unQvXF6/0GERQuqpBVvOVimKDO1IRRFSP4lEIjw/pDWeGtgSAQ7424oDWLaLLj0lhNwYrz+Ah5fswY4zZTBo5PhiWgYa6Wp3pkRdV9s5OxPWhRGn0wm/3w+NpuZvtVZu92D8gp3IMzvRtbEO8yd1h1JGRRFSfw3pkIDFD6RDo5Bi7ZFCPLBoF+xun9BhEULqme/35+H1NccAVK4pcjsVRUg9JxKJ8NTAVnh+SBtwHPD8qkP4cicVRwgh18cf4PD3FQew+ZQJsWoZvnqoB1LiarcYUB/UZs4eLKwLIxUVFQCAqKioGn1eu9uHyYt24XSxDa2MaiycnEZFEdIg9EiJwfKHMxGnkWNbdikmfpYFi8MrdFiEkHpi7eECPLP8QGXyOKQNrSlCGpQZ/Zrj33e0BQC88C0VRwgh1RcIcHh2xQF8vz8fKpkE8yZ2R3MqigCovZz9YmFdGDGbzQAAnU5XY8/p8VVOfzpwwYyk6EgsmZoBnbJ2r4ciJJTaJkRh+cOZaKRVYM+5coyeux0mm1vosAghddwfJ0vw+Ff74A9weLR/c8zo11zokAipcdP6pFQpjtAd3Qgh18JxHF798Si+2ZcHpUyCzx9IR9fG0UKHVWfURs5+OWFdGLFYLAAArVZbI8/nD3D424oD2HLahFi1HF9MzYAxqvZvBUxIqDWLVWHFjJ5IiVXheKEVo+dsR6HFJXRYhJA6andOGR5ashteP4cpvZri74NaCx0SIbXm4uLIit0XBI6IEFKXLdyag0XbciCTiLFgUhq6N9ULHVKdUtM5+5WEdWGETcupiZ0cCHD4x6qD+OFA5fSnhZPT0DRWddPPS0hdlaiLxLKHM9EmXoPsEjtGztmG3HKH0GERQuqYHJMd0xbvhssbwMjUJLx4RzuIRCKhwyKkVk3rk4IXbmdrjhzE9/vzhA6JEFIHrTlUgNd+OgoA+N/ITshsHiNwRHVPTebsVxPWhRG73Q4AUKluroDBcRz+88MRrNiTC0WEGJ9NTkPHpNr94AipC+I0cnz9UA90TtLiQpkTo+fswIUyKo4QQiqZHR488PkumB1eDGhjwJv3dIRYTEUREh4e6tscf7utFQIc8MzyA/j1WJHQIRFC6pDt2aV4atl+cBzw7ODWtO7WFdRUzn4tYV0YKS0tBQBER9/4NVwcx+GNNcfw+fZzkEnEmDexOzJSqNJHwodOKcOSaRnokqxDntmJMXOpOEIIAZweP6Z9vhtnSuxoE6/BR2O7QioJ62EHCUOP39oSM/o1hz/AYcbSvdiWbRI6JEJIHbD3fDmmfr4LHl8A43s0xiO07tYV1UTOXh1hPUIpLi4GABiNxhv6/6woMm/zWURIRJg1vhv6tIyryRAJqReiFBFYMjUdXRtXFkfun78DBRan0GERQgTi8QXw8Bd7sPtcORK0Cnw2OQ1qOd2djYSn5wa3xoQeTSq/F4v34HCeReiQCCEC2n/BjIkLsuDw+DGiSyO8elcHusT0Km42Z6+usC6MmM1myOVyREZGXvf/ZasHz9t8FlKxCJ/e3w23tq3dD4uQukyjiMDnD6Tzl9WMm78TJVa6Ww0h4SYQ4PDsygP442QJYlQyLJmagUa66+9nCWkoRCIRXrmrPe7olACr24dJn2Uhu8QmdFiEEAEcza/AxAU7YXP7MKxTAt4d2ZkuMb2Gm8nZr0dYF0YqKipu6H7I/gCHF749jIVbK1cPnjU+FYPax9dChITUL1GKCCx+IANtE6JwpsSO8fN3wuLwCh0WISSE/m/9CXy/v3Ih8kVT0tHCoBY6JEIEJxGL8P6oLujXOg6ldg8mLshCcQXdzY2QcHKhzIGJn2WhwuXD4PZGfDC6C11iWg03mrNfr7D+JEwmE/T667sdktcfwNPL9uOrrPOQS8WYOzEVt7WjmSKEMFpl5WU1LQxqnCiyYtriXXB5/UKHRQgJgWW7zuPT37IhEYswc3wqLUROSBCZVIxZ41L5y04fXLyb+kdCwkS53YMpi3bBZHOjV4sYWnfrOtxIzn4jwvrTKCsrQ0xM9RdKdXr8eHDxbqw+kA+1XIrPH0hHv9aGWoyQkPopVi3H5w+kI0GrwK6ccjz25T74/AGhwyKE1KJfjxXhn98cAgC8Orw9bmlFa24RcrFImQTzJnZHoi4SB3IteOzLvfBS/0hIg+by+vHQkt04XWxDa6MGs8anQi6VCB1WvXG9OfuNCuvCiN1ur/Ztf6wuLyZ9loXfT5RAr5Jh6bQM9KC7zxByRYm6SCx+IB3ayAhsOFaEf317GBzHCR0WIaQW7DlXWQANcMATA1pgXEYToUMipM6KVcuxaEoadMoIbDhWjL8tP4BAgPpHQhoinz+Ax7/ah1055YiPUuDzB9IRpYgQOqx65Xpy9psR1oURm80Gtfra1z6X2T0YN38nsnLKEB+lwPKHM9E5WVf7ARJSz7U0avDZ5DTIpWIs230BH2w4JXRIhJAadjS/AlMWZsHp9WNU9yQ8fVsroUMipM5radTg8ynpUMulWH0gHy+tppMHhDQ0gQCH51cdwvqjRYhSVF5tEK9VCB1WvVPdnP1mhXVhpLS09JrXK+WZnRg5exsO5lrQWK/EiumZtJAcIdchtUk0Ph7bFWIR8OGvp7B05zmhQyKE1JDTxVZM/GwnKlw+DGpnxBt3d6RbDhJSTZ2TdZg7MRUyqRhf7DiPD3+lkweENBQcx+GVH45g1d5cREZIsOiBdLSO1wgdVr1UnZy9JoR1YcRsNl91Jx/KteDuT7ciu8SONvEarJieiWS9MoQREtIwDGofjzfu7ggAePG7w9hwtEjgiAghN+usyY6x83bCZPOgT8tYfHw/LSRHyPXq2TyWP3nwwYZTWLT1rNAhEUJuEsdxePPn41i8/Rxkf96so1vjaKHDqreulbPXlLAdwXi9XrhcLmg0l6/cbTpZgtFzt6PY6kZGMz2WPZwJYxRNfSLkRo1Jb4wnbm2JAAc89tVe7DlXJnRIhJAbdL7Ugfvn7UCJ1Y3MlBjMndCdFpIj5AYNbh+Pt+7tBAD4z49H8dPBAoEjIoTcKI7j8L9fTmDuH2cgFYsw8/5u6NOSFiO/UdfK2WtS2BZGLBYLAECrvfRWgst3XcDURbvg8PhxT9dELJmaAW0kLZJDyM16emBLjElLhssbwLTPd+NMiU3okAgh1+lCmQNj5+1AgcWFtKbRmD+pOyJlVBQh5GaM6p6M54a0BscBTy/bj22nTUKHRAi5ThzH4b31JzHz98rb1n9yf1cMbGcUOqx67Wo5e02rU4WRffv2YdmyZTh27Fi1tj948CCWLVuGw4cPX/dr2e12AKiywi3Hcfhk4yk8t+ogfAEO029pjv8b1RkyaZ3aTYTUWyKRCP8d0QH9W8eh3OHFpIVZKLa6hA6LEFJN50sdGDN3B/LMTqQ2icbCKelQyaVCh0VIgzDjluaYlNkEHn8ADy7ejcN5FqFDIoRUE8dxeH/9SXy88XTlunpjumBIhwShw6r3Lpez15Y6kfFbrVaMGjUK3bp1w/jx49G+fXs89NBD8Pv9l93e4XBg3Lhx6Ny5M8aPH4+OHTti8uTJ8Hq91X5Nl6syGVMoKi+PCQQ4/PenY3h33UmIRMBrw9vjH0Pb0CJyhNQwqUSMT+7vhk5JWlwoc2LSZ7tgcVb/u0sIEUaOyY4xc7cjz+xEt8Y6LJqSBjUVRQipMSKRCC/f2R53dW4Eu8ePSZ9l4VypXeiwCCHXwHEc3l13Ah9tPA2JWIQPx3TFsE6NhA6rQbg4Z69N11UYee+997Bs2TL4fL4aDeLxxx/HH3/8gfXr18Pj8eCnn37Cl19+iblz5152+2eeeQbr1q3Dzz//DI/Hg/Xr1+Pbb7/FJ598Uu3XDN7J/gCHx7/ehwVbziJCIsLHY7tiQmbTmnhrhJDLUMml+GxyGlJiVThWUIEHP98Nl/fyhVBCiPBOFFoxcs525Ftc6N4kGounZkCjoEtMCalpYrEI747sjD4tY1Fq92DSZ1kos3uEDosQcgUcx+HttSfw6W/ZfxZFuuDOzlQUqSl1tjBy9OhRjBkzBq1bt8bMmTPhcDhuOoDi4mIsXrwY77//PgYOHAiRSIShQ4di8uTJ+Oijjy7Z3mw2Y/78+XjnnXcwZMgQiEQiDBw4EA8++OBlt7+S4OuVvt+fh58OFkAjl2Lh5HSq8BESArFqORZPTUd8lAJZOWV44dtD4DhO6LAIIRc5mGvG6Lnb+YVWP38gnWaKEFKLZFIxZo1PRftGUcgpdWD6kj108oCQOojjOLyx5hhmb8qGVCzCJ2NppkhNq7NrjMyfPx87d+5Ep06d8Nhjj6FJkyZ49dVXUVpaesMBbNiwAXK5HPfee2+Vx/v06YPjx4/zVSJm48aNAIAxY8Zcsn1OTg4qKiqqPH7s2DF8++23+P7777F27Vrs2LEDJSUlMJvNAACdTodjBVYYNHJ89VAP9G4Ze8PvhRByfZKilVgwuTsiIyT4Zm8ePt54WuiQCCFB9pwrw7h5O2F2eDGwrRELp6TRmiKEhIBaLsWCSWkwRsmRlVOGZ5bvhz9AJw8IqSv8AQ4vfn8Y8zafrSyK3N8NQzvSmiI1LThnr23XvcZIeno6vv32W5w+fRpDhgzByy+/jOTkZDz66KPVXjQ1WHZ2NpKSkiCTyao8HhtbWaAoLi6+ZPuEhARERkZedvvCwsIqjy9fvhz33HMPRowYgaFDhyIzMxPffPNNlYVc1HIJvn+sFzok1n4lihBSVftGWnw8titEIuC99Sexck+u0CERQgD8fqIY4+dnwer24Y5OCZg1vhsUEXT3GUJCJV6rwOcPpEMjl2LNoUK8svoIzawkpA5w+/x44qt9+GLHecikYswen4ohHeKFDqtBqvOLr+bl5eHDDz/EypUrodFoMHHiRKxZswbt2rVDz549MWvWLAQCgWo9l1KphMdz6bWTTqcTwKXXE13v9pdbwFWn0/HTcnQ6Hab2TkGCNvKS7QghoTGwnRH/vqMdAOD5VQfxx8kSgSMiJLz9dLAADy7eDafXj5GpSfhwdBdESOrEeu2EhJU28VGYN6k7ZFIxluw4h/mbzwodEiFhzeX14+Ele/DTocplGD6fkk635K1FwTl7bbuuUY7b7cZ///tftGjRAvPmzcMTTzyBs2fPYvbs2Th9+jR++OEHNGrUCG+//TbKy8ur9ZwGgwElJSWXFDDOnz+P6OhoxMXFXbJ9aWnpJXegOX/+PFQqFRITE6s83rZtWwwfPhzDhg3Dbbfdhu7duyM+Ph5WqxUAoNFooFbQtGBChDa1dzPM6Ne8cjHkr/bhVJFV6JAICUsLtpzFY1/thdfPYVrvZnjnvk6QUlGEEMH0SInB/+7rBAB4fc0x/HSwQOCICAlPFocXExdk4fcTJdCrZPj64R7IbB4jdFgNWnDOXtuuqyIwePBgbNu2DQ8//DBeeOEFJCT8dR2VRCLBsGHDMGzYsOsKICMjA06nE9u3b0fv3r35x9evX4/09PRLbpebnp4Ov9+PTZs2YeDAgVW27969OySSqtN8x44di7Fjx17yuhs2bIBYLIZSqbyueAkhtefZQa1xutiG9UeLMPGzLKya0RONdDSbi5BQYCvrz96UDQB4dnBrPNKvOd22npA6YHiXRBRVuPDGmuN4Zvl+GKPk6N5UL3RYhISNc6V2PLBoF7JL7IiPUmDJ1HS0NNZ+sh7uKioqQpazX9cpoJdeeglHjhzBxx9/XKUocjNatWqFbt264ZVXXuEXV/nyyy/x/fff4+67775k+yZNmqBnz55VFn1duXIlVqxYcdntr6SsrAw6nQ5iMZ0FI6SuEIsrb5fdvUk0CiwuTFiwE2YH3aaQkNrm9Qfwj1WH+JX13x/dGY/2b0FFEULqkAf7pGBsemO4fQE8sGgXThfTzEpCQuFwngX3zNyG7BI7Whs1+OaRnlQUCZFQ5uzX9QoDBgxAy5YtazyIefPm4fTp02jWrBnatWuHcePG4e6778bUqVMBANu2bYNer0dubuWijLNnz0ZeXh6aN2+O9u3bY+TIkRg6dChmzJhR7dd0OBw0W4SQOkgRIcH8Sd3R2qhBdokd07/YA7ePblNISG1xeHyY9vluLNt9AXKpGHMnpuLurklCh0UIuYhIJMJrw9vjtnZGVLh8mPTZLhRYnEKHRUiDtj27FGPm7kCp3YM+LWOxckYmzWYOoVDm7HViukS3bt1w5MgRvP766xg9ejR+//13rFq1ClJp5ZU+JSUlsNvtMJlMAICOHTvi0KFDeOuttzBq1Chs2LABq1evvuTONlfj9XoRERFRK++HEHJzdEoZPpuShjiNHDvOlOG5lQdpJX5CakGZ3YP75+3EppN/Xi/9UA8MaEOLyBFSV0klYnw0piu6NtYhz+zElIW7YHF4r/0fCSHXbd2RQkxemAWb24c7OzfCgklp0CgofwylUObsIi5Ms43Ro0dj//79OHHihNChEEKu4Gh+BUbO3ga7x49H+zfHs4PbCB0SIQ1GbrkDExZk4azJjqToSCx+IB0pcWqhwyKEVIPZ4cG9syqn9ndJ1mHptAyo5HQzAUJqypLtOXh59REEOGBcRmO8NrwDxGK6vDTUQpmz14kZI0Lw+Xz8jBRCSN3UrlEUPhnXDRKxCJ/+lo3F23OEDomQBuFEoRX3zdqOsyY72iVE4ZsZPakoQkg9olPKsGRqBhJ1kdh/wYxpn++Gy0uXnRJyswIBDm+uOYYXv68sijw1sCX+O4KKIkIJZc4etoURupSGkPqhf2sD3ri7AwDgpe+PYMXuCwJHREj9tuFoEe6btQ2FFS6kN9Xj64d7wBClEDosQsh1aqSLxNJpGYjTyLH9TCkeWboXHl9A6LAIqbdcXj9mLN2DOX+cgVQswrsjO+Opga1oIXIBhTJnD9vCiMfjua41SQghwhmd1hj/ur0tAOD5VQex5lCBwBERUj99vi0HDy7ZDavbhzs6JmDx1HRE0fXShNRbTWNVWDotA9HKCGw8XozHvtwLr5+KI4RcL7PDg4mfZeGXI0WIUkixaEo67kulhciFFsqcPWwLI3QpDSH1y4N9U/DUwJYIcMCTX+/Db8eLhQ6JkHrD5w/gtR+P4uXVR8BxwN8HtcIn93eFIkIidGiEkJvUyqjBkqkZiFJIse5oEZ78eh98VBwhpNpyTHaM+HQrss6WwRglx4rpPdG7ZazQYRHQpTQh4ff7IZHQgJCQ+uTJW1tiau9m8Po5PPzFHmzLNgkdEiF1ntPjx/Qv9mDBlrOIkIjwv/s64bEBLWlqMCENSIdELb6YlgGNQoo1hwrx1LL9VBwhpBq2Z5dixMytyCl1VK659UgvtI7XCB0W+VMoc/awLYxwHAexOGzfPiH1kkgkwr/vaItxGY3h8QUwddFuZJ0tEzosQuqscrsH4+bvwIZjxdApI7B0Wg+M7J4sdFiEkFrQKUmHzx9Ih1ouxY8HC/Dk1/vpshpCrmL5rguY+NlOmB1eDGhjwIrpmUjURQodFgkSypw9rCsDdLaMkPpHJBLhteEdcG+3JDi9fkxemIVdOVQcIeRiZ0123DtrG/aeN6ORVoGV0zOR3kwvdFiEkFrUrXE0Fk9Nh0YuxU+HCjDji71w++huNYQECwQ4vPXzcTy36iC8fg5TezfDvInd6ZbXdVSocvawLoxwHCd0CISQGyAWi/DOfZ0woksjODx+TPosC9uzS4UOi5A6Y+tpE4Z/sgVnTHa0/XNqcAsDTQ0mJBx0axyNpQ9mQBsZgQ3HivDQ4j10K19C/uTy+vH4V/swe1M2JGIR3ri7I14c1g4Suh1vnRWqnJ0KI4SQekkiFuH/RnXBPd0S4fD4MWVRFjafKhE6LEIEt+ZQASYvzEKFy4eBbY1YOT0T8Vq6HS8h4aRTkg5fPpgBvUqGTSdLMOmzLNjcPqHDIkRQFocXExdk4adDBdDIpVg0JQ33ZzQWOixyDVQYqWUSiQR+P1XPCanPJGIR/ndfZ4zungyXt3LNkXVHCoUOixDBLNlx7s/bdXKY0qsp5k5IpanBhISp9o20WP5wDxij5Nh5tgzj5++E2eEROixCBFFocWH03O3Iyqm888yyhzPRp2Wc0GGRawhlzh62hRGpVEqFEUIaAIlYhDfv6YhJmU3g8QfwyNK9WLUnV+iwCAmpQIDDG2uO4cXvDiPAAc/c1govDWsHMU0NJiSstTBosPzhygUl918wY/ScHSiqcAkdFiEhdbrYintnbcPxQiuax6nw7SO90K5RlNBhkWoIZc4etoURmUwGt9stdBiEkBogFovwyl3t8Ui/5vAFOPxtxQHM/P200GEREhIeXwBPL9+PuX+cgfTP9XeeuJVux0sIqdQkRoWVMzLRwqDGiSIr7pm5DaeLrUKHRUhIbM8uxT0ztyHP7ETXxjqsnN4TjejOM/VGKHP2sC2MREZGwul0Ch0GIaSGiEQiPDekDV6+sx1EIuCdtSfw9trjtJYQadBcXj+mf7EH3+/Ph0omwWeT0zCKbsdLCLlIgjYSyx/OROdkHfLMTtw9cxu2ZZuEDouQWvXjwXxM/GwnKlw+DGpnxJfTeiBaJRM6LHIdQpmzh21hRKVSwW63Cx0GIaSGTenVDB+M7gKJWIRZv2fjhW8PwecPCB0WITXO6vJi8sIsbDxeDJ0yAl8/lIm+reh6aULI5elVMnz9YA8MameE1eXDxAVZWLw9h04gkAbp8205ePyrffD6OUzu2RSzxqciUiYROixynUKZs4dtYUSpVNKMEUIaqOFdEjF3QirkUjG+yrqAx77cB7eP1hQiDUeZ3YP75+3EjjNlMGjkWP5wJjomaYUOixBSx0XKJJg1PhUP9mkGX4DDS98fwQvfHqI+kjQYHMfhgw0n8fLqI+A44LkhrfHynXQ73voqlDl72BZGIiIi4PHQytyENFS3tjVi6bQMaBRSrD1SiIkLslBspQXnSP1XVOHCqDnbcSjPgsZ6JVbN6IlWRo3QYRFC6gmJWIR/3dEOH4zuwp9AGDdvJwot1EeS+o3jOPz3p2P4YMMpiEXAO/d1wiP9WtCaW/VYKHP2sC2MyGQyKowQ0sB1b6rHsocyEaepvFXhnR9vwYELZqHDIuSG5ZmdGDVnO04X29DaqMHK6ZlI1iuFDosQUg+N6JqIldN7Ij5Kgd3nyjH0wz+w6WSJ0GERckMCAQ4vfHsYC7acRYREhE/u70ZrbjUAoczZw74wQtdVEtKwtWsUhZ+e6I30pnoUVbgxcs52rKTb+ZJ66FypHaNmb8e5Ugc6JEbh64d6wBClEDosQkg91jFJix8e740+LWNR7qhct+iDDSfhD9D4mNQf/gCHZ1cexFdZ5yGXijFvYnfc3jFB6LBIDQhlzh62hRG5XA6O4+Dz+YQOhRBSywwaBb6YloH7MxrD4wvg7ysO4D8/HIGXFmUl9cRZkx2j5+xAntmJbo11WEor6xNCakicRo7Pp6Tj6YGtAAAfbDiFyQuzYHF4BY6MkGvz+gN4etl+rNqbi8gICRZOTkO/1gahwyI1JJQ5e9gWRjSayuuxKyoqBI6EEBIKMqkYb9zdEW/e0xEREhEWbs3B+Pk7YbKF5t7ohNyoHJMdY+fuQGGFC2lNo7F4aga0kRFCh0UIaUDEYhGeHNgSn09Jh14lw+ZTJtw9aytyTHQHR1J3eXwBPPHVPqw+kA+1XIolU9PRs0Ws0GGRGhTKnD1sCyMxMTEAgPLycoEjIYSE0tj0xvj6oR78uiN3fLQZ206bhA6LkMvKMdkxdl5lUSS9qR6LpqRDLZcKHRYhpIHq2yoOPzzeG23iNThTYsfdM7diV06Z0GERcgmPL4DHvtyLnw8XQqOoLIp0b6oXOixSw0KZs4dtYSQ6OhoAUFZGjT0h4Sa1iR4/Pt4baU2jUVThxvgFO/HJxlN0TTWpU/acK8c9s7ahwFI5U2ThlDSoqChCCKllibpIrJzREwPaGFDu8GLcvJ34dh+tzUXqDq8/gMe/2ot1R4sQpZBi6bQMdG0cLXRYpBaEMmcP28KIVqsFAFgsFoEjIYQIwRilwFcP9sATA1ogwAHvrjuJcfN30O0KSZ2w7kgh7p+3A2V2D25pFYeFU9KpKEIICRm1XIq5E1IxMbMJPP4Anl52AC99f5jW5iKC8/oDePzLffjlCCuK9ECnJJ3QYZFaEsqcPWwLIyqVCgBgt9O1k4SEK6lEjGcGtcaiKWmIVcuw40wZhnz4B7bSpTVEQEu252D6F3vg9gUwNr0xFkzqTpfPEEJCTioR4z93tcdrIzpAJhFj8fZzGDN3B/LNTqFDI2HK5w/gqa/3Y+2RystnvpiWgY5JWqHDIrUolDl7nSqMnDx5Er/88gvy8/Ovup3JZML58+dx7tw5nDx5EidPnsShQ4euq5JEM0YIIUy/1gb8/GRf3NIqDmaHFxMW7MRHv55CgC6tISHEcRw+3HAKL35/BAEOeHpgK7xxdwdIJXWqqyaEhBGRSIQJPZrg64d7ID5KgT3nyjHs4y3Yc47W6COhFQhweG7VQfx0qAAauRRfTM2gmSJhIOxmjNhsNkyZMgWtW7fGkCFDkJKSgpdffvmK9yu+44470KRJEzRt2hStW7dG69at0alTJ8yZM6far8kWcjGZ6MwwIaTydoWfTU7DY/1bgAPw3vqTmLZ4N6wuul0hqX2BAIdXVh/B+xtOQiwC3r63I54c2BIikUjo0AghBN0aR2PNk33Qp2Usyuwe3D9vB9YeLhQ6LBIm3D4/nlq2H9/szYNSJsGiB9LQOVkndFgkBEKZs9eJwsijjz6KNWvWYPXq1bDZbJg9ezbeeOMNrFix4rLbN23aFL169cKePXtw6NAhHDhwAKdPn8Zzzz1X7dfUarVQKBQoKCioqbdBCKnnJGIR/j64NT6fkg6dMgIbjxfj3lnbcKHMIXRopAELBDj8+/vD+Hz7OcgkYnx6fzeMTmssdFiEEFKFXiXDwslpGJOWDLcvgBlL92DW79lXPJFJSE2wuryY/NkurD6QD5VMgvmTuiO1Cd19JlyEMmcXvDCSm5uLxYsX45NPPsGdd94JlUqFyZMnY9SoUfjggw8u+3/MZjPatWuHVq1aweutPJubkpJyXa8rEomQkJCAwkKqdhNCqurbKg7fPdILLQxqnCyyYcSnW7HzTKnQYZEGyOML4Mll+/HlzvOQS8WYP6k7hnZMEDosQgi5LKlEjDfv6YhnB7cGxwFvrz2Ov684CI+PFmUlNc/i8GL8gixsP1OKOI0cy6dnomfzWKHDIiEUypxd8NXc1q9fj6ioKIwYMaLK4/3798eqVasQCAQgFlet3xQUFCAvLw/x8fH8Qix9+/bFd999x9/Shzl27BiOHz8OsVgMuVwOnU4HtVqNDh06IDo6GmazuTbfHiGknmoaq8KqGT3x2Jd7sfmUCeMX7MSb93TCfalJQodGGgiHx4dHlu7F7ydKKu8AMTGVBnyEkDpPJBLh0f4t0NKgxpNf78eqvbkosDgxe0IqohQRQodHGogCixOTP9uFE0VWJOsjsXRqDzSOUQodFhFAqHJ2wWeMnDhxAs2aNUNERNWG1GAwwO12X3YnFBQU4NixY3j55Zdx4cIFbNu2DcePH8c777xzybbLly/HPffcgxEjRmDo0KHIzMxEp06dEAgEEBUVRYuvEkKuSBsZgUVT0vFAr2bw+jn8fcUBvP7TUfhpUVZyk8wOD8bN34nfT5RAr5Lhqwd7UFGEEFKvDGofj+UPZyJOI8e27FKMnLUdBRa6Yw25eedLHRg5eztOFFnRwqDGsocyqSgSxkKVswteGFEoFPD5fJc87nK5AAByufyS3zVr1gxz587Fs88+i6SkJGRmZuLBBx/EDz/8cMm2fr//ksc4joPVakVUVBSsVmsNvAtCSEMlEYvw0p3t8PrdHSAVizBv81lM/2IPHJ5L2y1CqqPU5saYuTuw77wZibpILH84k243SAiplzomafHNjJ5oYVDjRJEV987chtPFNLYmN+5kkRUj52xDbrkTXZJ1WDW9JxrpIoUOiwgoVDl7yAojgUAA7du3R2xsLFQqFWQyGX799VfExcWhqKjokoWbcnNzkZCQAKXy0upgVlYWpk6dWuWx+Pj4y1571LZtWwwfPhzDhg3Dbbfdhu7du6NNmzYwm82IiYlBcXFxzb5RQkiDNC6jCZZMzUCUQor1R4swZu4OlFjdQodF6hk2U+R4oRXN4yov12phUAsdFiGE3LBkvRIrp2citUk08i0u3Dd7O/aep9v5kuu3O6cM983ahqIKNzKa6fHFtAxolXR5VrgLVc4esjVGxGIxfvrpJzidTkilUmg0GhiNRqjVaphMJhw5cgQdOnTgt9+4cSPS09OrfavCQ4cOoXHjS1fxHzt2LMaOHXvZ/xMfH4/i4mJwHEe3RCSEXFNm8xh880gvTFmUhYO5Foz4dCsWTUlDS6NG6NBIPVDh8mLywl04XmhFSpwKXz3UAwaNQuiwCCHkpumUMnwxNQOPfbkXvx4vxv3zdmDWuFT0b2MQOjRST/x2vBgzlu6ByxvAoHZGfDS2KxQREqHDInVAqHL2kF5K07RpU7Rt2xYtW7ZEfHw8RCIR0tLS0LRpU7z22mv85TMrV67EmjVrcOedd17yHF6vF5MnT8aJEyf4x3bs2IFFixZh9OjR1xWP0WiE3+9HaSndbYIQUj0tDGp8+0gvdE7WIc/sxL2ztiHrbJnQYZE6zuzwYPz8ndh/ofLymaXTMqgoQghpUCJlEsyZkIr7UpPg8gYw9fNdeG/dCfj8dMcacnW/HivCQ0t2w+UNYFT3JMwan0pFEcILVc4u+BojYrEY8+fPx4YNG9CqVSv06NEDI0eOxO23346JEycCALZs2YKmTZuirKwMYrEYJ0+eRGZmJqZOnYoRI0agb9++6Ny5Mx5//PHrem2j0QgAKCkpqfH3RQhpuGLVcnz9YA/c1s6ICpcP4xfsxNrDdOtvcnkmmxv3z9uJg7kWNNYr8fVDPZCgpeulCSENj1Qixv/u64Qnb20JDsBHG0/j/nk7aVFWckW/nSjGjC/2wuvn8ECvZnj73k6QiGkmP/lLqHJ2wQsjAHDrrbfi2LFjmDZtGnr27Ikff/wRP/zwA3+nmiNHjuDChQuoqKiARCLBb7/9htdeew35+fngOA7z5s3DH3/8cdn1SK5Gra68rttms9X4eyKENGyRMglmj0/FuIzG8PgCeGTpHnyVdV7osEgdU1Thwug523G0oALNYlVY/nAmkvW0sj4hpOESiUR4+rZWf86MkyMrpwxDP9yM347Tun6kqrWHC/Hw4j3w+AOY3LMpXhzWlpY3IJcIVc4u4i5e9TSM/PHHH7jllluwYcMG3HrrrUKHQwiphziOw4e/nsIHG04BAJ4d3BqP9GtOHTtBsdWFsXN3ILvEjjbxGiyZmoE4zaV3WiOEkIaq1ObG31YcwO8nKs/0Tr+lOf4+qBWkkjpxbpYI6Ous83jh20MIcMDknk3x8p3taOxELitUOXtYt0pRUVEAgIqKCoEjIYTUVyKRCE8NbIXXhreHSAT875cTmLRwF4qtLqFDIwIy/XlLXlYU+erBHlQUIYSEnRi1HJ9NSsOzg1tDIhZh9qZs3D9/J4oqqI8MZ3P/yMY/vqksijx5a0sqipCrClXOHtaFEXbpjd1uFzgSQkh9NyGzKeaMT4VOGYE/TpZg6AebsfF4kdBhEQGwmSJn/iyKLJ2WgWiVTOiwCCFEEGKxCI/2b4Evp1XOmss6W4Y7PtqMbadNQodGBPDxr6fwxprjAIDXhrfH07e1oqIIuapQ5exhXRhh1Ser1SpwJISQhmBQ+3j88lRf9GoRg1K7Bw8s2o2Xvz8Mt88vdGgkRPLMToyZswOnim1oZVRjydQMxKhppgghhGSkxGDNE33Qs3kMTDYPJnyWhc+35SCMr+oPK4EAhzfXHMP/rT8JsQj4332dMCGzqdBhkXogVDl7WBdGNBoNACqMEEJqjjFKgSUPZOAfQ9sgQiLC59vP4b5Z23GulGamNXTZJTbcN2sbzpjsaJsQRZfPEELIReI0ciyZmoHptzSHP8Dh5dVH8MK3h+Dx0S19GzK3z48nl+3HnD/OQCoW4YMxXTGye7LQYZF6IlQ5e1gXRiIjK2+X6HA4BI6EENKQiMUiTL+lOb6Z0QvJ+kgcyrNg2EdbsPpAvtChkVpyNL8Co2ZvR4HFhe5NovH1Qz1opgghhFyGRCzCP4a2wYdjukAuFeOrrAsYM3c7rc3VQJXbPZgwPws/HMiHWi7FZ5PTcFfnRkKHReqRUOXsYV0YEYvFUCgUtMYIIaRWdEzS4sfH+2BI+3hY3T488dU+/GPVQbi8dGlNQ3Iw14z75+9Aqd2Dvq3isHhqOrSREUKHRQghddrwLolY/nAmGmkV2HvejLs+3or9F8xCh0VqUI7JjntnbUNWThnioxRY/nAm+raKEzosUs+EKmcP68IIULmYi9PpFDoMQkgDpY2MwKzx3fD63R0gl4rx9a4LGD1nOy6U0Uy1hmDNoQKMnbsDZocXA9saMG9iKpQyqdBhEUJIvdA5WYfVj/dGWtNoFFa4MGrOdqzakyt0WKQG7DlXjnv+vLy0TbwG3z3aC+0aRQkdFqmnQpGzh31hRK1Ww2azCR0GIaQBE4lEGJfRBKtm9ESiLhIHci2485Mt+O14sdChkRtUYnXjkaV78MjSvbB7/BjepRFmjkuFXCoROjRCCKlXYtVyLJ3WA+MyGsPjC+BvKw7gf78cRyBAi7LWVz8ezMfYeTtQZvegX+s4rJzRE/FahdBhkXosFDl72BdGVCoVFUYIISHRIVGLn57ojQFtDDA7vJiyaBdm/n6aVuSvRwIBDl9lncet//c71hwqhEomwWvD2+P9UV0gk4Z9l0oIITdEJhXj9bs74r8jOkAiFuHT37Lx6Jd7YXf7hA6NXAeO4/DRr6fw2Jf74PEFMC6jMeZN7A61nGZSkpsTipw97I/SiIgIeL1eocMghIQJnVKG+RO7Y9ambPzvlxN4Z+0JHMq14K17O9G6FHXckXwL/v3dYew7bwYA3NIqDq/f3QFJ0UphAyOEkAZifI8mSNYr8ejSvfj5cCHyLS58Nqk7LWZdD1hdXjy/6iDWHCqESAS8MLQtpvVpBpFIJHRopAEIRc4e9qe3ZDIZPB6P0GEQQsKIWCzCo/1bYO6EVKjlUvx8uBB3fbIFxwoqhA6NXIbZ4cHL3x/GnR9vwb7zZhg0cnw8tisWTUmjogghhNSwW1rF4fvHeiEpOhIHLpgxYuZWnCyq3dt0khvn8wfwxY5z6P9u5UxKjVyKBZO648G+KVQUITUmFDm7iAvzOdw9e/aEUqnEhg0bhA6FEBKGckx2PPrlXhzJr0BkhARv3NMBd3dNEjosgsrB3le7LuC9dSdQ7vBCLAIm9WyKp29rhSgFze4hhJDaVFzhwrTFu3Ew1wKVTIKP7++KAW2MQodF/sRxHNYfLcLba48ju6TybiHdGuvwv5Gd0TxOLXB0pKEJRc4e9oWRPn36QCqV4rfffhM6FEJImHJ5/fjXt4exam/lSvz3ZzTGf+5qjwhJ2E/qE8y2bBNe/eEojhdWnqXskaLHy3e2R9sEWlGfEEJCxenx4/lVB7H6QD4kYhFeGtYOEzOb0EwEge04U4p31h7H3j8vLW2sV+L5IW1we8d4+mxIrQhFzh72a4yIxWJa+JAQIihFhATvjuyE7k2j8crqI/hy53mcLrbhk7FdYYiiVdxD6XSxDe+sPY51R4sAAEnRkfjX7W0xpAMN9gghJNQiZRJ8OKYLGuuV+OS303h59REcyDXjjbs7QhFBdwELtf0XzPi/dSew+ZQJABCjkuHxAS1wf0YTWoCc1KpQ5OxhXxgJBAKQSsN+NxBCBCYSiTA2vTFax2swfckeZJ0twx0fb8HcCano2jha6PAavDK7B++tP4Gvsi7AH+CglEkw45bmeLBvCg2+CSFEQCKRCH8f3BotjWr8Y9UhfLM3DycKrZgzIZXWeQqRYwUVeH/9Sf6kgUYuxbQ+KZjapxndcYaERChy9rC/lIbWGCGE1DXFVhee+GofdpwpQ4REhOeHtMHU3rSye21wef1YvD0HH288DavLB4lYhNFpyXjq1pY0W4cQQuqYYwUVeHjJHpwvc0CvkuEJmq1Qqw7nWTDz99NYc6gQAKCIEGNyz2aYfksKdEqZwNGRcEJrjIRAeno69Ho91q5dK3QohBDC8/oDeP2nY1i0LQcAcEfHBLxzXyeo6MxMjeA4DqsP5OPtn48j3+ICAPRpGYsXh7VDK6NG4OgIIYRcicXhxWNf7eUv52gao8TfB7fG7R0SIBbTCYSasO98OT79LRsbjlXOEJFJxbg/vTEe6decThoQQYQiZw/7wkjnzp3RtGlTfP/990KHQgghl/jlSCH+vvwArG4fUmJV+OT+bmjXiBYAvVEcx2HzKRP+b/1JHLhgBgC0idfg+aFt0K9VHM3KIYSQeoDjOKz7844oZ/68I0r7RlF4emAr3NrWQG35Ddp7vhwf/3oKv50oAQDIpWJM6NEE0/qkIF5LBREinFDk7GFfGGndujW6dOmCZcuWCR0KIYRc1uliGx5duhcniqyQiEUY0iEe0/s2R8ckrdCh1SsHc814/adj2Hm2DAAQp5Hj2UGtcW9qEiR0lpEQQuodrz+AFbtz8eGvJ1FU4QZQWex+4taWGNI+nmaQVEMgwOG3E8WYt/kMdpyp7B+VMgkm9WyKqb2bIVYtFzhCQkKTs4d9YSQ5ORkDBw7EwoULhQ6FEEKuyOnx4401x/BV1nn4ApXNdo8UPab1TkH/NgZK7K8iz+zEu7+cwLf78gAAOmUEHu7bHJN6NoFSRpcmEUJIfefy+vHFjnOYt/kMXyBpZVTj0f4tcEfHBEgltAbJxSpcXqzYnYsl23OQU+oAULmo6oTMJpjauxliqCBC6pBQ5OxhXxiJiYnBmDFj8OmnnwodCiGEXFOBxYmFW3Pw5c7zsLl9AIBkfSQm92yGUd2ToFFECBxh3cBxHLJL7Fiw5SxW7c2FxxeATCLGlF5N8Uj/FtBG0n4ihJCGxu3zY/nuXMz67TS/flSyPhIP9knBqO7JdJcxAOdK7Viy/Ry+3nWBH0ck6iIxqWcTjElvjCgaR5A6KBQ5e9gXRjQaDaZNm4b3339f6FAIIaTaKlxeLMu6gMU7cnChzAmgcurr3V0TMS6jSditQ2J1eXGswIoj+RZknS3DvvNmFFZUDopFIuDOTo3w7ODWSNbTrR0JIaSh8/gC+HZfLmZvOoOzpso1SGJUMkzq2RQTejRBtCq87qjCcRx2nyvHgs1n8cvRQrDsLzMlBpN6NsXAtgaaVUPqtFDk7GFfGJHJZHjmmWfw1ltvCR0KIYRcN3+Aw4ZjRVi49Sx/bTAAdE7WYUxaMu7s3AjqBngnG5PNje3ZpdiVU4b9F8w4nGdB4KLeTBsZgUHtjHj4luZoYVALEyghhBDB+AMc1h0pxMzfs3EozwKg8pazo7onY2rvZmgSoxI4wtpjdnhwJL8C+y+Y8fuJYuzKKQcAREhEuKtzIib3bEprlZF6IxQ5e8MbLV8Hn88Hr9cLpZLOIBJC6ieJWITB7eMxuH08ThVZ8cWOc/hmXx4OXDDjwAUzXvvxKO7omIDRaclIbRJdL1fq5zgOueVO7Dxbhp1nSrH3fDmy/7wLARMhEaFdvAZt46PQrUk0ujeJRvM4NS28RwghYUwiFmFoxwQM6RCP7dmlmPPHGWw6WYLF289hyY5zuK2tEQ/0boaMZvp62T8yxRUu7LtgxpE8Cw7mWXAkvwIlVneVbaIUUn7GDN1yl9QnocrZ62RhxGQywel0Ijk5+arbFRUVIScnBy1btoRer7/u17HbKwfWKlXDrRYTQsJHS6MG/xneAf8Y2hZrDhVg2a4LyMopw4o9uVixJxfNYlW4t1siRnRNRFJ03S4Il9k92HrahD9OlmDLaRMK/rxWnFFEiNG9iR6ZzWPQtbEOnZN0UDXAmTGEEEJunkgkQs8WsejZIhYnCq2Yt/kMvt+fh3VHi7DuaBHaJURhUs8mGN4lsc6vQ2KyuXEo14IDuWbsO2/GkfwKmGzuS7ZTyiRoZdSgS7IOXRvr0K+1gdbXIvVSqHL2OncpzcaNGzFq1CiMHDkSs2bNuuw2Ho8HL774Ij744AN4PB6oVCr8+9//xvPPP39d1d6zZ88iJSUFCxYswAMPPFBTb4EQQuqMMyU2rNiTi1V7clEcdPaoR4oeI7okYmiHBGiVwg+UAgEOh/Is+PV4MTadKMbBPAuCeyedMgLdm+jRI0WP7k31aJcQBZmUrocmhBByY4qtLnyx4zy+3HkOJpsHQGVfc0/XJNyfkYwWBo3AEVbOmDxjsmPnmTLszinD7nPlOF/muGQ7jVyKjkladErSoWOiFh0TtUiKjqRZk6RBCFXOXqdOr61YsQJjxoxBIBC46lSZF154AXPmzMHMmTMxePBgrF69Gk8++SSaNWuG0aNHV/v1ysoqr8ePjY296dgJIaQuSolT4/khbfC321ph8ykTvtmXh3VHCrHjTBl2nCnDi98fRr/WBtzeMR4D2hhDejbJ6w9gx5lS/HKkEOuPFvG3WAQAmUSMtGbR6N0iDn1bxaJtfBQN8AghhNQYg0aBZ25rhUf6NceaQwVYtC0HB3Mt+GzrWXy29SzSm+lxd9dENIlRQimTQiYRQ6OQQiYVQy4VI0IiRmSEpMb7pnyzE1tPm7A9uxRbs01V+kagciZIh0QtOiVq0eXPGZOJOiqCkIYrVDl7nZoxsnbtWpw7dw7z5s1DRkbGZW/HU15eDoPBgE8++QQPP/ww//ikSZNw7NgxZGVlVfv1fvvtNwwYMAC//fYb+vXrVxNvgRBC6rwKlxdrDxVi9YF8bMs28YuWyiRi3NrWgDs7N4IuMgJuXwC+AAexCBCLRHD7/AhwlX8XiSoXtWM9iNPrBwCIRYDPX/kgm8Bnd/vg8Prh8QXg8gbg8QVgdXmx4VgRyh1ePq5GWgUGtDVgQBsDeqTEQCmrU7V7QgghDdyhXAu+zDqP7/fnweHxX3N7kQhQy6WIUkRAGxkBpUwCpVwKXWQEVHIpZBIRlHIplBF/PR6tqtxWGylDjEoGpVyCXWfL8duJYmw+VYKTRbYqrxGjkiEjRY/0ppUzJtvEa+gOMiSshCpnr1OjziFDhgAA3n//fURGRl52m40bN0IsFmPChAlVHr/tttvwxRdfwOv1IiLirzOex44dw/HjxyEWiyGXy6HT6aBUKtGqVStYLJWrU2u1tCIzISR8RCkiMCotGaPSklFc4cJPhwqw7kgRdpwtxc+HC/Hz4cKQxdLCoMaQPxeP7ZAYVa8XvyOEEFK/dUzS4s2kjvjXHW2xen8+tmabUFLhhsvnh9sbgM3tg8cfgNvrh9fPwen1w+ryweryIc/srJEY1HIpeqTokdk8Fr1axKC1UUN9IwlrocrZ61RhhCktLYXBYLjs744ePYrGjRtfcqmN0WhEIBBAUVERkpKS+MeXL1+OV1555ZLn+fzzz+H3V1aCo6Ojay54QgipRwxRCkzp1QxTejVDocWFb/flYVu2CV5/AHKpBFKxCAGOg58DFFIxxCIR/FzlTBGpuHLmCMcBkTIJRAACHMefyQr8OZ1EJZNCKZNALhVDIZNAJhFDJBKhe5NodE7WCffmCSGEkMtQy6W4P6Mx7s9ofNXt/AEOVpcXFU4fKlxeODx+2N0+mJ0e2Nx++P0B2D1+ODw+2N1+mB0elDu8MDu9sDg8KLV7YHX5kBKnwuD28ejTMhbdm+hpDS1CgpSXV95qurZz9jpXGAkEAigrK0N8fPxlfx8REYFAIHDJ415v5XRsmUxW5XFW/LiYSqVCfn4+AECtVt9MyIQQ0iDEaxWY0a85ZvRrLnQohBBCSJ0nEYugU8qgU8quvfEVBAIcrQ9CyFXYbJWXl9V2zi5IYeTJJ5/Ezz//DKfTCZfLhaeffhovvPACgMrb8QQCAcTExFz2/xoMBpSUlIDjuCrTyvLy8qDRaC5ZlKVt27YYPnw4/H4/3G43ysvLYbPZoNFo4HRWTnm70mU7hBBCCCGEEFJbqChCyNWFKmcXpDDy9NNPY/To0ZBIJFCpVEhJSeF/x2Z4SKWXD61Lly6wWq3Yv38/unbtyj/++++/Iy0tDWJx1alnY8eOxdixYy/7XJs2bYJEIrnqHXAIIYQQQgghhBASehaLJSQ5uyAXsDVt2hQ9e/ZERkYGOnToUOVNRkVFQSwW89cSXaxr165ISUnBO++8w18+s2nTJqxYsQJDhw69rjisVis0GlrQiBBCCCGEEEIIqWtClbPXqZV9Zs+ejZSUFAQCATz00EOYPn06AODEiRPo3LkzzGYzRCIRZs2ahZ9++gkdOnTA7bffjltvvRVdunTBI488cl2vZ7FYoNPpauGdEEIIIYQQQggh5GaUlZWFJGevU4uv9u3bt8riqp07dwYAnDx5EgcPHkROTg66dOmCQYMG4dixY/jggw9gMpkwf/58jB8//oqX31xJ8J1p6iOO42CxWFBaWgqLxQK73Q6LxYLy8nKUlpbCarXC7XbD4/HA4/HA6/XC4XDAbrfD6XTC4/HA5/Ndsg9EIhEkEgmkUilkMhkiIiIglUoRERGBiIgIKJVK6PV6REVFQaPRQKvVQqVSQafTQavVQqFQQKFQQKVSQavVVrl9ckPi8/lgNpths9lgt9tRUVHB71u2fo7NZoPVaoXD4eB/PB4P3G43XC4XvF4vfD4f/xMIBBAIBMD9eTcPVhll+z1438rlckRERECtVkOr1UKr1SIqKgpRUVH83w0GA7Rabb2dFWW1WlFWVga73c7/OBwOWK1WWK1Wfv+yv7N96nK54Ha74fV64fF4qhzjIpGIP7ZlMhkiIyOh0Wj4n+D9p9PpoNPp+L9HR0c3iOPZ7XYjPz8f5eXlKCsrQ1FREX/8ulwu/lh1u938Mc2OVfZn8D4Vi8WIiIiATCbj961cLodUKkVkZCTUajVUKhV//LJ9yfZ3TEwM4uPjIZfLBdwrtYvjOHg8Hv4YLikpQUFBAUpKSmAymVBSUgKLxYKKigrYbDa+ffb5fHx7ELyf2Z9qtZpvi9nxqlQqoVarodfr+ceMRuMll5rWN4FAACaTCcXFxbBYLHA4HHA6nbDZbHA4HLBYLCgrK+PbZNbesv7P7/fzP4xYLIZUKoVEIkFERAQUCgXkcjnfvrLjN3jfKhQKREVFwWg0IjY2FlFRUVAoFPW2nb0cjuPgcrmqtK12ux0lJSWX7GOr1Qq73c4f36y9cLvdVY5fkUjE72+lUonIyEi+/WV9GmsrNBoNDAYD9Ho9P5bQ6XSQy+UNaj8DlZeQm0wmlJaWory8HGazmR9HsGOc9XNs7ObxeOByufhjm7UXbAwRjI3nJBIJJBIJFAoFlEolZDIZVCoV/1mw45y1zWx8p9FooFarG+wMa9Y2OxwOvk80m80wm838frfb7VXGc2yMwT4Dv9/Pj93Y8Q5Uti9svwf3jUqlEiqVih+/BbfZCQkJiIuL47dRqVQNcr8HAgE4nU6UlpbyfWB+fj4sFgvcbjfcbjesVisqKir48UhwWx4IBKrsd+CvNobt7+A8Jri/1Ol0UKvV/L+DH4+Li4NWq4VEIhF4D9Usv9/PH9fl5eVVchPWbrO/B7fvrB+9UpvO2hTWZ7JjVi6XQ6FQIDIyEkqlEjqdDhqNhn9Mq9UiPj4eBoOhWuPqW265Be3atavt3QQRF/wNrsMCgUCND+qefPJJHD58GJGRkdDpdNDr9XxHwDqJ6OhoPknS6/V8J329RZgrYQ1D8Je/oqKCHxwXFRWhqKgIhYWFKC0t5X9XXl6OgoICuFyuqz6/SCTiE0CWBKpUKkRGRkIul/MNtkgkgkgkAsdxfGPj8/n4LwNLiFhxxWw2X/buQJfDEqGYmBh+0KPX6/kBpU6ng8FgQExMDFQqFZ+YsoQ0MjKyxjsFj8fDD/DYwK+0tBSlpaX8INBms6G8vBwVFRWwWCx858gaDZPJVO19AIAfBLKOUaFQ8EUn9iMWi/kfJhAIwOv1Vim4sI6ZDYiuRiaTwWAwIC4uDgaDAQkJCTAajTAajXxjFRsbi+joaMTGxvIdRk193ziOg9vt5htc1tiyol5BQQEKCwv5PwsLC1FWVsZ/FtXBBtSRkZGQSqVVGmmZTMYf40Dl/mTHNhtcsu8fW9zpatgARqPR8Ps0JiYGer0eSqUScXFxiI2N5Y91rVaL6OhoPrGqif0aPJCz2WyoqKhASUkJv7h0RUUF/55YsZQl4cXFxSgpKbnq87PrOOVyOd9eBBdI2SBbLBbzA/HgwTkr/vl8PjidTtjtdrjd7mu+L/Y5Bg/O9Xo9jEYj3wbHxMRUabODE6moqKgaL1xxHFel0FlSUsIfm06nE2VlZSgvL+cHEBaLhS9Ul5aWoqysDE6nkx/sXUlERAQ/cFCr1XyRibULQOXAhu1n9if7vO12+1Xfh1QqhV6vh1arRWxsLOLi4pCUlMQPwNlPVFQU3zazz1+j0SAyMhIKhaJGjl+/388nGSz+8vJyvr8rLi6GyWSCxWLhB3HsGL5We8fWLmM/wYUk1t+xY5fjOL6vY/uWJTussMo+92sRi8X8AvCsr4uLi0N8fDzUajVf0GZtB2sT2D5nx3JN9nV+v59P8tjxypINloiz9oH9FBcX48KFCygtLb2uPj440WbtBSuOsn3NxhesXQhO+tnC+9cil8thNBrRqFEjGAwGfjyRmJjInwhg+5kVDFkboVQqa3wswdoHp9NZJeEwm838eM1kMiE3NxfFxcVVjnm2TXXaRvbe2RhCJpNBoVDwf2ftMhs/sPd58THu9/vhcrn4Agvrk6tzklAsFvP7NDo6Go0aNUJcXBzfFrO+TqvVwmAw8OOKmJiYWksyOY7j2122v9nxFHxyir3P4P7QZDLBarWiuLgYDofjmq8lkUj4Y4kVT9lnwNoUlpiz8XRwMTa4b2T9hc1mg8/nu+ZrB5+4Yf0ESziD2xTWb158woyNSWQyWY19B9g4hB3/7JguKyuDxWJBcXExcnNz+Ta9oKCAP9HFxrDXEhkZyecBcrm8SlvOCiDsmA9uY9j+Ds5jgvvL6uxzrVaLRo0aISYmBlqtFjExMTAYDHzBirXdbN8bDAb+M1AoFDWxiy/h9XphNptRVFTE72s2pmMnxFkbw451m83G5zjVbdNZ38/eK+tH2biEjQPYvg7uM9lxzYpb1SkzxMTE8LlJYmIiEhMTER8fj7i4OP6YZiflmzRpclP78FrqTWGkNjz55JPYvXs3XC4XP3iwWq3V6iBYpy+TyfgBJUvGLk4aLh7MssS6ug2DRCKBwWCAwWDgCzc6nQ7x8fFISEhAbGwsPxjQarXQ6/WIjo5GVFQUpFJprVSaA4EAf6bIbDbDbrfDbDbDYrHA5XLxZ5dYQaGsrKzKWSaWMFRnUMAGuqyww5JfNoMluCIfnEAEd0QsJtYwVmegy4oGrHNhX8zgs1lsYMYeY4NC9sM6rJpKKC7H6/VWGWRZrVY+OWMdEks0WPGhuLiYX6PnckQiEd8ZBScW7BhnhQY2AGDJMeskWcPIqtDXambEYjEMBgMaNWqE+Ph4xMbGQq/X851S8JmT4M6IVfxrKhn2+/1VCmGsus72Lzubxzoftl9LSkpgNpuvObhi+zW4ownu7FnbwWJhxzDrYNjg22azVauzkUqlfHthNBr5fcs6HjZwNRqN/FkrVrCr6XbD5/Px38Pg/coSBzZrhRUkWYfO9m9FRcVVj1mGDRYVCgWfkAa3FcFJAzvTxAo77IfFyYpm1Xld1hewgatKpeKTAnZ2hLXTLJljiUVcXByioqJuap8HAgG+wMCKkCzxLS0tRW5uLt/PlZaW8oPWK63ndSURERFVzvAHD5jYgDV4//r9fr6Ix2bWWSyWqx6/CoUCBoOhykyt6OhoxMfHIykpCUajkS8qsGI/2/e1MUMuEAhU6evcbjc/OGVJgN1u59sEduyytra631f23tnsKtbmsmSY9ecXD0qDi71sBg070XItYrGYH/SzhDY5ORlxcXF8vxZc2ImNjeWTLDb2qan21+fzVZmBWVxczLd3rE9jBbS8vDyYTCZ+FovFYrnm8wcntqzQx8YTwSclgk8SsYJC8MkhdqKCxXqtZEMkEsFoNCIhIYEfEwTPTGTtgcFg4AvobAwRPJtDJrvx28FeCyuSXDxTgvWF7PgPHtPl5eXxY7uKioqrPj87XliCy/q9y+17tv/ZD0twWeEy+ESL0+ms9sxviUSCqKgovs8zGo1VipnBJ4rY2I8VJNh3srYKaywvKC0tRWFhIX9sB89UYeM7VnxjBYngIvO1sFkTrM28uHDM9n9w4ZjlLuy4Z59BdfpkmUzG7+uEhAT+5DL7DrJklxXSEhIS+JlhLJ+qDcEz3di4o6KiAmVlZTCZTHz7w4o57OSTyWSq1gk0sVh8SX7Ijvfgsd7FhTQ2lmbHe/AsPPaZX4lSqawyLmb5IPs3OyESfOKJfQbBn0dNnvwPPjHK2hU2tgo+GZKfn8/3l7m5uSgoKLhs8apv377YtGlTjcR2JWFbGLn4UoXgx4On55aXl/OVaJPJhPLycr7ayaZ1scovq5ixjpTdUphN0WWNDztIgwd0wY0vuxyCnXGKiYlpkNPoGIfDwQ8mWTGFnSlkHQGbWs7O3AYPUNjgkO1z4K/pi2waHZvCzzoFvV7Pn9lnDUl0dDQ/GKzNQkZdwJIou93OTx1lM2aC9z+bEs06RnaMs33Nfljjzs5oBV8+EZy8BFeg2SUUrMDUEPY3m+rPZhQEn6Fi1X2WXAUn4cEzs9ggO/gYZoMENnBjiQprO9i+ZJ0gKxzVxmwroXAcxyf+TqezSpGKDR5ZWx3chge3FxdPMw8eBLLiCUtG2Q9LYFihhQ0uWBvOzpbW1gCutrFZi6wfY4UTVrRmA0jW5wUnhMGXagZPcWZY/8cGiOzYZGeVg/s8dsaNJSsN5bgFKo/d4JMiLAFilwOx45rNWgueEchmCLLZWMFT9dn+ZZenXO7SQHaihB2vCoWCf5wVOGpzX3u9Xv4stVQqveaMnxvFLk9j/RpLdIL/HXw2lY0lnE7nZS9jBf667DL4Uiv2w/q44PEcS0RYQS/4bHJ9bR+qKxAIVOnriouLq4wrgi/VZOOIiy//ubjAxI5L9hmwE5LBl6IEF53Z2Dm4kMEKUayQWxfbFZ/Ph7Fjx/KzUN9+++0beh6Px1OlTwwuJl48m5TlMcHju+BxHVDZbrH2m+Uw7OSYQqGAVqvlxyKs3WGXb7IZyLGxsXVyn9+M4D6TtTGsoBK8ry/OD9nxHnw5MiuGAFXHI8HHOxtDB58Yj4uL469iYCdXGtIdVgOBAN92sL7R4XBAp9OhX79+tXpMhW1hpLy8HHq9HnK5HImJicjOzhY6JELqFJ/Ph7Nnz/KNc0JCgtAhEVKn2O12TJ06FSUlJeA4Dhs3bhQ6JELqFDbWAgCVSlXtyyMJCRf0HSHk6pxOJ+bMmcMXOCdOnFhrr9WwS9hXceHCBQCVixDW5vREQuqro0eP8gsgt2nTBseOHRM4IkLqluzsbCxbtgxA5XeEEFIVG2sBQHJysoCREFI30XeEkKs7deoUnn76aQCVY63aLIzU/7nrNyj42lu1Wi1gJITUTfQdIeTq6DtCyNXRd4SQq6PvCCFXF8rvSNgWRsxmM//36Oho4QIhpI6i7wghV0ffEUKujr4jhFwdfUcIubpQfkfCtjASfCcUuVwuYCSE1E30HSHk6ug7QsjV0XeEkKuj7wghVxfK70jYFkaCV7+urfurE1Kf0XeEkKuj7wghV0ffEUKujr4jhFxdKL8jYbv4aps2bfCf//wHfr+fFs0j5DLoO0LI1dF3hJCro+8IIVdH3xFCri6U35GwvV0vIYQQQgghhBBCSNheSkMIIYQQQgghhBBChRFCCCGEEEIIIYSErXpdGLHZbHjxxReRlpaGIUOGYOPGjVfd3uFw4D//+Q/S0tIwaNAgrFu37pJt9u7dixEjRiA1NRVPPvkkiouLq/ze4/HgvffeQ0ZGBvr164dVq1aBrkYidZnNZsNLL72EtLQ0DB48GL/++utVt3c4HHj11VeRnp6O2267Db/88sslx/i+ffv478kTTzxxyfeE4zi8//77GDt2bI2/H0Jq0vnz5zFlyhSkpqZi3LhxOHny5FW3z8vLw7Rp05CamoqxY8fi2LFjVX7PcRxWrVqFfv36ISMjA++//36VFdUBwOVy4ZNPPsHo0aPxyCOPYNeuXTX+vgipKadPn8a4ceOQmpqKKVOmICcn56rbnz17FpMmTUJqaiomTpyI7OzsKr8PBAJYunQp+vTpg8zMTMyaNQter7fKNmfOnMFLL72ERx55BLNnz4bdbq/pt0VIjdm+fTuGDRuG1NRUPPfccygvL7/q9rt27cJdd92F1NRUPPPMMygtLa3ye7fbjXfeeQcZGRno378/vvvuuyvmGtnZ2ejTpw8+/vjjGns/hNS0n3/+GQMHDkRaWhreeOMNOByOq26/fv16DBo0CGlpaXj11Vdhs9mq/N5qteKFF15AWloahg4dik2bNvG/CwQCmDZtGiZMmID7778f9957L0aOHInbb78d27Ztu+rr1tvCSFlZGbp27YrZs2ejf//+UKvVuPXWWzFv3rzLbm+xWNC9e3d8+OGHuOWWW6DX6zFkyJAqDcnSpUvRvXt3VFRUYPDgwVizZg26du2KkpISAJUNVb9+/fDSSy+hR48eaNq0KcaMGYMXX3wxJO+ZkOtVXl6Obt26YebMmejXrx+ioqIwcOBAzJkz57LbV1RUIC0tDR988AH69u2L2NhYDB06FB999BG/zZdffonU1FT+e/Lzzz+jS5cufHHE7XZjwoQJeOaZZy4ZEBNSl+zatQtt2rTB7t27MWTIEJw5cwadOnXCvn37Lrv9gQMH0KZNG2zduhVDhgxBbm4uOnfujJ07d/LbPProoxg5ciSSk5PRs2dP/Oc//8GwYcP4QW1ubi4yMjLwr3/9CxzH4Y8//kBGRsZlC/WECO33339H+/btcfz4cQwZMgQHDhxAx44dceLEictuv23bNrRr1w4HDhzAkCFDcPz4cXTs2BGHDx8GUFk4nDBhAiZNmoRWrVqhe/fueO655zBmzBj+ObZu3Yp27dph6dKlOHPmDP75z38iPT39kuIJIXXB7Nmz0bNnT/j9fgwaNAjLli1D9+7dYbFYLrv9woULkZ6eDqfTiUGDBuG7775Dt27dUFZWBgBwOp3o3bs3XnvtNfTs2RPJycm477778Nprr13yXF6vF6NGjcKWLVuwd+/eWn2fhNyoF154AbfffjtiYmJwyy234P3330f//v3h8/kuu/2rr76KQYMGISoqCv369cPMmTPRt29feDweAEBJSQk6d+6MBQsWYMCAAYiMjES/fv2waNEiAIBYLMbmzZuxdetWeL1eyOVyiMVipKSkICkp6erBcvXUk08+yTVq1IgrLCzkH3vxxRc5o9HIuVyuS7Z//vnnubi4OC4vL49/7PXXX+f0ej1nt9s5i8XC6XQ67qmnnuICgQDHcRxns9m4pKQk7sUXX+Q4juPee+89TqVSccePH+efY/78+ZxcLueKiopq660ScsOeeuopLiEhgSsoKOAfe/nllzmDwcA5nc5Ltv/HP/7BxcXFcbm5ufxjb775JhcdHc3ZbDauoqKC0+l03JNPPsl/T+x2O5eUlMT9+9//5jiO43755RfOYDBwPXr04Lp06VLL75CQG9e1a1du0KBBnNvt5jiO4wKBANe/f3/urrvuuuz2mZmZXL9+/fg+JhAIcEOHDuUGDRrEcRzHbd26lQPArVq1iv8/Bw8e5ABw69at4ziu8vvUp08f/jvm9Xq5Dh06cKNGjaq190nIjfD7/VyLFi24e++9l/N6vRzHcZzP5+O6d+/OTZgw4ZLtA4EA1759e27YsGGcx+Phn6NXr17cyJEjOY7juDVr1nAAuPXr1/P/j31vdu7cyXEcx2VnZ3OzZ8/mX7OwsJCLjIzkfvzxx1p9v4Rcr6KiIi4yMpJ76aWX+DFReXk5p9fruf/973+XbF9WVsZpNBruueee47evqKjgjEYj99prr3Ecx3FvvfUWFxUVxZ0+fZr/fzNnzuSUSiVXWlpa5fleeuklLjY2lktPT+cmT55cW2+TkBt26NAhTiQScQsWLOAfO3v2LCeVSrnly5dfsv2JEyc4sVjMffrpp/xjubm5nEwm4xYvXsxxHMfNmDGDa9y4MVdSUsJv8/zzz3OJiYl839O6dWs+f78e9bIwEggEuEaNGnFvvfVWlcdzcnIu6XCZlJQU7pVXXqnyWGFhIQeA++GHH7hVq1ZxcrmcM5lMVbb5+9//zrVu3ZrjuMpB8YwZM6r83uFwcAqFgps/f35NvDVCakwgEOASExO5N998s8rj58+f5wBwv/zyyyX/p3nz5tzLL79c5bHi4mIOAPf9999z3377LSeTyS75njz77LNcq1at+H/7/X5uxowZXLdu3WruDRFSg06fPs0B4LZt21bl8SVLlnAikYhzOBxVHr9w4QIHgNu4cWOVx1esWMEB4MrLy7mnnnrqssd8eno69+CDD142Do/HwyUlJXHTp0+/yXdESM3au3cvB4A7fPhwlcc/+eQTTqFQcH6/v8rjR48e5QBwe/bsqfL4/PnzOYlEwnk8Hm7KlClc//79L3mtNm3acH/7298uG0dJSQmnUqm41atX3+Q7IqRmLVy4kIuKiuJsNluVxx988EEuPT39ku2/+uorTqlUcmazucrjjz/+ONepUyeO4zguNTWVe+qpp6r83mq1clKplE8MOY7jsrKyOIlEwi1dupS7/fbbqTBC6qRXXnmFa968+SX9xeDBg7n77rvvku3feustLikpifP5fFUeHz58ODds2DDO7/dzcXFx3Pvvv1/l9ydPnuQAcJs2beI4juOio6O5f/7zn9zjjz/ODRw4kBs5ciS3a9eua8ZbLy+lycnJQX5+Pm655ZYqjycnJ0MikVxy/WtBQQHOnDlzyfZGoxFKpRI5OTnYunUrOnTogJiYmCrbpKSkICcnB263Gzt37rzkOSIjI5GQkHDNa24JCbXz588jLy8Pffv2rfJ4UlISIiIiLjlmi4qKkJ2dfcn2cXFxUKvVyMnJwZYtW674PTl37hwCgQCAymls5eXl0Ov1Nf/GCKkBW7duhUwmQ0ZGRpXHU1JSwHEczp8/X+Xxbdu2QSQSoVevXpdsDwDnzp3Dli1bLukj2DaX6yO8Xi8ee+wx5OXlYfLkyTf3hgipYVu2bEFMTAzat29f5fGUlBS4XK5L1pbasmULVCoVunbtesn2fr8feXl52Lp16yV9DNvm4u/IypUrMXHiRLRt2xbt27fH0KFDa+aNEVJDtmzZgu7du0OlUlV5/Ept/pYtW9ClSxdotdrLbu9wOLB3795LviNqtRpGo5F/TqfTiYkTJ2LQoEG0lhup07Zu3Yo+ffpALK5acrjSd2Tr1q3o3bs3JBLJZbc/deoUSkpKLvmONG3aFAD4nL28vBxvvvkmfvvtN7Rt2xZ5eXno1avXJevCXUx6/W9ReGwBlosbFrFYjMjISLhcrmptDwBKpRIulws2mw06ne6yv3e73XC5XAgEAld9DkLqEqvVCgCXHNcikeiy35MrbQ9U/3sSrLS0FEaj8cbfACG1yGazISoq6pLOWqlUAsBl+xGVSgWZTHbF7W022xX7iLy8vCqPnTlzBuPGjcO+ffuwaNGiSwo0hAjtau09cPnviE6ng0gkuuL2V3tOp9PJ/zsQCGDmzJnYvHkzOI7D1KlTIZXWyyEracD+v717D4qqfv8A/t5lzwIKC7IbStjmeCHJcUJBJ2zSGSg1NseUGi0bymyyspl00hl1tCaym2mlk5iTTQ0YCjoUioCJirfUUhfvNE6akoMMsSJycW88vz+cPeNxAe0X6fLl/foLPufZw+fAPrt7Hs75PB295rd1XnC7+ObmZohIh5/DAGD+/Pmorq7G9u3b/fKNKJB09JrfXo7Exsa2G+87p791nwaDAUajEdevX8fff/8NAEhPT8f69euhKAo8Hg8SEhKwevVqzbqJt+qSV4xYLBYA8Fv12eVyobGx0e+/2e3Ft7a2or6+HmazGWazuc1VpB0OB6KiomAymaAoSrsxt/5Monutvee9x+NBQ0PDP8qTK1eu3DZPevXqpTnJdDgcLIxQwPI9l+WWlf59C+Ddmh9msxmNjY1+C0DeHG+xWO7oPaKoqAgJCQnweDw4cuQIMjIyOuWYiDpTR6/3APyuCLxd/O3eQ27OEb1ej507d8LhcOD111/HzJkzsW3btn91PESd7U5f8+803ldY7Chm48aNWLlyJWJjY/HRRx9h9uzZOHPmDI4cOYKvv/663QUtie6FO33Nv9N432NujWlubobL5YLZbEZMTAwKCgqQm5sLRVEA3CicjB49GqdOnepwvl22MBISEuK3KnpFRQUAICkpSTMeGRmJ8PBwv/iTJ0/C4/EgKSkJVqsVf/zxh9+HXrvdjqSkJOh0OlitVr99XL58GdXV1X4/k+he8+VJZWWlZry9PImIiEB4eLhf/OnTp+F2uzV54lsZ2seXJzdrbm5GWFhYJx0NUeeyWq3wer1+nZPsdjt69+7t9x8Lq9UKAH7vAXa7HZGRkejfvz+sVqtf/vhifPlx/vx5tXXcgQMH/G5TIAoUVqsVDocDNTU1mnG73Y64uDiYTCa/+ObmZlRVVfnFW61W3HfffW3mSGtrKyoqKtr8HBUeHq6eBJaUlHTSkRF1Dt95ge82Yh+73Y7ExMQ248+ePQuv19tmvMFgQGxsrF+OXLx4EXV1dUhKSkJQUBAmTZqEBx98EGfPnsXRo0fhcDjw119/ITc3l62tKaDcyeeifxLfu3dvKIriF+PrJpiUlAS9Xo9Jkyb5XeF77dq1dtte+3TJwoiiKBg3bhw2bNigGc/JyYHFYsHAgQM14zqdDmlpadiwYYPmF5KTkwOTyYT4+HikpaXh6tWrKC0tVbc7HA4UFRUhOTkZAGCz2ZCfn695QVu3bh0MBgMLIxRwDAYDxo8fj7y8PM14dnY2zGYzBg0apBnX6XSw2WzIy8vT5El2djbCw8MxZMgQpKWloaGhQZMnV65cwZYtW9Q88QkLC7ttn3Kie2XEiBGwWCya9xGPx4Pc3FwkJyf7XZ78yCOP4P7779fEe71e/PDDD3j00Ueh1+ths9mwc+dOzdoLe/fuxYULF9T8yMvLg8lkwldffcVbAyigjRkzBj179kR+fr465nK5kJeX5/d6DwCjRo1CRESEJkfcbjfWr1+v+RxVXFysaWVaXFwMh8OhxmRnZ+PgwYPqdp1Ohx49eqi35BAFCpvNhurqauzbt08d893i0laO2Gw21NXVYceOHepYbW0tSkpK/M41bi625OTkwGg0Yvjw4Zg8eTIKCgpQXFyM7du3Y8+ePXjssccwceJE7Nmzp81bdYjuFZvNhkOHDuHcuXPq2LFjx1BRUdFujhw7dkxT+KisrMSvv/6K5ORkhIaGIjU1tc0aQExMDKxWK0TEr3DicDhQUlKClJSUjid8h4vKBpxdu3YJAHnppZekpKREZs2aJQBk6dKlaswvv/wi165dU7/W6XTy/PPPS0lJicyePVsASGZmphqfnp4uFotFVq9eLQUFBfLQQw9JZGSk2uL3zJkzYjQaxWazydatW+Xdd98VvV4vs2bNursHT3SHysvLBYBkZGRo8uTmjk4358mBAwc0eTJnzhwBoOno9Nxzz4nZbJasrCwpKCiQwYMHS0REhJondXV18vnnn8uAAQNk+PDhsnr16rt70ER3KDMzU4KDgyUzM1O2bt0qKSkpotfrZd++fSJyo7NTaWmpupr60qVLRVEUWbx4sWzdulXGjRsnOp1OduzYISI3Wrz369dPhg4dKps2bZI1a9aIyWSSESNGqPt47bXXpG/fvvLWW2/J1KlTZcKECTJ58mTZsmXLvfklEHVg7ty50qNHD/nkk0+kqKhIkpOTRVEUqaioEJEb7Xu3bdumth5dtGiRhISEyIcffihFRUUyevRoCQoKUlvxOhwO6dOnjyQlJcmPP/4oK1eulB49ekhKSoq6jxkzZkifPn0kNzdXdu/eLa+88ooYDAaprKy8N78Eona0trbKk08+KTExMbJ27VrJz8+Xfv36SXR0tNpat6mpSX1PERGZMGGCREdHy5o1a2TTpk0ycOBAiYqKkpqaGhG50eLdYDDIxIkTpbi4WBYuXCg6nU7mzJnT7jzGjx/PrjQUkJxOpwwZMkQGDRok69evl++//17MZrMMHjxYba1bW1srdrtdRETcbrcMGzZM+vfvL+vWrZOcnByJjo6WAQMGSEtLi4iIbNu2TQDIjBkzpKSkRGbOnCkA5MsvvxSRGy1/AciSJUvkzz//lF27dkliYqKYTCapqqrqcL5dtjAiIrJ9+3aJi4sTABIdHS0rVqxQP3w2NDQIAHniiSfU+PLycomPjxcAYrFYZPny5Zp2QE1NTTJ//nwxGo0CQFJTU9U/lM/hw4clMTFRAEh4eLi8//776h+KKBCVlZVp8uTLL79U86SxsVF9rvuUl5fLww8/LADEbDbLsmXL/PJkwYIFap6kpKTI0aNH1e27d++WpKQkGTZsmAwbNkyefvrpu3ewRP+Ax+ORVatWSWRkpACQoUOHSnFxsbo9OztbAMi3334rIjfaUH/zzTcSFRUlACQ+Pt6vhWhVVZVMmjRJAEhQUJC8+uqrcvnyZXX7xo0bJTExUVJTUyU9PV0yMjIkPT293ValRPeS2+2WZcuWSVhYmACQxMRETcvq5cuXCwApKioSkRs5tWLFCjGZTAJAEhIS5Oeff9bs89y5c/LUU08JAFEURWbNmqWeRIqI1NfXy4svvigGg0EAyKBBg6SwsPDuHDDRP9TQ0CBvv/22+ny12Wxy6tQpdbvNZhMA6nO8sbFR5s6dK4qiCAAZO3asHD9+XLPPgwcPSkJCggCQiIgIWbJkiTidznbnMGfOHFm8ePF/c4BE/1JNTY1MmzZNdDqd6HQ6mTZtmly8eFHd7js39xXHa2trZfr06aLX60Wn08mUKVPk/Pnzmn2WlJTIgAEDBID06dNHVq1apT5eRCQrK0vMZrMAEAAycuRIOXjw4G3nqhO5zc02XUBzczNCQ0P9Ln1eunQpJkyYgPj4+DuK9/F6vXC73QgJCWn3Z7a0tCA4ONivowFRoOooT2w2m99aB52RJ0RdgYigpaXF71L95uZmZGZmYtGiRZr1ctqLv5nT6URQUBBvl6H/Ca2trXA6nQgNDdWMX716FR9//DHee+89zbY7zRGDweDXlvHm7fX19YiOjmbnDQp4Ho8HXq8XwcHBmvGzZ88iPz8fCxYs0Jwz8FyDuhuXywWdTqcuiOpz6NAhHDlyBG+++aZm3O12Q0T81gq5WUfnKk1NTaisrERERITfMhvt+Z8ojBARERERERER/X+wBElERERERERE3RYLI0RERERERETUbbEwQkRERERERETdFgsjRERERERERNRtsTBCRERERERERN0WCyNERERERERE1G0Z7vUEiIiIiP6tsrIy/PTTT9Dr9UhLS8P48eM7jL906RI+++wziAgeeOABzJ079y7NlIiIiAINrxghIiKigFVXV4cpU6agubm5w7jCwkLk5ubCbrfj8uXLt91vU1MTTp48idLSUnzxxRedNV0iIiLqglgYISIiooBVXV2N/Px8HD9+vMO48PBwjBw5Env37sXLL7/cZoyIqF/HxcWhrKwM8+bNg8lk6swpExERURfDwggREREFHKfTibVr16KsrAwAUFxcjKysLFy4cKHN+NDQUE3hw6epqQn5+fl44YUXYDKZkJ2drdne2tqKkJCQzj8AIiIi6jK4xggREREFnOrqasybNw/19fUAgA8++AAhISHQ6XR44403/OK9Xi8URdGMVVZWYsyYMaitrcWoUaMQFRWF3377DRkZGWqMoihobW39T4+FiIiIAhsLI0RERBRw+vXrhytXrqCqqgpWqxWlpaUYN25cu/Fut9uvMLJw4UKEh4dj7969iIuLQ2lpKXr27KmJURQFbrf7PzkGIiIi6hpYGCEiIqKA1atXLwC4bfHC6XT6FUaMRiPq6uqwePFipKWlYerUqQgODtbEKIoCl8vVuZMmIiKiLoVrjBAREVHACg0NBQC0tLR0GOdyufwKI1lZWXjnnXdQU1ODGTNm4PHHH4fH49HEKIoCp9PZuZMmIiKiLoWFESIiIgpYQUFBUBQF169f7zDO6XTCaDRqxqKiorBo0SKUl5dj//79OHz4MDZv3qyJ4RUjRERExMIIERERBTS9Xn/bqzpcLpemMHLixAmMHTsWmzdvht1ux+nTpwHAb6FVo9HIK0aIiIi6Oa4xQkRERAFNRNC3b98OY1wuF8LCwtTvY2Ji0NTUhIkTJwIAdDodnn32WTzzzDOaxxmNRl4xQkRE1M2xMEJEREQB7cSJExg4cGCHMW63W3PFiMViwf79+3Hp0iXU1taib9++sFgsfo8LDg5mVxoiIqJujoURIiIiCmhxcXG3jfF6vSgsLMTvv/+OIUOG4NNPPwUAxMbGIjY21i++sLAQ3333Herq6uD1ejt9zkRERNR1sDBCREREXd706dORl5cHvV7v15K3LUFBQTCZTIiMjERqaupdmCEREREFKp2IyL2eBBERERERERHRvcCuNERERERERETUbbEwQkRERERERETdFgsjRERERERERNRtsTBCRERERERERN0WCyNERERERERE1G2xMEJERERERERE3RYLI0RERERERETUbf0fyomwW5nhS7oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xkcd()\n",
    "plt.figure(figsize=(18,3))\n",
    "plt.xlabel('t [s]')\n",
    "plt.ylabel('y')\n",
    "plt.xlim(0, 0.005)\n",
    "plt.plot(ts, amplitudes)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdef59e7",
   "metadata": {},
   "source": [
    "This figure show how this wave looks like in the range of $t \\in [0, 0.05]$. Here $y$ is a continous function. It's impossible to save all $y$ for every point of time $t$. Therefore, we can convert this sine wave into a sequence of \"samples\" like the following. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d83235e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import collections as matcoll\n",
    "lines = []\n",
    "for i in range(ts.size):\n",
    "    pair=[(ts[i],0), (ts[i], amplitudes[i])]\n",
    "    lines.append(pair)\n",
    "linecoll = matcoll.LineCollection(lines)\n",
    "\n",
    "plt.xkcd()\n",
    "fig, ax = plt.subplots(figsize=(18,3))\n",
    "ax.add_collection(linecoll)\n",
    "\n",
    "plt.xlabel('t [s]')\n",
    "plt.ylabel('y')\n",
    "plt.xlim(0, 0.005)\n",
    "plt.scatter(ts, amplitudes)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b138d518",
   "metadata": {},
   "source": [
    "This figure is the result of sampling 8000 points evenly during a second (s). Usually we call this number **sampling rate**. You may ask, \"Is this how computer store this sine wave for sound?\" Well, not exactly. You may have noticed that the values of $y$ are in the range from -1 to 1, which indicating most of $y$ values are floating number. However, it is much more easy to store these values as integers. \n",
    "\n",
    "In the computer world, we only have 0 and 1. The minimum storage unit in computer is called bit.  If I have one bit for storing, I can only store either 0 or 1. If I have two bits, I have four choices, $00_2$, $01_2$, $10_2$ and $11_2$, which correspond to 0, 1, 2, 3 in decimal system. (Here I use $xx_2$ to indecate $xx$ is a binary number.)  What about 3 bits, or 8 bits? You can try on yourself. It turns out there are $2^n$ choices for a $n$ bits storage. To make our life easier, we usually call 8 bits a byte, i.e., **1 byte equals to 8 bits**. Therefore you have $2^8 = 256$ choices in this byte storage range from 0 to 255. What if I use one bit to indecate the sign of this stored number? I mean, for example,  we can set it to 0 for a positive number and 1 for a negative number.  What is range of number a byte can represent? This is how I consider this question. As one bit has been occupied, so I have 7 bits to use. The largest number I can represent in 7 bits storage is $1111111_2$ which is 127 in decimal. If the sign bit is 1, this number is -127.  Thus,  a byte can represent integer numbers ranges from -127 to 127. \n",
    "\n",
    "Let's say the given storage for each $y$ value is a byte.  How can you represent the above sine wave? I'd like to say map the range $[-1, 1]$ to $[-127, 127]$ is a pretty strightfoward idea. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1f5f05cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "audio = amplitudes * (2**7 - 1)\n",
    "audio = audio.astype(np.int8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a2c3b2e7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import collections as matcoll\n",
    "lines = []\n",
    "for i in range(ts.size):\n",
    "    pair=[(ts[i],0), (ts[i], audio[i])]\n",
    "    lines.append(pair)\n",
    "linecoll = matcoll.LineCollection(lines)\n",
    "\n",
    "plt.xkcd()\n",
    "fig, ax = plt.subplots(figsize=(18,3))\n",
    "ax.add_collection(linecoll)\n",
    "\n",
    "plt.xlabel('t [s]')\n",
    "plt.ylabel('y')\n",
    "plt.xlim(0, 0.005)\n",
    "plt.scatter(ts, audio)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4604c729",
   "metadata": {},
   "source": [
    "Compare this figure to the former one, you can see the wave shape doesn't change much, while the $y$ range changes to $[-127, 127]$. Actually, another change which is hard to tell is the $y$ values changes to integers as we have used a method to force floating numbers to integers.  We should notice that this kind of conversion definitely brings some errors.  By the way,  the number of bits we choose to store $y$ values is called **bit depth** in digital audio, which directly corresponds to the resolution of each sample. ."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d18ab398",
   "metadata": {},
   "source": [
    "## Listen to the sine wave\n",
    "Let hear what this sine wave sounds like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "264f0ca2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def savewave(audio, name, channels, samplewidth, framerate):\n",
    "    '''Save data to a wave file'''\n",
    "    import wave\n",
    "    with wave.open(name, \"w\") as f:\n",
    "        f.setnchannels(channels)\n",
    "        f.setsampwidth(samplewidth)\n",
    "        f.setframerate(framerate)\n",
    "        f.writeframes(audio.tobytes())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3ff168a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# bit depth: 8; sampling rate 8000\n",
    "fs = 8000\n",
    "ts = np.linspace(0, 1, fs, False)\n",
    "freq = 450\n",
    "amplitudes = np.sin(2 * np.pi * freq * ts)\n",
    "audio = amplitudes * (2**7 - 1)\n",
    "audio = audio.astype(np.int8)\n",
    "play_object = sa.play_buffer(audio, 1, 1, fs)\n",
    "savewave(audio, \"8bit-450hz.wav\", 1, 1, 8000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b084ff5",
   "metadata": {},
   "source": [
    "<p align=\"center\">\n",
    "<audio ref='themeSong' src=\"8bit-450hz.wav\" controls  preload></audio>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "96a0a9a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# bit depth: 16; sampling rate 8000\n",
    "fs = 8000\n",
    "ts = np.linspace(0, 1, fs, False)\n",
    "freq = 450\n",
    "amplitudes = np.sin(2 * np.pi * freq * ts)\n",
    "audio = amplitudes * (2**15 - 1)\n",
    "audio = audio.astype(np.int16)\n",
    "play_object = sa.play_buffer(audio, 1, 2, fs)\n",
    "savewave(audio, \"16bit-450hz.wav\", 1, 2, 8000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23ef38c8",
   "metadata": {},
   "source": [
    "<p align=\"center\">\n",
    "<audio ref='themeSong' src=\"16bit-450hz.wav\" controls  preload></audio>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4d2c166b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in range(10):\n",
    "#     play_object = sa.play_buffer(audio, 1, 2, fs)\n",
    "#     time.sleep(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbf6b806",
   "metadata": {},
   "source": [
    "You can hear the two sound clips are very different bacause of the bit depth. The second clip with 16 bits depth sound much more soft than the 8 bits depth clip. Now we know how to generate sound, let make some music."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fd48114",
   "metadata": {},
   "source": [
    "## Volume\n",
    "**The volume or the loudness of a sound is represented as the amplitude**. A high amplitude produces a loud sound and a low amplitude produces a quiet sound. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ea3d7fe2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loud \n",
    "volume = 0.8\n",
    "fs = 8000\n",
    "ts = np.linspace(0, 1, fs, False)\n",
    "freq = 450\n",
    "amplitudes = np.sin(2 * np.pi * freq * ts)\n",
    "audio = amplitudes * (2**15 - 1)\n",
    "audio = volume * audio\n",
    "audio = audio.astype(np.int16)\n",
    "play_object = sa.play_buffer(audio, 1, 2, fs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6a8d4c03",
   "metadata": {},
   "outputs": [],
   "source": [
    "# quiet\n",
    "volume = 0.2\n",
    "fs = 8000\n",
    "ts = np.linspace(0, 1, fs, False)\n",
    "freq = 450\n",
    "amplitudes = np.sin(2 * np.pi * freq * ts)\n",
    "audio = amplitudes * (2**15 - 1)\n",
    "audio = volume * audio\n",
    "audio = audio.astype(np.int16)\n",
    "play_object = sa.play_buffer(audio, 1, 2, fs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2c8e32b",
   "metadata": {},
   "source": [
    "## Create the basic notes\n",
    "We create the basic botes in the 4-th octave based on **[equal temperamen](https://en.wikipedia.org/wiki/Equal_temperament)**.  There are twelve notes in each octave： `C` ,`C#`,`D` ,`D#`,`E` ,`F` ,`F#`,`G` ,`G#`, `A`, `A#` and `B`.  Each note corresponds a frequency. All these frequencies constitute a **geometric progression**, where each term after the first is found by multiplying the previous one by a fixed number. For example, we know the frequency of note `A` is 440 hz. To get the frequency of the next note `A#`, we can multiply 440 by a magic number $2^{1/12}$ so that we know the frequency of `A#` which is 440$\\times2^{1/12}$ = 466.2 hz.  Similarly, we can get all the frequencies of ther notes. By the way, this is magic number is called **common ratio** in geometric progression. \n",
    "\n",
    "Up till now, we are talking about the notes in the 4-th octave. You probabaly want to ask, what about the notes in other octave? Well, good question. Here we use the format like `A4` to reprent the note `A` in the 4-th octave. It turns out it is very easy to get the frequency of `A5`. You just need to double the frequency of `A4` to obtain the frequency of `A5`, i.e., 440$\\times2$ = 880 hz. You can use the same method to get the frequencies of all other notes.  When you know `A`, you know everthing. That pretty cool, right?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "103e5235",
   "metadata": {},
   "outputs": [],
   "source": [
    "ns = np.arange(-9, 3)\n",
    "frequencies = 440 * 2**(ns/12)\n",
    "note_names = [\"C\" ,\"C#\",\"D\" ,\"D#\",\"E\" ,\"F\" ,\"F#\",\"G\" ,\"G#\", \"A\", \"A#\", \"B\"]\n",
    "Notes = {note_names[i]:frequencies[i] for i in range(ns.size)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "57c50b33",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xkcd()\n",
    "fig, ax = plt.subplots(figsize=(18,3))\n",
    "plt.xlabel('note')\n",
    "plt.ylabel('freq. [Hz]')\n",
    "plt.ylim(210, 510)\n",
    "plt.scatter(note_names, frequencies)\n",
    "for i in range(12):\n",
    "    ax.annotate('{:.1f}'.format(frequencies[i]), (note_names[i], frequencies[i] - 30))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7937b015",
   "metadata": {},
   "source": [
    "## Play Notes\n",
    "Here we create a function to play notes. Besids, like sometimes we want to rest, a symbol `O` is used to indecate the absence of sound."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b66c78a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def has_numbers(inputString):\n",
    "    return any(char.isdigit() for char in inputString)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "872ed090",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "has_numbers('E4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "11dcf538",
   "metadata": {},
   "outputs": [],
   "source": [
    "def play(note, fs=8000, duration=0.5, volume=1):\n",
    "    global Notes\n",
    "    t = np.linspace(0, duration, int(duration * fs), False)\n",
    "    if note != 'O':\n",
    "        if has_numbers(note):\n",
    "            octave = int(note[-1])\n",
    "            frequency = Notes[note[:-1]]\n",
    "            frequency = frequency * 2**(octave - 4)\n",
    "        else:\n",
    "            frequency = Notes[note]   # 4-th octave by defaut\n",
    "        audio = np.sin(2 * np.pi * frequency * t)\n",
    "        audio = audio * (2**15 - 1)  / np.max(np.abs(audio))\n",
    "    else:\n",
    "        audio = np.zeros(int(duration * fs)) # rest\n",
    "    audio = volume * audio\n",
    "    audio = audio.astype(np.int16)\n",
    "    play_object = sa.play_buffer(audio, 1, 2, fs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9f4dbeda",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Play C4 note\n",
    "play('C')  # the same as play('C4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6ee92820",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Play multiple  notes at the 'same' time\n",
    "play('C')\n",
    "play('E4')\n",
    "play('G4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6771f22b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rest, nothing played\n",
    "play('O')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52d8b04e",
   "metadata": {},
   "source": [
    "Ignoring the notes with sharp symbol `#`, we get notes `C`, `D`, `E`, `F`, `G`, `A` and `B`, corresponding to `Do`, `Re`, `Mi`, `Fa`, `Sol`, `La` and `Ti`. These notes make up **C major scale**, which sounds like this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3a8a75e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "for note in ['C', 'D', 'E', 'F', 'G', 'A', 'B', 'C5', 'B', 'A', 'G', 'F', 'E', 'D', 'C']:\n",
    "    play(note)\n",
    "    time.sleep(0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86d36873",
   "metadata": {},
   "source": [
    "## Für Elise\n",
    "Let's try to play Für Elise. You can find the sheet [Here](https://cdn.jsdelivr.net/gh/HuangJiaLian/DataBase0@master/uPic/2022_04_13_20_g6YGx7.jpg) Another important attribute for a note is its **duration**, which is the length the sound lasts for.   A longer duration means that you hear the sound for longer. Here, we use `E5-4` to indicate this is a `E` note in the 5-th octave which lasts for **one quarter** of the duration of a whole note. Similarly, `E5-8` is to say this is a `E` note in the 5-th octave which lasts for **one eighth** the duration of a whole note. If the duration of a whole note is 4 seconds, then a quarter note lasts for a sencond, and an eighth note lasts for half a second.  Besides, to record the music notes more clearly, we use a `|` to represent a bar (measure) line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "be0e9395",
   "metadata": {},
   "outputs": [],
   "source": [
    "for_elise = ['E5-8', 'D#5-8', '|', \\\n",
    "                    'E5-8', 'D#5-8', 'E5-8', 'B-8', 'D5-8', 'C5-8', '|',\\\n",
    "                    'A-4', 'O-8', 'C-8', 'E-8', 'A-8', '|', \\\n",
    "                    'B-4', 'O-8', 'E-8', 'G#-8', 'B-8', '|', \\\n",
    "                    'C5-4', 'O-4', 'E5-8', 'D#5-8', '|', \\\n",
    "                    'E5-8', 'D#5-8', 'E5-8', 'B-8', 'D5-8', 'C5-8', '|',\\\n",
    "                    'A-4', 'O-8', 'C-8', 'E-8', 'A-8', '|', \\\n",
    "                    'B-4', 'O-8', 'E-8', 'C5-8', 'B-8', '|', \\\n",
    "                    'A4-4', 'O-4', 'E5-8', 'D#5-8', '|']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4b240428",
   "metadata": {},
   "outputs": [],
   "source": [
    "def playSheet(sheet, tempo=120):\n",
    "    for sign in sheet:\n",
    "        if sign == '|':\n",
    "            continue\n",
    "        if '-' in sign:\n",
    "            note, note_type= sign.split('-')\n",
    "            note_type = int(note_type)\n",
    "        else:\n",
    "            note = sign\n",
    "            note_type = 4 # 4-th note as default\n",
    "        play(note)\n",
    "        time.sleep( (60/tempo)*(4/note_type))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f0e73907",
   "metadata": {},
   "outputs": [],
   "source": [
    "playSheet(for_elise)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcb9fb2a",
   "metadata": {},
   "source": [
    "As there are some sections should be repeated, we add `|:` and `:|` to represent the left and right repeat signs, respectively. Then, we make some changes to the function of `playSheet` to make repeat signs work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1a9e1e31",
   "metadata": {},
   "outputs": [],
   "source": [
    "for_elise = \\\n",
    "['E5-8', 'D#5-8', '|:', 'E5-8', 'D#5-8', 'E5-8', 'B-8', 'D5-8', 'C5-8', '|', 'A-4', 'O-8', 'C-8', 'E-8', 'A-8', '|',  'B-4', 'O-8', 'E-8', 'G#-8', 'B-8', '|', 'C5-4', 'O-4', 'E5-8', 'D#5-8', '|',  'E5-8', 'D#5-8', 'E5-8', 'B-8', 'D5-8', 'C5-8', '|',\\\n",
    " 'A-4', 'O-8', 'C-8', 'E-8', 'A-8', '|', 'B-4', 'O-8', 'E-8', 'C5-8', 'B-8', '|', 'A-4', 'O-4', 'E5-8', 'D#5-8', ':|', 'A-4', 'O-8', 'B-8', 'C5-8', 'D5-8', '|:', 'E5-4', 'O-8', 'G-8', 'F5-8', 'E5-8', '|',\\\n",
    " 'D5-4', 'O-8', 'F-8', 'E5-8', 'D5-8', '|', 'C5-4', 'O-8','E-8', 'D5-8', 'C5-8',  '|', 'B-4', 'E5-8', 'E3-8', 'E-8', 'E-8', '|', 'E5-8', 'E-8', 'E5-8', 'D#-8', 'E-8', 'D-8', '|', 'E-8', 'D#-8', 'E-8', 'D#5-8',  'E-8', 'D-8', '|', \\\n",
    " 'E5-8', 'D#5-8', 'E5-8', 'B-8', 'D5-8', 'C5-8', '|', 'A-4', 'O-8', 'C-8', 'E-8', 'A-8', '|', 'B-4', 'O-8','E-8','G#-8','B-8','|','C5-4','O-4','E5-8','D#5-8', '|', 'E5-8','D#5-8','E5-8','B-8','D5-8','C5-8','|',\\\n",
    " 'A-4','O-8','C-8','E-8','A-8','|','B-4','O-8', 'E-4', 'C5-8','B-8','|','A-4','O-8','B-8','C5-8','D5-8', ':|', 'A-4', 'O-2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1af181c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def last_bar_index(sheet):\n",
    "    '''\n",
    "    This function is use to determine the index of  last bar sign\n",
    "    '''\n",
    "    max_index = len(sheet)-1\n",
    "    for i,sign in enumerate(reversed(sheet)):\n",
    "        if sign=='|':\n",
    "            return max_index - i     \n",
    "#  Test\n",
    "# li = ['E5-8', 'D#5-8', '|',  'E5-8', 'D#5-8', '|', 'E5-8']\n",
    "# idx = last_bar_index(li)\n",
    "# li[:idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "44f744af",
   "metadata": {},
   "outputs": [],
   "source": [
    "record_clip=[]\n",
    "record_status = False\n",
    "def playSheet(sheet, tempo=120):\n",
    "    global record_clip, record_status\n",
    "    for sign in sheet:\n",
    "        # print(sign, record_status)\n",
    "        if record_status == True:\n",
    "            record_clip.append(sign)\n",
    "        if sign == '|:':\n",
    "            record_status = True\n",
    "            continue\n",
    "        if sign == ':|' and record_status == True:\n",
    "            record_status = False\n",
    "            record_clip = record_clip[:last_bar_index(record_clip)]\n",
    "            playSheet(record_clip, tempo=tempo)\n",
    "            record_clip = []\n",
    "            continue\n",
    "        if sign == '|':\n",
    "            continue\n",
    "        if '-' in sign:\n",
    "            note, note_type= sign.split('-')\n",
    "            note_type = int(note_type)\n",
    "        else:\n",
    "            note = sign\n",
    "            note_type = 4 # 4-th note as default\n",
    "        play(note)\n",
    "        time.sleep( (60/tempo)*(4/note_type))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7723250a",
   "metadata": {},
   "outputs": [],
   "source": [
    "playSheet(for_elise)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "205165c4",
   "metadata": {},
   "source": [
    "## Fade in and out \n",
    "You may noticed that there are some click sounds at the beging and the end of a note. To smooth the trasitions, we can create an envelope, which gives you fine control over the amplitude of the sound. It’s like giving someone instructions on how to turn up and down the volume of a guitar amplifier. For example you might ask someone to “start at silence, slowly move up to full volume, hold it for a bit, then quickly fall back to silence.” This kind of envelope is called ADSR envelope, where ADSR represents four phase, attack, decay, sustain, and release. Here, for simplicity, we just use three of them, attack, sustain and release. In the attack phase, the volume goes up from 0 to 1. In the sustain phase, the volume keeps to 1. And in the release phase, the volume goes down from 1 to 0. Using the attack and rerlase phases, we can fade the note sound in and out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ba028f7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "duration = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d37e9dcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = np.linspace(0, duration, duration*fs, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "60b6e544",
   "metadata": {},
   "outputs": [],
   "source": [
    "attack = 0.1\n",
    "release = 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "36d68423",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_envelope(attack=0.1, release=0.2, duration=1, fs=8000):\n",
    "    ts = np.linspace(0, duration, int(duration*fs), False)\n",
    "    ts_attack = ts[ts<attack]\n",
    "    ts_sustain =  ts[np.logical_and(ts>=attack, ts<(duration - release))]\n",
    "    ts_release = ts[ts>= duration-release]\n",
    "    amplitude_attack = ts_attack/attack\n",
    "    amplitude_sustain = np.ones_like(ts_sustain)\n",
    "    amplitude_release = -ts_release/release + duration / release\n",
    "    amplitude_envelope = np.concatenate([amplitude_attack, amplitude_sustain, amplitude_release])\n",
    "    return ts, amplitude_envelope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e2b0ee93",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts, amplitude_envelope = get_envelope(attack=0.2, release=0.5, duration=1, fs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "92aed515",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xkcd()\n",
    "fig, ax = plt.subplots(figsize=(18,3))\n",
    "plt.xlabel('time [s]')\n",
    "plt.ylabel(' amplitude')\n",
    "plt.ylim(0,1.3)\n",
    "ax.plot( ts, amplitude_envelope, label='amplitude envelop')\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bc3c9d6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "freq = 6\n",
    "amplitudes = np.sin(2 * np.pi * freq * ts)\n",
    "fig, ax = plt.subplots(figsize=(18,3))\n",
    "plt.xlabel('t [s]')\n",
    "plt.ylabel('amplitude')\n",
    "plt.ylim(-1.8, 1.5)\n",
    "plt.scatter(ts, amplitudes, label='original wave')\n",
    "plt.scatter(ts, amplitudes*amplitude_envelope, label='envelop applied')\n",
    "plt.legend(loc='lower left', ncol=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "630d3caf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def play(note, fs=8000, duration=1, volume = 0.8, attack=0.01, release=0.3):\n",
    "    global Notes\n",
    "    t = np.linspace(0, duration, int(duration * fs), False)\n",
    "    if note != 'O':\n",
    "        if has_numbers(note):\n",
    "            octave = int(note[-1])\n",
    "            frequency = Notes[note[:-1]]\n",
    "            frequency = frequency * 2**(octave - 4)\n",
    "        else:\n",
    "            frequency = Notes[note]   # 4-th octave by defaut\n",
    "        audio = np.sin(2 * np.pi * frequency * t)\n",
    "        audio = audio * (2**15 - 1)  / np.max(np.abs(audio))\n",
    "    else:\n",
    "        audio = np.zeros(int(duration * fs)) # rest\n",
    "    _ , amplitude_envelope = get_envelope(attack=attack, release=release, duration=duration, fs=fs)\n",
    "    audio = amplitude_envelope * audio\n",
    "    audio = volume * audio\n",
    "    audio = audio.astype(np.int16)\n",
    "    play_object = sa.play_buffer(audio, 1, 2, fs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc2998f2",
   "metadata": {},
   "source": [
    "You can hear the difference before and after applying the amplitute envelop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "55b49667",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Play E without attack and  release phase\n",
    "play('E', attack=0, release=0.000000001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0796ebb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Play E with attack and  release phase\n",
    "play('E', attack=0.01, release=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fa98df1",
   "metadata": {},
   "source": [
    "Then let's play Für Elise again. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "84b7f902",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Play \n",
    "playSheet(for_elise)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5087968",
   "metadata": {},
   "source": [
    "## Capo\n",
    "When we play guitar, we can use a capo on the guitar neck to shorten the playable length of the guitar strings-hence raising the pitch. We can also apply this function in our `play` function by adding a paremeter `capo`.  This paremeter `capo` is 0 by default, indecating we don't use a capo.  For instance, if we set `capo=1` when we play note `A` (440 Hz), we  get the sound of the next note of `A` (466.2 Hz), which is `A#`. In another words, we multiply a magic number $2^{1/12}$ to the frequency of the orignal note, i.e., 440$\\times 2^{1/12}$ = 446.2. If we set `capo=12` when play note `A`, we actually get the frequency of 440$\\times 2^{12/12}$=880 Hz, which is frequency of `A5`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ea40298b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def play(note, fs=8000, duration=1, volume=0.8, attack=0.01, release=0.3, capo = 0):\n",
    "    global Notes\n",
    "    t = np.linspace(0, duration, int(duration * fs), False)\n",
    "    if note != 'O':\n",
    "        if has_numbers(note):\n",
    "            octave = int(note[-1])\n",
    "            frequency = Notes[note[:-1]]\n",
    "            frequency = frequency * 2**(octave - 4)\n",
    "        else:\n",
    "            frequency = Notes[note]   # 4-th octave by defaut\n",
    "        # Capo \n",
    "        frequency = frequency * 2**(capo/12)\n",
    "        audio = np.sin(2 * np.pi * frequency * t)\n",
    "        audio = audio * (2**15 - 1)  / np.max(np.abs(audio))\n",
    "    else:\n",
    "        audio = np.zeros(int(duration * fs)) # rest\n",
    "    _ , amplitude_envelope = get_envelope(attack=attack, release=release, duration=duration, fs=fs)\n",
    "    audio = amplitude_envelope * audio\n",
    "    audio = volume * audio\n",
    "    audio = audio.astype(np.int16)\n",
    "    play_object = sa.play_buffer(audio, 1, 2, fs)\n",
    "    return frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5b78fabd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "440.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "play('A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1f0b9778",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "466.1637615180899"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "play('A#')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "33810810",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "466.1637615180899"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "play('A', capo=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "32f36922",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "261.6255653005986"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "play('C4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "162672dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "523.2511306011972"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "play('C5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "74ee1181",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "523.2511306011972"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " play('C4', capo=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86098b3a",
   "metadata": {},
   "source": [
    "##  Chord\n",
    "A chord is any harmonic set of frequencies consisting of multiple notes that are heard as if sounding simultaneously. For example,  a C major chord consisted of note `C`, `E` and `G`. Here, to simulate a guitar, we use six notes to represent the chord notes on the guitar strings from 6th string to 1st string. Specially, a `X` is used to indicate a mute. \n",
    "<!-- ![](https://cdn.jsdelivr.net/gh/HuangJiaLian/DataBase0@master/uPic/2022_04_17_14_C_scale.svg) -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "d594620d",
   "metadata": {},
   "outputs": [],
   "source": [
    "C = ['X', 'C3', 'E3', 'G3', 'C4', 'E4']\n",
    "C7 = ['X', 'C3', 'E3', 'A#3', 'C4', 'E4']\n",
    "D = ['X', 'X', 'D3', 'A3', 'D4', 'F#4']\n",
    "Dm = ['X', 'X', 'D3', 'A3', 'D4', 'F4']\n",
    "Dm7 = ['X', 'X', 'D3', 'A3', 'C4', 'F4']\n",
    "Em = ['E2', 'B2', 'E3', 'G3', 'B3', 'E4']\n",
    "G = ['G2', 'B2', 'D3', 'G3', 'B3', 'G4']\n",
    "G7 = ['G2', 'B2', 'D3', 'G3', 'B3', 'F4']\n",
    "F = ['F2', 'C3', 'F3', 'A3', 'C4', 'F4']\n",
    "Am = ['X', 'A2', 'E3', 'A3',  'C4', 'E4']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "03645bec",
   "metadata": {},
   "outputs": [],
   "source": [
    "def playChord(chord, interval=0, capo=0):\n",
    "    for i, note in enumerate(chord):\n",
    "        if note == 'X':\n",
    "            continue\n",
    "        play(note, volume = 1, capo=capo)\n",
    "        time.sleep(interval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "cca761de",
   "metadata": {},
   "outputs": [],
   "source": [
    "capo = 12"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9a86119",
   "metadata": {},
   "source": [
    "Play chord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "696ee3c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "playChord(C, capo=capo)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3c8f3e6",
   "metadata": {},
   "source": [
    "Play chord notes one by one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "de0a1efb",
   "metadata": {},
   "outputs": [],
   "source": [
    "playChord(G, interval=0.1, capo=capo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "33b2f0e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "playChord(Dm, interval=0.1, capo=capo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "812a3c8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "playChord(Am, interval=0.2, capo=capo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "de2bfcd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "playChord(G7, interval=0.2, capo=capo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "6d458ff5",
   "metadata": {},
   "outputs": [],
   "source": [
    "playChord(F, interval=0.2, capo=capo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f713dc36",
   "metadata": {},
   "outputs": [],
   "source": [
    "playChord(C7, interval=0.2, capo=capo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ff243b76",
   "metadata": {},
   "outputs": [],
   "source": [
    "playChord(Dm7, interval=0.2, capo=capo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "9a82cbf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "playChord(G, interval=0.2, capo=capo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "55a0fd11",
   "metadata": {},
   "outputs": [],
   "source": [
    "playChord(C, interval=0.3, capo=capo)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57cd2016",
   "metadata": {},
   "source": [
    "## Rhythm Patterns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "1baf75a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def playChord(chord, interval=0, capo=0, pattern=None):\n",
    "    if pattern == None:\n",
    "        for i, note in enumerate(chord):\n",
    "            if note == 'X':\n",
    "                    continue\n",
    "            play(note, volume = 1, capo=capo)\n",
    "            time.sleep(interval)\n",
    "    else:\n",
    "        for i, p in enumerate(pattern):\n",
    "            if i == 0:\n",
    "                volume = 1\n",
    "            else:\n",
    "                volume = 0.6\n",
    "            if type(p) == list:\n",
    "                for element in p:\n",
    "                    if chord[6-element] == 'X':\n",
    "                            continue\n",
    "                    play(chord[6-element], volume = volume, capo=capo)\n",
    "            else:\n",
    "                if chord[6-p] == 'X':\n",
    "                        continue\n",
    "                play(chord[6-p], volume = volume, capo=capo)\n",
    "            time.sleep(interval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "66a54878",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xkcd()\n",
    "fig, ax = plt.subplots(figsize=(18,2))\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "plt.ylabel('TAB')\n",
    "plt.ylim(0,7)\n",
    "plt.xlim(-0.03,16.03)\n",
    "\n",
    "plt.tick_params(\n",
    "    axis='both',          # changes apply to the x-axis\n",
    "    which='both',      # both major and minor ticks are affected\n",
    "    bottom=False,      # ticks along the bottom edge are off\n",
    "    top=False,         # ticks along the top edge are off\n",
    "    left=False,\n",
    "    right = False\n",
    ")\n",
    "\n",
    "ax.axes.xaxis.set_visible(False)\n",
    "ax.axes.yaxis.set_visible(False)\n",
    "\n",
    "plt.gca().invert_yaxis()\n",
    "\n",
    "for s in range(1, 7):\n",
    "    plt.axhline(y = s, color='k', lw=1, zorder=0)\n",
    "for m in range(0,17,4):\n",
    "    plt.axvline(x = m, color='k',  lw=1, zorder=1, ymin=1/7, ymax=6/7)\n",
    "\n",
    "\n",
    "for i, x in enumerate(np.linspace(0, 4, 5, False)[1:]):\n",
    "    for j, y in  enumerate([1,2,3,4,5,6]):\n",
    "        if i == 0 and j == 0:\n",
    "            plt.text(x-0.1, y-1, 'G', weight=\"bold\")\n",
    "        plt.scatter(x, y, s=60, marker='x', color='k',  zorder=5)\n",
    "    plt.axvline(x = x, color='k',  lw=1, ymin = 0, ymax = (7-y)/7 -0.05)\n",
    "        \n",
    "for i, x in enumerate(np.linspace(4, 8, 5, False)[1:]):\n",
    "    for j, y in enumerate([1,2,3,4,5,6]):\n",
    "        if i == 0 and j == 0:\n",
    "            plt.text(x-0.1, y-1, 'Em', weight=\"bold\")\n",
    "        plt.scatter(x, y, s=60, marker='x', color='k',  zorder=5)\n",
    "    plt.axvline(x = x, color='k',  lw=1, ymin = 0, ymax = (7-y)/7 -0.05)\n",
    "        \n",
    "for i, x in enumerate(np.linspace(8, 12, 5, False)[1:]):\n",
    "    for j, y in enumerate([1,2,3,4]):\n",
    "        if i == 0 and j == 0:\n",
    "            plt.text(x-0.1, y-1, 'D', weight=\"bold\")\n",
    "        plt.scatter(x, y, s=60, marker='x', color='k',  zorder=5)\n",
    "    plt.axvline(x = x, color='k',  lw=1, ymin = 0, ymax = (7-y)/7 -0.05)\n",
    "\n",
    "for i, x in enumerate(np.linspace(12, 16, 5, False)[1:]):\n",
    "    for j, y in enumerate([1,2,3,4,5]):\n",
    "        if i == 0 and j == 0:\n",
    "            plt.text(x-0.1, y-1, 'C', weight=\"bold\")\n",
    "        plt.scatter(x, y, s=60, marker='x', color='k',  zorder=5)\n",
    "    plt.axvline(x = x, color='k',  lw=1, ymin = 0, ymax = (7-y)/7 -0.05)\n",
    "        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "ea341948",
   "metadata": {},
   "outputs": [],
   "source": [
    "playChord(G, interval=0.5, pattern=[[6,5,4,3,2,1], [6,5,4,3,2,1], [6,5,4,3,2,1], [6,5,4,3,2,1]])\n",
    "playChord(Em, interval=0.5, pattern=[[6,5,4,3,2,1], [6,5,4,3,2,1], [6,5,4,3,2,1], [6,5,4,3,2,1]])\n",
    "playChord(D, interval=0.5, pattern=[[4,3,2,1], [4,3,2,1], [4,3,2,1], [4,3,2,1]])\n",
    "playChord(C, interval=0.5, pattern=[[6,5,4,3,2,1], [6,5,4,3,2,1], [6,5,4,3,2,1], [6,5,4,3,2,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "fa1c7392",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VcOv9II2iqmqfFqQ6c7Sabu2ZXq+XazjVdL/fD0VREIlEIEkS/H4/gsEgzyZjY2OYnJxEIpGAJEnH7YtFB33TNOFwOPDa174WlUqFB1gSStFolJt8IpHgpPP7/X2Dgt/vZ0NJNAAvNc5Op4NWq4W9e/fi4osvZu4vfelLSKfTfcGs6zoX/0qlgtnZWRQKBWSzWVSrVXS7XW525JhEIsENkoaPWCwGSZLY8TQUkm2SJLHAcTqdHMTW5LGKexKdrVYL1Wq17xpVVcXBgwfxuc99jm276qqr4HQ6uaHNz89DVVUYhgG/349UKsVNIxaLIZVKsVglQUWFLRwOs38peKhokq+tdpimyYWXrpmWHo1GA5VKBcViEXNzc1BVlQfFfD7P10iNlezTNK2vmGzYsAGnnXYaNz0aFiVJ6vsgkUINle4DfVDjtd4L+qCm0e120Ww2eeCiYZeafbVa5Wul+KGGlc1mUSwWOcEjkQjGxsYwPj6OcDiMYDCIRCKBZrPZd/++/OUv4/zzz+fGREOb3+/nAkoFla6TroeuT1VVFuKNRgOapsEwDI7zYrGIQqGAmZkZXlxVKhXE43GEQiGkUilkMhlugjQsUkxY/U6f00IqEAiwSKeYcDgcHNudToeFKBU1avTlchmqqkJVVVQqFfZvqVTiz8nPhmEgGo0imUxys/D7/bj33nvZlzt37sRZZ52FeDzOYonuPdURt9vNtYOul66T/NpsNvmaSASSyK5UKtxEa7Ua5x1db6FQQLvdRiwWQyaTweTkJPs2nU4jHo9zbfD5fH01jeKWcu4Xv/gFtm3bxvb99Kc/xdTUFNeLTqfDArTdbvP9JpFK9956fblcjgWuqqrciLxeL9LpNIvPsbExyLLMcUv3n2KCFjZ+v59jhWKYfEsCieKW6sXRavQ999yDeDzOy7tyuYy5uTlUKhWO5fn5eV5GaZqGSqWCWq2GVqvFy4tgMIhoNMrXTsM3DSxjY2O83KA6srAu9Hq9vnjodDrcyKmGkQ8p/wqFAjRNw+zsLGZnZ1EsFjE7O8v2nXvuuUilUggEAuyvWCyGWCzW16Cp55FfSaRa45hyj3qJtS6QgKD8qlQqfQNjuVxGqVTiIT6bzXLtbbfb3H8pbqkXk2D4rd/6LSiKwnn+7LPPco21LqwX9gryabfb5TpLIpNEDQ0EVBcajQYMw8C+fftw1113sS/PPvtstFot5PN5aJoGSZIwPj6OaDTK/YB8SYKSYoGW53QPrL2b7oO17pKP6bp7vR4v4ejeUywWCgVehFLdoMUJ5WGxWOS4jcfjmJychNPpxOOPP872XXfddTj99NORSqVYHJNNsizD5/NxvaCc83q9fX2Z9AX1CYoLWhRYF7rFYpFzi/qZpmmYm5tDLpeDYRic/5lMBplMhusY5RlpCOuwFAgEMD09jauuuopt+8///E9s2rSpz79WHdHpdPo0ULvd5oGFYoKusVqtcp2mBV8+n8f09DTK5TJ6vR7neSQS4d6VSqVYu5FvaalPQyDVZ9IRFBfW+HY6nXj66aexefNmtu+JJ57AWWedxZqIFrqNRoOFvKZp0DSN44Xyk+ygWk29pVgsslajuhyPxzmW0+k0IpEIkslkn66mwwqqFwt1D/meagjFNuUnLYAp13/4wx/y0prqCtVjiie6V/Q9xWIRjUaDFxnpdJrvQyKRQCQSYe1fLpdxyy23sC+/8Y1vYPPmzVxf/H4/3G533/VTjyG9Ye3lrVaL44V0JsV8tVpFPp9HLpfjhW+tVmOtTTlNOprigw4oSDtRvFMs0X2h76O89Xg8OHjwIF7zmtf09fNNmzb11UeKeWt9pGuivDUMgzVSqVRCpVLBzMwML7Sr1SrXbYppuvZUKsXaJJVKsc6XJIl1qrUmkr+tsbOwxlDu7tmzB5dccgnbd/fddyOVSrEfrf2TtBLpD9KmnU6HDyRoAUN6lHKYeng0GkUkEuHYsPZO6umk9Y4WL6T96CDQuuyjnlgoFDA/P4+DBw/ijjvuYNvOOOMMdLtdrv8A2H/hcLivxlAtpGUr9Rmyi3QULWGpzlBNJ01C+UmHa7QcoDihvkM9lA6aDcPgWp7P5zEzM4NCoYBAIIDJyUk89NBDSKfTnOd33HEH3vve9+JoWHTQf8UrXoFHHnkE7XYblUqFi5r1hJyGKBJNNLiQ+KbtK4kD61aVgtHj8RyxWZIkCdu2bWOBR4LQurnLZDJIJpMYHx9HKBSCJEkjt1mywjRN6LqOXC7HRYsGUasQJN9Tg7EGDQ0QVl8v3GSToCPBQaKUNqgkrqnhBoNBXj5Qg6WG1Wg0cM0116zJU9rjQa/XQz6fx/z8PGZnZ7kYvPDCC7j99tv7nsjw+/2YnJxEr9fjIk8LE+s2leBwOOB2u49YclDhoA02FUhqQvF4HBMTExgbG8PExAQSicSKnIRRnq9fv36gv9c6/Om6jhdffBE333xz3zAVjUZx6aWXwjAMjm3rqQx9TmKGYI1hil8SJuRjErrhcBhjY2O8LKNmT8s0Ei/LrR3DPAGgjXQ2m+U6TH4mUW19mkNVVRZNtGDSdf2IWmEFiXpqwEfb/m/dupXFES11aCgLBAJ86kgNMxqNcqwP4tRlUDjavVu/fj1uu+02+Hw+fjKpVCqhVCpxQyah2Wg02K/WnkdxTDFshcPh4PilASwSifDpLNUCEh80xGcyGfYjLeuWgpXK84VYymmKpmmYmZnpEzfWAapQKLDPK5UKLw6ti1xa7NLibOETXyS4rHWCRJpV/FuHcRKniUSCF2gk/CKRCNxu96qfei8FtFhTVRXZbJaXhYZhsJ+tYp60hKqqeOGFF476NONC0EksfVifbLQKYqrFpOXooIKe/BobG8Mpp5yCWCwGr9e7ohpuWPeu2+3yAQ7V5UKhwItaOlwoFAqs5eiwgp5otD6FcKwnEaz3wOfz4amnnsLExATn+h/+4R+i1Wr1LU5Ie1hPbGnhEw6HEY/HEYvFFr0PazUPut0uLzKoHtOQau2JtBwrFAp8QFGtVjlHGo0GqtXqMX1PvZGebiINTfEfiUTYz5IkcfxTPZmYmODhPR6PD/1p3kHdPzrgzGazfBhIi35akNJTeaRVrAsROmi0HpoeK+6t9dzj8fQt++hpDjrc/frXv475+Xn+2dNOOw133303P0l9IqfjqwnTNFEsFjEzM4NXvvKVkCSJ8/wrX/kKdu7cecwffFlMTk6ahw8fXsZfBywOO/89hZ1tM01h36jbRxB5vnzY2T4722aa9rePIPJ8+bCzfXa2zTTtb58VK53rdvelsG907bOzbQuxlDxfdNDPZDLm9PT0wC5qIez8Col2ts00hX2jbp8VIs+XBzvbZ2fbTNP+9lkh8nx5sLN9drbNNO1v30KsZK7b3ZfCvtG1z862HQ1LyfNFB/1EIrHi248HHnjA3LJlyxE8uVzO3LJli/nAAw+sKP9Kws62maawb9TtI4g8Xz7sbJ+dbTNN+9tHEHm+fNjZPjvbZpr2t8+Klc51u/tS2De69tnZtoVYSp4v+jf60WgUzz33HGKx2Mt927LR6/WO+veax/r6KMHOtgHCPjtA5PlgYGf77GwbYH/7AJHng4Kd7bOzbYD97SMMI9ft7kth3+jCzrZZsZQ8X9TaVqsFj8cz0As76oUcw/F2uCF2tg0Q9tkBIs8HAzvbZ2fbAPvbB4g8HxTsbJ+dbQPsbx9hGLlud18K+0YXdrbNiqXk+ZIG/VF+L1ABAYHFIfJcQMD+EHkuIHByQOS6gID9sZQ8X3TQ7/V6Q3/LBwEBgeFC5LmAgP0h8lxA4OSAyHUBAftjKXm+pEHfbo86CAgI9EPkuYCA/SHyXEDg5IDIdQEB+2Mpeb6kKjCMYtHr9Y7r66MEO9sGCPvsApHny4ed7bOzbYD97SOIPF8+7GyfnW0D7G+fFSud63b3pbBvdGFn2xZiIIP+SmPXrl3Yvn078vl839fz+Ty2b9+OXbt2rdKVLR92tg0Q9o26fcOE3X1pZ/vsbBtgf/uGCbv70s722dk2wP72DRN296Wwb3Tts7NtJ4TF3qMPgNnr9Qb0jn9Hotvtmlu2bDEBmFNTU/x+gLlczpyamjIBmFu2bDG73e6KXcNKwc62maawb9Tts0Lk+fJgZ/vsbJtp2t8+K0SeLw92ts/Otpmm/e1biJXMdbv7Utg3uvbZ2bajYSl5vqRBv9PpDOyijgbrDZiamjL37t3b9/90o0YRdrbNNIV9o24fQeT58mFn++xsm2na3z6CyPPlw8722dk207S/fVasdK7b3ZfCvtG1z862LcRS8tzx/77xmPB4PNB1fcXfpiOfz2PHjh3Yt28ff21qagq7d+9GKpVa8u9pNpuo1WqYn5/H9PQ0CoUCisUiDMOAqqool8v8Ua/X0Ww20Wq10G630Wg0oOs66vU6Go0G2u02Op3OERxOpxNOpxNerxderxdutxsejwcejwderxfhcBjRaBTBYBCRSAQulwv33nsvCoUC/46JiQnceuutOPXUUyHLMpLJJMLhMAKBwMi9UurR7t1pp52GT33qU3A6nTAMA5qmQVVV1Ot1GIaBRqOBer0OVVWhaRrq9Tp0XUez2US73Ua9Xker1UKn0+H70O120ev1sDBkXS4XfD4fAoEAfD4fJElCIBCA1+uFz+dDOBxGKBRCJBJBMBhELBaDLMuIxWJIp9NIJpNIp9OIRqNHjfNBxeYg0Ol0MD8/j2q1ilwuh1wuh1qthnq9jlqtBsMwYBgGqtUqdF1HrVbjD8Mw+vzbbrdRLBbh8/k4z0OhENrtNtxuN3w+H7xeL2RZhqIoCIVCkGUZkUgEoVAIoVAI0WiU/RuLxfjfZFlGNBpFNBqFw+FYk75cCE3TUC6XMTs7i+npaWSzWdRqNeRyOZRKJei6zj6s1WpoNpsct1RDqI4shNPpZH+Sb6leBINByLLM8RsMBhGPx6EoCvs2nU4jkUggGAyyr8PhMNxu99D8M+h7R7Gs6zp0XWcfU9xms1nk83kUCgXMzc2hVCqhVqtxHaHaYP2bO7fb3edfv9+PcDgMRVGgKApisRgURUEgEEAoFEIikYAsy8hkMvD5fHj/+9+PAwcODMS+YaHZbKJcLqNQKHDcFgoF6LrOPe2v/uqvuEfpuo4LLrgAxWKR60Gz2USn00Gv10O32+2rsW63Gy6XCy6XC36/H16vl//r8XggyzLX11AoBL/fD9M08bWvfQ1zc3P8e0499VT80z/9E6ampqAoylBjd1DodrvI5XLYu3cv3v/+9+PFF1/kf0un07jxxhu515GW0DQNtVoNjUYDzWYTmqaxz1utVl/8OhyOvn4mSRKCwSAUReGvKYoCSZKQSCSQTCYRCoW4rymKgmQyiVQqhUQi0Vd7l4rVrtHtdhvVahW1Wg3lcpnrQS6XQ7VaRbFYhKZprCHIz9TzrDFNOo7qM2mIo8Hn83H9CAaD8Hq9kCQJsizD7/dDlmXWDdT/QqEQ67ZIJIJwOIxwOMyabjXf3m652p38XqlUkM/nUavVoGkaisUiyuUyKpUKcrkc7rvvPqiqyj/n9XqRTCbR7XY55km/mS8dLvbxuFwuOJ1O1tE+nw8+nw+KosDv9yMSiXDNDofDSCaTyGQyCIfDkCQJiqKw1qb/KooykNcnGGYutNttzM3NQdM01hqk63K5HGuTubk59mu9XkelUuFa32q1+mr50eq5w+GA0+mEx+OB0+lEo9Hoywm/348tW7ZgbGwMkiSxnqN4TyQSrE8mJiaQTCYRDAZPqNasJBa7d6Zp9s0gpKkrlQpKpVJfDbfeD9Ioqqr2aUGqM0er6dae6fV6uYZTTff7/VAUBZFIBJIkwe/3IxgM8mwyNjaGyclJJBIJSJLUZ+dS8nzRQT8QCKBQKOCiiy6Cw+HgAZaEUjQa5SafSCQ46fx+f9+g4Pf72VASDcBLjbPT6aDVamHv3r24+OKLmftLX/oS0ul0XzDrus7Fv1KpYHZ2FoVCAdlsFtVqFd1ul5sdOSaRSHCDpOEjFotBkiR2PA2FZJskSSxwnE4nB7E1eazinkRnq9VCtVrtu0ZVVXHw4EF87nOfY9uuuuoqOJ1Obmjz8/NQVRWGYcDv9yOVSnHTiMViSKVSLFZJUFFhC4fD7F8KHiqa5GurHaZpcuGla6alR6PRQKVSQbFYxNzcHFRV5UExn8/zNVJjJfs0TesrJhs2bMBpp53GTY+GRUmS+j5IpFBDpftAH9R4rfeCPqhpdLtdNJtNHrho2KVmX61W+VopfqhhZbNZFItFTvBIJIKxsTGMj48jHA4jGAwikUig2Wz23b8vf/nLOP/887kx0dDm9/u5gFJBpeuk66HrU1WVhXij0YCmaTAMg+O8WCyiUChgZmaGF1eVSgXxeByhUAipVAqZTIabIA2LFBNWv9PnJPYDgQCLdIfDwXnu9/s5J0mIUlGjRl8ul6GqKlRVRaVSYf+WSiX+nPxsGAai0SiSySQ3C7/fj3vvvZd9uXPnTpx11lmIx+MslujeUx1xu91cOyiG6TrJr81mk6+JRCCJ7Eqlwk20Vqtx3tH1FgoFtNttxGIxZDIZTE5Osm/T6TTi8TjXBp/P11fTKG4p537xi19g27ZtbN9Pf/pTTE1Ncb3odDosQNvtNt9vEql0763Xl8vlWOCqqsqNyOv1Ip1Os/gcGxuDLMsct3T/KSZoYeP3+zlWKIbJtySQKG6pXhytRt9zzz2Ix+O8vCuXy5ibm0OlUuFYnp+f52WUpmmoVCqo1WpotVq8vAgGg4hGo3ztNHzTwDI2NsbLDaojC+tCr9fri4dOp8ONnGoY+ZDyr1AoQNM0zM7OYnZ2FsViEbOzs2zfueeei1QqhUAgwP6KxWKIxWJ9DZp6HvmVRKo1jin3qJdY6wIJCMqvSqXSNzCWy2WUSiUe4rPZLNfedrvN/ZfilnoxCYb3ve99nPeFQgGHDh3iWLYurBf2CvJpt9vlOksik0QNDQRUFxqNBgzDwL59+3DXXXexL88++2y0Wi3k83lomgZJkjA+Po5oNMr9gHxJgpJigZbndA+svZvug7Xuko/punu9Hi/h6N5TLBYKBV6EUt2gxQnlYbFY5LiNx+OYnJyE0+nE448/zvZdd911OP3005FKpVgck02yLMPn83G9oJzzer19fZn0BfUJigtaFFgXusVikXOL+pmmaZibm0Mul4NhGJz/mUwGmUyG6xjlGWkI67AUCAQwPT2Nq666im37z//8T2zatKnPv1Yd0el0+jRQu93mgYVigq6xWq1ynaYFXz6fx/T0NMrlMnq9Hud5JBLh3pVKpVi7kW9pqU9DoDWmyd/Um6214umnn8bmzZvZvieeeAJnnXUWayJa6DYaDRbymqZB0zSOF8pPsoNqNfWWYrHIWo3qcjwe51hOp9OIRCJIJpN9upoOK6heLNQ95HuqIRTblJ/RaBROp5Nz/fHHH+drprpC9Zjiie4VfU+xWESj0eBFRjqd5vuQSCQQiURY+5fLZdxyyy3sy2984xvYvHkzazi/3w+32913/dRjSEtbe3mr1eJ4IZ1JMV+tVpHP55HL5XjhW6vVWGtTTpOOpvigAwrSThTvFEt0X+j7KG89Hg8OHjyI17zmNX39fNOmTX31kWLeWh/pmihvDcNgjVQqlVCpVDAzM8ML7Wq1ynWbYpquPZVKsTZJpVKs8yVJYp1qrYnkb2vsLKwxlLt79uzBJZdcwvbdfffdSKVS7Edr/yStRPqDtGmn0+EDCVrAkB6lHKYeHo1GEYlEODasvZN6Omm9o8ULaT86CLQu+6gnFgoFzM/P4+DBg7jjjjvYtjPOOAPdbpfrPwD2Xzgc7qsxVAtp2Up9huwiHUVLWKozVNNJk1B+0uEaLQcoTqjvUA+lg2bDMLiW5/N5zMzMoFAoIBAIYHJyEj/5yU84RgqFAr74xS/iwx/+MI6GRQf9WCyGAwcOcIGmomY9Iaevk2iiwYXEN21fSRxYt6oUjB6P54jNkiRJ2LZtGws8EoTWzV0mk0EymcT4+DhCoRAkSRq5zZIVpmlC13XkcjkuWjSIWoUg+Z4ajDVoaICw+nrhJpsEHQkOEqW0QSVxTQ03GAzy8oEaLDWsRqOBa665Zk2e0h4Per0e8vk85ufnMTs7y8XghRdewO233973RIbf78fk5CR6vR4XeVqYWLepBIfDAbfbfcSSgwoHbbCpQFITisfjmJiYwNjYGCYmJpBIJFbkJIzyPB6PD/T3Woc/Xdfx4osv4uabb+4bpqLRKC699FIYhsGxbT2Voc9JzBCsMUzxS8KEfExCNxwOY2xsjJdl1OxpmUbiZbm1Y5gnALSRzmazXIfJzySqrU9zqKrKookWTLquH1ErrCBRTw34aNv/rVu3sjiipQ4NZYFAgE8dqWFGo1GO9bX09k9Hu3fr16/HbbfdBp/Px08mlUollEolbsgkNBuNBvvV2vMojimGrXA4HBy/NIBFIhE+naVaQOKDhvhMJsN+pGXdUrBSeb4QSzlN0TQNMzMzfeLGOkAVCgX2eaVS4cWhdZFLi11anC184osEl7VOkEizin/rME7iNJFI8AKNhF8kEoHb7V71U++lgBZrqqoim83ystAwDPazVcyTllBVFS+88MJRn2ZcCDqJpQ/rk41WQUy1mLQcHVTQk19jY2M45ZRTEIvF4PV6V1TDDevedbtdPsChulwoFHhRS4cLhUKBtRwdVtATjdanEI71JIL1Hvh8Puzfv59j98CBA3j3u98NAH2LE9Ie1hNbWviEw2HE43HEYrFF78NazYNut8uLDKrHNKRaeyItxwqFAh9QVKtVzpFGo4FqtXpM31NvpKebSENT/EciEfazJEkc/1RPJiYmeHiPx+NDf5p3UPePDjiz2SwfBtKinxak9FQeaRXrQoQOGq2HpseKe2s993g8fcs+epqDDne//vWvY35+nn/2tNNOw913381PUi88HV/rME0TxWIRMzMz2LhxI3w+H+f5Zz/7WXzqU5865g++LMbHx83Dhw8v6W8FThR2/nsKO9tmmsK+UbePIPJ8+bCzfXa2zTTtbx9B5PnyYWf77GybadrfPitWOtft7kth3+jaZ2fbFmIpeb7ooH/mmWeazzzzzMAuaiHs/AqJdrbNNIV9o26fFSLPlwc722dn20zT/vZZIfJ8ebCzfXa2zTTtb99CrGSu292Xwr7Rtc/Oth0NS8nzRQf9c88919yzZ8/ALupoeOCBB8wtW7YcsWXJ5XLmli1bzAceeGBF+VcSdrbNNIV9o24fQeT58mFn++xsm2na3z6CyPPlw8722dk207S/fVasdK7b3ZfCvtG1z862LcRS8nzRv9HfunUrbr/9dmzfvv3lvm3Z6PV6R/17zWN9fZRgZ9sAYZ8dIPJ8MLCzfXa2DbC/fYDI80HBzvbZ2TbA/vYRhpHrdvelsG90YWfbrFhKni9qrdfrRavVGuiFHfVCjuF4O9wQ66tYH+3row5h3+hD5PlgYOdYsbNtgP3tA0SeDwp2jhU72wbY3z7CMHJd5Plow8722dk2K5aS54ta7PP5hiIMTgbIsgxd11f7MlYMwr7RhcjzwcLOsWJn2wB72yfyfLCwc6zY2TbA/vaJXB8c7B4rdrbPzrYBS8vzRQd9SZJQr9cHdlEnM1qt1oq8NdpagbBvdCHyfLCwc6zY2TbA3vaJPB8s7BwrdrYNsL99ItcHB7vHip3ts7NtwNLyfNFBPxqNolQqDeyiTmb0er2hv0/mMCHsG12IPB8s7BwrdrYNsLd9Is8HCzvHip1tA+xvn8j1wcHusWJn++xsG7C0PF/SoF8ulwd2UcdCr9c7rq+PGh/97mH8fchq2Ea/X9i3fI7j+fqgIPJ88Lx2jRWR54PjOZ6vDwIizwfPa9dYsbNt9PvtbN8wcl3k+eB5jufrg+QVemX5PMfz9UFhKXm+Jgb9Xbt2Yfv27cjn831fz+fz2L59O3bt2jXSfFasdMCtpm2AsG85WE3bRJ4PHnaNFUDk+XKxWvaJPB887BorgL1tA+xt30rnusjzwcLO9tnZNmAE8nyx9+j7sz/7M/Pmm29exrv8vTy63a65ZcsWE4A5NTXF73uYy+XMqakpE4C5ZcsWs9vtjiSfFUtw97KwmraZprBvOVht20SeDxZ2jhWR58vDaton8nywsHOs2Nk207S/fSuZ6yLPBws722dn20xz9e1bSp4v6oHPfvaz5u/8zu8M7KKOBqtDpqamzL179/b9PzluVPkIAMxer7civ5uwWraZprBvuVhN20SeDxZ2jhWR58vHatkn8nywsHOs2Nk207S/fSud6yLPBws722dn20xz7ee5wzQXvMngAtx999345je/ia9+9asv923LRj6fx44dO7Bv3z7+2tTUFHbv3o1UKrXk39NsNlGr1TA/P4/p6WkUCgUUi0UYhgFVVVEul1Eul5HL5fCTn/wEhmHwz3q9XkSjUbRaLTQaDbTbbXQ6nSM4nE4nnE4nvF4vvF4v3G43PB4PPB4PvF4vwuEwotEogsEgIpEI/v7v/x5OpxMOhwOdTgf/+I//CL/fD0VREIlEIMsykskkwuEwAoHAsl84YlC+PF6QfYcOHUKlUkE2m0W1WoVhGNA0Daqqol6vwzAMNBoN1Ot1qKoKTdNQr9eh6zqazSba7Tbq9TparRY6nQ7fh263i16vd8T7YhJ3NBqFJEmQJAmBQABerxc+nw/hcBihUAiRSATBYBCxWAyyLCMWiyGdTiOZTCKdTiMajcLr9S5q30q+sMdi967T6WB+fh7VahW5XA65XA61Wg31eh21Wg2GYcAwDFSrVei6jlqtxh+GYfT5t91u46qrrsLXvvY1zvPrr78e11xzDdxuN3w+H7xeL2RZhqIoCIVCkGUZkUgEoVAIoVAI0WiU/RuLxfjfZFlGNBpFNBqFw+FYsn3D9qcVmqahXC5jdnYW09PTyGazqNVqyOVyKJVK0HWdfVir1dBsNjluW60W2u02yuUyXC4Xx4rf7+eYsdYLn8/H9SIYDEKWZQQCAfh8PgSDQcTjcSiKwr5Np9NIJBIIBoPs60ajgSuvvHLV8ny5eUCxrOs6dF1nH1PcHjhwAF/84hf7HkvzeDxQFAXNZpNrg/Vv4Nxud59//X4/wuEwFEWBoiiIxWJQFAWBQAChUAiJRAKyLCOTyeANb3gDHA7HUPIcGGzPK5fLKBQKHLeFQgG6rqPRaEDXdZx55pn40Ic+xHn+/ve/HzfeeCPXA/Jnr9dDt9vtq7Futxsulwsulwt+vx9er5f/6/F4IMsy19dQKAS/3w/TNPG1r30Nc3Nz/HtOPfVU/NM//ROmpqagKMpAXwl5WHWl2+0il8thfHy8L1b+/u//njVGLpeDqqrQdR31eh2apqFWq6HRaKDZbELTNPZ5q9Xqi1+HwwGXywWfz4dAIABJkhAMBuH3+/Hss8/26ZVYLIbf/u3fxsTEBPc1RVGQTCaRSqWQSCT6au/xYjXzYMOGDbjrrrvgdru5HuRyOVSrVRSLRWiaxhqC/Ew9zxrTpOOoPne7XbRarb4aLUkSAHCMu91uBINBeL1eSJIEWZbh9/shyzLrBup/oVCIdVskEkE4HEY4HGZN5/V6V02PLVe7k98rlQry+TxqtRo0TUOxWES5XEalUkEul8N9990HVVX557xeL5LJJLrdLsc86TfzpcPFPh6XywWn08k62ufzwefzQVEU+P1+RCIRrtkejwcPPPAACoUC//wrXvEKfO5zn8Npp53GmltRlIE+rj2MXGi323j66afx67/+6zh48CB/fXJyEh/4wAfQ7XYxOzuLubk59mu9XkelUuFa32q1+mr50eq5w+GA0+mEx+OBpml9uXD22WezxohEIpAkifUcxXsikWB9MjExgWQyiWAwuGitWe25ZOG9M02zbwYhTV2pVFAqlfpqOGk/+lzXdaiq2qcFm80mdF0/6nzi8/m4Z3q9XiiKAkmSuM5b50FJkuD3+xEMBnk2GRsbw+TkJBKJBNcrwlLyfNFB/6GHHsJtt92Gv/mbv8GNN97IAywJpWg0ykGRSCSgKAo3Juug4Pf72VAqqMBLjbPT6aDVamHv3r24+OKLmftLX/oS0ul0XzDruo5yuQxd11GpVDA7O4tCocBDZbfb5WZHjkkkEggEAlAUhYePWCyGbDaLd7/73cz37//+7zj//PO5wZLAoSEdQF/yUPOgQZQaSrVa7bvGG264gYuYruv4wAc+wINvtVrlxYSqqjAMA36/H6lUiptGLBZDKpVisUqCigbXcDjM/qXg2b9/f58vn3zySWzevBkOhwOmaXLhpWuu1+vcGCuVCorFIubm5qCqKg+K+Xyer5Eaa6VSwXPPPcfCWtd1nHvuuQgEAshkMtz0aFikQZw+QqEQgsEgN1QazumDhLv1XjidTjz99NPYtGkT2/fjH/8Y69evR6vV4mGXmn21WuVrpfihhpXNZlEsFjnBI5EIxsbGMD4+jnA4jGAwiDvvvLPv/j3yyCMIhUJQFIUbEw1tfr8fHo+HY8bpdHJzo+uh61NVlYV4o9GApmkwDAMHDhzAbbfdxra96lWvQqPRwPT0NCqVCuLxOEKhEFKpFDKZDDdBGhYpJqx+p89pIUVNk/xNef7www+j0+mg0+mwEKWiRo2+XC5DVVWoqopKpcL+LZVK/Dn52TAMRKNRJJNJbhZ+vx/33nsv27dz506cddZZiMfjLJbo3lMdcbvdXDsohql2kF+bzSZfE4lATdPwzDPP4L//9//OfG984xvhdDpRq9X4eguFAtrtNmKxGDKZDCYnJ9m36XQa8Xic657P5+uraRS3brcb4XAYDoeDY8Xj8RxRLzqdDgvQdrvNdY1Eqq7rKBaLfdeXy+VY4Kqqyo3I7Xb3vYfqlVdeicnJSQSDQR5iqSbTwEv3gGKFYph8SwKJ4pbqhc/n67Pt+eefh2EYKJVKvLwrl8uYm5tDpVKBYRjQdR3z8/O8jNI0DZVKBbVaDa1Wi5cXwWAQ0WgUsizz4iOTyaDb7eLP/uzP2L5vf/vb2LZtG9dna13o9Xp98dDpdLiRUw0jH1L+FQoFaJqG2dlZ3HvvvTy86rqOs846C8lkkvOL/BWLxRCLxfoaNPU88iuJVGscU+5RLzFNE3v37sX555/f14OSySQqlQrfa8qlUqnEQ3w2m0WlUoGqqmi329x/KW6pF5NgOPXUU/GmN72J8/xb3/oW8vk892iKX+rNVL/Ip91uF91ul3sE1QaqD7VajetCo9GAYRjYt28f7rrrLrbt7LPPRqvVQj6fh6ZpkCQJ4+PjfQta8iUJSooFWp7TPZAkia+b7sMvfvELbN26lfl+9rOfYWpqihfE7Xabr43EXaVSQaFQ4EUo1Q1anFAeFotFjtt4PI7Dhw/3xcqHPvQheDwehMNhpFIpFsdkkyzLLPaoDpPvydemabK+oD5BwrJWq2Hfvn246aab2L6PfvSjcLvdfQOYpmmYm5tDLpeDYRic/5lMBplMhusY5RlpCPJ7JBLB1q1beRDQdR3dbrev99KCw6ojqGdYh2pN07h3UH8rFouoVqtcp2nBd+jQITzzzDNsG2lLOgiRZRmpVIq1G/mWlvqBQIAHbvIr+Zv6HMW32+3uq2Mul6svxtvtNi90G40GC3lN06BpGscL5SfZQbWaNF2xWGStFgwGsXfvXrbvXe96FzZt2oRIJIJkMtmnq30+HyRJ4nph9b01VqiGUGxTfvZ6PSSTSc71b37zm3jwwQd5aKfFU71e5wMCulf0PcViEY1GgxcZ6XSa70MikUAkEmHtXy6Xccstt7Bt3/jGN7B582bWcH6/H263u+/6qcdQb7T2cjpoowMg8icdYOzduxe33347811wwQVwOBxsG/Ue0sOkjSKRCGsnineKpXg8zpqJhi1FUXihR7FijX9rfaSYt9ZH0v9UU6hXklaqVCqYmZlBNptFPp9HtVpFIpGA1+vF4cOH2b7LL78cZ555JmuTVCrFOl+SJNap1ppI/l6Yt9YaQ0suq30vvvgi+5v8aO2fpJVIf8zMzGB+fh6dTocPJMLhMOt5WoDJsoyxsTEYhoFPfvKTbNs999yD8847j3sn9XTSekeLF9J+dBBoXfZRTywUCrj++uv7dPtVV13FBwtU/wGw/6h2U42JRCJ9y1bqM2QX6ShFUXgG2L9/Py644AK2b8+ePTj77LP7DtdoOUBxQn2Hemi9Xuc+RbU8n89jZmYGhUIBgUAAk5OTeO1rX4v//b//N+f5P//zPx9zYbLooP/MM8/gmmuuwc9//nPs2bOHi5r1hJyGKBJNNLiQ+KbtK4kDarwAOBg9Hg8ajUbfdluSJGzbtg2xWIxvhrU5RSIRZDIZJJNJjI+PIxQKceAuhtXYLgUCAb5Rx4JpmtB1HblcjosWDaK6rvcNWrRMqFarfUFDJzwv92qPJOhIcJAopQ1qIpHA2NgYDwfBYJCXD9RgSXxNTk7C4XAsyb5BYKXuXa/XQz6fx/z8PGZnZ7kYXH/99SwoC4UC3v72t2NmZgaaprHYtS5MrNtUgsPhgNvtPmLJQYWDNtjAS8s16ynmunXr8MUvfhGbNm1CIpFYkfcEpTx/9tlnB/p7rcMfNZKbb74Zs7Oz/D3RaBSXXnopDMPg2LaeytDnJGYI1him+CVhQj52u9149NFHUalU+OfGx8fx6U9/Gqeccgov00i8LOcEzIph5EIul8OOHTv6BPLExAQ+8pGPwOl08pBCIsm6WKQFk67rHLvWukwgQeN2u1EoFHjoLRQKuOiii2CaJg9gkiTxUoe+FggEkEwmuTbTQE9Lv5c7dVmtEwCyj07IaTFATyaVSiWUSiVuyLVajXse+dXa8yiOKYZfDl6vF1NTU7zYpYGMxAcN8ZlMhv0oy/KS43al8nwhFrt3pmlC0zTMzMz0iRvrAFUoFNjnlUqFF4fWRS4tdmlRvRB0Qm6tEyTSrOLfOoyTOE0kEnx6S8IvEon01d+13PNosaaqKrLZLC8LDcNgP1vFPGmJRx55hIVtoVDAunXrUKlUjvo0HZ3E0of1yUarICbhT1qODipkWQYAfPKTn8QLL7ywZNsGgZW+d91uF/Pz83j++efxjne8A4cOHeJ/Gxsbw5ve9Ca0Wi0UCgXWcvR0GD3RSD1voZ6wwnoPfD4fTj/9dDz++OOc6z/4wQ/wnve8p+8JUtIe1hNbWviEw2HE43HEYrE1d0q7VL5ut8uLDKrHNKRaeyItxwqFAh9Q0FOotVoN+/fv5zguFAoYHx+HqqqcB9QbaelnPbml+kJ+liSJ45/qycTEBA/v8XgchUJhVXveieYCHXBms1k+DKRFPy1IX3jhBXz1q19FtVrln/P7/chkMn09khY+x4p768zo8Xj6ln30NEcikcAdd9zB9b5QKOCnP/0pLz6p3i88HV8OhpELpmmiWCxy37zssss4zx977DGEw+Fj/uDLYn5+3oxGo0v8a4ETw8nytz7RaNQsFAor8rsJq/m3IsK+5WE1bRN5PljYOVZEni8fq2WfyPPBws6xYmfbTNP+9q10ros8HyzsbJ+dbTPNtZ/niw76jUbDdLvdK/ZiBifTq3eOj4+bhw8fHvjvJaz2qz8K+04cq22byPPBws6xIvJ8eVhN+0SeDxZ2jhU722aa9rdvJXNd5PlgYWf77Gybaa6+fUvJ8yW974DP5zN1XR/YhS3EAw88YG7ZsuWIrUculzO3bNliPvDAAyPNRzjzzDPNZ555ZkV+N2G1bDNNYd9ysZq2mabI80HCzrEi8nz5WE37RJ4PDnaOFTvbZpr2t880VzbXRZ4PFna2z862mebaz/MlDfpjY2NmNpsd2EUdDcfadqzUFmTYfKZpmueee665Z8+eFfv9hNWwzTSFfYPAatlmmiLPBwk7x4rI88FgtewTeT442DlW7GybadrfPtNc+VwXeT5Y2Nk+O9tmmms7z5f0/hOZTKbvxbNWAsd6UaZBvkXG0X6vueDtPlaKD3jpraGsr5C9UlgN2wBh3yCwWrYBIs8HCTvHisjzwWC17BN5PjjYOVbsbBtgf/uAlc91keeDhZ3ts7NtwNrO8yVdQSKRwPz8/MAuai3h8ssvx/e+972hcHm93qEEHGGYtgHCvkFi2LYBIs8HCTvHisjzwWLY9ok8HxzsHCt2tg2wv32AfXNdxMpgIfTK4LAW83xJg/74+Diy2ezALmotwe12971l10rC5/MNNeCGaRsg7Bskhm0bIPJ8kLBzrIg8HyyGbZ/I88HBzrFiZ9sA+9sH2DfXRawMFkKvDA5rMc+XNOhHIpG+96G2Ezwez1Hff3clIEkS6vX6ULiA4doGCPsGiWHbBog8HyTsHCsizweLYdsn8nxwsHOs2Nk2wP72AfbNdRErg4XQK4PDWszzJQ36wWAQuq4P6prWFKLRKMrl8tC4SqXSULiIb1i2EZ+wb3Bcw7QNEHk+aD67xorI88HzDdM+keeD5bNrrNjZNuKzs32AfXNdxMrg+YReGRzfWsvzJQ360WjUlltBYPiNc9gBLvhGk281ioXI89Hks7Ntgm9l+ESeC761xCX4Vo7Tjrlu93tnZz4727YafMQpTvRfBiLABd9a5FuLW8FRhogVwSf4XoLIc8G31rgE38rArrlu93tnZz4727YafMCATvTtWiwAIBQKoVar2Y5L8I0237BtA0SejyqfnW0TfIOHyHPBt9a4BN/KwK65bvd7Z2c+O9u2GnzA4nnufrkf7vV6cDqdiEQiqFarA7+4lUCz2UStVsP8/Dymp6dRKBRQLBZhGAZUVUW5XEa5XMYFF1yAD3/4wwgEAjAMA//+7/+O3//934eu66jX62g0Gmi32+h0OkdwOJ1OOJ1OeL1eeL1euN1ueDweeDweeL1ehMNhRKNRBINBRCIRvP71r8fb3vY25tq/fz+eeOIJKIqCSCQCWZaRTCYRDocRCATgcrkG4gviGxaIT1VVHDhwANlsFtVqFYZhQNM0qKqKer0OwzDQaDRQr9ehqio0TUO9Xoeu62g2m2i326jX62i1Wuh0Onwfut0uer0ebrrpJnzxi19kvn/8x3/E7/zO7yAQCMDn80GSJAQCAXi9Xvh8PoTDYYRCIUQiEQSDQcRiMciyjFgshnQ6jWQyiXQ6jWg0Cq/Xuyb8eSyuTqeD+fl5VKtV5HI55HI51Go11Ot11Go1GIYBwzBQrVah6zpqtRp/GIbR5992u41Wq4Urr7wS3/rWtzjPH3jgAVxzzTXw+Xzwer2QZRmKoiAUCkGWZUQiEYRCIYRCIUSjUfZvLBbjf5NlGdFoFNFoFA6HYyg+ezks5d5pmoZyuYzZ2VlMT08jm82iVqshl8uhVCpB13X2Ya1WQ7PZ5LhttVpot9u44YYb8Ld/+7fM96UvfQnvfOc74XK5+uqFz+fjehEMBiHLMsdvMBhEPB6Hoijs23Q6jUQigWAw2Odrl8u1anm+XFAs67oOXdfZxxS36XQab3rTm5jv3nvvxe/93u9xHaHa0Ov1+He63e4+//r9foTDYSiKAkVREIvFoCgKAoEAQqEQEokEZFlGJpPBOeecg3Xr1o2cP5vNJsrlMgqFAsdtoVCArutoNBrQdR2apuGVr3wlbrnlFs7z3bt3421vexva7TaazSY6nQ56vR663W7f+wC73W64XC64XC74/X54vV7+r8fjgSzLXF9DoRD8fj8CgQASiQTGxsYQiUSgKArXjrGxMSiKArf7ZeXHCWGl712320Uul0Or1cKpp57KfM8++yx27doFVVWRy+WgqiprCU3TUKvV0Gg00Gw2oWka+7zVavXFr8PhgMvlgs/nQyAQgCRJCAaDuP766/HHf/zHzPfoo4/iG9/4BpLJJEKhEPc1RVGQTCaRSqWQSCSWVXtXKw96vR5KpRJqtRrK5TLXg1wuh2q1imKxCE3TWEOQn6nnUY9rNpus46g+d7tdvOtd7+rTD1/60pfw3ve+l2Pc7XYjGAzC6/VCkiTIsgy/3w9Zllk3UP8LhUKs2yKRCMLhMMLhMGs6q54Ytj8BLEu7k98rlQry+TxqtRo0TUOxWES5XEalUoGqqqzh6PupT7ZaLY550m8L32McAFwuF5xOJ+ton88Hn88HRVHg9/u5fgQCAbzxjW/E9ddfz7588cUXsX//ftba9F9FUQb6/uXDuHftdhuapiEajTLf7OwsfvzjHyOXy7E2mZubY7/W63VUKhWu9a1Wq6+WH62eOxwOOJ1OeDwe3HTTTfjc5z7HfP/2b/+GW2+9leu5JEms5yjeE4kE65OJiQkkk0kEg8El15q10l9N0+ybQUhTVyoVlEqlvhpOMU2f67oOVVX7tODb3/52/M3f/M0R2s/hcPT1TK/XC0VRIEkS13m/38/zoCRJ8Pv9CAaDPJuMjY1hcnISiUQCkiQdYctief6ynfaKK67Ad7/7XUiSBF3X8cQTT+CP/uiPEAwGWShFo1EOikQiAUVREAwG4ff7+wYFv9/PhlJBBV5qnJ1Oh4e6RqPRVyzI0RTMuq5z8a9UKpidnUWhUOChstvtcrMjxyQSCQQCASiKgo0bNyIajeKss84CAMTjcRQKBVx22WW499572TZJkljgOJ1ODmJr8lDzoEGUGkq1Wu27RlmW+7gMw8B9990HVVVRrVZ5MaGqKgzDgN/vRyqV4qYRi8WQSqVYrJKgosIWDofZv5Ik8fcQHyU62WGaJhdeuuZ6vc6NsVKpoFgsYm5uDqqq8qCYz+f5GqmxVioVXHzxxbj77ruZ7/HHH8fNN9+MTCbDTY+GxXA4jHQ6DUmSuIgEg0FuqDSc0wcJd+u9oAJOfDfccAN+9Vd/lQcuGnap2VerVb5Wip9sNotiscj/pQQnITo+Po5wOIxgMIhLL70U73znO5lvbm4OL7zwAhRF4cZEQ5vf74fH42FfO51Obm50PXR9qqqyEG80GtA0DYlEAq973euYa8+ePfiDP/gDXlxVKhXE43GEQiGkUilkMhlugjQsxuNxnHPOOX1+p89pIRUIBPhzj8cDAJznr3/961GpVFiIUlGjRl8ul7nBVyoVFmClUol9TX42DAPRaBTJZJKbBV2zLMs81IZCIcTjcRZLdO+pjrjdbq4dFMNUO8ivzWaTr4lEYDgcxkUXXcT+fOqpp/Dnf/7nnHd0vYVCAe12G7FYDJlMBpOTk3yd69evx9atW7k2+Hy+vppGcUvXa43N3/zN38QNN9zQVy+ozlHtoLpGIlXXdRSLRRa6hw4dwoMPPsgCV1VVlEolXH755fj617/OXD/84Q/xmc98hv1KQyzVZBp4rcNYMBjkGCbfUn5R3FK96PV6HF+FQgG1Wg0HDhxAqVTi5V25XMbc3BwqlQrX7/n5eV5GaZqGSqWCWq2GVqvFy4tgMIhoNMrXLssyduzY0efLSy+9FA8++CA3yYV1odfr9cVDp9PhRk41jHxI+ffcc89B0zTMzs5iw4YN+MxnPsN8Dz30ED7+8Y9zfpG/YrEYYrFYX4Omnkd+JZFqjWPKNeolVJOJr16v48UXX2QRTfeacqlUKvEQn81mWWi3223uvxS31IsjkQgmJiYQDAaxfv36vjx/9atfjT179vQtoKg3U/0in3a7XXS7Xe4RVBuoPlCsUk0zDANzc3N4+umnUS6XoWka3/t8Pg9N0yBJEsbHxxGNRrkfkC9JUFIs0PKc7oEkSXzd9DWfz8e+JGFL193r9dBut/naSNxVKhWOZesCmhYnlIfFYpHjNh6P4x3veAf+8i//kvkajQYOHjyIcDiMjRs3sjgmm2RZhs/n43pBOef1evv6MukL6hMkLGkpQnzr16/HxMQE9wUawDRNw9zcHHK5HAzD4PzPZDLIZDKIx+PsU+twRH6PRCJIpVJYt25dn37odrvcex0OxxE6otPp9GkgGlqod1B/KxaLqFarXKd1XceGDRvw8Y9/nPnuv/9+/OZv/mbfQYgsy0ilUohGo4jFYli3bh3HDOUmDdzkV/I39WZrrVhYo9/2trexne12mxe6pEdpQaNpGscL+ZrsoF5CvaVYLCISieDaa6/F7bffznx79+7F3XffjXQ6jUgkgmQyyQdD1sMKqhdW3WONFaohFNuUn/SK3+Pj45zr8/Pz+Pa3v811heoxHRBYB3XSf41GgxcZ6XSa70MikeA4OfPMM/t6OX1OQwzdA7fb3Xf9dA9IS1t7OS0I6ACI/Elawnrv5ubm8Gd/9mestSmnSUdTfNCSkbQGxTsd+sTjcdaqNGxZc4L4er0eTNPkHKD6SDFvrY90TVRTDMNgjVQqlVCpVDAzM4NsNot8Po9qtYrrr78e//AP/8B8L774Iu666y6kUinEYjFs2LABl1xyCftXkiTWqdaaSP62xs7CGtPpdLgXEd/rX/96vOIVr+A4oFpJ/fP555/HY489xoPwzMwM5ufn0el0+EAiHA6znqcFmCzLePWrX41rrrmGuUqlEg4dOtTXO6mnk9Y7WryQ9qOZ0brso55YKBQQDofxlre8hfl+8IMf4H3vex/XJE3TAID9Fw6H+2oM1UbqQ9TvyS7SUYqi8MBu9eVv/dZv4cYbb+T8pMM1Wg5QnFDfoR5KB82GYeCJJ55ALpdDPp/HzMwMCoUCAoEA9/nLL78ct9xyC+f5CQ36Dz30EICXHgswDAMbN27EH/7hH/IQTifk1WoVhw8fRqFQ4IJBRlCBtIoDarwAOBitgigUCnHxpiZON8PajCKRCDKZDJLJJMbHxxEKhSBJ0nFtsROJBAdFOBxe9PspaejU6HhAXOeddx7uvvvuo36PaZrQdR25XI6LFg2idDJz4MAB9j01GGvQnHfeefj2t7/NfHv37sW2bdv6Tg5I0JHgIFFKG1Q6jaHhIJ1OY/PmzZzEVGDo1NZq32tf+1r89Kc/PS7fnAiIz+l0chNaDnq9HvL5PObn5zE7O8vFgAZh4qtWq/jgBz8ITdNY7FoXJtZtKsHhcMDtdrPvaMlBhYM22Oeeey5e97rXMdfk5CR+7/d+D2NjY5iYmEAikViRkzDg/+e50+nka1wurMMfFdjZ2VkWG4ZhYM+ePTy80rBgPZWhz0nMEKwxTPFLwoSuf8uWLbjooovYn+l0GldccQU3e8p7Ei+DevqA+OiEjk70BwkSe8R1zjnn4JprruE6USwWcfjwYfazdbFICyZd1zl2rXWZQILG7Xbjoosuwne/+13mO3z4MG666SYewCRJ4qVOJpPh+k2njnQ6GY1GeVhfyqnLUmu09SmrYDB4wn4lvksuuQR/9Vd/xYsBejKpVCqhVCphZmambzHdaDT6TtCpJlMcUwwTLrzwQvzwhz9kvoMHD+JXf/VXEYlE+HSWBHQ0GsX69etx4YUXIpFIIJPJsB9lWT6uuKU89/v9yGQyL/u95FPCcmsswTRNaJqGmZmZPnFDQweJtVKpxH2QFofWRS4tds877zz8n//zf9iXP/3pT7F161bu19Y6QSLNKv6tw7gsy3xIQKe3JPwikUhf/SW+zZs347Of/eyyfGIVtHTqsxDEd8opp+CjH/3oy/4+WqypqopsNotcLsdPN9ZqNZRKJTz//PNcG0hLvPKVr8Q///M/9+mH7du3s8i2grQQfVifbLQKYhL+sVgM4XAYkUgE69evhyzLOO200/pse+Mb34hisbgsXy4F1hq9UMvFYrFl//5ut4v5+Xl+D2/iy2QykGUZBw8e5EUTaTk6rKAnGqnnLdQTVljvAQ3XF1xwAb75zW9yrlcqFezevZuHrmg0ikwm03diS4N6OBxGPB5HLBZb8afxKD9JYy0V5Mvt27fj4YcfPuLfu90uLzKoHtNC3/qEY7FYRD6fx09+8hM+oKCnUGu1GjZv3owHHniA+fbs2YNt27ZxHlBvpKebSENT/EciEV44SJLE8b9+/XpewmYyGaRSKcTjcT4AJb4LL7wQ3/zmN5ftZ8LCGrPQn7IsY9u2bcf9e+mAM5vN8mEgLfppQXr48OE+rvn5edx00008pFOPpIXPseLeWs89Hk/fso+e5kgkEti+fXsf36ZNm/DlL3+ZD0xpITVoEB9dK+XnILhM00SxWOQFEelhyvNj4WUnBgoIehRBlmVcccUVy77YtYTJyUlMT0+vGS6HwwFZlnHGGWcMjO/888/vG5BWCsP05UrwOZ1OPvnYtGnTMfnOPPNMPP744wPjPRqIK5FI4A1veMOKchFW4pEqGiYWGyhWGuTPZDKJG2+8cWh8KwkSYcQVCoXwtre9bUU5rXxnn332UBZ6q1VXfD4fXv3qVw+N79xzz8X+/ftXnG81HiFeCIfDwU/YDRLkyy1bthwxlK4E1nLPo+EimUzi9NNPP2Gu888//6h/wjhoEN+w/tRrpe+dy+VCOp0+gi+ZTGLnzp0rxmsF5fqZZ56Ju+66ayicw8Bi987lcvHhTzKZHBjfli1bjrlwGSTWcl05GrxeL5LJ5JJ8TVwbNmwYqn5QFAWbN28eGt9KwOFw8FPqVlsW6+lLfjG+1RYGK4VkMon5+XnbcQm+0eYbtm2AyPNR5bOzbYJv8BB5LvjWGpfgWxnYNdftfu/szGdn21aDD1g8z5c06CuKAlVVB3ZRawmKoqBWqw1l+z9MLsE32nzDto04RZ6PHp+dbRN8K8Mn8lzwrSUuwbdynHbMdbvfOzvz2dm21eAjzpfL8yUN+n6/H41GY2AXtZZAL0JVr9dtxSX4Rptv2LYBIs9Hlc/Otgm+wUPkueBba1yCb2Vg11y3+72zM5+dbVsNPmDxPF/SoO92u4fyN1qrhXA4PLSt5zC5BN9o8w3bNpHno8tnZ9sE32Ah8lzwrUUuwTd42DnX7X7v7MxnZ9tWg2+xPF/SoO/1evktO+yITCaD2dlZ23EJvtHmG7ZtIs9Hl8/Otgm+wULkueBbi1yCb/Cwc67b/d7Zmc/Otq0G32J5vuQT/WG8avtqIZFIDO3FE4bJJfhGm2/Ytok8H10+O9sm+AYLkeeCby1yCb7Bw865bvd7Z2c+O9u2GnyL5fmSBn2XyzWUt5RYLYyPjyObzdqOS/CNNt+wbRN5Prp8drZN8A0WIs8F31rkEnyDh51z3e73zs58drZtNfgWy/MlDfrDwLEuchhFKhKJoFKprDjPsLkE32jzDds2u0PEiuATfPaH3e+dqCuCb63gZNHtgm90uU4GvsWwpEG/2+3C6Vy5ncCuXbuwfft25PP5vq/n83ls374du3btWjFu4KX3INR1fUU5VoNL8I0237BtW+k8X22IWBF8gk/kueBbm1yCb/BYyVw/mXS74BtdrpOBb7E8X/Kg73K5BnZRVvR6PezcuRNPPvkkduzYwUUjn89jx44dePLJJ7Fz584V3RBGo9GhbV+GySX4Rptv2LatZJ6vBYhYEXyCT+S54FubXIJv8FipXD/ZdLvgG12uk4FvsTxf0qDfarXg9XoHdlF9F+B04v7778fU1BT27duHHTt24KmnnsKOHTuwb98+TE1N4f7771/REwixyRJ8a5Fv2LatZJ6vBYhYEXyCT+S54FubXIJv8FipXD/ZdLvgG12uk4FvsTx3v9wP93o9OJ1ONBoN+Hy+gV8cIZVKYffu3VwkNm3aBACYmprC7t27kUqllvy7ms0marUa5ufnMT09jUKhgGKxCMMwoKoqyuUyf1x44YX4xCc+wTfl/vvvx3ve8x7U63U0Gg202+2jvjeh0+mE0+mE1+uF1+uF2+2Gx+OBx+OB1+tFOBxGNBpFMBhEJBKBoih4zWteg+uuu465XnzxRezduxeRSASyLCOZTCIcDiMQCAx0AzusgKNYIb5SqYQf/vCHqFarMAwDmqZBVVXU63UYhoFGo4F6vQ5VVaFpGur1OnRdR7PZRLvdRr1eR6vVQqfT4fvQ7XbR6/Vgmiauu+463H333cz3b//2b3jf+94Hn88HSZIQCATg9Xrh8/kQDocRCoUQiUQQDAYRi8UgyzJisRjS6TSSySTS6TSi0eiiTXG1i2Gn08H8/Dyq1SpyuRxyuRxqtRrq9TpqtRoMw4BhGKhWq9B1HbVajT8Mw+jzb7vdRqvVQiKRwOzsLOd5Pp/HunXr4PP54PV6IcsyFEVBKBSCLMuIRCIIhUIIhUKIRqPs31gsxv8myzKi0Sii0SgcDsdQ/LUYFrt3mqahXC5jdnYW09PTyGazqNVqyOVyKJVK0HWdfVir1dBsNjluW60Wx+m1116LL3/5y8z3L//yL3j729/eVy98Ph/Xi2AwCFmWEQgE4PP5EAwGEY/HoSgK+zadTiORSCAYDLKvw+Ew3G73kmwbNAbBR7Gs6zrXDPq8Vqshm81icnIS73znO5nv29/+Nn7rt34L9Xqda4P11Mjtdvf51+/3IxwOQ1EUKIqCWCwGRVEQCAQQCoWQSCQgyzIymQzGx8eRyWSQSqVGyp/NZhPlchmFQoHjtlAoQNd1NBoN6LoOTdPg8Xhwxx13cJ7Pz8/jnHPOQbvdRrPZRKfTQa/XQ7fbhWma/PvdbjdcLhdcLhf8fj+8Xi//1+PxQJZlrq+hUAh+vx+BQACJRAJjY2PcA6l2jI2NQVEUjt1BYyXvXbfbRS6Xw8zMDAKBAM455xzm279/P7761a8il8tBVVXouo56vQ5N01Cr1dBoNNBsNqFpGvu81Wr1xa/D4YDL5YLP50MgEIAkSQgGg1AUBVdffTU++clPMt+ePXvwwAMPIBQKcV9TFAXJZBKpVAqJRGLZtXe18qDZbGJ2dhblcpnrQS6XQ7VaRbFYhKZprCHIz9TzqMc1m03WcVSfSUMs1A9f+9rXcMMNN8DlcsHtdiMYDMLr9UKSJMiyDL/fD1mWWTdQ/wuFQqzbIpEIwuEwwuEwa7qFemLY/lyOdie/VyoV5PN51Go1aJqGYrGIcrmMSqWC888/H9PT03263ev1olqt4rzzzuOYJ/1mmmZfbQFeeiExp9PJOtrn88Hn80FRFPj9fq4fgUAA4XAYO3bswG/8xm+wL3O5HJ5//nnWd/T9g14yrPS9a7fbmJubg8PhwPj4OPNNT0/j3nvvZW0yNzfHfq3X66hUKlzrW61WXy0/Wj13OBxwOp3weDxwuVy47rrrcOeddzLfQw89hFtvvRWRSASSJLGeo3hPJBKsTyYmJpBMJhEMBo+r1qyF/mqaZt8MQpq6UqmgVCr11XDSfvS5rutQVbVPC7bbbVxzzTX4x3/8xz7td9111/X1TK/XC0VRIEkS13m/3w9FUdjnfr8fwWCQZ5OxsTFMTk4ikUhAkqQj7Fssz1+2027YsAEHDhxAo9GA3+/H7OwsbrzxRgSDQRZK0WiUm3wikYCiKAgGg/D7/X2Dgt/vZ0NJNAAvNc5OpwNJkvCFL3wBF198MfP/wR/8Afbs2dMXzLquc/GvVCqYnZ1FoVBANptFtVpFt9vlZkeOSSQSCAQCUBQFGzduRDQaRSwWw+TkJICXXjihWq3ikksuwY9//GNusCRwnE4nB7E1eah5kMCnhlKtVvuuUVVV/nniKpVKuP3221GtVnkxoaoqDMOA3+9HKpXiphGLxZBKpViskqCiwhYOh9m/FDySJPH3VqtVFhMOhwOmaXLhpWuu1+vcGCuVCorFIubm5qCqKg+K+Xyer5EaK9l36aWX4r777mO+ffv24fOf/zw3PRoWw+Ew0uk0JEniIhIMBrmh0nBOHyTcrfeCPqz+/JVf+RVcdNFFaLVaPOxSs69Wq3ytFD/ZbBbFYpH/SwlOQnR8fBzhcBjBYBCJRALbt2/HW9/6Vuaj2KPGREOb3++Hx+PhmHE6ndzc6Hro+lRVZSHeaDSgaRoMw0AymcSb3/xm5nrsscfw27/925ienkalUkE8HkcoFEIqlUImk+EmSMNiPB7HOeec0+d3+pwWUoFAgD/3eDxcLPx+P8bGxlCpVFiIUlGjRl8ul6GqKlRVRaVSYQFWKpXY1+RnwzAQjUaRTCa5WdA1y7LMQ20oFEI8HmexRPee6ojb7ebaQTFMtYP82mw2+ZpIBGqahkgkgte//vXsz5/97Gf4+Mc/jlqtxtdbKBTQbrcRi8WQyWQwOTnJ17l+/Xps3bqV657P5+uraRS3JFRI3BHfr//6r+Oaa67pWwaQAG2321zXSKTquo5isYharYZyuYxDhw7hwQcfZIGrqio3ov/6X/8rvvGNbzDXT3/6U3zuc5/juKX7T3WCFjY0jAWDQY5h8i3lFsUt1YtWqwWHw9FXV6rVKh5//HFe3pXLZczNzaFSqcAwDOi6jvn5eV5GaZqGSqWCWq3GS6ZgMIhgMIhoNMoxQcN3PB7v8+Vll12Gp59+muuztS70er2+eOh0OtzIqYaRDyn/nnvuOWiahtnZWczOzmLbtm34+7//e+b74Q9/iE9+8pMIBALsr1gshlgs1tegqeeRX0mkWuOYco16AfnW5XIxn6ZpeOqpp1CpVPheUy6VSiUe4rPZLNfedrvN/ZfilnpxJBLBxMQEgsEgxsbG+vI8Go1iz549fQso6s1Uv8in3W4X3W6XewTVBqoPFKtU0wzDwNzcHJ5++mmUy2Vomsb3Pp/PQ9M0SJKE8fFxRKNR7gfkSxKUFAu0PKd7IEkSXzfdB7KBfEk1goa7drvN10birlKpoFAo8CKU6gYtTigPi8Uix208Hsfk5CSuuOIK/Omf/inzUdxt3LiRxTHZJMsyfD4f1wvKOa/Xy742TZP1BfUJEpa1Wg2BQKAvF0KhEKrVKqanp3kA0zQNc3NzyOVyMAyD8z+TyXA+kU9pKKJFAv1/IBBAMpnkJQ3pB1rmOxyOI3REp9Pp00DtdhuapnHvoP5WLBZRrVa5TtOC78wzz8RnPvMZ5vv+97+P97znPXwQIssyUqkUa7d169ZxzFBu0sBNfiV/U01eqCGsvnzLW96CYrHImogWuo1Gg4U8xTDFC/ma7KB6SJquWCyyVrvmmmvwF3/xF8z37LPPYteuXYhEIkgmk3wwRNcvSRLXi4W6h3xPNYRim/KTat+6des413Vdx7/+679yXaF6TAcEdK9osC8Wi2g0GrzISKfTfB8SiQQikQhSqRTOPPNMnHPOObjllltYt3/1q1/F5s2bWcP5/X643e6+6yf/k5a29vJWq8XxQjqT8pPyzHrvXnzxRXz0ox9l26j3kB4mbRSJRFhrULzToU88HmetSsOWoijweDzcn6x1xTTNvvpIMW+tj6T/qaYYhsEaqVQqoVKpYGZmBtlsFvl8HtVqFYlEAm9+85txxx13MF8+n8fPfvYzxGIxbNiwAZdcckmfziedaq2J5G9r7CysMdbli9Wf/+W//Bd84hOfYD9a++fzzz+Pxx57jPXHzMwM5ufn0el0+EAiHA6znqcFmCzLGBsbw6ZNm3DllVf26YdcLse9k3o6ab2jxQvVdToItC77qCcWCgXMz88jnU7jhhtuYL5HH30Uv/mbv8n1HwD7LxwO99UYqo3Uh6jPkF2koxRF6TtYWKj9rr322r7DNVoOUJxQ36EeSgfNhmHgiSeeQC6XQz6fx8zMDAqFAgKBAPf5c889F3/5l3/JeX5Cg/6zzz4L4KWTLjqh+8M//EPebtAJebVaxeHDh1EoFLhgkBFUIK3igBovAA5Gj8eDRqPRx//+978f27Zt49Mra8KlUilEIhFkMhkkk0mMj48jFApBkqTj3mJLkgRd17mQvRwoaejU6HhBXOeffz6+/e1vH/HvpmnylpKKFg2idDJz4MAB9j01GGvQtNttvOpVr8K9997LfN///vdx+eWXMw8JOhIcJEppg0qnMTQcpNNpbN68mZOYCgwtehRF6bPvoosuwv3333/c/jlRf7rd7uN68uNo6PV6yOfzmJ+fx+zsLBcDauxWvueeew7XX389i13rwsS6TSU4HA643W72HS05qHDQBluSJGzZsqWP6/TTT8edd96JiYkJJBKJFTsJozx3OBx8jcuFdfgj0Tc7O8tiwzAM7Nmzh4dXGhaspzL0OYkZgjWGKX5JmND1y7KM7du34/Wvfz37M5VK4V3vehcvnsLhMIuXQT59QHwOh4Mb8CBhmiaazWYf1/j4OC688EIW1YcPH2Y/q6rKookWTLquc+xa6zKBRD0tMXbs2NFXVw4cOIBbb72Vl6O01MlkMizAk8kk12Ya6CnWl3rqQnzUA44G61NWx/qepYL4zj77bPz+7/8+L+c0TUOpVEKpVMLMzAw3aOp51hN0qskUxxTDVuzYsQPf/e53me/pp5/Ghz/8YT6dpYEsGo1i/fr1uPDCC5FIJJDJZNiPlLNLBeW52+1GJpN52e8lnxIW649LhWma0DQNMzMzfeKGhg4Sa6VSifsgLQ6ti1xa7DYaDVx00UV46KGH2JePPPIIXvva1/ZpDFrEWJ9OotMpqsOyLPMhAZ3ekvCLRCJH1F9rrPzJn/zJCfvEKmjp1OdoIL7TTjsNt9122zF/Hy3WVFVFNptFLpfjpxtrtRpKpRKef/55rg2kJQzDwKZNm3DPPfcw16OPPorLL7/8iNNY0kL0YX2y0SqIqRaTlotEIli/fj3rytNPP73PtiuuuAKHDh06YV8uFcTncrl4kQIAsVhs2b+72+3yAc7COu33+3Hw4EFeNJGWo6fD6IlG61MIx/p7d+s9oOF606ZN+M53vsO5XiwWsXv3bh66otEoMplM34ktLXzC4TDi8ThisdiidYX+Jt+KnTt3LvlJXNLTdNhwPCBfXnDBBfjRj37U92/dbpcXGVSPaaFvfcKxWCwin8/jJz/5CR9QUA7QMvpVr3oVvvWtb/XVFdLS1Bvp6SbS0BT/kUiEFw6SJHH8r1+/npew9BRZPB7ve5qX+LZu3YovfOELx+2fo2FhjTmaP8PhcN+ssBTQAWc2m+XDQFr004J0dnYW3W4XV155ZZ9+ePvb384HjdZD02PFvbWeezyevmUfPc2RSCT4ySarbeeffz4efvhhrveD0LhHg1X7UW4Ogss0TRSLRV4QUV2hPD8WXnZioIBotVq8PbriiiuWfbELQcWC/rbnnnvuwXXXXYd9+/ahWCziX//1X5c9xL0cgsEgDMNYsd9/PFwOhwOyLOOMM84YKN9ll112RJNeCQzTl4PmczqdfPJBj6Edi2/r1q145plnBsJ7LBBXPB7nk82VBOX5IOH3+9mnqw3yZzqdxjXXXDM0vpWCw+HgLS5xpVIpvPvd714xTgLxbdu2Dbt37x4a37BAfOFwGFdeeeXQ+C644AI89thjK8q1Enl+vKAnQzZu3Djw323tedbF4EphrfY8Gi6SySQP0ifKdemllw7lLdPWqi9PBC6XC+l0Gul0+gi+V7ziFfjsZz+7IrxWUK6vW7cOd91110B/98vp9h07dhz3n90eL17u3rlcLj64SyaTA+UTWvpIeL1eJJPJJfvaqh/2799/QpzHA+JbymHuIPkGDYfDwU+pb968mb++WE9f0nFKs9lcMWHQ6/Vw9dVXc7HYvXs3zj33XOzevZtf6OPqq69e0SYTCASGllDD5BJ8o803bNtWMs/XAkSsCD7BJ/Jc8K1NLsE3eKxUrp9sul3wjS7XycC3WJ4vadBf7Pn/5cDpdOLTn/40tmzZ0rcBpBfo27JlCz796U+v+Kt3rpUTfcEn+FaDC1jZPF8LELEi+ASfyHPBtza5BN/gsVK5frLpdsE3ulwnA9+y/kafQH8buVK46qqrcOWVVx5RFFKpFB5//PEVLRYAoCgKVFVdUY7V4BJ8o803bNtWOs9XGyJWBJ/gE3ku+NYml+AbPFYy108m3S74RpfrZOBbLM+XlIm1Wm3F/67hWEVhpYsF8NLfES98IUA7cAm+0eYbtm3DyPPVhIgVwSf4RJ4LvrXJJfgGj5XO9ZNFtwu+0eU6GfgWy/MlZWOhUEAikRjYRS0Fw3ixC4Lb7Uan07Edl+Abbb5h27YaeT5MiFgRfIJP5LngW5tcgm/wGHau21W3C77R5ToZ+BbL8yUN+vT+4sPCsF8V2Ov18tsU2IlL8I0237BtG3aeDxsiVgSf4BN5LvjWJpfgGzyGmet21u2Cb3S5Tga+xfJ8SYO+qqoIh8ODuqZF4XQ6h/K2OAS32z00vmFyCb7R5hu2bcPI82O9Cu8w3rpJxIrgE3wizwXf2uQSfIPHMLW7nXW74BtdrpOBb7E8X9KgXy6XEY1GB3ZRi8Hlcg1FEKwGn51tE3yjywWsfJ7v2rUL27dvRz6f7/t6Pp/H9u3bsWvXrhXjBkSsCD7BB4g8F3xrk0vwDR7D1O5296XgG02uk4FvsTxf8ol+KBQa2EUJCAisPaxknvd6PezcuRNPPvkkduzYwUNAPp/Hjh078OSTT2Lnzp1DLY4CAicjRJ4LCJwcENpdQMD+WCzPlzToG4aBQCAwsItaDN1udyiv2rkafHa2TfCNLhewsnnudDpx//33Y2pqCvv27cOOHTvw1FNPYceOHdi3bx+mpqZw//33r6i9IlYEn+ATeS741iaX4Bs8hqnd7e5LwTeaXCcD32J57n65HzZNEw6HY+jvu9vtduFyuU7oZ5vNJmq1Gubn5zE9PY1CoYBisQjDMKCqKsrlMn/0ej185zvfYb5Go4HTTz8d9XodjUYD7Xb7qK+c6HQ64XQ64fV64fV64Xa74fF44PF44PV6EQ6HEY1GEQwGEYlEoCgKotEo/sf/+B/M1el08MADDyASiUCWZSSTSYTDYQQCgRO2/WhYji9PhIf+2+v1cP/996NarcIwDGiaBlVVUa/XYRgGGo0G6vU6VFWFpmmo1+vQdR3NZhPtdhv1eh2tVgudTofvQ7fbRa/Xg2makCQJhmEwX71ex7p16+Dz+SBJEgKBALxeL3w+H8LhMEKhECKRCILBIGKxGGRZRiwWQzqdRjKZRDqdRjQahdfrXRP+PBZXp9PB/Pw8qtUqcrkccrkcarUa6vU6arUaDMOAYRioVqvQdR21Wo0/DMPo82+73Uar1cL3vvc9vPrVr+Y8v+yyy/CjH/0IPp8PXq8XsixDURSEQiHIsoxIJIJQKIRQKIRoNMr+jcVi/G+yLCMajSIajcLhcAB46f11d+/ezaJ/06ZNAICpqSns3r0bqVRq6P60QtM0lMtlzM7OYnp6GtlsFrVaDblcDqVSCbqusw9rtRqazSbHbavV4jh1u91QVbUvNhVF6asXPp+P60UwGIQsywgEAvD5fAgGg4jH41AUhX2bTqeRSCQQDAbZ1+FwGG63e0m2DRqD4KNY1nUduq6zjylus9ksSqUS/vqv/7qvRp9yyimo1+tcG6ynw263u8+/fr8f4XAYiqJAURTEYjEoioJAIIBQKIREIgFZlpHJZDA+Po5wOIxTTz11pPzZbDZRLpdRKBQ4bguFAnRdR6PRgK7r0DQNf/RHf4QNGzZwnv/ar/0aHn30UbTbbTSbTXQ6HfR6PXS73b5Xz3a73XC5XHC5XPD7/fB6vfxfj8cDWZa5voZCIfj9flx22WUoFAp9eX7qqafijjvugNfr5TxZCazkvet2u8jlcpiZmYGu67j88sv7evqf/umfIpfLQVVV6LqOer0OTdNQq9XQaDTQbDahaRr7vNVq9cWvw+GAy+WCz+dDIBCAJEkIBoNcfx966CHma7fb+Iu/+AuEQiHua4qiIJlMIpVKIZFIcO1djr2rkQemaeLQoUMol8tcD3K5HKrVKorFIjRNYw1BfqaeRz2u2WyyjqP6TBriaPohFovB5XLB7XYjGAzC6/VCkiTIsgy/3w9Zllk3UP8LhUKs2yKRCMLhMMLhMGu6hXpi2P5cjnYnv1cqFeTzedRqNWiahmKxiHK5jEqlAlVV0Wg0cNddd/XV6A0bNqDVanHMk34zTfOIV+Z3uVxwOp2so30+H3w+HxRFgd/vZw0dCAQQDocxPj6OT3ziE32a8yc/+QnrO/r+QQ9aK33v2u025ubmoOs6XvnKV/bZ94UvfIG1ydzcHPu1Xq+jUqlwrW+1Wn21/Gj13OFwwOl0wuPxwOVyIRwOY2Zmpu/+veENb0AkEoEkSaznKN4TiQTrk4mJCSSTSQSDweOqNWuhv5qm2TeDkKauVCoolUp9NZy0H32u6zpUVe3Tgu12Gy6XC6VSqa+uUC2hnun1eqEoCiRJ4jrv9/uhKAr73O/3IxgM8mwyNjaGyclJJBIJSJJ0hH2L5fnLdtqf//znOPfcc1EulxGJRHDdddehUCggGAyyUIpGo9zkE4kEFEVBMBiE3+/vGxT8fj8bSqKBbkCn00Gr1UK320U0GkWr1WIx8N3vfrcvmHVd5+JfqVQwOzuLQqGAbDaLarWKbrfLzY4ck0gkEAgEoCgKNm7ciGg0yk0RAPN5PB78+Mc/5gZLAsfpdHIQW5OHmgcJfGoo1Wq17xpVVeWFAXE1m03cfvvtqFarvJhQVRWGYcDv9yOVSnHTiMViSKVSLFZJUFFhC4fD7F8KHo/H0+dLKrAOhwOmaXLhpWuu1+vcGCuVCorFIubm5qCqKg+K+Xyer5EaK9nn8XhQLBaZT9d1fP7zn+emR8NiOBxGOp2GJElcRILBIDdUGs7pg4S79V7Qh9Wffr8fP//5z9FqtXjYpWZfrVb5Wil+stksisUi/5cSPBKJYGxsjEV/MBhEIpFAJpPBRz/60b7Y3L9/PzcmGtr8fj88Hg/HjNPpZN/T9dD1UZOkIq1pGgzDQKfTwXvf+17m0jQNF198Maanp1GpVBCPxxEKhZBKpZDJZLgJ0rAYj8dxzjnn9PmdPqeFVCAQ4M/plXIpzx966CF0Oh0WolTUqNGXy2WoqgpVVVGpVFiAlUol9jX52TAMRKNRJJNJbhZnnHEG9u3bx3XmLW95Cx5++GHE43EWS3TvqY643W6uHRTDVDvIr81mk6+JRKCmaWg2m7jxxhv7YvNtb3sbarUaX2+hUEC73UYsFkMmk8Hk5CT7dv369di6dSvXPZ/P11fTKG5JqNAAY41NwzD6lgEkQNvtNtc1Eqm6rqNYLKJWq6FcLuPQoUN48MEHWeCqqsqNaGxsDNlsts+2D33oQxy3dP+pTtDA4Pf7EQgEEAwGOQbIt5RbFLdUL45Vo7/3ve/x8q5cLmNubg6VSgWGYUDXdczPz/MyStM0VCoV1Go1tFotXl4Eg0FEo1HIssyLj0wmg40bNx5Ro59++mmuz9a60Ov1+uKh0+lwI6caRj6k/HvuueegaRpmZ2cxOzsLh8OBn/3sZ8xXq9Vw7bXXIhAIsL9isRhisVhfg6aeR34lkWqNY8o36iXkW5fLxXzdbhePPfYYKpUK32vKpVKpxEN8Npvl2ttut7n/UtxSL45EIpiYmEAwGORFGuX5Zz7zmSMW1tSbqX6RT7vdLrrdLvcIqg1UHyhWqaYZhoGrrroKd911F+d5IBDAO9/5TuTzeWiaBkmSMD4+jmg0yv2AfEmCkmKBlud0DyRJ4uum+0D+tfY8uu5er4d2u83XRuKuUqmgUCjwIpTqBi1OKA+LxSLHbTwex+TkJMbHx3H55ZczH4nsjRs3sjgmm2RZhs/n43pBOef1etnXpmmyvqA+QcKyVqsdoSG63S6q1Sqmp6d5ANM0DXNzc8jlcjAMg/M/k8kgk8kgHo+zT2kookUC/T9pu1NOOeUIX9K1LtQRnU6nTwO1221omsa9g/pbsVhEtVrlOk0LPsMw8L3vfY/5VFXF5ZdfzgchsiwjlUqxdlu3bh3HDOUmDdzkV/I39eaFGmJhjS4Wi6yJaKHbaDRYyGuaBk3TOF7I12QH9RLSdMVikbXa6aefju985zvMV6/XcccddyASiSCZTPLBEF2/JEkczwt1D/meagjFNuVns9lEIpGA1+vlXH/44YdZP9DiqV6v8wEB3Ssa7IvFIhqNBi8y0uk034dEIoFIJIJUKoUzzzwTsVjsiBr9ve99jzWc3++H2+3uu37yP2lpay+nBQEdAJE/6QCjWq328dXrdXz0ox9l26j3kB4mbRSJRFg7UbzToU88HmetSsOWoiicp+Fw+Khamuojxby1PpL+p5piGAZrpFKphEqlgpmZGWSzWeTzeVSrVSQSCUxMTOCJJ55gvkajgZ/97GeIxWLYsGEDLrnkEtb5kiSxTrXWRPK3NXYW1hjrDGD1p9vtxic+8Qn2o7V/Pv/883jsscdYf8zMzGB+fh6dTocPJMLhMOt5WoDJsoyxsTGk02lcf/31ffrhueee495JPZ203tHihWoRHQRal33UEwuFAubn59FqtbBz586+unLeeedx/QfA/guHw301hmoj9SHqM2QX6ShFUfpmgIV1hWKDDtdoOUBxQn2HeigdNBuGgSeeeAK5XA75fB4zMzMoFAoIBALc5//u7/4Op5xyCuf5CQ3655xzDoCXTrpkWcYtt9zCGyfrCXm1WsXhw4dRKBS4YJARVCCt4oAaLwAORp/Ph4mJCfziF79Ao9GAz+eDqqr4i7/4C74JJAipyEQiEWQyGSSTSYyPjyMUCkGSpOPeYhOfy+XCKaec8rLfS0lDp0bHC+IKBoP49re/fcS/m6YJXdeRy+W4aNEgSiczBw4cYN9Tg7EGDQ0sVl/Ozc0hnU4zDwk6KmQkSmmDmkgkMDY2xsNBOp3G5s2bOYmpwNCih5YmxKcoCu6///7j9s+J+tPhcCz7RLjX6yGfz2N+fh6zs7NcDAqFAlqtVh9ftVrFr/7qr7LYtS5MrNtUgsPhgNvtZt/RkoMKB/mMxK+Vy+Px4M4778TExAQSicSKnYRRnpPAONrm8HhhHf50XceLL76Im2++ue97Pv/5z+PSSy+FYRg8LFhPZehzEjMEawxT/JIwIR/LsozJyUnceOON7E+n04l3vetdvHgKh8MsXpZ7ArbQdopNasCDBG2krVwulwsXXnghi+rDhw+zSFJVlUUTLZh0XefYtdZlAol6WmJMTk721ZVqtYpbb72Vl6O01MlkMizAk8kk12Ya6CnWl3rqYrVvbGzsqN9jHVqX+wQa8fl8Pvz+7/8+L+c0TUOpVEKpVMLMzAw3aOp51hN0qskUxxTDVmQyGczOzjJfuVzGhz/8YV5E00AWjUaxfv16XHjhhbx4JD/KsnxccUt5PjExsej3kk8JsiwviYP+Jt8K0zTx6KOPIpVKwTRNaJqGmZmZPnFDQweJtVKpxH2QFofWRS4tdhuNBuLxOKanp/t63vj4OGsMqhMk0qzi3zqMU81IJBJ8ekvCLxKJHFF/iS8QCOBP/uRPluSfo8EqaOnU52ggPr/fj9tuu+2Yv48Wa6qqIpvNIpfL8dONtVoNpVIJzz//PNcG0hL0GOgTTzzR58tMJnPEaSxpIfqwPtloFcRUi+lJpEgkgvXr1/OTXxSLxBcOh3Ho0KET9uVSYa3R1kdfaXhdDrrdLh/glMvlPr5ut4uDBw/yoom0HD0dRk80Wp9CONbrWljvAdWsPXv2IJVKca7feeedqNfrPHRFo1FkMpm+E1ta+ITDYcTjccRiseOqK9Yafeqppy7pZ0hPezyeJfMs5JNlGT/60Y/6/q3b7fIig+oxLfStTzgWi0Xk83n85Cc/4QMKygFaRkejURw4cOCoWpp6Iy04KC8p/iORCC8cJEni+F+/fj0vYTOZDFKpFOLxeN+ps3VW+MIXvnDc/jkaFtaYo/nT7Xbj8ssvP67fSwec2WyWDwNp0U8L0tnZWfzyl7/E9ddf36cf3vzmN/NBo/XQ9Fhxb63npFNJa9DTHIlEAslk8oi6IkkSHn74Ya73g9C4R4O1rlBuDoLLNE0Ui0VeEMXjcQD/v6cfCy87MVCSz8/PI5lMIpFILPtCl4JGowG/349YLIaHHnpoaHzDwGJcDocDsizjjDPOGCgfiauVxjB9OWg+p9PJJx/0uOmx+OLxOJ555pmB8B4LxOXz+bB169YV5QL+f54PEn6/n32az+fxnve8B7Ozs5iamsI999yD6667Dvv27cP+/ftX/PF98qckSbjmmmtWjGch30rBKk6Jy+/3493vfveKcRKsebB79+6h8Q0LxOf1enHllVcOjS+RSOCxxx5bUa6VyHMraMinv8m35vmOHTs4z+kJu0HD2vOG8RZHa7Xn0XCRTCZx+umnL4srlUoN5QUU16ovTwQulwvpdLrvgIX4ZFnGZz/72RXhtYJyfVCD4sthLd07l8vFTyAMqtYJLX1seL1eJJPJJfvaqh/2799/QpzHA+LzeDw47bTThsY3aDgcDn5KffPmzfz1xXr6ko5Thv03+ottJ0aZz862Cb7R5QJWNs97vR6uvvpqFv+7d+/Gueeei927d/MLd1199dUrKiZFrAg+wSfyXPCtTS7BN3gMU7vb3ZeCbzS5Tga+xfJ80UGf/v5nmJulVqt1Qo/FjwKfnW0TfKPLtdJ57nQ68elPfxpbtmzpO7mnF+jbsmULPv3pT6/oK5WKWBF8JzufyHPBt1a5BN9gMWztbmdfCr7R5bI731LyfNE/9nU6neh2uwP929XF0Gw2h3pThslnZ9sE3+hyDSPPr7rqKlx55ZVHiPxUKoXHH398xd+ORMSK4DvZ+USeC761yiX4Botha3c7+1LwjS6X3fmWkudL6rjDHPKB0frblLXMJfhGm2/Ytg0jz48l8ofxnqMiVgSf4BN5LvjWJpfgGzyGqd3t7kvBN5pcJwPfYnm+8l33BDDs1wQYJp+dbRN8o8t1MkDEiuATfPaH3e+dqCuCby3C7r4UfKPJdTLwLYY1OejXarWhvpDBMPnsbJvgG12ukwEiVgSf4LM/7H7vRF0RfGsRdvel4BtNrpOBbzGsyUG/UCgM7a38hs1nZ9sE3+hyrQZ6vd5Q3qaGIGJF8Am+4UPk+ejy2dm2k4FvmLC7LwXfaHKdDHyLYU0O+pVKBZFIxJZ8drZN8I0u12rgtttuw8c+9rGh8YlYEXyCb/gQeT66fHa27WTgGybs7kvBN5pcJwPfYliTg76qqgiHw7bks7Ntgm90uVYDqqoO9fEmESuCT/ANHyLPR5fPzradDHzDhN19KfhGk+tk4FsMa3LQL5fLiEajtuSzs22Cb3S5VgN2vnfD5rOzbYJvtGF3X9qZz862nQx8w4TdfSn4RpPrZOBbDGty0FdVFaFQyJZ8drZN8I0u12rAzvdu2Hx2tk3wjTbs7ks789nZtpOBb5iwuy8F32hynQx8i2FNDvqGYSAQCNiSz862Cb7R5VoN2PneDZvPzrYJvtGG3X1pZz4723Yy8A0Tdvel4BtNrpOBbzGsyUHfzu95aGfbBN/ocq0G7Hzvhs1nZ9sE32jD7r60M5+dbTsZ+IYJu/tS8I0m18nAtxjW5KBfLpeH+oqFw+Szs22Cb3S5VgN2vnfD5rOzbYJvtGF3X9qZz862nQx8w4TdfSn4RpPrZOBbDO7VvoCjQdM0yLKM8847D/V6HbIsIxqNIhgMIhAIIBQKIRqNIhQKIRKJIJFIQFEUBINB+P1++Hw+eL1eyLIMv98Pv98Pr9cLl8sFl8sFAOh2u3C5XHA4HMwnbBP2rQX7AKyKbcOGiJXRjRVx70bbvmHC7r60s312tu1ksG+YsLsvhX1Cr6zVe7cYHKZpmqt9EQsRi8Wwf/9+OBwOzM3NQdM0VCoV6LoOwzCgqirK5TKq1SoqlQoKhQI0TYOu62g0Gmg2m2g2m9A0jf+/1Wqh2+2i1+sBAJxOJ1RVRTAYZL5EIiFsE/atun2apq2KbcOGiJXRjRVx70bbvmHC7r60s312tu1ksG+YsLsvhX1Cr6zVe7cY1uSg7/P5UKlUIEmS7fjsbJvgG12u1YCd792w+exsm+Abbdjdl3bms7NtJwPfMGF3Xwq+0eQ6GfgWw5oc9AUEBAQEBAQEBAQEBAQEBE4Ma/LF+AQEBAQEBAQEBAQEBAQEBE4MYtAXEBAQEBAQEBAQEBAQELARxKAvICAgICAgICAgICAgIGAjiEFfQEBAQEBAQEBAQEBAQMBGEIO+gICAgICAgICAgICAgICNIAZ9AQEBAQEBAQEBAQEBAQEb4f8CeGCQgj7OGxYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xkcd()\n",
    "fig, ax = plt.subplots(figsize=(18,2))\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "plt.ylabel('TAB')\n",
    "plt.ylim(0,7.3)\n",
    "plt.xlim(-0.03,16.03)\n",
    "\n",
    "plt.tick_params(\n",
    "    axis='both',          # changes apply to the x-axis\n",
    "    which='both',      # both major and minor ticks are affected\n",
    "    bottom=False,      # ticks along the bottom edge are off\n",
    "    top=False,         # ticks along the top edge are off\n",
    "    left=False,\n",
    "    right = False\n",
    ")\n",
    "\n",
    "ax.axes.xaxis.set_visible(False)\n",
    "ax.axes.yaxis.set_visible(False)\n",
    "\n",
    "plt.gca().invert_yaxis()\n",
    "\n",
    "for s in range(1, 7):\n",
    "    plt.axhline(y = s, color='k', lw=1, zorder=0)\n",
    "for m in range(0,17,4):\n",
    "    plt.axvline(x = m, color='k',  lw=1, zorder=1, ymin=1/7, ymax=6/7)\n",
    "\n",
    "\n",
    "xs = np.linspace(0, 4, 9, False)[1:]\n",
    "plt.text(xs[0]-0.05, 0, 'C', weight=\"bold\")\n",
    "for i, p in enumerate([[5, 3, 2, 1], 3, [2,1], 3, [2,1], 3, [2,1], 3]):\n",
    "    if type(p) == list:\n",
    "        plt.scatter(np.ones(len(p)) * xs[i], np.array(p), s=60, marker='x', color='k',  zorder=5)\n",
    "        plt.axvline(x = xs[i], color='k',  lw=1, ymin = 0, ymax = (7-p[0])/7 -0.05)\n",
    "    else:\n",
    "        plt.scatter(xs[i], p, s=60, marker='x', color='k',  zorder=5)\n",
    "        plt.axvline(x = xs[i], color='k',  lw=1, ymin = 0, ymax = (7-p)/7 -0.05)\n",
    "\n",
    "for i in range(0, xs.size, 2):\n",
    "    plt.plot([xs[i], xs[i+1]], [7.1, 7.1],  'k-', lw=1,zorder=0)\n",
    "    \n",
    "xs = np.linspace(4, 8, 9, False)[1:]\n",
    "plt.text(xs[0]-0.05, 0, 'G', weight=\"bold\")\n",
    "for i, p in enumerate([[6, 3, 2, 1], 3, [2,1], 3, [2,1], 3, [2,1], 3]):\n",
    "    if type(p) == list:\n",
    "        plt.scatter(np.ones(len(p)) * xs[i], np.array(p), s=60, marker='x', color='k',  zorder=5)\n",
    "        plt.axvline(x = xs[i], color='k',  lw=1, ymin = 0, ymax = (7-p[0])/7 -0.05)\n",
    "    else:\n",
    "        plt.scatter(xs[i], p, s=60, marker='x', color='k',  zorder=5)\n",
    "        plt.axvline(x = xs[i], color='k',  lw=1, ymin = 0, ymax = (7-p)/7 -0.05)\n",
    "\n",
    "for i in range(0, xs.size, 2):\n",
    "    plt.plot([xs[i], xs[i+1]], [7.1, 7.1],  'k-', lw=1,zorder=0)\n",
    "    \n",
    "xs = np.linspace(8, 12, 9, False)[1:]\n",
    "plt.text(xs[0]-0.05, 0, 'G7', weight=\"bold\")\n",
    "for i, p in enumerate([[6, 3, 2, 1], 3, [2, 1], 3, [2,1], 3, [2,1], 3]):\n",
    "    if type(p) == list:\n",
    "        plt.scatter(np.ones(len(p)) * xs[i], np.array(p), s=60, marker='x', color='k',  zorder=5)\n",
    "        plt.axvline(x = xs[i], color='k',  lw=1, ymin = 0, ymax = (7-p[0])/7 -0.05)\n",
    "    else:\n",
    "        plt.scatter(xs[i], p, s=60, marker='x', color='k',  zorder=5)\n",
    "        plt.axvline(x = xs[i], color='k',  lw=1, ymin = 0, ymax = (7-p)/7 -0.05)\n",
    "\n",
    "for i in range(0, xs.size, 2):\n",
    "    plt.plot([xs[i], xs[i+1]], [7.1, 7.1],  'k-', lw=1,zorder=0)\n",
    "    \n",
    "xs = np.linspace(12, 16, 9, False)[1:]\n",
    "plt.text(xs[0]-0.05, 0, 'C', weight=\"bold\")\n",
    "for i, p in enumerate([[5, 3, 2, 1], 3, [2, 1], 3, [2,1], 3, [2, 1], 3]):\n",
    "    if type(p) == list:\n",
    "        plt.scatter(np.ones(len(p)) * xs[i], np.array(p), s=60, marker='x', color='k',  zorder=5)\n",
    "        plt.axvline(x = xs[i], color='k',  lw=1, ymin = 0, ymax = (7-p[0])/7 -0.05)\n",
    "    else:\n",
    "        plt.scatter(xs[i], p, s=60, marker='x', color='k',  zorder=5)\n",
    "        plt.axvline(x = xs[i], color='k',  lw=1, ymin = 0, ymax = (7-p)/7 -0.05)\n",
    "\n",
    "for i in range(0, xs.size, 2):\n",
    "    plt.plot([xs[i], xs[i+1]], [7.1, 7.1],  'k-', lw=1,zorder=0)\n",
    "        \n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "plt.xkcd()\n",
    "fig, ax = plt.subplots(figsize=(18,2))\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "plt.ylabel('TAB')\n",
    "plt.ylim(0,7.3)\n",
    "plt.xlim(-0.03,16.03)\n",
    "\n",
    "plt.tick_params(\n",
    "    axis='both',          # changes apply to the x-axis\n",
    "    which='both',      # both major and minor ticks are affected\n",
    "    bottom=False,      # ticks along the bottom edge are off\n",
    "    top=False,         # ticks along the top edge are off\n",
    "    left=False,\n",
    "    right = False\n",
    ")\n",
    "\n",
    "ax.axes.xaxis.set_visible(False)\n",
    "ax.axes.yaxis.set_visible(False)\n",
    "\n",
    "plt.gca().invert_yaxis()\n",
    "\n",
    "for s in range(1, 7):\n",
    "    plt.axhline(y = s, color='k', lw=1, zorder=0)\n",
    "for m in range(0,17,4):\n",
    "    plt.axvline(x = m, color='k',  lw=1, zorder=1, ymin=1/7, ymax=6/7)\n",
    "\n",
    "\n",
    "xs = np.linspace(0, 4, 9, False)[1:]\n",
    "plt.text(xs[0]-0.05, 0, 'F', weight=\"bold\")\n",
    "for i, p in enumerate([[4, 3, 2, 1], 3, [2,1], 3, [2,1], 3, [2,1], 3]):\n",
    "    if type(p) == list:\n",
    "        plt.scatter(np.ones(len(p)) * xs[i], np.array(p), s=60, marker='x', color='k',  zorder=5)\n",
    "        plt.axvline(x = xs[i], color='k',  lw=1, ymin = 0, ymax = (7-p[0])/7 -0.05)\n",
    "    else:\n",
    "        plt.scatter(xs[i], p, s=60, marker='x', color='k',  zorder=5)\n",
    "        plt.axvline(x = xs[i], color='k',  lw=1, ymin = 0, ymax = (7-p)/7 -0.05)\n",
    "\n",
    "for i in range(0, xs.size, 2):\n",
    "    plt.plot([xs[i], xs[i+1]], [7.1, 7.1],  'k-', lw=1,zorder=0)\n",
    "    \n",
    "xs = np.linspace(4, 8, 9, False)[1:]\n",
    "plt.text(xs[0]-0.05, 0, 'C', weight=\"bold\")\n",
    "for i, p in enumerate([[6, 3, 2, 1], 3, [2,1], 3, [2,1], 3, [2,1], 3]):\n",
    "    if type(p) == list:\n",
    "        plt.scatter(np.ones(len(p)) * xs[i], np.array(p), s=60, marker='x', color='k',  zorder=5)\n",
    "        plt.axvline(x = xs[i], color='k',  lw=1, ymin = 0, ymax = (7-p[0])/7 -0.05)\n",
    "    else:\n",
    "        plt.scatter(xs[i], p, s=60, marker='x', color='k',  zorder=5)\n",
    "        plt.axvline(x = xs[i], color='k',  lw=1, ymin = 0, ymax = (7-p)/7 -0.05)\n",
    "\n",
    "for i in range(0, xs.size, 2):\n",
    "    plt.plot([xs[i], xs[i+1]], [7.1, 7.1],  'k-', lw=1,zorder=0)\n",
    "    \n",
    "xs = np.linspace(8, 12, 9, False)[1:]\n",
    "plt.text(xs[0]-0.05, 0, 'G7', weight=\"bold\")\n",
    "for i, p in enumerate([[6, 3, 2, 1], 3, [2, 1], 3, [2,1], 3, [2,1], 3]):\n",
    "    if type(p) == list:\n",
    "        plt.scatter(np.ones(len(p)) * xs[i], np.array(p), s=60, marker='x', color='k',  zorder=5)\n",
    "        plt.axvline(x = xs[i], color='k',  lw=1, ymin = 0, ymax = (7-p[0])/7 -0.05)\n",
    "    else:\n",
    "        plt.scatter(xs[i], p, s=60, marker='x', color='k',  zorder=5)\n",
    "        plt.axvline(x = xs[i], color='k',  lw=1, ymin = 0, ymax = (7-p)/7 -0.05)\n",
    "\n",
    "for i in range(0, xs.size, 2):\n",
    "    plt.plot([xs[i], xs[i+1]], [7.1, 7.1],  'k-', lw=1,zorder=0)\n",
    "    \n",
    "xs = np.linspace(12, 16, 9, False)[1:]\n",
    "plt.text(xs[0]-0.05, 0, 'C', weight=\"bold\")\n",
    "for i, p in enumerate([[5, 3, 2, 1], 3, [2, 1], 3, [2,1], 3, [2, 1], 3]):\n",
    "    if type(p) == list:\n",
    "        plt.scatter(np.ones(len(p)) * xs[i], np.array(p), s=60, marker='x', color='k',  zorder=5)\n",
    "        plt.axvline(x = xs[i], color='k',  lw=1, ymin = 0, ymax = (7-p[0])/7 -0.05)\n",
    "    else:\n",
    "        plt.scatter(xs[i], p, s=60, marker='x', color='k',  zorder=5)\n",
    "        plt.axvline(x = xs[i], color='k',  lw=1, ymin = 0, ymax = (7-p)/7 -0.05)\n",
    "\n",
    "for i in range(0, xs.size, 2):\n",
    "    plt.plot([xs[i], xs[i+1]], [7.1, 7.1],  'k-', lw=1,zorder=0)\n",
    "        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "742469c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Hey Jude\n",
    "tempo = 70\n",
    "capo = 12\n",
    "interval = (60/tempo) / 2\n",
    "for c in range(4):\n",
    "    play('E', duration=0.5, capo=capo)\n",
    "    time.sleep(interval*2)\n",
    "for i in range(2):\n",
    "    playChord(C, interval=interval, capo=capo, pattern=[[5, 3, 2, 1], 3, [1,2], 3, [1, 2], 3, [1,2], 3])\n",
    "    playChord(G, interval=interval, capo=capo, pattern=[[4, 3, 2, 1], 3, [1, 2], 3, [1, 2], 3, [1,2], 3])\n",
    "    playChord(G7, interval=interval, capo=capo, pattern=[[4, 3, 2, 1], 3, [1, 2], 3, [1, 2], 3, [1,2], 3])\n",
    "    playChord(C, interval=interval, capo=capo, pattern=[[5, 3, 2, 1], 3, [1, 2], 3, [1, 2], 3, [1,2], 3])\n",
    "    playChord(F, interval=interval, capo=capo, pattern=[[4, 3, 2, 1], 3, [1, 2], 3, [1, 2], 3, [1,2], 3])\n",
    "    playChord(C, interval=interval, capo=capo, pattern=[[5, 3, 2, 1], 3, [1, 2], 3, [1, 2], 3, [1,2], 3])\n",
    "    playChord(G7, interval=interval, capo=capo, pattern=[[4, 3, 2, 1], 3, [1, 2], 3, [1, 2], 3, [1,2], 3])\n",
    "    playChord(C, interval=interval, capo=capo, pattern=[[5, 3, 2, 1], 3, [1, 2], 3, [1, 2], 3, [1,2], 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5611f493",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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