SRIP_breath_audio_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cd5547f2",
   "metadata": {},
   "source": [
    "For extracting audio from video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "924627b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#file_path = \"D:\\Helpful folder\\for SRIP IITG\"\n",
    "#file_name = \"video_record\"\n",
    "#file_format = \".mp4\"\n",
    "\n",
    "#path = file_path + \"\\\\\" + file_name + file_format\n",
    "#path = path.replace (\"\\\\\" , \"\\\\\\\\\")\n",
    "\n",
    "path = \"D:\\\\Helpful folder\\\\for SRIP IITG\\\\video_record.mp4\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bccc4285",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "!pip install moviepy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1ee3d88d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Audio Header: {'duration': 16.51, 'sample_rate': 44100, 'channels': 2}\n",
      "Audio Data: [[ 0.          0.        ]\n",
      " [ 0.          0.        ]\n",
      " [ 0.          0.        ]\n",
      " ...\n",
      " [-0.00106812 -0.00106812]\n",
      " [-0.00198364 -0.00198364]\n",
      " [-0.00299072 -0.00299072]]\n"
     ]
    }
   ],
   "source": [
    "from moviepy.editor import VideoFileClip\n",
    "\n",
    "def extract_audio_data(video_file):\n",
    "    video = VideoFileClip(video_file)\n",
    "    audio = video.audio\n",
    "    audio_header = {\n",
    "        \"duration\": audio.duration,\n",
    "        \"sample_rate\": audio.fps,\n",
    "        \"channels\": audio.nchannels,\n",
    "    }\n",
    "    audio_data = audio.to_soundarray()\n",
    "    return audio_header, audio_data\n",
    "\n",
    "# Example usage\n",
    "audio_header, audio_data = extract_audio_data(video_file = path)\n",
    "print(\"Audio Header:\", audio_header)\n",
    "print(\"Audio Data:\", audio_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb3dfe46",
   "metadata": {},
   "source": [
    "Hence, the sample rate is 44.1 kHz, which corresponds to a 16 bit audio file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cef7886d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(728091, 2)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "audio_data.shapes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a910c319",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.00427246 -0.00427246]\n",
      " [-0.00396729 -0.00396729]\n",
      " [-0.0020752  -0.0020752 ]\n",
      " [-0.00167847 -0.00167847]\n",
      " [-0.00268555 -0.00268555]\n",
      " [-0.00286865 -0.00286865]\n",
      " [-0.00177002 -0.00177002]\n",
      " [-0.00106812 -0.00106812]\n",
      " [-0.00198364 -0.00198364]\n",
      " [-0.00299072 -0.00299072]]\n"
     ]
    }
   ],
   "source": [
    "print (audio_data[-10:])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5529e56a",
   "metadata": {},
   "source": [
    "The values in both the columns seems to remain same, hence, we remove one of the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f1b275ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "audio_data = audio_data [:,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "813117be",
   "metadata": {},
   "source": [
    "Now, to plot the file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "266a0bfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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p1vbKtMrCi8cvXMHl4poDbcV2pbhyMHvSwcprNl8rxUUbA4u1hskLuaSuqrhAzVs6e+tYAdgd1+dwZ3UNbnQEZ6mRo+R4yRw4Y8Z3O7LqPObr7Zmit3vk3GV8U+W8yccu1jjmg/V/id6u3DjbiGRVV9E6fqmmeqkgUSkqLYevt6fSYZALKhNe2284Yq1tNHJ+itXjC4UlQFPrVotLnR/UW9vb5XOL91g9Pvd3jZmqx18r0f7K6ex5IVm1ahJQ676OM9fKGAmRc4Z9vFXpEMhFSlRlqF6Y0VX2ftnbcDhX1HbVgskLSWZTxnn8lGa9sN7N0Y0Uiob0RMkZZsfOX7GMHyBtUnTlaPYwi4LJCxHJxtW7PleLy5Btvoab37xRlE6Jj6Env96lQKsklmt1VGSW+s6kWGP7HD2P3kpKyJK8zJkzBzExMfD19UVcXBy2bq292zU7OxujR49G27Zt4eHhgcmTJ8sRIhGp1PNV7uEfyb2MTzYctVmGnUgMu3S6Tpu+UhcZkpelS5di8uTJePnll7Fnzx7069cPQ4cORVaW7VHYxcXFaNKkCV5++WV06dJF6vBIB3T2hYKqWbnX+tZj9b/3wWyuXE7iKZGxMq5S489VMO7dZZInL++//z4ef/xxPPHEE2jfvj0+/PBDREVFYe7cuTaPj46OxkcffYRx48bBZDJJHR7JTA//aMh5TDTJ3VX9N6DU2Bs9vA1LmryUlJQgNTUVCQkJVtsTEhKQnJwsShvFxcUoKCiw+iF1Sj52AVOXpSsdBhGR5jn6RUBvXxwkTV4uXLiA8vJyhIWFWW0PCwtDTk6OKG0kJibCZDJZfqKiokQ5L9nHkUFjoxeIO1WQiEhrlMohdJa7yDNgt3oFTEEQRKuKOX36dJjNZsvPqVOnRDkvERGR2NLrqDIupb9yChVpVyqSVtgNCQmBp6dnjV6W3NzcGr0xzjIajTAajaKci7Tr0pUSBPv7KB0GEVGdxBoQ7GhPSsa5G8mLHsYeStrz4uPjg7i4OCQlJVltT0pKQp8+faRsmtzIl8mZ6P5GknML/RERKUQPSYRSJL9tNGXKFHz++ef44osvcOjQIbz44ovIysrChAkTAFTe9hk3bpzVc9LS0pCWlobLly/j/PnzSEtLw8GDB6UOlTTqjV8rXxtiLfRHRETqJvnCjCNHjsTFixcxe/ZsZGdnIzY2FqtXr0aLFi0AVBalq17zpVu3bpbfU1NT8d1336FFixbIzMyUOlwiUrmKCr0NPSR35UrHiysVcxVdHkEksqwqPXHiREycONHmvq+++qrGNr2VMdYz/qnIEWJ0ky/dxUH5RO6OaxsREYmksKhU6RBIxcZ/uVORdu+d84fVYz2MtWHyQkQkkld/2q90CKRimzLOWz1+L+kvlMtwG3RPVr7V4yV/nkJRHYtTagGTF3IJ7xoR3bDhcK7SIeheSZl8aw/J4Zdqa3fZy9Vb9v9LznTtBApj8kJERJrx6cajSocgqktXShRp93yhtldmZ/JCRLJJOX5J6RAkxZ5I6X38+xGlQyAVYPJCRLI5mntZ6RCIdMGRdeX0iMkL6UrysQtKh0BERBJj8kK6wpWr1UuOWRVE5B6YvBCRLF75KV3pEIhUSxAEhwq0ilkgVIuFYZm8EJEsFu/Uf2VcHdT+IpkZDJXJw8j5KXhk4Q7ZE4nHv/oT98xJ1tyyG7IsD0BERES2ZZuLsPNE5Uy8gqIymPy8JW/zepXd3/+uTfRXbiHaNQ2SvF2xsOeFiIhIIcXViu7JVbr/cnGZPA1JhMkLuUSL90qJiNRiwZbjVpOe7c1dXH3r1fptXCYvpDunLl1VOgQiIrtcdLLCrthfGw0aG7HF5IVcosZ+l37vbMS8zceUDoPcUGm5wCnh5LDs/GuW3+V89VRdBb24TFsLNTJ5IacJgoDV+7KVDsOmt387rHQI5IaulZZjyIdblA6DNOa5xXssv5eV25e+iHHL/tMNN9aJyjYXuXw+OTF5Iaet2Z+DacvVW7uj6rcKoPJ20tPf7MLurDyFIiJ3wCUQpJN54YrSIUgit8oiiXLevLlwWZlFIcXA5IWclnpS3UnApO/2WD2euGg31h44h/vmJCsUERG54rZ3NykdgiQaNfCx/O7pKV/64lGlqcb+PrUfqEJMXki3rlSZCvjXuUIcOGtWMBoiItuCfG+UXPPykC95qTotu2ED6WvLiInJC+nW9TvCuzIvIeGDLag6jnLoR1tx8qI+u6CJSFvKqg3yLq8Q8J9fD+K39NrHFIoxsLf62HJBEPB/aw9jxZ7TIpxdWkxeSLcuXi7GR+uP4IF522vsO5RdgGnL1Dteh7Tt90PnMOm73TBfK63/YHJ7WVXKO7z0w1688tN+fL7tBJ5ZtLvW54gxYPfH1BtJyqTv9uC/G4/ivxuP4cWle10+t9S4PADpVubFq/hg/V+17r9Sou0Kk6Rej/9vFwAgJMCIWXd1VDga0pLV6Tl2HZdbUFz/QQ44nFOIwzmFop5TSux5ISKSyLkCbU0/Je1w92pCTF7IbVXtdS0qLcfx85ziSkTq8v2fypTx/+qPE4q0ay8mL+Q0rRcSFap8dxn+yTbc/t5mJB+9oGBEpDdc+otc9e9l+xRpd9YvBxVp115MXshpB7P1M/X4emGxn9LOKBwJ6Yng9p37JBV3T4yZvJDTtP6PR+vxExG5KyYv5LYuF9ecbZR08JwCkZBeMUEmkgaTF3JbJy9W1lbIqbIgWd5V1uUg8TB3IZIGkxdye7Z6YIiI1Mzdx1MxeSGnncm/pnQIRERu6XrPsbti8kJOO52nj+SlrKJC6RBIp3JZpI4kckYn77/OYvJCbm/JTmWKQJH+7T1txtI/s7jGEZHImLyQ2+PtL5LS1GXpeOrrXUqHQTrDMS9Ebs6gdACkeztOXFI6BCJdYfJCbs9QLXvZ8td5ZQIhIrKTu9cQYvJCbs9Qre9l3Bc7FYqEiKimchsLycmRu5SVq3cyA5MXcmu/7jtrc/sfXKCRiFTivjl/KNLuoPc3K9KuPZi8kFv7aP0Rm9v/zOQYBSJSh72nlVkEN1PFtWS8lA6ASElHci+jqKxc6TBEdSb/GjYczkXKsYt4/e6OCAkwKh0SEYlMcPNBL0xeyO2dulRzqrTW3hdKyirg4+WBnScu4aHPtlu2GwzAp6O7KxgZEUlBY29RouNtIyIbPvr9CHZn5Skdhl3WHcjBTa/8hrd/O2yVuABAtlkdFV63H7uodAhEmvbL3rP4dIPt29xSWn/wHN5dm4EKG4OGlcSeF3JKYZH+K4beNycZmW8PUzqMej31TSoAYN7mYzX2pZ5URwL28IIUpUMgjStV8cwXOTy3eA8AoE/rEHRv3ki2dp/4u8BibLMg3BEbLlu79WHPCznlt/05SocgC7Unae5+31tLbE131TNBEPDmqoPYKVKBvnfXZohyHq27dLkEgPy3tnNU0ot7HZMXojpUf38oKi1HyvGLqql/UFSqjjiofq1mrMa1Em0NDi8uK8faAzkocCKJf3PVISzYeqLGrUxnnVXZh6dS3l2XgRMXrsje7v+tzcBf5wplb7c2TF6I6pCamYfE1Ydw7PxlAMCEb1Mxan4KPv7d9XvP10rKVXcfmaS1MSNX6RAckrj6MJ7+JhVPfFX72kxzNh3Fs9/trvFa/nzbCVFj8fLgQh4AcDinEAPe3ST7gN0rJeVI+GCLzK3WjsmLBpVXCMi/WoKKCgFPfr0Lib8dkj2GU5fUO/9fTP/46k98tuU4hn28FQCwKaNy6YCvU066dN7cgiK0f20Nxny+w6XzlFaw50VLtHbr6IddlSuu76yl7tH3u07hnTUZWLUvG2O/2IFTl67CfLUUk5fsET0Wk5+36OfUMqXegw/nFCjSbnUcsKshJWUViE/8HRevVN7zfOveTkg6eA4AMH1oe1lj+WTDUVnbU1pRaQX2n7lRKMrV+82/7MsGAGw/7tosHHdJIvVi9q8HMaJLhNJhuKSsvAJenh4wXyvFv3/cZ9n+x9GL6PfORpvPeWfNYTx2S4xLNYe6RjV0+rl6dOnvzwG5TV6ShjWTb1Wk7arY86Ihv+47a0lcgMp7n1UdO38ZWSquiKh1wz/ZZvldzIG8mQrcvyZlnC8sVjqEehWV3hiXc6XKGJ3E3w5h+Cdb0WnWOpzOu4qZP++3+5xzNh1Dj/+sd2nMz7cu9naSOEpUMt6PyYuGVB+cWTXzjp62CgPf24xb/28jx1HIoEJwbabPz2lnLL/f9u4mbDh8zqnzVF9UkshZn20+huhpq9Du1TX4bPMxnCsoqrb/OPafKcC10nLMWnkAqU7UQbrr0231H1SLXSqZ9u/u1PKOw+RFQwQ7h2i1nLEaYz5PsfoGVdWV4jIUl5WjuKwc87ccU9UIci3Zcyrf6efuq7ZWyWN1DIisiz2viUPZBTh16SrSTuUj21yzmjDRyYtXkPjbYcvjxN8O1zk+Z/2hXKcS5yO5l52Kj9TDYFBH+sIxLxryqQPjTP44ehHtXl1To8ja1ZIydJy5FgAwrFM4VqVn463VhzVRjE1tCovKlA7BrjEvQz/aavX4/Ye64L7ukTWOK68QYEBlt7Cvt6dYIZIG/HWuZlLR5+0NdT4ny8nxVucKihAW5OvUc4muY8+LhjhT6v16fYbS8goIgoAtf12w7FuVni1abO5IDTM3/8x0vCt9yvd7a2z7ZnsmWs1YjZYzVqPdq2vw1R/iTnMldZOzEnOvt36XrS0Snwre9gCw50X3Os9ahwCjFy4XK99LoDceKug+vexk78/R3EKEm/zgb6x8C3j15wNW+2f9chDj+8bAfK2UU1RF9vKKdEy6vTV+S8/BnZ3C0dSkbC9ERYVgc2kJKZWWV8Dbk9+dtejSlRJV/P346tGI2sav2MOexCV62ir8mHoaV5jk2E2K1EUQBBw/f9nuQdf2joOqbtD7W9Bx5lp88vsRdPr7NmJ1L/2wF11eX4foaatwKLsAR3ML8fKKdM1ViVWbRTuyEJ+4AbN/PYiR812vPpt89AKip61C+1fXIM+J6bNKLED64tI02dskcVy8UoK4N5KUDoM9L1pwKLugxrgFKbz0w178tOcMvn2il+Rt6YHYA9eip62y/N4xIghf/uNmhAZWfiu/VlIOPx/rcSil5RX4ftdpl9p8L+mvWvf9mHrj3FVff4t2ZGH60HZ4un8rl9om4KQIpQ1G/13o8FppOT7deBSvDu/g0PMfmCdO+X5H/LovG//v/jJLzx9pS4EKxvvxlaMipeUVKCotx4wV+xHZyA93dYmQJWmpatvRC/UfRABujHnZeuQ8iksrMKhDmGjnPnC2AD3f/B0JHcIQ0dAPXyVnYvbdHXEouxABRk+UlFXA10e5QbWJvx1m8qJCC7edwNfbM7Hkqd6IaxGMqyVlWHfgHAa0DYWpQc3bf6706LqqY5Uev8Nv3MFB4hpTXiHAU8GBf0xeVGLxzixMX55utW3uJnnvQ1+39kAOOkeaEG7yU6R9rRg5PwVv3N3RMl5k96uDEezvI2ob6w7eqP/yWrVxKUoTBEE10ybphtJyAffP3Y69ryWgy+x1AICe0cH4fkJ8jWPbvbpG7vBssjUzktTtl71ncU+3Zoq1zzEvKlE9cVHS09+kIj6x7mmSVKnqQFfzNfGq7mpBzPTVWPpnVr3F+q6WKN/FrFcPz0+pdd/1xAWoXJuouKwciasP4dSlq0g+egGD3t8sR4h2S3OhbhLJb7LC45bY80K1Ki4rh9GLXbli23rkvNIhiGbqsnR8v+s0lj3Tp9Zjjp/n8gd1eeTzHZg3Ng4BToz/cGRtrLavVPayfLbluMPtyGHj4VyuX0R2Y88L1artK2uQf7Xm7IUcJ+rNuAN7el6yzdcwduFOGaKRT+rJPJzNr71y75xN7rWIp6O2Hb2A2JlrceGyY+seLd/t2mBttfno9yNcaJTsJkvyMmfOHMTExMDX1xdxcXHYurXuQaibN29GXFwcfH190bJlS8ybN0+OMMmGrrOTcLRaSe/eiSwyZcs9//2j3q54vd6O6/P2BqSevGRz3+r0HJmj0aYe/1lv13FXS8oQO3OtzWKDWjeqjttgRFVJnrwsXboUkydPxssvv4w9e/agX79+GDp0KLKysmwef+LECdx5553o168f9uzZgxkzZuD555/HsmXLpA6VajHo/c1IcaB72p0dzb2MkjLbq66u2a/visb3z6055daVxSvdUfS0VbWOERIEAVeKy9DhtbW6LTp5Jv+aVcmA65ypX0P6ZhAkfnfp1asXunfvjrlz51q2tW/fHvfccw8SExNrHD916lSsXLkShw4dsmybMGEC9u7di+3b669HUFBQAJPJBLPZjKCgIHEuQga2/sESERGpldgzxBz5/JZ0wG5JSQlSU1Mxbdo0q+0JCQlITk62+Zzt27cjISHBatuQIUOwcOFClJaWwtvbulZBcXExiotv3CsuKCgQKXprJWUVGDl/OwQBlTVNhcraphWCULnt7+3C9ccQbGz7+/fKp1ueS0RERPaTNHm5cOECysvLERZmXbwrLCwMOTm274Pn5OTYPL6srAwXLlxAeHi41b7ExES8/vrr4gZugwABe7LyJW+HiIiI6ibLVOnqhazqK25l63hb2wFg+vTpmDJliuVxQUEBoqKiXAnXJm8PD3w2Ng6Gv+PwMAAGA2CAATBULtJXua9ym4cBwN+/G67vrzy08r+W4yv/e/d//xA9ZiIiIj2SNHkJCQmBp6dnjV6W3NzcGr0r1zVt2tTm8V5eXmjcuHGN441GI4xGo3hB18LDw4AhHZtK3o6avXN/Z2Sbi/DB+trXw6EbGvh4IqpRA9zWrgnKygUs3HZC6ZBkMTS2KcKCfPHdziyUlVfAzjUmqYrZd3dEYVEZMnIKMbxzOEx+3pi2PB0nLrhPzZxHejdHsL8RP6edQWigEX9myr+AJKmXpMmLj48P4uLikJSUhHvvvdeyPSkpCXfffbfN58THx+OXX36x2rZu3Tr06NGjxngXkkdChzDMH9fD8tjf6In/rDpUxzPcV2QjP2ybervNfa8O76Drgdl9WzfGoid6Wx7PuqsjAA5Gd0RdAyA3vnQbAGD/GTOGf7JNpojk9/s/+6NVkwDL4ymDbwLA1xFZk3yq9JQpU/D555/jiy++wKFDh/Diiy8iKysLEyZMAFB522fcuHGW4ydMmICTJ09iypQpOHToEL744gssXLgQL730ktShkg0mP2+rxAUAxveJViYYlfv9n/1rTVyuG9OruUzRyOuOjk2tEheSTmwzk27XAdo5Y6BV4kJUG8mTl5EjR+LDDz/E7Nmz0bVrV2zZsgWrV69GixYtAADZ2dlWNV9iYmKwevVqbNq0CV27dsUbb7yBjz/+GPfff7/UoVI1LyXchL0zE2ps52J8tkXYsZDlf+6JlSES+c0Z073Wfb9MukXGSLRndK/mOJF4p8MJybapAySKSBnLnumD0CBfpcMgjZBlwO7EiRMxceJEm/u++uqrGtv69++P3bt3SxwV1WX23R0xLj7a5j4FV0FXrX8NaQs/n/rXgTIYDNjyrwG49f82yhCVPD4Y2QUedbwoOkWaZIxGe966t5NTz4ts1AAjukTgl71n7X5OvzYh2H/GjHmPxOH5JXtwrsCxJQmkFNeikdIhkIZwYUaqwdvTUGviArDn5bqJt7VC+hkzRt3cHMM6h9f/hL81b9xAwqjkE2j0QvrrQ5QOw60Nah9qd/JyIvFOq3+7KdMHYuzCndh29IJU4dntm8d7Kh0CaQwXZlSJxU+qZ7zAgdfvUDoETWgcYMQ3j/dyKHFxRpvQAAxqHyppG874vwe7KB2C2xvROcLm9iVP9cavz92CN+7uiJAAH7xzf+caXzoMBgO+faKXHGHWqWdMMPq1aaJ0GOSgZwe0UrR99ryoRHyrxtj1yiC7F2eTko8Xc1p73NI6RJLzxrVohNSTeRjfJxqDO4ShS1RDBBi9IAgCLlwuQZNAI84VFGHiot1IPanc9NEhHW2XOyD7ufr/0MPDgKQXb8UXf2Ri0u2t0ayhn1UdrdhmJjzSu4Uqe0vv7x6J29o2weAOfB1p0UsJbRVtn8mLioQEGLH4yd5o4OOJLlENYb5WikCjFzw8DLJME+zRohFmDGsveTt60bZpoKjna9jAG+tevBWhgbYHLRoMBjQJrKxpFBbki2XP9MHUH/dh6a5TosZRn86RJvzvHz1V+YGoFRP6t0JJWQVeHNzG5XO1CQtE4n03xs3Y6mGpyyO9m+PbFNsL5UrlzXtjMaZXC1nbJHEp/e+fyYvKxLe6UYjP5CdfXZvJg9pg8qCbZGuPatr9yuA6B77a0sBY/yBhZ00a0Bpn8q/h6f4t4evliUe/3InmwQ3w9WNMXFw1bWg7pUOwmDa0vezJy+ie+iwZQPJh8uJGHusbgy/+sF3lNTaCM0Ic0U7kXhcADicuYnggLhLpp83IOFdYY99LQ6y7hTf/S19Tc6lSgNEL/dqEYOsReQbuNg9uwOSXXMbBDRrRsIFzvTAtQ/wx9Y52+OThbnhtRAcce+tOPHNbzYFWYt8C0bsGdkyLlkPlylj1e/721pbfQwJ8cCLxThx/6068+2AXeHvVPMeMO9XTM0DSe/yWGKvH4Sbp6q1ENqq/HhJRfZi8aMSKiX3xWN+Y+g+sZsNLt+GZ21phRJfKWQmeHgZMvaMddr862Oq4qGB9TN+Vi1qW67HnC+yRN4diSrXBdQaDwdLT41HtJBP6t8JTtyo7k4Dk1f8m69k+a164td7n9G4Z7FRb7HTRvn8NUXawLsDkRTNiQvzx2ogODj1n8qDaBwMG+/ugW/OGAIB3nZjyevytOx1+jp7c3z1S6RAA2Fcw0NvT+p+5UC3zGtapcqp3uMkXyyf2UcUbE8nLYDBYBv3e3z0Spnp6ene/OhhLnop3qq1nB7Su/yAbEjgryco7D3RWpN0fJ8Q7/TcUE8e8aNjAdqH4/XCuzX33dI3ACwPrnsmwYmJfFJeVw+jl+C0QJcZnKGH79NsxZ+MxPPf3bZf8a6UwXytFXHN1VAN1ZOzAbW2bYFPGeYyNt57l8fgtMWgdGoBuzRsh2N9H7BBJIx7u2Ry3tA5Bs4b139bx/Pt152i16LTXBqNhA77GXLViYh/4est/63rxk73RI9q5HjexMXnRsAd7RCI1Kw99W4Xg9bs7orG/Dy5eKUFIgNHucziTuLiTcJMf3qiyHpFYa68M7hCGpIPnAABdIk345OHa1waqiyMp5LxH4pB+xozu1RIvL08PDGzPb7Vkffu4T6vGSD520eZxhr878xypFv3p6G4uJS5quVWrBt2aN8Kh7ALZ2606G1ZpvG2kYQFGb+x6eRA+Hd0NIQFGGAwGhxIXUs5nj8RZfl/0ZG+nlwwY5EBXuq+3J26ODoanAr1m7PIHlk/sA19vD0svntp9/miPWvdVfQXZmgDw9WM98UBc5a3VFwa2wR/TbsfwWqoB22v9oXMuPV8v7u3WDID8Y4fiW6oncQHY86JpAgR4eTL/lIqUsyI8PAzY+fJAlJULCDA6/8+wsUZu8/Ru2RjrDrr3h0/35o1w4PU7FEkendHAxwvNGvrhTP61GvuqD/K+bu3kW+FhqCycd+tNTfB/D9RclsBZ1cdquaMukSZ8MLKr7O2GBPhg8VPqWcIGYM+LpvEfs7Tq+uYphtBAX0TYMb6gLi2bBIgUjbQ4w6SSVhKX6xaMq/lvYHjncPjXknC3bRqINmE3yi6wnovI+P/TgsmLxnz8cDfL75zeLJ2hsU3RrmmQ0mHoBt9ytalDRBDmPRKH5sENMO+ROOyblYBPR1uPzxp1cxSAmtOtiaTE20Yac1eXCLQIboCLV4oRE+KvdDi6ZdTQ4pTxLRtj+3HbAyuJXHVHbFPcEdu01v0tGvtj/+tD4K+Swo3kHrTzDk0WXaIa4vZ2HABJleY9Eqf6FZ55+0DfAoxekv+Ne8WoY4quEto1DYSXhwGz7+ooa7uxzYLgYQA+GtWt/oNlxp4XIo0zNfDGZ2N74N8/7sX3u05b7ZtXZVaTktw9d5k7xrmp8HRDoK/7flytmXwrysorZJ+g8etz/RRp1x7qi4hIBcJEqucip6r1W+7vHonkabfX2d1P8ujXJoR/B3KZUgmEGhMXgD0vRDWM6BKBSRqpxVHVA3GROJxTiD6tGiOho7o+LN254+Wbx3spHYIuDI0Nx/pDtiuKk/tRZ0pFpKBPHu6GQF/nVvFWkpenB2bd1VF1iQugrsqcpE33dW+mdAikIkxeyO01qmcROnJd69DA+g8iqgMHfVNVTF7I7S0cf7Pld073JCJSPyYv5PaqljqPbWZSMBIiIrIHkxdyexqr2E4a8u6DXZQOgUiXmLyQ2zO49VwYktL9HGRKJAkmL+Q0vSxPUHUcINe6JDFxkCmRNJi8kNN8VFq8iIhIq5oEGpUOQRP46UNOG9UzSukQROHrzX8GRKQt7n67m+/a5DSTn/brozzcszlaNQmwPHbvtwMiIm3g8gDktCANVqGtaljncCTe10npMIiIyEHseSG30a9NCD4dXffS7hywS0RKCgngmBd7sOeFdK93y2C0CQ3EG/fEAgD2nsrHF39k4sVBbRSOjIjImtHLuk/hmdtaydJuAx9PNPTzxllzEQDgkd7NZWnXWUxeSNfWTr4VbZtar6vz8rAO+Pcd7eDN2VJEpEIv39keb64+BACYekc7m8cIEvQT/+feWDz21a7K3+9R9y11Ji+ka/5G22sVMXEhIjWaPrQdLlwukb3dxPs6obG/dm5Z8R2cdE3gIBYi0pBeLRsr0u7A9mGKtOssJi9EAJ6/vTWAyu5aIiK1c/c6L7xtRLpmb8/LlIS2eG5gG95OIiLSAL5Tk645MqiNiQsRkTbw3ZqIiMjNae0mFJMX0jUO2CUi0h+OeSFdaxzgo3QI5Ibu7x6JySyCSBqite957Hkh2cwZ013W9h7rG4NAja+/RNr04uA2iApuoHQYuvNgXKTSIZBKMHkh2dzZKVzW9tqEBdR/EJEESsu19j1WGzwMWhuZQVJh8kJERJqQ0FFbhdTsNaaX9TpCsc2CZGl30oDWVo+11FvIMS9ERKQJt7cLVToE0f2jbzSGxoZj0Y4sy7YWjf2x6vlb6izX72on1OAOYRjdqzk+3XjUsi3Y3wfrXrwVft62l1VREyYv5LT4VsqUsSYi92Rwo9tGHSNMirR7U1hg/QepAG8bkdP8jV7YOWOg0mEQEWladIjjt2vEKAPRsMGNCQ1GL22lA+x5IZd4sSotEZHTDDAgNNAXv73QDwFG+T6SDQAa+Hhh/ZRb4WEwaK7COJMXcomg4ipwWvsmQfph8uMUfXJM+3B5BulW1zpUG7eJqmPyQi5RY+rSJjQAoUFGDO8coXQo5KbcZ2QGuUqpYTxaHz7E5IVcosaOly/G36ypKX9E5L6UyiEMGk+x2a9OREREmsKeF5JVSIAPLlwuUToMIkm0DQuEwcAxLyQ9rd/2cRWTF3KJ4OCol7WTb0Xcf9ZLFA2Rsn57oR8AwMPDzT9ZiCTG5IVk1Tig9oqRRFrHpIUcxQG7zuGYF3KNCgfsEhGRvjF5Id3R+jcKItKvV4d3kKUdz2q9gDNHyNOuXJi8kEvU2PHSrKGf0iGQhBo1UN9gWA8D0K9NiNJhkAp9MLKL1ePHb4mxehwh0fvVz8/2tXr8j77W7TZvrO1yEkxeSFeeua2VWy3epiVi/VkWP9VbnBOJ6HjiMHzzeC+lwyAVah7sX2Pb90/Ho0N4EMb0ao4xvVpI0m5oYM3xhT8/2xddoxri/u6ReP72NpK0KxcO2CWXqLFIHanTz8/2xb9/3IfDOYUunaddU2XKqBOJpWdMMFb/PTNNTl2iGuKnaj0yWsWeF5Ldew92qf8gJ8U1byTZuck1nSMb4uvHeyodhmgaqvD2FbkPd3/9SZq85OXlYezYsTCZTDCZTBg7dizy8/PrfM7y5csxZMgQhISEwGAwIC0tTcoQyUWO1nkBgEBfaTr85o+Nw8D2oZKcm6i6m8K0uaAdKefOTk1FO1dooK/dxyq16KOUJE1eRo8ejbS0NKxZswZr1qxBWloaxo4dW+dzrly5gr59++Ltt9+WMjRSkNHbU5LzJnRsyvEuOqeWyrUPxkViRBcu/KkELw3X0nn/oa6KtPvF+B6KtCslyca8HDp0CGvWrEFKSgp69aocyLZgwQLEx8cjIyMDbdu2tfm868lNZmamVKGRiPycSERuac1ZGW7LxTFSD8RFihOHix67JQa7s/KUDsMtbXzpNvR7Z6PSYTjFV6IvbvVp4K2/4a2S9bxs374dJpPJkrgAQO/evWEymZCcnCxaO8XFxSgoKLD6Ifk0bOCD/3d/J0SY7O/C9PQwYFy8uCPsvxx/s6jnI3X69x01v/S8OOgm/DAhXtY4OFBdOVwxngAJk5ecnByEhtYcfxAaGoqcnBzR2klMTLSMqTGZTIiKihLt3GSfkTc3x1v3dXLoOa/f1VG09r08DBjQjmNd3IHRq/Kb60ejuqJRA2/8MCEeLwxqg5ujg2WPpV/rJgCAIInGcJH2rZykzMyeH2VO5pXgcPIya9YsGAyGOn927doFADbHHwiCIOq4hOnTp8NsNlt+Tp06Jdq5yX5V/6Y97fggEes18PpdHbHhn7eJci7Sjru7NsPuVwfLlrQ83NP6S5HBUFnkK3na7UiZMVCWGEhbvnuiFzpHNpS93YWP9kAPBZJ5uTn8lWHSpEkYNWpUncdER0dj3759OHfuXI1958+fR1hYmKPN1spoNMJo5GJ/SquaigTI9E20Q3gQHu0TLUtbJL5wky+yzUVOP1/OwdketbQlVXVU0r4+f4/taxMWIGu7A9tXfr42bOBj2eZvVGasjZQc/pQJCQlBSEj9Ay7j4+NhNpuxc+dO9OxZWdthx44dMJvN6NOnj+ORElXTLpxTVbVMu3NGSGkGg3bGHQX5euPPlwfB11vesmo+Xh7Y8+pgGAyAl6f+SrpJdkXt27fHHXfcgSeffBIpKSlISUnBk08+ieHDh1vNNGrXrh1WrFhheXzp0iWkpaXh4MGDAICMjAykpaWJOk6GpMUPJRJT+/AgfPmPm/GbDBVJg/19bG53ZlYdSefers2UDsEhTQKNCPQVf5r/r8/dUuf+Rv4+Vj0weiJpOrZo0SJ06tQJCQkJSEhIQOfOnfHNN99YHZORkQGz2Wx5vHLlSnTr1g3Dhg0DAIwaNQrdunXDvHnzpAyVRORMb77eVjwl8Sx5qjcGtA2VpdCWyc/bZhHFZwe0RudIk+Ttk3wa+Gg/IY1t5r6vSUkHJwQHB+Pbb7+t8xihWt/f+PHjMX78eAmjIuk5lr10iWqIf/SNweu/HHToeY1r+ZZM6hVUpcicn50fHnIXpmsdGoA9WflW2xr5+2DlpFsQPW2VrLFQLUTo3n0gLhJfbz/p+olIEfq7EUaKk2sc5XMDtb0qqjvy9fbE1n8PwLapA+Ctw/vwpC3P3d5a6RDISXz3IFFUTVicrd79yrD2dh97X7dmCJLgHjJJLyq4ASIbqbfQmD0v3zN51ySPg6T3zwTbld5J/Zi8kOKuf1g80a+lonEQ2ZtAFxSVShwJ1cUgwn2jOzuFixAJKYXJC4muak2M6sW9bNHIjEfSsWGdw7FvVoKlRkZ92OunbR0jgtC7ZWOlwyAXsK41iS6+VWP8tr9yavu/hrRDtrkID/Xgsg2kbo4kJNEh/hJGQlJr1UTewnEkPiYvJLqqAzGD/X3w1T96it4GPzxICU/f2hIXr5SgVRO+/rSmWUM/nMnnWCW9YPJConN2wK4jnrqV42O0rkN4EA7nFCodBgCgX+v6q4YDwPQ77R9UTtKRcWUIUimOeSHRiTGYri4+nh7wZcVTzXttRAc81jem3iqhUntlWHve1iTSGCYvJIqQABkXx+S3Ll1o2MAHr43ooHiV0KGdwuEhR3chaYIYL4V3H+zi+kns9MHIG229fldH2dpVGm8bkSiqrgnDLl1yVb82Idh65IJqzkPaZ/S68V29tveoY2/diYQPNuPY+St1nqtv68b44+jFGtsPzh6CBj7yfqze2y0SQzo2lb1dpbHnhURRdZUHg8TZixxr3JCyBrQNla2t6kuUSP36Jdc58xcKsLFmVXWeLna7KJVAuFviAjB5IQ2aM6a70iGQxJqafJUOgVTMmfzyPRlv5ZD0mLyQKIQqpeZc+e4SbseHVngQP9j0rkmg42Oo/jn4phrb2ItC17UJC1Q6BBIRkxcSnSufF36cRUROem5gG6yY2MdqG1MXkhrzY2UweSFRWI95se854+JbAABeSrjxjbm2WR8LH+3hdGykLa7MXOvWvJHVwEwiV4Ta6AFs7O+DyIbqXVjUXfBfOYnO3jovr9/VEftmJaBfmyaWbbXdl44KvvFmwW86+rb6edfqvqyZfKtIkZA7alll6YCtUwfU2N+9RSNMv7Md7uvWDIue6CVnaFSF+w1RJklUna9hb3JhMBhqrCfTOdJ2zQ+Bqze6DV8f124delZ5AbYJDcDmv85b7Y8J8YfRy0M11X3JcUM7heP7Xaedfn5db1Fv3dsJjRp44+GezWH0qvlaNKCyRtH7I7s63T65jj0vpCoGgwE/TohXOgzSiUm3t7Z6/O6DXfD90/H432O1r7f1UkJbq8dv3O0+hb+0omUda5tVvQ3tjCaBRrzzQBd0a97IpfOQtNjzQpoggF0v7qRbVEO0Dw9Ci2DXxhZUX0bigbhIAMDVkrJanxPfqjH2vz4EAUYvXLhcLG/1aHLZpNvb4N11f8nWngHgu5MCmLyQKKoW+hJzeurgDmHoXK18PKe/6psgAF6eHlj9/C2K/a0DjJVvjUxciNSJyQuJTsyPmwXjKmcZHc4pEPGspAVMUomoNhzzQqKoOqD2JhaDIgUx56H6xEUHKx0CuYg9LyS65sENsHxiHwQ38Kn/YBu8PGvm1JxtRPaKbOSHobGVC9VVH/NynUeVDMfLg9/h9GLmiA517t/40m3488Ql3P/32CfSLiYvJInuLozU7xJpQkKHMEQ2YiEocpzBYMDcR+LqPMbX2xNP3doSRaXlXEdJR+7qElHn/pgQf8TUMVPJGU/0a4n5W47jzk5NRT0v1Y3JC4nO1W57g8GA+eNYUZekNePO9kqHQE5SchmRp/u3snr87yFtKycW1FKjiqTB/lISBW/rkCu+f5q1fch+oUG++Eff6BrbPSQc8BTk64W9MxMQ18K6V9nL0wM3RwfbLGhH0mHyQprA5EjfWjSucotQ5L/19aKH93fnOAc9mXhb6xrb/Fyszlwfk593/QeRLHjbiEQR4HvjpeRZy+KKRLWR8hXTIzoY+2YlINDItzsiveC/ZhJFsL8PPnm4G4xeHvC2MVuISEnV19AiIm1j8kKiGVHPSH8iIilJWeOHd67VhV+RSRO4thERVWUrUeGgWffB5IWIiNzW0qd6I7pxA3zzeO0rjZP68LYRERHpiocBqLCzs7ZXy8bY9K8B0gZEomPPCxEpLiTAiJgQf7Rq4o9AX36nItcsfrK3aOd6455YAMDHD3cT7ZzkOr5LkCa0DAkAAHh7chq2Hnl4GLB+Sn/L70T18fGq/bt3r5aNMaZXcyzakeVyO2N7t8Com6M4i1JlmLyQJvj5eCJ9VgLfQHSM9YHIEUG+3ugcacK+02ab+zs1E69cP9931Id/EdKMQF/vWlcJJiL389StLWvd92CPKDQN4qKbesXkhYiIdMfTw4Cx8S2UDoMkwuSFiIiINIXJCxEREWkKkxciIiLSFCYvREREpClMXoiIiEhTmLwQEZEmCVyv1W0xeSEiIiJNYfJCREREmsLkhYiIiDSFyQsREenSPd2aAQB6twxWOBISGxdmJCIiXWrW0A8HXh8CP66JpjtMXoiISLf8jfyY0yPeNiIiIiJNYfJCREREmsLkhYiIiDSFyQsRERFpCpMXIiIi0hQmL0RERKQpTF6IiIhIU5i8EBERkaYweSEiIiJNYfJCREREmsLkhYiIiDRF0uQlLy8PY8eOhclkgslkwtixY5Gfn1/r8aWlpZg6dSo6deoEf39/REREYNy4cTh79qyUYRIREZGGSJq8jB49GmlpaVizZg3WrFmDtLQ0jB07ttbjr169it27d+PVV1/F7t27sXz5cvz111+46667pAyTiIg0qHVogNIhkEIkW27z0KFDWLNmDVJSUtCrVy8AwIIFCxAfH4+MjAy0bdu2xnNMJhOSkpKstn3yySfo2bMnsrKy0Lx5c6nCJSIijWkfHgRfbw8UlVYoHQrJTLKel+3bt8NkMlkSFwDo3bs3TCYTkpOT7T6P2WyGwWBAw4YNbe4vLi5GQUGB1Q8REbmHge3ClA6BFCBZ8pKTk4PQ0NAa20NDQ5GTk2PXOYqKijBt2jSMHj0aQUFBNo9JTEy0jKkxmUyIiopyKW4iIiJSN4eTl1mzZsFgMNT5s2vXLgCAwWCo8XxBEGxur660tBSjRo1CRUUF5syZU+tx06dPh9lstvycOnXK0UsiIiKNEiAoHQIpwOExL5MmTcKoUaPqPCY6Ohr79u3DuXPnauw7f/48wsLq7uYrLS3FQw89hBMnTmDDhg219roAgNFohNFotC94IiIi0jyHk5eQkBCEhITUe1x8fDzMZjN27tyJnj17AgB27NgBs9mMPn361Pq864nLkSNHsHHjRjRu3NjREImIiEjHJBvz0r59e9xxxx148sknkZKSgpSUFDz55JMYPny41Uyjdu3aYcWKFQCAsrIyPPDAA9i1axcWLVqE8vJy5OTkICcnByUlJVKFSkRERBoiaZ2XRYsWoVOnTkhISEBCQgI6d+6Mb775xuqYjIwMmM1mAMDp06excuVKnD59Gl27dkV4eLjlx5EZSkRERKRfktV5AYDg4GB8++23dR4jCDcGW0VHR1s9JiIiIqqOaxsRERGRpjB5ISIiIk1h8kJERESawuSFiIiINIXJCxEREWkKkxciIiLSFCYvREREpClMXoiIiEhTmLwQERGRpjB5ISIiIk1h8kJERESawuSFiIiINIXJCxEREWkKkxciItKsuBbBSodACvBSOgAiIiJnPRrfAn7enujdkkmMO2HyQkREmuXl6YHRvZorHQbJjLeNiIiISFOYvBAREZGmMHkhIiIiTWHyQkRERJrC5IWIiIg0hckLERERaQqTFyIiItIUJi9ERESkKUxeiIiISFOYvBAREZGmMHkhIiIiTWHyQkRERJrC5IWIiIg0RXerSguCAAAoKChQOBIiIiKy1/XP7euf43XRXfJSWFgIAIiKilI4EiIiInJUYWEhTCZTnccYBHtSHA2pqKjA2bNnERgYCIPBIOq5CwoKEBUVhVOnTiEoKEjUc6sZr5vX7Q543bxud6Dm6xYEAYWFhYiIiICHR92jWnTX8+Lh4YHIyEhJ2wgKClLdH10OvG73wut2L7xu96LW666vx+U6DtglIiIiTWHyQkRERJrC5MUBRqMRM2fOhNFoVDoUWfG6ed3ugNfN63YHerlu3Q3YJSIiIn1jzwsRERFpCpMXIiIi0hQmL0RERKQpTF6IiIhIU5i82GnOnDmIiYmBr68v4uLisHXrVqVDstiyZQtGjBiBiIgIGAwG/PTTT1b7BUHArFmzEBERAT8/P9x22204cOCA1THFxcV47rnnEBISAn9/f9x11104ffq01TF5eXkYO3YsTCYTTCYTxo4di/z8fKtjsrKyMGLECPj7+yMkJATPP/88SkpKrI5JT09H//794efnh2bNmmH27Nl2rWVRVWJiIm6++WYEBgYiNDQU99xzDzIyMnR/3XPnzkXnzp0tBabi4+Px22+/6fqabUlMTITBYMDkyZN1fe2zZs2CwWCw+mnatKmur/m6M2fO4JFHHkHjxo3RoEEDdO3aFampqbq+9ujo6Bp/b4PBgGeffVa31+w0geq1ZMkSwdvbW1iwYIFw8OBB4YUXXhD8/f2FkydPKh2aIAiCsHr1auHll18Wli1bJgAQVqxYYbX/7bffFgIDA4Vly5YJ6enpwsiRI4Xw8HChoKDAcsyECROEZs2aCUlJScLu3buFAQMGCF26dBHKysosx9xxxx1CbGyskJycLCQnJwuxsbHC8OHDLfvLysqE2NhYYcCAAcLu3buFpKQkISIiQpg0aZLlGLPZLISFhQmjRo0S0tPThWXLlgmBgYHCu+++69A1DxkyRPjyyy+F/fv3C2lpacKwYcOE5s2bC5cvX9b1da9cuVJYtWqVkJGRIWRkZAgzZswQvL29hf379+v2mqvbuXOnEB0dLXTu3Fl44YUXLNv1eO0zZ84UOnbsKGRnZ1t+cnNzdX3NgiAIly5dElq0aCGMHz9e2LFjh3DixAlh/fr1wtGjR3V97bm5uVZ/66SkJAGAsHHjRt1es7OYvNihZ8+ewoQJE6y2tWvXTpg2bZpCEdWuevJSUVEhNG3aVHj77bct24qKigSTySTMmzdPEARByM/PF7y9vYUlS5ZYjjlz5ozg4eEhrFmzRhAEQTh48KAAQEhJSbEcs337dgGAcPjwYUEQKpMoDw8P4cyZM5ZjFi9eLBiNRsFsNguCIAhz5swRTCaTUFRUZDkmMTFRiIiIECoqKpy+7tzcXAGAsHnzZre6bkEQhEaNGgmff/65W1xzYWGh0KZNGyEpKUno37+/JXnR67XPnDlT6NKli819er1mQRCEqVOnCrfcckut+/V87VW98MILQqtWrYSKigq3uWZ78bZRPUpKSpCamoqEhASr7QkJCUhOTlYoKvudOHECOTk5VvEbjUb079/fEn9qaipKS0utjomIiEBsbKzlmO3bt8NkMqFXr16WY3r37g2TyWR1TGxsLCIiIizHDBkyBMXFxZbu3u3bt6N///5WBZKGDBmCs2fPIjMz0+nrNJvNAIDg4GC3ue7y8nIsWbIEV65cQXx8vFtc87PPPothw4Zh0KBBVtv1fO1HjhxBREQEYmJiMGrUKBw/flz317xy5Ur06NEDDz74IEJDQ9GtWzcsWLDAsl/P135dSUkJvv32Wzz22GMwGAxucc2OYPJSjwsXLqC8vBxhYWFW28PCwpCTk6NQVPa7HmNd8efk5MDHxweNGjWq85jQ0NAa5w8NDbU6pno7jRo1go+PT53HXH/s7P9PQRAwZcoU3HLLLYiNjbU6lx6vOz09HQEBATAajZgwYQJWrFiBDh066PqaAWDJkiXYvXs3EhMTa+zT67X36tULX3/9NdauXYsFCxYgJycHffr0wcWLF3V7zQBw/PhxzJ07F23atMHatWsxYcIEPP/88/j666+tzqfHa7/up59+Qn5+PsaPH291Hj1fsyN0t6q0VAwGg9VjQRBqbFMzZ+Kvfoyt48U4Rvh7gJez/z8nTZqEffv2Ydu2bTX26fG627Zti7S0NOTn52PZsmV49NFHsXnz5jrb0fo1nzp1Ci+88ALWrVsHX1/fWo/T27UPHTrU8nunTp0QHx+PVq1a4X//+x969+5daztavmYAqKioQI8ePfDWW28BALp164YDBw5g7ty5GDduXJ3taf3ar1u4cCGGDh1q1ftRWzt6uWZHsOelHiEhIfD09KyRSebm5tbIOtXo+syEuuJv2rQpSkpKkJeXV+cx586dq3H+8+fPWx1TvZ28vDyUlpbWeUxubi6Amt8o7PHcc89h5cqV2LhxIyIjIy3b9XzdPj4+aN26NXr06IHExER06dIFH330ka6vOTU1Fbm5uYiLi4OXlxe8vLywefNmfPzxx/Dy8qr1G58err0qf39/dOrUCUeOHNH13zs8PBwdOnSw2ta+fXtkZWVZ2gL0ee0AcPLkSaxfvx5PPPGEZZver9lRTF7q4ePjg7i4OCQlJVltT0pKQp8+fRSKyn4xMTFo2rSpVfwlJSXYvHmzJf64uDh4e3tbHZOdnY39+/dbjomPj4fZbMbOnTstx+zYsQNms9nqmP379yM7O9tyzLp162A0GhEXF2c5ZsuWLVZT7tatW4eIiAhER0fbfV2CIGDSpElYvnw5NmzYgJiYGLe47tr+XxQXF+v6mgcOHIj09HSkpaVZfnr06IExY8YgLS0NLVu21O21V1VcXIxDhw4hPDxc13/vvn371ih98Ndff6FFixYA9P/v+8svv0RoaCiGDRtm2ab3a3aYhIOBdeP6VOmFCxcKBw8eFCZPniz4+/sLmZmZSocmCELlDIw9e/YIe/bsEQAI77//vrBnzx7LVO63335bMJlMwvLly4X09HTh4Ycftjm9LjIyUli/fr2we/du4fbbb7c5va5z587C9u3bhe3btwudOnWyOb1u4MCBwu7du4X169cLkZGRVtPr8vPzhbCwMOHhhx8W0tPTheXLlwtBQUEOT6975plnBJPJJGzatMlqauHVq1ctx+jxuqdPny5s2bJFOHHihLBv3z5hxowZgoeHh7Bu3TrdXnNtqs420uu1//Of/xQ2bdokHD9+XEhJSRGGDx8uBAYGWt579HjNglA5Hd7Ly0t48803hSNHjgiLFi0SGjRoIHz77beWY/R67eXl5ULz5s2FqVOn1tin12t2BpMXO/33v/8VWrRoIfj4+Ajdu3e3TMlVg40bNwoAavw8+uijgiBUTiucOXOm0LRpU8FoNAq33nqrkJ6ebnWOa9euCZMmTRKCg4MFPz8/Yfjw4UJWVpbVMRcvXhTGjBkjBAYGCoGBgcKYMWOEvLw8q2NOnjwpDBs2TPDz8xOCg4OFSZMmWU2lEwRB2Ldvn9CvXz/BaDQKTZs2FWbNmuXw1Dpb1wtA+PLLLy3H6PG6H3vsMcvrsEmTJsLAgQMtiYter7k21ZMXPV779Toe3t7eQkREhHDfffcJBw4c0PU1X/fLL78IsbGxgtFoFNq1ayfMnz/far9er33t2rUCACEjI6PGPr1eszMMgiBXOTwiIiIi13HMCxEREWkKkxciIiLSFCYvREREpClMXoiIiEhTmLwQERGRpjB5ISIiIk1h8kJERESawuSFiIiINIXJCxEREWkKkxciIiLSFCYvREREpClMXoiIiEhT/j+y9MvAU37oDgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot (audio_data)\n",
    "plt.show ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "bccdd9e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nTtSxY8ckSZs3b9bBgwertTkAAIDLFVzZF2zdulU9evSQy+XS3r179cQTTygqKkpLlizRvn379O6779ZEnwBQJdz1HEClZ3bGjh2rYcOGadeuXapfv7413rdvX61Zs6bKjUyePFkOh0NJSUnWmDFGkyZNUmxsrMLCwtS1a1dt377d53Ver1djxoxRkyZNFBERoQEDBigrK6vKfQCwl/IOUD5VVFJ2EIBtVTrsbNy4USNHjiwz3rx5c+Xk5FSpiY0bN2rOnDm66aabfManTZum6dOna9asWdq4caPcbrd69uypgoICqyYpKUlLlixRSkqK1q5dqxMnTqh///4qKeEfMwDlm7LsO3+3AOAKqnTYqV+/vvLz88uM79y5U02bNq10AydOnNCjjz6qN998U40aNbLGjTF69dVX9cILL2jgwIFKSEjQO++8o1OnTmnhwoWSJI/Ho7lz5+qVV15Rjx491KFDB7333nvatm2bVq5cecH39Hq9ys/P93kAqDu2HfT4uwUAV1Clw87999+vF198UcXFZ+8343A4tH//fj3//PP65S9/WekGnnrqKfXr1089evTwGc/MzFROTo569epljTmdTnXp0kXr1q2TJKWnp6u4uNinJjY2VgkJCVZNeSZPniyXy2U94uLiKt03AACoHSoddl5++WUdPnxY0dHRKiwsVJcuXdSmTRtFRkbqz3/+c6XWlZKSos2bN2vy5Mlllp3bJRYTE+MzHhMTYy3LyclRaGioz4zQ+TXlmThxojwej/U4cOBApfoGAAC1R6XPxmrYsKHWrl2rzz//XJs3b1ZpaaluvfXWMjMzl3LgwAE988wzWrFihc+BzudzOHwv/2WMKTN2vkvVOJ1OOZ3OSvULAABqp0qHnXO6deumbt26VfmN09PTlZubq8TERGuspKREa9as0axZs7Rz505JZ2dvmjVrZtXk5uZasz1ut1tFRUXKy8vzmd3Jzc1V586dq9wbAACwjwqFnddee63CK3z66acrVNe9e3dt27bNZ+zXv/612rZtq+eee07XXHON3G63UlNT1aFDB0lSUVGR0tLSNHXqVElSYmKiQkJClJqaqkGDBkmSsrOzlZGRoWnTplW4ZwAAYF8VCjszZszweX748GGdOnVKV111laSzV1QODw9XdHR0hcNOZGSkEhISfMYiIiLUuHFjazwpKUnJycmKj49XfHy8kpOTFR4eriFDhkiSXC6XRowYoXHjxqlx48aKiorS+PHj1b59+0rvVgNgT6VcUxCo8yoUdjIzM62fFy5cqNdff11z587V9ddfL+nsaedPPPFEudffuRwTJkxQYWGhRo0apby8PHXq1EkrVqxQZGSkVTNjxgwFBwdr0KBBKiwsVPfu3TV//nwFBQVVay8AaivSDlDXOYwp7/qiF3bttdfq/ffft3YtnZOenq4HH3zQJxjVFvn5+XK5XPJ4PGrYsKG/2wFwGVo9/2mF6vZO6VfDnQCoaRX9+13pU8+zs7Ota+z8VElJiX788cfKrg4AAKBGVTrsdO/eXU888YQ2bdqkc5NCmzZt0siRIzlOBgAABJxKh523335bzZs312233ab69evL6XSqU6dOatasmd56662a6BEAAKDKKn2dnaZNm2rp0qX6/vvv9d1338kYoxtuuEHXXXddTfQHAABwWap8UcHrrruOgAMAAAJepcPO8OHDL7r87bffrnIzAAAA1a3SYScvL8/neXFxsTIyMnT8+PHLun0EAABATah02FmyZEmZsdLSUo0aNUrXXHNNtTQFAABQXSp9Nla5K6lXT88++2yZ20oAAAD4W7WEHUnas2ePzpw5U12rAwAAqBaV3o01duxYn+fGGGVnZ+vTTz/V0KFDq60xAKis08Ul/m4BQACqdNjZsmWLz/N69eqpadOmeuWVVy55phYA1CRvcam/WwAQgCoddlatWlUTfQAAANSISh+z061bNx0/frzMeH5+PqeeAwCAgFPpsLN69WoVFRWVGT99+rS++OKLamkKAACgulR4N9bWrVutn3fs2KGcnBzreUlJiZYvX67mzZtXb3cAAACXqcJh55ZbbpHD4ZDD4Sh3d1VYWJhmzpxZrc0BAABcrgqHnczMTBljdM011+jrr79W06ZNrWWhoaGKjo5WUFBQjTQJABVhZPzdAoAAVOGw07JlS0lnbw0BAABQW1Qo7Hz88cfq27evQkJC9PHHH1+0dsCAAdXSGAAAQHWoUNh54IEHlJOTo+joaD3wwAMXrHM4HCop4QqmAAAgcFQo7Px01xW7sQAAQG1SbTcCBQAACEQVmtl57bXXKrzCp59+usrNAMDlMJyMBaAcFQo7M2bMqNDKHA4HYQcAAASUCoWdzMzMmu4DAACgRlzWMTvGGBnmjQEAQACrUtiZO3euEhISVL9+fdWvX18JCQl66623qrs3AACAy1bhKyif8/vf/14zZszQmDFjdMcdd0iS1q9fr2effVZ79+7VSy+9VO1NAgAAVFWlw87s2bP15ptv6pFHHrHGBgwYoJtuukljxowh7AAAgIBS6d1YJSUl6tixY5nxxMREnTlzplqaAoCq4AhCAOWpdNj51a9+pdmzZ5cZnzNnjh599NFqaQoAAKC6VHo3lnT2AOUVK1bo9ttvlyRt2LBBBw4c0OOPP66xY8daddOnT6+eLgEAAKqo0mEnIyNDt956qyRpz549kqSmTZuqadOmysjIsOocDkc1tQgAAFB1lQ47q1atqok+AAAAagQ3AgUAALZW6Zmd06dPa+bMmVq1apVyc3NVWlrqs3zz5s3V1hwAVAZXdAdQnkqHneHDhys1NVUPPvigbrvtNo7NAQAAAa3SYefTTz/V0qVLdeedd172m8+ePVuzZ8/W3r17JUk33nij/vCHP6hv376Szv5f2p/+9CfNmTNHeXl56tSpk/7617/qxhtvtNbh9Xo1fvx4LVq0SIWFherevbtef/11tWjR4rL7AwAAtV+lj9lp3ry5IiMjq+XNW7RooSlTpmjTpk3atGmTunXrpvvvv1/bt2+XJE2bNk3Tp0/XrFmztHHjRrndbvXs2VMFBQXWOpKSkrRkyRKlpKRo7dq1OnHihPr376+SkpJq6REAANRuDlPJndzLli3Ta6+9pjfeeEMtW7as9oaioqL0l7/8RcOHD1dsbKySkpL03HPPSTo7ixMTE6OpU6dq5MiR8ng8atq0qRYsWKDBgwdLkg4dOqS4uDgtXbpUvXv3Lvc9vF6vvF6v9Tw/P19xcXHyeDxq2LBhtW8TgCvj6AmvEl9aWaHavVP61XA3AGpafn6+XC7XJf9+V3pmp2PHjjp9+rSuueYaRUZGKioqyudRVSUlJUpJSdHJkyd1xx13KDMzUzk5OerVq5dV43Q61aVLF61bt06SlJ6eruLiYp+a2NhYJSQkWDXlmTx5slwul/WIi4urct8AACCwVfqYnUceeUQHDx5UcnKyYmJiLvsA5W3btumOO+7Q6dOn1aBBAy1ZskTt2rWzwkpMTIxPfUxMjPbt2ydJysnJUWhoqBo1alSmJicn54LvOXHiRJ8rPZ+b2QEAAPZT6bCzbt06rV+/XjfffHO1NHD99dfrm2++0fHjx7V48WINHTpUaWlp1vLzw5Qx5pIB61I1TqdTTqfz8hoHEHA48RxAeSq9G6tt27YqLCystgZCQ0PVpk0bdezYUZMnT9bNN9+s//qv/5Lb7ZakMjM0ubm51myP2+1WUVGR8vLyLlgDAADqtkqHnSlTpmjcuHFavXq1jh49qvz8fJ/H5TLGyOv1qnXr1nK73UpNTbWWFRUVKS0tTZ07d5YkJSYmKiQkxKcmOztbGRkZVg0AAKjbKr0bq0+fPpKk7t27+4yf23VUmVO+//M//1N9+/ZVXFycCgoKlJKSotWrV2v58uVyOBxKSkpScnKy4uPjFR8fr+TkZIWHh2vIkCGSJJfLpREjRmjcuHFq3LixoqKiNH78eLVv3149evSo7KYBAAAbqtYbgW7ZsqVS6/rxxx/12GOPKTs7Wy6XSzfddJOWL1+unj17SpImTJigwsJCjRo1yrqo4IoVK3yu8zNjxgwFBwdr0KBB1kUF58+fr6CgoMpuGoBa7sMtBytcm3nkpFo3iajBbgAEikpfZ+d8Ho9H//M//6O33npL//znP2vlxfwqep4+gMCW+H9TdfRkUYVq/9C/nYb/vHUNdwSgJtXYdXbO+fzzz/WrX/1KzZo108yZM3Xvvfdq06ZNVV0dAFw2zsYCUJ5K7cbKysrS/Pnz9fbbb+vkyZMaNGiQiouLtXjxYrVr166megQAAKiyCs/s3HvvvWrXrp127NihmTNn6tChQ5o5c2ZN9gYAAHDZKjyzs2LFCj399NP67W9/q/j4+JrsCQAAoNpUeGbniy++UEFBgTp27KhOnTpp1qxZOnz4cE32BgCVcnk3rwFgVxUOO3fccYfefPNNZWdna+TIkUpJSVHz5s1VWlqq1NRUFRQU1GSfAFCtOJgZqDsqfTZWeHi4hg8frrVr12rbtm0aN26cpkyZoujoaA0YMKAmegQAAKiyKp96Lp29iee0adOUlZWlRYsWVVdPAFAlFb3GjnT2qu8A6obLCjvnBAUF6YEHHtDHH39cHasDAACoNtUSdgAAAAIVYQdAnXT8VLG/WwBwhRB2ANRJs1bt9ncLAK4Qwg4AALA1wg4AALA1wg4AALA1wg4AALA1wg4AALA1wg4AALA1wg4AALA1wg4AALA1wg4AALA1wg4AALA1wg4AALA1wg4AALA1wg4AALA1wg4AALA1wg4AALA1wg4AALA1wg4AALA1wg4AALA1wg4AALA1wg4AALA1wg4AWygtNf5uAUCAIuwAsIX1Pxz1dwsAAhRhB4AtnC4u8XcLAAIUYQcAANgaYQcAANiaX8PO5MmT9bOf/UyRkZGKjo7WAw88oJ07d/rUGGM0adIkxcbGKiwsTF27dtX27dt9arxer8aMGaMmTZooIiJCAwYMUFZW1pXcFAAAEKD8GnbS0tL01FNPacOGDUpNTdWZM2fUq1cvnTx50qqZNm2apk+frlmzZmnjxo1yu93q2bOnCgoKrJqkpCQtWbJEKSkpWrt2rU6cOKH+/furpIR9+EBd4XD4uwMAgcphjAmY8zUPHz6s6OhopaWl6e6775YxRrGxsUpKStJzzz0n6ewsTkxMjKZOnaqRI0fK4/GoadOmWrBggQYPHixJOnTokOLi4rR06VL17t37ku+bn58vl8slj8ejhg0b1ug2AqgZn3/3o4bP31Sp1+yd0q+GugFwJVT073dAHbPj8XgkSVFRUZKkzMxM5eTkqFevXlaN0+lUly5dtG7dOklSenq6iouLfWpiY2OVkJBg1ZzP6/UqPz/f5wEAAOwpYMKOMUZjx47Vz3/+cyUkJEiScnJyJEkxMTE+tTExMdaynJwchYaGqlGjRhesOd/kyZPlcrmsR1xcXHVvDgAACBABE3ZGjx6trVu3atGiRWWWOc7bGW+MKTN2vovVTJw4UR6Px3ocOHCg6o0DCAgOcdAOgPIFRNgZM2aMPv74Y61atUotWrSwxt1utySVmaHJzc21ZnvcbreKioqUl5d3wZrzOZ1ONWzY0OcBAADsya9hxxij0aNH64MPPtDnn3+u1q1b+yxv3bq13G63UlNTrbGioiKlpaWpc+fOkqTExESFhIT41GRnZysjI8OqAQAAdVewP9/8qaee0sKFC/XRRx8pMjLSmsFxuVwKCwuTw+FQUlKSkpOTFR8fr/j4eCUnJys8PFxDhgyxakeMGKFx48apcePGioqK0vjx49W+fXv16NHDn5sHAAACgF/DzuzZsyVJXbt29RmfN2+ehg0bJkmaMGGCCgsLNWrUKOXl5alTp05asWKFIiMjrfoZM2YoODhYgwYNUmFhobp376758+crKCjoSm0KAAAIUAF1nR1/4To7QO23ameufj1vY6Vew3V2gNqtVl5nBwAAoLoRdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgDYwpy0Hyr9mi92Ha6BTgAEGsIOAFtY/8PRSr/mjx9tr4FOAAQawg4AALA1wg4AALA1wg6AOquUWwMCdQJhB0CdtffoKX+3AOAKIOwAAABbI+wAAABbI+wAAABbI+wAAABbI+wAAABbI+wAAABbI+wAAABbI+wAAABbI+wAAABbI+wAAABbI+wAAABbI+wAAABbI+wAAABbI+wAAABbI+wAAABbI+wAAABbI+wAAABbI+wAAABbI+wAAABbI+wAAABbI+wAqPWMMf5uAUAAI+wAAABbI+wAAABbI+wAqPXYiwXgYgg7AADA1vwadtasWaP77rtPsbGxcjgc+vDDD32WG2M0adIkxcbGKiwsTF27dtX27dt9arxer8aMGaMmTZooIiJCAwYMUFZW1hXcCgAAEMj8GnZOnjypm2++WbNmzSp3+bRp0zR9+nTNmjVLGzdulNvtVs+ePVVQUGDVJCUlacmSJUpJSdHatWt14sQJ9e/fXyUlJVdqMwAAQAAL9ueb9+3bV3379i13mTFGr776ql544QUNHDhQkvTOO+8oJiZGCxcu1MiRI+XxeDR37lwtWLBAPXr0kCS99957iouL08qVK9W7d+8rti0A/IdDdgBcTMAes5OZmamcnBz16tXLGnM6nerSpYvWrVsnSUpPT1dxcbFPTWxsrBISEqya8ni9XuXn5/s8ANRe2Z5Cf7cAIIAFbNjJycmRJMXExPiMx8TEWMtycnIUGhqqRo0aXbCmPJMnT5bL5bIecXFx1dw9gCtp/7FTVX5taSnzQoDdBWzYOcfhcPg8N8aUGTvfpWomTpwoj8djPQ4cOFAtvQLwj+cWb63yaz/YcrAaOwEQiAI27LjdbkkqM0OTm5trzfa43W4VFRUpLy/vgjXlcTqdatiwoc8DQO114FjVd2PN+nxXNXYCIBAFbNhp3bq13G63UlNTrbGioiKlpaWpc+fOkqTExESFhIT41GRnZysjI8OqAYCL2Xu06rvAANQOfj0b68SJE9q9e7f1PDMzU998842ioqJ09dVXKykpScnJyYqPj1d8fLySk5MVHh6uIUOGSJJcLpdGjBihcePGqXHjxoqKitL48ePVvn176+wsAABQt/k17GzatEn33HOP9Xzs2LGSpKFDh2r+/PmaMGGCCgsLNWrUKOXl5alTp05asWKFIiMjrdfMmDFDwcHBGjRokAoLC9W9e3fNnz9fQUFBV3x7AABA4HEYw11l8vPz5XK55PF4OH4HqIVaPf/pZb1+75R+1dQJgCupon+/A/aYHQAAgOpA2AEAALZG2AEAALZG2AEAALZG2AEAALZG2AEAALZG2AFQq3H1DACXQtgBUKt9/M9Dl72OHM/paugEQKAi7ACo1f5fNYSdrDzujwXYGWEHQK12OXc8P4cdYYC9EXYAAICtEXYA1Go7fyy47HVM+nh7NXQCIFARdgDUedsP5fu7BQA1iLADAABsjbADAABsjbADoNb6OvNYta3rh8Mnqm1dAAILYQdArTXov9dX27q6vZJWbesCEFgIOwAAwNYIO0AAOVV0RvfNXKvpqd/7uxWgDGOMRi7YpKcWbvZ3K0ClEHaAAJLy9QFtO+jRa//Y5e9WgDIOn/Dqs+0/6tOt2fIUFvu7HaDCgv3dAIB/854ptX6+b+ZabTvo0bDOrfRgYgslNHf5sTPUVak7ftTfNx3Qih0/amCH5tY4d5tHbcLMDhBAXl6x0/p520GPJGn+ur3qP3Otvv+xQCWl/IHBlXGmpFSfbs3WE+9u0oodP0qSPthy0Fr+4U9+BgIdMztAALlYmOk1Y40kqXvbaM15vKOC6jmuVFuoQ46c8KrH9DQdP3Xx3VST/t8ODbuz9RXqCrg8zOwAAeB0cYk+/uehCtX+47tcLcvI9hn7cvcRZfxrJqiuqInr4pwuLqn2dQaytO8P69ts31tlPL942yWDzjkZBz3MNqJWIOwAAeDBN9bp6UVbKlw/Zdl31s8Hjxfq0be+Uv+Za5WVd6om2gtIw+ZtrPZ1LqlDu2beT8/S0Le/Vt//+sJnfOW3P1Z4Hf1nrtWfP/22ulsDqh1hBwgAGQcrdyPKrLxClf7r/6gXp2dZ4z+fukonvGeqtbdAtf9Y9Qe7/1yyrdrXGYh2HMrX+L//03p+8l//zXjPVH5m6+0vM6utL6CmEHaAWurcH+bzr8kz5M0N/mjnijpQA0FHkoyRiktKL11Yy937mu9szu8/ypB0NiwDdkTYgS2s2J6j9H3Vd5+kmvLJ1kPq+pdV2vVjgSTJc6pYs1fvqdK6UjYeKHd8a5ZHG/cG/mdRVTme07prWs39UY5/YZmtA89PZwLP+WDz2d13hwu8VVrnexv26cy/PrPlGTnq/srqWnEM2fKMbG3Zn+fvNnAFEHZQq+07elK/eusr/ceCdP1y9r/vk5SbfzogrwMyeuEW7T16Sj1nrJExRje/uEJTl3936RdewIWO0XnojcrdM6q4pFSvrNipJ97dZO0eC1QTP9ha4+/x8TcVO1jcX3ILTmvQG+v1fnpWpf87H/eT3Vc/dTnh5P98mKEOL6aquKRUT76Xrj2HT+o/3t1U5fXVlDMlpTpy4myg25bl0ZPvbdYvXl+nP3yUUWd2/9ZVhB3Uao++9ZXW7j5iPf9k6yEt+nq/bkv+h5KXBvaBk60nLr3sdVRlt8Prq3erz6trdPSEV6eLS7R6Z67iX1immZ/vVuqOH/XlniOXXomfnPCe0aqdh2v8fS4UCALFtOU79fXeYxr/93+q9cSl2rI/T6WlRt8cOK5uL6/Wiu055b7uYsGo/8y1l9VTgfeM4l9YZj0PxPDw8JwN6vjSSu04lK/7Zv17e99dv0//tZJbtNgZ19lBrZaVV+jzfPTCLaofcjbDv/lFpl7o184fbQWstbuOaNrysxcuTHxppcJDg3SqyPeg1JPeqp9+veb7w4qKCK3S1Z7P/SF2OC58/aCklG+q2lqlbdp7TB1bRV1wuTHmor1ezOqduWoa6dSNsVW7Kvb5p93/4vV1inXV1yHPaUnSfyxI17cv9lFYaJBP3fZDlTsQ/nLknw68sLNp39ldVu+Xsytv9c7DeqHfle4IVwozO7Cd08WBd7xFxkGPWj3/6RV9z1bPf6r0fces3ROeU8X61dyvfGrODzqSNGdN1Y4h2n/0lB5/++sqzRB8ufuIWk9cqoGz16m01Ki4pFSF5/XmPVNSqdOiL9eD5ewKLDhdLGOMjpzw6tb/m6pWz3+q7/91/FVF7c49oWHzNqrfa1WfSdm8/3iZsXNB55wb/rBcp4tLZIzRuj1HtOGHo5c9e1NZY//2jbI9hZcuvMLKO4NsV271X7cJgYOZHdRaH31Te66JcqX/yJzz0+OYKmrz/uOVnrV4fvFWnwOmC04Xq7C4RE0bOC+6nq8zj+mbA3lKXnr2uKUt+4/rhyMnNfTtr3XweKF2vNhb4aFn/5m6/v8sr/S2XK6klC169eEOkqRvs/PV97++UJ8b3Yq9Kkx5/7rwXq8Za/TeiE5qGunU9e7IC67rTEmp8k+f0eqdudZYq+c/1SsP3axfJraocE/5pyt+A862v7/yn9lPfbDloD7YclB7p/h/yqQiZ/Cl7zumxJYXns1D7eUwgXgU5xWWn58vl8slj8ejhg0b+rsdXEJpqdGT76Vb9+u5mD3J9/r1tgpFZ0p13f9ZdunCAPTRU3fq/r9+KUla93w3xV4VJkkavXCzPtmarV90aK6tWcfV+domWrBhX7nruDnuKn301J3lLpv1+S69vKLscRJLn76rzKnRPdvFKLUC33dN6NUupsx/a00jneWeufTJmJ9fcBfexWb23vjVrZq87Dv9rFWU3k/P0o2xDfXp03dJkr7YdViPzf1at159lfrfFKsXP9lxGVvjP/4OPK/9Y1eZyzSU563HO6pHu5gr0BGqQ0X/fhN2RNipbR6Zs0Hrfzhaodo1v7tHVzcOr+GOLuxK77oKROX9kbPz59LluqZ6Z/htPmOFRSW64Q/+nWXxt/G9rtPobvF+e/+b/7RCnsKKzYr93wcS9NjtLWu4I1QHwk4lEHYCnzFGf/70W721tvJXa935Uh85g4MuXVhNjp0s0sffHNR3OQUXvBZOXRMXFaY1v7tH7321X3fHN1GXv6z2d0s16n9H3qHjp4p093VN/b4rKZDMGtJBx08V68HEFqofcuV+J3fnnlCP6WmVft3UX7bX4J9dXQMdoboQdiqBsFM5hwu8Oni8ULfEXVXj71VaapSdf1p3Tvn8stYz6b522rz/uLLyTun3/dup/b92NQQH/fsY/W+z81VcUqqbWlwl6ewBseeHpNPFJaofEqTCohINeWuD7rk+WtNTv9fzfdv63K8KQMX0uCFa3xzwqP9NzXS4wKtZQzrI4XBYv2s/de530hijL3YdUZvoBtbuVenscVFG0tJt2Xr7y73q1DpKRwq8+uAy73n27Yt9VD+kXpXPvquoklKjjXuP6aYWLutYNVwcYacSCDsVc+SEV8+9v1X/+O7fB1g+3T1eP2vVSHfFN5X3TImMOXuF26Mnvfoup0AzUr9XVESoOsQ10uhubXTweKGC6zl0Y6xLQfUcCg0+GzaKzpRq0df7dVd8E8VFhWvDD0e1dvcR/XfaDzW+XXOHdtTkZd9pN2djALXOTS1cGtihuSb9v5o/lunlh25W6ybhSmwZpa8zjynbU6j7b2ku6ez/mJUao/zTZ5R55ISMkeqHBGnq8u90MK9Q7WIb6u74purYqpFKSo2uadpAxSWlqh8SpL9t3K/MI6f0Rtq/z4R8MLGFkn/R3vo3EuWrc2Hn9ddf11/+8hdlZ2frxhtv1Kuvvqq77rqrQq8l7FSMnY+zAIBA0/yqMH35fDd/txHQKvr32xaR8W9/+5uSkpL0wgsvaMuWLbrrrrvUt29f7d+/39+tAQBQJQePB941imorW8zsdOrUSbfeeqtmz55tjd1www164IEHNHny5Eu+viZndnbnFij3p6eomnJ/PPv8vAHzk4qyy85/rbngsvMHLrrei7zPEwF4rxsAsLP/uPsahQQ59MPhk2oUESpncD2FhQTpTKlRSalR0ZlSORzSoeOndWNsQ6Xvy1OLRmFyOBxqGBas5leF6YfDJ+U9U6JmrjBt3p+nVo0j1LNdjOr96xikjXuPqeB0sZq5wrT/2Cm1aBSmE94z+jHfK2dwPZ0qOqMzpUalpUbRDevLFRYiSQoNqqedPxYoyOFQ5zaNlV949vpax08VK8IZrPohQWr+r2OqrgoP0e3XNK72z6fO7MYqKipSeHi4/v73v+sXv/iFNf7MM8/om2++UVpa2SPwvV6vvN5/B5D8/HzFxcVVe9hZ9PV+TfxgW7WtDwCA2mpi37Ya2eXaal1nndmNdeTIEZWUlCgmxvciUDExMcrJKf9meJMnT5bL5bIecXFxNdJbdKSzRtYL4NLO3SPtp8Ku4OnOAHzFRfnvmme2Obft/FMCL3a5+4kTJ2rs2LHW83MzO9Wt+w0xfr9qKAAAdV2tDztNmjRRUFBQmVmc3NzcMrM95zidTjmdzLoAAFAX1PrdWKGhoUpMTFRqaqrPeGpqqjp37uynrgAAQKCo9TM7kjR27Fg99thj6tixo+644w7NmTNH+/fv15NPPunv1gAAgJ/ZIuwMHjxYR48e1Ysvvqjs7GwlJCRo6dKlatmSG7kBAFDX1fpTz6sDV1AGAKD2qTOnngMAAFwMYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANiaLW4XcbnOXUQ6Pz/fz50AAICKOvd3+1I3gyDsSCooKJAkxcXF+bkTAABQWQUFBXK5XBdczr2xJJWWlurQoUOKjIyUw+HwdzsXlZ+fr7i4OB04cID7eAUYvpvAxPcSmPheAlNt+16MMSooKFBsbKzq1bvwkTnM7EiqV6+eWrRo4e82KqVhw4a14j/EuojvJjDxvQQmvpfAVJu+l4vN6JzDAcoAAMDWCDsAAMDWCDu1jNPp1B//+Ec5nU5/t4Lz8N0EJr6XwMT3Epjs+r1wgDIAALA1ZnYAAICtEXYAAICtEXYAAICtEXYAAICtEXb8YO/evRoxYoRat26tsLAwXXvttfrjH/+ooqIin7r9+/frvvvuU0REhJo0aaKnn366TM22bdvUpUsXhYWFqXnz5nrxxRfL3CMkLS1NiYmJql+/vq655hq98cYbZXpavHix2rVrJ6fTqXbt2mnJkiXVv+G1wJ///Gd17txZ4eHhuuqqq8qt4XupPV5//XW1bt1a9evXV2Jior744gt/t1SrrVmzRvfdd59iY2PlcDj04Ycf+iw3xmjSpEmKjY1VWFiYunbtqu3bt/vUeL1ejRkzRk2aNFFERIQGDBigrKwsn5q8vDw99thjcrlccrlceuyxx3T8+HGfmor8HtYFkydP1s9+9jNFRkYqOjpaDzzwgHbu3OlTw/ciyeCKW7ZsmRk2bJj57LPPzJ49e8xHH31koqOjzbhx46yaM2fOmISEBHPPPfeYzZs3m9TUVBMbG2tGjx5t1Xg8HhMTE2Mefvhhs23bNrN48WITGRlpXn75Zavmhx9+MOHh4eaZZ54xO3bsMG+++aYJCQkx77//vlWzbt06ExQUZJKTk823335rkpOTTXBwsNmwYcOV+UACyB/+8Aczffp0M3bsWONyucos53upPVJSUkxISIh58803zY4dO8wzzzxjIiIizL59+/zdWq21dOlS88ILL5jFixcbSWbJkiU+y6dMmWIiIyPN4sWLzbZt28zgwYNNs2bNTH5+vlXz5JNPmubNm5vU1FSzefNmc88995ibb77ZnDlzxqrp06ePSUhIMOvWrTPr1q0zCQkJpn///tbyivwe1hW9e/c28+bNMxkZGeabb74x/fr1M1dffbU5ceKEVcP3YgxhJ0BMmzbNtG7d2nq+dOlSU69ePXPw4EFrbNGiRcbpdBqPx2OMMeb11183LpfLnD592qqZPHmyiY2NNaWlpcYYYyZMmGDatm3r814jR440t99+u/V80KBBpk+fPj41vXv3Ng8//HD1bWAtM2/evHLDDt9L7XHbbbeZJ5980mesbdu25vnnn/dTR/ZyftgpLS01brfbTJkyxRo7ffq0cblc5o033jDGGHP8+HETEhJiUlJSrJqDBw+aevXqmeXLlxtjjNmxY4eR5BPq169fbySZ7777zhhTsd/Duio3N9dIMmlpacYYvpdz2I0VIDwej6Kioqzn69evV0JCgmJjY62x3r17y+v1Kj093arp0qWLz8WfevfurUOHDmnv3r1WTa9evXzeq3fv3tq0aZOKi4svWrNu3bpq3UY74HupHYqKipSenl7m8+vVqxefXw3JzMxUTk6Oz2fudDrVpUsX6zNPT09XcXGxT01sbKwSEhKsmvXr18vlcqlTp05Wze233y6Xy+VTc6nfw7rK4/FIkvX3hO/lLMJOANizZ49mzpypJ5980hrLyclRTEyMT12jRo0UGhqqnJycC9ace36pmjNnzujIkSMXrTm3Dvwb30vtcOTIEZWUlPD5XUHnPteLfeY5OTkKDQ1Vo0aNLloTHR1dZv3R0dEX/f05//ewLjLGaOzYsfr5z3+uhIQESXwv5xB2qtGkSZPkcDgu+ti0aZPPaw4dOqQ+ffrooYce0m9+8xufZQ6Ho8x7GGN8xs+vMf86CLY6asp7/9qoKt/LxfC91B58fldeVT7zS/3+VLWmrhk9erS2bt2qRYsWlVlW17+XYL++u82MHj1aDz/88EVrWrVqZf186NAh3XPPPbrjjjs0Z84cnzq3262vvvrKZywvL0/FxcVWcna73WXScm5uriRdsiY4OFiNGze+aM35Cb22quz3cjF8L7VDkyZNFBQUxOd3Bbndbkln/+++WbNm1vhPP3O3262ioiLl5eX5zCLk5uaqc+fOVs2PP/5YZv2HDx/2Wc+lfg/rmjFjxujjjz/WmjVr1KJFC2uc7+VfrvRBQjgrKyvLxMfHm4cfftjnaPdzzh3odejQIWssJSWlzIGwV111lfF6vVbNlClTyhwIe8MNN/is+8knnyxzIGzfvn19avr06VOnD4S91AHKfC+B77bbbjO//e1vfcZuuOEGDlCuJrrAAcpTp061xrxeb7kHwv7tb3+zag4dOlTugbBfffWVVbNhw4ZyD4S92O9hXVFaWmqeeuopExsba77//vtyl/O9cDaWXxw8eNC0adPGdOvWzWRlZZns7Gzrcc65U/i6d+9uNm/ebFauXGlatGjhcwrf8ePHTUxMjHnkkUfMtm3bzAcffGAaNmxY7inOzz77rNmxY4eZO3dumVOcv/zySxMUFGSmTJlivv32WzNlypQ6e4rzvn37zJYtW8yf/vQn06BBA7NlyxazZcsWU1BQYIzhe6lNzp16PnfuXLNjxw6TlJRkIiIizN69e/3dWq1VUFBg/U5IMtOnTzdbtmyxTuefMmWKcblc5oMPPjDbtm0zjzzySLmnOLdo0cKsXLnSbN682XTr1q3cU5xvuukms379erN+/XrTvn37ck9xvtjvYV3x29/+1rhcLrN69WqfvyWnTp2yavheCDt+MW/ePCOp3MdP7du3z/Tr18+EhYWZqKgoM3r0aJ/TmY0xZuvWreauu+4yTqfTuN1uM2nSJGv24JzVq1ebDh06mNDQUNOqVSsze/bsMj39/e9/N9dff70JCQkxbdu2NYsXL67+Da8Fhg4dWu73smrVKquG76X2+Otf/2patmxpQkNDza233mqdjouqWbVqVbm/H0OHDjXGnJ1F+OMf/2jcbrdxOp3m7rvvNtu2bfNZR2FhoRk9erSJiooyYWFhpn///mb//v0+NUePHjWPPvqoiYyMNJGRkebRRx81eXl5PjUV+T2sCy70t2TevHlWDd+LMQ5jzrusKwAAgI1wNhYAALA1wg4AALA1wg4AALA1wg4AALA1wg4AALA1wg4AALA1wg4AALA1wg4AALA1wg4AVJOioiK1adNGX375ZbWu95NPPlGHDh1UWlparesF6grCDoByDRs2TA6Ho8xj9+7d/m4tYM2ZM0ctW7bUnXfeaY05HA59+OGHZWqHDRumBx54oELr7d+/vxwOhxYuXFhNnQJ1C2EHwAX16dNH2dnZPo/WrVuXqSsqKvJDd4Fn5syZ+s1vflMj6/71r3+tmTNn1si6Absj7AC4IKfTKbfb7fMICgpS165dNXr0aI0dO1ZNmjRRz549JUk7duzQvffeqwYNGigmJkaPPfaYjhw5Yq3v5MmTevzxx9WgQQM1a9ZMr7zyirp27aqkpCSrpryZkKuuukrz58+3nh88eFCDBw9Wo0aN1LhxY91///3au3evtfzcrMnLL7+sZs2aqXHjxnrqqadUXFxs1Xi9Xk2YMEFxcXFyOp2Kj4/X3LlzZYxRmzZt9PLLL/v0kJGRoXr16mnPnj3lflabN2/W7t271a9fv0p+ytLevXvLnUXr2rWrVTNgwAB9/fXX+uGHHyq9fqCuI+wAqJJ33nlHwcHB+vLLL/Xf//3fys7OVpcuXXTLLbdo06ZNWr58uX788UcNGjTIes3vfvc7rVq1SkuWLNGKFSu0evVqpaenV+p9T506pXvuuUcNGjTQmjVrtHbtWjVo0EB9+vTxmWFatWqV9uzZo1WrVumdd97R/PnzfQLT448/rpSUFL322mv69ttv9cYbb6hBgwZyOBwaPny45s2b5/O+b7/9tu666y5de+215fa1Zs0aXXfddWrYsGGltkeS4uLifGbPtmzZosaNG+vuu++2alq2bKno6Gh98cUXlV4/UOf5+a7rAALU0KFDTVBQkImIiLAeDz74oDHGmC5duphbbrnFp/73v/+96dWrl8/YgQMHjCSzc+dOU1BQYEJDQ01KSoq1/OjRoyYsLMw888wz1pgks2TJEp/1uFwuM2/ePGOMMXPnzjXXX3+9KS0ttZZ7vV4TFhZmPvvsM6v3li1bmjNnzlg1Dz30kBk8eLAxxpidO3caSSY1NbXcbT906JAJCgoyX331lTHGmKKiItO0aVMzf/78C35ezzzzjOnWrVuZcUmmfv36Pp9jRESECQ4ONvfff3+Z+sLCQtOpUyfTv39/U1JS4rOsQ4cOZtKkSRfsAUD5gv0btQAEsnvuuUezZ8+2nkdERFg/d+zY0ac2PT1dq1atUoMGDcqsZ8+ePSosLFRRUZHuuOMOazwqKkrXX399pXpKT0/X7t27FRkZ6TN++vRpn11MN954o4KCgqznzZo107Zt2yRJ33zzjYKCgtSlS5dy36NZs2bq16+f3n77bd1222365JNPdPr0aT300EMX7KuwsFD169cvd9mMGTPUo0cPn7HnnntOJSUlZWpHjBihgoICpaamql4938n3sLAwnTp16oI9ACgfYQfABUVERKhNmzYXXPZTpaWluu+++zR16tQytc2aNdOuXbsq9J4Oh0PGGJ+xnx5rU1paqsTERP3P//xPmdc2bdrU+jkkJKTMes+duh0WFnbJPn7zm9/oscce04wZMzRv3jwNHjxY4eHhF6xv0qSJFabO53a7y3yOkZGROn78uM/YSy+9pOXLl+vrr78uE+Yk6dixYz7bCKBiCDsAqsWtt96qxYsXq1WrVgoOLvtPS5s2bRQSEqINGzbo6quvliTl5eXp+++/95lhadq0qbKzs63nu3bt8pnNuPXWW/W3v/1N0dHRVTo+RpLat2+v0tJSpaWllZlxOefee+9VRESEZs+erWXLlmnNmjUXXWeHDh00e/ZsGWPkcDgq3dPixYv14osvatmyZeUeF3Ru5qpDhw6VXjdQ13GAMoBq8dRTT+nYsWN65JFHrLOGVqxYoeHDh6ukpEQNGjTQiBEj9Lvf/U7/+Mc/lJGRoWHDhpXZVdOtWzfNmjVLmzdv1qZNm/Tkk0/6zNI8+uijatKkie6//3598cUXyszMVFpamp555hllZWVVqNdWrVpp6NChGj58uD788ENlZmZq9erV+t///V+rJigoSMOGDdPEiRPVpk0bn91v5bnnnnt08uRJbd++vRKf2lkZGRl6/PHH9dxzz+nGG29UTk6OcnJydOzYMatmw4YNcjqdl+wDQFmEHQDVIjY2Vl9++aVKSkrUu3dvJSQk6JlnnpHL5bICzV/+8hfdfffdGjBggHr06KGf//znSkxM9FnPK6+8ori4ON19990aMmSIxo8f77P7KDw8XGvWrNHVV1+tgQMH6oYbbtDw4cNVWFhYqZme2bNn68EHH9SoUaPUtm1bPfHEEzp58qRPzYgRI1RUVKThw4dfcn2NGzfWwIEDy929dimbNm3SqVOn9NJLL6lZs2bWY+DAgVbNokWL9Oijj150VxqA8jnM+TvHAeAK6tq1q2655Ra9+uqr/m6ljC+//FJdu3ZVVlaWYmJiLlm/bds29ejRo9wDqC/H4cOH1bZtW23atKncizoCuDhmdgDgPF6vV7t379bvf/97DRo0qEJBRzp7LNC0adN8LnBYHTIzM/X6668TdIAq4gBlADjPokWLNGLECN1yyy1asGBBpV47dOjQau/ntttu02233Vbt6wXqCnZjAQAAW2M3FgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsLX/D4q5ZU9486JZAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to find the fourier transform,\n",
    "import numpy as np\n",
    "\n",
    "# Example usage\n",
    "fft_audio = np.fft.fft (audio_data)\n",
    "freqs = np.fft.fftfreq(len(audio_data)) * audio_header [\"sample_rate\"]\n",
    "\n",
    "plt.plot(freqs, np.abs(fft_audio))\n",
    "plt.xlabel('Frequency (Hz)')\n",
    "plt.ylabel('Amplitude')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "940987fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "728091"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len (fft_audio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "413d8e4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now, to filter this signal, and perform reverse fourier transform\n",
    "from scipy.signal import butter, filtfilt\n",
    "import scipy.fftpack as fftpack\n",
    "\n",
    "def butter_lowpass(cutoff, fs, order=5):\n",
    "    nyquist = 0.5 * fs\n",
    "    normal_cutoff = cutoff / nyquist\n",
    "    b, a = butter(order, normal_cutoff, btype='low', analog=False)\n",
    "    return b, a\n",
    "\n",
    "def butter_highpass(cutoff, fs, order=5):\n",
    "    nyquist = 0.5 * fs\n",
    "    normal_cutoff = cutoff / nyquist\n",
    "    b, a = butter(order, normal_cutoff, btype='high', analog=False)\n",
    "    return b, a\n",
    "\n",
    "def apply_lowpass_filter(data, cutoff_freq, fs, order=5):\n",
    "    b, a = butter_lowpass(cutoff_freq, fs, order=order)\n",
    "    return filtfilt(b, a, data)\n",
    "\n",
    "def apply_highpass_filter(data, cutoff_freq, fs, order=5):\n",
    "    b, a = butter_highpass(cutoff_freq, fs, order=order)\n",
    "    return filtfilt(b, a, data)\n",
    "\n",
    "def apply_inverse_fft(fft_data):\n",
    "    return fftpack.ifft(fft_data).real\n",
    "\n",
    "# Example usage\n",
    "# cutoff frequencies for low-pass and high-pass filters\n",
    "lowpass_cutoff = 22000  # Hz\n",
    "highpass_cutoff = 2000  # Hz\n",
    "# Sample rate of the audio data\n",
    "fs = 44100 \n",
    "\n",
    "# Apply low-pass filter\n",
    "filtered_audio = apply_lowpass_filter(fft_audio, lowpass_cutoff, fs)\n",
    "\n",
    "# Apply high-pass filter\n",
    "filtered_audio = apply_highpass_filter(filtered_audio, highpass_cutoff, fs)\n",
    "\n",
    "# Perform inverse FFT to obtain new audio data\n",
    "new_audio_data = apply_inverse_fft(filtered_audio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "636c781a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "728091"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len (new_audio_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "a2cbd537",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to find the fourier transform,\n",
    "import numpy as np\n",
    "\n",
    "# Example usage\n",
    "freqs = np.fft.fftfreq(len(audio_data)) * audio_header [\"sample_rate\"]\n",
    "\n",
    "plt.plot(freqs, np.abs(filtered_audio))\n",
    "plt.xlabel('Frequency (Hz)')\n",
    "plt.ylabel('Amplitude')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "02538aba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RamQrZ7xlDp4rwPIGJmj8POW0JOf6+XCuJMdRE442Iqeqr2gdb6qpQS6QqBSXVcDX21PpMMgBVQmv5Q8cqeY2uu/DFLPXl65UzYhe/azFZdIP8c+7Woom/j6SH9fVsOWFnKpNs4A613Wes8GJkZBWOLvD9qj3tzv1fCQ9JaoyTFiyo+GNbFDXu/68zAXxXAWTF5LNliMX8H26+cR6N0c3USga0hIlR5gdv3AVSQeV6zNBjlN05miJ3rru3lLN5IWInMbRpz5FJeXINl5D7zfkK0pnjcc+36Xo+ckx1+p5XCP3k0n27ZOGU5KXBQsWICYmBr6+voiLi8P27XU3u2ZnZ2P8+PFo3749PDw8MH36dGeESEQu6umv95h+P5p7BR/8cgwXCmuXYSeSwi7O06YKsicvK1aswPTp0zF79mzs2bMHAwcOxIgRI5CZabkXdklJCZo1a4bZs2ejW7ducodHGuDuzadat3qv+aPHmn/vw3XMaE5kj1LZK+Pe4Egrj7u34MievLzzzjt45JFH8Oijj6Jjx4549913ERUVhYULF1rcPjo6Gu+99x4mTZoEg8Egd3jkZC4wWIQUxEST3F31fwOK9r1ROVmTl9LSUqSlpSEhIcFseUJCApKTpakuWFJSgoKCArMfck3Jxy/ihZUZSodBREQqJ2vycvHiRVRUVCAsLMxseVhYGHJyciQ5R2JiIgwGg+knKipKkuOSdWxpuhy/WNqhgkREaiNV46O7t2I6pcNuzQqYQgjJqmLOmjULRqPR9JOVlSXJcYmIiKS2/2zdVcalUFkJt5j0UdYKuyEhIfD09KzVypKbm1urNcZeer0eer1ekmORel2+WopgN6gqSUTqVtLAlCqOmvrVbpy4eBU/PTUAsS20229U1pYXHx8fxMXFISkpyWx5UlIS+vXrJ+epyY18knwKPV9Lsm+iPyIihTg22siyExevAgA+/k3bkzXK/thoxowZWLJkCT7++GMcOnQIzzzzDDIzMzFlyhQAVY99Jk2aZLZPeno60tPTceXKFVy4cAHp6ek4ePCg3KGSSr32U9V7Q6qJ/oiIyLXJPjHj2LFjcenSJcybNw/Z2dmIjY3F2rVr0apVKwBVRelq1nzp0aOH6fe0tDR89dVXaNWqFU6dOiV3uETk4jjcnrSCb2X7OWVW6SeeeAJPPPGExXWffvpprWVKzltCtuGfimzBxIPIOVbtPovisgr85/6e8FBiJkqZcW4jIiKJuMMoD7LfI5/+LtmxrLnJX5uRg+TjlyQ7pyth8kJEJJGXv9+vdAjkwn4+nGv2+u2kP1BRKW/zdUl53ZNQqhmTF3IInxoR3fBLjS8nkl6pzEONne2nfeca3sgBWn1Uy+SFiFTlqx2WJ3Ul9/CfzceUDkFSl66U2rWftf0Nyyu0eYvJ5IWInCb1xGWlQ5CVNr8mXMv7Px9VOgRV2X70otIhyILJCxE5zbHcK0qHQORWKjQ6JJTJC2lK8nFt3mUQEVVny6S4WsTkhTSFM1e7LrlHVRBRbRos8QKAyQsROcns7zKUDoHI7VwpLlc6BFkweSEip1j+e5bSIchOoze5JCOdrqrg3MSlO/DQJzslrzD/fbq8Q7GV4pTpAYiIiMiynIJi06igwpJyBPl6N7iPRvvhWo0tL0RERAopKa90+0TEHkxeyCGcRJOIyH5Ltp8wGzfER4/WYfJCmpN1uUjpEIiIrHJR5gq7WsXkhRziiv9+Br65GYu2Hlc6DHJDZRWCQ8LJZucLik2/W/vucfd3GZMXspsQAmv2ZSsdhkXz1x1WOgRyQ9fKKpDw761Kh0Eq89RXe0y/a3UuIqkxeSG7rd+fg1mrXLd2R2FxmdnrrMtFePyLXdidmadQROQOjl+4qnQImnXqojb/2+ZUa3mxts+Lu/c3ZPJCdks77dpJwNRqdzMA8MSy3dhw4DzuXpCsUERE5Ihb3tqidAiyaNLoxtBoT0922bUGkxfSrKslNypL/nG+EAfOGRWMhojIsup1Xby0Ws9fYkxeSLOuN6ruOnUZCf/ehur9KG9/dxtOX9JmEzQRqUvNmZ+FEHj/56PY+scFhSJyfUxeSLMuXinBu5v+wD2LUmqtO5xTiJkrXbe/Dqnbz4fOY+pXu2G8VtbwxuT2Tl+6Ud5hzg8H8H/rj+CdpD/w4Mc769zHvXu8cHoA0rDTl4rw7qajda6/WqrNCctIeY98tgsAEBKgx9w7OyscDanJt2lnrNrO3UclseWFiEgm1et3EEnp0pUSpUNQFJMXclvVHzMXl1XgxIUrygVDRGTB2gzLtbTcu92FyQs5QO2FREW1f/6jP/gVt769FcnHLioYEWmNm5fiIAk8sWy3w8c4er5QgkhcC5MXstvBbO0MPT6WW9Xq8n36WYUjIS0Rbn9/TK5g2L+3KR2C5Ji8kN3Uflep9viJiNwVkxdyW1dKao822njwvAKRkFYxQSa5uPt7i8kLua3rtRWyjddMy/KLWJeDpOPm3y9EsmHyQm7vakmF0iEQEZENmLyQ3c7mX2t4IyIikpy7dwZn8kJ2O5OnjeSlvLJS6RBIo3JZpI5ksiczX+kQFMXkhdze8p1ZSodAGrX3jBErfs/kHEckuWtl7v24m8kLuT0+/iI5vbAyA499vkvpMEhrbHxqtOPEJXniUAiTF3J7OqUDIM3befKy0iGQxtja52XsR6kyRaIMJi/k9nQ1spdtf1xQJhAiIiuxzguRm9PVaHuZ9PFOhSIhIqpNWMhU3Dx3YfJC7u2nfecsLv+NEzQSkYuY8mWa0iG4HCYv5Nbe23TU4vLfT7GPAhG5hg0HOG1JTV5KB0CkpKO5V1Bcrq0hh2fzr+GXw7lIPX4Jr/6lM0IC9EqHREQkKSYv5PayLtceKq22znCl5ZXw8fLAzpOXcd+HKablOh3wn/E9FYyMiORgqR+MO+FjIyIL3vv5KHZn5ikdhlU2HsjBTS+tw/x1h80SFwDINrpGhdeU49qqMUHkbL8du4hvfmdBzeuYvJBdCou1XzH07gXJSodglb99UdWZb9HW47XWpZ12jQTs/sXaqjFBzldW4d7TeExYsgPPr9yHA+eMAOwbbaSl1homL2SXdftzlA7BKVw9SdPSh5HWVVS6199KCIHX1xyUrEDfWxuOSHIctcvOr2pNteeffsystRJHoxwmL0T1qPn5cK20AqknLqHcRe4Ci8tcIw5qWJsX1+Jaqbo6h5eUV2DDgRwU2JHEv77mEBZvP1nrUaa9zrnII1ClbTp0HnlXS+3eXys3PExeiOqRdioPiWsP4fiFKwCAvy9Lw7iPUvH+z5aHWNviWmkFKt3sbtzdbT6Sq3QINklcexiPf5GGRz+te26mBVuO4cmvdtd6Ly/59aSksXh5cCIPAFj+exZGvLfd7iJ1Wml9YfKiUmUVlRBCYPryPXXWKpFT1uUip59TCQ99+js+3HYCo97fDgDYcqRq6oDPU087dNzcgmJ0fGU9xi9xrC9IWSVbXtREbY+Ovt1V1UF0Zx11j77ZlYU31x/Bmn3ZmPjxDmRdLoKxqAzTl++RPBaDn7fkx1SrnIJiXLpSonQYiuJQaRWprBR45LPfsfnIBfh4eeDte7vh+/SqCrHTbmvn1Fg++OWYU8+ntOKySuw/azS9drTl9cd92QCA1BOO9QdwlyRSK+b9dBB3dItQOgyHlFdUwsvTA8ZrZXj+f/tMy387dgkD39xscZ831x/GQ/1j0CzQ/ppD3aMa272vFp3Jq13iwZ2w5UVFNh7MweY/7/xLyyvx5obDZutPX7qKs/nu/YaW0+gPfjX9LmVH3lMXr0p2LHJtFwpd/265er+cq9V+T1x3CKM/2I4uczfiTF4R5vyw3+pjLthyHDe/vsmhPj9fOtjaSdrC5EVF8orMvzCrF1eLnrkGg/+1Bf3n/8J+FE5QKRzr+PZD+lnT77e8tQU/H7Kv/HfNSSWJ7PXh1uOInrkGHV9Zjw+3Hsf5guIa609g/9kCXCurwNzVB5BmRx2kO//za8Mb1WGXiwz71wK1dRy3hMmLBrV+cS0mfbwTxWWW36BXS8pRUl6BkvIKLN52An+cL3RyhNqwJyvf7n33nTGavX7ks7o7RNZHWNFt71B2AbIuFyE9Kx/n2DJHFpy6eBWJ62605CauO1xv/5xNh3LtSpyP5l6xKz6S1sIt6n/szz4vKrI63fIMyJZs++MCOr6yHicTR5ktLyotR+c5GwAAo7qGY82+bLy+9hBOzR9l6TBUj8LicqVDsKrPy4j3tpu9fue+bri7Z2St7SoqBXQASisq4evtKVWIpAKWkop+83+pd59MO/tbnS8oRliQr137kjTOaOAmhi0vKpJywrYS60LAVJ/h+uikbX9cNK1f82enUbKPK4zc/P2U7U3pM77ZW2vZFymn0ObFtWj94lp0eHk9PvlN2mGu5NqcWYm5zxs/O+1cVAcN9Cxgy4vGdZ27EYG+Xi7RSqA1Hjrls5crdv5dj+UWItzgB3991UfAyz8cMFv/6o8H8VD/GBivlXGIqsRmf5eBqbe2xbqMHIzsEo7mBmVbISorhcWpJeRUVlEJb0/eOyulUgOF6vjuUQlHKrpak7hEz1yD/6WdwdUSJjnWkiN1EULgxIUrVne6tqbPiyW3vbMNnedswAc/H0Xsn48Ra3r2m73o9upGRM9cg0PZBTiWW4jZ32VoorOfkpbtyER84i+Y99NBjP3I8eqzyccuInrmGnR4eZ1dlVeVmID0mRXpTj8n3VDqIhXCHcGWFxUoLqtA4tpDsp/nuW/34vs9Z/Hlo31kP5cW6CRueYmeucb0e+eIIHzy0M0IDay6K79WWgE/H/N+KGUVlfhm1xmHzvl20h91rlu5+8axq/ebWbYjE7NGdMDjg9s4dG4CTl9yvE7P+CU7AFTVIvrP5mN4eXQnm/a/Z5E05ftt8dO+bPzfmHJTyx85lxZmeec7x4WVlFeg8ysbUO7Eoc+/HrvY8EYE4Eafl+1HL6C4rBLDOoVJduwD5wrQ+/WfkdApDBGN/fBp8im8emdnHM4pQIDeC6XllfD1Ua5TbeK6w0xeXNDSX0/is+RTWPF4X8S1CkZRaTk2HjiPIe1DYWhU+/FfXSMSnaFztRa/w6/dzk7iTlSz7IYaMXlxMelZ+bjrv78pGsOGAznoGmlAuMFP0Thc3diPUvHaXzqb+ovsfnkYgv19JD3HxoM36r/MWX2gni2dTwgheesTOa68UmDMwhTsfSUB3eZtBAD0jg7GN1Pia23b4eX1zg7Pog4vr+eIRyczFpVZTGjVgn1eXERpeSWiZ65RPHEBgMe/SEN8Yv3DJKlK9Y6uxmvqv5uxRcystVjxe2aDxfqKStmPSi7j6ukzcz1xAarmJiopr3r8nHW5CMnHLuK2d7Y6I0SrpTtQN4lst9DJnbSlxuTFRdz00jqlQ6ilpJwdM+Ww/egFpUOQzAsrMxrsM3HiAqc/qM8DS3bgip0d5W2ZG6v9S+vx4bYTGPjmZoxfsgPHXKxg3ObD6ppxW+2cPcJMakxeqE7tX1qP/KLaoxdyjMUWtiZrWl6yjdcwcelOJ0TjPGmn8+qt3LtAA9U85fTrsYuInbMBF22cJXhlmmOdtV3Nez8f5USjZDWnJC8LFixATEwMfH19ERcXh+3bt9e7/datWxEXFwdfX1+0bt0aixYtckaYZEH3eUm17tD6JrLIlCV3/fe3Bpvitfo4rt/8X5B22nIrwNqMHCdHo069/rnJqu2KSsvR+ZX1ePbb2sUG1W7cR6lKh0AqIXvysmLFCkyfPh2zZ8/Gnj17MHDgQIwYMQKZmZkWtz958iRGjhyJgQMHYs+ePXjxxRfx9NNPY+XKlXKHSnW47Z2tSLWxuq+7OpZ7BaXllmsorN+v7YrGYxbWfnzkyOSV7ih65po6+wgJIXC1pBydXtlgNtuzlpzNv2ZWMuA6e+rXUMPazV6rdAh20wmZP1369OmDnj17YuHChaZlHTt2xF133YXExMRa27/wwgtYvXo1Dh26UddkypQp2Lt3L1JSGq5HUFBQAIPBAKPRiKCgIGkuwgks/YMlIiKS28q/xyOuVbDSYdj0/S3rUOnS0lKkpaVh5syZZssTEhKQnJxscZ+UlBQkJCSYLRs+fDiWLl2KsrIyeHubD+0qKSlBScmNZ8UFBQUSRW+utLwS4z5KgUDVnEECVb/ceC2q/v/PdddzQrN1fy6v2vfG9BK8OyUiIqVYajXt2bJxvft4e3pgxeO1h987i6zJy8WLF1FRUYGwMPPiXWFhYcjJsfwcPCcnx+L25eXluHjxIsLDw83WJSYm4tVXX5U2cAsEBHZn5st+HiIiIqU19H3n46XseB+nFKmrWciqoeJWlra3tBwAZs2ahRkzZpheFxQUICoqypFwLfL28MBHE+NMcegA6HR//kCHP/9ntg6oWqf7cx105q91Op1pu3sVKNFNRERU03vjusOvgYrHSk9MK2vyEhISAk9Pz1qtLLm5ubVaV65r3ry5xe29vLzQtGnTWtvr9Xro9Xrpgq6Dh4cOCZ2by34eV/bmmK7IKSjGO/XMh0M3BOi9ENnED0M6hKKsvBJLfj2pdEhOMbJLc4QF+WL5ziyUVVQ6dXoLrXjtL51RUFyOIzmFGN01HI0b+WDmqn1uVTPngb4tEeyvxw/pZxEW6Iudp6yvaUPWO5k4UpWVsmVNXnx8fBAXF4ekpCT89a9/NS1PSkrCX/7yF4v7xMfH48cffzRbtnHjRvTq1atWfxdyjoROYfhoUi/T60Y+nvjnGvknilSjqGA/bH/+VovrXhrdSdMdswe0DTGb1HPOHZ0BsDO6Leorkf/Ls7cAAPafNWL0B786KSLn+/nZwWjTLMD0esawmwDwfSSHV0Z3UmXiAjhhqPSMGTOwZMkSfPzxxzh06BCeeeYZZGZmYsqUKQCqHvtMmjTJtP2UKVNw+vRpzJgxA4cOHcLHH3+MpUuX4rnnnpM7VLLA4OdtlrgAwOR+0coE4+J+fnZwnYnLdRP6tHRSNM41IrY5ZyN3ktgWBs3OA7TzxaFmiQvJ6+EBMUqHYDfZk5exY8fi3Xffxbx589C9e3ds27YNa9euRatWrQAA2dnZZjVfYmJisHbtWmzZsgXdu3fHa6+9hvfffx9jxoyRO1Sq4bmEm7B3TkKt5WrN1OUWYcVElv+8K9YJkTjff8f3rHPdj1MHODES9RnfpyVOJo60OSH59YUhMkWkjJV/74fQIF+lwyCVcEqH3SeeeAJPPPGExXWffvpprWWDBw/G7t27ZY6K6jPvL50xKT7a4joP5i61PH97e/j51N/BDahK/Lb9YwgG/WuzE6Jyjn+P7QaPet4UXSINToxGfd74axe79ots0gh3dovA6r3nrN5nYLsQ7D9rxKIH4jBteTpyClxnqo+4Vk2UDsGtJHSy3O9ULZySvFDDjr4+Au1mu8bkjN6eujoTF4AtL9c9cUsbZJw1YtzNLTGqa3jDO/ypZdNGMkblPIF6L2S8OlzpMNza0I6hVicvNTtmpsy6FROX7sSvxy7KFZ7Vvnikt9IhuJ3eMcoXpXMEJ2Z0Ed6eHtj98jClwwAAHHj1dqVDUIWmAXp88UgfmxIXe7QLDcBtHUNlPYc9/nVvN6VDcHt3dI2wuHz53/rip6cG4LW7YhES4IM3x3StddOh0+lcop9S75hgDGzXTOkw3M6DKu+7yJYXFxLs74Mfpw5AcXkFjudewdwfD6CRjxfaNgtw6jBBpYsPqcWAtiGyHDeuVROknc7D5H7RSOgUhq5RjRGg94IQAhevlKJZoB7nC4rx5LLd2HU6T5YYrDG8s7qbnV3B7Q6WX/Dw0CHpmUH4+LdTmHprW7Ro7GdWRyu2hQEP9Gnpkq2lY3pG4pb2zTBM5Y8v1MrbU92f80xeXMz1/gE3RwdjXO8bI1OcMUywV6smeHFUR9nPoxXtmwdKerwmjbyx4ZlBCA203GlRp9OhWWBVTaOwIF/87+/98ML/9mHFrixJ42hI10gDPnuot0t+IarFlMFtUFpeiWeGtXP4WO3CApF4941+M5ZaWOrzQN+W+DLV8kS5cnn9r7GY0KeVU89JNwy+Sf0tXUxeCAAw/bZ2mH7bTUqH4dbSXhpWb8dXSxrpG+4kbK+pQ9ribP41PD64NXy9PDH5k52ICm6Ezx9m4uKomSM6KB2CycwRHZ2evIzvrc2SAeQ8TF7cyCMDYrC0jiqvsREcEWKLDhK3ugCwOXGRwr1xkdh3xogj5wtrrXtueHuz11v+oa2huVQlQO+FQTc1w7Y/LjjlfC2DGzH5Vdg/avzbViN1P/RyI7Et6p8evC5tmvlj5ogO+OD+Hnh5dCccf2Mk/n5Lm1rbSf0IROsaWTEs2hmqZtJq2NO3tjX9HhLgg5OJI3HijZH4173d4O1V+xgvjnSdlgGS38P9o81ehxvkq7cS2aThekgkn0cGxCC2hfpvVpm8qMRnD/W2q8DZz8/egimD2+COblWjEjw9dHjh9g61RjZFBWtj+K6zuMpsPdbcwB59fQRmJJjfael0OlNLT80J1qYMboO/Daqd4JJ21ewDsX7aoAb36dvavqG2bHRRVrC/j9IhSILJi0o0DdDjgb62dXB7pp4+LMH+PujRsjEA4C07hryeeGOkzftoyZiekUqHAMC6goE1RxWIGpnXqC5VQ73DDb5Y9UQ/TTQpk210Op2p0++YnpEwNKp/HrndLw/D8r/F23WuJ4e0bXgjC9ReVE1qS2pM22Kth2q0sqkV+7yo2O2dm2P9gRyL6+7u2QLTbqt/JMN3T/RHSXkF9F62PwJRon+GElJm3YoFm4/jqT8fu+RfK0PBtTL0bOka1UBt6TswpH0zbD5yARPjzZPgRwbEoG1oAHq2bIImGrkrI9vd37slBrQNQYvGDT/W8fzzfWdrtej0V4ahcSO+xxy1acZglFVU2rVvIx9tfO1r4yrc1Ji4SOw/Z8SAtiGYNaIjgvy8kF9UZtMXkD2JizsJN/jhtWqP66Sae2VYpzAkHTwPAOgWacAH99c9N1B9bEkhFz4Qh4yzxlqJl5enB4Z25F0tmT8+7temKZKPX7K4ne7PxjxbqkX/Z3wPhxIXV3lU6wrahgbgcE6B0mEoio+NVKyRjye2Pz8E88d0haGRN3Q6He+cVeLDB+JMvy97rK/dUwbcZkNTuq+3J26ODoanAq1mbPIHVj3RD77eHqZWPFe35MG6H0tUfwdZGgDw+cO9cU9c1aPVaUPb4beZt2J0HdWArbXp0HmH9teKxwe3VjoEl8CWFxWrrFZJk6Qn56gIDw8dds4eivIKgQC9/f8Mm6okWe3buik2HnTvL5+eLZvgwKu3K5I82qORjxdaNPbD2fxrtdbV7OR93Ybpg+ChqyqcN+imZvjXPbWnJbBXzb5a7qhbpAGzRrCQKMCWF1XjP2Z51XfnKYXQQF9EWNG/oD6tmwVIFI28mGNXUUvict1iC51CR3cNh38dCXf75oFoF3aj7AJvriTG/54mTF5U5vOHb8y+2iZUHV9cajQitjk6NLevtg7Vxo9cdeoUEYRFD8ShZXAjLHogDvvmJuA/4837Z427OQqANkrOk3rwsZHKDLqpGbY/PwT5RWVWjQog++hVNDllfOumSDlhuWMlkaNuj22O22PrnkCyVVN/7H91OPxdpHCjlnk6eBfQr01TaQJxAer5hCaTqOBGpgkciRY9EOfw7MRy4+MDbQvQe8n+N+4TY19RPC0YfFMzNA/yxf+N6erQcb54pI9EESmPLS9EKmdo5I1FE+Pw/P/24ptdZ8zWLao2qklJ7p67LJxg31B4uiHQ132/rj6r1l3AEWrrc1UftrwQWRAmUT0XZ6pev+WeuEgkz7y13uZ+co5BNzXj34FIYu6byhLV4Y5uEZiqkloc1d0TF4nDOYXo16YpElzsMZJ27vds97lEd83ubkRsODYdylU6DHIRbHkhquGD+3sg0Lf+uV1ckZenB+be2dnlEhcAiNdQR0FSxt09WygdArkQJi/k9rQyy6oraxsa2PBGRPVgp2+qjskLub3qxeg43JOItEgrs0lfx+SF3F71UuexLTgEnYi05+VRnZQOQVJMXsjtaWj0ILmYt+7tpnQIRACq5lPTEiYv5PZ0bj0WhuQ0hp1MiWTB5IXsFhPir3QIkqjeD5BzXZKU2MmUSB5MXshuPp58+xARSalZoN6q7dy9xZjfPmS3cb2jlA5BEr7e/GdARKQm/NQmuxn81FfIrabxfVqiTbMA02v3vpchIlIHTg9AdgtSYRXa6kZ1Dccbf+2idBhERGQjtryQ2xh0UzP8Z3yPerdhh10iUlJIgHV9XtwdW15I8+JbN0Xb0AC8dlcsAGDfGSOW/noSz9zWTuHIiIjM6b3M2xSeS7jJ4nbCzW+1mLyQpm18ZhBuCjOfV+fFkR3xj+Ht4c3RUkTkgl4e3Qmv/XQQADD1Vt5kWcJPb9K0RnXMVcTEhYhc0cujO6J5kK/SYbg8foKTpgn3blklIpWJaxVs1Xa21Hn5x/D29objspi8EAF4+ta2AIDZIzsqHAkRkbQe6NtK6RAkxz4vpGnWtrzMSGiPp4a24+MkIiIV4Cc1aZotPfKZuBCRJmnw8Tk/rYmIiEhVmLyQprHDLhGR9jB5IU1rGuCjdAjkhsb0jMT254coHQa5sXahAQ1vpGJMXshpFkzo6dTzPdw/BoEqn3+J1OmZYe0QFdxI6TA05964SKVDUA2tNzozeSGnGdkl3Knnaxem7TsPcl1lFVr/6lCGh47zvlureoFOHy/tfdVr74qIiEiTEjqHKR2CLCbWqMPSvWXjBvdpKI/z9/HCB/f3wH/H94RfHZXG1Yx1XoiISBVu7RCqdAiSe6h/NEbEhuOL1NOmZS0a+2HrP26Bwc+xx953dItwNDyXxeSF7BbfpqnSIRCRG9G50WOjVk39613f0EjKsCC9hNG4Hj42Irv5672w88WhSodBRKRqMSH1Jyr2mD+mq+THdCVseSGHeLEqLRGR3XTQoVmgHknPDEIjvXRfyb7e2uvnUh2TF3KIcOEqcHoN9rAndXC0rwK5n3ZhgUqHoCpMXsghrpi63BQWgNBAX4zuqt3OauTa3KdnBjnKjbrxSIrJCznEFRtelj54MwuEaZinhw4VlS74xiOyA3MX+7BdnYhUxZO3qkRu32LDlhdyqpAAH1y8Uqp0GKRmLvyh3T4sEDod+7wQyY3JCzlE2NjrZcP0QYj75yaZoiFS1rppAwEAHh4unGERaQCTF3KqpgHaLpxE7o1JC9nK3R//2It9Xsgx7DdJREROxuSFNId3MkTkql4Z3UnpEDSByQs5xBUbXlo09lM6BJJR8yBfpUOoxdNDh0E3NVM6DHJB/x7bzez1wwNizF7L8Xn187ODJT+mq2HyQpry91vauNXkbWoi1Z/l48m9pDmQhI6/MRKfP9xb6TDIBbUMrj1v0bdT4tE10oBJ8a0woW8ryc/ZplmA5Md0NeywSw5xxSJ15Jp+eLI/nv/fPhzOKXToOG1DWUad1O3m6GCsnjrAoWNENXHvQpxseSGne/vebg1vZKe4lk1kOzY5pmtkY3z+iHZaJxo3Yi0XUo6fj+WJF6OC3eOxuazJS15eHiZOnAiDwQCDwYCJEyciPz+/3n1WrVqF4cOHIyQkBDqdDunp6XKGSA6ytc4LAAT6ytPgt3hSLwztGCrLsYlquokT6ZGNRnZpLvs51k0bJPs5XIGsycv48eORnp6O9evXY/369UhPT8fEiRPr3efq1avo378/5s+fL2dopCC9TFO1D+sUxv4uGucqlWvv6xWJO7px4k8leKm4ls4793WX/RwBevfoDSLbVR46dAjr169Hamoq+vTpAwBYvHgx4uPjceTIEbRv397ifteTm1OnTskVGknIz45EZEDbEBkiIVVwsI/UmJ6R0sThoIf6x2B3Zp7SYbilzc/dgoFvblY6DLv4ynTj5o5ka3lJSUmBwWAwJS4A0LdvXxgMBiQnJ0t2npKSEhQUFJj9kPM0buSD/xvTBREG64evenroMCle2h72n0y+WdLjkWt6/vbaNz3P3HYTvp0S79Q42FFdOZwxngAZk5ecnByEhtbufxAaGoqcnBzJzpOYmGjqU2MwGBAVFSXZsck6Y29uiTfu7mLTPq/e2Vmy83t56DCkA/u6uIPrd67vjeuOYH8ffDslHtNua4ebo4OdHsvAtlV1XYJk6sNF6rd6an+lQ9Asm5OXuXPnQqfT1fuza9cuALDY/0AIIWm/hFmzZsFoNJp+srKyJDs2Wa/637S3FV8kUr0HXr2zM3559hZJjkXq8ZfuLZD20m1OS1ru721+U6TTAS2bNkLKrFux48XbnBIDqctXj/ZB18jGSoehWTbfMkydOhXjxo2rd5vo6Gjs27cP58+fr7XuwoULCAsLs/W0ddLr9dDrOdmf0qqnIgFOuhPtFB6EB/tFO+VcJL1wgy+yjcV27+/MztkedZwr3OAew1LJdv3+7NvXLky+gnH3926Jr3dmynZ8V2bzt0xISAhCQhrucBkfHw+j0YidO3eid++q2g47duyA0WhEv379bI+UqIYO4RyqqmbqHTNCStPp1NPvKMjXG7teug16L+l7aSTe3cVtkxfZ+rx07NgRt99+Ox577DGkpqYiNTUVjz32GEaPHm020qhDhw747rvvTK8vX76M9PR0HDx4EABw5MgRpKenS9pPhuTFLyWSUsfwIHzy0M1YN22g7OcK9vexuNyeUXUkn792b6F0CDYJCdAj0Nc1hvlrhax1XpYtW4YuXbogISEBCQkJ6Nq1K7744guzbY4cOQKj0Wh6vXr1avTo0QOjRo0CAIwbNw49evTAokWL5AyVJGRPa/6cOzjTKlm2/G99MaR9KDqGB8l+LoOft8Uiik8OaYuukQbZz0/O06iOCrWkDrJ2TggODsaXX35Z7zaiRtvf5MmTMXnyZBmjIvnZlr10i2qMh/rH4NUfD9q0X9M67pLJdQVVKzJXV3nzmpxdmK5taAD2ZOabLWvi74PVUwcgeuYap8ZCdZCgefeeuEh8nnLa8QORIji3EUnOWf0onxrazjknIsn4enti+/ND8OsLQ+DtyY8fUtZTt7ZVOgSyEz89SBLVExZ7q3e/NKqj1dve3aMFgvgMWZWighsh0oVnxLXm7Xs275rscZD8nk2wXOmdXB+TF1Lc9S+LRwe2VjQOImsT6ILiMpkjofroJHhuNLJLuASRkFKYvJDkqtfEqFncyxKVjHgkDRvVNRwZcxMwtKN1NajY6qdunSOC0Ld1U6XDIAewrjVJLr5NU6zbXzW0/R/DOyDbWIz7enHaBnJttgxljQ7xlzESklubZvIVjlPS5uduUToEp2HLC0muekfMYH8ffPpQb8mbaPnlQUp4fFBr3BsXiTbN+P5TmxaNtVkN+c17ugIAZo/siBg3+lxkywtJzt4Ou7b42yD2j1G7TuFBOJxTqHQYAICBbRuuGg4As0Za36mc5OPEmSFc3n29ojAitrnbFcFjywtJTorOdPXx8fQwzS5M6vXKHZ3wcP8Y/PTUAEXjeGlURz7WJFVzt8QFYMsLSSQkwImTY/KuSxMaN/LBKy5QWXlEl3B4OKO5kFTBQwdUOjiK4K17u0kTDNWJLS8kiepzwrBJlxw1sJ11j3EaMsDKx0GkfdUnRqzrM+r4GyOt6jfSv63lkUoH5w3HqfmjcE9cpF0xkvWYvJAkqs/yoJM5e3HGHDekrCHtQyU5jjVvxZpTlMj9/iXH2fMXCrAwZ1VNng62wDXy4cMMZ2HyQqqzYEJPpUMgmTU3+EpyHMEiQppkT375Nh/laAqTF5KEqFZqzpF7l3ArvrTCg6T5YiPX1SzQ9j5Uzw67SYZISCvahQUqHQJJiMkLSc6RVnc/jiIiOz01tB2+f7K/0mGQm+FTRmUweSFJmPd5sW6fSfGtAADPJdy4Y65r1MfHk3vZHRupiyMj17pHNZYuEHJ7oRZaAJv6+yCysetOLOoumLyQ5Kyt8/LqnZ2xb24CBrZrZlpW13Pp6rMQ805H29Y+7Vjdl+qJruDMWWSj1tWmDtj+wpBa63u2aoJZIzvg7h4tsOzRPs4Mjaph12iSRPWvCGuTC51OV2uCu2513Dmz46X78PVx7NGhp8eNe7J2oYH47dgls/UxIf7Qe3m4THVfst2ILuH4ZtcZu/ev7yPqjb92QWM/b4zv0xJ6r9rvRR2qahS9M7a73ecnx7HlhVzO/6bEKx0CqVj1L6YnhrQxW/fWvd3wzePx+Ozh3nXu/1xCe7PXr90VK2V4JIHW9dRiqf4Y2h7NAvX4173d0KNlE4eOQ/JiywupApv/3UuPqMboGB6EVsGO9S2o2QH8evGwotLyOveJb9MU+18djgC9Fy5dKUFTZ1aPJodNvbUd3tr4h9POpwP46aQAJi8kieqFvqQs8jWsUxi6tjCYLWMRMW0TAvDy9MDapwco9rcO0Fd9NDJxIXJNTF5IclJ+3SyeVNX58nBOgYRHJTWwN3GpXkmViS6RNrHPC0mieofam1gMihTUrFpriRcnXCQL4qKDlQ6BHMSWF5Jcy+BGWPVEPwQ38ml4Ywu8PGvn1BxtRNaKbOKHEbHN0cjHC751FD30qNYi4+XBezitmNPALOWbn7sFv5+8jDGcOFH1mLyQLHo60FO/W6QBCZ3CzGq7EFlLp9Nh4QNx9W7j6+2Jvw1qjeKyCsnmUSLl3dktot71MSH+Vs0abYtHB7bGR9tOYGSX5pIel+rH5IUk52g3A51Oh48msaIuyevFkR2VDoHspOQ0Io8Pbm32+vnh7asGFkQa6tiD5MD2UpIEH+uQI755nLV9yHqhQb54qH90reUeMnbQDvL1wt45CYhrZd5fxsvTAzdHB1ssaEfyYfJCqsDkSNtaNa32iFDiv/X1oof3sJ+DpjxxS9tay/wcrM7cEIOfd8MbkVPwsRFJovrwVE+O8CAbyfmO6RUdjH1zExCo58cdkVbwXzNJItjfBx/c3wN6Lw94WxgtRKSkmnNoEZG6MXkhydzRQE9/IiI5yVmTkE+uXQtvkUkVOLcREVVnKVFhp1n3weSFiIjc1oq/9UV000b44pG6Zxon18PHRkREpCkeOqDSysbaPq2bYss/hsgbEEmOLS9EpLiQAD1iQvzRNjQAgb68pyLHfP1YX8mO9dpdsQCA9+/vIdkxyXH8lCBVaB0SAADw4UgmTfLw0GHTjMGm34ka4uNV92dBn9ZNMaFPSyzbkenweSb2bYVxN0dxFKWLYfJCquDn44mMuQn8ANEw1gciWwT5eqNrpAH7zhgtru/SQrpy/fzccT38i5BqBPp61zlLMBG5n78Nal3nunt7RaF5ECfd1ComL0REpDmeHjpMjG+ldBgkEyYvREREpCpMXoiIiEhVmLwQERGRqjB5ISIiIlVh8kJERESqwuSFiIhUSXC+VrfF5IWIiIhUhckLERERqQqTFyIiIlIVJi9ERKRJd/VoAQDo2zpY4UhIapyYkYiINKlFYz8ceHU4/DgnmuYweSEiIs3y1/NrTov42IiIiIhUhckLERERqQqTFyIiIlIVJi9ERESkKkxeiIiISFWYvBAREZGqMHkhIiIiVWHyQkRERKrC5IWIiIhUhckLERERqQqTFyIiIlIVWZOXvLw8TJw4EQaDAQaDARMnTkR+fn6d25eVleGFF15Aly5d4O/vj4iICEyaNAnnzp2TM0wiIiJSEVmTl/HjxyM9PR3r16/H+vXrkZ6ejokTJ9a5fVFREXbv3o2XX34Zu3fvxqpVq/DHH3/gzjvvlDNMIiJSobahAUqHQAqRbbrNQ4cOYf369UhNTUWfPn0AAIsXL0Z8fDyOHDmC9u3b19rHYDAgKSnJbNkHH3yA3r17IzMzEy1btpQrXCIiUpmO4UHw9fZAcVml0qGQk8nW8pKSkgKDwWBKXACgb9++MBgMSE5Otvo4RqMROp0OjRs3tri+pKQEBQUFZj9EROQehnYIUzoEUoBsyUtOTg5CQ0NrLQ8NDUVOTo5VxyguLsbMmTMxfvx4BAUFWdwmMTHR1KfGYDAgKirKobiJiIjItdmcvMydOxc6na7en127dgEAdDpdrf2FEBaX11RWVoZx48ahsrISCxYsqHO7WbNmwWg0mn6ysrJsvSQiIlIpAaF0CKQAm/u8TJ06FePGjat3m+joaOzbtw/nz5+vte7ChQsIC6u/ma+srAz33XcfTp48iV9++aXOVhcA0Ov10Ov11gVPREREqmdz8hISEoKQkJAGt4uPj4fRaMTOnTvRu3dvAMCOHTtgNBrRr1+/Ove7nrgcPXoUmzdvRtOmTW0NkYiIiDRMtj4vHTt2xO23347HHnsMqampSE1NxWOPPYbRo0ebjTTq0KEDvvvuOwBAeXk57rnnHuzatQvLli1DRUUFcnJykJOTg9LSUrlCJSIiIhWRtc7LsmXL0KVLFyQkJCAhIQFdu3bFF198YbbNkSNHYDQaAQBnzpzB6tWrcebMGXTv3h3h4eGmH1tGKBEREZF2yVbnBQCCg4Px5Zdf1ruNEDc6W0VHR5u9JiIiIqqJcxsRERGRqjB5ISIiIlVh8kJERESqwuSFiIiIVIXJCxEREakKkxciIiJSFSYvREREpCpMXoiIiEhVmLwQERGRqjB5ISIiIlVh8kJERESqwuSFiIiIVIXJCxEREakKkxciIlKtuFbBSodACvBSOgAiIiJ7PRjfCn7enujbmkmMO2HyQkREquXl6YHxfVoqHQY5GR8bERERkaoweSEiIiJVYfJCREREqsLkhYiIiFSFyQsRERGpCpMXIiIiUhUmL0RERKQqTF6IiIhIVZi8EBERkaoweSEiIiJVYfJCREREqsLkhYiIiFSFyQsRERGpiuZmlRZCAAAKCgoUjoSIiIisdf17+/r3eH00l7wUFhYCAKKiohSOhIiIiGxVWFgIg8FQ7zY6YU2KoyKVlZU4d+4cAgMDodPpJD12QUEBoqKikJWVhaCgIEmP7cp43bxud8Dr5nW7A1e+biEECgsLERERAQ+P+nu1aK7lxcPDA5GRkbKeIygoyOX+6M7A63YvvG73wut2L6563Q21uFzHDrtERESkKkxeiIiISFWYvNhAr9djzpw50Ov1SofiVLxuXrc74HXzut2BVq5bcx12iYiISNvY8kJERESqwuSFiIiIVIXJCxEREakKkxciIiJSFSYvVlqwYAFiYmLg6+uLuLg4bN++XemQTLZt24Y77rgDERER0Ol0+P77783WCyEwd+5cREREwM/PD7fccgsOHDhgtk1JSQmeeuophISEwN/fH3feeSfOnDljtk1eXh4mTpwIg8EAg8GAiRMnIj8/32ybzMxM3HHHHfD390dISAiefvpplJaWmm2TkZGBwYMHw8/PDy1atMC8efOsmsuiusTERNx8880IDAxEaGgo7rrrLhw5ckTz171w4UJ07drVVGAqPj4e69at0/Q1W5KYmAidTofp06dr+trnzp0LnU5n9tO8eXNNX/N1Z8+exQMPPICmTZuiUaNG6N69O9LS0jR97dHR0bX+3jqdDk8++aRmr9lughq0fPly4e3tLRYvXiwOHjwopk2bJvz9/cXp06eVDk0IIcTatWvF7NmzxcqVKwUA8d1335mtnz9/vggMDBQrV64UGRkZYuzYsSI8PFwUFBSYtpkyZYpo0aKFSEpKErt37xZDhgwR3bp1E+Xl5aZtbr/9dhEbGyuSk5NFcnKyiI2NFaNHjzatLy8vF7GxsWLIkCFi9+7dIikpSURERIipU6eatjEajSIsLEyMGzdOZGRkiJUrV4rAwEDx1ltv2XTNw4cPF5988onYv3+/SE9PF6NGjRItW7YUV65c0fR1r169WqxZs0YcOXJEHDlyRLz44ovC29tb7N+/X7PXXNPOnTtFdHS06Nq1q5g2bZppuRavfc6cOaJz584iOzvb9JObm6vpaxZCiMuXL4tWrVqJyZMnix07doiTJ0+KTZs2iWPHjmn62nNzc83+1klJSQKA2Lx5s2av2V5MXqzQu3dvMWXKFLNlHTp0EDNnzlQoorrVTF4qKytF8+bNxfz5803LiouLhcFgEIsWLRJCCJGfny+8vb3F8uXLTducPXtWeHh4iPXr1wshhDh48KAAIFJTU03bpKSkCADi8OHDQoiqJMrDw0OcPXvWtM3XX38t9Hq9MBqNQgghFixYIAwGgyguLjZtk5iYKCIiIkRlZaXd152bmysAiK1bt7rVdQshRJMmTcSSJUvc4poLCwtFu3btRFJSkhg8eLApedHqtc+ZM0d069bN4jqtXrMQQrzwwgtiwIABda7X8rVXN23aNNGmTRtRWVnpNtdsLT42akBpaSnS0tKQkJBgtjwhIQHJyckKRWW9kydPIicnxyx+vV6PwYMHm+JPS0tDWVmZ2TYRERGIjY01bZOSkgKDwYA+ffqYtunbty8MBoPZNrGxsYiIiDBtM3z4cJSUlJiae1NSUjB48GCzAknDhw/HuXPncOrUKbuv02g0AgCCg4Pd5rorKiqwfPlyXL16FfHx8W5xzU8++SRGjRqF2267zWy5lq/96NGjiIiIQExMDMaNG4cTJ05o/ppXr16NXr164d5770VoaCh69OiBxYsXm9Zr+dqvKy0txZdffomHH34YOp3OLa7ZFkxeGnDx4kVUVFQgLCzMbHlYWBhycnIUisp612OsL/6cnBz4+PigSZMm9W4TGhpa6/ihoaFm29Q8T5MmTeDj41PvNtdf2/vfUwiBGTNmYMCAAYiNjTU7lhavOyMjAwEBAdDr9ZgyZQq+++47dOrUSdPXDADLly/H7t27kZiYWGudVq+9T58++Pzzz7FhwwYsXrwYOTk56NevHy5duqTZawaAEydOYOHChWjXrh02bNiAKVOm4Omnn8bnn39udjwtXvt133//PfLz8zF58mSz42j5mm2huVml5aLT6cxeCyFqLXNl9sRfcxtL20uxjfizg5e9/z2nTp2Kffv24ddff621TovX3b59e6SnpyM/Px8rV67Egw8+iK1bt9Z7HrVfc1ZWFqZNm4aNGzfC19e3zu20du0jRoww/d6lSxfEx8ejTZs2+Oyzz9C3b986z6PmawaAyspK9OrVC2+88QYAoEePHjhw4AAWLlyISZMm1Xs+tV/7dUuXLsWIESPMWj/qOo9WrtkWbHlpQEhICDw9PWtlkrm5ubWyTld0fWRCffE3b94cpaWlyMvLq3eb8+fP1zr+hQsXzLapeZ68vDyUlZXVu01ubi6A2ncU1njqqaewevVqbN68GZGRkablWr5uHx8ftG3bFr169UJiYiK6deuG9957T9PXnJaWhtzcXMTFxcHLywteXl7YunUr3n//fXh5edV5x6eFa6/O398fXbp0wdGjRzX99w4PD0enTp3MlnXs2BGZmZmmcwHavHYAOH36NDZt2oRHH33UtEzr12wrJi8N8PHxQVxcHJKSksyWJyUloV+/fgpFZb2YmBg0b97cLP7S0lJs3brVFH9cXBy8vb3NtsnOzsb+/ftN28THx8NoNGLnzp2mbXbs2AGj0Wi2zf79+5GdnW3aZuPGjdDr9YiLizNts23bNrMhdxs3bkRERASio6Otvi4hBKZOnYpVq1bhl19+QUxMjFtcd13/LUpKSjR9zUOHDkVGRgbS09NNP7169cKECROQnp6O1q1ba/baqyspKcGhQ4cQHh6u6b93//79a5U++OOPP9CqVSsA2v/3/cknnyA0NBSjRo0yLdP6NdtMxs7AmnF9qPTSpUvFwYMHxfTp04W/v784deqU0qEJIapGYOzZs0fs2bNHABDvvPOO2LNnj2ko9/z584XBYBCrVq0SGRkZ4v7777c4vC4yMlJs2rRJ7N69W9x6660Wh9d17dpVpKSkiJSUFNGlSxeLw+uGDh0qdu/eLTZt2iQiIyPNhtfl5+eLsLAwcf/994uMjAyxatUqERQUZPPwur///e/CYDCILVu2mA0tLCoqMm2jxeueNWuW2LZtmzh58qTYt2+fePHFF4WHh4fYuHGjZq+5LtVHG2n12p999lmxZcsWceLECZGamipGjx4tAgMDTZ89WrxmIaqGw3t5eYnXX39dHD16VCxbtkw0atRIfPnll6ZttHrtFRUVomXLluKFF16otU6r12wPJi9W+u9//ytatWolfHx8RM+ePU1Dcl3B5s2bBYBaPw8++KAQompY4Zw5c0Tz5s2FXq8XgwYNEhkZGWbHuHbtmpg6daoIDg4Wfn5+YvTo0SIzM9Nsm0uXLokJEyaIwMBAERgYKCZMmCDy8vLMtjl9+rQYNWqU8PPzE8HBwWLq1KlmQ+mEEGLfvn1i4MCBQq/Xi+bNm4u5c+faPLTO0vUCEJ988olpGy1e98MPP2x6HzZr1kwMHTrUlLho9ZrrUjN50eK1X6/j4e3tLSIiIsTdd98tDhw4oOlrvu7HH38UsbGxQq/Xiw4dOoiPPvrIbL1Wr33Dhg0CgDhy5EitdVq9ZnvohHBWOTwiIiIix7HPCxEREakKkxciIiJSFSYvREREpCpMXoiIiEhVmLwQERGRqjB5ISIiIlVh8kJERESqwuSFiIiIVIXJCxEREakKkxciIiJSFSYvREREpCpMXoiIiEhV/h9zPkpPvtZ2JQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot (new_audio_data)\n",
    "plt.show ()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a89a9f35",
   "metadata": {},
   "source": [
    "Through trial and error and changing the low pass and high pass filter parameters, it seems the signal best retains its shape between frequencies of 2 kHz and 22 kHz. It would seem that higher frequencies are detected when exhaling, and lower ones when inhaling."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}