testing_soundtools_find_song.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computer: lookfar.local\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10ac23290>]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xCLTXunXroPeJfpZx+eqzmNfpVNu2MJKuVnlvNXD6NHwDYurj9yJ35XqcfPYp\ntLvrt/D85ws0fvJv6Fqp05xT0Tl0JBSnM+xa+UjkmrW/ZTxYq1aA02n+tW8mOytTyFxsLFataome\nLSwsRGFhoRC7SeN0XC4Xjh1riZByu91wuYLDQ/VcmLpGD1itwbxgETj1xotouuKGiPvUfxvPhQUA\ntQZ0nmibBRT9GtjxEerq64EevdEg8O8NRHlsHk5AAQLsn5hjzZDjiUxn5M+xfQdDtY5iXWs2bEzE\nIng1t10ad0I6ml128WWGyhAE2nM6nYa+I4lw/LPtYFEi+tQGY9FXTXW1YHn5YD8Z6S/zXPPP/wHf\n8A5Ob0isUm3dqdNgtbWmXKu6+hYX6/semF1Ur7a6GiyB9XXxcDqdMR/aEyFphtcKCgpw6NAhHD16\nFE1NTdi0aRMGDRpk2B5r00agutjw119M6HzP78Ijw2LNJfhQhoyGY8LDCbVtd4QuiGyXGbutAT+J\nus9oqKzsOSmjqLMeFG+0/iT45rVBBfh8WcQThWUnuCzAZvAP7F0uPhZJ43QURcH48eMxc+ZMTJ06\nFcOHD0e3bjZJf9K81iJqfRmDayfYz27xvvjhWFhRN3Xq7YZsmkLoDbfbj+ToCCS/s+5T2M8nxD6g\nQ5Q1PPAmaDVE67b6z2mVYawtE1FfKPVmuE6klk1zgAIbMkpz7SHZsOFjpLSrNSmsHUkapwMA/fv3\nR2lpKRYsWICxY8fKlgPA+wNR5nozHKiTW7qjnruuhbr2LagJpJlnw1vCbNVnnwIPSYMflbx8w20a\nIsTpKA/+ydr2I8CMLro0SkjPk2vNj3Wm/glu5dE5us8xE/7Nl/6gCfXh8QnbY+cUBpUcYDffmbBN\ns2CXmjuMlookldMRBbvtHnHGOA8b4lHfXOndtfI58L8aT7MfuiZB65CG8vtnEmjTACEdHZZpLJ+Y\nWIwMu8UbItM+hMbXvdWi5LJoa06MDQ8yO/QkA1CfFjuEy87sCuXBJ1reN9czMhUDvUdWNN52n0Uy\nkJ5OZ+hoccaaI0h8Y/PqxnfB17wizvzS4BXP/LP4a5OMjF+3vS1K3Rct5OQaP9dOxPMp2TF6T81J\nJbn7mDer8Wt/9e/yD5NaBLvhV5a0E3Ue66SxapyB+G7myu/ngbXLBJjJt6rW+ud42cXhC36txnPX\ntaYHLYgmPZ1OGwPj6NFsFXkn+dn13h86f3mx9/0vJyfe4+AcrFWroDop6qIZ4HGy5hohw2AFUcey\nNdHzlUnC9RyPAAAeVElEQVSEjbjMwElx9reLntGav/V38IaT3nRAOz8PNttWcCbsOLDO1qz/Ue++\nDmqEujGRVukbwbFsTUvWhjybfcey24O1jR14YjY8IDqTf79LohJ9pKXTEYmvV8Eys4K2KxdfBnZ2\nAZTSBHo9zXMzrNuPoDwXUKkwVh2QdlnR98XAITItkFVEypjtI1IGgHjE6+nEqbukPn6f18z2TTGP\nSyX4Ky1JL9mtd5vWDhunL3CG3a6/pIaf/C7x7V8c56Em1ndTEL5idACCctfZHXI6JuCrXQ80z29E\nWc8Qj8DxfsZYS0biGD0d5XExIabJgDIxel46Fi31fwxY19g9hLg95OYnT74xecNZjcJu+jXYqKvM\nsz9giPZjf3odlBGXG25LeVRLbrbYTyiWBLI0Z2xgV94Idn5/89sTBDkdgfgCFAJr1wMAWolZg+sr\ny8y/Lo9+UAcL83n1134jMAOhAQvOnMTKXMewazkW1aFjo670v1YuGwsmoLZRtEg1PQshfUPdfjRE\nCLKftmSHDh21iIiN1v0o1/9StgRdpJ3TUZ4ti3+QUdujroy8Ml1QOWyW683AwD/ZHP0YCytfKrEm\nrM2ex3B1FGvPhIzPAMB+dqspdu2AEiEKVFnyRmI2DYQg+9pkY3/h/T/kIU8pnhLXht7s3GxMHJ25\n5hbT8xE4qpIspJ3TiZh40WxELebz3RijlMJlQ0aJaUcz0YcY2FU3WahDACaVSGDde5piNyaiHXIM\nQkufswzrF66yjAyw2yeCXTYWyuwXwvf3Old8m3HuI+z6XwlPKhwRC+aORJN2TicmfaInxEwExcDT\nrnLff4dti1fhUBk/VXc7pmF6xJZ1PbpEEH3DY8X3xT8mCdeOsNFXGyoJzm7xBi8oI67wli0I6GGw\n636uw5DupmOby8iwJOJOxJCm1SSfYhNRbr3LHMNn63/aZRcYzytnGTGHEEx2Cp3ONNe+TVGsWCip\nBYNRktFgF/0UrLf+LMbKmGui2zy3r7ChbSOYnQU6WSGnE4hJK+lT9csXc52CDp9jJOpJGXub7nNi\ni0iOnpNWtPSIEkF5LJFMG5EQP7zJCs6H49n/iX8gYSnkdBLB7FXSkUia9TTab+KGMkQIduSKzPxe\nA4cJN8mGmZuIkonuaQruORlFmWF+Dah0h5xOAijPJhapY6jNKHnj2MDhFiuJQ1txWR+sgJ2pLWN5\n+4Wvim+7f/SSCYZtGuy5KX8WEN3ZRt98Hht9lXgnZpSO8XWwu3+n2ZxyT/S1ZOkKOZ0EkDFsxs45\nP/KOswusFRIHduEI7Qdn61/IifZy8r0pdrk5moSQ73R7nWuTbFiqIRZMzxytiZGEbOglptk2E3I6\nhCnouXkxDWlHws7peIbuc6yGjS+RLcEUlMmxq8IqtyaQPFYa3p5h6BqfiOiZfjJjwXEzgQtakwly\nOnYkTuSack9qVwNNFZQhArOZG6HvYHPsxgvT1Rsu3/ks41pkoGNNl6aQZgMJiJU//RnsrOQLjQfI\n6diTOF9q9uPwiWdWcJ5ZaiJruDQ5n7LSCdPmE84Tm+eLXaSxRIDOrAHJQu6L/wx6rzz8dPyTLM5c\nLhJyOrbEQPio5eUFzFnBT4hDy1CRkVXzce3qjGHQGvTA+vxYn2HTMPe7H29BMfvZrXJy+gmCnI4N\nYT8ylpXaUkxKG6MFXUEKRGziZLkwhjlrntjIK6H86VlTbOvCzK9+4YC4hyjX3mppjkXRkNMxSNx6\nGrHOzYq9CFW5NnWTRIoheX9wkbBNWvrm+k0JY6SWkQYYY4aCTjShK4LSRK+TxM5EK+R0AtFTCTCR\n9BpmhFqL+K7qWXhqJMxZK/FuWin2u2TtO8iWAABQbiyOf5CGSW+rK6WKgOXmRc4QHwmJvfxUgJxO\nAJrCJQWQPWOxeKMZ+mu8h6Kn98auuDHh9qKh/CbO4rs0eBq0DJ3XUnnqryYJSSLM7Ogkw9B6gpDT\nMUoX42GejjO7QnnwCYFiAOYUUVRKR+oaLZPU00uNycjvHHt/pzj7bQC7VkeGY4kEfY5aJqfT3eEz\nBeisLXuFEdJhaJ2cjkH0Tmaz0IJnPc4RqEY8QtYCaRiCZGN+Fr4t3vBjnDkxH8pS69IUsRFXBL+/\nbKxlbQsjyTID4MdDzbMdJRccGzRcbDmBZMgmL5j0cjoSF1MpV9wQvCHBlOvs8nFggy5KyEaYzZ69\nhdrTNHdloN4M66ltCIJZeBNlN3hLBrMLR8KxbA2YgQV/hD6YiQl3lXkrIu+IlobKICzLxLlRm5JW\nTod17SFbgp9EK5gqN94h/kkvcK2PxgSYsWBndIHy+2cSthOGDXuJLDMbyqJVYHfEL41MCMLEob5o\nv08jZThik35BCenldG4qFmdMT6RbEsK6dBfjeM4uAOwSEmwW/YcAAFibtpYFo6QK7KY7ZEsgLCa9\nnI7I0NQkLBMbl5AHR3HrR1J38pn9YmLqTP520JARWeuwoca5CtbL2vRNMkjWxJxmkYJ3TkIYMiKV\nJJUsMIoy8oqkTbwYCtOQhl/rsDA7w6RFnLIxsEaH/TQJg0pMhJwO0UJoxI4wp6MjK+9l4+Ifk+5h\nu8lAyM05LHpTBBK+B0Ij19IUuoKEH5bRWrYEIlUIWfPDLr9ekhDCbpDTSWKYzaqFRoP11B8WbSl9\nBspWkHoEFtnLcVHvlPBDTieZ0VsWWBY2H99nQ0bJlmAvWife42WDL255fdGlCduL3EiSOLJYMvUm\nR03CvHahkNOxmiTpnXiJ/GthN9+pz8pPRkbfZ1EdIMeyNXHLLGtGVDZmm8KGXpK4DQvmPtjPbjG9\nDbNho6/Wd4KepLw2hZxOCOy68JxZTEv2XQ0oJX+EMn6qEFuBsIHDhduMic4CUrGGVuIVrLIj7NwL\nZEvQDLv7QdkSgE5nmmKWCVhHJp1o67pMzLYgm9T9ywyiXBP89KQ8OhfsIuO1cwJh5/cHi5MskF0e\nP3or7JxrbjYqyRBssNj0O2aiTJkOducDsQ+yU6r6WE+yvQt1m9OaMij4JLHDVoY0pDms+F7ZEkyD\nnE4cWI9z4hZdE0qURIMxSSDjtRHiJuRMhHZiMz2wPgOhxBjeAwBmpyGL7OjZwln3XhYKEY/yh0Wy\nJSQNbOglwLl9w3fY6QHJIOR0tBLjZiASLetUws4xywnImKeVsfbCRos7lYnTxNasiZWFoyBa8kox\nn0HuyvXeF83fT9aluxC76QBjDCwwAjCFSH2nk8h4csC4Kuvz4+BdJt0cWUaSpZcnhMIys6NmBmCX\n6k+nEuv7pEQbyhXY21T+uzR2doJu9nH45hFjTjPWA2Oke0yyROzFIPWdTiKZpQPSmJsRAEAQemB5\n1kT6iQzFj9eLZG0Sr3ibTijjbpctIWFS3ukow8foP2fJ67EP0BtbTxC2JInnBxKsR5W0GJnztRkp\n/8mx/j/Rf06cdDDKbfcYlSMWs4qU+TIJp0BX3hR8Dx2pELIbEWs+dzZM/wMhACgzlsqZ+wupDiuH\nJH5QaCblezoiUX4/z/vCJmtLWKsMOJatEW/YfzNNMacjar1Itnf4if14mBh7aYpisOAdO7OrJVms\nWchQFrvgx1GOJPRATkcH7OxeQN/BsmWYT4quuBcVPcUuHOF9kZNcZRiIxGDNxfp0YzCRLrvyRmPt\n2ZyUH17zY2CYLRKOex8TYseuKPP/ZvhHki4wRYEy9yUgO83q24ssE57jEmfL5hhd58dMyuQgm7Rw\nOsr8vwGtNVY8THNYVprdSA3CkqzYXERY5HBddkbXiDMHbOjoxJscNgZ881qaLzRK8k/ppMfwGsty\nGlr/wgbSmD2RwkSZo2AXDPTOFf6XCTnmfE/9Vmb5SCVstJDZKKb1dF577TWsXbsWOTneSddbb70V\n/fv3BwCUlZVh/fr1cDgcKC4uRr9+/QAAu3btwpIlS9DY2IgBAwaguLgYANDU1IRFixZh165dcDqd\nKCkpQceOGuq5Jwjr3cf0NggCAOBwAB6PpU2ams4oHhZl+EgE1mcgeNnLsmUEkQqVS039C6655ho8\n9dRTeOqpp/wOZ9++fdiyZQvmzZuHadOmYfny5eDN+YSWL1+OCRMmoLS0FAcPHsSOHTsAAOvWrUN2\ndjYWLFiAq6++GitWrDBTdgtpNO6cNti0YBu75BrgvH7R9wfUp7EKUxJ1tvHWg1F+8zvxtgXDuveU\nLSElMdXp8AjJ6bZv345hw4bB4XAgPz8fnTt3RmVlJaqrq1FfX4+CAm+9mREjRmDbtm0AgG3btmHk\nSG/SxiFDhqC8vNxM2QC89VeY0/5PYylL1LxgiaFMesQUu4miFI2HY+qM6AdImGtjA4KjtUJTQRmy\neeUNUP6wCExneYy04IyushVYgqmBBO+88w42btyIXr164Ze//CUyMzPhdrvRu3dv/zEulwtutxsO\nhwN5eXn+7Xl5eXC73QAAt9vt36coCrKyslBXV4fsbBoXTjnaNuf9cpgz9MPMWlCbDghIQMlatwEo\n8Wc4nc4Mc/Jh5KbGyEtCTmfGjBmoqanxv+ecgzGGW265BZdffjluvPFGMMawcuVKvPTSS5gwYULC\ngn3tRKKiogIVFRX+90VFRXA67ReN1bp1azidTlRH2CdLr09TIPWtW+NUhGONaIz0twba8u1vP+py\nMEWBOvkRHL/31pjnGCVQS9bvnkCGDnuRrpMZhF6vjNYZyIzSbjxNWq99qI2mzEzU+fbN/iscOtcl\nWXWt9KBXU+C1S+RvifUZtG7dGrkLX417XpuRV6Cdhddz1apV/teFhYUoLNRfzykSCTmdxx7TtmZl\nzJgxeOqppwB4ezbHjh3z76uqqoLL5YLL5UJVVVXYdt85vveqqqK+vj5iLyfShamtrdX9d5mN0+mM\nqkuW3kia1NOnIx4rUmOorboTJwAA3BF9rZDI9uvr69Ggw16sz85MGk83Rm3XqKbQc0Lf85Mn/a9P\nZLQF09mGrGsVC8Oauvcy5W+pra3VrKnx/AFosuh6Op1OFBUVmWLbtDmd6uoWH/3RRx/hrLO8hcYG\nDRqEzZs3o6mpCUeOHMGhQ4dQUFCA3NxcZGZmorKyEpxzbNy4EYMHD/af88EHHwAAtmzZgj59KKos\n5aFlHDaAPgRRKHNeTNxIKqwNg4lzOitWrMCePXvAGEOnTp1w9913AwC6deuGoUOHoqSkBK1atcKd\nd97pr00zfvx4LF682B8y7Yt4u+SSS7Bw4ULcd999cDqdmDLFWM4mQifpEEhBE9qEBbCcGMX0tNpI\nkaJupjmdyZMnR903btw4jBsXXkCqZ8+emDt3btj2jIwMTJ1K9Wyshv10LPgbAp7QbAz7Ue/4B9kB\nE52jUvoKkALrP8yE/ReNroiCvmkSCety2yzXEnM4gNy8+AcSpsOuusk825nZYG3jVAttk95ppMy8\n/ukGOR2ZhKy9cDzxnCQhMaAcWbaAtZKYJrFDR7ntEykFOR3CppCzswts4HDZEtIWdvN42RKEQ06H\n0IcZSSD1QIXTLEdJwRtf0hAlE3gyQ06HiE1Ih4NJXhXN+l0otf1khN10h2wJBOGHnA5hDzQGLPir\ndqY4yowl4mxdFh4pStiANJ0nI6dDWELErMK9A7JHdA3JxxUlgCFdJrTZmd1kSyAAKM+WeV84xH/v\n2MgrhdtMBsjpyCQgqSUb8zOJQmIRfPM3PKncPsLiuFYtqW6Uex6B8vDTLe04HHDO/quxtjTChl9q\nqv2kpwOFyzOHA8rClWDt4oSUG6Fjfvxj2rUT365kyOlIhAU+zSfL4jyTaoywNm3Aep0btM1hcpVE\ndqldHb09YLkubwXRNCfuGiYz2x4yyvsihaoYJ8mdjpBFqCNgeRqezkxCmSi6Fg6FZRMWYqCAoNTq\nriZBToeICRt3uxhDXc9O2ETceiMEYWMcU6anbfBAIOR0CEtgWfYpuMeu+7lsCfJpHb10BGERkcuC\nhXNeP7AUWp9GTkcy7DYxhe0IgkhNHFNnQEmhpQLkdCTDBl3k/f/iyyQrsYC8fN0JRJUJD5kkJgk4\nt69sBQQhHBpgtAms81myJZiOMmMpsH8P1D89AHbdz8EKzo9/Upfu8Y8xis3rk7D2HTSPwBBEskA9\nHSI2DnHRMywjw7/ITrnmFjDJT/Ksrc3XQAgMrlNK/iDOGEEkADkdIibM1Um2hLRHmf1CwjbY+QOC\nN1AXipAEOR3ZZGaBXVUkW4V9EdjTSlZkJ1klCJHQnI5kmOIAG/cL2TJsC8vvIt7msDGAI0O4XeFQ\nAT0iBaGeDmF/zusn1BxzdYJy5Q1CbZqCaKfj6ijWHkEYgJwOYS05ERJ/EkQ6kqYdWXI6hKWw9rmU\nRNIOcIokkAkbfDHYxVfIliEFcjqE7WHtc2VLkEOv8wAzUuoT0lHufhCsTRvZMqRAgQSE7WG3TwK7\nsVi2DMtRRl4BjBT4NOzqBLiPibNHEAagng4RF19xNXbRT6W0z9q0BdOZPoeIAM2nETaAnA4Rn4zm\n8GIzU9IQBJEWkNMhtJOm0TYpSUhxPoKwCnI6BJEmKJe0lOdWxt4mUQmRzpDTIYg0gfUuBEzI8EAQ\neiCnQ+iAxteSHmd72QqINIecDhGfNKj1kzZkNzudM7vJ1UGkLbROh4gLy2gN9P8JWOGPZUshEkQZ\nPxU4eQIsm3o8hBzI6RCacEx6VLYEQgCsXSZlOSCkQsNrBEEQhGVQT4cgCMIqLhgEnGqQrUIq5HQI\ngiAswjHxEdkSpEPDawRBEIRlkNMhCIIgLIOcDkEQBGEZ5HQIgiAIyyCnQxAEQVgGOR2CIAjCMsjp\nEARBEJZBTocgCIKwDHI6BEEQhGWQ0yEIgiAsI6E0OP/+97/x2muvYd++fZg1axZ69uzp31dWVob1\n69fD4XCguLgY/fr1AwDs2rULS5YsQWNjIwYMGIDi4mIAQFNTExYtWoRdu3bB6XSipKQEHTt2BABs\n2LABZWVlAIDrr78eI0eOTEQ2QRAEIYmEejrdu3fHb3/7W5x//vlB2/ft24ctW7Zg3rx5mDZtGpYv\nXw7OOQBg+fLlmDBhAkpLS3Hw4EHs2LEDALBu3TpkZ2djwYIFuPrqq7FixQoAQF1dHd544w3MmjUL\nTzzxBF5//XWcPHkyEdkEQRCEJBJyOl26dEHnzp3Dtm/fvh3Dhg2Dw+FAfn4+OnfujMrKSlRXV6O+\nvh4FBQUAgBEjRmDbtm0AgG3btvl7MEOGDMEXX3wBAPjss8/Qt29fZGZmIisrC3379vU7KoIgCCK5\nMGVOx+12+4fGAMDlcsHtdsPtdiMvL8+/PS8vD26323+Ob5+iKMjMzERdXV3YOT5bBEEQRPIRd05n\nxowZqKmp8b/nnIMxhltuuQWDBg0yTZhvOI4gCIJIHeI6nccee0y3UZfLhWPHjvnfV1VVweVyweVy\noaqqKmy77xzfe1VVUV9fj+zsbLhcLlRUVASd06dPn4jtVlRUBB1bVFSELl266NZvBU6nU7aEMEiT\nNkiTduyoizRpY9WqVf7XhYWFKCwsFGLXlOG1QYMGYfPmzWhqasKRI0dw6NAhFBQUIDc3F5mZmais\nrATnHBs3bsTgwYP953zwwQcAgC1btvgdS79+/VBeXo6TJ0+irq4O5eXl/ki4UAoLC1FUVOT/F3jR\n7IQddZEmbZAm7dhRF2nSxqpVq4LupaIcDpBgyPTWrVvx/PPP4/jx43jyySfRo0cPPPLII+jWrRuG\nDh2KkpIStGrVCnfeeScYYwCA8ePHY/Hixf6Q6f79+wMALrnkEixcuBD33XcfnE4npkyZAgDIzs7G\nDTfcgIcffhiMMdx4443IyspK8M8mCIIgZJCQ07nwwgtx4YUXRtw3btw4jBs3Lmx7z549MXfu3LDt\nGRkZmDp1akRbo0aNwqhRoxKRShAEQdgAx+OPP/64bBFmkp+fL1tCROyoizRpgzRpx466SJM2zNLE\nOIWJEQRBEBZBudcIgiAIyyCnQxAEQVhGQoEEdmbHjh144YUXwDnH6NGjMXbsWKH2ly5dik8++QQ5\nOTmYM2cOAG+euPnz5+Po0aPIz89HSUkJMjMzAYhNgBqNqqoqLFq0CDU1NWCMYcyYMbjqqquk6mps\nbMT06dPR1NQEj8eDIUOG4KabbpJ+rQBAVVVMmzYNLpcLDz30kHRNkyZNQmZmJhhjcDgcmDVrlnRN\nJ0+exLPPPou9e/eCMYZ77rkHnTt3lqrpwIEDmD9/Phhj4Jzj8OHDuPnmmzFixAiput566y2sX78e\njDF0794dEydORENDg1RNb7/9NtauXQsAtrgfAAB4CuLxePjkyZP5kSNHeGNjI//tb3/L9+3bJ7SN\nr776iu/evZs/8MAD/m0vv/wyX716Neec87KyMr5ixQrOOed79+7lDz74IG9qauKHDx/mkydP5qqq\ncs45nzZtGv/mm28455w/8cQT/NNPP+Wcc/7uu+/yZcuWcc4537RpE583b15cTT/88APfvXs355zz\n+vp6ft999/F9+/ZJ19XQ0MA5934ujzzyCP/mm2+ka+Kc8zfffJOXlpbyJ598knMu//ObNGkSr62t\nDdomW9OiRYv4unXrOOecNzU18RMnTkjXFIjH4+F33303P3r0qFRdVVVVfNKkSbyxsZFzzvkzzzzD\n169fL1XT999/zx944AF++vRp7vF4+IwZM/jBgwelf34pObxWWVmJzp07o1OnTmjVqhWGDx/uTywq\ninPPPTdsvdD27dv9SUtHjRrlb1NEAtTy8vK4mnJzc9GjRw8AQNu2bdG1a1dUVVVJ19WmTRsA3l6P\nx+OxxbWqqqrCp59+ijFjxvi3ydbEOQ9L/yRT08mTJ7Fz506MHj0aAOBwOJCZmSn9OgVSXl6OM844\nAx07dpSuS1VVNDQ0wOPx4PTp03C5XFI17d+/HwUFBcjIyICiKDjvvPOwdetWfPzxx1KvU0oOr0VK\nElpZWWl6uzU1NcjNzQXgdQC+nHVutxu9e/cO0uN2u+FwODQnQM3KykJdXR2ys7M1aTly5Ai+++47\n9O7dW7ouVVXx8MMP4/Dhw7j88stRUFAgXdOLL76I22+/PahMhmxNjDHMnDkTiqLg0ksvxZgxY6Rq\nOnLkCJxOJ5YsWYLvvvsOPXv2RHFxsfTrFMjmzZtx0UUXAZD7+blcLlxzzTWYOHEi2rRpg759+6Jv\n375SNZ111llYuXIl6urqkJGRgU8//RQ9e/ZEdXW11M8vJZ2OXfBlYRBB6BNwLBoaGvDMM8+guLgY\nbdu2la5LURQ8/fTTOHnyJObMmYO9e/dK1eSbi+vRo0dQrj6ZmgBvct0OHTrg+PHjmDlzZsS8gVZq\nUlUVu3fvxvjx49GrVy+88MILWL16tVRNgTQ1NWH79u247bbbIu63UteJEyewfft2LFmyBJmZmXjm\nmWfwr3/9S6qmrl274rrrrsPMmTPRtm1b9OjRA4oSPrhl+f1AWGs2IjThqNvt9icWNZPc3FxUV1cD\nAKqrq5GTkxNRj54EqACCEqDGw+PxYO7cuRgxYoQ/r50ddAFAZmYmzj//fOzYsUOqpp07d2L79u2Y\nPHkySktL8cUXX2DhwoXSr1OHDh0AAO3bt8fgwYNRWVkpVZPL5UJeXh569eoFwDt8snv3bunXyceO\nHTvQs2dPtG/fHoDc73l5eTny8/ORnZ0NRVFw4YUX4uuvv5Z+rUaPHo0nn3wSjz/+OLKystClSxfp\nmlLS6RQUFODQoUM4evQompqasGnTJlPKMISOwQ8cOBAbNmwA4C2x7WtTZALUeCxduhTdunXDVVdd\nZQtdx48f9w9hnT59GuXl5ejatatUTT//+c+xdOlSLFq0CPfffz/69OmDe++9V6qmU6dOoaGhAYC3\np/r555+je/fuUjXl5uYiLy8PBw4cAOC9sXbr1s0W33MA+PDDDzF8+HD/e5m6OnbsiG+++QanT58G\n59w21+r48eMAgGPHjmHr1q246KKLpGtK2YwEO3bswPPPPw/OOS655BLhIdOlpaX48ssvUVtbi5yc\nHBQVFWHw4MGYN28ejh07hk6dOqGkpMQfbFBWVoZ169ahVatWYaGIgQlQ77jjDgDeSfeFCxdiz549\n/gSo8dJS7Ny5E9OnT0f37t3BGANjDLfeeisKCgqk6fr++++xePFiqKoKzjmGDRuG66+/HnV1dVKv\nlY8vv/wSb775pj9kWpamI0eOYPbs2WCMwePx4OKLL8bYsWOlX6c9e/bgz3/+M5qamnDGGWdg4sSJ\nUFVV+md36tQpTJw4EYsWLUK7du0AQPq1eu2117B582Y4HA706NEDEyZMQENDg1RN06dPR11dHRwO\nB371q1+hsLBQ+nVKWadDEARB2I+UHF4jCIIg7Ak5HYIgCMIyyOkQBEEQlkFOhyAIgrAMcjoEQRCE\nZZDTIQiCICyDnA5BEARhGeR0CIIgCMv4fylxdmPJxxnQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a74e310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pdb\n",
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.io as sio\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import scipy.signal as sg\n",
    "import math\n",
    "import scipy as sp\n",
    "import socket\n",
    "import os\n",
    "import wave\n",
    "import struct\n",
    "\n",
    "matplotlib.style.use('ggplot')\n",
    "\n",
    "comp_name=socket.gethostname()\n",
    "print 'Computer: ' + comp_name\n",
    "if  comp_name == 'chim':\n",
    "    sys.path.append('C:\\Users\\GentnerLab\\Documents\\Experiment\\scripts\\sound_tools')\n",
    "    experiment_folder = os.path.join('C:\\Users\\GentnerLab\\Documents\\Experiment')\n",
    "    r = os.path.join('C:\\Users\\GentnerLab\\Documents\\Experiment')\n",
    "    raw_data_folder = os.path.join(experiment_folder, 'raw_data')\n",
    "\n",
    "if 'lintu' in comp_name:\n",
    "    sys.path.append('/mnt/cube/earneodo/repos/soundflow/sound_tools')\n",
    "    experiment_folder = os.path.join('/mnt/cube/earneodo/bci_zf/')\n",
    "    raw_data_folder = os.path.join(experiment_folder, 'raw_data')\n",
    "\n",
    "if 'lookfar' in comp_name:\n",
    "    sys.path.append('/Users/zeke/repos/soundflow/sound_tools')\n",
    "    experiment_folder = os.path.join('/Volumes/gentner/earneodo/bci_zf/')\n",
    "    raw_data_folder = os.path.join(experiment_folder, 'raw_data')\n",
    "#test soundtools\n",
    "from soundtools import WavData, Chunk\n",
    "    \n",
    "#file structure\n",
    "bird_id = 'z003'\n",
    "sess = 1\n",
    "rec = 'a'\n",
    "run = 5\n",
    "bout = '2251_869'\n",
    "\n",
    "#load the bout and the whole sound file for searching\n",
    "raw_file_folder = os.path.join(raw_data_folder, bird_id, str(sess).zfill(3))\n",
    "raw_file_name = rec + '_' + str(run).zfill(2) + '_song.wav'\n",
    "raw_file_path = os.path.join(raw_file_folder, raw_file_name)\n",
    "\n",
    "#the whole sound\n",
    "all_sound = WavData(raw_file_path)\n",
    "\n",
    "#the bout\n",
    "if bout is not None:\n",
    "    raw_file_folder = os.path.join(raw_file_folder, 'bouts')\n",
    "    raw_file_name = rec + '_' + str(run).zfill(2) + '_song_' + bout + '.wav'\n",
    "\n",
    "raw_file_path = os.path.join(raw_file_folder, raw_file_name)\n",
    "bout_sound = WavData(raw_file_path)\n",
    "\n",
    "#get a piece, filter it, plot it\n",
    "window_t = 234700 #time window in msec\n",
    "window_size = 1000 # window len in msec\n",
    "\n",
    "window_t_samples = int(round(window_t*bout_sound.s_f/1000.))\n",
    "window_samples = int(round(window_size*bout_sound.s_f/1000.))\n",
    "window_samples = min(window_samples, bout_sound.n_samples - window_t_samples)\n",
    "\n",
    "if bout is not None:\n",
    "    segment = [0, None]\n",
    "else:\n",
    "    segment=[window_t_samples, window_t_samples + window_samples]\n",
    "\n",
    "window = Chunk(bout_sound, segment=segment)\n",
    "plt.plot(np.transpose(window.data))\n",
    "# window2 = window2 = Chunk(sound, segment=segment, chan_list = [0, 1])\n",
    "# plt.plot(np.transpose(window2.data));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<168930h\n",
      "168930\n",
      "(1, 2, 44100, 84465, 'NONE', 'not compressed')\n",
      "(1, 2, 44100, 168930, 'NONE', 'not compressed')\n"
     ]
    }
   ],
   "source": [
    "import struct\n",
    "import wav\n",
    "#write the chunk into a wav file\n",
    "stream = np.array(window.data, dtype=np.int16)[0]\n",
    "stream_bytes = window.samples * 2\n",
    "print '<{0:d}h'.format(stream_bytes)\n",
    "bin_stream = struct.pack('<{0:d}h'.format(window.samples), *stream )\n",
    "\n",
    "file_path = '/Users/zeke/bout_test.wav'\n",
    "wavfile = wave.open(file_path, 'w')\n",
    "wavfile.setparams((1, 2, 44100, 0, 'NONE', 'not compressed'))\n",
    "#insert a bout\n",
    "wavfile.writeframes(bin_stream)\n",
    "wavfile.writeframes(bin_stream)\n",
    "print wavfile.tell()\n",
    "print wavfile.getparams()\n",
    "wavfile.close()\n",
    "wavfile2 = wave.open(file_path, 'r')\n",
    "print wavfile2.getparams()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10d158790>]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T60HANBbEFFprjKgo8oqUDOBXkOMW+vwzakvghDji07+B+NIWtWVIhu55T20JYQF3LhzV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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ce34950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#filter the signal\n",
    "s_f = window.sound.s_f\n",
    "\n",
    "filt_lo = 10000 #Hz\n",
    "filt_hi = 400 #Hz\n",
    "hp_b, hp_a = sg.butter(4, filt_hi/(s_f/2.), btype='high')\n",
    "lp_b, lp_a = sg.butter(4, filt_lo/(s_f/2.), btype='low')\n",
    "\n",
    "def band_pass_filter(chunk, hp_b, hp_a, lp_b, lp_a):    \n",
    "    chunk_hi = sg.filtfilt(hp_b, hp_a, chunk)\n",
    "    chunk_filt = sg.filtfilt(lp_b, lp_a, chunk_hi)\n",
    "    return chunk_filt\n",
    "    \n",
    "window.apply_filter(band_pass_filter, hp_b, hp_a, lp_b, lp_a)\n",
    "plt.plot(np.transpose(window.data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'rms_band_pass_filter' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-44-cc72ee26fa6c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0mstarts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msamples\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0msize_sample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize_sample\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0mrms_vec_bout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbout_sound\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_repeated\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstarts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize_sample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrms_band_pass_filter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhp_b\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhp_a\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlp_b\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlp_a\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0mbout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mband_pass_filter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhp_b\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhp_a\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlp_b\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlp_a\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'rms_band_pass_filter' is not defined"
     ]
    }
   ],
   "source": [
    "def rms(x):\n",
    "    return np.linalg.norm(x)\n",
    "\n",
    "def mad(x):\n",
    "    med = np.median(x)\n",
    "    dev = np.abs(x - np.median(x))\n",
    "    return np.median(dev)\n",
    "\n",
    "def identity(x):\n",
    "    return x\n",
    "\n",
    "bout = Chunk(bout_sound)\n",
    "size = 100\n",
    "size_sample = np.int(size*bout_sound.s_f*0.001)\n",
    "starts = np.arange(0, bout.samples - size_sample, size_sample)\n",
    "\n",
    "rms_vec_bout = bout_sound.apply_repeated(starts, size_sample, rms_band_pass_filter, hp_b, hp_a, lp_b, lp_a)\n",
    "\n",
    "bout.apply_filter(band_pass_filter, hp_b, hp_a, lp_b, lp_a)\n",
    "\n",
    "#print rms_vec\n",
    "plt.plot(starts, rms_vec_bout, 'b*')\n",
    "\n",
    "plt.plot(range(bout.samples), np.transpose(bout.data))\n",
    "plt.plot(range(bout.samples), np.transpose(np.abs(bout.data)))\n",
    "#print rms_vec\n",
    "print np.median(rms_vec_bout)\n",
    "print np.int(np.median(rms(bout.data)))\n",
    "print mad(rms_vec_bout)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def rms_after_filter(filter_func):\n",
    "    def out_of_filtered(*args, **kwargs):\n",
    "        #print \"Arguments were: %s, %s\" % (args, kwargs)\n",
    "        #plt.plot(np.transpose(filter_func(*args, **kwargs)))\n",
    "        return np.linalg.norm(filter_func(*args, **kwargs))\n",
    "    return out_of_filtered\n",
    "\n",
    "\n",
    "window = Chunk(bout_sound)\n",
    "\n",
    "@rms_after_filter\n",
    "def rms_band_pass_filter(chunk, hp_b, hp_a, lp_b, lp_a):\n",
    "    #plt.plot(np.transpose(chunk))\n",
    "    chunk_hi = sg.filtfilt(hp_b, hp_a, chunk)\n",
    "    chunk_filt = sg.filtfilt(lp_b, lp_a, chunk_hi)\n",
    "    return chunk_filt\n",
    "\n",
    "@rms_after_filter\n",
    "def rms_identity(chunk):\n",
    "    return(chunk)\n",
    "\n",
    "print rms_band_pass_filter(window.data, hp_b, hp_a, lp_b, lp_a)\n",
    "print rms_identity(window.data)\n",
    "#window.apply_filter(band_pass_filter, hp_b, hp_a, lp_b, lp_a)\n",
    "#print window.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "all_starts = np.arange(0, all_sound.n_samples - size_sample, size_sample)\n",
    "starts = np.random.choice(all_starts, size = np.int(all_starts.size*0.25), replace=False)\n",
    "\n",
    "rms_vec = np.empty_like(starts)\n",
    "\n",
    "rms_vec = all_sound.apply_repeated(starts, size_sample, rms_band_pass_filter, hp_b, hp_a, lp_b, lp_a)\n",
    "\n",
    "#get a segment of 50 starts and plot it\n",
    "plot_starts = starts[1:30]\n",
    "#print rms_vec\n",
    "plt.plot(starts, rms_vec, 'b*')\n",
    "#plot_bout = Chunk(all_sound, segment=[starts[0], starts[0] + ] )\n",
    "#plt.plot(range(plot_bout.samples), np.transpose(plot_bout.data))\n",
    "#plt.plot(range(plot_bout.samples), np.transpose(np.abs(plot_bout.data)))\n",
    "np.median(rms_vec)\n",
    "\n",
    "print np.median(rms_vec)\n",
    "print mad(rms_vec)\n",
    "print np.median(rms_vec) + mad(rms_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#get a segment of 50 starts and plot it\n",
    "plot_starts = starts[1:30]\n",
    "#print rms_vec\n",
    "plt.plot(plot_starts, rms_vec[1:30], 'b*')\n",
    "plot_bout = Chunk(all_sound, segment=[starts[0], starts[-1]] )\n",
    "plt.plot(range(plot_bout.samples), np.transpose(plot_bout.data))\n",
    "plt.plot(range(plot_bout.samples), np.transpose(np.abs(plot_bout.data)))\n",
    "np.median(rms_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print all_sound.n_samples/size_sample\n",
    "print all_starts.size\n",
    "print starts.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# variables and parameters\n",
    "y = window.data[0,:]\n",
    "fs = window.sound.s_f\n",
    "t = np.arange(0,y.shape[0], dtype=np.float)/fs\n",
    "\n",
    "n_window = 1024\n",
    "n_overlap = n_window-1\n",
    "sigma = 7./1000.*fs\n",
    "\n",
    "# pad the window so the output is the same size as the input\n",
    "assert(n_window%2 == 0)\n",
    "before_padding = n_window/2\n",
    "after_padding = n_window/2 -1 \n",
    "x = np.pad(y, (before_padding, after_padding), 'constant', constant_values = (0,0))\n",
    "\n",
    "# Get the spectrogram of the padded segment\n",
    "# overlap is n_window - 1, so that it slides across each time point\n",
    "# window is gaussian so it ends up doing a sliding gabor transform, which is what I need to compute the fundamental\n",
    "f, t, Sxx = sg.spectrogram(x, fs, nperseg = n_window, noverlap=n_overlap, window = sg.gaussian(n_window, sigma), scaling='spectrum')\n",
    "\n",
    "ax1 = plt.subplot(311)\n",
    "plt.plot(t, y)\n",
    "plt.subplot(212, sharex=ax1)\n",
    "plt.pcolormesh(t, f, np.log(Sxx))\n",
    "plt.ylim(0, 7000)\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  }
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