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6VbN6euF+MpKV773x0Pqjk37H4FgsKtSyOf588whshFxfGzyjjvPLzz8r4vTj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} ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "ts.reference=0\n", "absplot(ts)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/usr/local/EPD/lib/python2.7/site-packages/pandas/core/frame.py:3576: FutureWarning: rename with inplace=True will return None from pandas 0.11 onward\n", " \" from pandas 0.11 onward\", FutureWarning)\n" ] }, { "output_type": "pyout", "prompt_number": 7, "text": [ "" ] }, { "output_type": "display_data", "png": 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Ojo7Y29tz7NgxJk6cyP3338/GjRtvu+OQkBD8/f3x8/NjyZIlNbYLCwvDzMyM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\n07H8/gypGk2j5zdWxpzfmLPrS63F4+m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xcjOjOoh/XPhPMX9OuPjss0/EBedcKc7p+qiw\nhX0t/p6b22LlamicCqfw6D8pJ+3zePzxx7nzzjtJSUmhuLiYp556ivvuu++MjVZWVha7du1qSm/f\nvp0BAwYcd96WLVu45ZZbmDNnDnb7id01R3B7YjEaju8ILy//FpEzmLy0Q7z+9Ym/z9DarMlfSa8+\nscxxv84PGcs5f8NFLJ38d76d1Z/c3EeoqhjHJwtf5oPIrryb2IuZu37AUbKFfh2/IyvjX3y34UEG\n9P436z/OO2lZJlnmPIeD59PT2dC3LwUDBzK1TRvMssxHJSVctG0b9uXLGbxhA3fu3ct7xcXsrq9H\nDfF+k7319Vy9YwcfZmbS1mhi4fbe2MPXIDtXsjx6DbsLVrdY2bc+fAubPYVIh1PpkFrD8q1LSIrd\ng7N2MPPvaZ6RXRoaweSkxiMnJ4f+/fujKApWq5UHHniAjz468w5Gmy3wjfFly5aRl5fHggULjlsz\n6+DBg1x66aXMmjWL9JOsLFtWVIjPl4iiP9Z4+Hw11NfvJPqHczlo24rlND701NKcd/75LFr+JUsK\nvuLjnh+hkyPo+/nf+HLazbhdZaxe3Yb/zRnHrrAoHu0ygpvyDnJzwVKi5J1cOuQvbNrflxHXFzJ+\n4I1Ubj710WMROh2jIyN5tG1bPu3alb39+1MwcCBPpKWRbDQyp6yMsVu24Fi+nJGbNvFobi6LKiup\n/8lorp824c8m8honUz7Zrh3D7XbOHzyFnIILOLfP14wMm8xHi99pUf2333U3Bw/8QNXaEWxe8TQX\nTxhDSVHAdeXdbWd73Yknsp4OZ3P9nwqhrD+UtTcXJ+3zCAsLw+12M3LkSG6//XbatGlDYmJisxQ+\nffp0Jk2ahNfrZcqUKURHRzNjRmD+xaRJk3jssceoqKjgllsC3xLX6/WsWbPmhHnddfkfEeJralyH\njtnvdH7P4dwoogrDKIrf0Sy6mxNTWASb967nsXun8tW8t+jcezAjNl3KhtsGUtDvZcZedwcP/Lsb\nlQVP8PA/9ZxXuo839y3m/rRMho1ooCK3M1+vnkb7IcvpmTaXv99yE4MndkMxn94qwRE6Hec6HJzr\n+PEbFmVeL2uqq/ne6eRvublsrquju9XKULudoTYb4iydX5LncnHupk3ck5zMnxMS+MtVD7No3W0M\n7jKD6H2V/NDhB1yitEUfAJIk4ZF9bCpK5twOB/lD2hPM2P8hXZXVbMntz8elpXSxWlusfA2Nluak\no63y8vKIiwt8h2L27Nnk5+fzpz/9ieTk5FYReDKOjBq4/NyJfLL0Ca647mE+eOedpuP79t3Fknd2\nUzf3OqasvxJZPqUVWYKC6hcMSO1EQsZo+rkGkrkzjjW95zLqoY9QdCW0bfs4t1/5HXG+CC4tXEaH\n2jomdz8ffVgcJQfasHnvKPz+RNolzqVd7FpuGHEN51+RhbW7Fdlw5tdd7/ezurqapU4ny6qqWFtT\nQ0ezmWF2O8MaDYqjBUZ1nQ7b6+o4f8sWHkhN5fakJNw1Hjp0+JgGt54uyS+TFfsHdtXN4atVi1pc\ny7xvP+e/L3zLbSO3UWDsyJ2PLWZ8n8H8b+Fr9Lv8CVZ9+AySJLW4Dg2Nn9Ico61OajyOUFBQANBs\nrY7m4kgljBs4hSUbr6K2vv8xBmL16o7sePJGtlfW89z304Ko9NRZ/OUi7rvnCXqkDWfUjsHURFRS\nMORlzr1mJSZTCjrfdB584HXSvJXcnrMOnRDc13k8tUYrDq+DHTkZHCwZhtebhC1sHfGRO3GYD5AZ\naeHR628i6pxILBkWJPnMHlweVWVdTQ3ZVVUsrarih+pq2pvNDD/KmLTEEOGfI7uqiit37OD59u25\nJi4OIQTDej3Eyq03cNG5TzC26GLeNb3N8nVzWk1Tp/admTroIszDPmbe6pHk/rCV9QcfJT15FW+v\n/QtdtdaHRhBoFeOxevVqbrrppqaCZFnm9ddfp1+/fmdUcHNxpBIGd3uEvYd7UFL5h6ZjXm8lSxbH\n4rvsa97s/j6fLp8ZPKEn4OghoidibFYvDPYBdPH3ZeC6NHZ3XI/jmldI73UQm20wC2cPZ9H3O2jn\nKWVgxQ7GFZSzyWJmercRlKtGMsLiOFRiZX9hW0qqOlFXn4Vetx9H+A6ibQeIthSRaXXwwJ9uJKq/\nA2tXK7Lp1FsoP9XvVVXW19aSXVVFdlUVK51O2pnNDLPZyIqIoIfVSieLBX0zt/7q/H4eysnhk9JS\n3u7UidGRkYH663sv8zc8wPkDniRrB+zs5+b9b19p+vDXyeq/ORjTfxid7aMYP+VpPPKbvPrUbJzV\nXdmcM4A7t3RmWlrar867NfS3JKGsP5S1QyvN83jggQd44403mozF2rVruf/++8+6DqPKmihsYcfO\n3nU6l1FW0BZflIvXv3gxSMp+PfPWbqSmoJRRA8ewa9AYBlX2J/LxN/h2wBIuevB9ssY+ycjL+1Ne\nMZkZj7zHZynhhDcU0r1iG32ryxlcUsubie3wZ6QR662nY2xnvBVRHCqzcag8idyi4SxzdeW/653Y\nrKuIsecS6zhIuLWIycOuZOhVA7B2sSIpp9ZC0csyAyIiGBARwV9TU5uMydKqKr4qK+OJAwc44HKR\nYbHQ3WqlvdlMW5OpKSQZjehOw41T5PHwdmEhrxUUMMRuZ2tWVlNL56J+k5m/4W+c1/cfqGVraOhx\nOft3v9ssX4w8HZ576xmm3vMC6sohbHa/wIodxYzPMrJsy73MnfpP/v7ek5rrSiMkOWnLo0+fPmRn\nZxMeHg5AbW0tw4YNY/36llvO/HQ4YkEToj8gJXY/a3b8+MnP3btvZuU71RTN78eD6+4Josoz55l7\n/8JXqzbS3dKdfvvG4JO9bM34jovvnw/SHmTZhNXajVrnObz5yB4kUz0HicTu8eJwlRLlqiHeXUOy\nu5pNSYns0sVT7DcQHWnC7HdQVBxJQUk8JZVp1NRnIkQqFvMe4h2bSIzcROcUM/+49z7sg2yn3Rl/\nNPV+P9vr6thaV0euy0XeUaHE4yHBaCRarydCUbDpdEQoChGNMUCt388ht5s9DQ0cdrv5Q0wMkxIS\nyIqIaCrjsoGT+Gz1NM7p/gJm9XsuVO9lpvU51q0+fsHM1iC5XQZ/y7qShIve5NsVF5GzbCurDz5E\nu+TVzFxzH92O+syzhkZr0KJuq08//RSAlStXsnbtWi655BKEEHz55Zf06dOHf/3r+MUHg8GRSgiz\nLGNw16/4bs2zTcfmzLHhev5Jsn0b+c+KN4OosnkQQjA6qSOicxf6+bPosrc/fsXNxjbbcCdlc9MD\nDdTXb8frLQdAUawYDAnUNySxaKadnav8SKn11KoCe4MZlycCi78Om6+KcH8VB+zp6HzV1OoNGIwJ\nOGvMHCpO5XBZT2rr+2M25ZIct442cTtIj4vjsdv+TOQQG4q1ed7mParKIbebCq+Xar+fap+vKXY2\njuyyyjLJRiMdLRY6mM1YftKSuGTwDXy1ahp9Mv6LXl7IHeV/Z1a3j5gzP3i//1UXXkyYtwtXTJqB\n3/o8/3n8c6pqurIlZyCTt2Ty+Bm4rjQ0fg0tajwmTpzY1JwWQhy3/fbbb59Rwc3FkUpQlFwuHfo0\nHy0JDPX1eErJXpyM66qvmFo1iS0iN8hKj+fX+k1dbjeXpPVD1zGNTkoPUst6krbfTnm0i9zEAsp0\nB/Gai0nKquSSKyQ8nly83mK83kr8/lpABRQUxQrCwaYVGSxbGElEdT0lxioifG6EHIbNX0x1RBo5\ntUZSrDHUllvZWxzP4fIeuDz9MRk+oE0SpMbtIilW4tGr7yfpvCj0Ua0/4srv8fN/Q+5m7rq/0rP9\nTMKM87ijeCqf9J3DC+9OJSE6/ri/aS2/dWlFAX/+w43c2s3GPvseHp/RwIU9uvPW/P/S8/p/seHN\nv/8q11Wo+91DWX8oa4cW7vOYOXMmPp+Pl156iXvuObtdPvdPfgJVvR+zr7JpX1VVNpWH0ilNKWVL\n5dlnOM4Ek9HItwWbEQKuG3odi6s3kNDfSHuSSajJpGtVHxwVVhxLZHa84afS4aLWUk+doYY6nZMa\ncyneyEPY4gu4+o8pDL2gmAHnbcfrLcXnc6KqLkDgc1n4ZHYXog7nU+stxmHw0rNHR+IqlhJniiLn\nUB3VDYP4fsNFuDx9mD1/N/FRc0mK2YU9MY97R/6VvqM6YW5vRrG0XF/DNy8s4K//2ca2/VMZ2u2f\nGMRabi2Yyme9vmBQ/4wTGo7WJCYykQ2H9rLTNpHM4Qu46spr2bRoE7bwH1DWCFbX1DDgKLebhkYo\ncNI+j759+7Jy5UoMBkNraTotJElixDnXsmTFc/RNv5Q1e5YDsGPH1Wz6r5HN61L45/LHgqyy5VFV\nwcv/epPvZ35FjVGi2i8RFeckxZeMxR+NUXVg8UViddkJrwnHXmXGVqWjOgLKon04bfXUWKupMVZS\no6/EbSrBFn6AmydH41U309CQg99fTVGhg09mdEexenGVGlCi4iktr8JmjMdVm0RhRRSFFe2oqusF\nWImwbiDGsQ2b7QARymEG0JdLzxtN8oAkrB2tAcPyK9xefo+frx77hmfnLmX9zmtQ5ArGZn2G17mN\nGw8/yJxe36KaS3n7q/ebv7J/BQ9Nvo3cfQZuvPpLPFH38OpTc3DWDGJrXhYT1qQzs1Prr/Ks8ful\nVYbqPvzww+Tm5nL11VeTmJjY5Lbq3bv3GRXcXEiSxJiBk1i09k7KyhKw2QLrX338URjWJ5/nPfti\n3l86O8gqg0dxeS1zl69l/fxsvMs3s8dcjkkfSU24F2uYno5FMYQ1hGHy27Codiw+B9aGCMJrrdgq\nDdRZBQWJdRRHllJkzqfOsYs//9lLmH0vLtd+/N5a1i9tz9ZFsdRKMk5dBwy6QwhXJladjQonFJQn\nUVadTHV9WzyedkhSCSZDHuHmImzWcsIsVVjM1RiNHqx+F0aXGZNsxByuw2JW0Mk+VFFDVUk5Zfgp\n8aVSWNmR/NJh6JRSBneei8W2G7eUz+SNj/Ndn7nklO/g283fBbv6m/B43fzfiJHc1KUzlWmreOhl\nmbEZXXl78X9oP/EF1vz34VadE6Px+6ZVjMfw4cNP6I9dsmTJGRXcXEiSxMisO1m59QrqGwYC4HYX\nkr24HcW3fcTIhT1Iap8aZJUn5mzxm/pdLrauWcWX/3uPwk15+AweDloseDwy3Tx2rFIqDk8KkTXx\nxBXZUSWZgsR61knLiYhz4IraRZ+hu+nZvw6XOwdfjZt583qzd1syznof9qRoLK5DNPhlbIY0ZFmh\nrNpEaZWF8ioblTWx1DfE4vEa8PpMqKoJ+GkwA3UY9IcJt+wj0bGXjkklNMi5KD4XIxzDSFrTm2+6\nzaWaYj5d/MFJr7u1679H2y5c1u0aBtzyFN9lT+KH71ayLXcaHdt+z5Xf3Mw9KSmnld/Zcv/8WkJZ\nfyhrh1aa53G2zec4EV6vGYPe2ZSurFxETU5nDsblk9T+xN8A0fgRxWSi59Dh9Bw6/ITHP/5iIfMe\n+QdbpTr0PWxkqsnYRDRSZT3ppT2J3tUTxxw9e6M9lDuqqLQWYYjfSZ8uWxg8wYPRuh6vtxxnrZtv\nlg5j864M5GIXkba9xMRE4E0sodJdiRsXimwgPMqO3hqGSTZhUQV6t4ynTk+FV0U0HCTa76EuIgW3\nsY5z4pLoNPdy9qYXMavHu9x4x7WMP8GXJM8GHnnsLt77YB0D9qfTtb0gx2qElFXsONiPf+fnc2dy\nMoo250MjRDhpy6Ompob33nuPOXMCSzpMmDCBa6+9lrCzZGy6JEn0z3yU3KJMissvB2DTpvHkvpzM\nd7lGXls8PcgKf5ssWraetx54jNLqAjxJVpJ8CVh9cYTrErB744moiSa22EJkBZTEuSiPqqYqvAyv\nPRdDZB5SRAkGk5PUDkYyejlQdD78/jrAjxCBACoCGbfXQE2FzP615VRXJCFyemIp7E3qoWSctnpW\ntl1G9qaF7C1Zf1avXQYwovc5TOrRF7XPPB6ZrpCZlMVXy6Zz6ZgpXPvev7g4OjrYEjV+B7SK2+rR\nRx+ltLSU66+/HiEE77zzDtHR0UybdnasEyVJEj3Sn6GqNpK8whsB+PIzG/pH/sWrxo/5asPZ8f2O\n3wOFFVU8Pu0Z8r9eQ303CQsWzOU2wuQY7P4YIhuisdXbsTSYCas1YGlQMLokVBncRnCZBG6jiseo\nIgmB4pdQ/KD4JYwumbBaGafdz8EUJ0X2Qop0eSzPX8HcOR+R2f70XD7BYlBGP4a2v5BRdzzN2s1/\n5dsPvmPXobuJi8wh6pPRLOnZM9gSNX4HtIrx6NmzJ+vWrUOnC3i4fD4fffv2ZdOmTWdUcHMhSRIZ\nqS+DpLIrbwpu92GyF3Ziz0Nvc8fmy4It7xcJdb/pqeovqSzm7ZmzWP1ZNof8h0ipj0WfUEWlZMRp\nCEPntWJvkNH59ZiFDr0IQ0h6XAp4jeAzSHh1XvyuPOprqqgpN3LB6L48M/3pVtHfnOzbvoH773+O\n2wfls6JcYtFCgcPak7lr7yLi3XUsmXA+PU+xVf97uX/ORkJZO7RSn0fv3r359NNPufzyyxFC8Pnn\nn581I62OUFUbT9v4rQA4nSvxHEjnoOO3NbcjlIl1xPHA3ffA3Wf3fKHWIL1Lb/bs3U9+5Ll0G/Ex\n2w73oWbrSoyGq8h4bgNP9czkoy5dgi1TQ+OknNRB/OCDDzJ79mxSU1Np06YNH374IQ8++GBraDtl\nKqoziY86DMDk267EuL8dhaY9QVZ1ckL5zQU0/b+Wbm3S2ViqwxZTysSJE/CZLAzqOJdtu8eyrKqK\nrbW1p5SPVv/BI5S1NxcnNR4dOnTgs88+Y//+/eTk5PDpp5/SoUOH1tB2ynh9adhseQCM6teF+opE\nVuwMziJ4Ghon452v36CAHfiXD2f78n9xsLaWpIgtNLjS6XzfLP6elxdsiRoaJ+WkxmP69OlUV1dj\nMBiYOnUqo0ePZtWqVa2h7ZTR6/ZwoDSwHLs9qp7yOge5B7YHWdXJCYVh0L+Epv/XYTSaWfb9Knbv\n6U/3frk8/MhkNlZ6yGzzMbv3DWJNTQ0rnM6T5qPVf/AIZe3NxUmNx1tvvUVERAQrV65k06ZNTJs2\njUceeaQ1tJ0ydttOFizfAEB4VCXldeYgK9LQ+GUee+7vrKurRi5JoGdmDT7JR/u2myksG8fwh17k\n7n37UM+wQ1NDoyU5qfHQNy6Z8O6773LzzTczcOBAysrKWlzY6RAbeRC9To/PV4fBUktpvRpsSadE\nqPtNNf2/nptvu5ldOcup/+YS9mx/lrunXEFF7S7io75jTc4gBPB+Sckv5qHVf/AIZe3NxUmNx6hR\noxg6dCjLly9nwoQJVFdXn3UTsaIdgdV0X/3HRPwH2lMqHQqyIg2NkxMRaWNVpR1buIcJE9rj8+rp\n3XUJew9eQ5t7X+TBnBzqG79joqFxtnFSK/D0008zc+ZMNmzYgE6nw+v1njXf8jiC2ewCIMy+Ft+C\nsWw+vC/Iik6NUPebavrPjK+Wfcpu/2b8/5tI9rfXcOUNf6CmehtpiV+waue5DIqI4NlDP/8iFGz9\nZ0oo6w9l7c3FKTUhKisrefbZZ3nqqac4cOAA3bt3b2ldp4WzrhCAmBg3efVxbNyzKMiKNDROjslk\nZv3WFfxQmkKESebSS9NocOnIzFhBQel4xC3P8HJ+PrkNDcGWqqFxHCc1Hi+++CJ33HEHRqMRg8HA\nlClTePHFF5ul8GXLltG5c2c6dOjAyy+/fNzxXbt2MXDgQEwmE88///zP5mOPtCCED0tkBXt8rmbR\n1hqEut9U03/mZG/6ju36FfjfvZFVS2/k+slXUF66g27tZjFv49VM1kdwx759J5wNfDboPxNCWX8o\na28uTmo8Xn/9debNm8d9993HfffdxzfffMPrr7/eLIXfeeedzJgxg4ULF/LKK68c1xEfFRXFyy+/\nzH333feL+Xw59w2uv3Ig6sG2+Pw7m0WbhkZr0K5dB7atX82KKgcWYeXiCdFgsJGS+D0udxuyb36b\nfQ0NzCkvD7ZUDY1jOKnxSE9PZ+/evU3p/fv3k56efsYFOxvHsQ8dOpQ2bdowevRoVq9efcw5MTEx\n9O3bt2nE18+h18v0H1qH75vxbMo9+2eWHyHU/aaa/uZhWc5KdhqX4p95I2tX3sq/X3+SyoN7Gd57\nJt9vncw5L3zInfv2UfeTzvOzRf+vJZT1h7L25uJn17YaPz7wHYz6+nr69etHl8b1drZv394sTba1\na9fS6ahPb2ZmZrJq1SrGjRt32nlNnDgRT+1h5u5Yz6WT/3TMomVHfmQtraXP5nThvgN8n3QOthVR\nVFQ9R9bFo9g3dy3hlnDmLXIz9JEIpuXlcUFjB3qw9f7e00c4W/Scit7s7GzymnH1gp9dVfdIoSda\nfVGSJIYNG3ZGBS9cuJA333yTDz4IfPHttddeIz8/n8cff/y4c6dNm0ZYWBj33nvv8RcgSaiqlyUL\nrHw5/W5e/ObMVlrV0AgGda56Lhv2RybWT8D+9CR69VvJHy98CIslns+XPsP4Qfex+pmb+aJrCE3M\njQAAIABJREFUVwZERARbrkaI0xyr6v6s22r48OEMHz6cYcOGNW0PHz4cRVGYPfvMvwmelZXFrl27\nmtLbt29nwIABvyqv/+vbG+G04/QVnbEuDY1gYDVZkKx1fB2zF8/7N7Fs4bnc/4+7KSjJo3u791my\n6Q88o7MzcdcuGrS5HxpnAac0VHfDhg3cf//9tGnThkceeYTOnTufccE2mw0IjLjKy8tjwYIF9O/f\n/4TnnsxCtu2WCIUJHNiz9xfPO9v4aRM41ND0Ny9zF33DwhXvsm1nFrqSjsRGzqZj784kxWXjcmfw\n3q2v0DMsjKm5gc8NnG36T5dQ1h/K2puLn+3z2L17Nx988AGzZ88mJiaGyy67DCFEs1ba9OnTmTRp\nEl6vlylTphAdHc2MGTMAmDRpEkVFRWRlZTXNan/xxRfZsWPHcZ/AjY8UuKpiWJK3oNm0aWi0NpIk\n8fbcN3jliZmkvvJXDqfcwLTHX+HWi/9Dn97/Y9G6vzDx1nv46On7GGK3Yw+2YI3fNT/b5yHLMhde\neCH//ve/SU1NBSAtLY3c3LPrI0uSJPHGE4OwVqVw5bMfBluOhsYZc26f0cQ5enKtsw26R+9DMc7k\nyQf/w56DtyFEPf/7bihXNRSxuEcPup3iVwc1NI6mRfs8PvvsM8xmM0OHDuWWW25h0aJFZ1xYS5HS\nfSv5u8+uWe8aGr+WJevns3HN58y3+nDN+SPOolvp3XsQHXt9R2nlCJ654QWmp6czYds2yrzeYMvV\n+J3ys8bj4osvZvbs2Wzbto0hQ4bwwgsvUFpayq233sr8+fNbU+NJkfKT2SaHXidiqPtNNf0tx5r8\n9Sws+JDc1aPQV7fhz5Pr8OftYPCA11m4ZSrf/t/99M/J4dJt20J28cSzuf5PRihrby5O2mEeFhbG\nNddcw9y5czl06BC9evXi6afPruGwDeuGUeYMrc5yDY1fwhYewaPP/o3s6K9wvzCVQznv8MrHkxC1\n35PZ7iM+WXcn0muf0sZkYvzWrSFrQDRCl5/t8wgVJEniX+NeZsoXt6Hozq6l4jU0zpQxQy/DZkrh\nOmcaxkce4EDhE3z40jdsOXQnis7J4v/15Om2CvluN19164ZFUYItWSMEaNE+j1Di64Q8zXBo/Cb5\nbtnHbFzyKR84KvDOuIfk6L+R3iudvlkfUlXTmQk3L+dZbxyJRiNjt2yh1OMJtmSN3wm/iSdu7I7i\nYEv4VYS631TT3zrs8eSxbvl/+NwVgzrvMsaM/hBLfTk9ek7lQNEghlw4i2eqozjHZqPfhg1sqa0N\ntuRTIlTq/0SEsvbm4mfneYQSM5e82Wx5CSFw5bio2VBD7aZaPPke/HX+poAK+lg9xmQjlk4WrF2s\nhPcNR7Fo7oLfEu5CN+VflVPxbQUNextw57uRFAnZLKOEK1gyLNiH24meEI2pralFtUiSxNbKQ/TL\nGILNdyvDHZVcdesSHn0kgfNGvsSixbcz+JL5zH8ti2790xi5eTOvdezIpTExLapL4/fNb6LP40wv\nwVvmpfybwIOickElslkmrHcY4b3CMaYaUawKSpiCYlVAAk+JB/dBN/W76qnbVkfd1jrCeoVhH27H\nPtxOxMAIzZiEGEII6nfUU/ZlGWVfltGwp4HI8yOJujAKa1crxiQjQhWoLhVflY/67fVUzK+g/Kty\nTGkmEm9JJO6aOGRjyzXmS501XDTmEi4Ut5B5zsf4+37Ni090xdyuPcsX3ECExcOMO1wk33cel2zb\nxg0JCfytTRtkSWoxTRqhSXM8N3+3xsNf76d4VjElH5RQs74Gx0gHURdEETk2EmOy8fTyqvPjXOmk\nKruKquwq6jbXEZ4Vjv1cOxH9IgjPCkcf9cvLymsEB3e+m8K3Cil+txjVoxJ9UTTRE6KxDbUhG05u\nCIRPUDG/gvyX8qnbWUfbR9sS/8d4JF3LPLA3b9vHHTfdw1j/dXTq+w2GUR/y7LT+6NPiWLfoUvz+\nFK7s/iqPZb/OH7Zvx67T8XanTsQaDC2iRyM00YwHp18Jqkel4NUCDjx1gIj+ESTckIBjjAPF3Hwt\nBX+tn6plVVQtraJmbQ0162vQR+kJzwonvG84lo4WzOlmVh9azYixI5qt3NYm+6il70MJ4RdUzKvg\ny6e+pOPOjsReEUvCjQmE9QlDOoO3dOcKJ7kP5+IudJP+QjpRF0Q1o+of2bc/jz9ffCup9OH/2ugw\n3/I0r/xrPB6Di12bLia/fACX9P0X73z7Ko9XHGZmUREvdejApdHRZ3R9zU2o3j8Q2tqheYzHb6LP\n41QQQlD+ZTn779+POd1Mz0U9sXa1tkhZSphC1AVRTQ8PoQoa9jRQvbaa2g21OJc6adjXwNacrZii\nTZjbmTG2MWJOM2Nq15hONWJMNCKbfhNjGoKOUAXVK6sp+bCE0k9KMbU1YRtqY+B3A1HCmufFwTbY\nRo8lPaj4toJ9d+6jYEYBHV7s0Ox9Iunt2zLnh48ZmNofQ9LNXHTPW9w09T427kpDLZ5FvL2MT1Y/\nze5+z/HkhDTGPjSWO/bt4+XDh5menk6v8PBm1aPx++R30fLwlnnZdcMuXDku2j/fnsgxka2k7pcR\nfoE7340rzxUIuYHQsL8B92E37gI3OpsOY7LxxCHFiDHJqPWv/AzeMi+ViyupXFRJxTcV6Ow6Yq+M\nJfaKWMzp5hYtW3WrHHruEIdfOEzKX1JIvjsZWd+8LwKqUEm3taF/v2u5aPdQdNf9B7XL1zz3zDkk\n2LqzZN0f8PjsjOr+FrPefZLZ9hr+lpfH+KgonkhLI05zZf1u0dxWnLwSnCud7Lh8B7HXxJL2eNop\n+bHPFoQq8JZ6A4bksBv3IfeP20cF2SpjiDOgs+nQ2XTIFhlJLyEbZCSDhGySA8H4Y5CMP+5XzEqg\nhaOApJMCo4oMMrJZRrbIKBYlMMrIojTlfTa5P6Cxdbe3gdqNtdSsq6FycSUN+xuwD7XjGOnAMdqB\nNbNlWpq/RMP+Bvbevhd3gZuOr3XENsjW7GWMHX09xjoY4L6ADG8dYvI03nw5ESnCSk31QJZvm0RC\n9EIuyJjPE2+8yjNKMW8XFXFNXBz3JCeTZm5ZQ6px9qEZD365Eko/L2XPpD10mtmpxfzPZ0Jz+E2F\nEPjKfXiKPficPnxVPtQGFeEVqB4V4QmMEFLdaiB2qYH9btGU9jf4UV0q+AOtIeEL/K3aoKLWNx6v\nV/HX+wN5+wWKRWGzbjN9bX2PNTBhCjqbDsWmoIvQBQyVQfox1gfiJuN2VCwZftxGDXRG/zSoHjVw\nnZU+PIUe3IfduA66qN9ejz5GT1ivMMJ7BwYrhPcL/8W3/dbyWwshKJ1dyr579xF1YRTt/9kenf3M\nPcZH6y+vqmF4mwH07X01Y3dm0TByCfKw6Tz+6iA6x8SxdcsY8orH0ybxQ4a1W8W0V15iRlgFrxcW\nMtrh4P6UlFZ3Z4Vyv0Eoawetz+MXyX8lnwNPHaD7t90J7/Pb9fFKkoQ+Wo8+uvVGc6negGFxLXLR\no2cP1PpA2l/nx1/rx+f04a/246v2BYyXW8Vb6/3RoHlFYL83YNyOMXSN+yRFCrSCfhr0Ejq7rsmd\nFzEgIjDnJtOC3nF2jmiTJInYK2OJHBtJzkM5rMlcQ/qL6cT8IabZWnBR9nC2Ordz+wP/ZHbtu6Tn\nDGPQgs946rp32KBbSn1RBT06rWbLrgG8u3w6i0d/xIUZy1h2xWN8c57MRdu2EanTcWVsLFfExtJO\na41onITfXMtDCEHuQ7mUflZK93ndMadp/wQaZxfOFU72TNqDPlpPu2faEdGv+b9J3rVdPzoktyNB\nOY+Re+Pgwi/4XlrP5lUpOOzJbNndn/2FE3BELOecDgsYZLEz8MlbmR1Xwyelpdh0OgbbbAyx2Rhq\ns5FuNp91rkqNX4/mtuL4Ssh/NZ/C1wvpMb9Hq76Na2icDsInKJpZRO6judiH2Gn7WFssHS3NXs7I\nUVdirfMSaR3DuXtSiEjbyvLMz1nyQzThiTaqc7qyM2c0Xl8kHVM/YHiH5YyyX0e7B85jdbybZU4n\ny5xOvEIw1GZjqN3OEJuNblarNvkwhNGMB8dWgqfIw7qe6+g+vzth3c/+L6yFut9U03/m+Ov8HH7h\nMIdfPIx9uJ3UB1IJ73tqbtbT0b94zRamXXs9USkjyazvRt8iM2VDvuT1TbkkmyIpVbpzYH93DpWN\nwmxeRs/0bPrHbiOjegQXP3ADNT3NrLTU873TybKqKoq9XvqEhXFxdDTD7XY6W63oTtOYnA31/2sJ\nZe2g9XkcQ/EHxez64y5S/pISEoZDQwNAsSq0mdqG5LuSKXyjkG3/tw1jkpH4ifHEXhHbLB3rACP6\ndWfEnvWoQmXI2IvYorqx5I/gYuUSuhV4Wd35fyyM2UhWxpfklg1kV+6FrNzyBIq8h0cnfUGb+F10\niDxA25pYPj1vMpZzYtgRJZhdU8krBQUccLlIM5lIN5vpYDaTajKRbDQ2hXiDAUVrqfym+E20PBry\nGljXex09s3sS1k0zHBqhi/AJKr6roGhmERXzK7APtxN7RSxR46PQhTfvu57X72X86IsJq9IjOQbQ\n1pVCRqGDcstiPtetpa0hgiIxgJqKaA6Xt6W8uid+1UdE2GoSInOJd+STZCkgrtbE6NRrsQ5JojLD\nzP5klZwIH4c9Hg673Rx2uyn3eokzGI4xKEdCgsFAtF5PtF5PpF5/2i0YjdNHc1sRqITdk3Yjm2TS\np6cHW46GRrPhq/JR9mUZJbNLcK5w4jjPQfRF0USOjcQQ1/wT/HYf3MVD59wCGanIxq5kVKTSocDG\nroiv+da1kc5toFwaTH1JFMVlkZRXp1JT3x6fPwmdbh/h1j1E23OIiTpIXHgxcQYfsTld6JOSRXyv\nJJR0G/WpJopj4GCkn0OSl8NuN4UeD+VeL2VeL5U+H+GKQpReT6ROR6Rej12nI0JRiNDpsDXGEYqC\nTac7drvxmEWWtc79k6AZDwKVsLrzanqt6HXWDtX8OULdb6rpbz285V7Kviyj4psKKhdWYu5gZnfG\nbkZdMgprdyvmdmYkpXkfmA1uN+MShhPVIQrJkUGEGk9sfQxRznAWez6mUD5EtzgDTkMPvDWRVDst\nlDkdlNYkU92QjteXiCwfwGQ8RJglH1tYMRHmasLM9YQZ62mozaWzPQJdlZ3k2vakR3Yior0dU1sb\ncpIVX6yJOptEtV3CGQGVVpVqVKp9Ppx+fyD2+ag+st0Ye4Q4qbEJVxTCdTrCFYWwxrRFUTBIEgZZ\nxtgYG46KjbLctL1s6dKQuXdOhNbn0UjfjX1bdClsDY1go4/Sk3BDAgk3JKB6VKpXVrPnrT0UvVtE\n3ZY6vKVeLF0shPUII6x7GNbuVqzdrGf0QmU2Gllc8cMx+3yqj4VrlrHmj4foHJ6OQlvi3ElEKFYa\nTE62qwuJtZfQ2RhPTbxEWVUCLlc4dXXh1NbHUFzejlyXjQZPJG7vPharEwAVRSnGUFSCaV8FRmMx\nRn0tRn0dRn0dJkMNRl09Jl09ZqUeq+TCosrEuaykVdmxe2KID4vFEeXAGG1EH2tCH2dBsulRwwx4\nLTq8Fh0us0KdXlCjqNTq/ZToXOxV/Dh1KlU6lRrFjwfwqCoeIZpi9wnS8ubNmBUFiyxjURTMsty0\nfXRs/km6af8J9gmgV1gYTr+f6BBw3wWt5bFs2TImTZqEz+djypQp3HHHHced8+CDDzJ79mwcDgez\nZs2iU6dOx53THBZUQyPU8Tl91G2to3ZLLXVbGuOtdYHJlI1roBmSDBgTj9pOCiy+2VwLQwIUVpUx\nddD1mCUDdalRuHR2PDhA2DH4TEillRys/4FSCXpGmPFHuagUkai1Yaj1RrwuPT6PAY/XhNtrwu0z\n4/GacfuteHzheH3h+PwR+NUIVNXeWKoTSXIiy1UocjV6XQ16XTV6pQG9zoNB8aFvDDrFh15S0cl+\n9DpfICgejDovBtmDTm7ApLrRywJFBgUJo6ogufVQZ8BYZyPMZcdmisJiDUMxG8CsA6MOYVTAoCBM\nCsKiB4sB1azgNyr4TDJeo4LXKOM2SDToBQ2KoE4vqNOp+NwqaUvcvHWxHxloiNcRpZPZN2BQs/02\nRxPSbqtevXrx4osv0qZNG8aMGcPy5cuJjo5uOr5mzRruuece5syZw3fffcesWbOYO3fucfloxkND\n48QIVfy4Hlq+G0+BB3e+uykcSUt6KWBQ4nTobTK6cNCFgS5cRhcuoYtojBuDYgHFCLIRZINAkgBV\nBSEC4ejtn6aP2haqyv79h5j+j5mUmsxIyRH4IvSoajhCtaL6TNTX1lBVtxt9cTlpkgd7mBWfw0Kd\nIRwXkfgIRxJ6hAqqX8Ln1eP16vB4DXg8erx+GZ9PwetX8Ph0eL16vH49Xp8Br8+Az2fE5zfi95tQ\nhREhzAhhQggTYALMBBw0DUhSA5JUjyy5kGUXihyIJcmPJIEEyLIXWfaiNAVfUxw45kORPY2xF0Xx\nokg+hCecMHs9ki4CZ3EKvWLexlCawW0fTqT7wB7Nfm+ErPFwOp0MHz6cjRs3AjBlyhTGjBnDuHHj\nms55+eWX8fv93HXXXQC0b9+e/fv3H5eXJEkIn4+Fn2aTkJBEx6wU9KbWXwDv1xBKPvcT8bvVr6rg\n8fwY3O4ft12uH4PbDV7vj8HjAZ8v8PD8JSQJZBkhSYEgBKrbjXC5EB4Pwu1GeDws27OHIXFxiCMa\nfqrD40FqPNYUu91IXm8gdruRPF78bj0+vx2XLg6vzoFXicAnR+ATVvzCik+14hMWfI3bqmpEFUb8\nwoAQOmTJhyx7kCVPIJY9yLIXqTEObHuRJAGSQJJUkGCtN4d+pjbQuB9JIMkqkiSQZAEyyJKKJHxI\nqg9J+JFUL7LwIaleJKECfiRUkHzsqS9nQYRCsU3CJfyoZgtusxVVb0TVKUh+gex246/xo7hlhFtF\n0cvoFRuKPgKDSY9iUJGUejw6P149eHQSQifjU3V4PRI+n4TwSZSXHyIsrC1+vw4QIFQQgCqDX0L1\nKQhVRvUr+P0yqiqjqnr8qtIY6/H7daiqDp8woKo6ahoSqa3vg163Cq+vLybTD/S3PkB22frTv0dP\nQsj2eaxdu/YYF1RmZiarVq06xnisWbOG6667rikdExPD/v37ad++/XH5JaSkYwszY0XF6ajFEWMj\nJbIrSfudVB4oAEkhPtaB36RwuKqMBuHHGhZOnaRQXFuBjI94ayR6r0RZfR0oAocjBqE3UFpbjVAU\nImPi8SkyZRVlqIqEIy4GnfBSXVyITpWIiY5FEYLykjIkBPGRcQgkiivKAIiLTkCVoLi8FFVSiIxM\norS8CKRnQBJExseBpFJaVoSKwBEfiypDeXEgbUuIQQiJyqISBBK2uHgEMlXFpYCEPSYOVKguKkAS\nAnt0HLJPUFVejCQgMjYOWZaoLCtClgP5C0mmorQEVQJ7YjwCicqiYoQkYU9IQEgSVUXFANgSA/qr\nCgpBkghLSsBZUAgvPBdIpyQiZImaggIQKvaEOBS/n5qDBchC4IiNRUKiqjCQnyM+BoREVWERAFHx\n8cgSVBQVB64nIR4hSYG0BI74eJCgsrAIEEQmxoMkUVlYEtCblIwEVBQWBPQmJYIQOPMLkYDIhHgk\nEdAvNZZXUViE9MwzyAKi4hOQhKC8oBCA6Ph4VElQXhhIRyUmIAFlRUUgAn8PUNGYjk4I1F9ZUREC\niExIQiBRUVTYeL2JIEmUFxUikLAnJgXqvzAfJAlbQjLIUFVwGCSwJaUAjWnAnpwMSFTlHwqkk5Jx\n1jRATR5CAntSCsJgpSr/MCBjb5eBEDLO/EOggiMxGUmoOA8fBARR8UmAoLLgMEgSjoQkJOGnqiAf\niXocCQ5U2UNFcR40Xk/g/PxAfcYnIiGozD+Mzq8QH52KzqdQUViI4ldItbdD51MoKj2E4pdJC++I\nLCQOOXNAlUgLT+dAtZklohRZlWgX1gFZSORV70NCor0lA1mVyKnbiywkOpi7IgmJ/XW7kYREhikT\nWZXY27ALSUh0MmYimyVEzU7iaiQy9YHzd7t3IAmJLrpuyKrEDu92ZAHdlO7Iqsw231YkIdFN6YaQ\nYKt/C2Cnu9wdWUhs9W9DFtBD6okkJLb6twTsHIn0ohcb2ABAd7kHqizYLDahSoJuSvdA2reRWqkK\nQ4qOA/Zi8qsOISET7YjCq/dQ5CzBJwRuR1tMmXWkVB0iZUc0HWJr8doj+Xp7+TEvOdnZ2QCnnT6y\nnZeXd9zz89cSlJbHwoULefPNN/nggw8AeO2118jPz+fxxx9vOufaa6/luuuuY8yYMQAMGDCA999/\nn3bt2h2TlyRJvJI6jNL+qWTu38UPsWZ+GDgcr8WM22zGYzAiFAW/TsEvK6iKgl9R8Cs6fI2xX1bw\nyzJ+RUGVJHyKgiwERq8HvdeHwedF5/Oh8/uRVRVFVZGECPxN09/JqIqMKkn45UDnvSwEkhDIjc10\nWajIqkBCgABZVZEQyKoABJIAufENJnAMpMY8mgIg+1VkoTbmrULgr/HLMmrjG6vaqO3o/I+OJQSS\nKhpf+NSjdNKY94/7JUBq1COrR65FHKdN5/MjC4FPkfEpOnw6BZ+i4NPpEBKA1BSDQDR2CApJIlAK\nCKmx3o5obMybxrqQG90ekhBIqorcGCShBvQHKgNJCtSJkH7MP7Ad2I8UeFEMvN2DkGSEBKosITX9\nR0hNZQsCL8cB6Uf/y4gm7YgjmjlO+7HHj/zWR10bovF44DoRjb89R11T433T+AcIpOOON5XX+LcC\nCSFLqI33hCorQCAt5EC9yEL8WBeyhCorgXptvN+b5B/5veQj9RlIq7J87HW2MOK0+5F/TtsvZyRO\neNaP90PgN5KQVQlZBb3Xj86nIqsgCZnA/SMjqxJCCsSKX0YSCkd+9cAvKKH4BeHV1SwdPpxhy3eT\ntcHC/EFFyDt+4JNln53uBZ+UkG15ZGVlcf/99zelt2/fztixY485p3///uzYsaPJeJSWlh5nOI5w\n+zt/57x16/j7bTcAMPqLz4jYn4vR7QM/eIUBv09G9UnIfgXJLyP7FLzocOl0qFYzjlgDiYk22rSP\nIjE2lrSYOFITY7GGh2MwGJFlBVnWRnRpaGi0DIf25PLZG4uZMO1yLr3+eS5LL+WvrzW/4Wg2RJDo\n2bOnWLp0qcjNzRUZGRmitLT0mOOrV68WgwcPFmVlZWLWrFli3LhxJ8wHEFMunSBGd+klpl73J/Hg\nFdcIv8/XGpdwxixZsiTYEs4ITX9w0fQHj1DWLkTguXmmBG2ex/Tp05k0aRJer5cpU6YQHR3NjBkz\nAJg0aRL9+vXjnHPOoW/fvkRGRvLee+/9bF4vfvJFYEM0+is0NDQ0NFqU38QM8xC/BA0NDY1WpTme\nm5oTX0NDQ0PjtNGMRxA5ehhdKKLpDy6a/uARytqbC814aGhoaGicNlqfh4aGhsbvDK3PQ0NDQ0Mj\nKGjGI4iEut9U0x9cNP3BI5S1Nxea8dDQ0NDQOG20Pg8NDQ2N3xlan4eGhoaGRlDQjEcQCXW/qaY/\nuGj6g0coa28uNOOhoaGhoXHaaH0eGhoaGr8ztD4PDQ0NDY2goBmPIBLqflNNf3DR9AePUNbeXGjG\nQ0NDQ0PjtNH6PDQ0NDR+Z2h9HhoaGhoaQUEzHkEk1P2mmv7goukPHqGsvbnQjIeGhoaGxmmj9Xlo\naGho/M7Q+jw0NDQ0NIKCZjyCSKj7TTX9wUXTHzxCWXtzoRmPILJp06ZgSzgjNP3BRdMfPEJZe3MR\nFONRU1PDhAkTSE1N5eKLL6a2tvaE591www3ExcXRrVu3VlbYOlRVVQVbwhmh6Q8umv7gEcram4ug\nGI9XX32V1NRU9u7dS3JyMq+99toJz7v++uuZN29eK6vT0NDQ0DgZQTEea9as4cYbb8RoNHLDDTew\nevXqE543ZMgQHA5HK6trPfLy8oIt4YzQ9AcXTX/wCGXtzUVQhuq2adOG3bt3YzKZqK+vp3Pnzhw4\ncOCE5+bl5TF+/Hi2bt16wuOSJLWkVA0NDY3fJGf66Nc1k47jGDVqFEVFRcftf/LJJ5t1XoY2x0ND\nQ0Oj9Wkx47FgwYKfPfbOO++wc+dOevXqxc6dO8nKymopGRoaGhoaLUBQ+jz69+/PW2+9RUNDA2+9\n9RYDBgwIhgwNDQ0NjV9JUIzHrbfeysGDB8nIyCA/P59bbrkFgIKCAsaNG9d03lVXXcWgQYPYs2cP\nKSkpvP3228GQq6GhoaHxE4JiPMLDw/nyyy85ePAgX3zxBWFhYQAkJiby9ddfN533zDPPkJGRQXp6\nOu3bt8doNAK/PE/kpZdeokOHDmRmZrJ8+fLWvbCf4HK56N+/Pz179mTAgAG88MILQOjoP4Lf76dX\nr16MHz8eCC39bdu2pXv37vTq1Yt+/foBoaW/rq6OP/3pT3Ts2JHMzExWr14dEvp3795Nr169moLN\nZuOll16itrb2rNd+hNdff51BgwbRp08f7rrrLiC07p3333+fYcOG0aVLF9544w2gmfWLs5jCwkKx\nceNGIYQQpaWlIi0tTVRXV4t//vOfYvLkycLlconbb79dPPvss0KI/2/v/mOqqv84jj8vlUTqiqlA\nWwomhcSP+0MR0UE31owcXJoKhZtks7FptVK0XDMbtVUuLMvMNrNyWP7YWiQGWJbXX9AF9e46ofyR\nINoPBMzuvXJBuff9/YN5vhDqV5Jveurz+It77vnc+zpnl/u+59f7iDQ3N0tMTIycOHFC7Ha7mM3m\n6xlfRETOnTsnIiIdHR0SFxcnR44c0VV+EZHly5fLzJkzJSsrS0REV/mjoqKkra2t1zQ95S8sLJQl\nS5aIz+eTCxcuyNmzZ3WVX0TE7/dLRESENDU16SZ7W1ubREVFidfrFb/fLw8//LBUVlbqJv/Zs2fl\n3nvvlTNnzojH45GkpKQB/+zc0O1JIiIiMJlMAAwfPpy4uDhqa2sve52Iw+EgIyODUaOJ9sN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} ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "ts=ts.ix[400.0:700.0]\n", "absplot(ts)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 9, "text": [ "" ] }, { "output_type": "display_data", "png": 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DtdVu3N5vy/4bnxJ2ry/37w/l5M211O/WABvL7awcMxMAdSUljru6ci0nh02vML+X+NnV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\npRWeLy8tF5ytNwkGuqeEPm1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F3eQ9ZUO+dnAQr7AX1Rj/WlR69uzJiRMngEf3ONm8eTMD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} ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "from pcakernel import PCA\n", "help(PCA)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Help on class PCA in module pcakernel:\n", "\n", "class PCA(__builtin__.object)\n", " | PCA object to perform Principal Component Analysis.\n", " | \n", " | Methods defined here:\n", " | \n", " | __init__(self, k=None, kernel=False, extern=False, index=None)\n", " | Constructor.\n", " | \n", " | arguments:\n", " | * k: number of principal components to compute. 'None'\n", " | (default) means that all components are computed.\n", " | * kernel: perform PCA on kernel matrices (default is False)\n", " | * extern: use extern product to perform PCA (default is \n", " | False). Use this option when the number of samples\n", " | is much smaller than the number of features.\n", " | \n", " | Notes:\n", " | * All data will be mean-cenetered. Np subroutines (eg np.cov())\n", " | do this in all cases except for the extern_pca() method, which\n", " | does this automatically.\n", " | \n", " | fit(self, X)\n", " | Performs PCA on the data array X.\n", " | arguments:\n", " | * X: 2D numpy array. In case the array represents a kernel\n", " | matrix, X should be symmetric. Otherwise each row\n", " | represents a sample and each column represents a\n", " | feature.\n", " | \n", " | transform(self, X, whiten=False)\n", " | Project data on the principal components. If the whitening\n", " | option is used, components will be normalized to that they\n", " | have the same contribution.\n", " | \n", " | arguments:\n", " | * X: 2D numpy array of data to project.\n", " | * whiten: (default is False) all components are normalized\n", " | so that they have the same contribution.\n", " | \n", " | returns:\n", " | * prX : projection of X on the principal components.\n", " | \n", " | Notes: In the case of Kernel PCA, X[i] represents the value\n", " | of the kernel between sample i and the j-th sample used\n", " | at train time. Thus, if fit was called with a NxN kernel\n", " | matrix, X should be a MxN matrix.\n", " | \n", " | The projection in the kernel case is made to be equivalent\n", " | to the projection in the linear case.\n", " | \n", " | X.T = U * S * v.T\n", " | C = 1/(N-1) * X.T * X\n", " | X.T * X = U*S^2*U.T\n", " | K = X * X.T = v*S^2*v.T\n", " | \n", " | U = X.T * v * S^(-1)\n", " | \n", " | The projection with PCA is :\n", " | X' = X * U\n", " | X' = X * X.T * v * S^(-1)\n", " | X' = K * v * S^(-1)\n", " | \n", " | For whiten PCA :\n", " | X' = X * U * S^(-1) * sqrt(N-1)\n", " | X' = X * X.T * v * S^(-1) * S^(-1) * sqrt(N-1)\n", " | X' = K * S^(-2) * sqrt(N-1)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " | \n", " | __dict__\n", " | dictionary for instance variables (if defined)\n", " | \n", " | __weakref__\n", " | list of weak references to the object (if defined)\n", "\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "pc=PCA(ts)\n" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "Cannot call bool() on DataFrame.", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mPCA\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m/home/hugadams/Dropbox/skspec/Master/skspec/core/pcakernel.pyc\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m 206\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 207\u001b[0m \u001b[0mpca_func\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpca\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meigen_values_\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meigen_vectors_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpca_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_k\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 209\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 210\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_kernel\u001b[0m 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\u001b[1;34m'LM'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 62\u001b[0m \u001b[1;31m# return w[::-1],u[:,::-1]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 63\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mu\u001b[0m \u001b[1;31m#(No need to reverse w,u because eigs does it using 'LM')\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/local/EPD/lib/python2.7/site-packages/scipy/sparse/linalg/eigen/arpack/arpack.pyc\u001b[0m in \u001b[0;36meigs\u001b[1;34m(A, k, M, sigma, which, v0, ncv, maxiter, tol, return_eigenvectors, Minv, OPinv, OPpart)\u001b[0m\n\u001b[0;32m 1203\u001b[0m \u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mA\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1204\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1205\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mk\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1206\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"k must be between 1 and rank(A)-1\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1207\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/local/EPD/lib/python2.7/site-packages/skspec/pandas_utils/metadframe.pyc\u001b[0m in \u001b[0;36m__nonzero__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 212\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 213\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 214\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__nonzero__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 215\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 216\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__contains__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/local/EPD/lib/python2.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__nonzero__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 585\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 586\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 587\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Cannot call bool() on DataFrame.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 588\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 589\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_need_info_repr_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mValueError\u001b[0m: Cannot call bool() on DataFrame." ] } ], "prompt_number": 14 } ], "metadata": {} } ] }