Kernel Matrix Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import trig_poly\n",
    "import dual_certificates\n",
    "import test_data\n",
    "import numpy as np\n",
    "import plt_util\n",
    "import phase_transition\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def kernel_matrix(kernel, support):\n",
    "    shifted_supports = np.outer(np.ones(N), support) - np.outer(support, np.ones(N))\n",
    "    return kernel.eval(shifted_supports).real"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fc = 30\n",
    "min_separation = 0.65 / (2.0 * fc + 1)\n",
    "N = int(0.5/min_separation)\n",
    "\n",
    "kernel = trig_poly.TrigPoly.multi_dirichlet(fc, [2.0])\n",
    "# kernel = trig_poly.TrigPoly.dirichlet(fc)\n",
    "\n",
    "support = test_data.uniform_supports(N, min_separation=min_separation)\n",
    "\n",
    "K0 = kernel_matrix(kernel, support)\n",
    "K1 = kernel_matrix(kernel.derivative(), support)\n",
    "K2 = kernel_matrix(kernel.derivative().derivative(), support)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.+0.j])"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kernel(0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Desired min separation: 0.0106557377049\n",
      "Actual min separation:  0.010950550572\n"
     ]
    }
   ],
   "source": [
    "print 'Desired min separation:', min_separation\n",
    "print 'Actual min separation: ', np.min(support[1:] - support[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1156eafd0>"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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ReyNi7/z8MytcHAAAQLM1vysqM3dK2ilJRx25eQQvIQcAoA58pMLKOzbfjIgTJan/3/3l\nVgkAAGBlVjqx2SXp0v73l0q6uczqAACA1ej1s2zW+mtUObd73yDpTkk/HBEzEXGZpPdL2h4RD0va\n3n8MAAAwVM5dUZc8z6/esJIFOkmeMWek2U6YzSYzZbJ1sDlRWPJSgP2UV++So4mn5xtrJg/MWmO5\nicK7b/uUVff6yy9vrDli5nvWWJ0jp606J+2zdCprb7pdcDCvzE01tV5T7lgFU1nd5ZZORXajv510\nan+7lcs6LbnflzWeM5z5vlt0meYxGeYVnNF1L/U0jt3CadglpEa7mzIIJA8DAIBqDPyzogAAwNoZ\n5VTgQaBjAwAAqkHHBgCAWiQ5NnRsAABANZjYAACAanAqCgCASvCRCnRsAABARejYAABQkXHv2Izk\nxMZJc3TTI+2MSTdRc95IP3WTT81lZttIvQ0vGddN5HUShSXpT3fubKw5b8urrbEmt55i1fU2GgnF\nLXPbuq9/N8g4jSPOTO3NCXPljHUrne6bbhCzk0Dr7gP3dWXWOfuq5FiSmY5rpvbaf7rM8ZyEXzdR\neCjLdN/s3Q3nvE7N1zIGayQnNgAAYPn4SAWusQEAABWhYwMAQEWSjg0AAEAd6NgAAFARPgQTAACg\nEnRsAACoRPIhmHRsAABAPQbesbHClowaN9zODc5yw51yqtxcsDXbteqcUD0r+EtSb9rb5UfMfM+q\nc8L39szcY4111r/9catu/bfmG2vahzrWWMU5gV1umNucuVOdEDkz5Kz068oaz32NusF17nO1gkDN\nwEI3PNApM9ZrWczxnPcQO7DQDYR03uvbZf/97e5T53gb1b4Id0UBAACsgYjYEREPRcS+iLjqeWp+\nJiIeiIj7I+J/rnaZXGMDAEA1Rid5OCLakq6WtF3SjKS7I2JXZj6wqGarpF+VdHZmPhURJ6x2uXRs\nAADAWjhD0r7MfCQz5yTdKOmiw2p+SdLVmfmUJGXm/tUulIkNAABYC5slPbro8Uz/Z4v9kKQfiogv\nRsSXI2LHahfKqSgAACoywIuHN0XE3kWPd2bmzkWPl1qRw6/KnpC0VdI5krZI+rOIeGVmHljpSjGx\nAQAAK/FEZm57gd/PSDpp0eMtkh5boubLmTkv6f9GxENamOjcvdKV4lQUAACVSC0E9A3iy3C3pK0R\ncUpETEm6WNKuw2r+WNJPSFJEbNLCqalHVrMNmNgAAIDiMrMj6QpJeyQ9KOmmzLw/It4XERf2y/ZI\n+nZEPCDpNkn/PjO/vZrlcioKAIBapJ95OQiZuVvS7sN+9t5F36ekd/e/ihj4xMZJrgxjr9jpom4C\nZpoJo06ZG97qprwaz8E9jltzXtpx58hpq25y6ymNNW6i8Jc+dI1Vd95P/WJjTZipzu6+creHw02J\ntt+bnARdM4i5+OvKSZZ102zdLeKOZ6REp9vTdhKn3aHctGMzkbdoerKbmu0e40NYpv/CMoYajbgY\nHIaODQAAFemN7Ic9DAbX2AAAgGrQsQEAoBIpPgSTjg0AAKgGHRsAAKoxOh+COSx0bAAAQDXo2AAA\nUJFRyrEZBjo2AACgGnRsAACoyLjfFTX4iU2hHlnp/ZYFk0P9hbrxnOX6immk1Ep+Am1vY3Mi7/pv\nzVtjOYnCkrTn07/XWHPBj223xtKxR1tlefQ6q85JzU4zPKtl7veSXWf7deWma7/IW+J2gm7Jt4/S\n70Ulx3PHGsJ7G/AcTkUBAIBqcCoKAIBKZHIqio4NAACoBh0bAAAqQkAfAABAJejYAABQkXG/2YyO\nDQAAqAYdGwAAKjLud0UNfGJjBV45NaWDokoeBz1zkWYInlvnyLbXpIuu+SRazeO1D3W8Zc52rTon\nfG/3126xxtpx0S9YdS13ezi65n63l9m8D9yxcrrtLdJdNed1Wrpv7r5enNe8HTTnlVlDue9FboCj\nOZ7z3pzmE81WuSBQdyz/eZbbp26AIwaLjg0AAJVIxdh3bLjGBgAAVIOODQAAFRn3M2R0bAAAQDXo\n2AAAUAs+K4qODQAAqAcdGwAAajLmF9nQsQEAANVgYgMAAKpR/amooSRDDiGtdFiKXqPmTrOPPbqx\nxE0U/vzNn7Dqtl/yNqvOYR+TboJuy6grmF4tyT/G28YB4gVOD4cbYuy+5J3XS+lUdfx97vZ1dr6z\nD4awm7h4GAAAoBLVd2wAABgn497Mo2MDAACqQccGAIBKpLjGho4NAACoBh0bAABqkSp8u+qLDx0b\nAABQDTo2AABUhLuiAAAAKjHwjo116m+ieb4VnZ63wJZ5rtFMo7SW6waHGs9TKhtcGV1vu/Wm296A\n5m5wdI6ctury6HWNNS3zebqJwrfc8LtW3Xlvbk487m6YtMbqbvRenu3Z5uea67yxJp+es+p6k96x\nG3PNscL268BJMZa8tGNJ0Wl+ZbnrFmays1Xn/nO7YICu5G1f93m6z8Fapvlatt8ozb8J6f7taDKM\ny13o2AAAANSBa2wAAKhGkGMz7BUAAAAohY4NAAA14RobAACAOjCxAQAA1eBUFAAAtUg+BJOODQAA\nqAYdGwAAajLmFw+P5sRmlD/owunwmSnGVXD2VeHtEUM4PpxEYUna88efaKw557JfssZyk3Zb80aC\nrtubtVNvC+7TcXq9AFhzozmxAQAAKzTe/1jgGhsAAFANOjYAANRkhK/mGAQ6NgAAoBp0bAAAqAkd\nGwAAgDrQsQEAoBYpieRhAACAOgy8YxPGub9sObPNsicR3Qlu9KzRzIV6ZUMJQ7OepzmeGfrmHBuS\nlE5GQ7fsMrsbJq06J3zv9mt/xxrr7Hf+slU39XTzzupOtK2x7IC+Xrntm+ZYmij87zDruZqvF3e7\nvdivfSi8/tbr78W+zYZglDNuB4GODQAAqEbjxCYiToqI2yLiwYi4PyLe2f/5sRFxS0Q83P/vS9Z+\ndQEAwAvKAX2NKKdj05F0ZWaeKulMSe+IiNMkXSXp1szcKunW/mMAAIChaZzYZObjmfnn/e+flvSg\npM2SLpJ0fb/seklvXquVBAAAcCzr4uGIOFnSqyTdJemlmfm4tDD5iYgTnuf/uVzS5ZK0bvro1awr\nAABowu3enojYKOkPJb0rM7/r/n+ZuTMzt2XmtsnJDStZRwAAAIvVsYmISS1Man4/Mz/d//E3I+LE\nfrfmREn712olAQCAx42xqJVzV1RIulbSg5n5wUW/2iXp0v73l0q6ufzqAQAA+JyOzdmSfkHS1yPi\n3v7P/oOk90u6KSIuk/S3kn56bVYRAABYRvxW7EFonNhk5v/W88dvvmFZS0t5kYgFA0HdhFQ/1bTQ\n+ksKc92yYIyin7BsrttE84AxZ45lVUkt4xiKrhmdbD7P7kbvOvtsN28PN1H4i7/9UavuTWe8qbFm\n7hVLXtv//Vrewdaa61h1vanm7dbqePuqM+2lJ4c5nnWMmz199/XiyLb5gneXaSW5l30O9vtzyYhc\nNyTa3B7WIp33mTGfZAwDH4IJAEA1gruihr0CAAAApdCxAQCgJmN++ouODQAAqAYdGwAAakLHBgAA\noA50bAAAqAkdGwAAgDrQsQEAoBapsc+xGezEJiSFscGdGjexsoYETGd7mPy0Y3OZTsirO5aR2iu5\nXVazGdnyRmvPemm2rfnm8aae9sZyEoUl6bNf+WxjjZt2fNSDB626XD9p1TkJxW7SbrivUfc46jbX\n2a8D9w+J8xRKJgAvZzznubr7wI4RH8Iy7b8dhf5WjfccYyg4FQUAAKrBxAYAgIpEDubLWpeIHRHx\nUETsi4irXqDuLRGREbFttc+fiQ0AACguItqSrpZ0vqTTJF0SEactUXekpH8j6a4Sy2ViAwBATXJA\nX83OkLQvMx/JzDlJN0q6aIm6/yzptyQdWu5TXQoTGwAAsBY2S3p00eOZ/s/+TkS8StJJmfmZUgvl\ndm8AALASmyJi76LHOzNz56LHS90T9ne9nohoSfqQpLeWXCkmNgAAYCWeyMwXuth3RtJJix5vkfTY\nosdHSnqlpNtj4db5H5C0KyIuzMzFE6ZlYWIDAEBF3DuWBuBuSVsj4hRJ35B0saR//twvM/M7kjY9\n9zgibpf071YzqZEGPbFJeeFIzk6xg/fMOjcEzzli3Nvg3OCsggFPbuBY0SA/c6xoznKzRdcLwXPX\nLdd5L5U0rlrrTrStseZecYJV54TvffG3P2qNteNNP2fVxcF5q6575HRjjRPiJ/nHbmvO2/fWMeK+\nLRQM1bMDC81jvDfhjjf4kNKiYYRDCDwt9vesUpnZiYgrJO2R1JZ0XWbeHxHvk7Q3M3etxXLp2AAA\nUJMR+kiFzNwtafdhP3vv89SeU2KZ3BUFAACqQccGAIBa+Bkz1aJjAwAAqkHHBgCAmtCxAQAAqAMd\nGwAAKjJCOTZDQccGAABUg44NAAA1GfOOzWAnNuGlalopnmZ6ZLbNpN2Ol+JpjecmWzqplSrbViz6\nPM3x0k0+dVOAjVXLaS/d1zX59JxXaCWRmju05W23ox482FjjJgp//rO/b9Wd9e63W3VH/vWzjTWz\nm9ZbY00/OWvVdc1939sw2VgTHW9f9abM14ux72PeTBQ2n2eYSczu69Rapvtadt5nCr9Puu+BTnqy\nlRI9Oll5Y4NTUQAAoBqcigIAoCZjfiqKjg0AAKgGHRsAACoRye3edGwAAEA16NgAAFAT59bRitGx\nAQAA1aBjAwBATbjGBgAAoA6D79gYKZLO6cHommmUVpWklpkcaqUie4tMc5klT5c6yacLhW6ys1Nk\nppCa28Oajpvhou669SbNfwM4281MZW3Nday6XG8k6B6ct8ZyE4W/9MFrrLrztry6sWbyzFdaY3XX\neW9X7r5qzXUba9Icy00Ldt4/7GRck/uad6qiW+55SlKGkURvvtfbSr7vuu+nA8ZdUQAAAJXgGhsA\nAGpCxwYAAKAOdGwAAKgFycN0bAAAQD3o2AAAUBM6NgAAAHVgYgMAAKrBqSgAAGoy5qeiBj+xMZJZ\nrXRLNz3STRQ20z6z7cQiu9HDZnpyyYPUDR42k0OdFN2cMNNb3bRSKyLV3AfO/pQURkqty92fvSnv\n5ekkFHePnLbGOvKvn7XqnERhSdozc09jzWuv/HFrrKP/8mmrThuak5glqTfVHJvdMhOF3YRi59h1\n2+juMmU+B2uZbTeBu1zCuZVuvoxFusnfVvCw+d6GwaJjAwBARbjdGwAAoBJMbAAAQDWY2AAAgGpw\njQ0AADXhGhsAAIA60LEBAKAWfAgmHRsAAFCPwXdsjIQqJ1QvuuVCliTZgX9WiJybz+eGDNpPolmY\noYDuunkpVoWXaQ1m/pPFzN2zg7icwDEzIKxlh0Y2r5sT4idJs5vWW3WTZ77SqnPC9+78wDXWWNsv\neZtV11nnJbpNPNO8TZwQP8kP8nPCR92wUJcfPtp8HFnhqVpG2GY4yyzcfij5/uy+zwzaiK7WoNCx\nAQAA1eAaGwAAakLHBgAAoA50bAAAqESIu6Lo2AAAgGowsQEAANXgVBQAADXhVBQAAEAd6NgAAFAL\nPlJhwBObNFMkncBHMwnWTcr0k3aNVGTzoCqdMOrItpmw7K6bs93MFNJR7p+6281iHrudaS/11kmT\ndo/v6SdnrbruOu+t4+i/fLqxxk0UvuWG37Xqzv8HZ1p1eeorGmsmDhzyxlo/adU5ScadjVPWWO1Z\nLza7u8Fbt/ZBI4nZPHZz0nx/nmt+n7Ffe8Z788KAZmq9U+a8t43u21q16NgAAFCTMZ9McY0NAACo\nBh0bAABqQscGAACgDnRsAACoyLjfFUXHBgAAVIOODQAANaFjAwAAUAc6NgAA1CI19h2bkZzYOCmp\n4abZujvYTK20koztZbp1BVNvXWZSbdF95aY/O+y0Y1PJ5GGTnf5srFvLSHiVpK6Zdtwzk2VlpN52\n1nnLdBOFP/fIl6268zYbKcuv/hFrrDg4b9W155vTgjO85OHWbHNSsCRly0sedl4zrTkv7Tgny6Vm\ny1ukff4hJ8z3NuMlY7+3YaBGcmIDAABWhruiAAAAKsHEBgAAVINTUQAA1IRTUQAAAHWgYwMAQEW4\neBgAAKASdGwAAKgJHRsAAIA6DLZjE1I6Ca5O0q6TACzZabZFEyTNkFontXdhvHKpt1ZysqRsuzGe\nzdvN2ufdy3f3AAAWeElEQVRS2YTlwkHB0Sl4fDhpq/KPyewa6c/mfu8ZScGSn0Dbm2pOoJ14xkzQ\nPfUVVp2VKCxpzze+2lhz1rtfY4214RveMp204InvzVljddd7+6ptpiJ3jX0fZoJ1GAnLknd8tNwE\n7q77GvXe29J4D/H+nlmLK4ePVGjewxGxLiK+EhFfi4j7I+I3+j8/JSLuioiHI+KTEWYOOAAAwBpx\npq6zks7NzB+TdLqkHRFxpqTflPShzNwq6SlJl63dagIAgCYxwK9R1TixyQXf6z+c7H+lpHMlfar/\n8+slvXlN1hAAAMBknWyMiHZE3Ctpv6RbJP2VpAOZ+dwJ4xlJm5/n/708IvZGxN65+WdKrDMAAHg+\nOaCvEWVNbDKzm5mnS9oi6QxJpy5V9jz/787M3JaZ26YmN6x8TQEAABos666ozDwQEbdLOlPSMREx\n0e/abJH02BqsHwAAWAaShxtExPERcUz/+/WS3ijpQUm3SXpLv+xSSTev1UoCAAA4nI7NiZKuj4i2\nFiZCN2XmZyLiAUk3RsR/kfRVSdeu4XoCAADHmHdsGic2mXmfpFct8fNHtHC9jS+90LGioUduyNmE\nF9pUMqjNbRdmwaPUDQW0w+GM8exQQDMT0NpuZgieu2nd48PjBhaW2wf2Is3jOye97dGab973Tkib\nJE0cOGTV6dU/YpU54Xtf+uA11lgX/KPXWXU64bjGks7xR1lD+cF7XsRYa7Y5VC/MELye+Xqxw/cM\naQZ85kTBwFPj+B73ScYw8JEKAACgGnwIJgAANRnzLhEdGwAAUA06NgAA1CK53ZuODQAAWBMRsSMi\nHoqIfRFx1RK/f3dEPBAR90XErRHx8tUuk4kNAAA1GZGPVOjHxFwt6XxJp0m6JCJOO6zsq5K2ZeY/\n1sLnT/7Wip7zIkxsAADAWjhD0r7MfCQz5yTdKOmixQWZeVtmPtt/+GUtfJLBqnCNDQAAFRngNTab\nImLvosc7M3PnosebJT266PGMpBcKkLpM0udWu1JMbAAAwEo8kZnbXuD3S6UhLjntioifl7RN0utX\nu1KDndiEn3zbyJ2RFk7a9QbzytLdFGaipjWUmwLcNs9SGgm/9j53k0OdMnN3uv+yKXp8mKnI9jKN\nDeKO1ZsyXy9O4qq8hGInnViScv2kVRdmIu+Gb8w21riJwrv/8g6r7oLX/1RjzcS3vmuN1XvJRquu\n9ayZUHxUc0Jx62DHW6aZKNydbk6ddsdy39vUMd9njPetkqnfRY3OXVEzkk5a9HjJD8yOiDdK+jVJ\nr8/M5hdmA66xAQAAa+FuSVsj4pSImJJ0saRdiwsi4lWSPirpwszcX2KhnIoCAKAio5Jjk5mdiLhC\n0h5JbUnXZeb9EfE+SXszc5ek/yppo6Q/iIWu/d9m5oWrWS4TGwAAsCYyc7ek3Yf97L2Lvn9j6WUy\nsQEAoBZmxkzNuMYGAABUg44NAAA1oWMDAABQByY2AACgGpyKAgCgEqHRud17WAY/sXHSZc1kVoeb\n7munvDrjuUnBBROFh8V5AdkJy0NQ/PiwFlpuqNLjReFUZGc4NzG2N9WcUitJ7fmuVdeaNVJ0TzjO\nGstJFJak3X/66caa87a82hqrd9aPWnUTZhJzb6K5gR9GjeQnU6ttpPt2zTRs94VgHrtWwHmpJH0U\nRccGAICajHnHhmtsAABANejYAABQEfeUcq3o2AAAgGrQsQEAoBZ8pAIdGwAAUA86NgAAVGTcc2zo\n2AAAgGqMZMcmus3TzTSCnRYGM+vMq8it5RZeZtFwOJO9TKfO3FduUJuzfe1QwML7qqRsm//uMPaB\nO5YbrBYdr85ZqjtWZ+OUVZfh1U18b655mccf5Y31re9adU743p6Ze6yxXnvlj1t1R5nb19E5wvuT\n0Z71lpnOa3naC2aMjvdabrnbw/k71PLWbeDo2AAAANRhJDs2AABgZbjGBgAAoBJ0bAAAqAkdGwAA\ngDowsQEAANXgVBQAALVILh6mYwMAAKpBxwYAgJqMecdmJCc2OdHcSHLTStMNhnSDjJ2kXTfMtuUV\n2inLBjfd1123cmu2jKRdazDzle3WlXyiroKJ0+5+75kpr66cLLdP27Ndq64127Hquusnm5d5cN4a\nq/eSjV7dWT/aWOMmCt/5gWusuvNedrpV1z2/ebnTT81aY4W5rzpHTzfWtA95+9NJrJeknvH3RTLf\ndznnMZJGcmIDAACWL8Q1Nsw3AQBANejYAABQkyF8aO8ooWMDAACqQccGAICKcI0NAABAJejYAABQ\ni9TY59jQsQEAANWgYwMAQEXCy+Os1khObJxUYTuNN9wYYK9356Qiu6wUY2kot+7Z6+bUmfvKTcd1\n9mmWTgoeRmvXTH929oGbthpz3j4I95icbx7PTRHvbmhOCpakbHl1Tqpwd8OUNVbrWS+heMJY5lHm\n9nAThfc8dq9Vd86/aE4ePrRpnTXW5DNeWrCjc4S3P1vGsSb5ydRhhCf3pkfyT+jYY68AAFATrrEB\nAACoAxMbAABQDU5FAQBQEQL6AAAAKkHHBgCAWqT4EMxhrwAAAEApdGwAAKgI19gAAABUYuAdGyfR\nNqea51tuQqo7dXWTjN2U1JLLVGvwacd2cq/xHNx/PaSbtGso/S8We18VZKc/G9stumWTtUtu3mx7\ny2wfNNNsze3mJBm3Zo34WUndo7yEYjcB2lrm+c1JwZKXKCxJt3/sdxprzt96tjVWvOylVl1n05GN\nNU5CtOQfu3aCtZFw7qYdDxwdGwAAgDpwjQ0AAJUIcY0NHRsAAFANOjYAANQikxybYa8AAABAKXRs\nAACoCNfYAAAAVIKODQAANRnzjs3AJzY5YQSdmWFi1vLM0Dc7DM0Zzgh2Wladw71YzH2eZtiVFbjo\nBu+5z8HYbln4lW0fH47SbzpDuFAwul4wmRO+547lhtu15rxQPSfk0w02bJnhgWE8h84R3tvy9FOz\nVt2hTeusOid873MPf9Ea64IfPdeqm9t6XGPNtHl8uMeRu69yst1Y05s0jsmS7/OwcCoKAABUg1NR\nAABUhIuHAQAAKkHHBgCAWqT8aykrRccGAABUg44NAAA1Ge+GDR0bAABQDzo2AABUhLuiAAAAKjGS\nHRsr5dVNs22bdWbCaDrjuUmTZmLsMGbfdtJuwX1lP09ju9lpx64hpPtaKdeSdz7dDcM297uf1N1c\n546VTsqrvMRYSYr55oRiO+24Y6bjzjfXtWfNsWa9hOXJZ8xU5Je9tLHGTRTe/fUvWHVnv+uHGmta\nnWlrrAnzebYOzVt1FvOYHLhhvF+NkBHdKwAAAMs3kh0bAACwMlxjAwAAUAk6NgAA1CJFjs2wVwAA\nAKAUOjYAAFQiJAV3RQEAANSBiQ0AAKgGp6IAAKiJl/FYrYFPbKJjpMYaaY4tM3VT6UWuWonCksJK\nKC6YYqyyKbp2mK1baDwHO1nWfZ7G+WM7Gdfk7itHyYRlSV6ys7sP3GMyzGavkcLtjhVzZiKvud16\nU80JxW6icHfaSzt2Xi9pJpd3jvYSeV2dTUc21sxtPc4ay0kUlqQvfviaxpodLz/DGqv18s1W3fzm\nY6w65/3ISZIe9xTgYbBPRUVEOyK+GhGf6T8+JSLuioiHI+KTETG1dqsJAAAckTmQr1G1nGts3inp\nwUWPf1PShzJzq6SnJF1WcsUAAACWy5rYRMQWSW+S9LH+45B0rqRP9Uuul/TmtVhBAABgygF+jSi3\nY/NhSb+i/39J0nGSDmTmcx+nOiNpyROcEXF5ROyNiL3z88+samUBAABeSOPEJiJ+UtL+zLxn8Y+X\nKF1y/paZOzNzW2Zum5zcsMLVBAAAzXLhguVBfI0op2NztqQLI+KvJd2ohVNQH5Z0TEQ8d1fVFkmP\nrckaAgCAF6WI2BERD0XEvoi4aonfT/dvQNrXvyHp5NUus3Fik5m/mplbMvNkSRdL+kJm/pyk2yS9\npV92qaSbV7syAABgdSIH89W4HhFtSVdLOl/SaZIuiYjTDiu7TNJTmfkPJX1ICzcmrcpqkoffI+nd\nEbFPC9fcXLvalQEAANU4Q9K+zHwkM+e0cNbnosNqLtLCDUjSwg1Jb+jfoLRiywroy8zbJd3e//4R\nLay0L0I9I3wvjFAsN8Qq22b4lxnE5YSh2eF27r4reS7TDa5zAwuN8dx9UPJ5lgw1lKToFozyLH1q\nuuTxYY7lBVVKaeTW+WOZ+9TM7nTD90qOlV3j/cMM+2sf6jQXSeocMemNd3C+sWbafB20Ol54oBO+\n9/m/+Yo11pvOutCqmzhwyKrrrW/ebs62df9WFTU6179slvToosczkl7zfDWZ2YmI72ihWfLEShfK\nRyoAAICV2BQRexc93pmZOxc9dm40sm9GcjGxAQCgFinF4D4r6onM3PYCv5+RdNKix0vdaPRczUz/\nhqSjJT25mpXi070BAMBauFvS1v5HME1p4QakXYfV7NLCDUjSwg1JX8hc3bk0OjYAANRkRK6x6V8z\nc4WkPZLakq7LzPsj4n2S9mbmLi3cePSJ/o1IT2ph8rMqTGwAAMCayMzdknYf9rP3Lvr+kKSfLrlM\nJjYAANRkNBo2Q8M1NgAAoBpMbAAAQDU4FQUAQEViRC4eHpaRnNhYSaRusqzZk3IPhJ6TIlk4Udj5\nTI7S7GUWXLeSz9NOf7YHLDyew30OzrqZr5fSx1rR8dzXlduHNt5n3NRYN5k6jJ0VHXeZ3sZtzZup\nyBNGKrz5PCee8VKRWy/f3FjjJgp/9kuH30W8tAte98+sumgZB5KZ6ozBGsmJDQAAWKEx79hwjQ0A\nAKgGHRsAAGqRkgb3kQojiY4NAACoBh0bAAAqEcqxvyuKjg0AAKgGHRsAAGpCxwYAAKAOdGwAAKjJ\nmHdsBjux6aVas93Gslan+V61zgYv8dFO3Wy7EcVGKqibKGwmh5aU7bKppk46rptWWjbF2F1/N822\ndJRxs3SX6TxXN0HXeO1J8rdHz1k3byj3dZUT7oDNr3l7LDMt2NkezvufJPWMpGBJas16KcBd4z21\nddBMFD40b9XNbz6msWbiwCFrLDdRePcdf2TVnX/+JY01k081r5v9/odi6NgAAFALcmy4xgYAANSD\njg0AABUhxwYAAKASTGwAAEA1OBUFAEBNOBUFAABQBzo2AABUI+nYDHsFAAAAShlsxyZTrUPNyZU5\nWW6+5aY+9ibb3oDGTDictFX5t+Slm45rLdSOeS03nrk9irIXae6DISQP24oeH15ZmnVOmbttw9yn\n6QZdF9xs9nNwiszUbz9F3Cqz3mfSfZ80Odutt95LmY+W93fDSRSWpM997obGmvNednrzQOklJxeT\nomMz7BUAAAAohWtsAACoCR+pAAAAUAc6NgAAVISPVAAAAKgEHRsAAGpCxwYAAKAOdGwAAKhFajjZ\nYSNksBObCOV0c8DT/MbmQKap78xZi3RDrNzeVeugkXZlhnX1JsyFFgxgc8MD7e1mrJsdCVgyZ859\nXY9wy9YNl7SODzcMsm0ek+54zjHu7gM3+LLgMR7z3j5wA/qcumyZIXjmrupNe2/zLeO59tzwVLPO\n2b6dI7yAPpl1k095gXlO+N6ex+5trDnjvGet5aEcOjYAAFSDz4riGhsAAFANJjYAAKAanIoCAKAm\nnIoCAACoAx0bAABqQscGAACgDnRsAACoBQF9dGwAAEA9BtqxefqZx574kzvf+zeH/XiTpCcGuR74\nPuyD4WMfDB/7YPheVPugfaJV9vI1Xo3DpJRmanmlBjqxyczjD/9ZROzNzG2DXA/8feyD4WMfDB/7\nYPjYByiBa2wAAKgJd0UBAADUYRQ6NjuHvQJgH4wA9sHwsQ+Gj32wWtwVNfyOTWZyIA8Z+2D42AfD\nxz4YPvYBShiFjg0AACiFa2yGIyJ2RMRDEbEvIq4a1nqMm4i4LiL2R8RfLPrZsRFxS0Q83P/vS4a5\njjWLiJMi4raIeDAi7o+Id/Z/zj4YkIhYFxFfiYiv9ffBb/R/fkpE3NXfB5+MiKlhr2vtIqIdEV+N\niM/0H7MPsGpDmdhERFvS1ZLOl3SapEsi4rRhrMsY+rikHYf97CpJt2bmVkm39h9jbXQkXZmZp0o6\nU9I7+sc++2BwZiWdm5k/Jul0STsi4kxJvynpQ/198JSky4a4juPinZIeXPSYfVBC5mC+RtSwOjZn\nSNqXmY9k5pykGyVdNKR1GSuZeYekJw/78UWSru9/f72kNw90pcZIZj6emX/e//5pLbypbxb7YGBy\nwff6Dyf7XynpXEmf6v+cfbDGImKLpDdJ+lj/cYh9gAKGNbHZLOnRRY9n+j/DcLw0Mx+XFv7wSjph\nyOszFiLiZEmvknSX2AcD1T8Fcq+k/ZJukfRXkg5kZqdfwnvS2vuwpF+R9FxM7nFiH6CAYU1sYomf\njW5fCygsIjZK+kNJ78rM7w57fcZNZnYz83RJW7TQQT51qbLBrtX4iIiflLQ/M+9Z/OMlStkHyzag\n01AjfCpqWHdFzUg6adHjLZIeG9K6QPpmRJyYmY9HxIla+Fcs1khETGphUvP7mfnp/o/ZB0OQmQci\n4nYtXO90TERM9DsGvCetrbMlXRgRF0haJ+koLXRw2AdYtWF1bO6WtLV/BfyUpIsl7RrSumBh21/a\n//5SSTcPcV2q1r+O4FpJD2bmBxf9in0wIBFxfEQc0/9+vaQ3auFap9skvaVfxj5YQ5n5q5m5JTNP\n1sL7/xcy8+fEPli9lNTrDeZrRA1lYtOfjV8haY8W3lBuysz7h7Eu4yYibpB0p6QfjoiZiLhM0vsl\nbY+IhyVt7z/G2jhb0i9IOjci7u1/XSD2wSCdKOm2iLhPC//IuiUzPyPpPZLeHRH7tHC9x7VDXMdx\nxT7AqkWO8HkyAADgO3ryhDzruLc0Fxbw+W9+5J5R/DT2oX+kAgAAQCl8pAIAADUZ8zMxdGwAAEA1\n6NgAAFCNlHp0bAAAAKpAxwYAgFqklDm6GTODQMcGAABUg44NAAA14RobAACAOtCxAQCgJuTYAAAA\n1IGJDQAAqAanogAAqEWm1ON2bwAAgCrQsQEAoCZcPAwAAFAHOjYAAFQkucYGAACgDnRsAACoRnKN\nzbBXAAAAoBQ6NgAA1CLFh2AOewUAAABKYWIDAEBNsjeYr1WIiGMj4paIeLj/35csUXN6RNwZEfdH\nxH0R8bPO2ExsAADAoF0l6dbM3Crp1v7jwz0r6Rcz80ck7ZD04Yg4pmlgrrEBAKASKSlfHNfYXCTp\nnP7310u6XdJ7Fhdk5v9Z9P1jEbFf0vGSDrzQwExsAADASmyKiL2LHu/MzJ3m//vSzHxckjLz8Yg4\n4YWKI+IMSVOS/qppYCY2AADUInPV178swxOZue35fhkRfyLpB5b41a8tZyERcaKkT0i6NLP5yTGx\nAQAAxWXmG5/vdxHxzYg4sd+tOVHS/uepO0rSZyX9emZ+2VkuFw8DAIBB2yXp0v73l0q6+fCCiJiS\n9EeSfi8z/8AdmIkNAAAVyV4O5GuV3i9pe0Q8LGl7/7EiYltEfKxf8zOSXifprRFxb//r9KaBI8f8\nMyUAAKjFUXFsvqa1fSDL+pPeTfe80DU2w8I1NgAA1GRwFw+PJDo2AABUIiI+L2nTgBb3RGbuGNCy\nbExsAABANbh4GAAAVIOJDQAAqAYTGwAAUA0mNgAAoBpMbAAAQDWY2AAAgGowsQEAANVgYgMAAKrB\nxAYAAFTj/wHVYIWYuXKx8AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1199edb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(10, 10))\n",
    "plt.imshow(K0)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x11b324750>"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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I+LCkcySti4jtkn5F0rWSbomISyV9Q9KPLedKAgAAT+0dl8aJS2ZefIhfnVt4XQAAAA5r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\nGBtMXAAAwNhg4gIAAMYGExcAADA2/j/4qTYPC0RWbQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b13c390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(10, 10))\n",
    "plt.imshow(np.linalg.inv(K0))\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.0221930992494\n",
      "1 0.0218955891338\n",
      "2 0.0252872441092\n",
      "3 0.0328142098962\n",
      "4 0.0309954818897\n",
      "5 0.0189923881125\n",
      "6 0.0375360345871\n",
      "7 0.0458171451828\n",
      "8 0.0275139859748\n",
      "9 0.0207577015452\n",
      "10 0.032319891601\n",
      "11 0.0216175424959\n",
      "12 0.027934284364\n",
      "13 0.0175806050943\n",
      "14 0.0402558921925\n",
      "15 0.0210642430093\n",
      "16 0.0285292505661\n",
      "17 0.0195517554025\n",
      "18 0.0293906161572\n",
      "19 0.039817997719\n",
      "20 0.0331424404378\n",
      "21 0.0160407275406\n",
      "22 0.0258226936258\n",
      "23 0.0190096011067\n",
      "24 0.0323731447305\n",
      "25 0.0195424699345\n",
      "26 0.013320621632\n",
      "27 0.0190712744998\n",
      "28 0.0300834114895\n",
      "29 0.0134106103251\n",
      "30 0.0146409639023\n",
      "31 0.0330055890553\n",
      "32 0.0409716655533\n",
      "33 0.0157102729114\n",
      "34 0.0365510418187\n",
      "35 0.0220874602223\n",
      "36 0.014232750474\n"
     ]
    }
   ],
   "source": [
    "for i in range(N - 1):\n",
    "    print i, (support[1:] - support[:-1])[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 39.20922154,  18.70234405,  12.57705425,   6.78705697,\n",
       "         3.04813984,   2.42624753,   3.04075544,   4.65817789,\n",
       "         1.61929928,   0.59051366,   1.90188384,   5.08347106,\n",
       "         6.2712505 ,  10.05952829,  -0.62267879,  -6.52390116,\n",
       "        -1.70479838,  -1.51200253,   4.14403315,   0.51437422,\n",
       "        -4.03943175,  -0.60713935,  -0.61779535,  -3.05909749,\n",
       "        -2.12068264,   3.65038307,   3.46655746,   0.07447356,\n",
       "        -2.72677997,  -4.8900445 ,  -3.91222766,  -2.47267172,\n",
       "        -2.54564283,  -3.20052687,  -6.11439943,  -8.20825839,\n",
       "        -8.6875    , -11.125     , -17.75      , -38.5703125 ])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diag(np.dot(K1, np.linalg.inv(K0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x11255b7d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Q3Z+ZpwfuPwNcvu/7y/jeQ0HXAlcBZObvRcR5wMXAfa0nteMiSZK24Tbgioh4ZkScAl4F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\nJElajP8fLqNdKQVTfYoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1126cf290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(10, 10))\n",
    "plt.imshow(np.linalg.inv(kernel_matrix(kernel, support)))\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tridiagonal Approximation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def tridiagonal_part(M):\n",
    "    M_ret = np.zeros_like(M)\n",
    "    for i in range(M.shape[0]):\n",
    "        M_ret[i, i] = M[i, i]\n",
    "        if i + 1 < M.shape[0]:\n",
    "            M_ret[i, i + 1] = M[i, i + 1]\n",
    "            M_ret[i + 1, i] = M[i + 1, i]\n",
    "        if i - 1 >= 0:\n",
    "            M_ret[i, i - 1] = M[i, i - 1]\n",
    "            M_ret[i - 1, i] = M[i - 1, i]\n",
    "    return M_ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def tricirculant_part(M):\n",
    "    M_ret = np.zeros_like(M)\n",
    "    n = M.shape[0]\n",
    "    for i in range(M.shape[0]):\n",
    "        M_ret[i, i] = M[i, i]\n",
    "        M_ret[i, (i + 1) % n] = M[i, (i + 1) % n]\n",
    "        M_ret[(i + 1) % n, i] = M[(i + 1) % n, i]\n",
    "        if i - 1 >= 0:\n",
    "            M_ret[i, i - 1] = M[i, i - 1]\n",
    "            M_ret[i - 1, i] = M[i - 1, i]\n",
    "    return M_ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fc = 50\n",
    "min_separation = 1.5 / (2.0 * fc + 1)\n",
    "N = int(1.0 / min_separation - 1)\n",
    "\n",
    "kernel = trig_poly.TrigPoly.dirichlet(fc)\n",
    "\n",
    "support = test_data.jittered_supports(N, min_separation=min_separation)\n",
    "\n",
    "K0 = kernel_matrix(kernel, support)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -4.87210679e-11])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.optimize import fsolve\n",
    "f = lambda t: kernel(0).real - kernel(t).real ** 2\n",
    "fsolve(f, x0=3.0 / 4.0 / fc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "K0_td = tridiagonal_part(K0)\n",
    "K0_ci = tricirculant_part(K0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x10f814d90>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DUbXGBrr5EiG5YFKoUtXfjq0vAb+Wsu9/JKDQWIEXrrG21w5j8bkglrHp2nO9bVkxw6Kk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\neEwM4QQTTLDhMTGEE0wwwYbH/w8eWMOTmwKlOAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f645f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(K0 - K0_td)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x10fdfaa10>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J4Pmq+rSJQf6/IvLinGN2DStikmBiJ8ectrhkWnU46E1wPnmovmYE5y2x3CEW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\nxhhjjAyMjeQYY4wxRgb+f6GXywEVfKz+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114873c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(np.linalg.inv(K0_td) - np.linalg.inv(K0))\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}