20150527T220000_MultiBand_test_time_units.ipynb

20150527T220000_MultiBand_test_time_units.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiband Periodograms with ``gatspy``\n",
    "\n",
    "``gatspy`` includes an implementation of the multiband periodogram, introduced in [VanderPlas & Ivezic 2015](http://jakevdp.github.io/multiband_LS/). If you've taken a look at the [Single Band Example](SingleBand.ipynb), the interface should be pretty familiar."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As in that example, let's start with some imports, and downloading some of the Sesear 2010 Stripe 82 RR Lyrae data, and plotting it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# use seaborn's default plotting styles for matplotlib\n",
    "import seaborn; seaborn.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from gatspy.datasets import fetch_rrlyrae\n",
    "rrlyrae = fetch_rrlyrae()\n",
    "lcid = rrlyrae.ids[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "t, y, dy, filts = rrlyrae.get_lightcurve(lcid)\n",
    "period = rrlyrae.get_metadata(lcid)['P']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "8P8BMG4NZ9KQpoEAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a0ac390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for filt in 'ugriz':\n",
    "    mask = (filts == filt)\n",
    "    plt.errorbar(t[mask] % period, y[mask], dy[mask], fmt='.', label=filt)\n",
    "plt.gca().invert_yaxis()\n",
    "plt.legend(ncol=3, loc='upper left');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll fit the multiband periodogram model to this data. For more information on the model, refer to the [VanderPlas and Ivezic paper](http://jakevdp.github.io/multiband_LS/) mentioned above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "WKFQKBSKWeB/AZy3EO2T6//0AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a3959d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from gatspy.periodic import LombScargleMultiband\n",
    "model = LombScargleMultiband(Nterms_base=1, Nterms_band=0)\n",
    "model.fit(t, y, dy, filts)\n",
    "periods = np.linspace(period - 0.1, period + 0.1, 2000)\n",
    "power = model.periodogram(periods)\n",
    "plt.plot(periods, power, lw=1)\n",
    "plt.xlim(periods[0], periods[-1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see what the multiterm model looks like by plotting it over the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a54d090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_model(model, lcid):\n",
    "    t, y, dy, filts = rrlyrae.get_lightcurve(lcid)\n",
    "    model.fit(t, y, dy, filts)\n",
    "    \n",
    "    tfit = np.linspace(0, period, 1000)\n",
    "    for filt in 'ugriz':\n",
    "        mask = (filts == filt)\n",
    "        eb = plt.errorbar(t[mask] % period, y[mask], dy[mask], fmt='.', label=filt)\n",
    "        yfit = model.predict(tfit, filt, period=period)\n",
    "        plt.plot(tfit, yfit, color=eb[0].get_color())\n",
    "    plt.gca().invert_yaxis()\n",
    "    plt.legend(ncol=3, loc='upper left')\n",
    "    \n",
    "plot_model(LombScargleMultiband(Nterms_base=1, Nterms_band=0), lcid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we'd like to do a higher-oder multiterm model, we can simply adjust the number of terms in the base and band models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "AElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a32bb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(LombScargleMultiband(Nterms_base=4, Nterms_band=1), lcid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The periodogram can also be computed for any model using the same interface as above.\n",
    "For more information and anlysis of this algorithm, see [our paper](http://jakevdp.github.io/multiband_LS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Added tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test LombScargleMultiband with different time units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding optimal frequency:\n",
      " - Using omega_step = 0.00038\n",
      " - Computing periods at 69190 steps from 0.20 to 1.20\n",
      "Zooming-in on 5 candidate peaks:\n",
      " - Computing periods at 1005 steps\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.61431668575678366"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# With times in MJD takes ~69e3 steps.\n",
    "model = LombScargleMultiband(Nterms_base=1, Nterms_band=0)\n",
    "model.fit(t, y, dy, filts)\n",
    "model.best_period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding optimal frequency:\n",
      " - Using omega_step = 0.00000\n",
      " - Computing periods at 5977868 steps from 0.20 to 1.20\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-a9154ab3ad3a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mt_unixtime_TCB\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mdt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime64\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint64\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m1e9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt_unixtime_TCB\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt_unixtime_TCB\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_period\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/harrold/anaconda/lib/python2.7/site-packages/gatspy/periodic/modeler.pyc\u001b[0m in \u001b[0;36mbest_period\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    101\u001b[0m         \u001b[0;34m\"\"\"Lazy evaluation of the best period given the model\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_best_period'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_best_period\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_best_period\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_calc_best_period\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    104\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_best_period\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/harrold/anaconda/lib/python2.7/site-packages/gatspy/periodic/modeler.pyc\u001b[0m in \u001b[0;36m_calc_best_period\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    111\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_calc_best_period\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m         \u001b[0;34m\"\"\"Compute the best period using the optimizer\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 113\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_period\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mperiods\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/harrold/anaconda/lib/python2.7/site-packages/gatspy/periodic/optimizer.pyc\u001b[0m in \u001b[0;36mbest_period\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mbest_period\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         periods = self.find_best_periods(model, n_periods=1,\n\u001b[0;32m---> 14\u001b[0;31m                                          return_scores=False)\n\u001b[0m\u001b[1;32m     15\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mperiods\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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/Users/harrold/anaconda/lib/python2.7/site-packages/gatspy/periodic/optimizer.pyc\u001b[0m in \u001b[0;36mfind_best_periods\u001b[0;34m(self, model, n_periods, return_scores)\u001b[0m\n\u001b[1;32m     65\u001b[0m         \u001b[0;31m# Compute the score on the initial grid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m         \u001b[0mN\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mwidth\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0momega_step\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m         \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mperiods\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m         \u001b[0;31m# find initial candidates of unique peaks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/harrold/anaconda/lib/python2.7/site-packages/gatspy/periodic/modeler.pyc\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, periods)\u001b[0m\n\u001b[1;32m     93\u001b[0m         \"\"\"\n\u001b[1;32m     94\u001b[0m         \u001b[0mperiods\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mperiods\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mperiods\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mperiods\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m     \u001b[0mperiodogram\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/harrold/anaconda/lib/python2.7/site-packages/gatspy/periodic/lomb_scargle.pyc\u001b[0m in \u001b[0;36m_score\u001b[0;34m(self, periods)\u001b[0m\n\u001b[1;32m    166\u001b[0m         \u001b[0;31m# Iterate through the omegas and compute the power for each\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0momega\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0momegas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 168\u001b[0;31m             \u001b[0mXw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mXTX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_construct_X_M\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0momega\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    169\u001b[0m             \u001b[0mXTy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myw_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m             \u001b[0mchi2_0_minus_chi2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXTy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXTX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mXTy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/harrold/anaconda/lib/python2.7/site-packages/gatspy/periodic/lomb_scargle.pyc\u001b[0m in \u001b[0;36m_construct_X_M\u001b[0;34m(self, omega, **kwargs)\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_construct_X_M\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0momega\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m         \u001b[0;34m\"\"\"Construct the weighted normal matrix of the problem\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_construct_X\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0momega\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweighted\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m         \u001b[0mM\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
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   ],
   "source": [
    "# With times in unixtime takes ~6e9 steps.\n",
    "# NOTE: Calculation is interrupted.\n",
    "# NOTE: Unit conversion is not correct in this cell in order to show number of required steps.\n",
    "# For unix time conversion:\n",
    "# http://docs.scipy.org/doc/numpy/reference/arrays.datetime.html\n",
    "# http://stackoverflow.com/questions/11865458/how-to-get-unix-timestamp-from-numpy-datetime64\n",
    "from __future__ import division\n",
    "import astropy.time as ast_time\n",
    "model = LombScargleMultiband(Nterms_base=1, Nterms_band=0)\n",
    "t_unixtime_TCB = ast_time.Time(t, format='mjd', scale='utc').tcb.datetime\n",
    "t_unixtime_TCB = map(lambda dt: np.datetime64(dt).astype(np.int64) / 1e9, t_unixtime_TCB) # 1e9 is WRONG; 1e6 is CORRECT\n",
    "model.fit(t_unixtime_TCB, y, dy, filts)\n",
    "model.best_period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# With times in unixtime takes ~6e9 steps.\n",
    "# NOTE: Calculation is interrupted.\n",
    "# NOTE: Calculation will overfill 8GB RAM.\n",
    "# For unix time conversion:\n",
    "# http://docs.scipy.org/doc/numpy/reference/arrays.datetime.html\n",
    "# http://stackoverflow.com/questions/11865458/how-to-get-unix-timestamp-from-numpy-datetime64\n",
    "from __future__ import division\n",
    "import astropy.time as ast_time\n",
    "model = LombScargleMultiband(Nterms_base=1, Nterms_band=0)\n",
    "t_unixtime_TCB = ast_time.Time(t, format='mjd', scale='utc').tcb.datetime\n",
    "t_unixtime_TCB = map(lambda dt: np.datetime64(dt).astype(np.int64) / 1e6, t_unixtime_TCB) # 1e6 is CORRECT\n",
    "model.fit(t_unixtime_TCB, y, dy, filts)\n",
    "model.best_period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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