zonca/Run bls_pulse_vec on simulated light curve with detrend.ipynb

## Run bls_pulse_vec on simulated light curve with detrend.ipynb
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from bls_vec_simulator import bls_vec_simulator\n",
      "import sys\n",
      "sys.path.append(\"..\")\n",
      "import bls_pulse_vec\n",
      "import simplejson\n",
      "import base64\n",
      "import cStringIO\n",
      "from zlib import compress\n",
      "import bls_pulse"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext autoreload\n",
      "%autoreload 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Simulate a test light curve with high signal to noise"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "period = 7.4634186472543185\n",
      "transit_ratio = 0.014427218793100851\n",
      "transit_depth = 0.06055135677305074\n",
      "phase = 0.15497227080241027\n",
      "signal_to_noise = 10000.0\n",
      "n_samples = 129600.0\n",
      "time_span = 90.0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "simulated_light_curve = bls_vec_simulator(period, transit_ratio, \n",
      "        transit_depth, phase, signal_to_noise, n_samples, time_span)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "true_p = [1e-6, -0.0002, .01, .03]\n",
      "simulated_light_curve[\"lc\"].flux += np.polyval(true_p, \n",
      "        np.array(simulated_light_curve[\"lc\"].index).astype(np.double))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "simulated_light_curve[\"lc\"].flux.plot()\n",
      "plt.title(\"Simulated light curve flux\")\n",
      "plt.xlabel(\"Time [days]\")\n",
      "plt.ylabel(\"Flux\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.text.Text at 0x7f17a1002890>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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EE3xfxPzwt3Vl9xcZqcy+srL4/mJjTfeYGRmPj9mNG6Z7XPKYoe+dqn2mLC4u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\nSBy5DugAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f17a1435190>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "min_duration = 0.0416667 #in days\n",
      "max_duration = 1.0 #in days\n",
      "n_bins_blspulse = 1000\n",
      "segment_size = 10 #in days\n",
      "#segment_size = period"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Run `bls_pulse_vec`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bls_pulse_vec_result_nodetrend = \\\n",
      "               bls_pulse_vec.bls_pulse_vec(simulated_light_curve[\"lc\"].copy(), \n",
      "                                                   segment_size, \n",
      "                                                   min_duration, \n",
      "                                                   max_duration, \n",
      "                                                   n_bins_blspulse, \n",
      "                                                   detrend_order=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "detrended_light_curve = simulated_light_curve[\"lc\"].copy()\n",
      "bls_pulse_vec_result_detrend, detrend_coefficients = \\\n",
      "               bls_pulse_vec.bls_pulse_vec(detrended_light_curve, \n",
      "                                                   segment_size, \n",
      "                                                   min_duration, \n",
      "                                                   max_duration, \n",
      "                                                   n_bins_blspulse, \n",
      "                                                   detrend_order=4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bls_pulse_vec_result_nodetrend"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>depth</th>\n",
        "      <th>duration</th>\n",
        "      <th>midtime</th>\n",
        "      <th>phase</th>\n",
        "      <th>signal_residual</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>segment</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0.105423</td>\n",
        "      <td> 23.783517</td>\n",
        "      <td>  9.485000</td>\n",
        "      <td> 899</td>\n",
        "      <td> 0.002515</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0.155543</td>\n",
        "      <td> 23.783517</td>\n",
        "      <td> 19.485077</td>\n",
        "      <td> 899</td>\n",
        "      <td> 0.003638</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0.177064</td>\n",
        "      <td> 23.783517</td>\n",
        "      <td> 29.485154</td>\n",
        "      <td> 899</td>\n",
        "      <td> 0.004107</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 0.178991</td>\n",
        "      <td> 23.783517</td>\n",
        "      <td> 33.335231</td>\n",
        "      <td> 284</td>\n",
        "      <td> 0.004142</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 0.173609</td>\n",
        "      <td> 23.783517</td>\n",
        "      <td> 40.495309</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.004032</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 0.153356</td>\n",
        "      <td> 23.783517</td>\n",
        "      <td> 50.495386</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.003578</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td> 0.119404</td>\n",
        "      <td> 23.783517</td>\n",
        "      <td> 61.465463</td>\n",
        "      <td>  97</td>\n",
        "      <td> 0.002825</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td> 0.090625</td>\n",
        "      <td> 23.783517</td>\n",
        "      <td> 70.495540</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.002134</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td> 0.060149</td>\n",
        "      <td> 23.783517</td>\n",
        "      <td> 80.495617</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.001423</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>9 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "            depth   duration    midtime  phase  signal_residual\n",
        "segment                                                        \n",
        "0        0.105423  23.783517   9.485000    899         0.002515\n",
        "1        0.155543  23.783517  19.485077    899         0.003638\n",
        "2        0.177064  23.783517  29.485154    899         0.004107\n",
        "3        0.178991  23.783517  33.335231    284         0.004142\n",
        "4        0.173609  23.783517  40.495309      0         0.004032\n",
        "5        0.153356  23.783517  50.495386      0         0.003578\n",
        "6        0.119404  23.783517  61.465463     97         0.002825\n",
        "7        0.090625  23.783517  70.495540      0         0.002134\n",
        "8        0.060149  23.783517  80.495617      0         0.001423\n",
        "\n",
        "[9 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bls_pulse_vec_result_detrend"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>depth</th>\n",
        "      <th>duration</th>\n",
        "      <th>midtime</th>\n",
        "      <th>phase</th>\n",
        "      <th>signal_residual</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>segment</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>-0.060554</td>\n",
        "      <td> 2.400019</td>\n",
        "      <td>  8.660000</td>\n",
        "      <td> 861</td>\n",
        "      <td> 0.000432</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>-0.060553</td>\n",
        "      <td> 2.400019</td>\n",
        "      <td> 16.130077</td>\n",
        "      <td> 608</td>\n",
        "      <td> 0.000424</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>-0.060553</td>\n",
        "      <td> 2.400019</td>\n",
        "      <td> 23.590154</td>\n",
        "      <td> 354</td>\n",
        "      <td> 0.000430</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>-0.060555</td>\n",
        "      <td> 2.400019</td>\n",
        "      <td> 31.060231</td>\n",
        "      <td> 101</td>\n",
        "      <td> 0.000429</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>-0.060554</td>\n",
        "      <td> 2.400019</td>\n",
        "      <td> 45.980309</td>\n",
        "      <td> 593</td>\n",
        "      <td> 0.000438</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>-0.060554</td>\n",
        "      <td> 2.400019</td>\n",
        "      <td> 53.450386</td>\n",
        "      <td> 340</td>\n",
        "      <td> 0.000424</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>-0.060555</td>\n",
        "      <td> 2.400019</td>\n",
        "      <td> 68.370463</td>\n",
        "      <td> 832</td>\n",
        "      <td> 0.000430</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>-0.060554</td>\n",
        "      <td> 2.400019</td>\n",
        "      <td> 75.830540</td>\n",
        "      <td> 578</td>\n",
        "      <td> 0.000424</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>-0.060554</td>\n",
        "      <td> 2.400019</td>\n",
        "      <td> 83.300617</td>\n",
        "      <td> 325</td>\n",
        "      <td> 0.000432</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>9 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "            depth  duration    midtime  phase  signal_residual\n",
        "segment                                                       \n",
        "0       -0.060554  2.400019   8.660000    861         0.000432\n",
        "1       -0.060553  2.400019  16.130077    608         0.000424\n",
        "2       -0.060553  2.400019  23.590154    354         0.000430\n",
        "3       -0.060555  2.400019  31.060231    101         0.000429\n",
        "4       -0.060554  2.400019  45.980309    593         0.000438\n",
        "5       -0.060554  2.400019  53.450386    340         0.000424\n",
        "6       -0.060555  2.400019  68.370463    832         0.000430\n",
        "7       -0.060554  2.400019  75.830540    578         0.000424\n",
        "8       -0.060554  2.400019  83.300617    325         0.000432\n",
        "\n",
        "[9 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "simulated_light_curve[\"lc\"].flux.plot()\n",
      "detrended_light_curve.flux.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f17a0fa32d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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LJ7WFXm1EU8gZOIDU+OA//wlkZ/PeHB07iqFJbUTU5SnjDFxVafv58TWZ//Mf4OWXeTfU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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f17a0f366d0>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    }
   ],
   "metadata": {}
  }
 ]
}