CentralFrequencyDumb.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Burst Center Frequency\n",
    "\n",
    "Finds the center frequency of a burst by integrating over time and fitting a gaussian to the spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>edge</th>\n",
       "      <th>angle</th>\n",
       "      <th>peak</th>\n",
       "      <th>sigmax</th>\n",
       "      <th>sigmay</th>\n",
       "      <th>Slope</th>\n",
       "      <th>drift (mhz/ms)</th>\n",
       "      <th>drift error (mhz/ms)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01_puppi_57747_C0531+33_0558_5.dm559.72.calibP...</td>\n",
       "      <td>1525</td>\n",
       "      <td>1.720000</td>\n",
       "      <td>10429.0</td>\n",
       "      <td>173.731526</td>\n",
       "      <td>47.364587</td>\n",
       "      <td>-6.652439</td>\n",
       "      <td>-1039.308995</td>\n",
       "      <td>0.617101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02_puppi_57747_C0531+33_0558_1183.dm559.72.cal...</td>\n",
       "      <td>1850</td>\n",
       "      <td>23.770000</td>\n",
       "      <td>5692.0</td>\n",
       "      <td>177.080918</td>\n",
       "      <td>44.911875</td>\n",
       "      <td>-4.736866</td>\n",
       "      <td>-739.419463</td>\n",
       "      <td>1.279131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03_puppi_57747_C0531+33_0558_1202.dm559.72.cal...</td>\n",
       "      <td>1975</td>\n",
       "      <td>-1.520000</td>\n",
       "      <td>5668.0</td>\n",
       "      <td>169.749001</td>\n",
       "      <td>19.463885</td>\n",
       "      <td>-19.669528</td>\n",
       "      <td>-2813.966383</td>\n",
       "      <td>5.239849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04_puppi_57747_C0531+33_0558_25437.dm559.72.ca...</td>\n",
       "      <td>1450</td>\n",
       "      <td>1.710000</td>\n",
       "      <td>5038.0</td>\n",
       "      <td>160.360329</td>\n",
       "      <td>26.706231</td>\n",
       "      <td>-7.137257</td>\n",
       "      <td>-1109.319273</td>\n",
       "      <td>40.321594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05_puppi_57747_C0531+33_0558_3683.dm559.72.cal...</td>\n",
       "      <td>1450</td>\n",
       "      <td>1.639204</td>\n",
       "      <td>5031.0</td>\n",
       "      <td>37.747383</td>\n",
       "      <td>166.280492</td>\n",
       "      <td>-14.595503</td>\n",
       "      <td>-2109.819670</td>\n",
       "      <td>124.715830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>06_puppi_57747_C0531+33_0558_3687.dm559.72.cal...</td>\n",
       "      <td>1150</td>\n",
       "      <td>1.590000</td>\n",
       "      <td>5569.0</td>\n",
       "      <td>156.557708</td>\n",
       "      <td>12.416683</td>\n",
       "      <td>-52.066970</td>\n",
       "      <td>-6317.730503</td>\n",
       "      <td>30.736619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>07_puppi_57747_C0531+33_0558_3688.dm559.72.cal...</td>\n",
       "      <td>1590</td>\n",
       "      <td>1.720000</td>\n",
       "      <td>5821.0</td>\n",
       "      <td>102.259272</td>\n",
       "      <td>38.969184</td>\n",
       "      <td>-6.652439</td>\n",
       "      <td>-1028.172298</td>\n",
       "      <td>2.175251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>08_puppi_57747_C0531+33_0558_3689.dm559.72.cal...</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.990000</td>\n",
       "      <td>5666.0</td>\n",
       "      <td>171.395398</td>\n",
       "      <td>64.792725</td>\n",
       "      <td>-2.244076</td>\n",
       "      <td>-340.010864</td>\n",
       "      <td>0.162096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>09_puppi_57747_C0531+33_0558_3690.dm559.72.cal...</td>\n",
       "      <td>1600</td>\n",
       "      <td>2.240000</td>\n",
       "      <td>5041.0</td>\n",
       "      <td>133.024941</td>\n",
       "      <td>89.628038</td>\n",
       "      <td>-1.264288</td>\n",
       "      <td>-191.824534</td>\n",
       "      <td>9.704662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10_puppi_57747_C0531+33_0558_12568.dm559.72.ca...</td>\n",
       "      <td>800</td>\n",
       "      <td>-1.390000</td>\n",
       "      <td>5132.0</td>\n",
       "      <td>130.605616</td>\n",
       "      <td>47.074113</td>\n",
       "      <td>-5.470689</td>\n",
       "      <td>-846.471217</td>\n",
       "      <td>34.730967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11_puppi_57748_C0531+33_0594_2.dm559.72.calibP...</td>\n",
       "      <td>1800</td>\n",
       "      <td>1.720000</td>\n",
       "      <td>5196.0</td>\n",
       "      <td>182.570112</td>\n",
       "      <td>39.810758</td>\n",
       "      <td>-6.652439</td>\n",
       "      <td>-1028.948558</td>\n",
       "      <td>12.832905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12_puppi_57748_C0531+33_0594_48.dm559.72.calib...</td>\n",
       "      <td>1000</td>\n",
       "      <td>4.860000</td>\n",
       "      <td>4672.0</td>\n",
       "      <td>137.483220</td>\n",
       "      <td>28.822386</td>\n",
       "      <td>-6.725287</td>\n",
       "      <td>-1012.871320</td>\n",
       "      <td>2.193128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13_puppi_57748_C0531+33_0594_49.dm559.72.calib...</td>\n",
       "      <td>1900</td>\n",
       "      <td>1.740000</td>\n",
       "      <td>6789.0</td>\n",
       "      <td>141.354014</td>\n",
       "      <td>40.669821</td>\n",
       "      <td>-5.853528</td>\n",
       "      <td>-915.975701</td>\n",
       "      <td>0.710367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14_puppi_57748_C0531+33_0594_50.dm559.72.calib...</td>\n",
       "      <td>1550</td>\n",
       "      <td>4.760000</td>\n",
       "      <td>5313.0</td>\n",
       "      <td>159.613074</td>\n",
       "      <td>11.696348</td>\n",
       "      <td>-20.987668</td>\n",
       "      <td>-3424.532932</td>\n",
       "      <td>3.589301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15_puppi_57748_C0531+33_0594_1269.dm559.72.cal...</td>\n",
       "      <td>1550</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5922.0</td>\n",
       "      <td>65.081975</td>\n",
       "      <td>47.789210</td>\n",
       "      <td>-2.185040</td>\n",
       "      <td>-329.395555</td>\n",
       "      <td>0.926590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16_puppi_57772_C0531+33_0007_2695.dm559.72.cal...</td>\n",
       "      <td>700</td>\n",
       "      <td>1.710000</td>\n",
       "      <td>12309.0</td>\n",
       "      <td>124.668892</td>\n",
       "      <td>46.778198</td>\n",
       "      <td>-7.137257</td>\n",
       "      <td>-1129.833809</td>\n",
       "      <td>1.641886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>gajjar/11A_16sec.calib.4p.RM.scr.ASCII</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.525292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>651.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-785.400000</td>\n",
       "      <td>0.276000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>gajjar/11D_323sec.calib.4p.RM.scr.ASCII</td>\n",
       "      <td>0</td>\n",
       "      <td>1.592000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1673.440000</td>\n",
       "      <td>3.060000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             filename  edge      angle  \\\n",
       "0   01_puppi_57747_C0531+33_0558_5.dm559.72.calibP...  1525   1.720000   \n",
       "1   02_puppi_57747_C0531+33_0558_1183.dm559.72.cal...  1850  23.770000   \n",
       "2   03_puppi_57747_C0531+33_0558_1202.dm559.72.cal...  1975  -1.520000   \n",
       "3   04_puppi_57747_C0531+33_0558_25437.dm559.72.ca...  1450   1.710000   \n",
       "4   05_puppi_57747_C0531+33_0558_3683.dm559.72.cal...  1450   1.639204   \n",
       "5   06_puppi_57747_C0531+33_0558_3687.dm559.72.cal...  1150   1.590000   \n",
       "6   07_puppi_57747_C0531+33_0558_3688.dm559.72.cal...  1590   1.720000   \n",
       "7   08_puppi_57747_C0531+33_0558_3689.dm559.72.cal...  2000   1.990000   \n",
       "8   09_puppi_57747_C0531+33_0558_3690.dm559.72.cal...  1600   2.240000   \n",
       "9   10_puppi_57747_C0531+33_0558_12568.dm559.72.ca...   800  -1.390000   \n",
       "10  11_puppi_57748_C0531+33_0594_2.dm559.72.calibP...  1800   1.720000   \n",
       "11  12_puppi_57748_C0531+33_0594_48.dm559.72.calib...  1000   4.860000   \n",
       "12  13_puppi_57748_C0531+33_0594_49.dm559.72.calib...  1900   1.740000   \n",
       "13  14_puppi_57748_C0531+33_0594_50.dm559.72.calib...  1550   4.760000   \n",
       "14  15_puppi_57748_C0531+33_0594_1269.dm559.72.cal...  1550   2.000000   \n",
       "15  16_puppi_57772_C0531+33_0007_2695.dm559.72.cal...   700   1.710000   \n",
       "16             gajjar/11A_16sec.calib.4p.RM.scr.ASCII     0  -1.525292   \n",
       "17            gajjar/11D_323sec.calib.4p.RM.scr.ASCII     0   1.592000   \n",
       "\n",
       "       peak      sigmax      sigmay      Slope  drift (mhz/ms)  \\\n",
       "0   10429.0  173.731526   47.364587  -6.652439    -1039.308995   \n",
       "1    5692.0  177.080918   44.911875  -4.736866     -739.419463   \n",
       "2    5668.0  169.749001   19.463885 -19.669528    -2813.966383   \n",
       "3    5038.0  160.360329   26.706231  -7.137257    -1109.319273   \n",
       "4    5031.0   37.747383  166.280492 -14.595503    -2109.819670   \n",
       "5    5569.0  156.557708   12.416683 -52.066970    -6317.730503   \n",
       "6    5821.0  102.259272   38.969184  -6.652439    -1028.172298   \n",
       "7    5666.0  171.395398   64.792725  -2.244076     -340.010864   \n",
       "8    5041.0  133.024941   89.628038  -1.264288     -191.824534   \n",
       "9    5132.0  130.605616   47.074113  -5.470689     -846.471217   \n",
       "10   5196.0  182.570112   39.810758  -6.652439    -1028.948558   \n",
       "11   4672.0  137.483220   28.822386  -6.725287    -1012.871320   \n",
       "12   6789.0  141.354014   40.669821  -5.853528     -915.975701   \n",
       "13   5313.0  159.613074   11.696348 -20.987668    -3424.532932   \n",
       "14   5922.0   65.081975   47.789210  -2.185040     -329.395555   \n",
       "15  12309.0  124.668892   46.778198  -7.137257    -1129.833809   \n",
       "16      NaN  651.000000    9.000000        NaN     -785.400000   \n",
       "17      NaN    9.000000  248.000000        NaN    -1673.440000   \n",
       "\n",
       "    drift error (mhz/ms)  \n",
       "0               0.617101  \n",
       "1               1.279131  \n",
       "2               5.239849  \n",
       "3              40.321594  \n",
       "4             124.715830  \n",
       "5              30.736619  \n",
       "6               2.175251  \n",
       "7               0.162096  \n",
       "8               9.704662  \n",
       "9              34.730967  \n",
       "10             12.832905  \n",
       "11              2.193128  \n",
       "12              0.710367  \n",
       "13              3.589301  \n",
       "14              0.926590  \n",
       "15              1.641886  \n",
       "16              0.276000  \n",
       "17              3.060000  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from __future__ import division\n",
    "import math\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "import numpy as np\n",
    "import scipy.stats\n",
    "from scipy.optimize import curve_fit\n",
    "from math import log10\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "from scipy import pi as nombrepi\n",
    "from scipy import signal, ndimage\n",
    "from tqdm import tqdm\n",
    "from matplotlib import colors as mcolors\n",
    "import functools\n",
    "print = functools.partial(print, flush=True) # print doesn't happen til script ends so force it to flush... windows thing?\n",
    "import pandas as pd\n",
    "\n",
    "bursts = pd.read_csv('bursts.csv')\n",
    "bursts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>edge</th>\n",
       "      <th>angle</th>\n",
       "      <th>peak</th>\n",
       "      <th>sigmax</th>\n",
       "      <th>sigmay</th>\n",
       "      <th>Slope</th>\n",
       "      <th>drift (mhz/ms)</th>\n",
       "      <th>drift error (mhz/ms)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01_puppi_57747_C0531+33_0558_5.dm559.72.calibP...</td>\n",
       "      <td>1525</td>\n",
       "      <td>1.720000</td>\n",
       "      <td>10429.0</td>\n",
       "      <td>173.731526</td>\n",
       "      <td>47.364587</td>\n",
       "      <td>-6.652439</td>\n",
       "      <td>-1039.308995</td>\n",
       "      <td>0.617101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02_puppi_57747_C0531+33_0558_1183.dm559.72.cal...</td>\n",
       "      <td>1850</td>\n",
       "      <td>23.770000</td>\n",
       "      <td>5692.0</td>\n",
       "      <td>177.080918</td>\n",
       "      <td>44.911875</td>\n",
       "      <td>-4.736866</td>\n",
       "      <td>-739.419463</td>\n",
       "      <td>1.279131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03_puppi_57747_C0531+33_0558_1202.dm559.72.cal...</td>\n",
       "      <td>1975</td>\n",
       "      <td>-1.520000</td>\n",
       "      <td>5668.0</td>\n",
       "      <td>169.749001</td>\n",
       "      <td>19.463885</td>\n",
       "      <td>-19.669528</td>\n",
       "      <td>-2813.966383</td>\n",
       "      <td>5.239849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04_puppi_57747_C0531+33_0558_25437.dm559.72.ca...</td>\n",
       "      <td>1450</td>\n",
       "      <td>1.710000</td>\n",
       "      <td>5038.0</td>\n",
       "      <td>160.360329</td>\n",
       "      <td>26.706231</td>\n",
       "      <td>-7.137257</td>\n",
       "      <td>-1109.319273</td>\n",
       "      <td>40.321594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05_puppi_57747_C0531+33_0558_3683.dm559.72.cal...</td>\n",
       "      <td>1450</td>\n",
       "      <td>1.639204</td>\n",
       "      <td>5031.0</td>\n",
       "      <td>37.747383</td>\n",
       "      <td>166.280492</td>\n",
       "      <td>-14.595503</td>\n",
       "      <td>-2109.819670</td>\n",
       "      <td>124.715830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>06_puppi_57747_C0531+33_0558_3687.dm559.72.cal...</td>\n",
       "      <td>1150</td>\n",
       "      <td>1.590000</td>\n",
       "      <td>5569.0</td>\n",
       "      <td>156.557708</td>\n",
       "      <td>12.416683</td>\n",
       "      <td>-52.066970</td>\n",
       "      <td>-6317.730503</td>\n",
       "      <td>30.736619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>07_puppi_57747_C0531+33_0558_3688.dm559.72.cal...</td>\n",
       "      <td>1590</td>\n",
       "      <td>1.720000</td>\n",
       "      <td>5821.0</td>\n",
       "      <td>102.259272</td>\n",
       "      <td>38.969184</td>\n",
       "      <td>-6.652439</td>\n",
       "      <td>-1028.172298</td>\n",
       "      <td>2.175251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>08_puppi_57747_C0531+33_0558_3689.dm559.72.cal...</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.990000</td>\n",
       "      <td>5666.0</td>\n",
       "      <td>171.395398</td>\n",
       "      <td>64.792725</td>\n",
       "      <td>-2.244076</td>\n",
       "      <td>-340.010864</td>\n",
       "      <td>0.162096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>09_puppi_57747_C0531+33_0558_3690.dm559.72.cal...</td>\n",
       "      <td>1600</td>\n",
       "      <td>2.240000</td>\n",
       "      <td>5041.0</td>\n",
       "      <td>133.024941</td>\n",
       "      <td>89.628038</td>\n",
       "      <td>-1.264288</td>\n",
       "      <td>-191.824534</td>\n",
       "      <td>9.704662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10_puppi_57747_C0531+33_0558_12568.dm559.72.ca...</td>\n",
       "      <td>800</td>\n",
       "      <td>-1.390000</td>\n",
       "      <td>5132.0</td>\n",
       "      <td>130.605616</td>\n",
       "      <td>47.074113</td>\n",
       "      <td>-5.470689</td>\n",
       "      <td>-846.471217</td>\n",
       "      <td>34.730967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11_puppi_57748_C0531+33_0594_2.dm559.72.calibP...</td>\n",
       "      <td>1800</td>\n",
       "      <td>1.720000</td>\n",
       "      <td>5196.0</td>\n",
       "      <td>182.570112</td>\n",
       "      <td>39.810758</td>\n",
       "      <td>-6.652439</td>\n",
       "      <td>-1028.948558</td>\n",
       "      <td>12.832905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12_puppi_57748_C0531+33_0594_48.dm559.72.calib...</td>\n",
       "      <td>1000</td>\n",
       "      <td>4.860000</td>\n",
       "      <td>4672.0</td>\n",
       "      <td>137.483220</td>\n",
       "      <td>28.822386</td>\n",
       "      <td>-6.725287</td>\n",
       "      <td>-1012.871320</td>\n",
       "      <td>2.193128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13_puppi_57748_C0531+33_0594_49.dm559.72.calib...</td>\n",
       "      <td>1900</td>\n",
       "      <td>1.740000</td>\n",
       "      <td>6789.0</td>\n",
       "      <td>141.354014</td>\n",
       "      <td>40.669821</td>\n",
       "      <td>-5.853528</td>\n",
       "      <td>-915.975701</td>\n",
       "      <td>0.710367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14_puppi_57748_C0531+33_0594_50.dm559.72.calib...</td>\n",
       "      <td>1550</td>\n",
       "      <td>4.760000</td>\n",
       "      <td>5313.0</td>\n",
       "      <td>159.613074</td>\n",
       "      <td>11.696348</td>\n",
       "      <td>-20.987668</td>\n",
       "      <td>-3424.532932</td>\n",
       "      <td>3.589301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15_puppi_57748_C0531+33_0594_1269.dm559.72.cal...</td>\n",
       "      <td>1550</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5922.0</td>\n",
       "      <td>65.081975</td>\n",
       "      <td>47.789210</td>\n",
       "      <td>-2.185040</td>\n",
       "      <td>-329.395555</td>\n",
       "      <td>0.926590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16_puppi_57772_C0531+33_0007_2695.dm559.72.cal...</td>\n",
       "      <td>700</td>\n",
       "      <td>1.710000</td>\n",
       "      <td>12309.0</td>\n",
       "      <td>124.668892</td>\n",
       "      <td>46.778198</td>\n",
       "      <td>-7.137257</td>\n",
       "      <td>-1129.833809</td>\n",
       "      <td>1.641886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             filename  edge      angle  \\\n",
       "0   01_puppi_57747_C0531+33_0558_5.dm559.72.calibP...  1525   1.720000   \n",
       "1   02_puppi_57747_C0531+33_0558_1183.dm559.72.cal...  1850  23.770000   \n",
       "2   03_puppi_57747_C0531+33_0558_1202.dm559.72.cal...  1975  -1.520000   \n",
       "3   04_puppi_57747_C0531+33_0558_25437.dm559.72.ca...  1450   1.710000   \n",
       "4   05_puppi_57747_C0531+33_0558_3683.dm559.72.cal...  1450   1.639204   \n",
       "5   06_puppi_57747_C0531+33_0558_3687.dm559.72.cal...  1150   1.590000   \n",
       "6   07_puppi_57747_C0531+33_0558_3688.dm559.72.cal...  1590   1.720000   \n",
       "7   08_puppi_57747_C0531+33_0558_3689.dm559.72.cal...  2000   1.990000   \n",
       "8   09_puppi_57747_C0531+33_0558_3690.dm559.72.cal...  1600   2.240000   \n",
       "9   10_puppi_57747_C0531+33_0558_12568.dm559.72.ca...   800  -1.390000   \n",
       "10  11_puppi_57748_C0531+33_0594_2.dm559.72.calibP...  1800   1.720000   \n",
       "11  12_puppi_57748_C0531+33_0594_48.dm559.72.calib...  1000   4.860000   \n",
       "12  13_puppi_57748_C0531+33_0594_49.dm559.72.calib...  1900   1.740000   \n",
       "13  14_puppi_57748_C0531+33_0594_50.dm559.72.calib...  1550   4.760000   \n",
       "14  15_puppi_57748_C0531+33_0594_1269.dm559.72.cal...  1550   2.000000   \n",
       "15  16_puppi_57772_C0531+33_0007_2695.dm559.72.cal...   700   1.710000   \n",
       "\n",
       "       peak      sigmax      sigmay      Slope  drift (mhz/ms)  \\\n",
       "0   10429.0  173.731526   47.364587  -6.652439    -1039.308995   \n",
       "1    5692.0  177.080918   44.911875  -4.736866     -739.419463   \n",
       "2    5668.0  169.749001   19.463885 -19.669528    -2813.966383   \n",
       "3    5038.0  160.360329   26.706231  -7.137257    -1109.319273   \n",
       "4    5031.0   37.747383  166.280492 -14.595503    -2109.819670   \n",
       "5    5569.0  156.557708   12.416683 -52.066970    -6317.730503   \n",
       "6    5821.0  102.259272   38.969184  -6.652439    -1028.172298   \n",
       "7    5666.0  171.395398   64.792725  -2.244076     -340.010864   \n",
       "8    5041.0  133.024941   89.628038  -1.264288     -191.824534   \n",
       "9    5132.0  130.605616   47.074113  -5.470689     -846.471217   \n",
       "10   5196.0  182.570112   39.810758  -6.652439    -1028.948558   \n",
       "11   4672.0  137.483220   28.822386  -6.725287    -1012.871320   \n",
       "12   6789.0  141.354014   40.669821  -5.853528     -915.975701   \n",
       "13   5313.0  159.613074   11.696348 -20.987668    -3424.532932   \n",
       "14   5922.0   65.081975   47.789210  -2.185040     -329.395555   \n",
       "15  12309.0  124.668892   46.778198  -7.137257    -1129.833809   \n",
       "\n",
       "    drift error (mhz/ms)  \n",
       "0               0.617101  \n",
       "1               1.279131  \n",
       "2               5.239849  \n",
       "3              40.321594  \n",
       "4             124.715830  \n",
       "5              30.736619  \n",
       "6               2.175251  \n",
       "7               0.162096  \n",
       "8               9.704662  \n",
       "9              34.730967  \n",
       "10             12.832905  \n",
       "11              2.193128  \n",
       "12              0.710367  \n",
       "13              3.589301  \n",
       "14              0.926590  \n",
       "15              1.641886  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bursts.head(16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Burst #1\n",
      "Data loaded\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Burst #2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loaded\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Burst #3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "Burst #4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loaded\n"
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    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Burst #5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Burst #6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Burst #7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Burst #8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "Burst #9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "Burst #10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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    },
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    },
    {
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     "output_type": "stream",
     "text": [
      "Burst #11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "Burst #12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "Burst #13\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loaded\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "Burst #14\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "Burst #15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "Burst #16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loaded\n"
     ]
    },
    {
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     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x135ff850>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def findcenter(burst):\n",
    "    burstnum = burst[0][:2].strip('0') if burst[0][:2][0] == '0' else burst[0][:2]\n",
    "    print('Burst #{}'.format(burstnum))\n",
    "\n",
    "    folder = 'data'\n",
    "    filename = burst.filename\n",
    "    edge = burst.edge\n",
    "\n",
    "    junk, nchan, nbin, I, Q, U, V = np.loadtxt('{}/{}'.format(folder, filename), delimiter=' ', unpack=True)\n",
    "    Q, U, V = None, None, None\n",
    "    n = len(junk)\n",
    "    print(\"Data loaded\")\n",
    "\n",
    "    binmax = int(nbin[n-1])+1\n",
    "    frequencymax = (int(nchan[n-1])+1)\n",
    "    intensity = np.zeros((frequencymax, binmax))\n",
    "\n",
    "    X = np.zeros(binmax)\n",
    "    Y = np.zeros(frequencymax)\n",
    "\n",
    "    # what are these?\n",
    "    tmin = 500 \n",
    "    tmax = 1500\n",
    "\n",
    "    #### 1. remove noise\n",
    "    intensitynoise1 = np.zeros(tmin-1)\n",
    "    intensitynoise2 = np.zeros(binmax-tmax)\n",
    "    for i in tqdm(range(frequencymax-50,51,-1), desc='noise removal', disable=False):\n",
    "\n",
    "        Y[i-1] = 4.15 + (i-1) * 1.5625 # ?\n",
    "\n",
    "        for j in range(1,tmin) :\n",
    "\n",
    "            intensitynoise1[j-1] = (I[j-1 + binmax*(frequencymax-i)])/(tmin-1)\n",
    "\n",
    "\n",
    "        for j in range(tmax+1,binmax+1) :\n",
    "\n",
    "            intensitynoise2[j-1-tmax] = (I[j-1 + binmax*(frequencymax-i)])/(binmax-tmax)\n",
    "\n",
    "        a = sum(intensitynoise1)\n",
    "        b = sum(intensitynoise2)\n",
    "\n",
    "        for j in range(1,binmax+1) :\n",
    "            X[j-1] = j-1\n",
    "            intensity[i-1,j-1] = I[j-1 + binmax*(frequencymax-i)] - (a+b)/2\n",
    "\n",
    "    burstwindow = intensity[:,edge:edge+frequencymax]\n",
    "    plt.title('Burst #{}'.format(burstnum))\n",
    "    freqspectrum = pd.DataFrame(burstwindow[:,:].sum(axis=1)[:, None])\n",
    "    data = freqspectrum[50:463][0]\n",
    "    x = data.keys()\n",
    "    xo = sum(x*data)/sum(data)\n",
    "    return xo # return the central frequency \n",
    "\n",
    "bursts['center'] = bursts.head(16).apply(findcenter, axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     228.755888\n",
       "1     209.060564\n",
       "2     249.051665\n",
       "3     322.029396\n",
       "4     214.396765\n",
       "5     263.938012\n",
       "6     160.167242\n",
       "7     296.067560\n",
       "8     216.215087\n",
       "9     310.581676\n",
       "10    263.108513\n",
       "11    194.684720\n",
       "12    315.871984\n",
       "13    239.876953\n",
       "14    113.702082\n",
       "15    167.929660\n",
       "16           NaN\n",
       "17           NaN\n",
       "Name: center, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bursts['center']"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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