spectrum_error_lines_plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "path = 'Dropbox/OpenUniversity/S382_Astrophysics/SDSS_Project/sdss_abell/spectrum_z03_z18/master_z03_z18/auto_z03_z18'\n",
    "file = 'spectrum_z03_z18_auto.csv' #this would be the SpecCombine csv output file\n",
    "df = pd.read_csv(os.path.join(path, file))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wavelength</th>\n",
       "      <th>flux</th>\n",
       "      <th>is_good</th>\n",
       "      <th>data_points</th>\n",
       "      <th>scatter</th>\n",
       "      <th>error</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>power_law_continuum_model</th>\n",
       "      <th>flux_minus_model</th>\n",
       "      <th>SD</th>\n",
       "      <th>SNR</th>\n",
       "      <th>+SEM</th>\n",
       "      <th>-SEM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1279</td>\n",
       "      <td>439.459226</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>70.662066</td>\n",
       "      <td>NaN</td>\n",
       "      <td>384.422391</td>\n",
       "      <td>55.036835</td>\n",
       "      <td>70.662066</td>\n",
       "      <td>6.219168</td>\n",
       "      <td>510.121292</td>\n",
       "      <td>368.797160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1280</td>\n",
       "      <td>436.743732</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>47.443813</td>\n",
       "      <td>26.924685</td>\n",
       "      <td>NaN</td>\n",
       "      <td>383.963882</td>\n",
       "      <td>52.779850</td>\n",
       "      <td>46.634923</td>\n",
       "      <td>9.365165</td>\n",
       "      <td>463.668417</td>\n",
       "      <td>409.819047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1281</td>\n",
       "      <td>362.859198</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>21.129959</td>\n",
       "      <td>20.269848</td>\n",
       "      <td>NaN</td>\n",
       "      <td>383.506277</td>\n",
       "      <td>-20.647078</td>\n",
       "      <td>40.539695</td>\n",
       "      <td>8.950714</td>\n",
       "      <td>383.129046</td>\n",
       "      <td>342.589351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1282</td>\n",
       "      <td>362.789802</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>42.506316</td>\n",
       "      <td>20.371594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>383.049573</td>\n",
       "      <td>-20.259772</td>\n",
       "      <td>40.743187</td>\n",
       "      <td>8.904306</td>\n",
       "      <td>383.161395</td>\n",
       "      <td>342.418208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1283</td>\n",
       "      <td>366.022180</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>17.272179</td>\n",
       "      <td>14.473232</td>\n",
       "      <td>NaN</td>\n",
       "      <td>382.593769</td>\n",
       "      <td>-16.571590</td>\n",
       "      <td>38.292572</td>\n",
       "      <td>9.558569</td>\n",
       "      <td>380.495411</td>\n",
       "      <td>351.548948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1284</td>\n",
       "      <td>358.241820</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>23.588301</td>\n",
       "      <td>14.879732</td>\n",
       "      <td>NaN</td>\n",
       "      <td>382.138862</td>\n",
       "      <td>-23.897042</td>\n",
       "      <td>36.447752</td>\n",
       "      <td>9.828914</td>\n",
       "      <td>373.121553</td>\n",
       "      <td>343.362088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1285</td>\n",
       "      <td>315.458069</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>16.675302</td>\n",
       "      <td>13.064740</td>\n",
       "      <td>NaN</td>\n",
       "      <td>381.684849</td>\n",
       "      <td>-66.226781</td>\n",
       "      <td>34.566053</td>\n",
       "      <td>9.126239</td>\n",
       "      <td>328.522809</td>\n",
       "      <td>302.393329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1286</td>\n",
       "      <td>336.179327</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>28.475498</td>\n",
       "      <td>13.461027</td>\n",
       "      <td>NaN</td>\n",
       "      <td>381.231729</td>\n",
       "      <td>-45.052402</td>\n",
       "      <td>35.614530</td>\n",
       "      <td>9.439387</td>\n",
       "      <td>349.640354</td>\n",
       "      <td>322.718300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1287</td>\n",
       "      <td>347.289320</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>33.694090</td>\n",
       "      <td>14.496967</td>\n",
       "      <td>NaN</td>\n",
       "      <td>380.779497</td>\n",
       "      <td>-33.490177</td>\n",
       "      <td>35.510172</td>\n",
       "      <td>9.779995</td>\n",
       "      <td>361.786287</td>\n",
       "      <td>332.792353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1288</td>\n",
       "      <td>379.044185</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>25.204758</td>\n",
       "      <td>12.470155</td>\n",
       "      <td>NaN</td>\n",
       "      <td>380.328153</td>\n",
       "      <td>-1.283969</td>\n",
       "      <td>35.270924</td>\n",
       "      <td>10.746648</td>\n",
       "      <td>391.514339</td>\n",
       "      <td>366.574030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1289</td>\n",
       "      <td>395.427894</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>29.167110</td>\n",
       "      <td>14.251941</td>\n",
       "      <td>NaN</td>\n",
       "      <td>379.877694</td>\n",
       "      <td>15.550200</td>\n",
       "      <td>34.909983</td>\n",
       "      <td>11.327072</td>\n",
       "      <td>409.679835</td>\n",
       "      <td>381.175953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1290</td>\n",
       "      <td>366.075190</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>18.092639</td>\n",
       "      <td>12.569569</td>\n",
       "      <td>NaN</td>\n",
       "      <td>379.428116</td>\n",
       "      <td>-13.352926</td>\n",
       "      <td>33.255953</td>\n",
       "      <td>11.007809</td>\n",
       "      <td>378.644759</td>\n",
       "      <td>353.505621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1291</td>\n",
       "      <td>380.967091</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>32.694083</td>\n",
       "      <td>13.023109</td>\n",
       "      <td>NaN</td>\n",
       "      <td>378.979419</td>\n",
       "      <td>1.987673</td>\n",
       "      <td>34.455907</td>\n",
       "      <td>11.056655</td>\n",
       "      <td>393.990200</td>\n",
       "      <td>367.943983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1292</td>\n",
       "      <td>442.357944</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>38.399339</td>\n",
       "      <td>15.417826</td>\n",
       "      <td>NaN</td>\n",
       "      <td>378.531599</td>\n",
       "      <td>63.826345</td>\n",
       "      <td>46.253479</td>\n",
       "      <td>9.563777</td>\n",
       "      <td>457.775770</td>\n",
       "      <td>426.940118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1293</td>\n",
       "      <td>408.884947</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>32.856624</td>\n",
       "      <td>13.218602</td>\n",
       "      <td>NaN</td>\n",
       "      <td>378.084654</td>\n",
       "      <td>30.800293</td>\n",
       "      <td>41.800888</td>\n",
       "      <td>9.781729</td>\n",
       "      <td>422.103549</td>\n",
       "      <td>395.666345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1294</td>\n",
       "      <td>406.113654</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>26.440316</td>\n",
       "      <td>12.707081</td>\n",
       "      <td>NaN</td>\n",
       "      <td>377.638581</td>\n",
       "      <td>28.475072</td>\n",
       "      <td>42.144618</td>\n",
       "      <td>9.636192</td>\n",
       "      <td>418.820734</td>\n",
       "      <td>393.406573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1295</td>\n",
       "      <td>395.982785</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>31.182099</td>\n",
       "      <td>13.686971</td>\n",
       "      <td>NaN</td>\n",
       "      <td>377.193379</td>\n",
       "      <td>18.789406</td>\n",
       "      <td>41.060914</td>\n",
       "      <td>9.643789</td>\n",
       "      <td>409.669756</td>\n",
       "      <td>382.295814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1296</td>\n",
       "      <td>379.332401</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>27.545776</td>\n",
       "      <td>11.158447</td>\n",
       "      <td>NaN</td>\n",
       "      <td>376.749045</td>\n",
       "      <td>2.583356</td>\n",
       "      <td>38.653996</td>\n",
       "      <td>9.813537</td>\n",
       "      <td>390.490848</td>\n",
       "      <td>368.173953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1297</td>\n",
       "      <td>351.375119</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>24.124722</td>\n",
       "      <td>10.059309</td>\n",
       "      <td>NaN</td>\n",
       "      <td>376.305576</td>\n",
       "      <td>-24.930457</td>\n",
       "      <td>41.475594</td>\n",
       "      <td>8.471853</td>\n",
       "      <td>361.434428</td>\n",
       "      <td>341.315810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1298</td>\n",
       "      <td>359.825954</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>26.466505</td>\n",
       "      <td>9.973806</td>\n",
       "      <td>NaN</td>\n",
       "      <td>375.862971</td>\n",
       "      <td>-16.037017</td>\n",
       "      <td>39.895223</td>\n",
       "      <td>9.019274</td>\n",
       "      <td>369.799760</td>\n",
       "      <td>349.852148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1299</td>\n",
       "      <td>370.480300</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>25.017219</td>\n",
       "      <td>8.586511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>375.421226</td>\n",
       "      <td>-4.940926</td>\n",
       "      <td>34.346044</td>\n",
       "      <td>10.786695</td>\n",
       "      <td>379.066811</td>\n",
       "      <td>361.893789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1300</td>\n",
       "      <td>367.534028</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>22.782950</td>\n",
       "      <td>8.957736</td>\n",
       "      <td>NaN</td>\n",
       "      <td>374.980340</td>\n",
       "      <td>-7.446312</td>\n",
       "      <td>40.060212</td>\n",
       "      <td>9.174540</td>\n",
       "      <td>376.491764</td>\n",
       "      <td>358.576292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1301</td>\n",
       "      <td>388.226436</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>23.489251</td>\n",
       "      <td>10.019519</td>\n",
       "      <td>NaN</td>\n",
       "      <td>374.540310</td>\n",
       "      <td>13.686126</td>\n",
       "      <td>44.808653</td>\n",
       "      <td>8.664095</td>\n",
       "      <td>398.245955</td>\n",
       "      <td>378.206916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1302</td>\n",
       "      <td>385.059743</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>26.293185</td>\n",
       "      <td>9.890984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>374.101134</td>\n",
       "      <td>10.958609</td>\n",
       "      <td>43.113799</td>\n",
       "      <td>8.931241</td>\n",
       "      <td>394.950727</td>\n",
       "      <td>375.168759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1303</td>\n",
       "      <td>405.466895</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>28.453307</td>\n",
       "      <td>9.356524</td>\n",
       "      <td>NaN</td>\n",
       "      <td>373.662809</td>\n",
       "      <td>31.804086</td>\n",
       "      <td>42.876979</td>\n",
       "      <td>9.456517</td>\n",
       "      <td>414.823419</td>\n",
       "      <td>396.110371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1304</td>\n",
       "      <td>419.167788</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>27.330196</td>\n",
       "      <td>10.171559</td>\n",
       "      <td>NaN</td>\n",
       "      <td>373.225334</td>\n",
       "      <td>45.942455</td>\n",
       "      <td>45.488594</td>\n",
       "      <td>9.214789</td>\n",
       "      <td>429.339347</td>\n",
       "      <td>408.996230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1305</td>\n",
       "      <td>387.170682</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>23.463165</td>\n",
       "      <td>9.466522</td>\n",
       "      <td>NaN</td>\n",
       "      <td>372.788706</td>\n",
       "      <td>14.381976</td>\n",
       "      <td>41.263615</td>\n",
       "      <td>9.382859</td>\n",
       "      <td>396.637204</td>\n",
       "      <td>377.704159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1306</td>\n",
       "      <td>390.682590</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>27.909863</td>\n",
       "      <td>8.825722</td>\n",
       "      <td>NaN</td>\n",
       "      <td>372.352922</td>\n",
       "      <td>18.329668</td>\n",
       "      <td>40.444540</td>\n",
       "      <td>9.659711</td>\n",
       "      <td>399.508312</td>\n",
       "      <td>381.856867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1307</td>\n",
       "      <td>402.827149</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>24.119624</td>\n",
       "      <td>9.458939</td>\n",
       "      <td>NaN</td>\n",
       "      <td>371.917981</td>\n",
       "      <td>30.909168</td>\n",
       "      <td>42.301663</td>\n",
       "      <td>9.522726</td>\n",
       "      <td>412.286088</td>\n",
       "      <td>393.368209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1308</td>\n",
       "      <td>380.458479</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>23.174037</td>\n",
       "      <td>9.110764</td>\n",
       "      <td>NaN</td>\n",
       "      <td>371.483879</td>\n",
       "      <td>8.974600</td>\n",
       "      <td>39.712898</td>\n",
       "      <td>9.580224</td>\n",
       "      <td>389.569243</td>\n",
       "      <td>371.347716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5203</th>\n",
       "      <td>6482</td>\n",
       "      <td>35.200137</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.865976</td>\n",
       "      <td>1.559453</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.249302</td>\n",
       "      <td>2.950835</td>\n",
       "      <td>2.205399</td>\n",
       "      <td>15.960890</td>\n",
       "      <td>36.759590</td>\n",
       "      <td>33.640684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5204</th>\n",
       "      <td>6483</td>\n",
       "      <td>34.799461</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1.863754</td>\n",
       "      <td>1.275051</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.241706</td>\n",
       "      <td>2.557756</td>\n",
       "      <td>2.550102</td>\n",
       "      <td>13.646300</td>\n",
       "      <td>36.074513</td>\n",
       "      <td>33.524410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5205</th>\n",
       "      <td>6484</td>\n",
       "      <td>34.175254</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2.402038</td>\n",
       "      <td>1.319984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.234113</td>\n",
       "      <td>1.941141</td>\n",
       "      <td>2.639967</td>\n",
       "      <td>12.945332</td>\n",
       "      <td>35.495238</td>\n",
       "      <td>32.855270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5206</th>\n",
       "      <td>6485</td>\n",
       "      <td>36.704960</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.911343</td>\n",
       "      <td>1.922466</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.226523</td>\n",
       "      <td>4.478437</td>\n",
       "      <td>2.718778</td>\n",
       "      <td>13.500538</td>\n",
       "      <td>38.627426</td>\n",
       "      <td>34.782494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5207</th>\n",
       "      <td>6486</td>\n",
       "      <td>35.066142</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2.483615</td>\n",
       "      <td>1.222480</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.218937</td>\n",
       "      <td>2.847205</td>\n",
       "      <td>2.444959</td>\n",
       "      <td>14.342220</td>\n",
       "      <td>36.288621</td>\n",
       "      <td>33.843662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5208</th>\n",
       "      <td>6487</td>\n",
       "      <td>37.079212</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2.670827</td>\n",
       "      <td>1.408202</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.211353</td>\n",
       "      <td>4.867860</td>\n",
       "      <td>2.816405</td>\n",
       "      <td>13.165442</td>\n",
       "      <td>38.487415</td>\n",
       "      <td>35.671010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5209</th>\n",
       "      <td>6488</td>\n",
       "      <td>38.139619</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.241050</td>\n",
       "      <td>1.925329</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.203772</td>\n",
       "      <td>5.935847</td>\n",
       "      <td>2.722826</td>\n",
       "      <td>14.007364</td>\n",
       "      <td>40.064948</td>\n",
       "      <td>36.214290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5210</th>\n",
       "      <td>6489</td>\n",
       "      <td>34.804446</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2.686285</td>\n",
       "      <td>1.211089</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.196194</td>\n",
       "      <td>2.608252</td>\n",
       "      <td>2.422178</td>\n",
       "      <td>14.369071</td>\n",
       "      <td>36.015535</td>\n",
       "      <td>33.593357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5211</th>\n",
       "      <td>6490</td>\n",
       "      <td>38.511596</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1.025839</td>\n",
       "      <td>1.382452</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.188619</td>\n",
       "      <td>6.322977</td>\n",
       "      <td>2.764903</td>\n",
       "      <td>13.928733</td>\n",
       "      <td>39.894048</td>\n",
       "      <td>37.129145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5212</th>\n",
       "      <td>6491</td>\n",
       "      <td>39.838651</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4.994918</td>\n",
       "      <td>1.961911</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.181047</td>\n",
       "      <td>7.657604</td>\n",
       "      <td>2.774562</td>\n",
       "      <td>14.358539</td>\n",
       "      <td>41.800562</td>\n",
       "      <td>37.876739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5213</th>\n",
       "      <td>6492</td>\n",
       "      <td>35.450826</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2.519280</td>\n",
       "      <td>1.195374</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.173478</td>\n",
       "      <td>3.277348</td>\n",
       "      <td>2.390748</td>\n",
       "      <td>14.828343</td>\n",
       "      <td>36.646200</td>\n",
       "      <td>34.255453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5214</th>\n",
       "      <td>6493</td>\n",
       "      <td>36.548730</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2.114583</td>\n",
       "      <td>1.329870</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.165912</td>\n",
       "      <td>4.382818</td>\n",
       "      <td>2.659739</td>\n",
       "      <td>13.741470</td>\n",
       "      <td>37.878600</td>\n",
       "      <td>35.218860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5215</th>\n",
       "      <td>6494</td>\n",
       "      <td>34.711165</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.645016</td>\n",
       "      <td>1.856967</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.158349</td>\n",
       "      <td>2.552816</td>\n",
       "      <td>2.626148</td>\n",
       "      <td>13.217519</td>\n",
       "      <td>36.568132</td>\n",
       "      <td>32.854198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5216</th>\n",
       "      <td>6495</td>\n",
       "      <td>37.525723</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2.303375</td>\n",
       "      <td>1.261888</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.150788</td>\n",
       "      <td>5.374935</td>\n",
       "      <td>2.523776</td>\n",
       "      <td>14.868881</td>\n",
       "      <td>38.787611</td>\n",
       "      <td>36.263835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5217</th>\n",
       "      <td>6496</td>\n",
       "      <td>38.542747</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3.271796</td>\n",
       "      <td>1.703344</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.143231</td>\n",
       "      <td>6.399516</td>\n",
       "      <td>2.950279</td>\n",
       "      <td>13.064102</td>\n",
       "      <td>40.246092</td>\n",
       "      <td>36.839403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5218</th>\n",
       "      <td>6497</td>\n",
       "      <td>37.353156</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.577430</td>\n",
       "      <td>1.863169</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.135677</td>\n",
       "      <td>5.217479</td>\n",
       "      <td>2.634919</td>\n",
       "      <td>14.176207</td>\n",
       "      <td>39.216325</td>\n",
       "      <td>35.489987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5219</th>\n",
       "      <td>6498</td>\n",
       "      <td>37.254067</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.675240</td>\n",
       "      <td>1.521850</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.128125</td>\n",
       "      <td>5.125942</td>\n",
       "      <td>2.635921</td>\n",
       "      <td>14.133225</td>\n",
       "      <td>38.775917</td>\n",
       "      <td>35.732218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5220</th>\n",
       "      <td>6499</td>\n",
       "      <td>39.998275</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.089388</td>\n",
       "      <td>1.635990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.120577</td>\n",
       "      <td>7.877698</td>\n",
       "      <td>2.833618</td>\n",
       "      <td>14.115618</td>\n",
       "      <td>41.634265</td>\n",
       "      <td>38.362284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5221</th>\n",
       "      <td>6500</td>\n",
       "      <td>39.654649</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.049847</td>\n",
       "      <td>2.021598</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.113031</td>\n",
       "      <td>7.541618</td>\n",
       "      <td>2.858971</td>\n",
       "      <td>13.870251</td>\n",
       "      <td>41.676247</td>\n",
       "      <td>37.633051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5222</th>\n",
       "      <td>6501</td>\n",
       "      <td>35.847156</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3.923575</td>\n",
       "      <td>1.452339</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.105489</td>\n",
       "      <td>3.741667</td>\n",
       "      <td>2.515524</td>\n",
       "      <td>14.250372</td>\n",
       "      <td>37.299494</td>\n",
       "      <td>34.394817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5223</th>\n",
       "      <td>6502</td>\n",
       "      <td>35.944520</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3.137278</td>\n",
       "      <td>1.597070</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.097949</td>\n",
       "      <td>3.846571</td>\n",
       "      <td>2.766207</td>\n",
       "      <td>12.994154</td>\n",
       "      <td>37.541591</td>\n",
       "      <td>34.347450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5224</th>\n",
       "      <td>6503</td>\n",
       "      <td>50.167850</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.879597</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.090412</td>\n",
       "      <td>18.077437</td>\n",
       "      <td>2.879597</td>\n",
       "      <td>17.421831</td>\n",
       "      <td>53.047446</td>\n",
       "      <td>47.288253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5225</th>\n",
       "      <td>6504</td>\n",
       "      <td>47.790735</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.738268</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.082878</td>\n",
       "      <td>15.707856</td>\n",
       "      <td>2.738268</td>\n",
       "      <td>17.452906</td>\n",
       "      <td>50.529003</td>\n",
       "      <td>45.052467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5226</th>\n",
       "      <td>6506</td>\n",
       "      <td>44.051668</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.721568</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.067819</td>\n",
       "      <td>11.983849</td>\n",
       "      <td>2.721568</td>\n",
       "      <td>16.186133</td>\n",
       "      <td>46.773236</td>\n",
       "      <td>41.330100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5227</th>\n",
       "      <td>6507</td>\n",
       "      <td>45.297697</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.765799</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.060294</td>\n",
       "      <td>13.237403</td>\n",
       "      <td>2.765799</td>\n",
       "      <td>16.377799</td>\n",
       "      <td>48.063496</td>\n",
       "      <td>42.531899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5228</th>\n",
       "      <td>6509</td>\n",
       "      <td>43.611472</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.621564</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.045253</td>\n",
       "      <td>11.566219</td>\n",
       "      <td>2.621564</td>\n",
       "      <td>16.635667</td>\n",
       "      <td>46.233037</td>\n",
       "      <td>40.989908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5229</th>\n",
       "      <td>6510</td>\n",
       "      <td>44.491810</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.608400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.037737</td>\n",
       "      <td>12.454073</td>\n",
       "      <td>2.608400</td>\n",
       "      <td>17.057127</td>\n",
       "      <td>47.100210</td>\n",
       "      <td>41.883410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5230</th>\n",
       "      <td>6512</td>\n",
       "      <td>52.324994</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.797475</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.022713</td>\n",
       "      <td>20.302282</td>\n",
       "      <td>2.797475</td>\n",
       "      <td>18.704366</td>\n",
       "      <td>55.122469</td>\n",
       "      <td>49.527520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5231</th>\n",
       "      <td>6513</td>\n",
       "      <td>43.335573</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.718371</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.015205</td>\n",
       "      <td>11.320368</td>\n",
       "      <td>2.718371</td>\n",
       "      <td>15.941743</td>\n",
       "      <td>46.053944</td>\n",
       "      <td>40.617202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5232</th>\n",
       "      <td>6515</td>\n",
       "      <td>44.464527</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.739051</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.000199</td>\n",
       "      <td>12.464328</td>\n",
       "      <td>2.739051</td>\n",
       "      <td>16.233552</td>\n",
       "      <td>47.203578</td>\n",
       "      <td>41.725476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5233 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      wavelength        flux  is_good  data_points    scatter      error  \\\n",
       "0           1279  439.459226        1            1   0.000000  70.662066   \n",
       "1           1280  436.743732        1            3  47.443813  26.924685   \n",
       "2           1281  362.859198        1            4  21.129959  20.269848   \n",
       "3           1282  362.789802        1            4  42.506316  20.371594   \n",
       "4           1283  366.022180        1            7  17.272179  14.473232   \n",
       "5           1284  358.241820        1            6  23.588301  14.879732   \n",
       "6           1285  315.458069        1            7  16.675302  13.064740   \n",
       "7           1286  336.179327        1            7  28.475498  13.461027   \n",
       "8           1287  347.289320        1            6  33.694090  14.496967   \n",
       "9           1288  379.044185        1            8  25.204758  12.470155   \n",
       "10          1289  395.427894        1            6  29.167110  14.251941   \n",
       "11          1290  366.075190        1            7  18.092639  12.569569   \n",
       "12          1291  380.967091        1            7  32.694083  13.023109   \n",
       "13          1292  442.357944        1            9  38.399339  15.417826   \n",
       "14          1293  408.884947        1           10  32.856624  13.218602   \n",
       "15          1294  406.113654        1           11  26.440316  12.707081   \n",
       "16          1295  395.982785        1            9  31.182099  13.686971   \n",
       "17          1296  379.332401        1           12  27.545776  11.158447   \n",
       "18          1297  351.375119        1           17  24.124722  10.059309   \n",
       "19          1298  359.825954        1           16  26.466505   9.973806   \n",
       "20          1299  370.480300        1           16  25.017219   8.586511   \n",
       "21          1300  367.534028        1           20  22.782950   8.957736   \n",
       "22          1301  388.226436        1           20  23.489251  10.019519   \n",
       "23          1302  385.059743        1           19  26.293185   9.890984   \n",
       "24          1303  405.466895        1           21  28.453307   9.356524   \n",
       "25          1304  419.167788        1           20  27.330196  10.171559   \n",
       "26          1305  387.170682        1           19  23.463165   9.466522   \n",
       "27          1306  390.682590        1           21  27.909863   8.825722   \n",
       "28          1307  402.827149        1           20  24.119624   9.458939   \n",
       "29          1308  380.458479        1           19  23.174037   9.110764   \n",
       "...          ...         ...      ...          ...        ...        ...   \n",
       "5203        6482   35.200137        1            2   2.865976   1.559453   \n",
       "5204        6483   34.799461        1            4   1.863754   1.275051   \n",
       "5205        6484   34.175254        1            4   2.402038   1.319984   \n",
       "5206        6485   36.704960        1            2   2.911343   1.922466   \n",
       "5207        6486   35.066142        1            4   2.483615   1.222480   \n",
       "5208        6487   37.079212        1            4   2.670827   1.408202   \n",
       "5209        6488   38.139619        1            2   3.241050   1.925329   \n",
       "5210        6489   34.804446        1            4   2.686285   1.211089   \n",
       "5211        6490   38.511596        1            4   1.025839   1.382452   \n",
       "5212        6491   39.838651        1            2   4.994918   1.961911   \n",
       "5213        6492   35.450826        1            4   2.519280   1.195374   \n",
       "5214        6493   36.548730        1            4   2.114583   1.329870   \n",
       "5215        6494   34.711165        1            2   0.645016   1.856967   \n",
       "5216        6495   37.525723        1            4   2.303375   1.261888   \n",
       "5217        6496   38.542747        1            3   3.271796   1.703344   \n",
       "5218        6497   37.353156        1            2   0.577430   1.863169   \n",
       "5219        6498   37.254067        1            3   0.675240   1.521850   \n",
       "5220        6499   39.998275        1            3   2.089388   1.635990   \n",
       "5221        6500   39.654649        1            2   1.049847   2.021598   \n",
       "5222        6501   35.847156        1            3   3.923575   1.452339   \n",
       "5223        6502   35.944520        1            3   3.137278   1.597070   \n",
       "5224        6503   50.167850        1            1   0.000000   2.879597   \n",
       "5225        6504   47.790735        1            1   0.000000   2.738268   \n",
       "5226        6506   44.051668        1            1   0.000000   2.721568   \n",
       "5227        6507   45.297697        1            1   0.000000   2.765799   \n",
       "5228        6509   43.611472        1            1   0.000000   2.621564   \n",
       "5229        6510   44.491810        1            1   0.000000   2.608400   \n",
       "5230        6512   52.324994        1            1   0.000000   2.797475   \n",
       "5231        6513   43.335573        1            1   0.000000   2.718371   \n",
       "5232        6515   44.464527        1            1   0.000000   2.739051   \n",
       "\n",
       "      Unnamed: 6  power_law_continuum_model  flux_minus_model         SD  \\\n",
       "0            NaN                 384.422391         55.036835  70.662066   \n",
       "1            NaN                 383.963882         52.779850  46.634923   \n",
       "2            NaN                 383.506277        -20.647078  40.539695   \n",
       "3            NaN                 383.049573        -20.259772  40.743187   \n",
       "4            NaN                 382.593769        -16.571590  38.292572   \n",
       "5            NaN                 382.138862        -23.897042  36.447752   \n",
       "6            NaN                 381.684849        -66.226781  34.566053   \n",
       "7            NaN                 381.231729        -45.052402  35.614530   \n",
       "8            NaN                 380.779497        -33.490177  35.510172   \n",
       "9            NaN                 380.328153         -1.283969  35.270924   \n",
       "10           NaN                 379.877694         15.550200  34.909983   \n",
       "11           NaN                 379.428116        -13.352926  33.255953   \n",
       "12           NaN                 378.979419          1.987673  34.455907   \n",
       "13           NaN                 378.531599         63.826345  46.253479   \n",
       "14           NaN                 378.084654         30.800293  41.800888   \n",
       "15           NaN                 377.638581         28.475072  42.144618   \n",
       "16           NaN                 377.193379         18.789406  41.060914   \n",
       "17           NaN                 376.749045          2.583356  38.653996   \n",
       "18           NaN                 376.305576        -24.930457  41.475594   \n",
       "19           NaN                 375.862971        -16.037017  39.895223   \n",
       "20           NaN                 375.421226         -4.940926  34.346044   \n",
       "21           NaN                 374.980340         -7.446312  40.060212   \n",
       "22           NaN                 374.540310         13.686126  44.808653   \n",
       "23           NaN                 374.101134         10.958609  43.113799   \n",
       "24           NaN                 373.662809         31.804086  42.876979   \n",
       "25           NaN                 373.225334         45.942455  45.488594   \n",
       "26           NaN                 372.788706         14.381976  41.263615   \n",
       "27           NaN                 372.352922         18.329668  40.444540   \n",
       "28           NaN                 371.917981         30.909168  42.301663   \n",
       "29           NaN                 371.483879          8.974600  39.712898   \n",
       "...          ...                        ...               ...        ...   \n",
       "5203         NaN                  32.249302          2.950835   2.205399   \n",
       "5204         NaN                  32.241706          2.557756   2.550102   \n",
       "5205         NaN                  32.234113          1.941141   2.639967   \n",
       "5206         NaN                  32.226523          4.478437   2.718778   \n",
       "5207         NaN                  32.218937          2.847205   2.444959   \n",
       "5208         NaN                  32.211353          4.867860   2.816405   \n",
       "5209         NaN                  32.203772          5.935847   2.722826   \n",
       "5210         NaN                  32.196194          2.608252   2.422178   \n",
       "5211         NaN                  32.188619          6.322977   2.764903   \n",
       "5212         NaN                  32.181047          7.657604   2.774562   \n",
       "5213         NaN                  32.173478          3.277348   2.390748   \n",
       "5214         NaN                  32.165912          4.382818   2.659739   \n",
       "5215         NaN                  32.158349          2.552816   2.626148   \n",
       "5216         NaN                  32.150788          5.374935   2.523776   \n",
       "5217         NaN                  32.143231          6.399516   2.950279   \n",
       "5218         NaN                  32.135677          5.217479   2.634919   \n",
       "5219         NaN                  32.128125          5.125942   2.635921   \n",
       "5220         NaN                  32.120577          7.877698   2.833618   \n",
       "5221         NaN                  32.113031          7.541618   2.858971   \n",
       "5222         NaN                  32.105489          3.741667   2.515524   \n",
       "5223         NaN                  32.097949          3.846571   2.766207   \n",
       "5224         NaN                  32.090412         18.077437   2.879597   \n",
       "5225         NaN                  32.082878         15.707856   2.738268   \n",
       "5226         NaN                  32.067819         11.983849   2.721568   \n",
       "5227         NaN                  32.060294         13.237403   2.765799   \n",
       "5228         NaN                  32.045253         11.566219   2.621564   \n",
       "5229         NaN                  32.037737         12.454073   2.608400   \n",
       "5230         NaN                  32.022713         20.302282   2.797475   \n",
       "5231         NaN                  32.015205         11.320368   2.718371   \n",
       "5232         NaN                  32.000199         12.464328   2.739051   \n",
       "\n",
       "            SNR        +SEM        -SEM  \n",
       "0      6.219168  510.121292  368.797160  \n",
       "1      9.365165  463.668417  409.819047  \n",
       "2      8.950714  383.129046  342.589351  \n",
       "3      8.904306  383.161395  342.418208  \n",
       "4      9.558569  380.495411  351.548948  \n",
       "5      9.828914  373.121553  343.362088  \n",
       "6      9.126239  328.522809  302.393329  \n",
       "7      9.439387  349.640354  322.718300  \n",
       "8      9.779995  361.786287  332.792353  \n",
       "9     10.746648  391.514339  366.574030  \n",
       "10    11.327072  409.679835  381.175953  \n",
       "11    11.007809  378.644759  353.505621  \n",
       "12    11.056655  393.990200  367.943983  \n",
       "13     9.563777  457.775770  426.940118  \n",
       "14     9.781729  422.103549  395.666345  \n",
       "15     9.636192  418.820734  393.406573  \n",
       "16     9.643789  409.669756  382.295814  \n",
       "17     9.813537  390.490848  368.173953  \n",
       "18     8.471853  361.434428  341.315810  \n",
       "19     9.019274  369.799760  349.852148  \n",
       "20    10.786695  379.066811  361.893789  \n",
       "21     9.174540  376.491764  358.576292  \n",
       "22     8.664095  398.245955  378.206916  \n",
       "23     8.931241  394.950727  375.168759  \n",
       "24     9.456517  414.823419  396.110371  \n",
       "25     9.214789  429.339347  408.996230  \n",
       "26     9.382859  396.637204  377.704159  \n",
       "27     9.659711  399.508312  381.856867  \n",
       "28     9.522726  412.286088  393.368209  \n",
       "29     9.580224  389.569243  371.347716  \n",
       "...         ...         ...         ...  \n",
       "5203  15.960890   36.759590   33.640684  \n",
       "5204  13.646300   36.074513   33.524410  \n",
       "5205  12.945332   35.495238   32.855270  \n",
       "5206  13.500538   38.627426   34.782494  \n",
       "5207  14.342220   36.288621   33.843662  \n",
       "5208  13.165442   38.487415   35.671010  \n",
       "5209  14.007364   40.064948   36.214290  \n",
       "5210  14.369071   36.015535   33.593357  \n",
       "5211  13.928733   39.894048   37.129145  \n",
       "5212  14.358539   41.800562   37.876739  \n",
       "5213  14.828343   36.646200   34.255453  \n",
       "5214  13.741470   37.878600   35.218860  \n",
       "5215  13.217519   36.568132   32.854198  \n",
       "5216  14.868881   38.787611   36.263835  \n",
       "5217  13.064102   40.246092   36.839403  \n",
       "5218  14.176207   39.216325   35.489987  \n",
       "5219  14.133225   38.775917   35.732218  \n",
       "5220  14.115618   41.634265   38.362284  \n",
       "5221  13.870251   41.676247   37.633051  \n",
       "5222  14.250372   37.299494   34.394817  \n",
       "5223  12.994154   37.541591   34.347450  \n",
       "5224  17.421831   53.047446   47.288253  \n",
       "5225  17.452906   50.529003   45.052467  \n",
       "5226  16.186133   46.773236   41.330100  \n",
       "5227  16.377799   48.063496   42.531899  \n",
       "5228  16.635667   46.233037   40.989908  \n",
       "5229  17.057127   47.100210   41.883410  \n",
       "5230  18.704366   55.122469   49.527520  \n",
       "5231  15.941743   46.053944   40.617202  \n",
       "5232  16.233552   47.203578   41.725476  \n",
       "\n",
       "[5233 rows x 13 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SD']=df['error']*df['data_points']**0.5\n",
    "df['SNR']=df['flux']/df['SD']\n",
    "s = 1 #s=SNR_scaling_factor\n",
    "df['SNR'] = df['SNR']*s\n",
    "df['+SEM'] = df['flux']+df['error']\n",
    "df['-SEM'] = df['flux']-df['error']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cd61940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "line_1 = 1549; # y1 =  # C IV emission line\n",
    "line_2 = 2798; # y1 =  # Mg II emission line\n",
    "line_3 = 4861; # y1 =  # H-beta I emission line\n",
    "line_4 = 4340; # y1 =  # H-gamma emission line\n",
    "line_5 = 1908; # y1 =  # CIII] emission line - semi-forbidden line\n",
    "line_6 = 1396; # y1 =  # Si IV emission line\n",
    "line_7 = 5006; # y1 =  # [O III] emission line - semi-forbidden line\n",
    "line_8 = 3203; # y1 =  # He II emission line\n",
    "line_9 = 2326; # y1 =  # C II] emission line - semi-forbidden line\n",
    "line_10 = 2418; # y1 =  # [Ne IV] emission line - semi-forbidden line\n",
    "line_11 = 5875; # y1 =  # He I \n",
    "line_12 = 4101; # y1 =  # H-delta emission line\n",
    "line_13 = 3726; # y1 =  # [O II] forbidden emission\n",
    "line_14 = 3868; # y1 =  # [Ne III] emission line - forbidden\n",
    "\n",
    "wave = df['wavelength']\n",
    "err_1 = df['flux']+df['SD'] # +SD\n",
    "err_2 = df['flux']-df['SD'] # -SD\n",
    "# err_3 = df['flux']+*df['error'] # +SEM\n",
    "# err_4 = df['flux']-*df['error'] # -SEM\n",
    "\n",
    "ax1 = df.plot(x='wavelength', y='flux', lw=0.3, color='red', label='mean') \n",
    "\n",
    "ax2 = df.plot(x='wavelength', y='+SEM', lw=0.000001\n",
    "             , color='yellow', label='±SD', ax=ax1)\n",
    "\n",
    "# ax3 = df.plot(x='wavelength', y ='+SEM', lw=0.05\n",
    " #            , color='grey', label='±SD', ax=ax1)\n",
    "\n",
    "#ax2 = df.plot(x='wavelength', y='SNR', lw=0.2\n",
    "#             , color='red', ax=ax1)\n",
    "\n",
    "plt.legend()\n",
    "plt.title('Composite Spectrum Of Broadline QSO, z = 0.3 - 1.8', fontsize=13, color='black')\n",
    "# plt.grid()\n",
    "\n",
    "plt.fill_between(wave, err_1, err_2, color='#CCCCCC') # ±SD\n",
    "# plt.fill_between(wave, err_3, err_4, color='grey') # ±SEM\n",
    "\n",
    "# ax1.set_facecolor('black') # creates a black background to the plot\n",
    "\n",
    "ax1.text(0.82, 0.75, '(n = 297)',\n",
    "        verticalalignment='bottom', horizontalalignment='left',\n",
    "        transform=ax1.transAxes,\n",
    "        color='blue', fontsize=10)\n",
    "\n",
    "# emission & absorption line labelling\n",
    "plt.vlines(x=line_1, ymin=0, ymax=480, label='line_1', linestyles='dashed', color='purple', lw=0.2)\n",
    "plt.text(line_1, 500, 'C IV', color='purple', fontsize=5, rotation=45)\n",
    "\n",
    "plt.vlines(x=line_2, ymin=0, ymax=480, label='line_1', linestyles='dashed', color='purple', lw=0.2)\n",
    "plt.text(line_2, 500,'Mg II', color='purple', fontsize=5, rotation=45)\n",
    "\n",
    "plt.vlines(x=line_3, ymin=0, ymax=480, label='line_1', linestyles='dashed', color='purple', lw=0.2)\n",
    "plt.text(line_3, 490, r'H$\\beta$', color='purple', fontsize=5, rotation=45)\n",
    "\n",
    "plt.vlines(x=line_4, ymin=0, ymax=480, label='line_1', linestyles='dashed', color='purple', lw=0.2)\n",
    "plt.text(line_4, 495, r'H$\\gamma$', color='purple', fontsize=5, rotation=45)\n",
    "\n",
    "plt.vlines(x=line_5, ymin=0, ymax=480, label='line_1', linestyles='dashed', color='purple', lw=0.2)\n",
    "plt.text(line_5, 500,'CIII]', color='purple', fontsize=5, rotation=45)\n",
    "\n",
    "plt.vlines(x=line_6, ymin=0, ymax=500, label='line_1', linestyles='dashed', color='purple', lw=0.2)\n",
    "plt.text(line_6, 518,'Si IV', color='purple', fontsize=5, rotation=45)\n",
    "\n",
    "plt.vlines(x=line_7, ymin=0, ymax=460, label='line_1', linestyles='dashed', color='purple', lw=0.2)\n",
    "plt.text(line_7, 480,'[O III]', color='purple', fontsize=5, rotation=45)\n",
    "\n",
    "plt.vlines(x=line_8, ymin=0, ymax=480, label='line_1', linestyles='dashed', color='purple', lw=0.2)\n",
    "plt.text(line_8, 500,'He II', color='purple', fontsize=5, rotation=45)\n",
    "\n",
    "plt.vlines(x=line_9, ymin=0, ymax=480, label='line_1', linestyles='dashed', color='purple', lw=0.2)\n",
    "plt.text(line_9, 500, 'C II]', color='purple', fontsize=5, rotation=45)\n",
    "\n",
    "plt.vlines(x=line_10, ymin=0, ymax=460, label='line_1', linestyles='dashed', color='purple', lw=0.2)\n",
    "plt.text(line_10, 485,'[Ne IV]', color='purple', fontsize=5, rotation=45)\n",
    "\n",
    "plt.vlines(x=line_11, ymin=0, ymax=340, label='line_1', linestyles='dashed', color='purple', lw=0.2)\n",
    "plt.text(line_11, 355,'He I', color='purple', fontsize=5, rotation=45)\n",
    "\n",
    "plt.vlines(x=line_12, ymin=0, ymax=460, label='line_1', linestyles='dashed', color='purple', lw=0.2)\n",
    "plt.text(line_12, 475, r'H$\\delta$', color='purple', fontsize=5, rotation=45)\n",
    "\n",
    "plt.vlines(x=line_13, ymin=0, ymax=460, label='line_1', linestyles='dashed', color='purple', lw=0.2)\n",
    "plt.text(line_13, 480,'[O II]', color='purple', fontsize=5, rotation=45)\n",
    "\n",
    "plt.vlines(x=line_14, ymin=0, ymax=340, label='line_1', linestyles='dashed', color='purple', lw=0.2)\n",
    "plt.text(line_14, 360, '[Ne III]', color='purple', fontsize=4, rotation=45)\n",
    "\n",
    "plt.xlim(1250, 6550)\n",
    "plt.ylim(0,530)\n",
    "\n",
    "plt.xlabel(r'Emitted Wavelength / $\\AA$',  fontsize=13, color='grey')\n",
    "plt.ylabel('Spectral Flux Density / Arbitrary Units', fontsize=10, color='grey')\n",
    "\n",
    "plt.savefig(os.path.join(path, 'spectrum_z03_z18_auto.png'), dpi = 1080)\n",
    "#plt.savefig(os.path.join(path, 'spectrum_z03_z18_auto.pdf'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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