03-Photometry_CV.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astroquery.svo_fps import SvoFps\n",
    "import numpy as np\n",
    "from astropy.table import Table\n",
    "from synphot import SourceSpectrum, Observation, units, SpectralElement\n",
    "from synphot.models import Empirical1D\n",
    "from astropy.io import fits\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import astropy.units as u\n",
    "from specutils import Spectrum1D\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the list of OAJ/JPCam filters\n",
    "JPAS_filter_list = SvoFps.get_filter_list(facility='OAJ', instrument='JPCam').to_pandas()\n",
    "# Filters of interest (56 narrow - J0378)\n",
    "JPAS_filter_ids = ('OAJ/JPAS.J0390', 'OAJ/JPAS.J0400',\n",
    "                   'OAJ/JPAS.J0410', 'OAJ/JPAS.J0420', 'OAJ/JPAS.J0430', 'OAJ/JPAS.J0440', 'OAJ/JPAS.J0450',\n",
    "                   'OAJ/JPAS.J0460', 'OAJ/JPAS.J0470', 'OAJ/JPAS.J0480', 'OAJ/JPAS.J0490',\n",
    "                   'OAJ/JPAS.J0500', 'OAJ/JPAS.J0510', 'OAJ/JPAS.J0520', 'OAJ/JPAS.J0530', 'OAJ/JPAS.J0540',\n",
    "                   'OAJ/JPAS.J0550', 'OAJ/JPAS.J0560', 'OAJ/JPAS.J0570', 'OAJ/JPAS.J0580', 'OAJ/JPAS.J0590',\n",
    "                   'OAJ/JPAS.J0600', 'OAJ/JPAS.J0610', 'OAJ/JPAS.J0620', 'OAJ/JPAS.J0630',\n",
    "                   'OAJ/JPAS.J0640', 'OAJ/JPAS.J0650', 'OAJ/JPAS.J0660', 'OAJ/JPAS.J0670', 'OAJ/JPAS.J0680',\n",
    "                   'OAJ/JPAS.J0690', 'OAJ/JPAS.J0700', 'OAJ/JPAS.J0710', 'OAJ/JPAS.J0720', 'OAJ/JPAS.J0730',\n",
    "                   'OAJ/JPAS.J0740', 'OAJ/JPAS.J0750', 'OAJ/JPAS.J0760', 'OAJ/JPAS.J0770',\n",
    "                   'OAJ/JPAS.J0780', 'OAJ/JPAS.J0790', 'OAJ/JPAS.J0800', 'OAJ/JPAS.J0810', 'OAJ/JPAS.J0820',\n",
    "                   'OAJ/JPAS.J0830', 'OAJ/JPAS.J0840', 'OAJ/JPAS.J0850', 'OAJ/JPAS.J0860', 'OAJ/JPAS.J0870',\n",
    "                   'OAJ/JPAS.J0880', 'OAJ/JPAS.J0890', 'OAJ/JPAS.J0900', 'OAJ/JPAS.J0910')\n",
    "# Filters transmission data\n",
    "JPAS_waveRefAA = {} # WavelengthRef dict\n",
    "JPAS_bp = {} # Bandpass dict\n",
    "for filter_id in JPAS_filter_ids:\n",
    "    JPAS_filter_tx = SvoFps.get_transmission_data(filter_id)\n",
    "    JPAS_waveRefAA[filter_id] = JPAS_filter_list[JPAS_filter_list.filterID==filter_id].WavelengthRef.values[0]\n",
    "    JPAS_bp[filter_id] = SpectralElement(Empirical1D, points=JPAS_filter_tx['Wavelength'], lookup_table=JPAS_filter_tx['Transmission'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "RKcat_cb_spec_class=Table.read('tables/cbdata_pre_final.ecsv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture cell_output\n",
    "#                       SDSS            BOSS\n",
    "# Wavelength coverage \t3800-9200 Å \t3650-10,400 Å\n",
    "\n",
    "dfCVmags = pd.DataFrame()\n",
    "for i in range(len(RKcat_cb_spec_class)):\n",
    "    p = RKcat_cb_spec_class[i]['plate']\n",
    "    m = RKcat_cb_spec_class[i]['mjd']\n",
    "    f = RKcat_cb_spec_class[i]['fiberID']\n",
    "    filename = 'spec-'+str(p).zfill(4)+'-'+str(m)+'-'+str(f).zfill(4)+'.fits'\n",
    "    sp = Spectrum1D.read('cbdata_w_spec/'+filename)\n",
    "    # Fotometría\n",
    "    JPAS_obs_effstim = {}\n",
    "    good = True\n",
    "    for k in JPAS_bp:\n",
    "        try:\n",
    "            obs = Observation(sp, JPAS_bp[k], force='taper')\n",
    "        except:\n",
    "            good = False\n",
    "        JPAS_obs_effstim[k[9:]] = round(obs.effstim(u.ABmag).value, 4)\n",
    "    dfEffstim = pd.DataFrame.from_dict([JPAS_obs_effstim])\n",
    "    dfObj = pd.DataFrame(columns=['class', 'plate', 'mjd', 'fiber', 'good'])\n",
    "    dfObj = dfObj.append(pd.Series({'class':'CV', 'plate':p, 'mjd':m, 'fiber':f, 'good':good}), ignore_index=True)\n",
    "    concatenated_dataframes = pd.concat([dfObj, dfEffstim], axis=1)\n",
    "    dfCVmags = dfCVmags.append(concatenated_dataframes, ignore_index=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfCVmags.to_csv('tables/cv_mag.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>plate</th>\n",
       "      <th>mjd</th>\n",
       "      <th>fiber</th>\n",
       "      <th>good</th>\n",
       "      <th>J0390</th>\n",
       "      <th>J0400</th>\n",
       "      <th>J0410</th>\n",
       "      <th>J0420</th>\n",
       "      <th>J0430</th>\n",
       "      <th>...</th>\n",
       "      <th>J0820</th>\n",
       "      <th>J0830</th>\n",
       "      <th>J0840</th>\n",
       "      <th>J0850</th>\n",
       "      <th>J0860</th>\n",
       "      <th>J0870</th>\n",
       "      <th>J0880</th>\n",
       "      <th>J0890</th>\n",
       "      <th>J0900</th>\n",
       "      <th>J0910</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CV</td>\n",
       "      <td>1916</td>\n",
       "      <td>53269</td>\n",
       "      <td>424</td>\n",
       "      <td>True</td>\n",
       "      <td>17.2737</td>\n",
       "      <td>17.3589</td>\n",
       "      <td>17.3420</td>\n",
       "      <td>17.4940</td>\n",
       "      <td>17.2049</td>\n",
       "      <td>...</td>\n",
       "      <td>15.9307</td>\n",
       "      <td>15.9274</td>\n",
       "      <td>15.9068</td>\n",
       "      <td>15.8799</td>\n",
       "      <td>15.8895</td>\n",
       "      <td>15.8850</td>\n",
       "      <td>15.8483</td>\n",
       "      <td>15.8476</td>\n",
       "      <td>15.8479</td>\n",
       "      <td>15.8121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CV</td>\n",
       "      <td>635</td>\n",
       "      <td>52145</td>\n",
       "      <td>387</td>\n",
       "      <td>True</td>\n",
       "      <td>17.5318</td>\n",
       "      <td>17.6467</td>\n",
       "      <td>17.6285</td>\n",
       "      <td>17.8215</td>\n",
       "      <td>17.4566</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0421</td>\n",
       "      <td>16.0398</td>\n",
       "      <td>16.0194</td>\n",
       "      <td>15.9717</td>\n",
       "      <td>15.9718</td>\n",
       "      <td>15.9497</td>\n",
       "      <td>15.9389</td>\n",
       "      <td>15.9506</td>\n",
       "      <td>15.9508</td>\n",
       "      <td>15.9145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CV</td>\n",
       "      <td>1468</td>\n",
       "      <td>52999</td>\n",
       "      <td>163</td>\n",
       "      <td>True</td>\n",
       "      <td>17.6002</td>\n",
       "      <td>17.7148</td>\n",
       "      <td>17.7002</td>\n",
       "      <td>17.9280</td>\n",
       "      <td>17.6191</td>\n",
       "      <td>...</td>\n",
       "      <td>16.7773</td>\n",
       "      <td>16.7757</td>\n",
       "      <td>16.7763</td>\n",
       "      <td>16.7670</td>\n",
       "      <td>16.7540</td>\n",
       "      <td>16.7361</td>\n",
       "      <td>16.7262</td>\n",
       "      <td>16.7704</td>\n",
       "      <td>16.7634</td>\n",
       "      <td>16.7547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CV</td>\n",
       "      <td>439</td>\n",
       "      <td>51877</td>\n",
       "      <td>582</td>\n",
       "      <td>True</td>\n",
       "      <td>19.8255</td>\n",
       "      <td>19.6789</td>\n",
       "      <td>19.5170</td>\n",
       "      <td>19.5924</td>\n",
       "      <td>19.3274</td>\n",
       "      <td>...</td>\n",
       "      <td>17.2581</td>\n",
       "      <td>17.2465</td>\n",
       "      <td>17.2542</td>\n",
       "      <td>17.2151</td>\n",
       "      <td>17.1992</td>\n",
       "      <td>17.1841</td>\n",
       "      <td>17.1608</td>\n",
       "      <td>17.1527</td>\n",
       "      <td>17.1259</td>\n",
       "      <td>17.0471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CV</td>\n",
       "      <td>3686</td>\n",
       "      <td>55268</td>\n",
       "      <td>46</td>\n",
       "      <td>True</td>\n",
       "      <td>18.4175</td>\n",
       "      <td>18.5666</td>\n",
       "      <td>18.5130</td>\n",
       "      <td>18.7694</td>\n",
       "      <td>18.3293</td>\n",
       "      <td>...</td>\n",
       "      <td>16.9848</td>\n",
       "      <td>16.9741</td>\n",
       "      <td>16.9608</td>\n",
       "      <td>16.9337</td>\n",
       "      <td>16.9309</td>\n",
       "      <td>16.9276</td>\n",
       "      <td>16.9210</td>\n",
       "      <td>16.9271</td>\n",
       "      <td>16.9033</td>\n",
       "      <td>16.8551</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",
       "      <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>289</th>\n",
       "      <td>CV</td>\n",
       "      <td>4494</td>\n",
       "      <td>55569</td>\n",
       "      <td>72</td>\n",
       "      <td>True</td>\n",
       "      <td>18.1685</td>\n",
       "      <td>18.1298</td>\n",
       "      <td>18.3218</td>\n",
       "      <td>18.4809</td>\n",
       "      <td>18.5303</td>\n",
       "      <td>...</td>\n",
       "      <td>18.5325</td>\n",
       "      <td>18.5201</td>\n",
       "      <td>18.5028</td>\n",
       "      <td>18.4873</td>\n",
       "      <td>18.4842</td>\n",
       "      <td>18.4695</td>\n",
       "      <td>18.4556</td>\n",
       "      <td>18.4412</td>\n",
       "      <td>18.4304</td>\n",
       "      <td>18.3269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>CV</td>\n",
       "      <td>2062</td>\n",
       "      <td>53381</td>\n",
       "      <td>22</td>\n",
       "      <td>True</td>\n",
       "      <td>19.6721</td>\n",
       "      <td>19.7700</td>\n",
       "      <td>19.7837</td>\n",
       "      <td>19.7580</td>\n",
       "      <td>19.7907</td>\n",
       "      <td>...</td>\n",
       "      <td>20.7106</td>\n",
       "      <td>20.8218</td>\n",
       "      <td>20.7339</td>\n",
       "      <td>20.8178</td>\n",
       "      <td>21.0406</td>\n",
       "      <td>20.8381</td>\n",
       "      <td>20.5716</td>\n",
       "      <td>20.6713</td>\n",
       "      <td>20.6931</td>\n",
       "      <td>20.8623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>CV</td>\n",
       "      <td>2922</td>\n",
       "      <td>54612</td>\n",
       "      <td>406</td>\n",
       "      <td>True</td>\n",
       "      <td>19.2764</td>\n",
       "      <td>19.3913</td>\n",
       "      <td>19.3989</td>\n",
       "      <td>19.3798</td>\n",
       "      <td>19.4096</td>\n",
       "      <td>...</td>\n",
       "      <td>19.9295</td>\n",
       "      <td>19.8617</td>\n",
       "      <td>19.8449</td>\n",
       "      <td>20.0219</td>\n",
       "      <td>20.1631</td>\n",
       "      <td>19.9825</td>\n",
       "      <td>19.7924</td>\n",
       "      <td>19.6884</td>\n",
       "      <td>19.8122</td>\n",
       "      <td>19.9799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>CV</td>\n",
       "      <td>336</td>\n",
       "      <td>51999</td>\n",
       "      <td>358</td>\n",
       "      <td>True</td>\n",
       "      <td>19.4675</td>\n",
       "      <td>19.5747</td>\n",
       "      <td>19.5457</td>\n",
       "      <td>19.5092</td>\n",
       "      <td>19.5562</td>\n",
       "      <td>...</td>\n",
       "      <td>20.1608</td>\n",
       "      <td>20.2732</td>\n",
       "      <td>20.2594</td>\n",
       "      <td>20.2946</td>\n",
       "      <td>20.0895</td>\n",
       "      <td>20.0733</td>\n",
       "      <td>20.2890</td>\n",
       "      <td>20.0587</td>\n",
       "      <td>20.4395</td>\n",
       "      <td>20.2959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>CV</td>\n",
       "      <td>1274</td>\n",
       "      <td>52995</td>\n",
       "      <td>505</td>\n",
       "      <td>True</td>\n",
       "      <td>17.6400</td>\n",
       "      <td>17.6510</td>\n",
       "      <td>17.6660</td>\n",
       "      <td>17.7014</td>\n",
       "      <td>17.7080</td>\n",
       "      <td>...</td>\n",
       "      <td>18.1131</td>\n",
       "      <td>18.1214</td>\n",
       "      <td>18.1344</td>\n",
       "      <td>18.1512</td>\n",
       "      <td>18.1569</td>\n",
       "      <td>18.1696</td>\n",
       "      <td>18.1531</td>\n",
       "      <td>18.0931</td>\n",
       "      <td>18.1190</td>\n",
       "      <td>18.1054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>294 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    class  plate    mjd  fiber  good    J0390    J0400    J0410    J0420  \\\n",
       "0      CV   1916  53269    424  True  17.2737  17.3589  17.3420  17.4940   \n",
       "1      CV    635  52145    387  True  17.5318  17.6467  17.6285  17.8215   \n",
       "2      CV   1468  52999    163  True  17.6002  17.7148  17.7002  17.9280   \n",
       "3      CV    439  51877    582  True  19.8255  19.6789  19.5170  19.5924   \n",
       "4      CV   3686  55268     46  True  18.4175  18.5666  18.5130  18.7694   \n",
       "..    ...    ...    ...    ...   ...      ...      ...      ...      ...   \n",
       "289    CV   4494  55569     72  True  18.1685  18.1298  18.3218  18.4809   \n",
       "290    CV   2062  53381     22  True  19.6721  19.7700  19.7837  19.7580   \n",
       "291    CV   2922  54612    406  True  19.2764  19.3913  19.3989  19.3798   \n",
       "292    CV    336  51999    358  True  19.4675  19.5747  19.5457  19.5092   \n",
       "293    CV   1274  52995    505  True  17.6400  17.6510  17.6660  17.7014   \n",
       "\n",
       "       J0430  ...    J0820    J0830    J0840    J0850    J0860    J0870  \\\n",
       "0    17.2049  ...  15.9307  15.9274  15.9068  15.8799  15.8895  15.8850   \n",
       "1    17.4566  ...  16.0421  16.0398  16.0194  15.9717  15.9718  15.9497   \n",
       "2    17.6191  ...  16.7773  16.7757  16.7763  16.7670  16.7540  16.7361   \n",
       "3    19.3274  ...  17.2581  17.2465  17.2542  17.2151  17.1992  17.1841   \n",
       "4    18.3293  ...  16.9848  16.9741  16.9608  16.9337  16.9309  16.9276   \n",
       "..       ...  ...      ...      ...      ...      ...      ...      ...   \n",
       "289  18.5303  ...  18.5325  18.5201  18.5028  18.4873  18.4842  18.4695   \n",
       "290  19.7907  ...  20.7106  20.8218  20.7339  20.8178  21.0406  20.8381   \n",
       "291  19.4096  ...  19.9295  19.8617  19.8449  20.0219  20.1631  19.9825   \n",
       "292  19.5562  ...  20.1608  20.2732  20.2594  20.2946  20.0895  20.0733   \n",
       "293  17.7080  ...  18.1131  18.1214  18.1344  18.1512  18.1569  18.1696   \n",
       "\n",
       "       J0880    J0890    J0900    J0910  \n",
       "0    15.8483  15.8476  15.8479  15.8121  \n",
       "1    15.9389  15.9506  15.9508  15.9145  \n",
       "2    16.7262  16.7704  16.7634  16.7547  \n",
       "3    17.1608  17.1527  17.1259  17.0471  \n",
       "4    16.9210  16.9271  16.9033  16.8551  \n",
       "..       ...      ...      ...      ...  \n",
       "289  18.4556  18.4412  18.4304  18.3269  \n",
       "290  20.5716  20.6713  20.6931  20.8623  \n",
       "291  19.7924  19.6884  19.8122  19.9799  \n",
       "292  20.2890  20.0587  20.4395  20.2959  \n",
       "293  18.1531  18.0931  18.1190  18.1054  \n",
       "\n",
       "[294 rows x 58 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfCVmags = pd.read_csv('tables/cv_mag.csv')\n",
    "dfCVmags\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Plugging 442-51882-494')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "a = dfCVmags.iloc[[36]]\n",
    "df1 = a.iloc[:,5:59]\n",
    "plt.figure(figsize=(16,4))\n",
    "#plt.plot(df1.values[0])\n",
    "plt.scatter(JPAS_waveRefAA.values(), df1.values[0], s=5, c='r')\n",
    "ax = plt.gca()\n",
    "ax.set_ylim(ax.get_ylim()[::-1])\n",
    "plt.xlabel('Filter #')\n",
    "plt.ylabel('AB mag')\n",
    "plt.title('Plugging '+str(a.plate.values[0])+'-'+str(a.mjd.values[0])+'-'+str(a.fiber.values[0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "3df98a109a5cc23581108261c6c72118143224d8842a21ce60f5415bfb914c48"
  },
  "kernelspec": {
   "display_name": "Python 3.9.10 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.2"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}