{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 800100715151 Astronomide Veritabanları #\n",
    "\n",
    "## Ders - 03 Astronomide Veritabanlarına Erişim ve Sorgulama ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Doç. Dr. Özgür Baştürk <br>\n",
    "Ankara Üniversitesi, Astronomi ve Uzay Bilimleri Bölümü <br>\n",
    "obasturk at ankara.edu.tr <br>\n",
    "http://ozgur.astrotux.org"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bu derste neler öğreneceksiniz? #\n",
    "\n",
    "* [Veri Tabanlarına Erişim ve Sorgulama](#Veri-Tabanlarına-Erişim-ve-Sorgulama)\n",
    "    * [astroquery İle Veritabanlarına Erişim](#astroquery-İle-Veritabanlarına-Erişim)\n",
    "* [astroquery API Kullanımı](#astroquery-API-Kullanımı)\n",
    "* [astroquery İle SIMBAD Kataloğu Sorguları](#astroquery-İle-SIMBAD-Kataloğu-Sorguları)\n",
    "    * [Sanal Gözlemevi Alanlarıyla Sorgunun Genişletilmesi](#Sanal-Gözlemevi-Alanlarıyla-Sorgunun-Genişletilmesi)\n",
    "    * [SIMBAD Veritabanında Kaynakça Sorguları](#SIMBAD-Veritabanında-Kaynakça-Sorguları)\n",
    "    * [SIMBAD Veritabanında Kritere Dayalı Sorgular](#SIMBAD-Veritabanında-Kritere-Dayalı-Sorgular)\n",
    "    * [SIMBAD Veritabanı Sorgularındaki Ayarlar](#SIMBAD-Veritabanı-Sorgularındaki-Ayarlar)\n",
    "* [astroquery İle VizieR Sorguları](#astroquery-İle-VizieR-Sorguları)\n",
    "    * [VizieR'de Bölge Sorguları](#VizieR'de-Bölge-Sorguları)\n",
    "    * [SkyView ve Vizier Kataloglarını Kullanarak Görüntü Üzerinde Kaynak Belirleme](#SkyView-ve-Vizier-Kataloglarını-Kullanarak-Görüntü-Üzerinde-Kaynak-Belirleme)\n",
    "* [astroquery İle SAO/NASA ADS Sorguları](#astroquery-İle-SAO/NASA-ADS-Sorguları)\n",
    "* [ads Paketiyle SAO/NASA ADS Sorguları](#ads-Paketiyle-SAO/NASA-ADS-Sorguları)\n",
    "* [astroquery İle Gaia Veritabanı Sorguları](#astroquery-İle-Gaia-Veritabanı-Sorguları)\n",
    "* [astroquery İle NASA Exoplanet Archive Sorguları](#astroquery-İle-NASA-Exoplanet-Archive-Sorguları)\n",
    "* [Ödev 2](#Ödev-2)\n",
    "* [Kaynaklar](#Kaynaklar)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Veri Tabanlarına Erişim ve Sorgulama #\n",
    "\n",
    "Astronomide yıldız, ötegezegen, Güneş sistemi cisimleri, kuyrukluyıldız ve asteroidler, gökadalar, gökada kümeleri gibi cisim katalog ve veritabanlarından, teleskopların gözlemsel verilerini içeren veritabanlarına ve  bibliyografi / literatür veritabanlarına kadar pek çok tür veritabanı neredeyse günlük olarak kullanılmaktadır. Bu derste bu veritabanlarına erişim ve sorgulamalara yönelik giriş niteliğindeki bilgi, Python betikleri yardımıyla uygulamalı olarak verilecektir.\n",
    "\n",
    "## astroquery İle Veritabanlarına Erişim ##\n",
    "\n",
    "[astroquery](https://github.com/astropy/astroquery) astronomiye yönelik Python uygulamalarının bulunduğu büyük bir modül olan [astropy](https://www.astropy.org/) 'la koordina bir şekilde geliştirilen ve çeşitli astronomi veritabanlarına erişim için araçlar sunan bir pakettir ve geniş bir [dokümantasyonu](https://astroquery.readthedocs.io/en/latest/) da bulunmaktadır. \n",
    "\n",
    "Örnek olarak aşağıda [SIMBAD](https://simbad.u-strasbg.fr/simbad/sim-fid) veritabanında basit bir cisim sorgusu (ing. identifier query) yapabilmeye olanak sağlayan bir Python betiği verilmiştir. `astroquery` ile [erişebileceğiniz tüm servislerin bir listesini](https://astroquery.readthedocs.io/en/latest/index.html#available-services) astroquery dokümantasyonunda bulabilirsiniz. Bunların arasında [astronomide sık kullanılan kataloglar](https://astroquery.readthedocs.io/en/latest/index.html#catalogs), [veri arşivleri](https://astroquery.readthedocs.io/en/latest/index.html#archives), [simülasyonlar](https://astroquery.readthedocs.io/en/latest/index.html#simulations) ve [çizgi listesi servisleri](https://astroquery.readthedocs.io/en/latest/index.html#line-list-services) gibi spesifik veri ve bilgi kaynakları bulunmaktadır.\n",
    "\n",
    "Her servisin kendine özgü nesneleri, metotları, parametreleri ve veri setleri bulunduğundan çok sayıdaki servisin her birine örnek vermek mümkün değildir. Ancak bazı çok kullanılan servisler üzerinden verilecek genel örnekleri takip ederek diğer servislerin temelde nasıl çalıştığı konusunda temel bir fikir edinmek mümkündür. Neredeyse tüm astronomlar tarafından sıkça kullanılan [Simbad Veritabanı](https://simbad.u-strasbg.fr/simbad/) bunun için iyi bir örnektir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.4.6'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import astroquery\n",
    "astroquery.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " MAIN_ID        RA           DEC      ...     COO_BIBCODE     SCRIPT_NUMBER_ID\n",
      "             \"h:m:s\"       \"d:m:s\"    ...                                     \n",
      "--------- ------------- ------------- ... ------------------- ----------------\n",
      "V* EL CVn 13 23 57.0187 +43 35 55.438 ... 2020yCat.1350....0G                1\n"
     ]
    }
   ],
   "source": [
    "from astroquery.simbad import Simbad\n",
    "ELCVn = Simbad.query_object('EL CVn')\n",
    "ELCVn.pprint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MAIN_ID',\n",
       " 'RA',\n",
       " 'DEC',\n",
       " 'RA_PREC',\n",
       " 'DEC_PREC',\n",
       " 'COO_ERR_MAJA',\n",
       " 'COO_ERR_MINA',\n",
       " 'COO_ERR_ANGLE',\n",
       " 'COO_QUAL',\n",
       " 'COO_WAVELENGTH',\n",
       " 'COO_BIBCODE',\n",
       " 'SCRIPT_NUMBER_ID']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ELCVn.colnames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sorgunun sonucu bir astropy tablosudur (astropy tabloları ile ilgili geniş bilgi için [bkz.](https://docs.astropy.org/en/stable/table/)) Bir astropy tablosundaki tüm sütunları görmek için `astropy.table` nesnesinin `columns` metodu kullanılır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TableColumns names=('MAIN_ID','RA','DEC','RA_PREC','DEC_PREC','COO_ERR_MAJA','COO_ERR_MINA','COO_ERR_ANGLE','COO_QUAL','COO_WAVELENGTH','COO_BIBCODE','SCRIPT_NUMBER_ID')>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ELCVn.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Herhangi bir sütundaki bilgi sütun adını indeks olarak vermek suretiyle alınabilir. Örneğin cismin koordinatlarının hangi dalgaboyunda yapılan gözlemlerle belirlendiği bilinmek isteniyorsa <i>COO_WAVELENGTH</i> sütunundaki bilgiye aşağıdaki şekillerde erişilebilir. Görüldüğü gibi veritabanlarında herhangi bir bilginin aranan sütunda bulunabilmesi için öncelikle o bilginin veritabanına girilmiş olması gereklidir ve maalesef bunun garantisi de yoktur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "COO_WAVELENGTH\n",
      "--------------\n",
      "             O\n"
     ]
    }
   ],
   "source": [
    "print(ELCVn['COO_WAVELENGTH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;MaskedColumn name=&apos;COO_BIBCODE&apos; dtype=&apos;object&apos; description=&apos;Coordinate reference&apos; length=1&gt;\n",
       "<table>\n",
       "<tr><td>2020yCat.1350....0G</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<MaskedColumn name='COO_BIBCODE' dtype='object' description='Coordinate reference' length=1>\n",
       "2020yCat.1350....0G"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ELCVn['COO_BIBCODE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;MaskedColumn name=&apos;COO_QUAL&apos; dtype=&apos;str1&apos; description=&apos;Coordinate quality&apos; length=1&gt;\n",
       "<table>\n",
       "<tr><td>A</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<MaskedColumn name='COO_QUAL' dtype='str1' description='Coordinate quality' length=1>\n",
       "A"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ELCVn['COO_QUAL']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gördüğünüz bu örnek basit bir nesne sorgusudur. `astroquery` modül fonksiyonlarının diğer Python modülleriyle birleştirilerek kullanılmasıyla daha gelişkin sorgular da tasarlanabilir. Aşağıdaki sorguda `astropy.coordinates` kütüphanesindeki `SkyCoord` fonkskiyonu <i>ICRS</i> koordinat sisteminde bir koordinat oluşturmak üzere kullanılmaktadır. Daha sonra `astropy.units` modülündeki `arcminute` metoduyla yaydakikası biriminde bir yarıçap tanımı yapılıp, SIMBAD veritabanı bu yarıçap dahilindeki cisimleri verecek şekilde sorgulanmaktadır. Sorgunun sonucunun her satırda en fazla 80 karakteri ve toplamda 10 cismi geçmeyecek şekilde sınırlandırılması ve birimlerinin de gösterilmesi istenmiştir. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          MAIN_ID                  RA      ... SCRIPT_NUMBER_ID\n",
      "                                \"h:m:s\"    ...                 \n",
      "---------------------------- ------------- ... ----------------\n",
      "                   V* BB Peg 22 22 56.8915 ...                1\n",
      "                LEDA 1505483    22 22 45.2 ...                1\n",
      "             TYC 1682-1530-1 22 22 48.2065 ...                1\n",
      "    SDSS J222302.48+161354.3 22 23 02.4831 ...                1\n",
      "    SDSS J222300.12+162519.5  22 23 00.125 ...                1\n",
      "                  LP  460-13 22 22 21.3776 ...                1\n",
      "                         ...           ... ...              ...\n",
      "             TYC 1682-1413-1 22 22 02.0334 ...                1\n",
      "    SDSS J222205.50+162455.4 22 22 05.5012 ...                1\n",
      "             TYC 1682-1330-1 22 23 33.5844 ...                1\n",
      "             LSPM J2223+1618 22 23 42.4743 ...                1\n",
      "             TYC 1682-1444-1 22 22 01.8116 ...                1\n",
      "       CRTS J222318.6+160655 22 23 18.6883 ...                1\n",
      "Gaia DR2 2737346454909302912 22 21 54.5218 ...                1\n",
      "Length = 17 rows\n"
     ]
    }
   ],
   "source": [
    "from astropy import coordinates\n",
    "import astropy.units as u\n",
    "from astroquery.simbad import Simbad\n",
    "# Sadece ICRS koordinatlarıyla calisir \n",
    "koordinat = coordinates.SkyCoord(\"22h22m50s +16d19m20s\", frame='fk5')\n",
    "yaricap = 15.0 * u.arcminute\n",
    "sorgu = Simbad.query_region(koordinat, radius=yaricap)\n",
    "sorgu.pprint(show_unit=True, max_width=80)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sonuçları bir `pandas` tablosuna almak ve `pandas` fonksiyonlarından yararlanmak da ayrı bir esneklik sağlayacaktır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "V* BB Peg                       1\n",
       "BD+15  4631                     1\n",
       "CRTS J222318.6+160655           1\n",
       "TYC 1682-1444-1                 1\n",
       "LSPM J2223+1618                 1\n",
       "TYC 1682-1330-1                 1\n",
       "SDSS J222205.50+162455.4        1\n",
       "TYC 1682-1413-1                 1\n",
       "Gaia DR2 2737147202786480896    1\n",
       "LEDA 1505483                    1\n",
       "BD+15  4634                     1\n",
       "TYC 1682-1466-1                 1\n",
       "LP  460-13                      1\n",
       "SDSS J222300.12+162519.5        1\n",
       "SDSS J222302.48+161354.3        1\n",
       "TYC 1682-1530-1                 1\n",
       "Gaia DR2 2737346454909302912    1\n",
       "Name: MAIN_ID, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas_tablosu = sorgu.to_pandas()\n",
    "pandas_tablosu['MAIN_ID'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Başa Dön](#Bu-derste-neler-öğreneceksiniz?)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# astroquery API Kullanımı #\n",
    "\n",
    "`astroquery` pek çok veritabanında sorgular yapmayı sağlayan pek çok alt modül barınıdırır. Sorgulanabilecek [servislerin listesi](https://astroquery.readthedocs.io/en/latest/#available-services) üzerinden her birinin dokümantasyonuna ulaşmak mümkündür. Bazı veritabanlarının sunucu ve bağlantıda zaman aşımı gibi seçeneklerin ayarlanmasına olanak sağlayan konfigürasyon dosyası UNIX sistemler için kullanıcı dizininde <i>\\$HOME/.astropy/config/astroquery.cfg</i> dosyasında bulunmaktadır. Dosyanın konumu astropy'ın kurulum türüne göre değişiklik gösterebilir. Her bir servisin sağlayıcısı ayrıca [github hesapları](https://github.com/astropy/astroquery/wiki/Maintainers) üzerinden dokümantasyon ve özellikle bu servislere bağlı programlar geliştirmek isteyenler için bilgiler sağlamaktadır. \n",
    "\n",
    "`astroquery` tüm bu veritabanlarına erişim için ortak olan araçlar paketini içeren bir [Uygulama Programlama Arayüzü](https://astroquery.readthedocs.io/en/latest/api.html)'ne (ing. Application Programming Interface, API) sahiptir. Bir API, modül, yazılım ve sistem bileşenlerinin söz konusu modüü nasıl kullanabileceğini tanımlayan bir arabirimidir. Bo modülle (örneğin `astroquery` ile) yapılabilecek arama veya istek türlerini, nasıl yapılacağını, kullanılması gereken veri formatlarını ve izlenecek kuralları tanımlar. `astroquery` API'de, çoğu modül için ortak olan standart araçlar; <i>query_object</i> ve <i>query_region</i> 'dır.\n",
    "\n",
    "Tüm veritabanları için sorgulama yapısı benzer nitelikler içerir.\n",
    "\n",
    "` from astroquery.service import Servis ` \n",
    "<br>\n",
    "` result = Servis.query_object('Nesne Adı') `\n",
    "\n",
    "Öncelikle `astroquery.service` alt modülünden istenen <i>Servis</i> (ya da veritabanı) (IRSA, UKIDSS, SIMBAD, ALMA vs.) $import$ edilir. Daha sonra bu serviste istenen cisimler (tek tek ya da bir liste / döngü / dosya içinden) istenen servisin `query_object` metoduyla aranabilir. \n",
    "\n",
    "Daha önce de örneği verilen bir SIMBAD sorgusu aşağıdaki şekide örneklenebilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=1</i>\n",
       "<table id=\"table140486263973216\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>MAIN_ID</th><th>RA</th><th>DEC</th><th>RA_PREC</th><th>DEC_PREC</th><th>COO_ERR_MAJA</th><th>COO_ERR_MINA</th><th>COO_ERR_ANGLE</th><th>COO_QUAL</th><th>COO_WAVELENGTH</th><th>COO_BIBCODE</th><th>SCRIPT_NUMBER_ID</th></tr></thead>\n",
       "<thead><tr><th></th><th>&quot;h:m:s&quot;</th><th>&quot;d:m:s&quot;</th><th></th><th></th><th>mas</th><th>mas</th><th>deg</th><th></th><th></th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>object</th><th>str13</th><th>str13</th><th>int16</th><th>int16</th><th>float32</th><th>float32</th><th>int16</th><th>str1</th><th>str1</th><th>object</th><th>int32</th></tr></thead>\n",
       "<tr><td>M  31</td><td>00 42 44.330</td><td>+41 16 07.50</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>C</td><td>I</td><td>2006AJ....131.1163S</td><td>1</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=1>\n",
       "MAIN_ID      RA          DEC      ...     COO_BIBCODE     SCRIPT_NUMBER_ID\n",
       "          \"h:m:s\"      \"d:m:s\"    ...                                     \n",
       " object    str13        str13     ...        object            int32      \n",
       "------- ------------ ------------ ... ------------------- ----------------\n",
       "  M  31 00 42 44.330 +41 16 07.50 ... 2006AJ....131.1163S                1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from astroquery.simbad import Simbad\n",
    "Simbad.query_object(\"M 31\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "API'nin diğer metodları yarıçap ($radius$) ya da genişliği ($width$) ile tanımlı bir alanda arama yapmak için kullanılan `query_region` ve eğer veritabanı görüntü sağlıyorsa `get_images` metotlarıdır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=96</i>\n",
       "<table id=\"table140487254081248\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>MAIN_ID</th><th>RA</th><th>DEC</th><th>RA_PREC</th><th>DEC_PREC</th><th>COO_ERR_MAJA</th><th>COO_ERR_MINA</th><th>COO_ERR_ANGLE</th><th>COO_QUAL</th><th>COO_WAVELENGTH</th><th>COO_BIBCODE</th><th>SCRIPT_NUMBER_ID</th></tr></thead>\n",
       "<thead><tr><th></th><th>&quot;h:m:s&quot;</th><th>&quot;d:m:s&quot;</th><th></th><th></th><th>mas</th><th>mas</th><th>deg</th><th></th><th></th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>object</th><th>str13</th><th>str13</th><th>int16</th><th>int16</th><th>float32</th><th>float32</th><th>int16</th><th>str1</th><th>str1</th><th>object</th><th>int32</th></tr></thead>\n",
       "<tr><td>HAT-P-19</td><td>00 38 04.0135</td><td>+34 42 41.553</td><td>14</td><td>14</td><td>0.010</td><td>0.008</td><td>90</td><td>A</td><td>O</td><td>2020yCat.1350....0G</td><td>1</td></tr>\n",
       "<tr><td>HAT-P-19b</td><td>00 38 04.0136</td><td>+34 42 41.552</td><td>14</td><td>14</td><td>0.029</td><td>0.025</td><td>90</td><td>A</td><td>O</td><td>2018yCat.1345....0G</td><td>1</td></tr>\n",
       "<tr><td>GSC 02283-01197</td><td>00 38 09.3713</td><td>+34 41 37.191</td><td>14</td><td>14</td><td>0.009</td><td>0.007</td><td>90</td><td>A</td><td>O</td><td>2020yCat.1350....0G</td><td>1</td></tr>\n",
       "<tr><td>Gaia DR3 365427023114195456</td><td>00 37 53.9011</td><td>+34 45 06.947</td><td>14</td><td>14</td><td>1.615</td><td>1.572</td><td>90</td><td>C</td><td>O</td><td>2020yCat.1350....0G</td><td>1</td></tr>\n",
       "<tr><td>TYC 2283-664-1</td><td>00 38 16.2619</td><td>+34 45 03.412</td><td>14</td><td>14</td><td>0.015</td><td>0.012</td><td>90</td><td>A</td><td>O</td><td>2020yCat.1350....0G</td><td>1</td></tr>\n",
       "<tr><td>DIR 120-28</td><td>00 38</td><td>+34.8</td><td>2</td><td>2</td><td>--</td><td>--</td><td>0</td><td></td><td></td><td></td><td>1</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>CRTS J003644.8+343323</td><td>00 36 44.9018</td><td>+34 33 22.880</td><td>14</td><td>14</td><td>0.063</td><td>0.046</td><td>90</td><td>A</td><td>O</td><td>2020yCat.1350....0G</td><td>1</td></tr>\n",
       "<tr><td>SDSS J003718.56+345901.6</td><td>00 37 18.560</td><td>+34 59 01.69</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>C</td><td>O</td><td>2016MNRAS.455.3413K</td><td>1</td></tr>\n",
       "<tr><td>SDSS J003936.58+344023.1</td><td>00 39 36.5800</td><td>+34 40 23.116</td><td>9</td><td>9</td><td>--</td><td>--</td><td>0</td><td>C</td><td>O</td><td>2015ApJS..219...12A</td><td>1</td></tr>\n",
       "<tr><td>SDSS J003638.71+345045.1</td><td>00 36 38.716</td><td>+34 50 45.18</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>C</td><td>O</td><td>2020ApJS..250....8L</td><td>1</td></tr>\n",
       "<tr><td>CRTS J003657.7+342856</td><td>00 36 57.7566</td><td>+34 28 55.964</td><td>14</td><td>14</td><td>0.032</td><td>0.027</td><td>90</td><td>A</td><td>O</td><td>2020yCat.1350....0G</td><td>1</td></tr>\n",
       "<tr><td>SDSS J003639.00+343341.6</td><td>00 36 39.008</td><td>+34 33 41.61</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>C</td><td>O</td><td>2020ApJS..250....8L</td><td>1</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=96>\n",
       "          MAIN_ID                 RA      ... SCRIPT_NUMBER_ID\n",
       "                               \"h:m:s\"    ...                 \n",
       "           object               str13     ...      int32      \n",
       "--------------------------- ------------- ... ----------------\n",
       "                   HAT-P-19 00 38 04.0135 ...                1\n",
       "                  HAT-P-19b 00 38 04.0136 ...                1\n",
       "            GSC 02283-01197 00 38 09.3713 ...                1\n",
       "Gaia DR3 365427023114195456 00 37 53.9011 ...                1\n",
       "             TYC 2283-664-1 00 38 16.2619 ...                1\n",
       "                 DIR 120-28         00 38 ...                1\n",
       "                        ...           ... ...              ...\n",
       "      CRTS J003644.8+343323 00 36 44.9018 ...                1\n",
       "   SDSS J003718.56+345901.6  00 37 18.560 ...                1\n",
       "   SDSS J003936.58+344023.1 00 39 36.5800 ...                1\n",
       "   SDSS J003638.71+345045.1  00 36 38.716 ...                1\n",
       "      CRTS J003657.7+342856 00 36 57.7566 ...                1\n",
       "   SDSS J003639.00+343341.6  00 36 39.008 ...                1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from astropy import coordinates\n",
    "import astropy.units as u\n",
    "sorgu = Simbad.query_object(\"HAT-P-19\")\n",
    "koordinatlar = coordinates.SkyCoord(sorgu['RA'], sorgu['DEC'], \\\n",
    "                                    unit=(u.hourangle, u.deg), frame='icrs')\n",
    "yaricap = 20*u.arcminute\n",
    "yeni_sorgu = Simbad.query_region(koordinatlar,radius=yaricap)\n",
    "yeni_sorgu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Görüntü sağlayan veritabanlarından görüntü de çekilmek istenebilir. Ancak bu noktada bazı veritabanlarının her kullanıcıya açık olmadğını, ancak kullanıcı adı ve şifreyle giriş yapılabildiğini belirtmekte fayda vardır. Örneğin UKIRT Teleskobu'yla süren Kızılöte Derin Gökyüzü Araştırması ([UKIRT Infrared Deep Sky Survey](http://www.ukidss.org/)) 'den aşağıdaki gibi bir betikle görüntü çekilebilir. Ancak kullanıcı adresi ve şifreye ihtiyaç duyulur. \n",
    "\n",
    "from astroquery.ukidss import Ukidss <br>\n",
    "Ukidds.login(username,password)<br>\n",
    "images = Ukidss.get_images(\"m1\")\n",
    "\n",
    "Yukarıda HAT-P-19'un koordinatlarını elde etmeye yönelik sorgudan hareketle bu cisim merkezinde bir bölgeinin görüntüsü [CDS.Vizier servisinden](https://vizier.u-strasbg.fr/viz-bin/VizieR) alınabilir. Ancak bunun için gökyüzü taraması yapan hangi araştırmada (survey) elde edilmiş imajların istendiği de belirtilmelidir. `SkyView` tüm [gökyüzü araştırmalarının (surveys) listesinden](https://skyview.gsfc.nasa.gov/current/cgi/survey.pl) hangi araştırmada, hangi dalgaboyunda ve çözünürlüte elde edilen imajları görüntülemek istiyorsanız ona uygun olanları seçebilirsiniz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fc57b5e1240>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from astropy import wcs\n",
    "from astroquery.vizier import Vizier\n",
    "from astroquery.skyview import SkyView\n",
    "from matplotlib import pyplot as plt\n",
    "goruntuler = SkyView.get_images(position=koordinatlar, survey='SDSSg')\n",
    "# Fonksiyon bir imaj listesi dondurur\n",
    "goruntu = goruntuler[0]\n",
    "\n",
    "# Bir FITS goruntusu fits.HDUList nesnesi olup; 0. indeksinde goruntu bulunur\n",
    "# Bu indeksin header nesnesi basligi data nesnesi ise goruntunun kendisini verir\n",
    "# Bu goruntunun WCS koordinatlari goruntu basliginda bulunur\n",
    "mywcs = wcs.WCS(goruntu[0].header)\n",
    "\n",
    "fig = plt.figure(1)\n",
    "# Bu WCS koordinatlari uzerinden istenen buyuklukte bir goruntu kesilip \n",
    "# ekrana getirlebilir\n",
    "ax = fig.add_axes([0.15, 0.1, 0.8, 0.8], projection=mywcs)\n",
    "ax.set_xlabel(\"RA\")\n",
    "ax.set_ylabel(\"Dec\")\n",
    "\n",
    "ax.imshow(goruntu[0].data, cmap='gray_r', interpolation='none', origin='lower',\n",
    "          norm=plt.matplotlib.colors.LogNorm())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Başa Dön](#Bu-derste-neler-öğreneceksiniz?)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# astroquery İle SIMBAD Kataloğu Sorguları #\n",
    "\n",
    "[Simbad veritabanı](http://simbad.u-strasbg.fr/simbad/) Güneş sistemi dışındaki gökcisimleri için temel verileri, farklı kataloglardaki isimlerini, bu cisimler üzerine yapılmış çalışmaların kaynakçasını ve cisim için yapılan çeşitli ölçümleri sağlar. SIMBAD veritabanı sınırlı sorgulama kapasitesine sahiptir. Sunucuya kısa sürelerde büyük sorgular gönderirseniz IP'niz geçici olarak kara listeye alınabilir. Sınırı değişmekle birlikte saniyede 5 ile 10 sorgudan fazla sorgu göndermemek gerekir. Çok sayıda cismi içeren büyük sorgularda nesne isimlerini bir döngü dahilinde dosyalardan okuyarak tek tek sorguya geçirmek yerine bir cisim listesi oluşturup, bu listeyi sorguya göndermekte fayda vardır. \n",
    "\n",
    "Daha önce basit sorgular için bazı örnekler vermiştik. Şimdi biraz daha ileri sorgularla veritabanından veri almaya çalışalım. Yeni Nesil Transit Araştırması (ing. [Next Generation Transit Survey, NGTS](https://ngtransits.org/)), temelde parlak yıldızların etrafında ($V < 13^m$) Neptün'nden daha küçük olan ötegezegenleri geçiş yöntemiyle keşfetmek ve karakterize etmek için tasarlanmış, geniş bir gökyüzü alanında yapılan bir fotometrik araştırmadır. Bu araştırmanın cisimleri <i>NGTS</i> ismi ile kataloglanmaktadır. Şu ana kadar ne kadar NGTS cisminin kataloglandığını öğrenmek için bir Simbad sorgusu aşağıdaki gibi yazılabilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    MAIN_ID            RA      ...     COO_BIBCODE     SCRIPT_NUMBER_ID\n",
      "                    \"h:m:s\"    ...                                     \n",
      "---------------- ------------- ... ------------------- ----------------\n",
      "          NGTS-1 05 30 51.4522 ... 2020yCat.1350....0G                1\n",
      "     CD-30 11338 14 20 29.4891 ... 2020yCat.1350....0G                1\n",
      "          NGTS-3 06 17 46.7534 ... 2020yCat.1350....0G                1\n",
      "        NGTS-3Ab 06 17 46.7535 ... 2018yCat.1345....0G                1\n",
      "          NGTS-4 05 58 23.7549 ... 2020yCat.1350....0G                1\n",
      "          NGTS-5 14 44 13.9703 ... 2020yCat.1350....0G                1\n",
      "             ...           ... ...                 ...              ...\n",
      "UCAC4 279-005438 04 51 36.1377 ... 2020yCat.1350....0G                1\n",
      "        NGTS-17b 04 51 36.1377 ... 2020yCat.1350....0G                1\n",
      "UCAC4 273-062781 12 02 11.0956 ... 2020yCat.1350....0G                1\n",
      "        NGTS-18b 12 02 11.0956 ... 2020yCat.1350....0G                1\n",
      "        NGTS-19B 15 16 31.5959 ... 2018yCat.1345....0G                1\n",
      "         NGTS-1b 05 30 51.4522 ... 2018yCat.1345....0G                1\n",
      "         NGTS-4b 05 58 23.7549 ... 2020yCat.1350....0G                1\n",
      "Length = 46 rows\n"
     ]
    }
   ],
   "source": [
    "from astroquery.simbad import Simbad\n",
    "sonuc_tablosu = Simbad.query_object(\"NGTS-*\", wildcard=True)\n",
    "print(sonuc_tablosu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Görüldüğü gibi tüm <i>NGTS</i> cisimlerini görebilmek için <i>\"*\"</i> karakterini kullandık. Bu karakterler (<i>\"*\",\"?\", \"[1-9]\", \"[a-b]\"</i> gibi) bir ya da daha çok karakterin yerini tutmak üzere kullanılır (ing. wildcard). Örneğin NGTS'in keşfettiği gezegenleri listelemek için <i>NGTS</i> katalog isminden sonra olası tüm seçenekleri listeleyecek olan (katalogdaki tüm NGTS adıyla kodlanmış cisimler) <i>\"*\"</i> karakterini kullandıktan sonra gezegen isimlerinin $b$ ile $i$ harfleri arasında isimlerle yıldız isminin sonuna getirileceğini düşünerek (bugüne kadar etrafında en fazla gezegen bulunan sistemde 8 gezegen keşfedilmiştir) aşağıdaki şekilde kurgulanan bir sorgu kullanılabilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=24</i>\n",
       "<table id=\"table140486239633216\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>MAIN_ID</th><th>RA</th><th>DEC</th><th>RA_PREC</th><th>DEC_PREC</th><th>COO_ERR_MAJA</th><th>COO_ERR_MINA</th><th>COO_ERR_ANGLE</th><th>COO_QUAL</th><th>COO_WAVELENGTH</th><th>COO_BIBCODE</th><th>SCRIPT_NUMBER_ID</th></tr></thead>\n",
       "<thead><tr><th></th><th>&quot;h:m:s&quot;</th><th>&quot;d:m:s&quot;</th><th></th><th></th><th>mas</th><th>mas</th><th>deg</th><th></th><th></th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>object</th><th>str13</th><th>str13</th><th>int16</th><th>int16</th><th>float32</th><th>float32</th><th>int16</th><th>str1</th><th>str1</th><th>object</th><th>int32</th></tr></thead>\n",
       "<tr><td>NGTS-3Ab</td><td>06 17 46.7535</td><td>-35 42 23.046</td><td>14</td><td>14</td><td>0.016</td><td>0.017</td><td>90</td><td>A</td><td>O</td><td>2018yCat.1345....0G</td><td>1</td></tr>\n",
       "<tr><td>NGTS-10B</td><td>06 07 29.3136</td><td>-25 35 40.758</td><td>14</td><td>14</td><td>0.270</td><td>0.133</td><td>90</td><td>A</td><td>O</td><td>2020yCat.1350....0G</td><td>1</td></tr>\n",
       "<tr><td>NGTS-14B</td><td>21 54 03.9205</td><td>-38 22 38.653</td><td>14</td><td>14</td><td>0.082</td><td>0.069</td><td>90</td><td>A</td><td>O</td><td>2020yCat.1350....0G</td><td>1</td></tr>\n",
       "<tr><td>NGTS-20b</td><td>03 05 10.2104</td><td>-21 56 01.116</td><td>14</td><td>14</td><td>0.012</td><td>0.014</td><td>90</td><td>A</td><td>O</td><td>2020yCat.1350....0G</td><td>1</td></tr>\n",
       "<tr><td>CD-30 11338b</td><td>14 20 29.4890</td><td>-31 12 07.437</td><td>14</td><td>14</td><td>0.053</td><td>0.040</td><td>90</td><td>A</td><td>O</td><td>2018yCat.1345....0G</td><td>1</td></tr>\n",
       "<tr><td>NGTS-5b</td><td>14 44 13.9704</td><td>+05 36 19.417</td><td>14</td><td>14</td><td>0.022</td><td>0.021</td><td>90</td><td>A</td><td>O</td><td>2018yCat.1345....0G</td><td>1</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>NGTS-16b</td><td>03 53 03.3314</td><td>-30 48 16.632</td><td>14</td><td>14</td><td>0.010</td><td>0.012</td><td>90</td><td>A</td><td>O</td><td>2020yCat.1350....0G</td><td>1</td></tr>\n",
       "<tr><td>NGTS-17b</td><td>04 51 36.1377</td><td>-34 13 34.181</td><td>14</td><td>14</td><td>0.011</td><td>0.013</td><td>90</td><td>A</td><td>O</td><td>2020yCat.1350....0G</td><td>1</td></tr>\n",
       "<tr><td>NGTS-18b</td><td>12 02 11.0956</td><td>-35 32 55.017</td><td>14</td><td>14</td><td>0.015</td><td>0.012</td><td>90</td><td>A</td><td>O</td><td>2020yCat.1350....0G</td><td>1</td></tr>\n",
       "<tr><td>NGTS-19B</td><td>15 16 31.5959</td><td>-25 42 17.824</td><td>14</td><td>14</td><td>0.028</td><td>0.018</td><td>90</td><td>A</td><td>O</td><td>2018yCat.1345....0G</td><td>1</td></tr>\n",
       "<tr><td>NGTS-1b</td><td>05 30 51.4522</td><td>-36 37 50.896</td><td>14</td><td>14</td><td>0.016</td><td>0.020</td><td>90</td><td>A</td><td>O</td><td>2018yCat.1345....0G</td><td>1</td></tr>\n",
       "<tr><td>NGTS-4b</td><td>05 58 23.7549</td><td>-30 48 42.363</td><td>14</td><td>14</td><td>0.010</td><td>0.011</td><td>90</td><td>A</td><td>O</td><td>2020yCat.1350....0G</td><td>1</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=24>\n",
       "  MAIN_ID          RA      ...     COO_BIBCODE     SCRIPT_NUMBER_ID\n",
       "                \"h:m:s\"    ...                                     \n",
       "   object        str13     ...        object            int32      \n",
       "------------ ------------- ... ------------------- ----------------\n",
       "    NGTS-3Ab 06 17 46.7535 ... 2018yCat.1345....0G                1\n",
       "    NGTS-10B 06 07 29.3136 ... 2020yCat.1350....0G                1\n",
       "    NGTS-14B 21 54 03.9205 ... 2020yCat.1350....0G                1\n",
       "    NGTS-20b 03 05 10.2104 ... 2020yCat.1350....0G                1\n",
       "CD-30 11338b 14 20 29.4890 ... 2018yCat.1345....0G                1\n",
       "     NGTS-5b 14 44 13.9704 ... 2018yCat.1345....0G                1\n",
       "         ...           ... ...                 ...              ...\n",
       "    NGTS-16b 03 53 03.3314 ... 2020yCat.1350....0G                1\n",
       "    NGTS-17b 04 51 36.1377 ... 2020yCat.1350....0G                1\n",
       "    NGTS-18b 12 02 11.0956 ... 2020yCat.1350....0G                1\n",
       "    NGTS-19B 15 16 31.5959 ... 2018yCat.1345....0G                1\n",
       "     NGTS-1b 05 30 51.4522 ... 2018yCat.1345....0G                1\n",
       "     NGTS-4b 05 58 23.7549 ... 2020yCat.1350....0G                1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sonuc_tablosu = Simbad.query_object(\"NGTS-*[b-i]\", wildcard=True)\n",
    "sonuc_tablosu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bir ya da daha fazla karakterin yerini tutmak üzere kulanabileceğiniz tüm karakterleri (ing. wildcard) öğrenmek için `list_wildcards()` metodu kullanılabilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* : Any string of characters (including an empty one)\n",
      "\n",
      "? : Any character (exactly one character)\n",
      "\n",
      "[abc] : Exactly one character taken in the list. Can also be defined by a range of characters: [A-Z]\n",
      "\n",
      "[^0-9] : Any (one) character not in the list.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Simbad.list_wildcards()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bölge sorgularında (`query_region`) varsayılan yarıçap $20$ yaydakikasıdır ve `astropy.units` kullanılarak değiştirilebilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          MAIN_ID                 RA      ... SCRIPT_NUMBER_ID\n",
      "                               \"h:m:s\"    ...                 \n",
      "--------------------------- ------------- ... ----------------\n",
      "                  * alf UMi 02 31 49.0945 ...                1\n",
      "                * alf UMi B 02 30 36.0860 ...                1\n",
      "Gaia DR3 576402619921730176 02 34 30.8477 ...                1\n",
      "              UCAC3 359-258 02 35 12.0618 ...                1\n",
      "              UCAC3 359-247 02 30 44.3425 ...                1\n",
      "                     PWP  8   02 35 09.04 ...                1\n",
      "                      RN  6      02 41 17 ...                1\n",
      "              UCAC3 359-198 02 10 05.9552 ...                1\n",
      "    2MASX J02341709+8920469 02 34 16.4080 ...                1\n",
      "                 AG+88    8 02 16 19.8253 ...                1\n",
      "            IRAS 02057+8857    02 49 46.1 ...                1\n"
     ]
    }
   ],
   "source": [
    "from astroquery.simbad import Simbad\n",
    "import astropy.units as u\n",
    "sonuc_tablosu = Simbad.query_region(\"Polaris\", radius=0.1 * u.deg)\n",
    "# yaricap derece-dakika-saniye ('dms') formatinda da tanimlanabilir\n",
    "sonuc_tablosu = Simbad.query_region(\"Polaris\", radius='0d6m0s')\n",
    "print(sonuc_tablosu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Koordinat sorgularına daha önce örnekler verilmişti. Eğer bu tür sorgularda verilen koordinatların <i>epoch</i> değerleri (bu ölçümlerin referans verildiği zaman) biliniyorsa sorguya parametre olarak geçirilebilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          MAIN_ID                  RA      ... SCRIPT_NUMBER_ID\n",
      "                                \"h:m:s\"    ...                 \n",
      "---------------------------- ------------- ... ----------------\n",
      "Gaia DR3 1152903847998259712 11 30 48.1736 ...                1\n",
      "                TYC 4644-6-1 11 35 12.6253 ...                1\n",
      "Gaia DR2 1152914602596449408 09 16 57.5993 ...                1\n",
      "               TYC 4643-26-1 08 12 13.9402 ...                1\n",
      "                TYC 4645-2-1 13 38 05.8259 ...                1\n",
      "Gaia DR3 1152911231045163776 07 58 42.9159 ...                1\n",
      "                         ...           ... ...              ...\n",
      "                   Shk 172-5    15 42 52.8 ...                1\n",
      "                   Shk 172-2    15 40 42.3 ...                1\n",
      "       1RXS J050421.1+893321  05 04 21.101 ...                1\n",
      " Gaia DR3 576439316121957888 04 03 47.6985 ...                1\n",
      "Gaia DR2 1152887561482454016 08 53 14.4154 ...                1\n",
      "               TYC 4644-20-1 10 49 28.3745 ...                1\n",
      " Gaia DR3 576385646213013120 05 47 17.0370 ...                1\n",
      "Length = 73 rows\n"
     ]
    }
   ],
   "source": [
    "from astropy import coordinates as coord\n",
    "sonuc_tablosu = Simbad.query_region(coord.SkyCoord(ra=02.50, dec=89.90,\n",
    "                                    unit=(u.hour, u.deg), frame='fk5'),\n",
    "                                    radius=0.5 * u.deg,\n",
    "                                    epoch='B1950',\n",
    "                                    equinox=1950)\n",
    "print(sonuc_tablosu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bölge sorgularında vektörleştirilmiş koordinat aralıklarını kullanmak hem hız açısından daha etkilidir, hem de daha kullanışlıdır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=36</i>\n",
       "<table id=\"table140486154092496\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>MAIN_ID</th><th>RA</th><th>DEC</th><th>RA_PREC</th><th>DEC_PREC</th><th>COO_ERR_MAJA</th><th>COO_ERR_MINA</th><th>COO_ERR_ANGLE</th><th>COO_QUAL</th><th>COO_WAVELENGTH</th><th>COO_BIBCODE</th><th>SCRIPT_NUMBER_ID</th></tr></thead>\n",
       "<thead><tr><th></th><th>&quot;h:m:s&quot;</th><th>&quot;d:m:s&quot;</th><th></th><th></th><th>mas</th><th>mas</th><th>deg</th><th></th><th></th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>object</th><th>str13</th><th>str13</th><th>int16</th><th>int16</th><th>float32</th><th>float32</th><th>int16</th><th>str1</th><th>str1</th><th>object</th><th>int32</th></tr></thead>\n",
       "<tr><td>NVSS J235956-010125</td><td>23 59 56.486</td><td>-01 01 25.61</td><td>7</td><td>7</td><td>1000.000</td><td>1000.000</td><td>90</td><td>C</td><td>R</td><td>2015ApJ...801...26H</td><td>1</td></tr>\n",
       "<tr><td>2SLAQ J000006.80-010135.1</td><td>00 00 06.800</td><td>-01 01 35.16</td><td>7</td><td>7</td><td>65.000</td><td>63.000</td><td>0</td><td>C</td><td>O</td><td>2009ApJS..182..543A</td><td>1</td></tr>\n",
       "<tr><td>PB  5664</td><td>23 59 46</td><td>-00 58.3</td><td>4</td><td>4</td><td>--</td><td>--</td><td>0</td><td>E</td><td></td><td>1980A&amp;AS...39...39B</td><td>1</td></tr>\n",
       "<tr><td>SDSS J235955.88-010346.8</td><td>23 59 55.884</td><td>-01 03 46.86</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>C</td><td>O</td><td>2020ApJS..250....8L</td><td>1</td></tr>\n",
       "<tr><td>2SLAQ J000015.84-005941.6</td><td>00 00 15.8519</td><td>-00 59 41.649</td><td>14</td><td>14</td><td>1.692</td><td>2.216</td><td>90</td><td>C</td><td>O</td><td>2020yCat.1350....0G</td><td>1</td></tr>\n",
       "<tr><td>[VV2006] J235945.5-005819</td><td>23 59 45.4964</td><td>-00 58 19.708</td><td>14</td><td>14</td><td>0.179</td><td>0.111</td><td>90</td><td>A</td><td>O</td><td>2020yCat.1350....0G</td><td>1</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>WiggleZ R01J005952967+01044853</td><td>00 59 52.9763</td><td>+01 04 48.432</td><td>8</td><td>8</td><td>--</td><td>--</td><td>0</td><td>C</td><td>O</td><td>2018MNRAS.474.4151D</td><td>2</td></tr>\n",
       "<tr><td>SDSS J010018.10+010226.7</td><td>01 00 18.104</td><td>+01 02 26.74</td><td>7</td><td>7</td><td>23.000</td><td>17.000</td><td>90</td><td>C</td><td>O</td><td>2012ApJS..203...21A</td><td>2</td></tr>\n",
       "<tr><td>WiggleZ R01J010016989+00565316</td><td>01 00 16.9983</td><td>+00 56 53.063</td><td>8</td><td>8</td><td>--</td><td>--</td><td>0</td><td>C</td><td>O</td><td>2018MNRAS.474.4151D</td><td>2</td></tr>\n",
       "<tr><td>SDSS J010014.44+010418.3</td><td>01 00 14.4496</td><td>+01 04 18.400</td><td>9</td><td>9</td><td>--</td><td>--</td><td>0</td><td>C</td><td>O</td><td>2020ApJS..249....3A</td><td>2</td></tr>\n",
       "<tr><td>Gaia DR2 2537368959381150592</td><td>01 00 12.1957</td><td>+00 55 14.155</td><td>14</td><td>14</td><td>0.064</td><td>0.048</td><td>90</td><td>A</td><td>O</td><td>2018yCat.1345....0G</td><td>2</td></tr>\n",
       "<tr><td>WiggleZ R01J010007335+00542459</td><td>01 00 07.3442</td><td>+00 54 24.487</td><td>8</td><td>8</td><td>--</td><td>--</td><td>0</td><td>C</td><td>O</td><td>2018MNRAS.474.4151D</td><td>2</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=36>\n",
       "           MAIN_ID                   RA      ... SCRIPT_NUMBER_ID\n",
       "                                  \"h:m:s\"    ...                 \n",
       "            object                 str13     ...      int32      \n",
       "------------------------------ ------------- ... ----------------\n",
       "           NVSS J235956-010125  23 59 56.486 ...                1\n",
       "     2SLAQ J000006.80-010135.1  00 00 06.800 ...                1\n",
       "                      PB  5664      23 59 46 ...                1\n",
       "      SDSS J235955.88-010346.8  23 59 55.884 ...                1\n",
       "     2SLAQ J000015.84-005941.6 00 00 15.8519 ...                1\n",
       "     [VV2006] J235945.5-005819 23 59 45.4964 ...                1\n",
       "                           ...           ... ...              ...\n",
       "WiggleZ R01J005952967+01044853 00 59 52.9763 ...                2\n",
       "      SDSS J010018.10+010226.7  01 00 18.104 ...                2\n",
       "WiggleZ R01J010016989+00565316 01 00 16.9983 ...                2\n",
       "      SDSS J010014.44+010418.3 01 00 14.4496 ...                2\n",
       "  Gaia DR2 2537368959381150592 01 00 12.1957 ...                2\n",
       "WiggleZ R01J010007335+00542459 01 00 07.3442 ...                2"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sonuc_tablosu = Simbad.query_region(coord.SkyCoord(ra=[0, 1], dec=[-1,1],\n",
    "                                    unit=(u.hour, u.deg), frame='fk5'),\n",
    "                                    radius=0.1 * u.deg)\n",
    "sonuc_tablosu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bir cismin alternatif isimlerini öğrenmek için aşağıdaki gibi bir sorgu kurgulanabilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Gaia DR3 6220602384081327104', 'TIC 125739286', 'GSC 07282-01298', 'TYC 7282-1298-1', '2MASS J14202949-3112074', 'RAVE J142029.5-311208', 'NGTS-2', 'Gaia DR1 6220602379784456320', 'Gaia DR2 6220602384081327104', 'CD-30 11338']\n"
     ]
    }
   ],
   "source": [
    "sorgu = Simbad.query_objectids(\"TYC 7282-1298-1\")\n",
    "sonuc_tablosu = [isim[0] for isim in sorgu]\n",
    "print(sonuc_tablosu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Başa Dön](#Bu-derste-neler-öğreneceksiniz?)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SIMBAD Veritabanında Kaynakça Sorguları ##\n",
    "\n",
    "Bibliyografik kodu bilinen herhangi bir yayında veri tabanında aranabilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                                                                References                                                                                                                                               \n",
      "---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "2011A&A...535A..17B = DOI 10.1051/0004-6361/201117740\\nAstronomy and Astrophysics, volume 535A, 17-17 (2011/11-1)\\nBASTURK O., DALL T.H., COLLET R., LO CURTO G. and SELAM S.O.\\nBisectors of the HARPS cross-correlation function. The dependence on  stellar atmospheric parameters.\\nFiles: (abstract)\n"
     ]
    }
   ],
   "source": [
    "from astroquery.simbad import Simbad\n",
    "sonuc_tablosu = Simbad.query_bibcode('2011A&A...535A..17B')\n",
    "print(sonuc_tablosu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bibliyografik kodu bilinen yayının içinde geçen kataloglanmış ve yayın içinde <i>\\astrobj</i> latex anahtarıyla kodlanmış cisimleri almak için de aşağıdaki gibi bir sorgu yazılabilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  MAIN_ID         RA      ...     COO_BIBCODE     SCRIPT_NUMBER_ID\n",
      "               \"h:m:s\"    ...                                     \n",
      "----------- ------------- ... ------------------- ----------------\n",
      "  HD   1461 00 18 41.8676 ... 2020yCat.1350....0G                1\n",
      "  HD   4915 00 51 10.8478 ... 2020yCat.1350....0G                1\n",
      "  HD   7449 01 14 29.3222 ... 2020yCat.1350....0G                1\n",
      "  * tau Cet 01 44 04.0831 ... 2020yCat.1350....0G                1\n",
      "* lam02 For 02 36 58.6077 ... 2020yCat.1350....0G                1\n",
      "* zet02 Ret 03 18 12.8188 ... 2020yCat.1350....0G                1\n",
      "        ...           ... ...                 ...              ...\n",
      "  HD 179949 19 15 33.2300 ... 2020yCat.1350....0G                1\n",
      "  * del Pav 20 08 43.6088 ... 2020yCat.1350....0G                1\n",
      "  HD 207129 21 48 15.7511 ... 2020yCat.1350....0G                1\n",
      "  * eps Ind 22 03 21.6536 ... 2020yCat.1350....0G                1\n",
      "  HD 210918 22 14 38.6528 ... 2020yCat.1350....0G                1\n",
      "  HD 215456 22 46 08.0342 ... 2020yCat.1350....0G                1\n",
      "  HD 221420 23 33 19.5789 ... 2020yCat.1350....0G                1\n",
      "Length = 66 rows\n"
     ]
    }
   ],
   "source": [
    "from astroquery.simbad import Simbad\n",
    "sonuc_tablosu = Simbad.query_bibobj('2011A&A...535A..17B')\n",
    "print(sonuc_tablosu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TableColumns names=('MAIN_ID','RA','DEC','RA_PREC','DEC_PREC','COO_ERR_MAJA','COO_ERR_MINA','COO_ERR_ANGLE','COO_QUAL','COO_WAVELENGTH','COO_BIBCODE','SCRIPT_NUMBER_ID')>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sonuc_tablosu.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Başa Dön](#Bu-derste-neler-öğreneceksiniz?)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SIMBAD Veritabanında Kritere Dayalı Sorgular ##\n",
    "\n",
    "SIMBAD veritabanının istenen kriter(ler)e göre arama yapılmasını sağlayan bir [kriter arayüzü (criteria interface)](http://simbad.u-strasbg.fr/simbad/sim-fsam) de bulunmaktadır. `astroquery.simbad` bu arayüzün kullanılmasına olanak sağlayan fonksiyonlara da sahiptir. Örneğin Simbad kataloğunda bulunan 12 kadirden daha parlak kataklizmik değişenlerin bir listesine aşağıdaki sorguyla ulaşılabilir. Bu tür sorgular herhangi bir araştırma için hedef listesi oluşturulurken vazgeçilmezdir. Simbad veritabanında yapılabilecek kritere dayalı sorgular için öncelikle [ilgili dokümantasyonu](http://simbad.u-strasbg.fr/simbad/sim-fsam) dikkatle okumak, sorgularda kullanılabilecek anahtar kelime ve olası karşılaştırma seçeneklerini öğrenmek gereklidir. Ayrıca nesne türü bazında araştırma yapmak için de [Simbad'ın nesne sınıflandırma listesini](http://simbad.cds.unistra.fr/guide/otypes.htx) incelemek gerekir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        MAIN_ID               RA      ...     COO_BIBCODE     SCRIPT_NUMBER_ID\n",
      "                           \"h:m:s\"    ...                                     \n",
      "----------------------- ------------- ... ------------------- ----------------\n",
      "              NSV   786 02 22 22.6232 ... 2020yCat.1350....0G                0\n",
      "               V* Z Cam 08 25 13.1986 ... 2020yCat.1350....0G                0\n",
      "            V* V648 Ori 04 52 32.3611 ... 2020yCat.1350....0G                0\n",
      "              V* CW Mon 06 36 54.5810 ... 2020yCat.1350....0G                0\n",
      "              HD  54738    07 10 08.0 ...                                    0\n",
      "              V* FV Cnc 08 48 01.7386 ... 2020yCat.1350....0G                0\n",
      "                    ...           ... ...                 ...              ...\n",
      "              V* AE Aqr 20 40 09.1598 ... 2020yCat.1350....0G                0\n",
      " Cl* NGC  104    SBG V3 00 24 06.2913 ... 2018yCat.1345....0G                0\n",
      "CXOGlb J002407.7-720527 00 24 07.5339 ... 2020yCat.1350....0G                0\n",
      "2MASS J00240462-7204462 00 24 04.6189 ... 2018yCat.1345....0G                0\n",
      "           V* V1129 Cen 12 39 07.8948 ... 2020yCat.1350....0G                0\n",
      "  1RXS J071404.0+700413 07 14 04.6544 ... 2020yCat.1350....0G                0\n",
      "              V* IM Eri 04 24 41.1074 ... 2020yCat.1350....0G                0\n",
      "Length = 30 rows\n"
     ]
    }
   ],
   "source": [
    "from astroquery.simbad import Simbad\n",
    "sonuc_tablosu = Simbad.query_criteria('Vmag < 12 & maintype = \"CV*\"')\n",
    "print(sonuc_tablosu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Başa Dön](#Bu-derste-neler-öğreneceksiniz?)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SIMBAD Veritabanı Sorgularındaki Ayarlar ##\n",
    "\n",
    "SIMBAD veritabanı sorgularında ya da sonuçların görüntülenmesinde varsayılan değerler değiştirilebilir. Bir sorgunun sonucundan belirli sayıda satır çekmek için <i>ROW_LIMIT</i>, veritabanının belirli bir sürede sonuç vermediği vakit sorgunun başarısız olarak sonuçlandırılmasını denetleyen <i>TIMEOUT</i> gibi parametreler aşağıdaki şekilde ayarlanıp, istendiğinde değiştirilebilir. Bu ayarlar ayrıca <i>astroquery.cfg</i> konfigürasyon dosyasında da yer almaktadır. Bu dosyadan değiştirilerek kalıcı hale de getirilebilir.\n",
    "\n",
    "Bunun yanı sıra tüm veriyi bir `pandas` veriçerçevesine alıp, pandas paketi görüntüleme opsiyonları (`pandas.options`) ile de bu tür ayarlar yapılablir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "Simbad.ROW_LIMIT = 15 \n",
    "Simbad.TIMEOUT = 60"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Başa Dön](#Bu-derste-neler-öğreneceksiniz?)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sanal Gözlemevi Alanlarıyla Sorgunun Genişletilmesi ##\n",
    "\n",
    "<i>Simbad</i> gibi gelişmiş astronomi veritabanları [Sanal Gözlemevi](https://en.wikipedia.org/wiki/Virtual_observatory) (virtual observatory -- VO) yapılarında tanımlı standartlara uydukları için sütun isimlendirme ve yapıları da bu standartlar dahilindedir. Simbad'da erişilebilir sanal gözlemevi alanları ($VOTABLES$) elde etmek için `votable.fields()` fonksiyonu kullanılabilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--NOTES--\n",
      "\n",
      "1. The parameter filtername must correspond to an existing filter. Filters include: B,V,R,I,J,K.  They are checked by SIMBAD but not astroquery.simbad\n",
      "\n",
      "2. Fields beginning with rvz display the data as it is in the database. Fields beginning with rv force the display as a radial velocity. Fields beginning with z force the display as a redshift\n",
      "\n",
      "3. For each measurement catalog, the VOTable contains all fields of the first measurement. When applicable, the first measurement is the mean one. \n",
      "\n",
      "Available VOTABLE fields:\n",
      "\n",
      "bibcodelist(y1-y2)\n",
      "biblio\n",
      "cel\n",
      "cl.g\n",
      "coo(opt)\n",
      "coo_bibcode\n",
      "coo_err_angle\n",
      "coo_err_maja\n",
      "coo_err_mina\n",
      "coo_qual\n",
      "coo_wavelength\n",
      "coordinates\n",
      "dec(opt)\n",
      "dec_prec\n",
      "diameter\n",
      "dim\n",
      "dim_angle\n",
      "dim_bibcode\n",
      "dim_incl\n",
      "dim_majaxis\n",
      "dim_minaxis\n",
      "dim_qual\n",
      "dim_wavelength\n",
      "dimensions\n",
      "distance\n",
      "distance_result\n",
      "einstein\n",
      "fe_h\n",
      "flux(filtername)\n",
      "flux_bibcode(filtername)\n",
      "flux_error(filtername)\n",
      "flux_name(filtername)\n",
      "flux_qual(filtername)\n",
      "flux_system(filtername)\n",
      "flux_unit(filtername)\n",
      "fluxdata(filtername)\n",
      "gcrv\n",
      "gen\n",
      "gj\n",
      "hbet\n",
      "hbet1\n",
      "hgam\n",
      "id(opt)\n",
      "ids\n",
      "iras\n",
      "irc\n",
      "iso\n",
      "iue\n",
      "jp11\n",
      "link_bibcode\n",
      "main_id\n",
      "measurements\n",
      "membership\n",
      "mesplx\n",
      "mespm\n",
      "mk\n",
      "morphtype\n",
      "mt\n",
      "mt_bibcode\n",
      "mt_qual\n",
      "otype\n",
      "otype(opt)\n",
      "otypes\n",
      "parallax\n",
      "plx\n",
      "plx_bibcode\n",
      "plx_error\n",
      "plx_prec\n",
      "plx_qual\n",
      "pm\n",
      "pm_bibcode\n",
      "pm_err_angle\n",
      "pm_err_maja\n",
      "pm_err_mina\n",
      "pm_qual\n",
      "pmdec\n",
      "pmdec_prec\n",
      "pmra\n",
      "pmra_prec\n",
      "pos\n",
      "posa\n",
      "propermotions\n",
      "ra(opt)\n",
      "ra_prec\n",
      "rot\n",
      "rv_value\n",
      "rvz_bibcode\n",
      "rvz_error\n",
      "rvz_qual\n",
      "rvz_radvel\n",
      "rvz_type\n",
      "rvz_wavelength\n",
      "sao\n",
      "sp\n",
      "sp_bibcode\n",
      "sp_nature\n",
      "sp_qual\n",
      "sptype\n",
      "td1\n",
      "typed_id\n",
      "ubv\n",
      "uvby\n",
      "uvby1\n",
      "v*\n",
      "velocity\n",
      "xmm\n",
      "z_value\n",
      "For more information on a field:\n",
      "Simbad.get_field_description ('field_name') \n",
      "Currently active VOTABLE fields:\n",
      " ['main_id', 'coordinates']\n"
     ]
    }
   ],
   "source": [
    "Simbad.list_votable_fields()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Measure of distances by several means\n"
     ]
    }
   ],
   "source": [
    "Simbad.get_field_description('distance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Örnek olarak EL CVn yıldızı için oluşturulan tabloya sanal gözlemevi alanları `add_votable_fields` fonksiyonuyla eklenebilir. Bu amaçla standart sütunları döndüren bir Simbad sorgusu yerine yeni bir Simbad sorgusu kurgularken bu sütunların da sorguya dahil edilmesi istenir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " MAIN_ID        RA           DEC      ...     RVZ_BIBCODE     SCRIPT_NUMBER_ID\n",
      "             \"h:m:s\"       \"d:m:s\"    ...                                     \n",
      "--------- ------------- ------------- ... ------------------- ----------------\n",
      "V* EL CVn 13 23 57.0187 +43 35 55.438 ... 2014MNRAS.437.1681M                1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['MAIN_ID',\n",
       " 'RA',\n",
       " 'DEC',\n",
       " 'RA_PREC',\n",
       " 'DEC_PREC',\n",
       " 'COO_ERR_MAJA',\n",
       " 'COO_ERR_MINA',\n",
       " 'COO_ERR_ANGLE',\n",
       " 'COO_QUAL',\n",
       " 'COO_WAVELENGTH',\n",
       " 'COO_BIBCODE',\n",
       " 'OTYPE',\n",
       " 'Distance_distance',\n",
       " 'Distance_Q',\n",
       " 'Distance_unit',\n",
       " 'Distance_merr',\n",
       " 'Distance_perr',\n",
       " 'Distance_method',\n",
       " 'Distance_bibcode',\n",
       " 'RVZ_TYPE',\n",
       " 'RVZ_RADVEL',\n",
       " 'RVZ_ERROR',\n",
       " 'RVZ_QUAL',\n",
       " 'RVZ_WAVELENGTH',\n",
       " 'RVZ_BIBCODE',\n",
       " 'SCRIPT_NUMBER_ID']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customSimbad = Simbad()\n",
    "customSimbad.add_votable_fields('otype','distance', 'velocity')\n",
    "ELCVn = customSimbad.query_object(\"EL CVn\")\n",
    "ELCVn.pprint(show_unit=True)\n",
    "ELCVn.colnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EL CVn is a EclBin at a distance of 260.27 pc\n"
     ]
    }
   ],
   "source": [
    "print(\"EL CVn is a {} at a distance of {:.2f} {}\".format(ELCVn['OTYPE'][0], ELCVn['Distance_distance'][0], \n",
    "                                                     ELCVn['Distance_unit'][0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bir yıldızın tayf türünü (veritabanında bulunmak şartıyla) elde etmek üzere başka bir örnek aşağıda verilmiştir. Yine öcelikle Simbad() veritabanı nesnesinin bir olgusu (instance) oluşturulur. Daha sonra bu olguya VO tablosu sütunları eklenir (`add_votable_fields`). Bu durumda eğer bilgi veritabanında varsa ona da sütun adı (<i>sptype</i>) üzerinden erişilebilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=1</i>\n",
       "<table id=\"table140486152673584\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>MAIN_ID</th><th>RA</th><th>DEC</th><th>RA_PREC</th><th>DEC_PREC</th><th>COO_ERR_MAJA</th><th>COO_ERR_MINA</th><th>COO_ERR_ANGLE</th><th>COO_QUAL</th><th>COO_WAVELENGTH</th><th>COO_BIBCODE</th><th>SP_TYPE</th><th>SP_QUAL</th><th>SP_BIBCODE</th><th>SCRIPT_NUMBER_ID</th></tr></thead>\n",
       "<thead><tr><th></th><th>&quot;h:m:s&quot;</th><th>&quot;d:m:s&quot;</th><th></th><th></th><th>mas</th><th>mas</th><th>deg</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>object</th><th>str13</th><th>str13</th><th>int16</th><th>int16</th><th>float32</th><th>float32</th><th>int16</th><th>str1</th><th>str1</th><th>object</th><th>object</th><th>str1</th><th>object</th><th>int32</th></tr></thead>\n",
       "<tr><td>V* EL CVn</td><td>13 23 57.0187</td><td>+43 35 55.438</td><td>14</td><td>14</td><td>0.009</td><td>0.014</td><td>90</td><td>A</td><td>O</td><td>2020yCat.1350....0G</td><td>A1V</td><td>C</td><td>1969RGOB..156...35W</td><td>1</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=1>\n",
       " MAIN_ID        RA           DEC      ...      SP_BIBCODE     SCRIPT_NUMBER_ID\n",
       "             \"h:m:s\"       \"d:m:s\"    ...                                     \n",
       "  object      str13         str13     ...        object            int32      \n",
       "--------- ------------- ------------- ... ------------------- ----------------\n",
       "V* EL CVn 13 23 57.0187 +43 35 55.438 ... 1969RGOB..156...35W                1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customSimbad = Simbad()\n",
    "customSimbad.add_votable_fields('sptype')\n",
    "result = customSimbad.query_object('EL CVn')\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;MaskedColumn name=&apos;SP_TYPE&apos; dtype=&apos;object&apos; description=&apos;MK spectral type&apos; length=1&gt;\n",
       "<table>\n",
       "<tr><td>A1V</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<MaskedColumn name='SP_TYPE' dtype='object' description='MK spectral type' length=1>\n",
       "A1V"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['SP_TYPE']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standart sütunlara yenileri eklenebileceği gibi (<i>add_votable_fields()</i>) çıkarılabilir de (<i>remove_votable_fields()</i>). Sanal gözlemevi tabloları eski haline döndürülmek istendiğinde <i>reset_votable_fields</i> fonksiyonu kullanılır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=3</i>\n",
       "<table id=\"table140486125871184\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>MAIN_ID</th><th>RA</th><th>DEC</th><th>RA_PREC</th><th>DEC_PREC</th><th>COO_ERR_MAJA</th><th>COO_ERR_MINA</th><th>COO_ERR_ANGLE</th><th>COO_QUAL</th><th>COO_WAVELENGTH</th><th>COO_BIBCODE</th><th>TYPED_ID</th><th>SCRIPT_NUMBER_ID</th></tr></thead>\n",
       "<thead><tr><th></th><th>&quot;h:m:s&quot;</th><th>&quot;d:m:s&quot;</th><th></th><th></th><th>mas</th><th>mas</th><th>deg</th><th></th><th></th><th></th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>object</th><th>str13</th><th>str13</th><th>int16</th><th>int16</th><th>float32</th><th>float32</th><th>int16</th><th>str1</th><th>str1</th><th>object</th><th>object</th><th>int32</th></tr></thead>\n",
       "<tr><td>M  51</td><td>13 29 52.698</td><td>+47 11 42.93</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>C</td><td>I</td><td>2006AJ....131.1163S</td><td>M51</td><td>1</td></tr>\n",
       "<tr><td>NAME Proxima Centauri</td><td>14 29 42.9461</td><td>-62 40 46.164</td><td>14</td><td>14</td><td>0.024</td><td>0.034</td><td>90</td><td>A</td><td>O</td><td>2020yCat.1350....0G</td><td>Proxima</td><td>2</td></tr>\n",
       "<tr><td>* alf Sco</td><td>16 29 24.4597</td><td>-26 25 55.209</td><td>9</td><td>9</td><td>10.710</td><td>6.680</td><td>90</td><td>A</td><td>O</td><td>2007A&amp;A...474..653V</td><td>Antares</td><td>3</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=3>\n",
       "       MAIN_ID              RA           DEC      ... TYPED_ID SCRIPT_NUMBER_ID\n",
       "                         \"h:m:s\"       \"d:m:s\"    ...                          \n",
       "        object            str13         str13     ...  object       int32      \n",
       "--------------------- ------------- ------------- ... -------- ----------------\n",
       "                M  51  13 29 52.698  +47 11 42.93 ...      M51                1\n",
       "NAME Proxima Centauri 14 29 42.9461 -62 40 46.164 ...  Proxima                2\n",
       "            * alf Sco 16 29 24.4597 -26 25 55.209 ...  Antares                3"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Simbad.add_votable_fields('typed_id')\n",
    "Simbad.query_objects(['M51', 'Proxima', 'Antares'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Başa Dön](#Bu-derste-neler-öğreneceksiniz?)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# astroquery İle VizieR Sorguları #\n",
    "\n",
    "[VizieR](https://vizier.cds.unistra.fr/index.gml), astronomide sık kullanılan kataloglara ve bilimsel yayınların elektronik verilerine birden çok arabirim aracılığıyla erişim sağlayan büyük bir veritabanıdır. Sorgu araçları, kullanıcının ilgili veri tablolarını seçmesine ve verilen kriterlere uyan kayıtları çıkarmasına ve biçimlendirmesine olanak sağlar. 13 Mart 2023 tarihi itibarı ile 23136 katalog içeren VizieR'in tüm bu katalogları sorgulamak için bir de [web arayüzü](https://vizier.cds.unistra.fr/viz-bin/VizieR) bulunmaktadır. \n",
    "\n",
    "<b>VizieR</b>, <i>CDS</i> (Centre de Données astronomiques de Strasbourg) ve <i>ESA-ESRIN'in</i> (Bilgi Sistemleri Bölümü) ortak çabası olarak 1996'da kurulmuş bir servistir ve şu anda sadece <i>CDS</i> tarafından yönetilmektedir. Hal-i hazırda <i>VizieR</i> mevcut tüm çevrimiçi katalogları, bazı kataloglarının yapısının uygun olmaması, bazılarının ise VizieR'e hiç duyurulmayan, az kullanılan kataloglar olması nedeniyle içermemektedir. Bu tür katalogların bulunduğu sonunculara [Astronomer's Bazaar FTP sunucusu](https://cdsarc.cds.unistra.fr/viz-bin/Cat?menu=on) yoluyla erişilebilir.\n",
    "\n",
    "Belirli bir nesne hakkında bilgi için Vizier'deki çok sayıda katalog aşağıdaki şekilde taranabilir. Sonuç olarak aranan cismin adının geçtiği ve bir satır olarak içinde bulunduğu kataloglar ve bilimsel yayınların veri tabloları listelenir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: UnitsWarning: The unit 'pix' has been deprecated in the VOUnit standard. [astropy.units.format.utils]\n",
      "WARNING: UnitsWarning: The unit 'ct' has been deprecated in the VOUnit standard. [astropy.units.format.utils]\n",
      "WARNING: UnitsWarning: Unit 'Sun' not supported by the VOUnit standard. Did you mean uN? [astropy.units.format.vounit]\n",
      "WARNING: UnitsWarning: The unit 'au' has been deprecated in the VOUnit standard. Suggested: 1.4959787e+11m. [astropy.units.format.utils]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TableList with 159 tables:\n",
      "\t'0:I/197A/tic' with 10 column(s) and 3 row(s) \n",
      "\t'1:I/252/out' with 8 column(s) and 50 row(s) \n",
      "\t'2:I/254/out' with 10 column(s) and 15 row(s) \n",
      "\t'3:I/255/out' with 9 column(s) and 15 row(s) \n",
      "\t'4:I/259/tyc2' with 10 column(s) and 2 row(s) \n",
      "\t'5:I/261/fonac' with 9 column(s) and 3 row(s) \n",
      "\t'6:I/271/out' with 11 column(s) and 50 row(s) \n",
      "\t'7:I/275/ac2002' with 9 column(s) and 3 row(s) \n",
      "\t'8:I/280B/ascc' with 12 column(s) and 2 row(s) \n",
      "\t'9:I/284/out' with 14 column(s) and 50 row(s) \n",
      "\t'10:I/289/out' with 13 column(s) and 11 row(s) \n",
      "\t'11:I/297/out' with 19 column(s) and 50 row(s) \n",
      "\t'12:I/304/out' with 9 column(s) and 39 row(s) \n",
      "\t'13:I/305/out' with 11 column(s) and 50 row(s) \n",
      "\t'14:I/306A/stars' with 20 column(s) and 1 row(s) \n",
      "\t'15:I/312/sample' with 13 column(s) and 6 row(s) \n",
      "\t'16:I/317/sample' with 13 column(s) and 50 row(s) \n",
      "\t'17:I/319/xpm' with 25 column(s) and 50 row(s) \n",
      "\t'18:I/322A/out' with 24 column(s) and 24 row(s) \n",
      "\t'19:I/324/igsl3' with 18 column(s) and 50 row(s) \n",
      "\t'20:I/327/cmc15' with 9 column(s) and 41 row(s) \n",
      "\t'21:I/329/urat1' with 16 column(s) and 50 row(s) \n",
      "\t'22:I/337/gaia' with 14 column(s) and 50 row(s) \n",
      "\t'23:I/337/tgas' with 20 column(s) and 2 row(s) \n",
      "\t'24:I/337/tgasptyc' with 22 column(s) and 2 row(s) \n",
      "\t'25:I/339/hsoy' with 18 column(s) and 50 row(s) \n",
      "\t'26:I/340/ucac5' with 20 column(s) and 23 row(s) \n",
      "\t'27:I/342/f3' with 18 column(s) and 9 row(s) \n",
      "\t'28:I/345/gaia2' with 32 column(s) and 50 row(s) \n",
      "\t'29:I/347/gaia2dis' with 9 column(s) and 50 row(s) \n",
      "\t'30:I/348/catalog' with 35 column(s) and 5 row(s) \n",
      "\t'31:I/349/starhorse' with 19 column(s) and 46 row(s) \n",
      "\t'32:I/350/gaiaedr3' with 39 column(s) and 50 row(s) \n",
      "\t'33:I/350/tyc2tdsc' with 16 column(s) and 2 row(s) \n",
      "\t'34:I/350/comscanl' with 13 column(s) and 1 row(s) \n",
      "\t'35:I/351/gps1_p' with 22 column(s) and 50 row(s) \n",
      "\t'36:I/352/gedr3dis' with 10 column(s) and 50 row(s) \n",
      "\t'37:I/353/gsc242' with 35 column(s) and 50 row(s) \n",
      "\t'38:I/354/starhorse2021' with 20 column(s) and 50 row(s) \n",
      "\t'39:I/355/gaiadr3' with 57 column(s) and 50 row(s) \n",
      "\t'40:I/355/paramp' with 24 column(s) and 50 row(s) \n",
      "\t'41:I/355/paramsup' with 24 column(s) and 50 row(s) \n",
      "\t'42:I/355/epphot' with 48 column(s) and 50 row(s) \n",
      "\t'43:I/355/rvsmean' with 9 column(s) and 50 row(s) \n",
      "\t'44:I/355/xpsummary' with 22 column(s) and 35 row(s) \n",
      "\t'45:I/355/xpsample' with 6 column(s) and 50 row(s) \n",
      "\t'46:I/355/xpcont' with 26 column(s) and 35 row(s) \n",
      "\t'47:I/355/spectra' with 5 column(s) and 11 row(s) \n",
      "\t'48:I/358/varisum' with 24 column(s) and 3 row(s) \n",
      "\t'49:I/358/vclassre' with 8 column(s) and 3 row(s) \n",
      "\t'50:I/358/veb' with 27 column(s) and 1 row(s) \n",
      "\t'51:I/360/syntphot' with 5 column(s) and 35 row(s) \n",
      "\t'52:II/246/out' with 15 column(s) and 50 row(s) \n",
      "\t'53:II/270/catal' with 9 column(s) and 23 row(s) \n",
      "\t'54:II/271A/patch2' with 10 column(s) and 3 row(s) \n",
      "\t'55:II/287/skydot' with 11 column(s) and 3 row(s) \n",
      "\t'56:II/311/wise' with 22 column(s) and 50 row(s) \n",
      "\t'57:II/321/iphas2' with 11 column(s) and 50 row(s) \n",
      "\t'58:II/328/allwise' with 28 column(s) and 50 row(s) \n",
      "\t'59:II/336/apass9' with 20 column(s) and 13 row(s) \n",
      "\t'60:II/349/ps1' with 35 column(s) and 50 row(s) \n",
      "\t'61:II/356/xmmom41s' with 44 column(s) and 50 row(s) \n",
      "\t'62:II/360/catalog' with 21 column(s) and 41 row(s) \n",
      "\t'63:II/363/unwise' with 11 column(s) and 50 row(s) \n",
      "\t'64:II/365/catwise' with 38 column(s) and 50 row(s) \n",
      "\t'65:II/370/xmmom5s' with 44 column(s) and 50 row(s) \n",
      "\t'66:III/284/allstars' with 99 column(s) and 2 row(s) \n",
      "\t'67:III/284/allvis' with 10 column(s) and 6 row(s) \n",
      "\t'68:IV/35/wn18_b10' with 18 column(s) and 50 row(s) \n",
      "\t'69:IV/36/giphasl' with 18 column(s) and 10 row(s) \n",
      "\t'70:IV/38/tic' with 48 column(s) and 50 row(s) \n",
      "\t'71:IV/39/tic82' with 48 column(s) and 50 row(s) \n",
      "\t'72:V/136/tycall' with 14 column(s) and 2 row(s) \n",
      "\t'73:V/146/dr1' with 14 column(s) and 5 row(s) \n",
      "\t'74:V/146/fgkstars' with 14 column(s) and 1 row(s) \n",
      "\t'75:V/149/dr2' with 13 column(s) and 4 row(s) \n",
      "\t'76:V/149/stellar2' with 15 column(s) and 3 row(s) \n",
      "\t'77:V/153/dr4' with 13 column(s) and 8 row(s) \n",
      "\t'78:V/153/stellar4' with 15 column(s) and 6 row(s) \n",
      "\t'79:V/155/gdr2ap' with 22 column(s) and 17 row(s) \n",
      "\t'80:V/156/dr7lrs' with 18 column(s) and 8 row(s) \n",
      "\t'81:V/156/dr7slrs' with 23 column(s) and 5 row(s) \n",
      "\t'82:V/156/dr7melrs' with 13 column(s) and 1 row(s) \n",
      "\t'83:V/156/dr7mrs' with 23 column(s) and 50 row(s) \n",
      "\t'84:V/156/dr7smrs' with 34 column(s) and 10 row(s) \n",
      "\t'85:V/164/dr5' with 12 column(s) and 8 row(s) \n",
      "\t'86:V/164/stellar5' with 15 column(s) and 6 row(s) \n",
      "\t'87:V/165/igapsdr1' with 17 column(s) and 50 row(s) \n",
      "\t'88:VI/42/out' with 3 column(s) and 1 row(s) \n",
      "\t'89:VI/135/table15' with 15 column(s) and 2 row(s) \n",
      "\t'90:VI/137/gum_mw' with 17 column(s) and 50 row(s) \n",
      "\t'91:VI/145/attitude' with 18 column(s) and 5 row(s) \n",
      "\t'92:VII/293/catalog' with 14 column(s) and 13 row(s) \n",
      "\t'93:VIII/65/nvss' with 12 column(s) and 1 row(s) \n",
      "\t'94:VIII/106/hprsl' with 5 column(s) and 6 row(s) \n",
      "\t'95:IX/54/xmm3r7s' with 17 column(s) and 6 row(s) \n",
      "\t'96:IX/55/xmm3r8s' with 17 column(s) and 6 row(s) \n",
      "\t'97:IX/58/2sxps' with 12 column(s) and 3 row(s) \n",
      "\t'98:IX/58/2sxpscle' with 12 column(s) and 2 row(s) \n",
      "\t'99:IX/59/xmm4dr9s' with 17 column(s) and 6 row(s) \n",
      "\t'100:IX/61/xmm4r9st' with 23 column(s) and 20 row(s) \n",
      "\t'101:IX/63/xmm4d10s' with 16 column(s) and 6 row(s) \n",
      "\t'102:IX/64/xmm410st' with 23 column(s) and 20 row(s) \n",
      "\t'103:IX/65/xmm4d11s' with 16 column(s) and 6 row(s) \n",
      "\t'104:IX/66/xmm411st' with 23 column(s) and 20 row(s) \n",
      "\t'105:B/swift/swiftlog' with 10 column(s) and 1 row(s) \n",
      "\t'106:J/ApJ/737/45/table1' with 8 column(s) and 1 row(s) \n",
      "\t'107:J/ApJ/833/119/distall' with 16 column(s) and 2 row(s) \n",
      "\t'108:J/ApJ/833/119/distallw' with 16 column(s) and 2 row(s) \n",
      "\t'109:J/ApJ/836/5/table1' with 14 column(s) and 2 row(s) \n",
      "\t'110:J/ApJ/858/L7/table1' with 10 column(s) and 2 row(s) \n",
      "\t'111:J/ApJ/858/L7/table2' with 13 column(s) and 1 row(s) \n",
      "\t'112:J/ApJ/867/105/refcat2' with 23 column(s) and 50 row(s) \n",
      "\t'113:J/ApJ/878/21/table1' with 19 column(s) and 1 row(s) \n",
      "\t'114:J/ApJ/891/23/catalog' with 26 column(s) and 27 row(s) \n",
      "\t'115:J/ApJ/900/4/table3' with 19 column(s) and 1 row(s) \n",
      "\t'116:J/ApJ/901/109/table1' with 10 column(s) and 1 row(s) \n",
      "\t'117:J/ApJ/914/116/table2' with 14 column(s) and 3 row(s) \n",
      "\t'118:J/ApJS/245/34/catalog' with 47 column(s) and 6 row(s) \n",
      "\t'119:J/ApJS/246/9/catalog' with 21 column(s) and 4 row(s) \n",
      "\t'120:J/ApJS/258/26/table4' with 27 column(s) and 32 row(s) \n",
      "\t'121:J/ApJS/259/11/table4' with 24 column(s) and 1 row(s) \n",
      "\t'122:J/ApJS/261/36/rrnetcat' with 40 column(s) and 33 row(s) \n",
      "\t'123:J/A+A/594/A116/nhi_hpx' with 6 column(s) and 1 row(s) \n",
      "\t'124:J/A+A/628/A67/catalog' with 11 column(s) and 2 row(s) \n",
      "\t'125:J/A+A/638/A21/sample' with 17 column(s) and 2 row(s) \n",
      "\t'126:J/A+A/638/A76/apogee' with 14 column(s) and 2 row(s) \n",
      "\t'127:J/A+A/638/A76/lamost' with 14 column(s) and 6 row(s) \n",
      "\t'128:J/A+A/644/A69/news' with 27 column(s) and 1 row(s) \n",
      "\t'129:J/A+A/648/A44/tabled1' with 41 column(s) and 4 row(s) \n",
      "\t'130:J/A+A/657/A138/table6' with 13 column(s) and 2 row(s) \n",
      "\t'131:J/A+A/657/A138/table7' with 13 column(s) and 5 row(s) \n",
      "\t'132:J/A+A/659/A38/xmmaster' with 31 column(s) and 1 row(s) \n",
      "\t'133:J/A+A/659/A95/sos' with 12 column(s) and 9 row(s) \n",
      "\t'134:J/A+A/659/A95/apogee' with 18 column(s) and 2 row(s) \n",
      "\t'135:J/A+A/659/A95/lamost' with 18 column(s) and 6 row(s) \n",
      "\t'136:J/AJ/154/259/table6' with 14 column(s) and 2 row(s) \n",
      "\t'137:J/AJ/155/39/table5' with 10 column(s) and 2 row(s) \n",
      "\t'138:J/AJ/155/181/table1' with 16 column(s) and 4 row(s) \n",
      "\t'139:J/AJ/156/18/table2' with 5 column(s) and 2 row(s) \n",
      "\t'140:J/AJ/156/102/table9' with 10 column(s) and 2 row(s) \n",
      "\t'141:J/AJ/156/241/table4' with 16 column(s) and 5 row(s) \n",
      "\t'142:J/AJ/158/93/table2' with 5 column(s) and 15 row(s) \n",
      "\t'143:J/AJ/158/147/table1' with 10 column(s) and 1 row(s) \n",
      "\t'144:J/AJ/159/84/table2' with 5 column(s) and 15 row(s) \n",
      "\t'145:J/AJ/159/182/table4' with 13 column(s) and 2 row(s) \n",
      "\t'146:J/AJ/163/152/table1' with 12 column(s) and 2 row(s) \n",
      "\t'147:J/PASP/120/1128/catalog' with 15 column(s) and 1 row(s) \n",
      "\t'148:J/MNRAS/443/2907/amap' with 13 column(s) and 1 row(s) \n",
      "\t'149:J/MNRAS/463/4210/ucac4rpm' with 12 column(s) and 6 row(s) \n",
      "\t'150:J/MNRAS/471/770/table1' with 42 column(s) and 1 row(s) \n",
      "\t'151:J/MNRAS/474/5008/spidxcat' with 21 column(s) and 1 row(s) \n",
      "\t'152:J/MNRAS/476/2117/apogeenn' with 13 column(s) and 2 row(s) \n",
      "\t'153:J/MNRAS/489/176/dr14ages' with 8 column(s) and 1 row(s) \n",
      "\t'154:J/MNRAS/489/2079/table1' with 17 column(s) and 2 row(s) \n",
      "\t'155:J/MNRAS/495/3087/catalog' with 15 column(s) and 4 row(s) \n",
      "\t'156:J/MNRAS/505/1135/haexlit' with 15 column(s) and 10 row(s) \n",
      "\t'157:J/MNRAS/505/1135/haexfull' with 21 column(s) and 10 row(s) \n",
      "\t'158:J/other/PASA/33.45/frbcat' with 15 column(s) and 1 row(s) \n"
     ]
    }
   ],
   "source": [
    "from astroquery.vizier import Vizier\n",
    "# Hızlı radyo patlaması (Fast radio burst -- FRB)\n",
    "# 121102 için bir arama aşağıdaki gibi kurgulanabilir.\n",
    "\n",
    "result = Vizier.query_object(\"FRB121102\")\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Örneğin $\\gamma$-ışını gözlemleri yapan Fermi Geniş Alan Teleskobu'nun (<i>Fermi-LAT</i>) kataloğunun kataloğu aşağıdaki şekilde aranabilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: UnitsWarning: The unit 'ct' has been deprecated in the VOUnit standard. [astropy.units.format.utils]\n",
      "WARNING: UnitsWarning: The unit 'a' has been deprecated in the VOUnit standard. Suggested: 365.25d. [astropy.units.format.utils]\n",
      "WARNING: UnitsWarning: Unit 'uas' not supported by the VOUnit standard. Did you mean 1e-06a, uA or ua (deprecated)? [astropy.units.format.vounit]\n",
      "WARNING: UnitsWarning: The unit 'ph' has been deprecated in the VOUnit standard. [astropy.units.format.utils]\n",
      "WARNING: UnitsWarning: The unit 'pix' has been deprecated in the VOUnit standard. [astropy.units.format.utils]\n",
      "WARNING: UnitsWarning: Unit 'mCrab' not supported by the VOUnit standard.  [astropy.units.format.vounit]\n",
      "WARNING: UnitsWarning: Unit 'Sun' not supported by the VOUnit standard. Did you mean uN? [astropy.units.format.vounit]\n",
      "WARNING: UnitsWarning: The unit 'barn' has been deprecated in the VOUnit standard. Suggested: 1e-28m**2. [astropy.units.format.utils]\n",
      "WARNING: UnitsWarning: The unit 'mbarn' has been deprecated in the VOUnit standard. Suggested: 0.001barn. [astropy.units.format.utils]\n",
      "WARNING: UnitsWarning: The unit 'G' has been deprecated in the VOUnit standard. Suggested: 0.0001T. [astropy.units.format.utils]\n",
      "WARNING: UnitsWarning: Unit 'kibyte' not supported by the VOUnit standard. Did you mean Kibyte, Tibyte or kbyte? [astropy.units.format.vounit]\n",
      "WARNING: UnitsWarning: Unit 'Crab' not supported by the VOUnit standard.  [astropy.units.format.vounit]\n",
      "WARNING: UnitsWarning: The unit 'kct' has been deprecated in the VOUnit standard. Suggested: 1000count. [astropy.units.format.utils]\n",
      "WARNING: UnitsWarning: Unit 'cmg' not supported by the VOUnit standard. Did you mean 0.001G, 1000000G, MG (deprecated), Mg, cm, cmag, mG (deprecated) or mg? [astropy.units.format.vounit]\n",
      "WARNING: UnitsWarning: Unit 'arcs' not supported by the VOUnit standard. Did you mean arcsec? [astropy.units.format.vounit]\n",
      "WARNING: UnitsWarning: Unit 'ppm' not supported by the VOUnit standard. Did you mean Pm or pm? [astropy.units.format.vounit]\n"
     ]
    }
   ],
   "source": [
    "catalog_list = Vizier.find_catalogs('Gamma', max_catalogs=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Toplam 520 katalog listelenmiştir\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "OrderedDict([('III/29', </>),\n",
       "             ('VIII/108', </>),\n",
       "             ('IX/20A', </>),\n",
       "             ('IX/36', </>),\n",
       "             ('IX/51', </>),\n",
       "             ('IX/60', </>),\n",
       "             ('IX/67', </>),\n",
       "             ('J/ApJ/446/115', </>),\n",
       "             ('J/ApJ/481/95', </>),\n",
       "             ('J/ApJ/491/93', </>),\n",
       "             ('J/ApJ/508/314', </>),\n",
       "             ('J/ApJ/522/846', </>),\n",
       "             ('J/ApJ/563/80', </>),\n",
       "             ('J/ApJ/590/109', </>),\n",
       "             ('J/ApJ/607/L33', </>),\n",
       "             ('J/ApJ/608/L93', </>),\n",
       "             ('J/ApJ/609/564', </>),\n",
       "             ('J/ApJ/609/935', </>),\n",
       "             ('J/ApJ/615/887', </>),\n",
       "             ('J/ApJ/615/897', </>),\n",
       "             ('J/ApJ/616/1072', </>),\n",
       "             ('J/ApJ/626/95', </>),\n",
       "             ('J/ApJ/626/L5', </>),\n",
       "             ('J/ApJ/633/L77', </>),\n",
       "             ('J/ApJ/634/L89', </>),\n",
       "             ('J/ApJ/636/765', </>),\n",
       "             ('J/ApJ/641/L13', </>),\n",
       "             ('J/ApJ/642/L99', </>),\n",
       "             ('J/ApJ/647/1375', </>),\n",
       "             ('J/ApJ/649/L9', </>),\n",
       "             ('J/ApJ/654/527', </>),\n",
       "             ('J/ApJ/654/L21', </>),\n",
       "             ('J/ApJ/654/L25', </>),\n",
       "             ('J/ApJ/657/706', </>),\n",
       "             ('J/ApJ/672/449', </>),\n",
       "             ('J/ApJ/678/102', </>),\n",
       "             ('J/ApJ/678/1127', </>),\n",
       "             ('J/ApJ/681/113', </>),\n",
       "             ('J/ApJ/681/1464', </>),\n",
       "             ('J/ApJ/686/1195', </>),\n",
       "             ('J/ApJ/686/1209', </>),\n",
       "             ('J/ApJ/687/1201', </>),\n",
       "             ('J/ApJ/690/163', </>),\n",
       "             ('J/ApJ/691/723', </>),\n",
       "             ('J/ApJ/693/1484', </>),\n",
       "             ('J/ApJ/700/597', </>),\n",
       "             ('J/ApJ/701/824', </>),\n",
       "             ('J/ApJ/702/489', </>),\n",
       "             ('J/ApJ/704/1405', </>),\n",
       "             ('J/ApJ/706/L7', </>),\n",
       "             ('J/ApJ/707/L56', </>),\n",
       "             ('J/ApJ/709/1407', </>),\n",
       "             ('J/ApJ/711/495', </>),\n",
       "             ('J/ApJ/715/429', </>),\n",
       "             ('J/ApJ/715/1438', </>),\n",
       "             ('J/ApJ/718/587', </>),\n",
       "             ('J/ApJ/720/862', </>),\n",
       "             ('J/ApJ/720/1146', </>),\n",
       "             ('J/ApJ/720/1513', </>),\n",
       "             ('J/ApJ/722/520', </>),\n",
       "             ('J/ApJ/722/L7', </>),\n",
       "             ('J/ApJ/726/16', </>),\n",
       "             ('J/ApJ/727/73', </>),\n",
       "             ('J/ApJ/731/103', </>),\n",
       "             ('J/ApJ/740/104', </>),\n",
       "             ('J/ApJ/741/30', </>),\n",
       "             ('J/ApJ/742/27', </>),\n",
       "             ('J/ApJ/742/66', </>),\n",
       "             ('J/ApJ/743/154', </>),\n",
       "             ('J/ApJ/743/171', </>),\n",
       "             ('J/ApJ/744/141', </>),\n",
       "             ('J/ApJ/744/177', </>),\n",
       "             ('J/ApJ/746/156', </>),\n",
       "             ('J/ApJ/746/170', </>),\n",
       "             ('J/ApJ/748/49', </>),\n",
       "             ('J/ApJ/748/68', </>),\n",
       "             ('J/ApJ/748/134', </>),\n",
       "             ('J/ApJ/752/61', </>),\n",
       "             ('J/ApJ/753/83', </>),\n",
       "             ('J/ApJ/754/23', </>),\n",
       "             ('J/ApJ/754/121', </>),\n",
       "             ('J/ApJ/756/33', </>),\n",
       "             ('J/ApJ/756/44', </>),\n",
       "             ('J/ApJ/756/112', </>),\n",
       "             ('J/ApJ/757/25', </>),\n",
       "             ('J/ApJ/760/10', </>),\n",
       "             ('J/ApJ/760/12', </>),\n",
       "             ('J/ApJ/761/125', </>),\n",
       "             ('J/ApJ/763/15', </>),\n",
       "             ('J/ApJ/764/135', </>),\n",
       "             ('J/ApJ/769/108', </>),\n",
       "             ('J/ApJ/771/57', </>),\n",
       "             ('J/ApJ/772/73', </>),\n",
       "             ('J/ApJ/774/114', </>),\n",
       "             ('J/ApJ/774/157', </>),\n",
       "             ('J/ApJ/777/132', </>),\n",
       "             ('J/ApJ/778/128', </>),\n",
       "             ('J/ApJ/779/133', </>),\n",
       "             ('J/ApJ/780/73', </>),\n",
       "             ('J/ApJ/781/37', </>),\n",
       "             ('J/ApJ/782/41', </>),\n",
       "             ('J/ApJ/784/154', </>),\n",
       "             ('J/ApJ/784/159', </>),\n",
       "             ('J/ApJ/786/120', </>),\n",
       "             ('J/ApJ/787/66', </>),\n",
       "             ('J/ApJ/787/90', </>),\n",
       "             ('J/ApJ/788/30', </>),\n",
       "             ('J/ApJ/789/23', </>),\n",
       "             ('J/ApJ/789/135', </>),\n",
       "             ('J/ApJ/790/L21', </>),\n",
       "             ('J/ApJ/794/82', </>),\n",
       "             ('J/ApJ/795/L21', </>),\n",
       "             ('J/ApJ/799/86', </>),\n",
       "             ('J/ApJ/800/16', </>),\n",
       "             ('J/ApJ/802/103', </>),\n",
       "             ('J/ApJ/806/52', </>),\n",
       "             ('J/ApJ/807/76', </>),\n",
       "             ('J/ApJ/807/169', </>),\n",
       "             ('J/ApJ/808/162', </>),\n",
       "             ('J/ApJ/810/14', </>),\n",
       "             ('J/ApJ/810/85', </>),\n",
       "             ('J/ApJ/810/L9', </>),\n",
       "             ('J/ApJ/811/93', </>),\n",
       "             ('J/ApJ/813/51', </>),\n",
       "             ('J/ApJ/814/1', </>),\n",
       "             ('J/ApJ/814/128', </>),\n",
       "             ('J/ApJ/816/38', </>),\n",
       "             ('J/ApJ/818/18', </>),\n",
       "             ('J/ApJ/818/110', </>),\n",
       "             ('J/ApJ/818/187', </>),\n",
       "             ('J/ApJ/819/44', </>),\n",
       "             ('J/ApJ/820/8', </>),\n",
       "             ('J/ApJ/826/45', </>),\n",
       "             ('J/ApJ/826/228', </>),\n",
       "             ('J/ApJ/828/36', </>),\n",
       "             ('J/ApJ/829/7', </>),\n",
       "             ('J/ApJ/831/89', </>),\n",
       "             ('J/ApJ/832/108', </>),\n",
       "             ('J/ApJ/833/88', </>),\n",
       "             ('J/ApJ/833/117', </>),\n",
       "             ('J/ApJ/833/159', </>),\n",
       "             ('J/ApJ/833/170', </>),\n",
       "             ('J/ApJ/833/196', </>),\n",
       "             ('J/ApJ/833/284', </>),\n",
       "             ('J/ApJ/835/L38', </>),\n",
       "             ('J/ApJ/838/139', </>),\n",
       "             ('J/ApJ/843/40', </>),\n",
       "             ('J/ApJ/846/34', </>),\n",
       "             ('J/ApJ/846/98', </>),\n",
       "             ('J/ApJ/846/L19', </>),\n",
       "             ('J/ApJ/850/74', </>),\n",
       "             ('J/ApJ/851/31', </>),\n",
       "             ('J/ApJ/851/33', </>),\n",
       "             ('J/ApJ/854/99', </>),\n",
       "             ('J/ApJ/866/83', </>),\n",
       "             ('J/ApJ/869/157', </>),\n",
       "             ('J/ApJ/880/32', </>),\n",
       "             ('J/ApJ/880/76', </>),\n",
       "             ('J/ApJ/881/125', </>),\n",
       "             ('J/ApJ/881/154', </>),\n",
       "             ('J/ApJ/887/18', </>),\n",
       "             ('J/ApJ/887/134', </>),\n",
       "             ('J/ApJ/891/170', </>),\n",
       "             ('J/ApJ/892/97', </>),\n",
       "             ('J/ApJ/892/105', </>),\n",
       "             ('J/ApJ/893/L20', </>),\n",
       "             ('J/ApJ/894/88', </>),\n",
       "             ('J/ApJ/897/177', </>),\n",
       "             ('J/ApJ/902/133', </>),\n",
       "             ('J/ApJ/902/L1', </>),\n",
       "             ('J/ApJ/902/L43', </>),\n",
       "             ('J/ApJ/902/L46', </>),\n",
       "             ('J/ApJ/905/76', </>),\n",
       "             ('J/ApJ/907/67', </>),\n",
       "             ('J/ApJ/907/L30', </>),\n",
       "             ('J/ApJ/908/83', </>),\n",
       "             ('J/ApJ/910/134', </>),\n",
       "             ('J/ApJ/910/160', </>),\n",
       "             ('J/ApJ/911/92', </>),\n",
       "             ('J/ApJ/913/120', </>),\n",
       "             ('J/ApJ/913/146', </>),\n",
       "             ('J/ApJ/914/42', </>),\n",
       "             ('J/ApJ/915/13', </>),\n",
       "             ('J/ApJ/916/28', </>),\n",
       "             ('J/ApJ/917/69', </>),\n",
       "             ('J/ApJ/939/106', </>),\n",
       "             ('J/ApJS/101/259', </>),\n",
       "             ('J/ApJS/107/227', </>),\n",
       "             ('J/ApJS/111/377', </>),\n",
       "             ('J/ApJS/115/185', </>),\n",
       "             ('J/ApJS/120/399', </>),\n",
       "             ('J/ApJS/120/409', </>),\n",
       "             ('J/ApJS/123/79', </>),\n",
       "             ('J/ApJS/126/19', </>),\n",
       "             ('J/ApJS/127/79', </>),\n",
       "             ('J/ApJS/134/385', </>),\n",
       "             ('J/ApJS/135/155', </>),\n",
       "             ('J/ApJS/154/585', </>),\n",
       "             ('J/ApJS/156/69', </>),\n",
       "             ('J/ApJS/156/217', </>),\n",
       "             ('J/ApJS/157/324', </>),\n",
       "             ('J/ApJS/166/298', </>),\n",
       "             ('J/ApJS/169/62', </>),\n",
       "             ('J/ApJS/170/175', </>),\n",
       "             ('J/ApJS/175/97', </>),\n",
       "             ('J/ApJS/175/179', </>),\n",
       "             ('J/ApJS/180/192', </>),\n",
       "             ('J/ApJS/183/46', </>),\n",
       "             ('J/ApJS/186/1', </>),\n",
       "             ('J/ApJS/188/405', </>),\n",
       "             ('J/ApJS/191/179', </>),\n",
       "             ('J/ApJS/194/29', </>),\n",
       "             ('J/ApJS/195/2', </>),\n",
       "             ('J/ApJS/196/1', </>),\n",
       "             ('J/ApJS/197/34', </>),\n",
       "             ('J/ApJS/199/18', </>),\n",
       "             ('J/ApJS/199/31', </>),\n",
       "             ('J/ApJS/206/12', </>),\n",
       "             ('J/ApJS/206/13', </>),\n",
       "             ('J/ApJS/207/4', </>),\n",
       "             ('J/ApJS/207/16', </>),\n",
       "             ('J/ApJS/207/38', </>),\n",
       "             ('J/ApJS/207/39', </>),\n",
       "             ('J/ApJS/208/17', </>),\n",
       "             ('J/ApJS/208/21', </>),\n",
       "             ('J/ApJS/208/25', </>),\n",
       "             ('J/ApJS/209/9', </>),\n",
       "             ('J/ApJS/209/10', </>),\n",
       "             ('J/ApJS/209/20', </>),\n",
       "             ('J/ApJS/209/34', </>),\n",
       "             ('J/ApJS/211/13', </>),\n",
       "             ('J/ApJS/216/32', </>),\n",
       "             ('J/ApJS/217/2', </>),\n",
       "             ('J/ApJS/217/4', </>),\n",
       "             ('J/ApJS/218/11', </>),\n",
       "             ('J/ApJS/218/23', </>),\n",
       "             ('J/ApJS/222/5', </>),\n",
       "             ('J/ApJS/222/6', </>),\n",
       "             ('J/ApJS/223/15', </>),\n",
       "             ('J/ApJS/223/28', </>),\n",
       "             ('J/ApJS/224/8', </>),\n",
       "             ('J/ApJS/224/10', </>),\n",
       "             ('J/ApJS/224/20', </>),\n",
       "             ('J/ApJS/224/26', </>),\n",
       "             ('J/ApJS/226/20', </>),\n",
       "             ('J/ApJS/227/7', </>),\n",
       "             ('J/ApJS/229/31', </>),\n",
       "             ('J/ApJS/232/18', </>),\n",
       "             ('J/ApJS/233/17', </>),\n",
       "             ('J/ApJS/235/39', </>),\n",
       "             ('J/ApJS/238/13', </>),\n",
       "             ('J/ApJS/247/16', </>),\n",
       "             ('J/ApJS/247/27', </>),\n",
       "             ('J/ApJS/247/33', </>),\n",
       "             ('J/ApJS/248/8', </>),\n",
       "             ('J/ApJS/248/23', </>),\n",
       "             ('J/ApJS/254/26', </>),\n",
       "             ('J/ApJS/255/10', </>),\n",
       "             ('J/ApJS/256/13', </>),\n",
       "             ('J/ApJS/256/40', </>),\n",
       "             ('J/ApJS/257/30', </>),\n",
       "             ('J/ApJS/257/37', </>),\n",
       "             ('J/ApJS/259/55', </>),\n",
       "             ('J/ApJS/260/12', </>),\n",
       "             ('J/ApJS/262/18', </>),\n",
       "             ('J/A+A/284/515', </>),\n",
       "             ('J/A+A/327/61', </>),\n",
       "             ('J/A+A/367/86', </>),\n",
       "             ('J/A+A/370/468', </>),\n",
       "             ('J/A+A/371/1078', </>),\n",
       "             ('J/A+A/378/861', </>),\n",
       "             ('J/A+A/385/377', </>),\n",
       "             ('J/A+A/390/39', </>),\n",
       "             ('J/A+A/394/187', </>),\n",
       "             ('J/A+A/406/L9', </>),\n",
       "             ('J/A+A/410/813', </>),\n",
       "             ('J/A+A/411/L59', </>),\n",
       "             ('J/A+A/427/87', </>),\n",
       "             ('J/A+A/436/799', </>),\n",
       "             ('J/A+A/438/1175', </>),\n",
       "             ('J/A+A/447/685', </>),\n",
       "             ('J/A+A/448/697', </>),\n",
       "             ('J/A+A/448/L9', </>),\n",
       "             ('J/A+A/450/715', </>),\n",
       "             ('J/A+A/455/813', </>),\n",
       "             ('J/A+A/456/261', </>),\n",
       "             ('J/A+A/458/21', </>),\n",
       "             ('J/A+A/458/245', </>),\n",
       "             ('J/A+A/465/965', </>),\n",
       "             ('J/A+A/467/585', </>),\n",
       "             ('J/A+A/469/1069', </>),\n",
       "             ('J/A+A/472/241', </>),\n",
       "             ('J/A+A/473/347', </>),\n",
       "             ('J/A+A/474/793', </>),\n",
       "             ('J/A+A/475/775', </>),\n",
       "             ('J/A+A/482/247', </>),\n",
       "             ('J/A+A/484/783', </>),\n",
       "             ('J/A+A/489/849', </>),\n",
       "             ('J/A+A/489/1255', </>),\n",
       "             ('J/A+A/492/923', </>),\n",
       "             ('J/A+A/498/399', </>),\n",
       "             ('J/A+A/499/967', </>),\n",
       "             ('J/A+A/502/951', </>),\n",
       "             ('J/A+A/506/1563', </>),\n",
       "             ('J/A+A/507/241', </>),\n",
       "             ('J/A+A/508/593', </>),\n",
       "             ('J/A+A/509/A24', </>),\n",
       "             ('J/A+A/522/A68', </>),\n",
       "             ('J/A+A/523/A61', </>),\n",
       "             ('J/A+A/525/A23', </>),\n",
       "             ('J/A+A/525/A53', </>),\n",
       "             ('J/A+A/530/A21', </>),\n",
       "             ('J/A+A/530/A72', </>),\n",
       "             ('J/A+A/531/A39', </>),\n",
       "             ('J/A+A/535/A57', </>),\n",
       "             ('J/A+A/535/A69', </>),\n",
       "             ('J/A+A/537/A59', </>),\n",
       "             ('J/A+A/539/A149', </>),\n",
       "             ('J/A+A/540/A53', </>),\n",
       "             ('J/A+A/541/A160', </>),\n",
       "             ('J/A+A/542/A94', </>),\n",
       "             ('J/A+A/545/A27', </>),\n",
       "             ('J/A+A/545/L2', </>),\n",
       "             ('J/A+A/548/A106', </>),\n",
       "             ('J/A+A/549/A23', </>),\n",
       "             ('J/A+A/551/A142', </>),\n",
       "             ('J/A+A/552/A60', </>),\n",
       "             ('J/A+A/553/A33', </>),\n",
       "             ('J/A+A/556/A52', </>),\n",
       "             ('J/A+A/556/A56', </>),\n",
       "             ('J/A+A/556/A71', </>),\n",
       "             ('J/A+A/556/A87', </>),\n",
       "             ('J/A+A/557/A12', </>),\n",
       "             ('J/A+A/557/A18', </>),\n",
       "             ('J/A+A/557/A100', </>),\n",
       "             ('J/A+A/558/A137', </>),\n",
       "             ('J/A+A/559/A9', </>),\n",
       "             ('J/A+A/560/A70', </>),\n",
       "             ('J/A+A/562/A29', </>),\n",
       "             ('J/A+A/562/A64', </>),\n",
       "             ('J/A+A/562/A70', </>),\n",
       "             ('J/A+A/563/A94', </>),\n",
       "             ('J/A+A/564/A38', </>),\n",
       "             ('J/A+A/565/A72', </>),\n",
       "             ('J/A+A/565/L12', </>),\n",
       "             ('J/A+A/566/A50', </>),\n",
       "             ('J/A+A/567/A55', </>),\n",
       "             ('J/A+A/568/A19', </>),\n",
       "             ('J/A+A/568/A75', </>),\n",
       "             ('J/A+A/568/A107', </>),\n",
       "             ('J/A+A/572/A12', </>),\n",
       "             ('J/A+A/573/A50', </>),\n",
       "             ('J/A+A/573/A81', </>),\n",
       "             ('J/A+A/574/A17', </>),\n",
       "             ('J/A+A/575/A4', </>),\n",
       "             ('J/A+A/578/A22', </>),\n",
       "             ('J/A+A/579/A34', </>),\n",
       "             ('J/A+A/580/A139', </>),\n",
       "             ('J/A+A/581/A125', </>),\n",
       "             ('J/A+A/581/A126', </>),\n",
       "             ('J/A+A/582/A111', </>),\n",
       "             ('J/A+A/584/A48', </>),\n",
       "             ('J/A+A/586/A71', </>),\n",
       "             ('J/A+A/587/A93', </>),\n",
       "             ('J/A+A/588/A135', </>),\n",
       "             ('J/A+A/588/A141', </>),\n",
       "             ('J/A+A/589/A70', </>),\n",
       "             ('J/A+A/589/A97', </>),\n",
       "             ('J/A+A/589/A98', </>),\n",
       "             ('J/A+A/593/A91', </>),\n",
       "             ('J/A+A/596/A45', </>),\n",
       "             ('J/A+A/597/A80', </>),\n",
       "             ('J/A+A/598/A17', </>),\n",
       "             ('J/A+A/598/A134', </>),\n",
       "             ('J/A+A/601/A83', </>),\n",
       "             ('J/A+A/602/A31', </>),\n",
       "             ('J/A+A/602/A85', </>),\n",
       "             ('J/A+A/602/A86', </>),\n",
       "             ('J/A+A/607/A29', </>),\n",
       "             ('J/A+A/607/A107', </>),\n",
       "             ('J/A+A/608/A68', </>),\n",
       "             ('J/A+A/608/A103', </>),\n",
       "             ('J/A+A/609/A112', </>),\n",
       "             ('J/A+A/612/A1', </>),\n",
       "             ('J/A+A/612/A3', </>),\n",
       "             ('J/A+A/612/A5', </>),\n",
       "             ('J/A+A/612/A6', </>),\n",
       "             ('J/A+A/612/A7', </>),\n",
       "             ('J/A+A/617/A122', </>),\n",
       "             ('J/A+A/618/A22', </>),\n",
       "             ('J/A+A/619/A23', </>),\n",
       "             ('J/A+A/621/A116', </>),\n",
       "             ('J/A+A/623/A175', </>),\n",
       "             ('J/A+A/626/A60', </>),\n",
       "             ('J/A+A/626/A121', </>),\n",
       "             ('J/A+A/627/A13', </>),\n",
       "             ('J/A+A/627/A100', </>),\n",
       "             ('J/A+A/627/A140', </>),\n",
       "             ('J/A+A/628/A9', </>),\n",
       "             ('J/A+A/629/A131', </>),\n",
       "             ('J/A+A/630/A56', </>),\n",
       "             ('J/A+A/631/A150', </>),\n",
       "             ('J/A+A/632/A77', </>),\n",
       "             ('J/A+A/634/A112', </>),\n",
       "             ('J/A+A/636/A62', </>),\n",
       "             ('J/A+A/638/A130', </>),\n",
       "             ('J/A+A/640/A31', </>),\n",
       "             ('J/A+A/640/A43', </>),\n",
       "             ('J/A+A/640/A91', </>),\n",
       "             ('J/A+A/641/A152', </>),\n",
       "             ('J/A+A/642/A190', </>),\n",
       "             ('J/A+A/643/A103', </>),\n",
       "             ('J/A+A/646/A50', </>),\n",
       "             ('J/A+A/647/A88', </>),\n",
       "             ('J/A+A/647/A195', </>),\n",
       "             ('J/A+A/652/A6', </>),\n",
       "             ('J/A+A/653/A83', </>),\n",
       "             ('J/A+A/655/A89', </>),\n",
       "             ('J/A+A/655/A93', </>),\n",
       "             ('J/A+A/660/A87', </>),\n",
       "             ('J/A+A/662/A58', </>),\n",
       "             ('J/A+A/662/A65', </>),\n",
       "             ('J/A+A/667/A130', </>),\n",
       "             ('J/A+A/668/A75', </>),\n",
       "             ('J/A+A/669/A32', </>),\n",
       "             ('J/A+A/671/A47', </>),\n",
       "             ('J/A+A/671/A112', </>),\n",
       "             ('J/A+AS/129/1', </>),\n",
       "             ('J/A+AS/131/11', </>),\n",
       "             ('J/A+AS/143/145', </>),\n",
       "             ('J/AJ/112/2274', </>),\n",
       "             ('J/AJ/129/2026', </>),\n",
       "             ('J/AJ/129/2815', </>),\n",
       "             ('J/AJ/133/1027', </>),\n",
       "             ('J/AJ/133/1421', </>),\n",
       "             ('J/AJ/141/36', </>),\n",
       "             ('J/AJ/141/163', </>),\n",
       "             ('J/AJ/142/39', </>),\n",
       "             ('J/AJ/144/114', </>),\n",
       "             ('J/AJ/148/37', </>),\n",
       "             ('J/AJ/149/68', </>),\n",
       "             ('J/AJ/151/26', </>),\n",
       "             ('J/AJ/151/142', </>),\n",
       "             ('J/AJ/155/120', </>),\n",
       "             ('J/AJ/159/24', </>),\n",
       "             ('J/AJ/163/180', </>),\n",
       "             ('J/PASP/106/949', </>),\n",
       "             ('J/PASP/124/297', </>),\n",
       "             ('J/MNRAS/342/1299', </>),\n",
       "             ('J/MNRAS/376/573', </>),\n",
       "             ('J/MNRAS/393/538', </>),\n",
       "             ('J/MNRAS/397/1177', </>),\n",
       "             ('J/MNRAS/403/945', </>),\n",
       "             ('J/MNRAS/406/2473', </>),\n",
       "             ('J/MNRAS/407/2075', </>),\n",
       "             ('J/MNRAS/411/1367', </>),\n",
       "             ('J/MNRAS/418/1526', </>),\n",
       "             ('J/MNRAS/418/2202', </>),\n",
       "             ('J/MNRAS/421/1874', </>),\n",
       "             ('J/MNRAS/421/2692', </>),\n",
       "             ('J/MNRAS/424/2832', </>),\n",
       "             ('J/MNRAS/424/L64', </>),\n",
       "             ('J/MNRAS/425/477', </>),\n",
       "             ('J/MNRAS/426/2', </>),\n",
       "             ('J/MNRAS/426/416', </>),\n",
       "             ('J/MNRAS/426/1750', </>),\n",
       "             ('J/MNRAS/428/220', </>),\n",
       "             ('J/MNRAS/428/729', </>),\n",
       "             ('J/MNRAS/431/3608', </>),\n",
       "             ('J/MNRAS/438/3058', </>),\n",
       "             ('J/MNRAS/439/690', </>),\n",
       "             ('J/MNRAS/441/1899', </>),\n",
       "             ('J/MNRAS/442/1693', </>),\n",
       "             ('J/MNRAS/448/2210', </>),\n",
       "             ('J/MNRAS/448/2624', </>),\n",
       "             ('J/MNRAS/449/268', </>),\n",
       "             ('J/MNRAS/449/L6', </>),\n",
       "             ('J/MNRAS/451/2750', </>),\n",
       "             ('J/MNRAS/453/2599', </>),\n",
       "             ('J/MNRAS/454/L31', </>),\n",
       "             ('J/MNRAS/458/56', </>),\n",
       "             ('J/MNRAS/458/2307', </>),\n",
       "             ('J/MNRAS/458/3411', </>),\n",
       "             ('J/MNRAS/460/19', </>),\n",
       "             ('J/MNRAS/462/2747', </>),\n",
       "             ('J/MNRAS/462/3180', </>),\n",
       "             ('J/MNRAS/464/4545', </>),\n",
       "             ('J/MNRAS/467/4565', </>),\n",
       "             ('J/MNRAS/469/4341', </>),\n",
       "             ('J/MNRAS/470/512', </>),\n",
       "             ('J/MNRAS/470/1291', </>),\n",
       "             ('J/MNRAS/473/1512', </>),\n",
       "             ('J/MNRAS/477/4749', </>),\n",
       "             ('J/MNRAS/480/2165', </>),\n",
       "             ('J/MNRAS/480/5517', </>),\n",
       "             ('J/MNRAS/489/2595', </>),\n",
       "             ('J/MNRAS/490/927', </>),\n",
       "             ('J/MNRAS/491/3586', </>),\n",
       "             ('J/MNRAS/493/1926', </>),\n",
       "             ('J/MNRAS/493/2438', </>),\n",
       "             ('J/MNRAS/496/5423', </>),\n",
       "             ('J/MNRAS/505/2662', </>),\n",
       "             ('J/AZh/72/80', </>),\n",
       "             ('J/AZh/72/864', </>),\n",
       "             ('J/AZh/84/1110', </>),\n",
       "             ('J/PAZh/30/430', </>),\n",
       "             ('J/PAZh/30/589', </>),\n",
       "             ('J/PAZh/35/10', </>),\n",
       "             ('J/PAZh/36/744', </>),\n",
       "             ('J/PAZh/40/271', </>),\n",
       "             ('J/other/APh/26.367', </>),\n",
       "             ('J/other/GCN/1070.1', </>),\n",
       "             ('J/other/Nat/515.376', </>),\n",
       "             ('J/other/RAA/13.259', </>),\n",
       "             ('J/other/RAA/14.1135', </>),\n",
       "             ('J/other/RAA/16.13', </>),\n",
       "             ('J/other/RAA/18.56', </>),\n",
       "             ('J/other/RAA/21.254', </>),\n",
       "             ('J/other/PhRvD/103.H3016', </>),\n",
       "             ('J/other/Univ/8.587', </>)])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Toplam {:d} katalog listelenmiştir\".format(len(catalog_list)))\n",
    "catalog_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu kadar geniş bir listede Fermi-LAT kataloğunun hangi yayında yayınlanmış olduğunu belirlemek çok zor olacaktır. Eğer ilgili yayının en azından baş yazarı biliniyorsa ona başvurulabilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'J/ApJ/700/597': 'FERMI LAT detected blazars (Abdo+, 2009)', 'J/ApJ/715/429': 'First Fermi-LAT AGN catalog (1LAC) (Abdo+, 2010)', 'J/ApJ/716/30': 'SED of Fermi bright blazars (Abdo+, 2010)', 'J/ApJ/722/520': 'Gamma-ray light curves of Fermi blazars (Abdo+, 2010)', 'J/ApJS/183/46': 'Fermi/LAT bright gamma-ray source list (0FGL) (Abdo+, 2009)', 'J/ApJS/188/405': 'Fermi-LAT first source catalog (1FGL) (Abdo+, 2010)', 'J/ApJS/199/31': 'Fermi LAT second source catalog (2FGL) (Nolan+, 2012)', 'J/ApJS/208/17': '2nd Fermi LAT cat. of gamma-ray pulsars (2PC) (Abdo+, 2013)'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: UnitsWarning: The unit 'ct' has been deprecated in the VOUnit standard. [astropy.units.format.utils]\n",
      "WARNING: UnitsWarning: The unit 'ph' has been deprecated in the VOUnit standard. [astropy.units.format.utils]\n"
     ]
    }
   ],
   "source": [
    "# Fewer results if we directly search for the author name:\n",
    "catalog_list = Vizier.find_catalogs('Abdo', max_catalogs=10000)\n",
    "print({k:v.description for k,v in catalog_list.items()})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu listede seçim yapmak daha kolaydır. Örneğin 2nd Fermi-LAT catalog of gamma-ray pulsars (<i>'J/ApJS/208/17'</i>) kataloğu seçilebilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: UnitsWarning: The unit 'ph' has been deprecated in the VOUnit standard. [astropy.units.format.utils]\n"
     ]
    }
   ],
   "source": [
    "catalog = Vizier.get_catalogs('J/ApJS/208/17')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TableList with 3 tables:\n",
      "\t'0:J/ApJS/208/17/psrcat' with 27 column(s) and 50 row(s) \n",
      "\t'1:J/ApJS/208/17/offpk' with 23 column(s) and 50 row(s) \n",
      "\t'2:J/ApJS/208/17/refs' with 4 column(s) and 50 row(s) \n"
     ]
    }
   ],
   "source": [
    "catalog.pprint()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu yayında yayınlanan 3 tablodan herhangi biri seçilip içeriğine bakılabilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=50</i>\n",
       "<table id=\"table140485994472608\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>Name</th><th>RAJ2000</th><th>DEJ2000</th><th>Per</th><th>Edot</th><th>pm</th><th>Lum</th><th>Eff</th><th>S1400</th><th>Npk</th><th>XFlux</th><th>Type</th><th>LC</th><th>SED</th><th>par</th><th>fits</th><th>Simbad</th><th>TS-c</th><th>pInd1</th><th>Sc1</th><th>Ec1</th><th>Fph</th><th>Ferg</th><th>pInd0</th><th>Sc0</th><th>Fph0</th><th>Ferg0</th></tr></thead>\n",
       "<thead><tr><th></th><th>deg</th><th>deg</th><th>ms</th><th>1e-07 W</th><th>mas / yr</th><th>1e-07 W</th><th>%</th><th>mJy</th><th></th><th>mW / m2</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>MeV</th><th>MeV</th><th>ph / (cm2 s)</th><th>mW / m2</th><th></th><th>MeV</th><th>ph / (cm2 s)</th><th>mW / m2</th></tr></thead>\n",
       "<thead><tr><th>str11</th><th>float64</th><th>float64</th><th>float32</th><th>float64</th><th>float32</th><th>float64</th><th>float64</th><th>float64</th><th>uint8</th><th>float32</th><th>str3</th><th>str2</th><th>str3</th><th>str3</th><th>str4</th><th>str6</th><th>int32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int16</th><th>float32</th><th>float32</th></tr></thead>\n",
       "<tr><td>J0007+7303</td><td>1.7565</td><td>73.0523</td><td>315.89</td><td>4.5e+35</td><td>--</td><td>9.39e+34</td><td>0.2099</td><td>--</td><td>2</td><td>9.8e-14</td><td>YRQ</td><td>LC</td><td>SED</td><td>par</td><td>fits</td><td>Simbad</td><td>1884</td><td>1.397</td><td>727.4</td><td>4662.0</td><td>3.29e-07</td><td>4.01e-10</td><td>1.871</td><td>1000</td><td>4.16e-07</td><td>8.14e-10</td></tr>\n",
       "<tr><td>J0023+0923</td><td>5.8203</td><td>9.3900</td><td>3.05</td><td>1.5e+34</td><td>--</td><td>4.56e+32</td><td>0.0302</td><td>0.189</td><td>2</td><td>2.1e-14</td><td>MSP</td><td>LC</td><td>SED</td><td>par</td><td>fits</td><td>Simbad</td><td>16</td><td>1.405</td><td>1000.0</td><td>1408.0</td><td>1.18e-08</td><td>8.01e-12</td><td>2.367</td><td>1000</td><td>2.02e-08</td><td>1.14e-11</td></tr>\n",
       "<tr><td>J0030+0451</td><td>7.6143</td><td>4.8610</td><td>4.87</td><td>3.6e+33</td><td>5.70</td><td>5.76e+32</td><td>0.1589</td><td>0.600</td><td>2</td><td>2.5e-13</td><td>MSP</td><td>LC</td><td>SED</td><td>par</td><td>fits</td><td>Simbad</td><td>316</td><td>1.211</td><td>699.8</td><td>1820.0</td><td>6.6e-08</td><td>6.14e-11</td><td>2.046</td><td>1000</td><td>9.88e-08</td><td>1.1e-10</td></tr>\n",
       "<tr><td>J0034-0534</td><td>8.5910</td><td>-5.5769</td><td>1.88</td><td>1.7e+34</td><td>31.00</td><td>5.67e+32</td><td>0.0330</td><td>0.610</td><td>2</td><td>--</td><td>MSP</td><td>LC</td><td>SED</td><td>par</td><td>fits</td><td>Simbad</td><td>45</td><td>1.436</td><td>755.7</td><td>1831.0</td><td>2.18e-08</td><td>1.62e-11</td><td>2.219</td><td>1000</td><td>3.32e-08</td><td>2.44e-11</td></tr>\n",
       "<tr><td>J0101-6422</td><td>15.2963</td><td>-64.3750</td><td>2.57</td><td>1e+34</td><td>15.62</td><td>3.79e+32</td><td>0.0377</td><td>0.280</td><td>2</td><td>--</td><td>MSP</td><td>LC</td><td>SED</td><td>par</td><td>fits</td><td>Simbad</td><td>59</td><td>0.693</td><td>1139.0</td><td>1523.0</td><td>7.51e-09</td><td>1.05e-11</td><td>2.051</td><td>1000</td><td>1.84e-08</td><td>2.02e-11</td></tr>\n",
       "<tr><td>J0102+4839</td><td>15.7111</td><td>48.6619</td><td>2.96</td><td>1.7e+34</td><td>--</td><td>8.51e+33</td><td>0.4861</td><td>0.218</td><td>2</td><td>--</td><td>MSP</td><td>LC</td><td>SED</td><td>par</td><td>fits</td><td>Simbad</td><td>29</td><td>1.401</td><td>1000.0</td><td>3160.0</td><td>1.32e-08</td><td>1.32e-11</td><td>2.145</td><td>1000</td><td>2.29e-08</td><td>1.99e-11</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>J1231-1411</td><td>187.7971</td><td>-14.1955</td><td>3.68</td><td>5.2e+33</td><td>62.20</td><td>2.36e+33</td><td>0.4587</td><td>0.161</td><td>3</td><td>4.1e-13</td><td>MSP</td><td>LC</td><td>SED</td><td>par</td><td>fits</td><td>Simbad</td><td>446</td><td>1.241</td><td>864.1</td><td>2673.0</td><td>9.2e-08</td><td>1.03e-10</td><td>1.952</td><td>1000</td><td>1.35e-07</td><td>2.01e-10</td></tr>\n",
       "<tr><td>J1357-6429</td><td>209.2601</td><td>-64.4917</td><td>166.17</td><td>3.1e+36</td><td>--</td><td>2.53e+34</td><td>0.0082</td><td>0.440</td><td>1</td><td>4.2e-14</td><td>YRL</td><td>LC</td><td>SED</td><td>par</td><td>fits</td><td>Simbad</td><td>20</td><td>1.803</td><td>949.6</td><td>908.2</td><td>7.76e-08</td><td>3.39e-11</td><td>2.609</td><td>1000</td><td>9.06e-08</td><td>3.79e-11</td></tr>\n",
       "<tr><td>J1410-6132</td><td>212.5920</td><td>-61.5331</td><td>50.05</td><td>1e+37</td><td>--</td><td>7.66e+35</td><td>0.0766</td><td>6.600</td><td>2</td><td>--</td><td>YRL</td><td>LC</td><td>SED</td><td>par</td><td>fits</td><td>Simbad</td><td>9</td><td>1.564</td><td>6601.0</td><td>4262.0</td><td>2.8e-08</td><td>2.63e-11</td><td>2.151</td><td>1000</td><td>4.1e-08</td><td>3.51e-11</td></tr>\n",
       "<tr><td>J1413-6205</td><td>213.3759</td><td>-62.0935</td><td>109.74</td><td>8.2e+35</td><td>--</td><td>--</td><td>--</td><td>--</td><td>2</td><td>1.4e-13</td><td>YRQ</td><td>LC</td><td>SED</td><td>par</td><td>fits</td><td>Simbad</td><td>180</td><td>1.514</td><td>1429.0</td><td>4074.0</td><td>1.61e-07</td><td>1.57e-10</td><td>2.128</td><td>1000</td><td>2.66e-07</td><td>2.4e-10</td></tr>\n",
       "<tr><td>J1418-6058</td><td>214.6783</td><td>-60.9667</td><td>110.58</td><td>4.9e+36</td><td>--</td><td>9.24e+34</td><td>0.0187</td><td>--</td><td>2</td><td>3.6e-14</td><td>YRQ</td><td>LC</td><td>SED</td><td>par</td><td>fits</td><td>Simbad</td><td>172</td><td>1.765</td><td>2978.0</td><td>5489.0</td><td>3.82e-07</td><td>3.02e-10</td><td>2.206</td><td>1000</td><td>4.82e-07</td><td>3.64e-10</td></tr>\n",
       "<tr><td>J1420-6048</td><td>215.0343</td><td>-60.8046</td><td>68.20</td><td>1e+37</td><td>--</td><td>6.39e+35</td><td>0.0619</td><td>0.900</td><td>2</td><td>1.6e-13</td><td>YRL</td><td>LC</td><td>SED</td><td>par</td><td>fits</td><td>Simbad</td><td>51</td><td>1.511</td><td>2749.0</td><td>1634.0</td><td>2.56e-07</td><td>1.7e-10</td><td>2.224</td><td>1000</td><td>2.26e-07</td><td>1.64e-10</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=50>\n",
       "   Name    RAJ2000  DEJ2000    Per   ...  Sc0      Fph0       Ferg0   \n",
       "             deg      deg       ms   ...  MeV  ph / (cm2 s)  mW / m2  \n",
       "  str11    float64  float64  float32 ... int16   float32     float32  \n",
       "---------- -------- -------- ------- ... ----- ------------ ----------\n",
       "J0007+7303   1.7565  73.0523  315.89 ...  1000     4.16e-07   8.14e-10\n",
       "J0023+0923   5.8203   9.3900    3.05 ...  1000     2.02e-08   1.14e-11\n",
       "J0030+0451   7.6143   4.8610    4.87 ...  1000     9.88e-08    1.1e-10\n",
       "J0034-0534   8.5910  -5.5769    1.88 ...  1000     3.32e-08   2.44e-11\n",
       "J0101-6422  15.2963 -64.3750    2.57 ...  1000     1.84e-08   2.02e-11\n",
       "J0102+4839  15.7111  48.6619    2.96 ...  1000     2.29e-08   1.99e-11\n",
       "       ...      ...      ...     ... ...   ...          ...        ...\n",
       "J1231-1411 187.7971 -14.1955    3.68 ...  1000     1.35e-07   2.01e-10\n",
       "J1357-6429 209.2601 -64.4917  166.17 ...  1000     9.06e-08   3.79e-11\n",
       "J1410-6132 212.5920 -61.5331   50.05 ...  1000      4.1e-08   3.51e-11\n",
       "J1413-6205 213.3759 -62.0935  109.74 ...  1000     2.66e-07    2.4e-10\n",
       "J1418-6058 214.6783 -60.9667  110.58 ...  1000     4.82e-07   3.64e-10\n",
       "J1420-6048 215.0343 -60.8046   68.20 ...  1000     2.26e-07   1.64e-10"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "catalog[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Başa Dön](#Bu-derste-neler-öğreneceksiniz?)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VizieR'de Bölge Sorguları ##\n",
    "\n",
    "Simbad veritabanındakine benzer şekilde VizieR katalogları için de bölge sorguları yazılabilir. Bu amaçla `astropy.SkyCoord` fonksiyonları kullanılarak sorgu daha da esnek ve verimli hale getirilebilir.\n",
    "\n",
    "Örneğin [NOMAD kataloğounda (Zacharias+2004)](https://ui.adsabs.harvard.edu/abs/2004AAS...205.4815Z/abstract) (Naval Observatory Merged Astrometric Dataset) [Crab Bulutsusu](https://en.wikipedia.org/wiki/Crab_Nebula) etrafında $2^{\\circ}$ yarıçaplı bir alanda yer alan, B-bandında $9^{m}$ kadirden daha parlak kaynakları belirlemek için aşağıdaki bir sorgu yazılabilir. Bu amaçla `Vizier` modülü başlatılırken B-bandı parlaklığına ($Bmag$) göre bir filtre yapmak üzere `column_filters` parametresi kullanılabilir. Listelenen kaynak sayısını sınırlamamak amacıyla query nesnesi için satır limiti `ROW_LIMIT=-1` 'e ayarlanır. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=32</i>\n",
       "<table id=\"table140486108398400\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>NOMAD1</th><th>YM</th><th>RAJ2000</th><th>DEJ2000</th><th>r</th><th>pmRA</th><th>e_pmRA</th><th>pmDE</th><th>e_pmDE</th><th>Bmag</th><th>r_Bmag</th><th>Vmag</th><th>r_Vmag</th><th>Rmag</th><th>r_Rmag</th><th>Jmag</th><th>Hmag</th><th>Kmag</th><th>R</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th>deg</th><th>deg</th><th></th><th>mas / yr</th><th>mas / yr</th><th>mas / yr</th><th>mas / yr</th><th>mag</th><th></th><th>mag</th><th></th><th>mag</th><th></th><th>mag</th><th>mag</th><th>mag</th><th></th></tr></thead>\n",
       "<thead><tr><th>str12</th><th>str2</th><th>float64</th><th>float64</th><th>str1</th><th>float64</th><th>float32</th><th>float64</th><th>float32</th><th>float32</th><th>str1</th><th>float32</th><th>str1</th><th>float32</th><th>str1</th><th>float32</th><th>float32</th><th>float32</th><th>str1</th></tr></thead>\n",
       "<tr><td>1119-0082234</td><td>..</td><td>81.8766917</td><td>21.9363778</td><td>B</td><td>256.0</td><td>72.0</td><td>-88.0</td><td>101.0</td><td>8.340</td><td>B</td><td>--</td><td></td><td>16.470</td><td>B</td><td>--</td><td>--</td><td>--</td><td></td></tr>\n",
       "<tr><td>1119-0082297</td><td>YM</td><td>81.9086881</td><td>21.9369650</td><td>H</td><td>1.7</td><td>0.9</td><td>-6.9</td><td>0.5</td><td>4.720</td><td>T</td><td>4.858</td><td>T</td><td>4.940</td><td>B</td><td>5.179</td><td>5.270</td><td>5.283</td><td></td></tr>\n",
       "<tr><td>1124-0097499</td><td>YM</td><td>82.6806150</td><td>22.4622544</td><td>H</td><td>42.8</td><td>0.9</td><td>-20.5</td><td>0.4</td><td>7.477</td><td>T</td><td>6.298</td><td>T</td><td>5.580</td><td>B</td><td>4.517</td><td>3.862</td><td>3.713</td><td></td></tr>\n",
       "<tr><td>1124-0100914</td><td>YM</td><td>83.8579511</td><td>22.4380619</td><td>H</td><td>24.6</td><td>1.1</td><td>-26.8</td><td>0.5</td><td>8.880</td><td>T</td><td>8.352</td><td>T</td><td>7.990</td><td>B</td><td>7.309</td><td>7.080</td><td>7.018</td><td></td></tr>\n",
       "<tr><td>1113-0087658</td><td>YM</td><td>82.3149911</td><td>21.3158950</td><td>H</td><td>9.4</td><td>1.2</td><td>-27.0</td><td>0.5</td><td>8.699</td><td>T</td><td>8.166</td><td>T</td><td>7.810</td><td>B</td><td>7.081</td><td>6.884</td><td>6.828</td><td></td></tr>\n",
       "<tr><td>1119-0088794</td><td>.M</td><td>84.1088183</td><td>21.9931567</td><td>H</td><td>-34.6</td><td>2.7</td><td>-76.9</td><td>2.8</td><td>7.533</td><td>T</td><td>7.031</td><td>T</td><td>6.690</td><td>B</td><td>6.216</td><td>5.987</td><td>5.985</td><td></td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>1131-0109812</td><td>YM</td><td>84.4024200</td><td>23.1388575</td><td>H</td><td>0.6</td><td>1.7</td><td>-7.0</td><td>0.8</td><td>8.733</td><td>T</td><td>8.793</td><td>T</td><td>8.830</td><td>B</td><td>8.758</td><td>8.836</td><td>8.838</td><td></td></tr>\n",
       "<tr><td>1131-0109842</td><td>YM</td><td>84.4091506</td><td>23.1474531</td><td>H</td><td>-0.7</td><td>1.8</td><td>-5.0</td><td>0.9</td><td>8.983</td><td>T</td><td>8.960</td><td>T</td><td>8.950</td><td>B</td><td>8.563</td><td>8.474</td><td>8.282</td><td></td></tr>\n",
       "<tr><td>1107-0083198</td><td>.M</td><td>84.2603597</td><td>20.7307878</td><td>H</td><td>-79.7</td><td>1.3</td><td>-419.9</td><td>0.8</td><td>8.316</td><td>T</td><td>7.659</td><td>T</td><td>--</td><td></td><td>6.358</td><td>6.020</td><td>5.951</td><td></td></tr>\n",
       "<tr><td>1107-0083199</td><td>..</td><td>84.2605528</td><td>20.7317750</td><td>B</td><td>0.0</td><td>2.0</td><td>0.0</td><td>0.0</td><td>8.020</td><td>B</td><td>--</td><td></td><td>7.220</td><td>B</td><td>--</td><td>--</td><td>--</td><td></td></tr>\n",
       "<tr><td>1105-0077905</td><td>YM</td><td>82.5627531</td><td>20.5515931</td><td>H</td><td>-8.8</td><td>1.0</td><td>-17.7</td><td>0.5</td><td>7.018</td><td>T</td><td>7.044</td><td>T</td><td>7.050</td><td>B</td><td>7.046</td><td>7.075</td><td>7.078</td><td></td></tr>\n",
       "<tr><td>1104-0078939</td><td>YM</td><td>83.4118258</td><td>20.4742161</td><td>H</td><td>2.9</td><td>1.0</td><td>-7.4</td><td>0.5</td><td>6.153</td><td>T</td><td>6.186</td><td>T</td><td>6.220</td><td>B</td><td>6.155</td><td>6.168</td><td>6.177</td><td></td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=32>\n",
       "   NOMAD1     YM    RAJ2000     DEJ2000    r   ...   Jmag    Hmag    Kmag   R  \n",
       "                      deg         deg          ...   mag     mag     mag       \n",
       "   str12     str2   float64     float64   str1 ... float32 float32 float32 str1\n",
       "------------ ---- ----------- ----------- ---- ... ------- ------- ------- ----\n",
       "1119-0082234   ..  81.8766917  21.9363778    B ...      --      --      --     \n",
       "1119-0082297   YM  81.9086881  21.9369650    H ...   5.179   5.270   5.283     \n",
       "1124-0097499   YM  82.6806150  22.4622544    H ...   4.517   3.862   3.713     \n",
       "1124-0100914   YM  83.8579511  22.4380619    H ...   7.309   7.080   7.018     \n",
       "1113-0087658   YM  82.3149911  21.3158950    H ...   7.081   6.884   6.828     \n",
       "1119-0088794   .M  84.1088183  21.9931567    H ...   6.216   5.987   5.985     \n",
       "         ...  ...         ...         ...  ... ...     ...     ...     ...  ...\n",
       "1131-0109812   YM  84.4024200  23.1388575    H ...   8.758   8.836   8.838     \n",
       "1131-0109842   YM  84.4091506  23.1474531    H ...   8.563   8.474   8.282     \n",
       "1107-0083198   .M  84.2603597  20.7307878    H ...   6.358   6.020   5.951     \n",
       "1107-0083199   ..  84.2605528  20.7317750    B ...      --      --      --     \n",
       "1105-0077905   YM  82.5627531  20.5515931    H ...   7.046   7.075   7.078     \n",
       "1104-0078939   YM  83.4118258  20.4742161    H ...   6.155   6.168   6.177     "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from astroquery.vizier import Vizier\n",
    "from astropy.coordinates import SkyCoord\n",
    "from astropy import units as u\n",
    "\n",
    "# Select only bright stars\n",
    "v = Vizier(column_filters={\"Bmag\":\"<9\"})\n",
    "v.ROW_LIMIT = -1\n",
    "crabNebula = SkyCoord.from_name('Crab Nebula')  \n",
    "result = v.query_region(crabNebula, radius=2.0*u.deg, catalog='NOMAD')\n",
    "result[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Listelenen kaynakların sağaçıklık, dikaçıklık ve B-bandı parlaklıklarını listelemek için aşağıdaki gibi bir kod yazılabilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=32</i>\n",
       "<table id=\"table140486126716576\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>RAJ2000</th><th>DEJ2000</th><th>Bmag</th></tr></thead>\n",
       "<thead><tr><th>deg</th><th>deg</th><th>mag</th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>float32</th></tr></thead>\n",
       "<tr><td>81.8766917</td><td>21.9363778</td><td>8.340</td></tr>\n",
       "<tr><td>81.9086881</td><td>21.9369650</td><td>4.720</td></tr>\n",
       "<tr><td>82.6806150</td><td>22.4622544</td><td>7.477</td></tr>\n",
       "<tr><td>83.8579511</td><td>22.4380619</td><td>8.880</td></tr>\n",
       "<tr><td>82.3149911</td><td>21.3158950</td><td>8.699</td></tr>\n",
       "<tr><td>84.1088183</td><td>21.9931567</td><td>7.533</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>84.4024200</td><td>23.1388575</td><td>8.733</td></tr>\n",
       "<tr><td>84.4091506</td><td>23.1474531</td><td>8.983</td></tr>\n",
       "<tr><td>84.2603597</td><td>20.7307878</td><td>8.316</td></tr>\n",
       "<tr><td>84.2605528</td><td>20.7317750</td><td>8.020</td></tr>\n",
       "<tr><td>82.5627531</td><td>20.5515931</td><td>7.018</td></tr>\n",
       "<tr><td>83.4118258</td><td>20.4742161</td><td>6.153</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=32>\n",
       "  RAJ2000     DEJ2000     Bmag \n",
       "    deg         deg       mag  \n",
       "  float64     float64   float32\n",
       "----------- ----------- -------\n",
       " 81.8766917  21.9363778   8.340\n",
       " 81.9086881  21.9369650   4.720\n",
       " 82.6806150  22.4622544   7.477\n",
       " 83.8579511  22.4380619   8.880\n",
       " 82.3149911  21.3158950   8.699\n",
       " 84.1088183  21.9931567   7.533\n",
       "        ...         ...     ...\n",
       " 84.4024200  23.1388575   8.733\n",
       " 84.4091506  23.1474531   8.983\n",
       " 84.2603597  20.7307878   8.316\n",
       " 84.2605528  20.7317750   8.020\n",
       " 82.5627531  20.5515931   7.018\n",
       " 83.4118258  20.4742161   6.153"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nomad_query = result[0]\n",
    "nomad_query['RAJ2000', 'DEJ2000','Bmag']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu tablo bir `astropy.table` yapısındadır.\n",
    "type(nomad_query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "astropy.table.table.Table"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(nomad_query)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu kaynaklardan hangisinin gözlem sırasında (`astropy.time.Time.now()`) problem yaratacağı aşağıdaki gibi bir sorguyla ufuk koordinat sistemine (alt-az) dönüşüm yapılarak denetlenebilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy import coordinates as c\n",
    "from astropy.coordinates import SkyCoord\n",
    "from astropy import time as t\n",
    "\n",
    "gozlemevi = c.EarthLocation( lat=28.76194399284443*u.deg, lon=-17.890066533603996*u.deg )\n",
    "star_coord = SkyCoord(ra=nomad_query['RAJ2000'], dec=nomad_query['DEJ2000'], frame='icrs')\n",
    "star_coord_altAz = star_coord.transform_to( c.AltAz(obstime=t.Time.now(), location=gozlemevi) )\n",
    "nomad_query['alt'] = star_coord_altAz.alt\n",
    "nomad_query['az'] = star_coord_altAz.az"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Bmag          alt                  az        \n",
      " mag           deg                 deg        \n",
      "------ -------------------- ------------------\n",
      " 2.800   -2.831231557035601  63.93679285646915\n",
      " 2.938    0.213227782274814    63.294758212037\n",
      " 4.720  -0.4339967824125438  64.48811412411288\n",
      " 6.153  -2.3941018144889092  65.00021984923464\n",
      " 6.489   -2.854388122303114  63.18671616801318\n",
      " 7.018  -1.6767600434626462  65.35248455733101\n",
      "   ...                  ...                ...\n",
      " 8.938    -3.26401200478877  63.49179681220911\n",
      " 8.962  -2.1375599847342053  64.51537962169634\n",
      " 8.983  -1.7584333867057866 62.241436223321784\n",
      " 8.990  -2.8442934638034494 63.193082850560714\n",
      " 8.990 -0.35310662341273225 63.068990034439224\n",
      " 8.992   -2.202369950227752  63.18061626350741\n",
      " 8.996   -3.217523180331807 63.457226604131826\n",
      "Length = 32 rows\n"
     ]
    }
   ],
   "source": [
    "nomad_query.sort('Bmag')\n",
    "nomad_query['Bmag', 'alt', 'az'].pprint()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Başa Dön](#Bu-derste-neler-öğreneceksiniz?)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SkyView ve Vizier Kataloglarını Kullanarak Görüntü Üzerinde Kaynak Belirleme ##\n",
    "\n",
    "Aşağıda [SkyView](https://skyview.gsfc.nasa.gov/current/cgi/titlepage.pl) kataloğunu kullanarak istenen bir alanın görüntüsünü çektikten sonra [Vizier](https://vizier.u-strasbg.fr/viz-bin/VizieR) kataloğunu kullanarak bu görüntü üzerindeki kaynakları belirleyip işaretlemeye yönelik bir örnek verilmiştir. `astroquery` her [SkyView](https://astroquery.readthedocs.io/en/latest/skyview/skyview.html) ve [Vizier](https://astroquery.readthedocs.io/en/latest/vizier/vizier.html) kataloglarında sorgulama yapaiblmek için birer alt modül sunmaktadır. Katalog sorgularını kullanırken `astropy.coordinates` ve `astropy.units` modüllerini de kullanmak gerekecektir. Grafikler için yine `maptlotlib.pyplot` kullanılabilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from astropy import coordinates, units as u, wcs\n",
    "from astroquery.skyview import SkyView\n",
    "from astroquery.vizier import Vizier\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "merkez = coordinates.SkyCoord.from_name('Orion KL')\n",
    "\n",
    "# Orion Nebula'nin 2MASS-J bandinda bir SkyView goruntusunu alalim\n",
    "goruntuler = SkyView.get_images(position=merkez, survey='2MASS-H')\n",
    "\n",
    "# sonucta birden fazla goruntu dondurulurse sadece ilkini kullanalim\n",
    "goruntu = goruntuler[0]\n",
    "\n",
    "# dondurulen goruntu bir HDU nesnesidir ve goruntu (imaj)\n",
    "# bu nesnenin 0. indeksinde yer almaktadir.\n",
    "koorwcs = wcs.WCS(goruntu[0].header)\n",
    "\n",
    "fig = plt.figure(1)\n",
    "# RA / DEC koordinatlarina sahip bir goruntu olusturalim\n",
    "ax = fig.add_axes([0.15, 0.1, 0.8, 0.8], projection=koorwcs)\n",
    "ax.set_xlabel(\"RA\")\n",
    "ax.set_ylabel(\"Dec\")\n",
    "\n",
    "ax.imshow(goruntu[0].data, cmap='gray_r', interpolation='none', origin='lower',\n",
    "          norm=plt.matplotlib.colors.LogNorm())\n",
    "\n",
    "\n",
    "# Vizier'den bu bolgedeki nesne bilgilerini cekelim\n",
    "# Bunun icin J/ApJ/826/16/table1 tablosunu kullanalim\n",
    "# Herhangi baska bir tabloyu da kullanabilirdik\n",
    "tablolar = Vizier.query_region(merkez, radius=5*u.arcmin,\n",
    "                                catalog='J/ApJ/826/16/table1')\n",
    "# istedigimiz koordinatlar bu tablolarin birincisindedir\n",
    "tablo = tablolar[0]\n",
    "\n",
    "# RA / DEC girdiyi astropy koordinatlarina donusturelim\n",
    "tablo_koor = coordinates.SkyCoord(tablo['RAJ2000'], tablo['DEJ2000'],\n",
    "                                unit=(u.hour, u.deg), frame='fk5')\n",
    "\n",
    "# cismin cevresindeki diger cisimleri de J/ApJ/540/236 tablosundan alalim\n",
    "tablolar2 = Vizier(row_limit=10000).query_region(merkez, radius=5*u.arcmin,\n",
    "                                                  catalog='J/ApJ/540/236')\n",
    "tablo2 = tablolar[0]\n",
    "tablo2_koor = coordinates.SkyCoord(tablo2['RAJ2000'], tablo2['DEJ2000'],\n",
    "                                 unit=(u.hour, u.deg), frame='fk5')\n",
    "\n",
    "\n",
    "# goruntuyu cizdirelim\n",
    "ax.plot(tablo_koor.ra, tablo_koor.dec, '*', transform=ax.get_transform('fk5'),\n",
    "        mec='b', mfc='none')\n",
    "ax.plot(tablo2_koor.ra, tablo2_koor.dec, 'o', transform=ax.get_transform('fk5'),\n",
    "        mec='r', mfc='none')\n",
    "# istedigimiz bolgeye zoom yapabiliriz\n",
    "ax.axis([100, 200, 100, 200])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Başa Dön](#Bu-derste-neler-öğreneceksiniz?)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# astroquery İle SAO/NASA ADS Sorguları #\n",
    "\n",
    "[SAO/NASA Astrophysics Data System](https://ui.adsabs.harvard.edu/) astronomların literatür taramak üzere neredeyse her gün, belki defalarca sorguladıkları eşsiz bir dijital kütüphane portalıdır. ADS, Astronomi ve Astrofizik, Fizik ve arXiv e-baskılarındaki yayınları kapsayan 13 milyondan fazla kayıt içeren üç bibliyografik veri tabanı bulundurmaktadır. Astronomi ve fizik yayınlarının özetleri ve tam metni, yeni ADS modern arama formunun yanı sıra klasik bir arama formu aracılığıyla taranabilir.\n",
    "\n",
    "`astroquery` SAO/NASA ADS veritabanında temel ve ileri düzey sorgular yapabilmek için pek çok fonksiyon sunar. Ancak öncelikle bir ADS hesabınızın olması gerekir. Yeni bir ADS hesabı oluşturmak, ADS hesabınızın ayarlarını kontrol etmek / güncellemek ve bu modülün ilgili fonksiyonlarının çalışabilmesi için gerekli API şifresini almak için öncelikle [SAO/NASA ADS login sayfasına](https://ui.adsabs.harvard.edu/user/account/login) giriş yapmanız gerekir. API şifrenizi aldıktan sonra $na.ADS.TOKEN$ değişkenine verebileceğiniz gibi, bir dosyaya alabilir ya da bir çevre değişkeni olarak belirleyebilirsiniz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Homogeneous transit timing analyses of 10 exoplanet systems\n",
      "['Baştürk, Ö.', 'Esmer, E. M.', 'Yalçınkaya, S.', 'Torun, Ş.', 'Mancini, L.', 'Helweh, F.', 'Karamanlı, E.', 'Southworth, J.', 'Aliş, S.', 'Wünsche, A.', 'Tezcan, F.', 'Aladağ, Y.', 'Aksaker, N.', 'Tunç, E.', 'Davoudi, F.', 'Fişek, S.', 'Bretton, M.', 'Evans, D. F.', 'Yeşilyaprak, C.', 'Yılmaz, M.', 'Tezcan, C. T.', 'Yelkenci, K.']\n"
     ]
    }
   ],
   "source": [
    "from astroquery import nasa_ads as na\n",
    "# Modul fonksiyonlarini kullanmadan once \n",
    "# API sifrenizi girmeniz beklenir. Bunun icin oncelikle\n",
    "# https://ui.adsabs.harvard.edu/user/account/login\n",
    "# sayfasina giderek (kaydolduktan sonra) API sifrenizi aliniz\n",
    "# API sifrenizi buradan verebileceğiniz gibi \n",
    "# bir dosyaya kaydedip adini buraya verebilir \n",
    "# ya da bir cevre degiskeni olarak ayarlayabilirsiniz.\n",
    "na.ADS.TOKEN = 'sizin_token_degeriniz'\n",
    "\n",
    "# varsayilan kayit dondurme sayisi 10'dur ve kayitlar\n",
    "# tarihe gore yeniden eskiye dogru siralanir\n",
    "# bu ayarlar degistirilebilir. \n",
    "\n",
    "# dondurulen kayit sayisini 30'a cikarmak icin\n",
    "na.ADS.NROWS = 30\n",
    "\n",
    "# sadece belirli alanlari almak icin\n",
    "#na.ADS.ADS_FIELDS = ['author','title','abstract','pubdate','bibcode']\n",
    "\n",
    "# ornegin bir yazarin ilk isim oldugu yayinlari gorebilmek icin\n",
    "# yazar adinin onune ^ isareti konur \n",
    "# (bu durum ADS arama sayfasinda da aynidir)\n",
    "# Bu yapisiyla query_simple yeni ADS arama kutusuyla ayni sekilde calisir\n",
    "sonuclar = na.ADS.query_simple(query_string=\"^Basturk exoplanet\")\n",
    "\n",
    "# aramanizi istediginiz bir kritere gore sonradan da siralayabilirsiniz\n",
    "sonuclar.sort(['pubdate'])\n",
    "\n",
    "#  en son sonucunu basligi\n",
    "sonsonuc_baslik = sonuclar[-1]['title'][0]\n",
    "print(sonsonuc_baslik)\n",
    "\n",
    "# en son sonucun yazar listesi\n",
    "print(sonuclar[-1]['author'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bibcode',\n",
       " 'title',\n",
       " 'author',\n",
       " 'aff',\n",
       " 'pub',\n",
       " 'volume',\n",
       " 'pubdate',\n",
       " 'page',\n",
       " 'citation',\n",
       " 'citation_count',\n",
       " 'abstract',\n",
       " 'doi',\n",
       " 'eid']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na.ADS.ADS_FIELDS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`astroquery.nasa_ads` henüz NASA / ADS veritabanı üzerinde çalışan web arayüzünün yeteneklerinin bütününe sahip değildir. Örneğin yayının yayıncıdan ya da arXiv'dan indirilebileceği linkleri sağlamaz, makalenizde doğrudan kullanabileceğiniz bibtex'ini vermez. Ancak [dokümantasyounundan](https://buildmedia.readthedocs.org/media/pdf/astroquery/v0.3.7/astroquery.pdf) da takip edebileceğiniz gibi bu konularda ve modülü geliştirmek üzere çalışılmaktadır. İleride projeleriniz için yazmanız gerekecek kodlarda bu tür aramalara ihtyaç duyabilirsiniz, bu nedenle bu paketin gelişimini takip etmekte fayda vardır."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Başa Dön](#Bu-derste-neler-öğreneceksiniz?)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ads Paketiyle SAO/NASA ADS Sorguları #\n",
    "\n",
    "[ads paketi](https://ads.readthedocs.io/en/latest/) SAO/NASA ADS veritabanı ile etkileşim için önemli bir alternatif sunar. Bu paketi kullanma için de API şifresine ihtiyaç duyulur ve [ilgili bölümde](#astroquery-İle-SAO/NASA-ADS-Sorguları) anlatıldığı şekilde alınabilir. Eğer python'u Anaconda üzerinden kurduysanız bu paketi kurmamış olmanız ihtimal dahilindedir. Her zamanki gibi $pip$ kurulum paketini kullanarak \n",
    "\n",
    "<i> $ pip install ads--user </i>\n",
    "\n",
    "komutu ile kurabileceğiniz gibi projenin [github sayfasından](https://github.com/andycasey/ads) kaynak kodu indirip de kurabilirsiniz.\n",
    "\n",
    "Aşağıda $ads$ paketinin kullanımına bir örnek verilmiştir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obliquities of Hot Jupiter Host Stars: Evidence for Tidal Interactions and Primordial Misalignments 2012ApJ...757...18A\n",
      "Starspots, Spin-Orbit Misalignment, and Active Latitudes in the HAT-P-11 Exoplanetary System 2011ApJ...743...61S\n",
      "3.6 and 4.5 μm Spitzer Phase Curves of the Highly Irradiated Hot Jupiters WASP-19b and HAT-P-7b 2016ApJ...823..122W\n",
      "HST hot Jupiter transmission spectral survey: detection of water in HAT-P-1b from WFC3 near-IR spatial scan observations 2013MNRAS.435.3481W\n",
      "Adaptive Optics Images. II. 12 Kepler Objects of Interest and 15 Confirmed Transiting Planets 2013AJ....146....9A\n",
      "Observational constraints on tidal effects using orbital eccentricities 2012MNRAS.422.3151H\n",
      "The HAT-P-13 Exoplanetary System: Evidence for Spin-Orbit Alignment and a Third Companion 2010ApJ...718..575W\n",
      "HAT-P-18b and HAT-P-19b: Two Low-density Saturn-mass Planets Transiting Metal-rich K Stars 2011ApJ...726...52H\n",
      "The Continuing Search for Evidence of Tidal Orbital Decay of Hot Jupiters 2020AJ....159..150P\n",
      "System Parameters, Transit Times, and Secondary Eclipse Constraints of the Exoplanet Systems HAT-P-4, TrES-2, TrES-3, and WASP-3 from the NASA EPOXI Mission of Opportunity 2011ApJ...726...94C\n",
      "HAT-P-25b: A Hot-Jupiter Transiting a Moderately Faint G Star 2012ApJ...745...80Q\n",
      "Transmission spectroscopy of the inflated exo-Saturn HAT-P-19b 2015A&A...580A..60M\n",
      "Low albedos of hot to ultra-hot Jupiters in the optical to near-infrared transition regime 2019A&A...624A..62M\n",
      "Clear and Cloudy Exoplanet Forecasts for JWST: Maps, Retrieved Composition, and Constraints on Formation with MIRI and NIRCam 2018AJ....156...40S\n",
      "The Stellar Obliquity and the Long-period Planet in the HAT-P-17 Exoplanetary System 2013ApJ...772...80F\n",
      "Ground-based transit observations of the HAT-P-18, HAT-P-19, HAT-P-27/WASP40 and WASP-21 systems 2015MNRAS.451.4060S\n",
      "The Refined Physical Properties of Transiting Exoplanetary System WASP-11/HAT-P-10 2014AJ....147...92W\n",
      "Atmospheric mass-loss of extrasolar planets orbiting magnetically active host stars 2018MNRAS.477..808L\n",
      "A holistic and probabilistic approach to the ground-based and spaceborne data of HAT-P-19 system 2020MNRAS.496.4174B\n",
      "Reassessing Exoplanet Light Curves with a Thermal Model 2018AJ....156...28A\n",
      "Observational Consequences of Shallow-water Magnetohydrodynamics on Hot Jupiters 2021ApJ...916L...8H\n",
      "New transit timing observations for GJ 436 b, HAT-P-3 b, HAT-P-19 b, WASP-3 b, and XO-2 b 2018IBVS.6243....1M\n",
      "HAT-P-68b: A Transiting Hot Jupiter around a K5 Dwarf Star 2021AJ....161...64L\n",
      "Two stacked tandem white organic light-emitting diodes employing WO<SUB>3</SUB> as a charge generation layer 2016SPIE.9941E..1TB\n",
      "Are Am stars and hot-Jupiter planets related? 2022A&A...668A.157S\n",
      "Probing Giant Planet Formation with MOSFIRE Exoplanet Transmission Spectroscopy 2014koa..prop..511M\n",
      "An X-ray survey of ultra-active planet hosts 2013xmm..prop..123S\n",
      "VizieR Online Data Catalog: Spectroscopy and photometry for HAT-P-50--HAT-P-53 (Hartman+, 2015) 2016yCat..51500168H\n",
      "A Bayesian Atmospheric Retrieval Performed on HAT-P-16b and WASP-11b/HAT-P-10b 2016DPS....4821207M\n",
      "Near UV Observation of HAT-P-16b 2013AAS...22221709P\n",
      "In Pursuit of New Worlds: Searches for and Studies of Transiting Exoplanets from Three Space-Based Observatories 2012PhDT.......138B\n",
      "Towards Generalized Characterization of Exoplanet Atmospheres with Transit Spectroscopy 2021PhDT........15W\n",
      "Tidal Distortions as a Bottleneck on Constraining Exoplanet Compositions 2022ApJ...941..155B\n",
      "Evidence of Long-term Period Variations in the Exoplanet Transit Database (ETD) 2022AJ....164..220H\n",
      "The T025 - BD4SB a pro-am collaboration for planetary sciences 2022EPSC...16.1222N\n",
      "VizieR Online Data Catalog: 25 hot-Jupiter properties from HST &amp; Spitzer (Changeat+, 2022) 2022yCat..22600003C\n",
      "VizieR Online Data Catalog: Transit times of 11 hot Jupiters (Patra+, 2020) 2020yCat..51590150P\n",
      "VizieR Online Data Catalog: Differential photometry &amp; RVs of HAT-P-69 &amp; HAT-P-70 (Zhou+, 2019) 2019yCat..51580141Z\n",
      "Investigating Trends in Atmospheric Composition of Gas Giant Planets Using Spitzer Secondary Eclipses 2019ESS.....432640W\n",
      "Transit Spectroscopy of Mature Planets 2017jwst.prop.1185G\n",
      "MIRI observations of transiting exoplanets 2017jwst.prop.1177G\n",
      "Clear and Cloudy Exoplanet Forecasts for JWST: Maps, Retrieved Composition, and Constraints on Formation with MIRI and NIRCam 2019AAS...23320507S\n",
      "VizieR Online Data Catalog: Differential photometry of the F-subgiant HAT-P-67 (Zhou+, 2017) 2018yCat..51530211Z\n",
      "Atmospheric Retrievals of HAT-P-16b and WASP-11b/HAT-P-10b 2018AAS...23114802M\n",
      "HAT-P-16b: A Bayesian Atmospheric Retrieval 2017DPS....4941601M\n"
     ]
    }
   ],
   "source": [
    "import ads\n",
    "ads.config.token = 'Ug9VjbHlfR8araNkCVmxRs6vR65EgCLTU0oWAhoq'\n",
    "sonuclar = ads.SearchQuery(q=\"HAT-P-19\", sort=\"citation_count\")\n",
    "for sonuc in sonuclar:\n",
    "    print(sonuc.title[0], sonuc.bibcode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['author', 'first_author', 'bibcode', 'id', 'year', 'title']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sonuclar.DEFAULT_FIELDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['abstract', 'title', 'body', 'ack', 'aff', 'author']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sonuclar.HIGHLIGHT_FIELDS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yayın üzerinden arama yapıp başlık ($title$), yazar ($author$), bibliyografi kodu ($bibcode$), özet ($abstract$), makalenizin biyografi bölümünü oluşturmak üzere bibtex girdisi ($bibtex$), atıfları ($citation$), atıf sayısı (<i>citation_count</i>) gibi pek çok bilgiye ulaşabilirsiniz. Sorgunuzun sonucunu atadığınız değişken (yukarıdaki örnekte $sonuclar$ değişkeni) sonuna nokta koyduktan sonra <TAB> 'a basarak olası diğer metot seçeneklerini inceleyebilirsiniz.\n",
    "    \n",
    "ADS veritabanında üzerinde sorgu yapabileceğiniz bütün alanlarda $ads$ paketiyle de sorgu yapabilirsiniz. Aşağıda yazar ismiyle yapılmış bir sorgu örneği görüyorsunuz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Homogeneous transit timing analyses of 10 exoplanet systems']\n",
      "['Detection of two additional circumbinary planets around Kepler-451']\n",
      "['Discovery of a young low-mass brown dwarf transiting a fast-rotating F-type star by the Galactic Plane eXoplanet (GPX) survey']\n",
      "['The ultra-hot-Jupiter KELT-16 b: dynamical evolution and atmospheric properties']\n",
      "['Transit timing variation analysis of the low-mass brown dwarf KELT-1 b']\n",
      "['RR Draconis: An Algol-Type Eclipsing Binary with a Possible Massive Tertiary Companion']\n",
      "['Revisiting the analysis of HW Virginis eclipse timing data. I. A frequentist data modeling approach and a dynamical stability analysis']\n",
      "['TESS Discovery of a Super-Earth and Three Sub-Neptunes Hosted by the Bright, Sun-like Star HD 108236']\n",
      "['Transits of Known Planets Orbiting a Naked-eye Star']\n",
      "['Orbital Period Analysis of Four Eclipsing Binaries XY Boo, RW Com, MR Del and AK Her']\n",
      "['A list of minima times of some eclipsing binaries']\n",
      "['The KELT Follow-up Network and Transit False-positive Catalog: Pre-vetted False Positives for TESS']\n",
      "['Investigation of Orbital Decay and Global Modeling of the Planet WASP-43 b']\n",
      "['A simultaneous spectroscopic and photometric study of two eclipsing binaries: V566 Oph and V972 Her']\n",
      "['Analysis of the Most Precise Light Curves of HAT-P-36 Detrended from Spot Signals']\n",
      "['The First Light Curve Solutions and Period Study of BQ Ari']\n",
      "['Discovery of a pre-cataclysmic binary with unusual chromaticity of the eclipsed white dwarf by the GPX survey']\n",
      "['Homogeneously derived transit timings for 17 exoplanets and reassessed TTV trends for WASP-12 and WASP-4']\n",
      "['A holistic and probabilistic approach to the ground-based and spaceborne data of HAT-P-19 system']\n",
      "['Benchmarking the power of amateur observatories for TTV exoplanets detection']\n",
      "['Photometric Analysis of Overcontact Binaries AK Her, HI Dra, V1128 Tau, and V2612 Oph']\n",
      "[\"KPS-1b: The First Transiting Exoplanet Discovered Using an Amateur Astronomer's Wide-field CCD Data\"]\n",
      "['KELT-18b: Puffy Planet, Hot Host, Probably Perturbed']\n",
      "['Photometric Analysis of HS Aqr, EG Cep, VW LMi, and du Boo']\n",
      "['Analysis of HAT-P-16b and TrES-3b Exoplanets by the Transit Timing Variations Method']\n",
      "['Physical parameters of some close binaries: ET Boo, V1123 Tau, V1191 Cyg, V1073 Cyg and V357 Peg']\n",
      "['Physical parameters of close binaries QX Andromedae, RW Comae Berenices, MR Delphini, and BD +079° 3142']\n",
      "['BI CVn - A spotted, overcontact, type-W eclipsing binary']\n",
      "['High-precision multiband time series photometry of exoplanets Qatar-1b and TrES-5b']\n",
      "['First results of the Kourovka Planet Search: discovery of transiting exoplanet candidates in the first three target fields']\n",
      "['Times of Minima of Some Eclipsing Binaries']\n",
      "['A Python-based GUI Software to Calculate Times of Maximum and Minimum: Xtrema']\n",
      "['Photoelectric Minima of Some Eclipsing Binary Stars']\n",
      "['Light curve analysis of ground-based data from exoplanets transit database']\n",
      "['Photometric, Spectroscopic, and Orbital Period Study of Three Early-type Semi-detached Systems: XZ Aql, UX Her, and AT Peg']\n",
      "['Photoelectric Minima of Some Eclipsing Binary Stars']\n",
      "['VizieR Online Data Catalog: RR Dra RV curves and mid eclipse times (Senavci+, 2022)']\n",
      "['Orbital Period Variations in the NY Vir System, Revisited in the Light of New Data']\n",
      "['Times of Minima of Eclipsing Binaries and Mid-Transit Times of Transiting Exoplanets']\n",
      "['The stable isotope composition of waters of the eastern Mediterranean Sea']\n",
      "['New Times of Minima of Some Eclipsing Binary Stars and Maxima of Pulsating Stars']\n",
      "['Transit timing variations of five transiting planets']\n",
      "['Changes in the hydrochemistry of the Black Sea inferred from water density profiles']\n",
      "['The absolute parameters of two W UMa type binaries in Ursa Majoris']\n",
      "['Photoelectric Minima of Some Eclipsing Binary Stars']\n",
      "['VizieR Online Data Catalog: HAT-P-36 T80, T100 light curves and O-C (Yalcinkaya+, 2021)']\n",
      "['Bisectors of the HARPS cross-correlation function. The dependence on stellar atmospheric parameters']\n",
      "['VizieR Online Data Catalog: Light curves of KELT-16 (Mancini+, 2022)']\n",
      "['Defocused Observations of Selected Exoplanet Transits with T100 at the TÜBİTAK National Observatory of Turkey (TUG)']\n",
      "['A Tale on Two Close Binaries in Pegasus']\n"
     ]
    }
   ],
   "source": [
    "sonuclar = list(ads.SearchQuery(author=\"Basturk, O\"))\n",
    "for sonuc in sonuclar:\n",
    "    print(sonuc.title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Her sorgu bir $ads.Article$ nesnesi döndürür. Bu nesnenin oldukça kullanışlı pek çok metodu bulunmaktadır. Aşağıda bu metotlara bazı örnekler verilmiştir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Baştürk, Ö. et al. 2022, 2022MNRAS.512.2062B>\n",
      "['Baştürk, Ö.', 'Esmer, E. M.', 'Yalçınkaya, S.', 'Torun, Ş.', 'Mancini, L.', 'Helweh, F.', 'Karamanlı, E.', 'Southworth, J.', 'Aliş, S.', 'Wünsche, A.', 'Tezcan, F.', 'Aladağ, Y.', 'Aksaker, N.', 'Tunç, E.', 'Davoudi, F.', 'Fişek, S.', 'Bretton, M.', 'Evans, D. F.', 'Yeşilyaprak, C.', 'Yılmaz, M.', 'Tezcan, C. T.', 'Yelkenci, K.']\n",
      "2022MNRAS.512.2062B\n"
     ]
    }
   ],
   "source": [
    "en_son_yayin = sonuclar[0]\n",
    "# Yayinla ilgili bazı özet bilgiler\n",
    "print(en_son_yayin)\n",
    "print(en_son_yayin.author)\n",
    "print(en_son_yayin.bibcode)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Başa Dön](#Bu-derste-neler-öğreneceksiniz?)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# astroquery İle Gaia Veritabanı Sorguları #\n",
    "\n",
    "[Gaia](https://gea.esac.esa.int/archive/), galaksimizin kompozisyonunu, oluşumunu ve evrimini ortaya koymak amacıyla Samanyolu'nun üç boyutlu bir haritasını çıkarmak üzere geliştirilmiş bir uzay teleskobu projesidir. Gaia, Galaksimizde ve Yerel Grup genelinde yaklaşık bir milyar yıldızın tayfsal ve kinematik gözlemlerini gerçekleştirmek için bugüne kadar eşi görülmemiş bir duyarlılıkta konum ve radyal hız ölçümleri yapmaktadır.\n",
    "\n",
    "Gaia arşivi TAP (Table Access Protocol) + REST servisleri üzerine kuruludur. Üzerine kurulu olduğu $TAP+$, Uluslararası Sanal Gözlemevleri Birliği ([IVOA](http://www.ivoa.net)) tarafından belirtilen Tablo Erişim Protokolü'nün (TAP: http://www.ivoa.net/documents/TAP/) bir uzantısıdır. TAP sorgu dili, veritabanlarını sorgulamak için yaygın olarak kullanılan Yapılandırılmış Sorgu Dili'ne (ing, Structured Query Language, SQL) benzer bir dil olan Astronomi Veri Sorgu Dili ([Astronomical Data Query Language, ADQL] (http://www.ivoa.net/documents/ADQL/2.0)) 'dir.\n",
    "\n",
    "TAP, Eşzamanlı (ya da Senkron: Synchronous) ve Asenkron (Asynchronous) olmak üzere iki çalışma modu sunar:\n",
    "\n",
    "* Eşzamanlı: istenen cevap sunucu tarafından alındığında oluşturulur. Bu yöntem büyük boyutlarda sonuç üreten sorgular için kullanılmamalıdır. Eşzamanlı (senkronize) sorgularda sorgunun sonucu döndürülene kadar uygulamadaki başka hiçbir iş (task) gerçekleştirilmez. Uygulamalar, sorgunun veritabanına ilk gönderildiği andan sonuçların alınıp uygulamaya döndürülmesine kadar tüm gidiş-dönüş boyunca bloke edilirler. Bu nedenle, sorgular bir sıra dahilinde gönderilir; biri bitmeden diğeri başlamaz ve beşer dakika çalışacak üç sorgu toplamda 15 dakikada sonuçlanır. Birinin çıktısı diğerine girdi yapılabilir.\n",
    "\n",
    "* Asenkron: sunucu, sorgulama isteğini yürütecek bir iş (task) başlatır. İsteğe ilk yanıt, task durumunu elde etmek için gerekli bilgilerdir. Sorgulama işi bittikten sonra sonuçlar alınabilir. bu durumda sorgular aynı anda gönderilip, aynı anda çalışabilir; yukarıdaki örnekten hareketle beşer dakika sürecek üç sorgunun tamamlanması toplamda yine beş dakika sürer.\n",
    "\n",
    "Gaia $TAP+$ sunucusu herkese açık, genel (public) ve kullanıcı erişimli (authenticated) olmak üzere iki erişim modu sağlar: \n",
    "\n",
    "* <u>Genel kullanıcı:</u> Bu standart $TAP$ erişimidir. Bir kullanıcı, 'anında' sorguda kullanılacak ADQL sorgularını yürütebilir ve tabloları yükleyebilir (sorgu yürütüldüğünde bu tablolar kaldırılır). Sonuçlar diğer kullanıcılar tarafından kullanılabilir ve sınırlı bir süre için sunucuda kalır.\n",
    "\n",
    "* <u>Kullanıcı erişimli sorgulamalar:</u> bazı işlevler yalnızca kimliği doğrulanmış kullanıcılarla sınırlıdır. Sonuçlar özel bir kullanıcı alanına kaydedilir ve sonsuza kadar sunucuda kalır (kullanıcı tarafından kaldırılabilir).\n",
    "\n",
    "ADQL sorguları ve sonuçları, kullanıcının özel alanına kaydedilir. Bir kullanıcı özel bir alana tablo yükleyebilir. Bu tablolar sorgularda ve çapraz eşleşme işlemlerinde kullanılabilir.\n",
    "\n",
    "Örnek olarak basit bir Gaia sorgusu `astroquery.gaia` alt modülüyle aşağıdaki şekilde kurgulanabilir. Bu şekilde koordinatları bilinen bir cismin istenen yakınlığında başka bir kaynağın olup olmadığı konusunda bilgi verebilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Query finished. [astroquery.utils.tap.core]\n",
      "         dist         ...\n",
      "                      ...\n",
      "--------------------- ...\n",
      "7.814272017040687e-05 ...\n"
     ]
    }
   ],
   "source": [
    "import astropy.units as u\n",
    "from astropy.coordinates import SkyCoord\n",
    "from astroquery.gaia import Gaia\n",
    "koordinatlar = \"00h38m04s +34d42m41s\"\n",
    "koor = SkyCoord(koordinatlar, obstime=\"J2000.\", frame='icrs')\n",
    "ra_aralik = u.Quantity(1, u.arcmin)\n",
    "dec_aralik = u.Quantity(1, u.arcmin)\n",
    "sorgu = Gaia.query_object_async(coordinate=koor, width=ra_aralik, height=dec_aralik)\n",
    "sorgu.pprint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;MaskedColumn name=&apos;DESIGNATION&apos; dtype=&apos;object&apos; description=&apos;Unique source designation (unique across all Data Releases)&apos; length=1&gt;\n",
       "<table>\n",
       "<tr><td>Gaia DR2 365426679516816512</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<MaskedColumn name='DESIGNATION' dtype='object' description='Unique source designation (unique across all Data Releases)' length=1>\n",
       "Gaia DR2 365426679516816512"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorgu['DESIGNATION']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Gaia DR3 365426679516816512', 'WISE J003803.98+344241.2', 'UCAC4 624-001874', 'TIC 267650535', 'Gaia DR2 365426679516816512', 'UCAC2  43902188', 'UCAC3 250-4408', '2MASS J00380401+3442416', 'HAT-P-19', 'GSC 02283-00589']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ozbasturk/.local/lib/python3.10/site-packages/astroquery/simbad/core.py:135: UserWarning: Warning: The script line number 2 raised an error (recorded in the `errors` attribute of the result table): 'DESIGNATION': No known catalog could be found \n",
      "  warnings.warn(\"Warning: The script line number %i raised \"\n"
     ]
    }
   ],
   "source": [
    "sorgu = Simbad.query_objectids(sorgu['DESIGNATION'])\n",
    "sonuc_tablosu = [isim[0] for isim in sorgu]\n",
    "print(sonuc_tablosu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "İstenirse böyle bir sorgu isim üzerinden yapılmak üzere SIMBAD sorgularıyla birleştirilebilir.\n",
    "\n",
    "[Başa Dön](#Bu-derste-neler-öğreneceksiniz?)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# astroquery İle NASA Exoplanet Archive Sorguları #\n",
    "\n",
    "NASA Exoplanet Archive veritabanı keşfedilen aday ve onaylanmış ötegezegenler ile onların barınak yıldızları hakkında farklı literatür kaynaklarında yayınlanmış bilgi ve parametrelere erişmek için NASA tarafından geliştirilmiş bir veritabanıdır. Veritabanını `astroquery` fonksyonlarıyla sorgulamak için `astroquery.ipac.nexsci.nasa_exoplanet_archive` modülü kullanılır. Veritabanının sorgulamalar için bir [web arayüzü](https://exoplanetarchive.ipac.caltech.edu/cgi-bin/TblView/nph-tblView?app=ExoTbls&config=PS) bulunduğu gibi [TAP](https://exoplanetarchive.ipac.caltech.edu/docs/TAP/usingTAP.html) ve [API](https://exoplanetarchive.ipac.caltech.edu/docs/program_interfaces.html) arayüzleri de bulunmaktadır. \n",
    "\n",
    "Örneğin WASP-43 b ötegezegenine ilişkin bilgilere erişmek için aşağıdaki gibi bir sorgu yazılabilir. Bu fonksiyonun parametrelerini doğru belirlemek önem taşımaktadır. Zira bir gezegen sisteminin farklı isimlerle de bulunabildiği <i>\"aliastable\"</i> için API desteği artık bulunmamaktadır. Bu nedenel `regularize` parametresinin `False` değerine atanması gerekir. Aksi takdirde fonksiyonu ismi verilen gezegen ya da barınak yıldız için aynı gezegenin farklı isimlerle geçtiği satırları da getirmek için aliastable tablosunu taramaktadır ancak bu tabloya API desteği bulunmamaktadır. Ayrıca, `table` parametresinin doğru ayarlanması da önemlidir. Bu parametrenin varsayılan değeri olan `ps` birden fazla yayından parametrelerin alınabildiği ve her bir gezegen için veritabanında parametre alınan yayın sayısı kadar satırın bulunduğu tablodur. Genellikle keşif yayınından ilgili parametrelerin alınıp, diğer parametrelerin de başka yayınlardan tamamlandığı, dolayısıyla her gezegenin tek bir parametre setiyle ve satırla temsil edildiği tablo (Composite Planet Table) kullanılmak istendiğinde bu parametre `pscomppars` değerine ayarlanır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: column st_lum has a unit but is kept as a MaskedColumn as an attempt to convert it to Quantity failed with:\n",
      "UnitTypeError(\"MaskedQuantity instances require normal units, not <class 'astropy.units.function.logarithmic.DexUnit'> instances.\") [astropy.table.table]\n",
      "WARNING: column st_lumerr1 has a unit but is kept as a MaskedColumn as an attempt to convert it to Quantity failed with:\n",
      "UnitTypeError(\"MaskedQuantity instances require normal units, not <class 'astropy.units.function.logarithmic.DexUnit'> instances.\") [astropy.table.table]\n",
      "WARNING: column st_lumerr2 has a unit but is kept as a MaskedColumn as an attempt to convert it to Quantity failed with:\n",
      "UnitTypeError(\"MaskedQuantity instances require normal units, not <class 'astropy.units.function.logarithmic.DexUnit'> instances.\") [astropy.table.table]\n",
      "WARNING: column st_logg has a unit but is kept as a MaskedColumn as an attempt to convert it to Quantity failed with:\n",
      "UnitTypeError(\"MaskedQuantity instances require normal units, not <class 'astropy.units.function.logarithmic.DexUnit'> instances.\") [astropy.table.table]\n",
      "WARNING: column st_loggerr1 has a unit but is kept as a MaskedColumn as an attempt to convert it to Quantity failed with:\n",
      "UnitTypeError(\"MaskedQuantity instances require normal units, not <class 'astropy.units.function.logarithmic.DexUnit'> instances.\") [astropy.table.table]\n",
      "WARNING: column st_loggerr2 has a unit but is kept as a MaskedColumn as an attempt to convert it to Quantity failed with:\n",
      "UnitTypeError(\"MaskedQuantity instances require normal units, not <class 'astropy.units.function.logarithmic.DexUnit'> instances.\") [astropy.table.table]\n"
     ]
    }
   ],
   "source": [
    "from astroquery.ipac.nexsci.nasa_exoplanet_archive import NasaExoplanetArchive\n",
    "exo_sorgu = NasaExoplanetArchive.query_object(object_name=\"WASP-43\",table=\"ps\", regularize=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['pl_name',\n",
       " 'pl_letter',\n",
       " 'hostname',\n",
       " 'hd_name',\n",
       " 'hip_name',\n",
       " 'tic_id',\n",
       " 'gaia_id',\n",
       " 'default_flag',\n",
       " 'pl_refname',\n",
       " 'sy_refname',\n",
       " 'disc_pubdate',\n",
       " 'disc_year',\n",
       " 'discoverymethod',\n",
       " 'disc_locale',\n",
       " 'disc_facility',\n",
       " 'disc_instrument',\n",
       " 'disc_telescope',\n",
       " 'disc_refname',\n",
       " 'ra',\n",
       " 'rastr',\n",
       " 'dec',\n",
       " 'decstr',\n",
       " 'glon',\n",
       " 'glat',\n",
       " 'elon',\n",
       " 'elat',\n",
       " 'pl_orbper',\n",
       " 'pl_orbpererr1',\n",
       " 'pl_orbpererr2',\n",
       " 'pl_orbperlim',\n",
       " 'pl_orbperstr',\n",
       " 'pl_orblpererr1',\n",
       " 'pl_orblper',\n",
       " 'pl_orblpererr2',\n",
       " 'pl_orblperlim',\n",
       " 'pl_orblperstr',\n",
       " 'pl_orbsmax',\n",
       " 'pl_orbsmaxerr1',\n",
       " 'pl_orbsmaxerr2',\n",
       " 'pl_orbsmaxlim',\n",
       " 'pl_orbsmaxstr',\n",
       " 'pl_orbincl',\n",
       " 'pl_orbinclerr1',\n",
       " 'pl_orbinclerr2',\n",
       " 'pl_orbincllim',\n",
       " 'pl_orbinclstr',\n",
       " 'pl_orbtper',\n",
       " 'pl_orbtpererr1',\n",
       " 'pl_orbtpererr2',\n",
       " 'pl_orbtperlim',\n",
       " 'pl_orbtperstr',\n",
       " 'pl_orbeccen',\n",
       " 'pl_orbeccenerr1',\n",
       " 'pl_orbeccenerr2',\n",
       " 'pl_orbeccenlim',\n",
       " 'pl_orbeccenstr',\n",
       " 'pl_eqt',\n",
       " 'pl_eqterr1',\n",
       " 'pl_eqterr2',\n",
       " 'pl_eqtlim',\n",
       " 'pl_eqtstr',\n",
       " 'pl_occdep',\n",
       " 'pl_occdeperr1',\n",
       " 'pl_occdeperr2',\n",
       " 'pl_occdeplim',\n",
       " 'pl_occdepstr',\n",
       " 'pl_insol',\n",
       " 'pl_insolerr1',\n",
       " 'pl_insolerr2',\n",
       " 'pl_insollim',\n",
       " 'pl_insolstr',\n",
       " 'pl_dens',\n",
       " 'pl_denserr1',\n",
       " 'pl_denserr2',\n",
       " 'pl_denslim',\n",
       " 'pl_densstr',\n",
       " 'pl_trandep',\n",
       " 'pl_trandeperr1',\n",
       " 'pl_trandeperr2',\n",
       " 'pl_trandeplim',\n",
       " 'pl_trandepstr',\n",
       " 'pl_tranmid',\n",
       " 'pl_tranmiderr1',\n",
       " 'pl_tranmiderr2',\n",
       " 'pl_tranmidlim',\n",
       " 'pl_tranmidstr',\n",
       " 'pl_trandur',\n",
       " 'pl_trandurerr1',\n",
       " 'pl_trandurerr2',\n",
       " 'pl_trandurlim',\n",
       " 'pl_trandurstr',\n",
       " 'sy_kmagstr',\n",
       " 'sy_umag',\n",
       " 'sy_umagerr1',\n",
       " 'sy_umagerr2',\n",
       " 'sy_umagstr',\n",
       " 'sy_rmag',\n",
       " 'sy_rmagerr1',\n",
       " 'sy_rmagerr2',\n",
       " 'sy_rmagstr',\n",
       " 'sy_imag',\n",
       " 'sy_imagerr1',\n",
       " 'sy_imagerr2',\n",
       " 'sy_imagstr',\n",
       " 'sy_zmag',\n",
       " 'sy_zmagerr1',\n",
       " 'sy_zmagerr2',\n",
       " 'sy_zmagstr',\n",
       " 'sy_w1mag',\n",
       " 'sy_w1magerr1',\n",
       " 'sy_w1magerr2',\n",
       " 'sy_w1magstr',\n",
       " 'sy_w2mag',\n",
       " 'sy_w2magerr1',\n",
       " 'sy_w2magerr2',\n",
       " 'sy_w2magstr',\n",
       " 'sy_w3mag',\n",
       " 'sy_w3magerr1',\n",
       " 'sy_w3magerr2',\n",
       " 'sy_w3magstr',\n",
       " 'sy_w4mag',\n",
       " 'sy_w4magerr1',\n",
       " 'sy_w4magerr2',\n",
       " 'sy_w4magstr',\n",
       " 'sy_gmag',\n",
       " 'sy_gmagerr1',\n",
       " 'sy_gmagerr2',\n",
       " 'sy_gmagstr',\n",
       " 'sy_gaiamag',\n",
       " 'sy_gaiamagerr1',\n",
       " 'sy_gaiamagerr2',\n",
       " 'sy_gaiamagstr',\n",
       " 'sy_tmag',\n",
       " 'sy_tmagerr1',\n",
       " 'sy_tmagerr2',\n",
       " 'sy_tmagstr',\n",
       " 'pl_controv_flag',\n",
       " 'pl_tsystemref',\n",
       " 'st_metratio',\n",
       " 'st_spectype',\n",
       " 'sy_kepmag',\n",
       " 'sy_kepmagerr1',\n",
       " 'sy_kepmagerr2',\n",
       " 'sy_kepmagstr',\n",
       " 'st_rotp',\n",
       " 'st_rotperr1',\n",
       " 'st_rotperr2',\n",
       " 'st_rotplim',\n",
       " 'st_rotpstr',\n",
       " 'pl_projobliq',\n",
       " 'pl_projobliqerr1',\n",
       " 'pl_projobliqerr2',\n",
       " 'pl_projobliqlim',\n",
       " 'pl_projobliqstr',\n",
       " 'x',\n",
       " 'y',\n",
       " 'z',\n",
       " 'htm20',\n",
       " 'pl_rvamp',\n",
       " 'pl_rvamperr1',\n",
       " 'pl_rvamperr2',\n",
       " 'pl_rvamplim',\n",
       " 'pl_rvampstr',\n",
       " 'pl_radj',\n",
       " 'pl_radjerr1',\n",
       " 'pl_radjerr2',\n",
       " 'pl_radjlim',\n",
       " 'pl_radjstr',\n",
       " 'pl_rade',\n",
       " 'pl_radeerr1',\n",
       " 'pl_radeerr2',\n",
       " 'pl_radelim',\n",
       " 'pl_radestr',\n",
       " 'pl_ratror',\n",
       " 'pl_ratrorerr1',\n",
       " 'pl_ratrorerr2',\n",
       " 'pl_ratrorlim',\n",
       " 'pl_ratrorstr',\n",
       " 'pl_ratdor',\n",
       " 'pl_trueobliq',\n",
       " 'pl_trueobliqerr1',\n",
       " 'pl_trueobliqerr2',\n",
       " 'pl_trueobliqlim',\n",
       " 'pl_trueobliqstr',\n",
       " 'sy_icmag',\n",
       " 'sy_icmagerr1',\n",
       " 'sy_icmagerr2',\n",
       " 'sy_icmagstr',\n",
       " 'rowupdate',\n",
       " 'pl_pubdate',\n",
       " 'st_refname',\n",
       " 'releasedate',\n",
       " 'dkin_flag',\n",
       " 'pl_ratdorerr1',\n",
       " 'pl_ratdorerr2',\n",
       " 'pl_ratdorlim',\n",
       " 'pl_ratdorstr',\n",
       " 'pl_imppar',\n",
       " 'pl_impparerr1',\n",
       " 'pl_impparerr2',\n",
       " 'pl_impparlim',\n",
       " 'pl_impparstr',\n",
       " 'pl_cmassj',\n",
       " 'pl_cmassjerr1',\n",
       " 'pl_cmassjerr2',\n",
       " 'pl_cmassjlim',\n",
       " 'pl_cmassjstr',\n",
       " 'pl_cmasse',\n",
       " 'pl_cmasseerr1',\n",
       " 'pl_cmasseerr2',\n",
       " 'pl_cmasselim',\n",
       " 'pl_cmassestr',\n",
       " 'pl_massj',\n",
       " 'pl_massjerr1',\n",
       " 'pl_massjerr2',\n",
       " 'pl_massjlim',\n",
       " 'pl_massjstr',\n",
       " 'pl_masse',\n",
       " 'pl_masseerr1',\n",
       " 'pl_masseerr2',\n",
       " 'pl_masselim',\n",
       " 'pl_massestr',\n",
       " 'pl_bmassj',\n",
       " 'pl_bmassjerr1',\n",
       " 'pl_bmassjerr2',\n",
       " 'pl_bmassjlim',\n",
       " 'pl_bmassjstr',\n",
       " 'pl_bmasse',\n",
       " 'pl_bmasseerr1',\n",
       " 'pl_bmasseerr2',\n",
       " 'pl_bmasselim',\n",
       " 'pl_bmassestr',\n",
       " 'pl_bmassprov',\n",
       " 'pl_msinij',\n",
       " 'pl_msinijerr1',\n",
       " 'pl_msinijerr2',\n",
       " 'pl_msinijlim',\n",
       " 'pl_msinijstr',\n",
       " 'pl_msinie',\n",
       " 'pl_msinieerr1',\n",
       " 'pl_msinieerr2',\n",
       " 'pl_msinielim',\n",
       " 'pl_msiniestr',\n",
       " 'st_teff',\n",
       " 'st_tefferr1',\n",
       " 'st_tefferr2',\n",
       " 'st_tefflim',\n",
       " 'st_teffstr',\n",
       " 'st_met',\n",
       " 'st_meterr1',\n",
       " 'st_meterr2',\n",
       " 'st_metlim',\n",
       " 'st_metstr',\n",
       " 'st_radv',\n",
       " 'st_radverr1',\n",
       " 'st_radverr2',\n",
       " 'st_radvlim',\n",
       " 'st_radvstr',\n",
       " 'st_vsin',\n",
       " 'st_vsinerr1',\n",
       " 'st_vsinerr2',\n",
       " 'st_vsinlim',\n",
       " 'st_vsinstr',\n",
       " 'st_lum',\n",
       " 'st_lumerr1',\n",
       " 'st_lumerr2',\n",
       " 'st_lumlim',\n",
       " 'st_lumstr',\n",
       " 'st_logg',\n",
       " 'st_loggerr1',\n",
       " 'st_loggerr2',\n",
       " 'st_logglim',\n",
       " 'st_loggstr',\n",
       " 'st_age',\n",
       " 'st_ageerr1',\n",
       " 'st_ageerr2',\n",
       " 'st_agelim',\n",
       " 'st_agestr',\n",
       " 'st_mass',\n",
       " 'st_masserr1',\n",
       " 'st_masserr2',\n",
       " 'st_masslim',\n",
       " 'st_massstr',\n",
       " 'st_dens',\n",
       " 'st_denserr1',\n",
       " 'st_denserr2',\n",
       " 'st_denslim',\n",
       " 'st_densstr',\n",
       " 'st_rad',\n",
       " 'st_raderr1',\n",
       " 'st_raderr2',\n",
       " 'st_radlim',\n",
       " 'st_radstr',\n",
       " 'ttv_flag',\n",
       " 'ptv_flag',\n",
       " 'tran_flag',\n",
       " 'rv_flag',\n",
       " 'ast_flag',\n",
       " 'obm_flag',\n",
       " 'micro_flag',\n",
       " 'etv_flag',\n",
       " 'ima_flag',\n",
       " 'pul_flag',\n",
       " 'soltype',\n",
       " 'sy_snum',\n",
       " 'sy_pnum',\n",
       " 'sy_mnum',\n",
       " 'cb_flag',\n",
       " 'st_nphot',\n",
       " 'st_nrvc',\n",
       " 'st_nspec',\n",
       " 'pl_nespec',\n",
       " 'pl_ntranspec',\n",
       " 'pl_nnotes',\n",
       " 'sy_pm',\n",
       " 'sy_pmerr1',\n",
       " 'sy_pmerr2',\n",
       " 'sy_pmstr',\n",
       " 'sy_pmra',\n",
       " 'sy_pmraerr1',\n",
       " 'sy_pmraerr2',\n",
       " 'sy_pmrastr',\n",
       " 'sy_pmdec',\n",
       " 'sy_pmdecerr1',\n",
       " 'sy_pmdecerr2',\n",
       " 'sy_pmdecstr',\n",
       " 'sy_plx',\n",
       " 'sy_plxerr1',\n",
       " 'sy_plxerr2',\n",
       " 'sy_plxstr',\n",
       " 'sy_dist',\n",
       " 'sy_disterr1',\n",
       " 'sy_disterr2',\n",
       " 'sy_diststr',\n",
       " 'sy_bmag',\n",
       " 'sy_bmagerr1',\n",
       " 'sy_bmagerr2',\n",
       " 'sy_bmagstr',\n",
       " 'sy_vmag',\n",
       " 'sy_vmagerr1',\n",
       " 'sy_vmagerr2',\n",
       " 'sy_vmagstr',\n",
       " 'sy_jmag',\n",
       " 'sy_jmagerr1',\n",
       " 'sy_jmagerr2',\n",
       " 'sy_jmagstr',\n",
       " 'sy_hmag',\n",
       " 'sy_hmagerr1',\n",
       " 'sy_hmagerr2',\n",
       " 'sy_hmagstr',\n",
       " 'sy_kmag',\n",
       " 'sy_kmagerr1',\n",
       " 'sy_kmagerr2',\n",
       " 'sky_coord']"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exo_sorgu.colnames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "WASP-43 b örneğinde (bir yarıçap değerinin verildiği) ilgili yayınlardaki yarıçap değerleri Jüpiter yarıçapı cinsinden alınmak istenirse aşağıdaki gibi bir sorgu yazılabilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WASP-43 b gezegeninin <a refstr=BONOMO_ET_AL__2017 href=https://ui.adsabs.harvard.edu/abs/2017A&A...602A.107B/abstract target=ref>Bonomo et al. 2017</a> referasindaki yaricapi 1.04 jupiterRad\n",
      "WASP-43 b gezegeninin <a refstr=ESPOSITO_ET_AL__2017 href=https://ui.adsabs.harvard.edu/abs/2017A&A...601A..53E/abstract target=ref>Esposito et al. 2017</a> referasindaki yaricapi 1.01 jupiterRad\n",
      "WASP-43 b gezegeninin <a refstr=HELLIER_ET_AL__2011 href=https://ui.adsabs.harvard.edu/abs/2011arXiv1104.2823H/abstract target=ref> Hellier et al. 2011 </a> referasindaki yaricapi 0.93 jupiterRad\n",
      "WASP-43 b gezegeninin <a refstr=SALZ_ET_AL__2015 href=https://ui.adsabs.harvard.edu/abs/2015A&A...576A..42S/abstract target=ref>Salz et al. 2015</a> referasindaki yaricapi 0.93 jupiterRad\n",
      "WASP-43 b gezegeninin <a refstr=EXOFOP_TESS_TOI href=https://exofop.ipac.caltech.edu/tess/view_toi.php target=ref>ExoFOP-TESS TOI</a> referasindaki yaricapi 1.17 jupiterRad\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "for satir in exo_sorgu:\n",
    "    if not(pd.isna(satir['pl_radj'])):\n",
    "        print(\"{:s} gezegeninin {:s} referasindaki yaricapi {:.2f}\".\n",
    "              format(satir['pl_name'], satir['pl_refname'], satir['pl_radj']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Başa Dön](#Bu-derste-neler-öğreneceksiniz?)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ödev 2 #\n",
    "## Teslim Tarihi: 28 Mart 2023, Salı 17:30 ##\n",
    "\n",
    "Size verilen veritabanlarını `astroquery` 'nin ilgili alt modül ve fonksiyonlarını kullanarak 10 örnek sorgu üretiniz. Sorgularınızla neleri elde etmeyi amaçladığınızı ve sorgularınızın sonuç olarak ne yaptığını oluşturacağınız Jupyter defterinde açıklamalı olarak anlatınız.\n",
    "\n",
    "Her bir öğrenci çalışma alanına uygun kendi istediği bir veritabanını seçebilir. Sorguların işinize yarar, kullanılabilir bilgiler döndüren sorgular olmasına özen gösteriniz. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kaynaklar #\n",
    "\n",
    "* [astropy.table Dokümantasyonu](https://docs.astropy.org/en/stable/table/)\n",
    "* [astroquery Dokümantasyonu](https://astroquery.readthedocs.io/en/latest/index.html)\n",
    "* [SAO/NASA Astrophysics Data System](https://ui.adsabs.harvard.edu/)\n",
    "* [SIMBAD Veritabanı](http://simbad.u-strasbg.fr/simbad/)\n",
    "* [Gaia Veritabanı](https://gea.esac.esa.int/archive/)\n",
    "* [SkyView Veritabanı](https://skyview.gsfc.nasa.gov/current/cgi/titlepage.pl)\n",
    "* [Vizier Veritabanı](https://vizier.u-strasbg.fr/viz-bin/VizieR)\n",
    "* [ads Paketi github sayfası](https://github.com/andycasey/ads)\n",
    "* [ads Paketi Dokümantasyonu](https://ads.readthedocs.io/en/latest/)\n",
    "* Databases & On-line Data in Astronomy, Eds. Miguel A. Albrecht, Daniel Egret, 1991, Springer\n",
    "* [Simbad ve VizieR sorgu örnekleri](https://notebook.community/Python4AstronomersAndParticlePhysicists/PythonWorkshop-ICE/notebooks/10_02_Astronomy_Astroquery)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Başa Dön](#Bu-derste-neler-öğreneceksiniz?)"
   ]
  }
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