{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 800100715151 Astronomide Veritabanları #\n", "\n", "## Ders - 04a Pandas Paketiyle Veri İşleme Uygulaması##" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Doç. Dr. Özgür Baştürk
\n", "Ankara Üniversitesi, Astronomi ve Uzay Bilimleri Bölümü
\n", "obasturk at ankara.edu.tr
\n", "http://ozgur.astrotux.org" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Pandas Paketi İleri Konular Uygulama #\n", "## Ders-4a: Ötegezegen Veritabanları Uygulaması ##\n", "\n", "* [Soru 1.](#Soru-1.)\n", " * [Çözüm 1.](#Çözüm-1.)\n", "* [Soru 2.](#Soru-2.)\n", " * [Çözüm 2.](#Çözüm-2.)\n", "* [Soru 3.](#Soru-3.)\n", " * [Çözüm 3.](#Çözüm-3.)\n", "* [Soru 4.](#Soru-4.)\n", " * [Çözüm 4.](#Çözüm-4.)\n", "* [Soru 5.](#Soru-5.)\n", " * [Çözüm 5.](#Çözüm-5.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ötegezegenler ve onların barınak yıldızlarına ilişkin pek çok parametrenin bulunduğu ötegezegen veritabanları arasında öne çıkan iki tanesi [NASA Exoplanet Archive](https://exoplanetarchive.ipac.caltech.edu/) tarafından sağlanan veritabanı ile Cenevre Üniversitesi'nde tutulan [exoplanet.eu](http://exoplanet.eu/catalog/) veritabanıdır. İki veritabanında da benzer veriler yer aldığı gibi bir veritabanında olup diğerinde olmayan (barınak yıldız metal bolluğu gibi) ya da farklı kaynaklardan alınan veriler de bulunmaktadır. Bu iki veritabanını `pandas` paketinin sağladığı olanaklarla çeşitli şekillerde birleştirerek sorgulayabiliriz.\n", "\n", "Öncelikle her iki tabloyu `astroquery` fonksiyonlarıyla sorgulayarak birer `pandas` veriçerçevesine alalım. [NASA Exoplanet Archive](https://exoplanetarchive.ipac.caltech.edu/docs/data.html) ötegezegenler ve gözlemleriyle ilgili pek çok tablo ve veri sunmaktadır. Bu veri tablolarından ikisi tüm ötegezegen, sistem ve barınak yıldız parametrelerinin farklı literatür kaynaklarından alınarak listelendiği [Planetary Systems](https://exoplanetarchive.ipac.caltech.edu/cgi-bin/TblView/nph-tblView?app=ExoTbls&config=PS), diğeri bu parametrelerin farklı kaynaklardan derlenerek her bir gezegen için tek bir satır halinde sunulduğu [Planetary Systems Composite Data](https://exoplanetarchive.ipac.caltech.edu/cgi-bin/TblView/nph-tblView?app=ExoTbls&config=PSCompPars) tablosudur. Bu tablolara `astroquery` modüllerinden `NasaExoplanetArchive` NASA Exoplanet Archive veritabanını kritere göre tarayan fonksiyonu `query_criteria` ile erişilebilir. Tüm literatür kaynaklarından alınan verilerin sunulduğu tablo `ps`, parametrelerin derlenerek tek bir satıra dönüştürüldüğü tablo `pscommpars` adıyla `table` anahtarına sağlanmalı; tüm tablo çekilmek isteniyorsa, SQL sorgu nesnesinin sağlandığı `select` parametresi \" * \" değerine atanmalıdır. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from astroquery.ipac.nexsci.nasa_exoplanet_archive import NasaExoplanetArchive\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "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 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 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 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 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 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 instances.\") [astropy.table.table]\n" ] } ], "source": [ "nasatablo = NasaExoplanetArchive.query_criteria(table=\"pscomppars\", select=\"*\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nasatablo.columns" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "nasaexo = nasatablo.to_pandas()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[exoplanet.eu veritabanına](http://exoplanet.eu/catalog/) bağlanmak için bir `astroquery` servisi tanımlanmadığından bu veritabanını sorgulayabilmek ya da çevrimiçi çekip kullanabilmek için ya [`requests` modülü fonksiyonlarını](https://requests.readthedocs.io/en/latest/) kullanmak ya da sitenilen formatta (Virtual Observatory --VO-- tablosu, csv, dat) indirip uygun `pandas` fonksiyonuyla açmak gerekecektir. [DACE platformunun](https://dace.unige.ch/dashboard/) bir parçası olan katalog, Sanal Gözlemevi standardı Tablo Erişim Protokolü (Table Access Protocol, TAP) fonksiyonları ile sorgulamaya açıktır ve [pyvo](https://pyvo.readthedocs.io/en/latest/) modülü fonksiyonlarıyla sorgulanabilir ve tıpkı NASA Exoplanet Archive veritabanı gibi kullanılabilir." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NASA Exooplanet Archive veritabani sutunlari\n", "---------------------------------------------\n", "Index(['pl_name', 'pl_letter', 'hostname', 'hd_name', 'hip_name', 'tic_id',\n", " 'disc_pubdate', 'disc_year', 'discoverymethod', 'disc_locale',\n", " ...\n", " 'sy_pmra', 'sy_pmraerr1', 'sy_pmraerr2', 'sy_pmrastr', 'x', 'y', 'z',\n", " 'htm20', 'sky_coord.ra', 'sky_coord.dec'],\n", " dtype='object', length=375)\n", "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n", "exoplanet.eu veritabani sutunlari\n", "---------------------------------------------\n", "Index(['# name', 'planet_status', 'mass', 'mass_error_min', 'mass_error_max',\n", " 'mass_sini', 'mass_sini_error_min', 'mass_sini_error_max', 'radius',\n", " 'radius_error_min', 'radius_error_max', 'orbital_period',\n", " 'orbital_period_error_min', 'orbital_period_error_max',\n", " 'semi_major_axis', 'semi_major_axis_error_min',\n", " 'semi_major_axis_error_max', 'eccentricity', 'eccentricity_error_min',\n", " 'eccentricity_error_max', 'inclination', 'inclination_error_min',\n", " 'inclination_error_max', 'angular_distance', 'discovered', 'updated',\n", " 'omega', 'omega_error_min', 'omega_error_max', 'tperi',\n", " 'tperi_error_min', 'tperi_error_max', 'tconj', 'tconj_error_min',\n", " 'tconj_error_max', 'tzero_tr', 'tzero_tr_error_min',\n", " 'tzero_tr_error_max', 'tzero_tr_sec', 'tzero_tr_sec_error_min',\n", " 'tzero_tr_sec_error_max', 'lambda_angle', 'lambda_angle_error_min',\n", " 'lambda_angle_error_max', 'impact_parameter',\n", " 'impact_parameter_error_min', 'impact_parameter_error_max', 'tzero_vr',\n", " 'tzero_vr_error_min', 'tzero_vr_error_max', 'k', 'k_error_min',\n", " 'k_error_max', 'temp_calculated', 'temp_calculated_error_min',\n", " 'temp_calculated_error_max', 'temp_measured', 'hot_point_lon',\n", " 'geometric_albedo', 'geometric_albedo_error_min',\n", " 'geometric_albedo_error_max', 'log_g', 'publication', 'detection_type',\n", " 'mass_detection_type', 'radius_detection_type', 'alternate_names',\n", " 'molecules', 'star_name', 'ra', 'dec', 'mag_v', 'mag_i', 'mag_j',\n", " 'mag_h', 'mag_k', 'star_distance', 'star_distance_error_min',\n", " 'star_distance_error_max', 'star_metallicity',\n", " 'star_metallicity_error_min', 'star_metallicity_error_max', 'star_mass',\n", " 'star_mass_error_min', 'star_mass_error_max', 'star_radius',\n", " 'star_radius_error_min', 'star_radius_error_max', 'star_sp_type',\n", " 'star_age', 'star_age_error_min', 'star_age_error_max', 'star_teff',\n", " 'star_teff_error_min', 'star_teff_error_max', 'star_detected_disc',\n", " 'star_magnetic_field', 'star_alternate_names'],\n", " dtype='object')\n" ] } ], "source": [ "#nasaexo = pd.read_csv('veri/PSCompPars_2023.04.04_01.20.39',\n", "# comment=\"#\", skipinitialspace=True)\n", "exoeu = pd.read_csv('veri/exoplanet.eu_catalog_20230404.csv', \n", " skipinitialspace=True)\n", "\n", "print(\"NASA Exooplanet Archive veritabani sutunlari\")\n", "print(\"-\"*45)\n", "print(nasaexo.columns)\n", "print(\"x\"*80)\n", "print(\"exoplanet.eu veritabani sutunlari\")\n", "print(\"-\"*45)\n", "print(exoeu.columns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Görüldüğü üzere farklı veritabanlarında aynı sütunlar farklı isimlerle bulunabilmektedir. Örneğin NASA Exoplanet Archive veritabanında pl_name sütununda yer alan gezegen isimleri # name sütununda yer almaktadır. \n", "\n", "[Başa Dön](#Pandas-Paketi-İleri-Konular-Uygulama)\n", "\n", "### Soru 1. ### \n", "\n", "NASA Exoplanet Archive'dan bu uygulama için indirilen veritabanı gezegen doğaları başka gözlemlerle (örneğin hem geçiş, hem dikine hız) kesinleştirilmiş ve bu nedenle \"onaylanmış\" (ing. confirmed) ötegezegenleri içermektedir. exoplanet.eu veritabanında ise bu şekilde kesinleştirilmemiş gezegenler de bulunmaktadır. Her iki veritabanını bağlamak üzere gezegen isimlerini kullanınız ve bu iki veritabanında birden bulunan gezegenleri alınız. Gezegen ismini bu bağlanmış veritabanının indeksi haline getiriniz." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Çözüm 1. ####\n", "\n", "`pandas` paketinin iki veriçerçevesindeki verileri bağlamak için kullandığı fonksiyon `pd.merge()` fonksiyonudur. Fonksiyon varsayılan olarak bizim de amaçladığımız kesişim işlemini `how = \"inner\"` parametresi ile sağlar. Ancak her iki verçerçevesinde gezegen isimlerinin bulunduğu sütunlar farklıdır. Bu sütunların fonksiyona veritabanlarının verildiği sırayla hangi isimlerde olduğu `left_on` ve `right_on` parametrelerine sağlanmalıdır. Sonrasında oluşacak yeni verçerçevesinin indeksini gezegen ismi yapmak üzere veriçerçeveleri üzerine tanımlı `set_index` metoduna `pl_name` sütun ismi sağlanarak istenen hedefe ulaşılabilir." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NASA Exoplanet Archive veritabani buyuklugu: (5322, 375)\n", "exoplanet.eu veritabani buyuklugu: (5346, 98)\n", "İki veritabaninin kesisiminin buyuklugu: (4443, 472)\n" ] } ], "source": [ "og = pd.merge(nasaexo, exoeu, how=\"inner\",\\\n", " left_on=\"pl_name\", right_on=\"# name\")\n", "og.set_index(\"pl_name\", inplace=True)\n", "print(\"NASA Exoplanet Archive veritabani buyuklugu: \", nasaexo.shape)\n", "print(\"exoplanet.eu veritabani buyuklugu: \", exoeu.shape)\n", "print(\"İki veritabaninin kesisiminin buyuklugu: \", og.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Görüldüğü üzere her iki veritabanında sırasıylsa 52322 ve 5346 ötegezegen olmasına karşın, kesişimlerinde toplam 4443 ötegezegen bulunmaktadır. Aslında aynı isimle olmasa dahi her iki veritabanında yer alan bazı ötegezegenler maalesef bulunmaktadır. Bunun nedeni zaman zaman ortaya çıkan isim karışıklığıdır. Örneğin NASA Exoplanet Archive veritabanında Kepler-16 b ismiyle yer alan ötegezegen, exoplanet.eu veritabanında bir çift yıldızın etrafında dolanan bir gezegen olduğu (ing. circumbinary planet) Kepler 16 (AB) b ismiyle yer almaktadır. Yukarıdaki gibi bir birleştirmeyle bu tür ötegezegenleri veritabanında kaybetme tehlikesi vardır. Maalesef bu özel durumdan kaçınmanın kolay bir yolu olmayabilir. Özelde çift yıldız gezegenlerinin isimlerini kontrol edip eşleştirebilecek $string$ fonksiyonlarından faydalanılabilir. Ancak amacımız bu olmadığı için bu hataları bu uygulamada görmezden geliyoruz. Çalışma alanında deneyimli ve bu veritabanları üzerinden bir bilimsel araştırma yürütecek bir araştırıcının bu tür durumları da dikkate alması ve bunlar için de fonksiyonlar yazması ve öncesinde üzerinde çalışacağı veritabanlarını iyice incelemesi gerekir.\n", "\n", "Daha önemlisi birbirine bağlanan veritabanlarında sırasıyla 375 ve 98 sütun bulunduğu için bağlama işlemi sonunda oluşan $og$ veritabanında 472 sütunun bulunmasıdır. Bu durumda her iki veritabanında birden çok kez bulunan sütunların hangilerinin nihai veritabanında bulunup hangilerinin bulunmayacağına karar vermek gerek. Bu bilgiler, farklı veritabanlarında farklı kaynaklardan (literatür referanslarından) geliyor olabilir. Üzerinde çalışılacak birleştirilmiş veritabanının sağlıklı veriler içermesi için yapılması gereken bu seçimde öncelikler araştırmacıdan araştırmacıya, ya da yapılacak araştırmanın niteliğine göre değişebilir. Biz bu uygulamada her iki veritabanında da bulunan bilgiler için NASA Exoplanet Archive'da bulunanları kullanmayı tercih edeceğiz." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Başa Dön](#Pandas-Paketi-İleri-Konular-Uygulama)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Soru 2. ### \n", "\n", "Her iki veritabanında da bulunan sütunları belirleyerek bunlardan exoplanet.eu veritabanında bulunanları bağlanmış veriçerçevenizden atınız.\n", "\n", "#### Çözüm 2. #### \n", "\n", "Öncelikle her iki veritabanında aynı bilgiyi barındıran (aynı referanstan alınmayabildiği için bazıları tam olarak aynı olmayabilir ancak biz NASA Explanet Archive veritabanında bulunan bilgileri kullanmayı tercih ettik), ancak farklı isimle bulunan sütunların exoplanet.eu veritabanındaki isimlerini bir listeye toplayalım. Sonra bu listeyi `pandas` veriçerçevelerine uygulanan `drop()` fonksiyonuna sütun silecek şekilde `axis` parametresini $1$ değerine ayarlayarak atalım ve yaptığımız değişikliği kalıcı hale getirmek için `inplace` parametresini de $True$ değerine ayarlayalım." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['pl_letter', 'hostname', 'hd_name', 'hip_name', 'tic_id',\n", " 'disc_pubdate', 'disc_year', 'discoverymethod', 'disc_locale',\n", " 'disc_facility',\n", " ...\n", " 'star_sp_type', 'star_age', 'star_age_error_min', 'star_age_error_max',\n", " 'star_teff', 'star_teff_error_min', 'star_teff_error_max',\n", " 'star_detected_disc', 'star_magnetic_field', 'star_alternate_names'],\n", " dtype='object', length=450)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "atilacak_sutunlar = ['star_name','# name','detection_type',\n", " 'orbital_period','orbital_period_error_min','orbital_period_error_max',\n", " 'semi_major_axis','semi_major_axis_error_min','semi_major_axis_error_max',\n", " 'eccentricity','eccentricity_error_min','eccentricity_error_max',\n", " 'inclination','inclination_error_min','inclination_error_max',\n", " 'mass','mass_error_min','mass_error_max','mass_detection_type',\n", " 'radius','radius_error_min','radius_error_max']\n", "og.drop(atilacak_sutunlar,axis=1,inplace=True)\n", "og.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Görüldüğü gibi her iki veriçerçevesinde de \"aynı\" bilgiyi içeren, ancak farklı isimle bulunan toplamda 22 sütunu attık ve sütun sayımız 450'ye indi." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Başa Dön](#Pandas-Paketi-İleri-Konular-Uygulama)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Soru 3. ### \n", "\n", "Her iki veritabanında da aynı isimle bulunan ve barınak yıldızın ekvatoryal koordinat sistemindeki koordinatlarını içeren $ra$ ve $dec$ sütunlarından exoplanet.eu veritabanı sağlananları atacak ve birer $ra$,$dec$ sütunu bırakacak düzenlemeyi yapınız.\n", "\n", "#### Çözüm 3. ####\n", "\n", "`pandas.merge()` işlemi sırasında her iki taraftan aynıı isimle gelecek sütunlardan fonksiyona ilk sağlanan veriçerçevesindekinin sonuna \"_x\", ikinci sağlanacak veriçerçevesindekinin sonuna ise \"_y\" eki gelecektir. Bu nedenle sonu \"_y\" ile biten $ra$ ve $dec$ sütunlarını yine `drop()` fonksiyonunu uygun şekilde kullanarak atabiliriz. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "og.drop(['ra_y','dec_y'], axis=1,inplace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Daha sonra da ra_x ve dec_x isimli sütunları yeniden isimlendirerek amacımıza ulaşabiliriz. Bunun için veriçerçeveleri üzerinde tanımlı `rename()` fonksiyonunu aşağıdaki şekilde istediğimiz değişiklikleri bir sözlük değişkenine yazıp, bu değişkeni `columns` parametresine sağlayarak kullanmalı ve yine yaptığımız değişikliği kalıcı hale getirmek için fonksiyonun `inplace` parametresini $True$ değerine ayarlamalıyız." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n", "
" ], "text/plain": [ " ra dec\n", "pl_name \n", "Kepler-276 c 293.568197 39.036312\n", "Kepler-829 b 282.332831 42.463813\n", "K2-283 b 13.194368 9.692918\n", "Kepler-477 b 288.067445 42.355305\n", "TOI-1260 c 157.144071 65.854199" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "og.rename(columns={'ra_x':'ra','dec_x':'dec'},inplace=True)\n", "og[['ra','dec']].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Gördüğünüz gibi istediğmiz değişiklikleri yaptık ve bağlanmış veritabanımızı her iki veritabanında ortak olan ötegezegenler için her iki veritabanında ortak olmayan bilgilerini de içerecek şekilde her türlü analiz için hazır hale getirmiş olduk. Herhangi bir veri için tekrar orjinal veritabanlarına dönmemiz gerekse de bu veritabanlarını sırasıyla $nasaexo$ ve $exoeu$ veriçerçevelerinde sakladığımız için bir kaybımız olmayacak. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Başa Dön](#Pandas-Paketi-İleri-Konular-Uygulama)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Soru 4. ### \n", "\n", "Sadece geçiş yöntemiyle keşfedilmiş gezegenlerden oluşan bir veriçerçevesi oluşturunuz.\n", "\n", "#### Çözüm 4. #### \n", "\n", "Bu amaçla yapmamız gereken daha önce de yaptığımız gibi bize sadece geçiş yapan gezegenleri verecek bir koşul üretip bu koşulu mevcut veritabanında uygulayarak bir maske oluşturmak ve uyanları yeni bir veritabanına toplamaktan ibaret. Ancak önce keşif yöntemlerinin hangileri olduğunu görelim. Bunun için discoverymethod sütununa bakmalı ve bu sütundaki tüm teknikleri `unique()` metodunu kullanarak birer kez geçecek listelemeliyiz." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Transit' 'Radial Velocity' 'Microlensing' 'Astrometry' 'Imaging'\n", " 'Transit Timing Variations' 'Orbital Brightness Modulation'\n", " 'Eclipse Timing Variations' 'Pulsar Timing']\n" ] } ], "source": [ "print(og['discoverymethod'].unique())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Geçiş yöntemiyle keşfedilen gezegenlerin discoverymethod sütununda $Transit$ adıyla listelendiğini görmüş olduk. Koşulu buna uygun olarak yazabiliriz." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(3464, 448)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kosul = (og['discoverymethod'] == 'Transit')\n", "gecis_og = og[kosul]\n", "gecis_og.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Gördüğünüz gibi her iki veritabanında (NASA ve exoplanet.eu) ortak olan 4443 ötegezegenden 3464'ü geçişle keşfedilmiş (4 Nisan 2023 itibarı ile güncel). Bu önemli bir yüzde. Elimizdeki bu veriçerçevesinde NASA Exoplanet Arcihve'da bulunan onaylanmış tüm geçiş yapan ötegezegenler olduğuna göre bu ötegezegenlerin kütle ve yarıçapları biliniyor olmalı. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Başa Dön](#Pandas-Paketi-İleri-Konular-Uygulama)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Soru 5. ### \n", "\n", "Geçiş yapan ötegezegenler için bir kütle-yarıçap grafiği oluşturunuz.\n", "\n", "#### Çözüm 5. #### \n", "\n", "Çok kolay görünen bu iş için öncelikle elimizdeki verinin gerçekte kütle ve yarıçapları bilinen verilerden oluşup oluşmadığını sorgulamalıyız. Bunun için `isnull()` fonksiyonunu kullanalım." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pl_name\n", "Kepler-762 b NaN\n", "Kepler-785 b NaN\n", "Kepler-686 b NaN\n", "Kepler-723 b NaN\n", "Kepler-548 b NaN\n", "Kepler-731 b NaN\n", "Kepler-670 b NaN\n", "Kepler-468 b NaN\n", "Kepler-706 b NaN\n", "Kepler-553 c NaN\n", "TOI-5398 b NaN\n", "Kepler-487 b NaN\n", "Kepler-490 b NaN\n", "Kepler-302 c NaN\n", "Kepler-730 b NaN\n", "K2-253 b NaN\n", "Name: pl_bmassj, dtype: float64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gecis_og[gecis_og['pl_bmassj'].isnull()]['pl_bmassj']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Gördüğünüz gibi durum hiç de öyle değil. Bildiğiniz gibi [geçiş yöntemi](http://ozgur.astrotux.org/ast413/Ders_04/Ders_04_Gecis_Yontemi.pdf) gezegen yarıçapının yıldız yarıçapına oranının bulunmasını sağlar, yıldız modellerinden elde edilen yıldız yarıçapları kullanılarak gezegen yarıçapları elde edilebilirken kütlelerini elde etmek mümkün değildir. Burada dikine hız (ya da bir başka yöntemle) kütleleri onaylanmamış gezegenler (adaylar) de bulunuyor; hatta çoğunluktalar (2125 / 2772). Bunun için yapabileceğimiz pek fazla bir şey yok; zira bu gezegenlerin büyük çoğunluğu, ki bunların da büyük bölümü de Kepler uzay teleskobuyla keşfedilmiş gezegenlerdir; dikine hızlarını elde etmek üzere yüksek çözünürlüklü tayfları alınamayacak kadar sönük yıldızların etrafında bulunuyorlar. O vakit sadece kütlesi bulunanlara odaklanalım. Ancak önce yarıçapların da bulunup bulunmadığını bir test edelim. `isnull()` fonksiyonu kullanabileceğimiz gibi `isna()` fonksiyonunu da kullanabiliriz çünkü veriçerçevemizde değer bulunmayan bütün sütunlar `NaN` olarak işaretmenmiş durumda." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Series([], Name: pl_radj, dtype: float64)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gecis_og[gecis_og['pl_radj'].isna()]['pl_radj']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "İşte bu şaşırtıcı. Zira geçiş yöntemiyle keşfedilmiş 16 ötegezegenin veriçerçevemizde yarıçapları bulunmuyor. Bunun nedeni basit olarak bu değerlerin veritabanına girilmemiş olması olabilir. Astronomide (ya da veri bilimi içeren herhangi başka bir alanda) kullanılan veritabanlarında bu durumla sıklıkla karşılaşılır. Bu tür durumlarda bu bilgiler başka veritabanlarında ya da literatürden alınabilir. Ya da sadece bu durumu yönetmekle yetinilir. Bir başka deyişle, iş eldeki veriyle yapılıp bu durum sonuçların paylaşıldığı yerde belirtilir. 4 Nisan 2023 tarihi itibarı ile ortak tabloda geçiş yöntemiyle keşfedilip yarıçapı veritabanına girilmemiş gezegen bulunmamaktadır. Ancak tekrar oluşabilecek böyle bir durum için aşağıdakine benzer bir çözüm takip edilebilir: \n", "\n", "Biz örneğimizde yarıçap değerlerini barındıran pl_radj sütununu NASA Exoplanet Archive'dan almıştık. Bu gezegenler exoplanet.eu veritabanında da bulundukları için orada yarıçapları bulunuyor olabilir. Dolayısıyla yukarıdaki koşulun sağlandığı gezegenlerin yarıçap değerlerini exoplanet.eu ($exoeu$) veritabanından alıp, veriçerçevemizdeki pl_radj sütununa kopyalayabiliriz. Öncelikle exoplanet.eu veritabanında bu gezegenlerden hangilerinin yarıçaplarının olduğuna bakalım. Bunun için $exoeu$ şeklinde adlandırdığımız bu veritabanının indeks sütununu gezegen adının bulunduğu # name sütununa ayarlayalım ve gecis_og veriçerçevemizde yarıçapı bulunmayan (`NaN` olan) gezegenlerin bu veritabanında olup olmadığını sorgulayalım. Yarıçap değerleri $exoeu$ veriçerçevesinde $radius$ sütununda bulunmaktadır." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "exoeu.set_index('# name', inplace=True)\n", "for planet in gecis_og[gecis_og['pl_radj'].isna()]['pl_radj'].index:\n", " print(planet,exoeu.loc[planet,'radius'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Gördüğünüz gibi bu değerler exoplanet.eu veritabanında bulunuyor. Bulunmuyor da olabilirdi. Bu durumda da gidip başka bir veritabanına bakabilir ya da literatürden toplayıp manuel olarak kendimiz girebilirdik. Ama buna gerek kalmadı. Yukarıdaki gibi bir döngü yapısıyla ilgili satırlara gereken bilgileri ekleyebiliriz." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Series([], Name: pl_radj, dtype: float64)\n", "1.24\n" ] } ], "source": [ "for planet in gecis_og[gecis_og['pl_radj'].isna()]['pl_radj'].index:\n", " gecis_og.at[planet,'pl_radj'] = exoeu.at[planet,'radius']\n", "print(gecis_og[gecis_og['pl_radj'].isna()]['pl_radj'])\n", "print(gecis_og.at['WASP-18 b','pl_radj'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Görüldüğü üzere artık yarıçap bulunmayan sütun yok. Geçiş yöntemiyle keşfedilen tüm gezegenler için bu değerin belirlenmiş olması gerektiğinden bu bilgiyi veriçerçevemizde bulundurmak istedik. Ancak bu gezegenlerin tümü için kütle değerinin veriçerçevemizde olmadığını gördük. Ancak amacımız olan kütle-yarıçap grafiğini `seaborn` paketi fonksiyonları kullanarak çizdirirken bu durum bir problem teşkil etmeyecek. Çünkü kütlesi veriçerçevesinde bulunmayan gezegenler dikkate alınmayacak. Söz konusu parametreleri `numpy` dizilerine alarak `matplotlib.pyplot` fonksiyonları ile grafik elde etmek isterseniz bu durumu dikkat almanız gerekecektir." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib import pyplot as plt\n", "%matplotlib inline\n", "plt.plot(gecis_og[\"pl_bmassj\"], gecis_og[\"pl_radj\"],'b+')\n", "plt.xlabel(\"Kutle (M$_{jup}$)\")\n", "plt.ylabel(\"Yaricap (R$_{jup}$)\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Görüldüğü üzere bu veritabanlarından birinde (NASA Exoplanet Archive) bazı düşük kütleli yıldızlar da bulunmaktadır. Bunları seçerek veritabanından çıkarmakta fayda var." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "gecis_og = gecis_og[gecis_og['pl_bmassj'] < 100]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bu grafik genellikle farklı gezegen türleri için çizdirildiğinde anlam kazanan bir grafiktir. Bu nedenle yine daha önce yaptığımız gibi kütle limitleri koyarak kütle-yarıçap grafiğini farklı gezegen grupları için elde edebiliriz. Örneğin $0.1 - 0.4~M_{jup}$ kütle aralığındaki gezegenler için böyle bir uygulama yapalım." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "kosul = (gecis_og['pl_bmassj'] >= 0.1) & (gecis_og['pl_bmassj'] <= 0.4)\n", "saturnler = gecis_og[kosul]\n", "plt.plot(saturnler[\"pl_bmassj\"], saturnler[\"pl_radj\"],'b+')\n", "plt.xlabel(\"Kutle (M$_{jup}$)\")\n", "plt.ylabel(\"Yaricap (R$_{jup}$)\")\n", "plt.title(\"Gecis Yapan Saturn Kutlesindeki Gezegenler Icin Kutle-Yaricap Grafigi\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bu grafikte bir doğru üzerine dizilen gezegenler için kütle değeri [kütle-yarıçap ilişkilerinden](https://ui.adsabs.harvard.edu/abs/2017ApJ...834...17C/abstract) türetilmiş olmalıdır. Bu gezegenler için barınak yıldızlarının sönük olması nedeniyle dikine hız alınmamış olabilir, ayrıca gezegen kütlesini belirlemeye yardımcı olabilecek, gözlenebilecek düzeyde bir geçiş zamanlaması değişimi yaratabilecek bir ilave cisim bulunmayabilir. Grafikte daha ilgi çekici olan, genel olarak Satürn kütlesi civarındaki gezegenler için saçılması yüksek de olsa kütleyle yarıçap arasında bir pozitif korelasyonun gözlendiği, ancak yarıçapı $0.25 R_{jup}$''ten küçük gezegenler için bu ilişkinin bozulduğudur. Bu ilginç bir araştırma sorusu olabilir; ancak öncelikle bu verilerin kaynağına bakmak ve grafiği bir de belirsizlik değerleriyle birlikte çizdirmeyi denemekte fayda vardır." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "kosul = (gecis_og['pl_bmassj'] < 0.04)\n", "karasal = gecis_og[kosul]\n", "plt.plot(karasal[\"pl_bmassj\"], karasal[\"pl_radj\"],'b+')\n", "plt.xlabel(\"Kutle (M$_{jup}$)\")\n", "plt.ylabel(\"Yaricap (R$_{jup}$)\")\n", "plt.title(\"Gecis Yapan Karasal Gezegenler Icin Kutle-Yaricap Grafigi\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "kosul = (gecis_og['pl_bmassj'] > 0.5)\n", "devgaz = gecis_og[kosul]\n", "plt.plot(devgaz[\"pl_bmassj\"], devgaz[\"pl_radj\"],'b+')\n", "plt.xlabel(\"Kutle (M$_{jup}$)\")\n", "plt.ylabel(\"Yaricap (R$_{jup}$)\")\n", "plt.title(\"Gecis Yapan Dev Gaz Gezegenler Icin Kutle-Yaricap Grafigi\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Başa Dön](#Pandas-Paketi-İleri-Konular-Uygulama)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Kaynaklar #\n", "\n", "* [NASA Exoplanet Archive](https://exoplanetarchive.ipac.caltech.edu/)\n", "* [exoplanet.eu](http://exoplanet.eu/)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 1 }