| Software | Version |
|---|
| Python | 2.7.9 64bit [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] |
| IPython | 2.3.1 |
| OS | Linux 3.13.0 45 generic x86_64 with debian jessie sid |
| numpy | 1.9.1 |
| matplotlib | 1.4.2 |
| pandas | 0.15.2 |
| quantities | 0.10.1 |
| Mon Feb 23 23:37:19 2015 CET |
"
],
"json": [
"{\"Software versions\": [{\"version\": \"2.7.9 64bit [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\", \"module\": \"Python\"}, {\"version\": \"2.3.1\", \"module\": \"IPython\"}, {\"version\": \"Linux 3.13.0 45 generic x86_64 with debian jessie sid\", \"module\": \"OS\"}, {\"version\": \"1.9.1\", \"module\": \"numpy\"}, {\"version\": \"1.4.2\", \"module\": \"matplotlib\"}, {\"version\": \"0.15.2\", \"module\": \"pandas\"}, {\"version\": \"0.10.1\", \"module\": \"quantities\"}]}"
],
"latex": [
"\\begin{tabular}{|l|l|}\\hline\n",
"{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n",
"Python & 2.7.9 64bit [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] \\\\ \\hline\n",
"IPython & 2.3.1 \\\\ \\hline\n",
"OS & Linux 3.13.0 45 generic x86\\_64 with debian jessie sid \\\\ \\hline\n",
"numpy & 1.9.1 \\\\ \\hline\n",
"matplotlib & 1.4.2 \\\\ \\hline\n",
"pandas & 0.15.2 \\\\ \\hline\n",
"quantities & 0.10.1 \\\\ \\hline\n",
"\\hline \\multicolumn{2}{|l|}{Mon Feb 23 23:37:19 2015 CET} \\\\ \\hline\n",
"\\end{tabular}\n"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 1,
"text": [
"Software versions\n",
"Python 2.7.9 64bit [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\n",
"IPython 2.3.1\n",
"OS Linux 3.13.0 45 generic x86_64 with debian jessie sid\n",
"numpy 1.9.1\n",
"matplotlib 1.4.2\n",
"pandas 0.15.2\n",
"quantities 0.10.1\n",
"Mon Feb 23 23:37:19 2015 CET"
]
}
],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Preparation to the access to the SDSS database"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# URL SQL Search DR10\n",
"url = \"http://skyserver.sdss3.org/dr10/en/tools/search/sql.aspx\"\n",
"# Open a Browser instance to make the queries\n",
"br = mechanize.Browser()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's define a function to make the database SQL queries more easily. It will return a Pandas dataframe"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def SDSS_select(sql):\n",
" br.open(url)\n",
" br.select_form(name=\"sql\")\n",
" br['cmd'] = sql\n",
" br['format']=['csv']\n",
" response = br.submit()\n",
" file_like = StringIO.StringIO(response.get_data())\n",
" return pd.read_csv(file_like, skiprows=1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Creating a dataframe with stars on the main sequence"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we want to obtain both photometric and spectrum (the spectral classification of the star) data, the most appropriate table in the SDSS database can be [SpecPhoto](http://skyserver.sdss3.org/dr10/en/help/browser/browser.aspx#&&history=description+SpecPhoto+V) view. From it we will be interested in particular in the data fields with the magnitudes in each filter, the \"class\" and \"type\" to select stars only, and the \"subclass\" with the information on the spectral classification of the star.\n",
"\n",
"We will prepare a SQL query to get a dataframe with the stars which, according to their spectra data, were classified in the SDSS database as belonging to the main sequence.\n",
"\n",
"Keep in mind that the \"type\" field sets a classification of objects according to their morphology: type = 6 means \"STAR\". Instead, \"class\" classifies objects according to their spectrum into three categories: \"GALAXY\", \"QSO\" or \"STAR\". Thus, objects with a \"type\" equal to 6 in the database can be classified spectrographically as stars, galaxies or quasars. Conversely, although the spectral classification is safer, in the database there are also some object with \"class\" equal to \"STAR\" and with \"types\" such as 0 (UNKNOWN) or 3 (GALAXY). So the best to make sure that we are selecting only stars is to search with type = 6 and class = 'STAR'\n",
"\n",
"We know from the previous post that in the Morgan-Keenan system the stars of the main sequence are those that have a luminosity class of \"V\". So, let's prepare a SQL query to find precisely the stars of this class:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"SQL_V = ' \\\n",
"SELECT \\\n",
" objID, subclass, flags, zwarning, \\\n",
" modelMag_u, modelMag_g, modelMag_r, \\\n",
" modelMag_i, modelMag_z \\\n",
"FROM SpecPhoto \\\n",
"WHERE \\\n",
" class = \"STAR\" \\\n",
"AND \\\n",
" subclass LIKE \"%V%\" \\\n",
"AND \\\n",
" subclass NOT LIKE \"%IV%\" \\\n",
"AND \\\n",
" subclass NOT LIKE \"%VI%\" \\\n",
"AND \\\n",
" type = 6'"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And now we can run the query to the database:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df_V = SDSS_select(SQL_V)\n",
"df_V.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
"(93061, 9)"
]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's change the column names and have a look:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df_V.columns = ['objID','subclass','flags','zwarning','u','g','r','i','z']\n",
"df_V.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"