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@pkaf
Last active February 13, 2017 08:25
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Toomre Diagram for the Milky Way stars"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A Toomre diagram is basically cartesian X and Z velocities plotted as a function of Y component, i.e.,\n",
"\n",
"$\\sqrt{U_{LSR}^2 + W_{LSR}^2}$ vs $V_{LSR}$,\n",
"\n",
"where $U_{LSR}$, $V_{LSR}$, $W_{LSR}$ are solar-motion corrected heliocentric cartesian velocities of each star.\n",
"Note, there is no need to correct for the velocity of the Local Standard of Rest (vlsr), which is roughly 235km/s.\n",
"\n",
"The Toomre Diagram helps to distinguish halo and disk(even thin/thick) stars. Refer to [Bensby et al. 2003](http://www.aanda.org/articles/aa/pdf/2003/41/aah4451.pdf), Hawkins et al. 2015 etc for more details."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import ebf\n",
"import sys\n",
"sys.path.insert(0, '/home/prajwal/pyroutines/')\n",
"import transcoordinate as tns\n",
"import seaborn as sns\n",
"\n",
"sns.set(context='notebook', style='whitegrid', palette='deep', \n",
" font='sans-serif', font_scale=1.3, color_codes='Summer', rc=None)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, I generated the MW stars using the [Galaxia](http://galaxia.sourceforge.net/Galaxia3pub.html#mozTocId149119) software with the following parameters: $r_{SDSS}<16$ mag, geometryOption=0, popID=-1.\n",
"\n",
"Once the stars are generated we read the binary data (Galaxia unfortunately reads/write its own version of binary file.)\n",
"We read the .ebf file and then convert into Pandas Dataframe for an easy handling for the later use. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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15XWNgmqGEkKIuUI7Gvut1oHtngjOkZNTcq/BqUMAysvL877GFVGzaGlpmekiXLbZ/h6k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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f27279e4610>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Reading the Galaxia output\n",
"fn = ebf.read('gxstars.ebf')\n",
"\n",
"#Creating a dictionary with useful information taken from the above file.\n",
"df = {}\n",
"for i in ['vx', 'vy', 'vz', 'px', 'py', 'pz', 'glon', 'glat', 'grav', 'teff', 'rad', 'popid']:\n",
" df[i] = fn[i]\n",
"\n",
"#Creates dataframe out of above selected information\n",
"table = pd.DataFrame(df)\n",
"\n",
"table = table[table['rad']<2]\n",
"\n",
"#Plot an analogue of the Color-Magnitude diagram\n",
"ax = sns.lmplot(x='teff', y='grav', data=table, fit_reg=False, hue='popid')\n",
"ax.set(xlabel='$T_\\mathsf{eff}$', ylabel='$log g$', title='CMD')\n",
"plt.gca().invert_xaxis()\n",
"plt.gca().invert_yaxis()\n",
"\n",
"#Heliocentric cartesian coordinates are converted into galacto-centric spherical coordinates,\n",
"#that is what cx2sp does.\n",
"#fn['center'] correct for the relative position and motion of Sun wrt to galactic-center.\n",
"table['r'],_,_,table['vr'],table['vt'],table['vp'] = tns.cx2sp(table.px + fn['center'][0], \n",
" table.py + fn['center'][1], \n",
" table.pz + fn['center'][2], \n",
" table.vx + fn['center'][3], \n",
" table.vy + fn['center'][4], \n",
" table.vz + fn['center'][5])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Galaxia uses $U_\\odot=11.1, V_\\odot=12.2, W_\\odot=7.25$ (km/s) values for the Solar motion, which we now apply \n",
"to each star velocity and finally compute $U/V/W_{LSR}$."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"table['Ulsr'] = table.vx + fn['center'][3]\n",
"table['Vlsr'] = table.vy + 12.24\n",
"table['Wlsr'] = table.vz + fn['center'][5]\n",
"table['UWlsr'] = np.sqrt(table.Ulsr**2+table.Wlsr**2)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 66687 entries, 0 to 256498\n",
"Data columns (total 20 columns):\n",
"glat 66687 non-null float32\n",
"glon 66687 non-null float32\n",
"grav 66687 non-null float32\n",
"popid 66687 non-null int32\n",
"px 66687 non-null float32\n",
"py 66687 non-null float32\n",
"pz 66687 non-null float32\n",
"rad 66687 non-null float32\n",
"teff 66687 non-null float32\n",
"vx 66687 non-null float32\n",
"vy 66687 non-null float32\n",
"vz 66687 non-null float32\n",
"r 66687 non-null float64\n",
"vr 66687 non-null float64\n",
"vt 66687 non-null float64\n",
"vp 66687 non-null float64\n",
"Ulsr 66687 non-null float64\n",
"Vlsr 66687 non-null float64\n",
"Wlsr 66687 non-null float64\n",
"UWlsr 66687 non-null float64\n",
"dtypes: float32(11), float64(8), int32(1)\n",
"memory usage: 7.6 MB\n"
]
}
],
"source": [
"table.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## A Toomre Diagram for Galaxia stars"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Galaxia popid labels 0-10 means the following:\n",
"\n",
"0: Thin Disk < 0.15 Gyr \n",
"\n",
"1: Thin Disk 0.15-1 Gyr\n",
"\n",
"2: Thin Disk 1-2 Gyr\n",
"\n",
"3: Thin Disk 2-3 Gyr\n",
"\n",
"4: Thin Disk 3-5 Gyr\n",
"\n",
"5: Thin Disk 5-7 Gyr\n",
"\n",
"6: Thin Disk 7-10 Gyr\n",
"\n",
"7: Thick Disk\n",
"\n",
"8: Stellar Halo\n",
"\n",
"9: Bulge\n",
"\n",
"10: Bullock & Johnston stellar halos"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f2727544d90>]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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OpwPcbqBVX5Xx4KzpzJ07lyG6O4mw9lPjA2w2G0VFRSQkJGC1Wtm3b59PbI3R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19exLzBUjM/79B/xcBkHIZxzAiiKTTMJaXp3ywxVJ7EIGlAx9pu1j7ton1RkqjDzF4mk+B0UdQi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\nUCgUsxwlBAqFQjHL+X+RRz3ZF6iYCAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2727578690>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set_style(\"ticks\", {\"xtick.major.size\": 8, \"ytick.major.size\": 8})\n",
"#sns.color_palette('rainbow')\n",
"sns.color_palette(sns.hls_palette(8, l=.3, s=.8))\n",
"\n",
"#plt.figure(figsize=(10,8))\n",
"ax = sns.lmplot(x = 'Vlsr', y='UWlsr', data=table, hue=\"popid\", fit_reg=False, aspect=1,\n",
" markers='o', scatter_kws={'s':10}) \n",
"ax.set(xlabel='$V_\\mathsf{LSR}$ (km/s)', \n",
" ylabel='$\\sqrt{U_\\mathsf{LSR}^2 + W_\\mathsf{LSR}^2}$ (km/s)', \n",
" xlim=[-450,250], ylim=[0,450])\n",
"\n",
"#Plotting circles with radius 240, 300 km/s etc.\n",
"circ = np.linspace(-500, 500, 5000)\n",
"\n",
"plt.text(-100,100, 'Disk')\n",
"plt.plot(circ, np.sqrt((fn['center'][4]-12.24)**2 - circ**2), 'k--', alpha=1, lw=2.5)\n",
"plt.text(-100,245, '$v_{LSR}=%1.1f$'%(fn['center'][4]-12.24))\n",
"plt.text(-300,300, 'Halo')\n",
"\n",
"plt.text(-100,305, '$v_{LSR}=300.0$')\n",
"plt.plot(circ, np.sqrt(300**2 - circ**2), 'k--', alpha=1, lw=2.5)\n",
"#plt.plot(circ, np.sqrt(500**2 - circ**2), 'k--', alpha=1, lw=2.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively, we can just look at the $v_\\phi$ distributions and \n",
"call anything with say >-50 km/s halo stars.\n",
"In the following figure we label histograms according to the Galaxia popid given above."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f27279d2ed0>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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WvfPOO8qbMEFzprmznNnY84fjp3T0xKlsp2H7RP4MTbxqfLbTAEYciglggK67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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2727eb3a10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set_style(\"ticks\", {\"xtick.major.size\": 8, \"ytick.major.size\": 8})\n",
"sns.distplot(table['vp'], kde=False, \n",
" hist_kws={'histtype':'step', 'lw':3, \n",
" 'normed':True, 'alpha':1., 'color':'k'}, label='All')\n",
"sns.distplot(table[table['popid']!=8]['vp'], kde=False, \n",
" hist_kws={'histtype':'step', 'lw':3, \n",
" 'normed':True, 'alpha':1., 'color':'r'}, \n",
" label='disk')\n",
"ax = sns.distplot(table[table['popid']==8]['vp'], kde=False, \n",
" hist_kws={'histtype':'step', 'lw':3, \n",
" 'normed':True, 'alpha':1., 'color':'b'}, label='halo')\n",
"ax.set(xlabel='Galacto-centric velocity: $v_\\phi$', ylabel='pdf')\n",
"\n",
"plt.legend()"
]
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