From c17b69c92c251b1e181d573d210d940a17b02fc1 Mon Sep 17 00:00:00 2001 From: Jhon Yana Date: Sat, 27 Nov 2021 12:58:02 -0300 Subject: [PATCH] Delete 6. Stellar parameters from isochrones.ipynb --- 6. Stellar parameters from isochrones.ipynb | 814 -------------------- 1 file changed, 814 deletions(-) delete mode 100644 6. Stellar parameters from isochrones.ipynb diff --git a/6. Stellar parameters from isochrones.ipynb b/6. Stellar parameters from isochrones.ipynb deleted file mode 100644 index 822c1e3..0000000 --- a/6. Stellar parameters from isochrones.ipynb +++ /dev/null @@ -1,814 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## q2 Tutorial - 6. Stellar parameters from isochrones" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fundamental stellar parameters like mass, age, luminosity, and radius are difficult to derive from basic principles. For many stars, however, theoretical isochrones can be used to constrain these parameters. The isopars module in q2 contains a number of functions that may be employed for this purpose, including a few that allow us to extract the isochrones themselves.\n", - "\n", - "Let's begin by importing q2 and using the isopars.get_isochrone function to request an isochrone of 4.5 Gyr of age and [Fe/H]=0:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import q2\n", - "\n", - "iso = q2.isopars.get_isochrone(age=4.5, feh=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can find out which stellar parameters are available in the isochrone extracted as follows and then print the first five rows of one of its data columns (logg in this example):" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['mass', 'logt', 'logl', 'logg', 'mv']\n", - "[ 4.777 4.7769 4.7769 4.7767 4.7765]\n" - ] - } - ], - "source": [ - "print(list(iso))\n", - "print(iso['logg'][:5])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With these isochrone data we can make a classic $T_\\mathrm{eff}-\\log\\,g$ plot:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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jk/7KzOY754raq4v2CGANcFI75QfjDQkBUA50+euKmd2E18P45PYaf5GBrqo+yBWPz92l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\nqeRnppC3y3Mqg7N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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pylab as plt\n", - "%matplotlib inline\n", - "\n", - "plt.plot(10**iso['logt'], iso['logg'])\n", - "plt.gca().invert_xaxis()\n", - "plt.gca().invert_yaxis()\n", - "plt.xlabel('$T_\\mathrm{eff}$ (K)')\n", - "plt.ylabel('$\\log\\ g$ [cgs]');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By default, the isochrone grid employed is the Yonsei-Yale one (\"yy\"), spaced in [Fe/H] intervals of \"0.02\" dex. This grid is in an SQLite (\"sql3\") database called \"yy02.sql3\". To get an isochrone of equal parameters, but from the finely-spaced grid version (0.01 dex steps in [Fe/H]), we use the \"yy01.sql3\" database instead. We can also grab a solar-age, solar-metallicity isochrone from a finely-spaced Darmouth database (\"dm01.sql3\") for comparison:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "iso_yy = q2.isopars.get_isochrone(age=4.5, feh=0, db='yy01.sql3')\n", - "iso_dm = q2.isopars.get_isochrone(age=4.5, feh=0, db='dm01.sql3')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let's see them on the H-R diagram:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Jk3L62f/1119zRv5atWoVI0eOBGD9+vWMGDGCb7/9lsmTJ/P000/zxx9/8MYbb7Bo0SI2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\nLM66Pm5l39W7JAAhRIkppYzr+6ZSNPk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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(10**iso_yy['logt'], iso_yy['logg'], label='Yonsei-Yale', color='green')\n", - "plt.plot(10**iso_dm['logt'], iso_dm['logg'], '--', label='Darmouth', color='blue')\n", - "\n", - "plt.gca().invert_xaxis()\n", - "plt.gca().invert_yaxis()\n", - "plt.xlabel('$T_\\mathrm{eff}$ (K)')\n", - "plt.ylabel('$\\log\\ g$ [cgs]')\n", - "\n", - "plt.legend(loc='best', fontsize=14);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Finding parameters for one star\n", - "\n", - "It is possible to determine stellar parameters like mass and age by comparing the location of stars in stellar parameter space with those of a given set of isochrones. The \"location\" of a star is of course never known with infinite precision. Thus, to derive isochrone parameters, a star object must have its stellar parameters *and* errors defined, even if they are very small. Let's try creating a sun star object with tiny error bars:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "sun = q2.Star('Sun', teff=5777, logg=4.437, feh=0.0)\n", - "sun.err_teff = 5\n", - "sun.err_logg = 0.01\n", - "sun.err_feh = 0.005" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The isopars.solve_one function takes a star object and performs a maximum-likelihood calculation to determine which mass, age, etc., values are the most probable (\"m.p.\"). It also calculates 1-$\\sigma$-like and 2-$\\sigma$-like ranges for these parameters, as well as simple mean and standard deviation values of all isochrone points that fell inside the star's location box.\n", - "\n", - "In the example below, we first define SolvePars (required) and PlotPars (optional) controllers. Use the former to specify the isochrone database to employ (Yonsei-Yale, 0.01 dex [Fe/H] spacing in this case) and the 'key_parameter_known', which can be either the parallax ('plx', the default) or a $\\log\\,g$ value ('logg'), determined for example spectroscopically. In this case (Sun), we do not have a parallax available, so we will use its known $\\log\\,g$ value instead.\n", - "\n", - "By default, q2 creates a generic figure with all probability distributions included (\\*\\_isopar\\_\\*). Since the age probability distribution is used frequently, you can also create a nicer figure that shows only the age result (\\*\\_isoage\\_\\*) by changing the PlotPars.make_age_plot attribute to True:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using 114 isochrone points\n", - "\n", - "Parameter m.p. | 1-sigma range | 2-sigma range | mean +/- stdev\n", - "---------- ------ | --------------- | --------------- | -----------------\n", - "age 4.100 | 3.704 - 4.712 | 3.283 - 5.200 | 4.204 +/- 0.490\n", - "mass 1.010 | 1.003 - 1.024 | 1.000 - 1.029 | 1.012 +/- 0.005\n", - "logl 0.000 | -0.009 - 0.012 | -0.018 - 0.021 | 0.003 +/- 0.009\n", - "mv 4.840 | 4.821 - 4.859 | 4.784 - 4.882 | 4.829 +/- 0.022\n", - "r 1.000 | 0.991 - 1.012 | 0.982 - 1.045 | 1.004 +/- 0.009\n" - ] - } - ], - "source": [ - "sp = q2.isopars.SolvePars()\n", - "sp.db = 'yy01.sql3'\n", - "sp.key_parameter_known = 'logg'\n", - "\n", - "pp = q2.isopars.PlotPars()\n", - "pp.make_age_plot = True\n", - "\n", - "q2.isopars.solve_one(sun, sp, pp)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The relevant results are given in this table, in particular the most probable values. In some cases, q2 will not be able to do the statistics properly and an m.p. value will not be shown. However, it might still be able to provide a simple mean and stdev of the input isochrone points, which could be useful. In other cases, q2 will be able to solve for some, but not all parameters. In this case, the calculation was fully successful.\n", - "\n", - "The output figures are not shown on the notebook by default, but we can always take a look at, for example the age probability distribution plot (a PNG image) as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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jzUXuguiO0ZhztuIPut0XdyPhrwQMDBhokXiIyDoZ3L3z5s2baN++PcaPH48v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\n/v37i/b29qJKpRJHjBhh9f9+lb+H2t3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- "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "Image(filename='Sun_isoage_logg.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice that this is a very smooth distribution even though the original probability distribution plot is quite spiky, as shown in the generic figure (top panel):" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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GjRoFHx8f3Lt3r8iXax599FG0a9cOr732GurUqYNOnTqV+X7Pnj2LlJQUdO3a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\nQ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- "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Image(filename='Sun_isopar_logg.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Smoothing is controlled by SolvePars. By default, SolvePars.smooth_window_len_age = 13, but this can be changed to shorter or longer windows for less or more smoothing, respectively. One can also smooth the mass, log L, etc. distributions by setting their corresponding smoothing windows lengths (none of these are smoothed out by default).\n", - "\n", - "The figures we have generated so far in this part of the Tutorial are in PNG format, but as we saw before, they could be changed to, for example PDF by using the figure_format attribute of PlotPars. In addition, we can change the limits of the x-axis in the age figure, as well as the title inside the box, as long as we are requesting the age figure (make_age_plot = True).\n", - "\n", - "Note that the age derived above for the Sun is a bit young (the Sun's age is 4.6 Gyr), while the mass is 1% over the actual solar mass. This is an inevitable limitation of the isochrones. One way to address this problem is to shift the [Fe/H] values of the isochrones by a certain amount. For Yonsei-Yale isochrones, for example, an offset of -0.04 dex produces better solar age and mass values (but note that the absolute magnitude is now farther from the accepted value of 4.83):" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using 113 isochrone points\n", - "\n", - "Parameter m.p. | 1-sigma range | 2-sigma range | mean +/- stdev\n", - "---------- ------ | --------------- | --------------- | -----------------\n", - "age 4.600 | 4.134 - 5.108 | 3.581 - 5.510 | 4.627 +/- 0.481\n", - "mass 1.000 | 0.992 - 1.014 | 0.985 - 1.019 | 1.000 +/- 0.005\n", - "logl 0.010 | -0.007 - 0.024 | -0.019 - 0.029 | -0.001 +/- 0.009\n", - "mv 4.850 | 4.822 - 4.874 | 4.806 - 4.924 | 4.835 +/- 0.018\n", - "r 1.000 | 0.990 - 1.012 | 0.980 - 1.028 | 1.000 +/- 0.010\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pp = q2.isopars.PlotPars()\n", - "pp.figure_format = 'pdf'\n", - "pp.make_age_plot = True\n", - "pp.age_xlim = [2.5, 7]\n", - "pp.title_inside = 'Sun, Y$^2$'\n", - "\n", - "sp.feh_offset = -0.04\n", - "q2.isopars.solve_one(sun, sp, pp)\n", - "\n", - "from IPython.display import IFrame\n", - "IFrame('Sun_isoage_logg.pdf', width=600, height=400)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the previous examples we used surface gravity as an input parameter. When $\\log\\,g$ can be measured accurately, using it as a luminosity indicator is a reasonable approach. Very often, however, a known trigonometric parallax allows us to determine a star's absolute magnitude, which in some cases may be more reliable than $\\log\\,g$ as a luminosity index.\n", - "\n", - "In the following lines, we are going to use isopars to determine stellar parameters for the star iota Horologii (iotaHor), which has a known photometric $T_\\mathrm{eff}=6163\\pm80$ K and [Fe/H]$=0.15\\pm0.05$. Its measured trigonometric parallax is $58.25\\pm0.22$ mas and its apparent magnitude is $V=5.40\\pm0.02$:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "s = q2.Star('iotaHor')\n", - "s.teff, s.err_teff = 6263, 80\n", - "s.feh, s.err_feh = 0.15, 0.05\n", - "s.plx, s.err_plx = 58.25, 0.22\n", - "s.v, s.err_v = 5.40, 0.02" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are going to apply the -0.04 dex offset to the [Fe/H] of the isochrones to \"normalize\" the results to the solar values. Then, we are also going to smooth the mass and $\\log\\,g$ probability distributions. We are not passing a PlotPars controller here so we are not going to request the separate age probability figure. It turns out that iotaHor is a young star so its isochrone age is uncertain anyway. However, other parameters such as mass and $\\log\\,g$ can be estimated accurately:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using 2165 isochrone points\n", - "\n", - "Parameter m.p. | 1-sigma range | 2-sigma range | mean +/- stdev\n", - "---------- ------ | --------------- | --------------- | -----------------\n", - "age 1.000 | 0.502 - 1.829 | 0.156 - 2.822 | 1.254 +/- 0.690\n", - "mass 1.190 | 1.164 - 1.209 | 1.138 - 1.231 | 1.184 +/- 0.022\n", - "logl 0.220 | 0.208 - 0.248 | 0.197 - 0.245 | 0.223 +/- 0.010\n", - "mv 4.210 | 4.195 - 4.245 | 4.174 - 4.270 | 4.223 +/- 0.024\n", - "r 1.140 | 1.123 - 1.158 | 1.108 - 1.184 | 1.141 +/- 0.018\n", - "logg 4.400 | 4.376 - 4.418 | 4.347 - 4.434 | 4.396 +/- 0.021\n" - ] - }, - { - "data": { - "image/png": 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bPmvWLOzfvx8HDx7E8ePHMXLkyDrryxjD3bt3nf5lusS1UUuaNJrffvsNmZmZ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\nV/5fxv+gM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- "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sp = q2.isopars.SolvePars()\n", - "sp.feh_offset = -0.04\n", - "sp.smooth_window_len_mass = 7\n", - "sp.smooth_window_len_logg = 7\n", - "\n", - "q2.isopars.solve_one(s, sp)\n", - "\n", - "Image(filename='iotaHor_isopar_plx.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "While the isochrone age of this star is very uncertain (all we can say is that it is younger than about 2 Gyr), its mass is very well constrained to about $1.19\\pm0.02\\,M_\\odot$, as is its $\\log\\,g=4.40\\pm0.02$. Smoothing the distributions can provide more accurate results as they are sometimes too spiky and finding the most probable value (the highest peak) might produce biased results.\n", - "\n", - "Note that the figure generated in the latter case has the format \\*\\_plx.png. This is because the key_parameter_known was set to plx. Otherwise the figure format is \\*\\_logg.png.\n", - "\n", - "### Working with a sample\n", - "\n", - "The examples above show how to use q2 to analyze one star at a time. If you need to analyze a sample instead, you should use the isopars.solve_all function, which takes as input an entire q2.Data object. Both SolvePars and PlotPars controllers are required in this case, as well as an output file name where the results will be recorded.\n", - "\n", - "In the following example, we use isopars.solve_all to find isochrone stellar parameters for a sample of solar twin stars which all have parallaxes measured by Hipparcos (columns plx, err_plx in file 'stps_hipparcos.csv'). We are going to apply the $-0.04$ dex offset to the isochrone [Fe/H] values, use the finely-spaced Yonsei-Yale isochrone database ('yy01.sql3'), and smooth-out the logg distributions. Figures will be saved into a directory called stps_hipparcos. Since we know that these are all Galactic disk stars, we will restrict the age axis of the age plots to 0-9 Gyr. Both the generic and age plots will be generated." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "------------------------------------------------------\n", - "Initializing ...\n", - "- Date and time: 16-Jul-2017, 17:58:13\n", - "- Star data: stps_hipparcos.csv\n", - "------------------------------------------------------\n", - "\n", - "********\n", - "HIP19911\n", - "********\n", - "Using 1699 isochrone points\n", - "\n", - "Parameter m.p. | 1-sigma range | 2-sigma range | mean +/- stdev\n", - "---------- ------ | --------------- | --------------- | -----------------\n", - "age 3.900 | 1.782 - 6.066 | 0.729 - 7.725 | 3.849 +/- 1.932\n", - "mass 0.990 | 0.973 - 1.004 | 0.961 - 1.019 | 0.989 +/- 0.014\n", - "logl -0.070 | -0.087 - -0.017 | -0.099 - 0.039 | -0.037 +/- 0.038\n", - "mv 5.060 | 4.896 - 5.071 | 4.738 - 5.121 | 4.937 +/- 0.096\n", - "r 0.930 | 0.912 - 0.986 | 0.901 - 1.062 | 0.962 +/- 0.042\n", - "logg 4.500 | 4.437 - 4.521 | 4.371 - 4.539 | 4.466 +/- 0.044\n", - "\n", - "********\n", - "HIP44713\n", - "********\n", - "Using 98 isochrone points\n", - "\n", - "Parameter m.p. | 1-sigma range | 2-sigma range | mean +/- stdev\n", - "---------- ------ | --------------- | --------------- | -----------------\n", - "age 7.500 | 7.146 - 7.824 | 6.776 - 8.114 | 7.480 +/- 0.334\n", - "mass 1.020 | 1.013 - 1.032 | 1.010 - 1.065 | 1.024 +/- 0.006\n", - "logl 0.160 | 0.135 - 0.176 | 0.120 - 0.187 | 0.152 +/- 0.016\n", - "mv 4.430 | 4.413 - 4.491 | 4.357 - 4.529 | 4.448 +/- 0.043\n", - "r 1.210 | 1.169 - 1.230 | 1.147 - 1.278 | 1.195 +/- 0.024\n", - "logg 4.280 | 4.265 - 4.310 | 4.246 - 4.329 | 4.292 +/- 0.021\n", - "\n", - "********\n", - "HIP72043\n", - "********\n", - "Using 387 isochrone points\n", - "\n", - "Parameter m.p. | 1-sigma range | 2-sigma range | mean +/- stdev\n", - "---------- ------ | --------------- | --------------- | -----------------\n", - "age 7.000 | 6.595 - 7.411 | 6.165 - 7.789 | 6.997 +/- 0.405\n", - "mass 1.010 | 1.003 - 1.028 | 1.000 - 1.037 | 1.016 +/- 0.005\n", - "logl 0.150 | 0.125 - 0.176 | 0.103 - 0.190 | 0.148 +/- 0.022\n", - "mv 4.460 | 4.395 - 4.520 | 4.362 - 4.567 | 4.454 +/- 0.055\n", - "r 1.160 | 1.123 - 1.197 | 1.095 - 1.263 | 1.161 +/- 0.029\n", - "logg 4.320 | 4.293 - 4.341 | 4.271 - 4.362 | 4.315 +/- 0.023\n", - "\n", - "********\n", - "HIP79672\n", - "********\n", - "Using 12 isochrone points\n", - "\n", - "Parameter m.p. | 1-sigma range | 2-sigma range | mean +/- stdev\n", - "---------- ------ | --------------- | --------------- | -----------------\n", - "age 3.000 | 2.761 - 3.279 | 2.556 - 3.624 | 3.038 +/- 0.261\n", - "mass 1.040 | 1.033 - 1.054 | 1.030 - 1.059 | 1.040 +/- 0.001\n", - "logl 0.010 | 0.003 - 0.025 | 0.000 - 0.038 | 0.011 +/- 0.006\n", - "mv 4.800 | 4.782 - 4.814 | 4.765 - 4.819 | 4.796 +/- 0.011\n", - "r 1.000 | 0.993 - 1.019 | 0.990 - 1.016 | 1.001 +/- 0.006\n", - "logg 4.450 | 4.437 - 4.463 | 4.425 - 4.485 | 4.449 +/- 0.011\n", - "\n", - "*********\n", - "HIP102040\n", - "*********\n", - "Using 154 isochrone points\n", - "\n", - "Parameter m.p. | 1-sigma range | 2-sigma range | mean +/- stdev\n", - "---------- ------ | --------------- | --------------- | -----------------\n", - "age 3.300 | 2.849 - 3.928 | 2.318 - 4.650 | 3.421 +/- 0.571\n", - "mass 1.010 | 1.001 - 1.027 | 0.991 - 1.043 | 1.008 +/- 0.005\n", - "logl 0.000 | -0.013 - 0.009 | -0.028 - 0.019 | -0.003 +/- 0.011\n", - "mv 4.840 | 4.815 - 4.857 | 4.785 - 4.908 | 4.838 +/- 0.023\n", - "r 0.980 | 0.967 - 0.989 | 0.952 - 0.999 | 0.977 +/- 0.010\n", - "logg 4.460 | 4.442 - 4.478 | 4.424 - 4.494 | 4.460 +/- 0.017\n", - "\n", - "*********\n", - "HIP109110\n", - "*********\n", - "Using 1696 isochrone points\n", - "\n", - "Parameter m.p. | 1-sigma range | 2-sigma range | mean +/- stdev\n", - "---------- ------ | --------------- | --------------- | -----------------\n", - "age 3.900 | 2.205 - 5.206 | 0.787 - 6.311 | 3.650 +/- 1.424\n", - "mass 1.010 | 1.001 - 1.028 | 0.991 - 1.043 | 1.018 +/- 0.013\n", - "logl 0.000 | -0.030 - 0.028 | -0.049 - 0.055 | -0.001 +/- 0.027\n", - "mv 4.850 | 4.770 - 4.918 | 4.699 - 4.970 | 4.838 +/- 0.068\n", - "r 1.000 | 0.963 - 1.030 | 0.941 - 1.064 | 0.997 +/- 0.031\n", - "logg 4.440 | 4.410 - 4.482 | 4.379 - 4.510 | 4.448 +/- 0.034\n", - "\n", - "*********\n", - "HIP114615\n", - "*********\n", - "Using 1220 isochrone points\n", - "\n", - "Parameter m.p. | 1-sigma range | 2-sigma range | mean +/- stdev\n", - "---------- ------ | --------------- | --------------- | -----------------\n", - "age 8.300 | 5.019 - 8.662 | 1.598 - 8.965 | 6.319 +/- 2.167\n", - "mass 0.990 | 0.982 - 1.010 | 0.973 - 1.047 | 1.004 +/- 0.020\n", - "logl 0.010 | -0.033 - 0.181 | -0.065 - 0.416 | 0.108 +/- 0.115\n", - "mv 4.520 | 4.198 - 4.813 | 3.824 - 4.939 | 4.563 +/- 0.287\n", - "r 1.100 | 0.982 - 1.279 | 0.927 - 1.617 | 1.124 +/- 0.155\n", - "logg 4.440 | 4.271 - 4.489 | 4.113 - 4.527 | 4.341 +/- 0.111\n", - "\n", - "*********\n", - "HIP118115\n", - "*********\n", - "Using 413 isochrone points\n", - "\n", - "Parameter m.p. | 1-sigma range | 2-sigma range | mean +/- stdev\n", - "---------- ------ | --------------- | --------------- | -----------------\n", - "age 7.700 | 7.094 - 8.101 | 6.451 - 8.456 | 7.542 +/- 0.517\n", - "mass 1.010 | 1.002 - 1.017 | 0.994 - 1.020 | 1.010 +/- 0.006\n", - "logl 0.140 | 0.108 - 0.185 | 0.071 - 0.212 | 0.151 +/- 0.036\n", - "mv 4.410 | 4.327 - 4.518 | 4.242 - 4.621 | 4.450 +/- 0.092\n", - "r 1.200 | 1.134 - 1.244 | 1.087 - 1.295 | 1.179 +/- 0.049\n", - "logg 4.280 | 4.251 - 4.327 | 4.219 - 4.366 | 4.297 +/- 0.037\n", - "\n", - "------------------------------------------------------\n", - "- Date and time: 16-Jul-2017, 17:58:45\n", - "- Time elapsed: 0H 0M 31S\n", - "Done!\n", - "------------------------------------------------------\n", - "\n" - ] - } - ], - "source": [ - "d = q2.Data('stps_hipparcos.csv')\n", - "\n", - "sp = q2.isopars.SolvePars()\n", - "sp.db = 'yy01.sql3'\n", - "sp.feh_offset = -0.04\n", - "sp.smooth_window_len_logg = 7\n", - "\n", - "pp = q2.isopars.PlotPars()\n", - "pp.make_age_plot = True\n", - "pp.age_xlim = [0, 9]\n", - "pp.directory = 'stps_hipparcos'\n", - "\n", - "q2.isopars.solve_all(d, sp, pp, 'stps_hipparcos_isopars_plx.csv')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you'd rather employ the very precise $\\log\\,g$ values of your stars and ignore the parallax information, set the key_parameter_known to 'logg'. Since the solutions will now be different, we will use a different filename for the output. Figures will be sent to the same directory as before because we will pass the same PlotPars controller ('pp'). You will notice that the figure filenames are different this time, so sending them to that same directory will not be an issue:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "------------------------------------------------------\n", - "Initializing ...\n", - "- Date and time: 16-Jul-2017, 17:59:44\n", - "- Star data: stps_hipparcos.csv\n", - "------------------------------------------------------\n", - "\n", - "********\n", - "HIP19911\n", - "********\n", - "Using 1283 isochrone points\n", - "\n", - "Parameter m.p. | 1-sigma range | 2-sigma range | mean +/- stdev\n", - "---------- ------ | --------------- | --------------- | -----------------\n", - "age 4.000 | 2.424 - 5.511 | 1.022 - 6.776 | 3.996 +/- 1.465\n", - "mass 0.990 | 0.975 - 1.002 | 0.962 - 1.010 | 0.987 +/- 0.012\n", - "logl -0.030 | -0.063 - -0.008 | -0.087 - 0.030 | -0.036 +/- 0.026\n", - "mv 4.940 | 4.872 - 5.011 | 4.794 - 5.062 | 4.933 +/- 0.067\n", - "r 0.970 | 0.935 - 0.994 | 0.912 - 1.028 | 0.964 +/- 0.028\n", - "\n", - "********\n", - "HIP44713\n", - "********\n", - "Using 91 isochrone points\n", - "\n", - "Parameter m.p. | 1-sigma range | 2-sigma range | mean +/- stdev\n", - "---------- ------ | --------------- | --------------- | -----------------\n", - "age 7.600 | 7.311 - 7.911 | 7.005 - 8.199 | 7.625 +/- 0.297\n", - "mass 1.030 | 1.021 - 1.044 | 1.012 - 1.049 | 1.026 +/- 0.007\n", - "logl 0.170 | 0.155 - 0.181 | 0.132 - 0.206 | 0.165 +/- 0.015\n", - "mv 4.420 | 4.368 - 4.446 | 4.332 - 4.523 | 4.423 +/- 0.042\n", - "r 1.210 | 1.190 - 1.233 | 1.162 - 1.261 | 1.215 +/- 0.022\n", - "\n", - "********\n", - "HIP72043\n", - "********\n", - "Using 440 isochrone points\n", - "\n", - "Parameter m.p. | 1-sigma range | 2-sigma range | mean +/- stdev\n", - "---------- ------ | --------------- | --------------- | -----------------\n", - "age 6.500 | 6.021 - 6.909 | 5.393 - 7.297 | 6.446 +/- 0.468\n", - "mass 1.010 | 1.002 - 1.018 | 0.993 - 1.029 | 1.010 +/- 0.006\n", - "logl 0.100 | 0.077 - 0.134 | 0.054 - 0.149 | 0.110 +/- 0.024\n", - "mv 4.530 | 4.472 - 4.599 | 4.431 - 4.684 | 4.554 +/- 0.062\n", - "r 1.120 | 1.084 - 1.146 | 1.052 - 1.179 | 1.109 +/- 0.031\n", - "\n", - "********\n", - "HIP79672\n", - "********\n", - "Using 16 isochrone points\n", - "\n", - "Parameter m.p. | 1-sigma range | 2-sigma range | mean +/- stdev\n", - "---------- ------ | --------------- | --------------- | -----------------\n", - "age 3.000 | 2.703 - 3.387 | 2.287 - 3.837 | 3.072 +/- 0.369\n", - "mass 1.040 | 1.033 - 1.054 | 1.030 - 1.059 | 1.040 +/- 0.001\n", - "logl 0.010 | 0.003 - 0.021 | -0.005 - 0.078 | 0.014 +/- 0.009\n", - "mv 4.800 | 4.781 - 4.817 | 4.760 - 4.833 | 4.792 +/- 0.022\n", - "r 1.000 | 0.990 - 1.011 | 0.974 - 1.068 | 1.003 +/- 0.011\n", - "\n", - "*********\n", - "HIP102040\n", - "*********\n", - "Using 189 isochrone points\n", - "\n", - "Parameter m.p. | 1-sigma range | 2-sigma range | mean +/- stdev\n", - "---------- ------ | --------------- | --------------- | -----------------\n", - "age 1.800 | 1.262 - 3.093 | 0.364 - 4.148 | 2.465 +/- 0.903\n", - "mass 1.010 | 1.002 - 1.019 | 0.994 - 1.029 | 1.011 +/- 0.006\n", - "logl -0.030 | -0.040 - -0.004 | -0.053 - 0.019 | -0.022 +/- 0.018\n", - "mv 4.900 | 4.842 - 4.930 | 4.787 - 4.968 | 4.883 +/- 0.042\n", - "r 0.950 | 0.938 - 0.977 | 0.922 - 0.995 | 0.956 +/- 0.019\n", - "\n", - "*********\n", - "HIP109110\n", - "*********\n", - "Using 1582 isochrone points\n", - "\n", - "Parameter m.p. | 1-sigma range | 2-sigma range | mean +/- stdev\n", - "---------- ------ | --------------- | --------------- | -----------------\n", - "age 1.800 | 0.945 - 3.378 | 0.284 - 4.892 | 2.311 +/- 1.212\n", - "mass 1.030 | 1.016 - 1.041 | 1.003 - 1.053 | 1.027 +/- 0.012\n", - "logl -0.040 | -0.051 - -0.009 | -0.067 - 0.027 | -0.026 +/- 0.023\n", - "mv 4.930 | 4.854 - 4.960 | 4.770 - 4.995 | 4.899 +/- 0.056\n", - "r 0.950 | 0.940 - 0.985 | 0.930 - 1.023 | 0.968 +/- 0.025\n", - "\n", - "*********\n", - "HIP114615\n", - "*********\n", - "Using 213 isochrone points\n", - "\n", - "Parameter m.p. | 1-sigma range | 2-sigma range | mean +/- stdev\n", - "---------- ------ | --------------- | --------------- | -----------------\n", - "age 0.500 | 0.244 - 1.684 | 0.121 - 2.476 | 1.038 +/- 0.711\n", - "mass 1.020 | 1.011 - 1.033 | 1.002 - 1.046 | 1.022 +/- 0.008\n", - "logl -0.060 | -0.069 - -0.042 | -0.078 - -0.023 | -0.055 +/- 0.012\n", - "mv 4.970 | 4.933 - 4.999 | 4.900 - 5.051 | 4.962 +/- 0.027\n", - "r 0.920 | 0.911 - 0.938 | 0.902 - 0.957 | 0.926 +/- 0.013\n", - "\n", - "*********\n", - "HIP118115\n", - "*********\n", - "Using 196 isochrone points\n", - "\n", - "Parameter m.p. | 1-sigma range | 2-sigma range | mean +/- stdev\n", - "---------- ------ | --------------- | --------------- | -----------------\n", - "age 7.800 | 7.460 - 8.116 | 7.096 - 8.397 | 7.791 +/- 0.324\n", - "mass 1.010 | 1.003 - 1.018 | 1.000 - 1.027 | 1.012 +/- 0.005\n", - "logl 0.170 | 0.151 - 0.201 | 0.132 - 0.218 | 0.169 +/- 0.019\n", - "mv 4.410 | 4.336 - 4.464 | 4.311 - 4.496 | 4.406 +/- 0.051\n", - "r 1.200 | 1.173 - 1.223 | 1.152 - 1.258 | 1.209 +/- 0.030\n", - "\n", - "------------------------------------------------------\n", - "- Date and time: 16-Jul-2017, 18:00:13\n", - "- Time elapsed: 0H 0M 29S\n", - "Done!\n", - "------------------------------------------------------\n", - "\n" - ] - } - ], - "source": [ - "sp.key_parameter_known = 'logg'\n", - "\n", - "q2.isopars.solve_all(d, sp, pp, 'stps_hipparcos_isopars_logg.csv')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's compare the age probability distributions of HIP 72043, as determined by the parallax and $\\log\\,g$, respectively:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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khBBePcl7AhkjAwDYGtvCRGTCc0QvCIVCiAQiuJi/WMGH2gkbhnMirKiowJkz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\nCygREsKTO3fu4LfffkNaWhpatWqFV1999f/bt2M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Q0FC8+uqr6NevX5PX8Pl8BAYGqiUwQghpiRt5TxKht4U3h5E0T35QPZUIuady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\nDBo0CAAQFxeHoUOHIiMjQ6FE6Obmho8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- "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Image(filename='stps_hipparcos/HIP72043_isoage_logg.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "They are clearly very similar to each other, but not identical. Isochrone stellar parameters are highly model dependent as well as input dependent. It is up to the user to decide what is the best combination of input parameters and models to use. q2 is relatively flexible with regards to this. The Yonsei-Yale and Darmouth isochrones are included in the q2 Data file. Any other isochrone set could in principle be adapted by creating a SQLite3 database in the same format.\n", - "\n", - "_Note: q2 uses a frequentist approach to determine stellar parameters from isochrones. This approach is known to have severe limitations, which are alleviated by using Bayesian techniques instead. Users are encouraged to try the [isochrones Python package](http://isochrones.readthedocs.org/en/latest/) if a more comprehensive analysis is required._" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The End\n", - "\n", - "This concludes the q2 Tutorial. If you have any questions or requests, please e-mail . Happy q2-ing!" - ] - } - ], - "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": "3.6.1" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -}