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20150218T220000_SDSS_J160036.83+272117.8_analytical_lightcurve_solution.ipynb
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{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Estimate physical quantities of a binary system using observed quantities."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Models from other eclipsing papers:\n",
" * J0651: Hermes, J. J., Kilic, M., Brown, W. R., et al. 2012b, ApJL, 757, L21 \n",
" Models radial velocities using Kenyon & Garcia (1986).\n",
" * J0651: Brown, W. R., Kilic, M., Hermes, J. J., et al. 2011, ApJL, 737, L23 \n",
" Models the light curve using JKTEBOP (Southworth et al. 2004) and verified with PHOEBE (Prsa & Zwitter 2005). Based on Eclipsing Binary Orbit Program (Popper & Etzel 1981) and the Wilson & Devinney (1971) codes, respectively.\n",
" * J0751: Kilic, M., Hermes, J. J., Gianninas, A., et al. 2014b, MNRAS, 438, L26 \n",
" Modeled lightcurve using JKTEBOP (Southworth et al. 2005) and the limb darkening coefficients of Gianninas et al. (2013)\n",
" * NLTT 11748: Kaplan, D. L., Marsh, T. R., Walker, A. N., et al. 2014a, ApJ, 780, 167 \n",
" Very detailed model with limb darkening and thorough explanation. \n",
" Modeled light curve with emcee (http://dan.iel.fm/emcee/) and ForemanMackey et al. 2013 \n"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Initialization"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Imports"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Standard libraries.\n",
"from __future__ import absolute_import, division, print_function\n",
"import collections\n",
"import copy\n",
"import os\n",
"import re\n",
"import StringIO\n",
"import sys\n",
"import warnings\n",
"# Third-party installed packages.\n",
"import astroML.plotting as astroML_plt\n",
"import astroML.stats as astroML_stats\n",
"import astroML.time_series as astroML_ts\n",
"import astropy.constants as ast_con\n",
"import astropy.time as ast_time\n",
"import astropysics.phot as astpy_phot\n",
"import binstarsolver as bss\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import scipy.constants as sci_con\n",
"import scipy.signal as sci_sig\n",
"# IPython magic.\n",
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 140
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Globals"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Custom functions"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def calc_mass_function_from_period_velr(period, velr1):\n",
" \"\"\"Calculate the mass function of a binary system from the binary period and\n",
" the observed radial velocity, e.g. for a single-line spectroscopic binary.\n",
" \n",
" Parameters\n",
" ----------\n",
" period : float\n",
" Period of eclipse. Unit is seconds.\n",
" velr1 : float\n",
" Semi-amplitude of radial velocity of star 1, the brighter star that is observed.\n",
" Unit is m/s.\n",
" \n",
" Returns\n",
" -------\n",
" mfunc : float\n",
" Mass function for the binary system. Without knowing the mass of star 1 or the inclination,\n",
" the mass function sets a lower limit for the mass of star 2. Unit is kg.\n",
" \n",
" Notes\n",
" -----\n",
" mass function =\n",
" (m2 * sin(i))**3 / (m1 + m2)**2 = (P * v1r**3) / (2*pi*G)\n",
" From equation 7.7 of [1]_.\n",
" \n",
" References\n",
" ----------\n",
" .. [1] Carroll and Ostlie, 2007, An Introduction to Modern Astrophysics\n",
" \n",
" \"\"\"\n",
" mfunc = (period * velr1**3.0) / (2.0*np.pi*sci_con.G)\n",
" return mfunc\n",
"\n",
"\n",
"def test_calc_mass_function_from_period_velr(period=8.6*sci_con.year,\n",
" velr1=33.0*sci_con.kilo,\n",
" mfunc=2.324294844333284e+31):\n",
" \"\"\"Test that calculations are correct using examples 7.3.1, 7.3.2 of [1]_\n",
"\n",
" References\n",
" ----------\n",
" .. [1] Carroll and Ostlie, 2007, An Introduction to Modern Astrophysics\n",
" \n",
" \"\"\"\n",
" assert np.isclose(calc_mass_function_from_period_velr(period=period,\n",
" velr1=velr1),\n",
" mfunc)\n",
" return None\n",
"\n",
"\n",
"# No output means that test passed.\n",
"test_calc_mass_function_from_period_velr()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def calc_velr2_from_masses_period_incl_velr1(mass1, mass2, velr1, period, incl):\n",
" \"\"\"Calculate the semi-amplitude of the radial velocity of star2 from given masses,\n",
" period, orbital inclination, and semi-amplitude of the radial velocity of star1.\n",
" \n",
" Convenience function for handling modeled data from Gianninas et al 2014.\n",
" Assumes orbital excentricity << 1.\n",
" \n",
" Parameters\n",
" ----------\n",
" mass1 : float\n",
" Mass of star 1. Unit is kg.\n",
" mass2 : float\n",
" Mass of star 2. Unit is kg.\n",
" velr1 : float\n",
" Semi-amplitude of radial velocity of star 1. Unit is m/s.\n",
" period : float\n",
" Period of eclipse. Unit is seconds.\n",
" incl : float\n",
" Orbital inclination. Angle between line of sight and the axis of the orbit. Unit is radians.\n",
"\n",
" Returns\n",
" -------\n",
" velr2 : float\n",
" Semi-amplitude of radial velocity of star 1. Unit is m/s.\n",
" \n",
" Notes\n",
" -----\n",
" a = (P/(2*pi)) * (v1 + v2), where a is semi-major axis of low-eccentricity orbit,\n",
" P is orbital period, v is orbital velocity.\n",
" P**2 = ((4*pi**2) / (G*(m1 + m2))) * a**3, where G is gravitational constant,\n",
" m is stellar mass. Kepler's Third Law.\n",
" v = vr / sin(i), where vr is observed radial orbital velocity, i is orbital inclination.\n",
" => m1 + m2 = P/(2*pi*G) * ((v1r + v2r) / sin(i))**3 \n",
" v2r = ((m1 + m2)((2*pi*G)/P)(sin(i)**3))**(1/3) - v1r\n",
" From equation 7.6 in section 7.3 of [1]_.\n",
" Function adapted from [2]_.\n",
" \n",
" References\n",
" ----------\n",
" .. [1] Carroll and Ostlie, 2007, An Introduction to Modern Astrophysics\n",
" .. [2] https://pypi.python.org/pypi/binstarsolver/0.1.2\n",
" \n",
" \"\"\"\n",
" velr2 = ((mass1 + mass2) * ((2.0*np.pi*sci_con.G) / period) * (np.sin(incl))**3.0)**(1.0/3.0) - velr1\n",
" return velr2\n",
"\n",
"\n",
"def test_calc_velr2_from_masses_period_incl_velr1(mass1=3.0427831666779509e+31/2.0,\n",
" mass2=3.0427831666779509e+31/2.0,\n",
" velr1=33000.0,\n",
" period=271209600.0,\n",
" incl=1.5708021113113511,\n",
" velr2=3100.0):\n",
" \"\"\"Test that calculations are correct using examples 7.3.1, 7.3.2 of [1]_\n",
"\n",
" References\n",
" ----------\n",
" .. [1] Carroll and Ostlie, 2007, An Introduction to Modern Astrophysics\n",
" \n",
" \"\"\"\n",
" assert np.isclose(calc_velr2_from_masses_period_incl_velr1(mass1=mass1,\n",
" mass2=mass2,\n",
" velr1=velr1,\n",
" period=period,\n",
" incl=incl),\n",
" velr2)\n",
" return None\n",
"\n",
"\n",
"# No output means that test passed.\n",
"test_calc_velr2_from_masses_period_incl_velr1()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def calc_logg_from_mass_radius(mass, radius):\n",
" \"\"\"Calculate the surface gravity of a star from its mass.\n",
" \n",
" Parameters\n",
" ----------\n",
" mass : float\n",
" Stellar mass. Unit is kg.\n",
" radius : float\n",
" Stellar radius. Unit is meters.\n",
"\n",
" \n",
" Returns\n",
" -------\n",
" logg : float\n",
" Log10 of surface gravity of the star. Unit is dex cm/s^2 (dex Gal).\n",
" \n",
" Notes\n",
" -----\n",
" g = G*M/R**2\n",
" From Eqn 2.12 of [1]_.\n",
" \n",
" References\n",
" ----------\n",
" .. [1] Carroll and Ostlie, 2007, An Introduction to Modern Astrophysics\n",
" \n",
" \"\"\"\n",
" logg = np.log10((sci_con.G*mass/(radius**2.0)) * sci_con.hecto)\n",
" return logg\n",
"\n",
"\n",
"def test_calc_logg_from_mass_radius(mass=5.9736e24, radius=6.378136e6,\n",
" logg=np.log10(9.80*sci_con.hecto)):\n",
" \"\"\"Test that calculations are correct using page 36 of [1]_\n",
"\n",
" References\n",
" ----------\n",
" .. [1] Carroll and Ostlie, 2007, An Introduction to Modern Astrophysics\n",
" \n",
" \"\"\"\n",
" assert np.isclose(calc_logg_from_mass_radius(mass=mass, radius=radius),\n",
" logg)\n",
" return None\n",
"\n",
"\n",
"# No output means that test passed.\n",
"test_calc_logg_from_mass_radius()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def calc_loglum_from_radius_teff(radius, teff):\n",
" \"\"\"Calculate the log luminosity of a star from its radius and effective temperature.\n",
" \n",
" Parameters\n",
" ----------\n",
" radius : float\n",
" Stellar radius. Unit is meters.\n",
" teff : float\n",
" Stellar effective temperature. Unit is Kelvin.\n",
" \n",
" Returns\n",
" -------\n",
" loglum : float\n",
" Log10 luminosity of the star. Unit is dex Lsun.\n",
" \n",
" Notes\n",
" -----\n",
" L = 4*pi*R^2*sig*Teff^4,\n",
" where sig is the Stefan-Boltzmann constant.\n",
" From Eqn 3.17 of [1]_.\n",
" \n",
" References\n",
" ----------\n",
" .. [1] Carroll and Ostlie, 2007, An Introduction to Modern Astrophysics\n",
" \n",
" \"\"\"\n",
" loglum = np.log10((4.0*np.pi*(radius**2.0)*sci_con.Stefan_Boltzmann*(teff**4.0)) / \\\n",
" ast_con.L_sun.value)\n",
" return loglum\n",
"\n",
"\n",
"def test_calc_loglum_from_radius_teff(radius=6.95508e8, teff=5777.0,\n",
" loglum=np.log10(3.839e26/ast_con.L_sun.value)):\n",
" \"\"\"Test that calculations are correct using example 3.4.2. of [1]_\n",
"\n",
" References\n",
" ----------\n",
" .. [1] Carroll and Ostlie, 2007, An Introduction to Modern Astrophysics\n",
" \n",
" \"\"\"\n",
" assert np.isclose(calc_loglum_from_radius_teff(radius=radius, teff=teff),\n",
" loglum, atol=1e-4)\n",
" return None\n",
"\n",
"\n",
"# No output means that test passed.\n",
"test_calc_loglum_from_radius_teff()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def read_params_gianninas(fobj):\n",
" \"\"\"Read and parse custom file format of physical stellar parameters from\n",
" Gianninas et al 2014, [1]_.\n",
" \n",
" Parameters\n",
" ----------\n",
" fobj : file object\n",
" An opened file object to the text file with parameters.\n",
" Example file format:\n",
" line 0: 'Name SpT Teff errT log g errg '...\n",
" line 1: '========== ===== ======= ====== ===== ====='...\n",
" line 2: 'J1600+2721 DA6.0 8353. 126. 5.244 0.118'...\n",
" \n",
" Returns\n",
" -------\n",
" dobj : collections.OrderedDict\n",
" Ordered dictionary with parameter field names as keys and\n",
" parameter field quantities as values.\n",
" \n",
" Examples\n",
" --------\n",
" >>> with open('path/to/file.txt', 'rb') as fobj:\n",
" ... dobj = read_params_gianninas(fobj)\n",
" \n",
" References\n",
" ----------\n",
" .. [1] http://adsabs.harvard.edu/abs/2014ApJ...794...35G\n",
" \n",
" \"\"\"\n",
" # Read in lines of file and use second line (line number 1, 0-indexed) to parse fields.\n",
" # Convert string values to floats. Split specific values that have mixed types (e.g. '1.000 Gyr').\n",
" lines = []\n",
" for line in fobj:\n",
" lines.append(line.strip())\n",
" if len(lines) != 3:\n",
" warnings.warn((\"File has {num_lines}. File is expected to only have 3 lines.\\n\" +\n",
" \"Example file format:\\n\" +\n",
" \"line 0: 'Name SpT Teff errT log g errg '...\\n\" +\n",
" \"line 1: '========== ===== ======= ====== ===== ====='...\\n\" +\n",
" \"line 2: 'J1600+2721 DA6.0 8353. 126. 5.244 0.118'...\").format(num_lines=len(lines)))\n",
" dobj = collections.OrderedDict()\n",
" for mobj in re.finditer('=+', lines[1]):\n",
" key = lines[0][slice(*mobj.span())].strip()\n",
" value = lines[2][slice(*mobj.span())].strip()\n",
" try:\n",
" value = float(value)\n",
" except ValueError:\n",
" try:\n",
" value = float(value.rstrip('Gyr'))\n",
" except ValueError:\n",
" pass\n",
" if key == 'og L/L':\n",
" key = 'log L/Lo'\n",
" dobj[key] = value\n",
" return dobj\n",
"\n",
"\n",
"def test_read_params_gianninas(fobj=StringIO.StringIO(\"Name SpT Teff log L/Lo t_cool \\n\" +\n",
" \"========== ===== ======= ====== =========\\n\" +\n",
" \"J1600+2721 DA6.0 8353. -1.002 1.107 Gyr\"),\n",
" dobj=collections.OrderedDict([('Name', 'J1600+2721'),\n",
" ('SpT', 'DA6.0'),\n",
" ('Teff', 8353.0),\n",
" ('log L/Lo', -1.002),\n",
" ('t_cool', 1.107)])):\n",
" \"\"\"Test that parameters from Gianninas are read correctly.\n",
" \n",
" \"\"\"\n",
" assert dobj == read_params_gianninas(fobj=fobj)\n",
" return None\n",
"\n",
"# No output means that test passed.\n",
"test_read_params_gianninas()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def has_nans(obj):\n",
" \"\"\"Recursively iterate through an object to find a `numpy.nan` value.\n",
" \n",
" Parameters\n",
" ----------\n",
" obj : object\n",
" Object may be a singleton. If the object has the '__iter__' attribute,\n",
" nested objects such as `dict`, `list`, `tuple` are iterated through.\n",
" \n",
" Returns\n",
" -------\n",
" found_nan : bool\n",
" If `True`, a `numpy.nan` value was found within `obj`.\n",
" If `False`, no `numpy.nan` values were found within `obj`.\n",
" \n",
" \"\"\"\n",
" found_nan = False\n",
" if hasattr(obj, '__iter__'):\n",
" if isinstance(obj, dict):\n",
" for value in obj.itervalues():\n",
" found_nan = has_nans(value)\n",
" if found_nan:\n",
" break\n",
" else:\n",
" for item in obj:\n",
" found_nan = has_nans(item)\n",
" if found_nan:\n",
" break\n",
" else:\n",
" try:\n",
" if np.isnan(obj):\n",
" found_nan = True\n",
" except TypeError:\n",
" pass\n",
" return found_nan\n",
"\n",
"\n",
"def test_has_nans(obj1={'a': None, 'b': {'b1': True, 'b2': [False, 1, np.nan, 'asdf']}}, found_nan1=True,\n",
" obj2={'a': None, 'b': {'b1': True, 'b2': [False, 1, 'nan', ('asdf', 2.0)]}}, found_nan2=False):\n",
" \"\"\"Test that nans are found correctly.\n",
" \n",
" \"\"\"\n",
" assert((has_nans(obj1) == found_nan1) and\n",
" (has_nans(obj2) == found_nan2))\n",
" return None\n",
"\n",
"\n",
"# No output means that test passed.\n",
"test_has_nans()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def calc_periods_powers(mjds, fluxes_rel, fluxes_rel_err,\n",
" period_min=0.04, period_max=None, num_periods=None,\n",
" sigs=(95.0, 99.0, 99.9), num_bootstraps=100, show_plot=True):\n",
" \"\"\"Calculate periods and powers using generalized Lomb-Scargle periodogram.\n",
" Convenience function for methods from [1]_.\n",
" \n",
" Parameters\n",
" ----------\n",
" mjds : numpy.ndarray\n",
" 1D array of time coordinates for data. Unit is days in Modified Julian Date.\n",
" fluxes_rel : numpy.ndarray\n",
" 1D array of relative fluxes. Unit is relative integrated flux.\n",
" fluxes_rel_err : numpy.ndarray\n",
" 1D array of errors for relative fluxes. Unit is relative integrated flux.\n",
" period_min : {0.04}, float, optional\n",
" Minimum period to sample. Unit is days.\n",
" Default is 0.04 days, ~1 hour, from [2]_.\n",
" period_max : {None}, float, optional\n",
" Maximum period to sample. Unit is days.\n",
" If default `None`, uses `max(mjds) - min(mjds)`, from [2]_.\n",
" num_periods : {None}, int, optional\n",
" Number of periods to sample. If default `None`,\n",
" 10 periods are sampled per data point, from [2]_.\n",
" sigs : {(95.0, 99.0, 99.9)}, tuple of floats, optional\n",
" Levels of statistical significance for which to compute corresponding\n",
" powers via bootstrap analysis.\n",
" num_bootstraps : {100}, int, optional\n",
" Number of bootstrap resamplings to compute significance levels.\n",
" show_plot : {True, False}, bool, optional\n",
" If `True`, display periodogram plot.\n",
" \n",
" Returns\n",
" -------\n",
" periods : numpy.ndarray\n",
" 1D array of periods. Unit is days.\n",
" powers : numpy.ndarray\n",
" 1D array of powers. Unit is power from angular frequency, 2*pi/days.\n",
" sigs_powers : list of tuple of floats\n",
" Powers corresponding to levels of statistical significance from bootstrap analysis.\n",
" Example: [(95.0, 0.04), (99.0, 0.05), (99.9, 0.06)]\n",
" \n",
" See Also\n",
" --------\n",
" calc_best_period\n",
" \n",
" Notes\n",
" -----\n",
" - Shortest detectable period is 2x the sampling period (Nyquist rate).\n",
" - Period sampling is linear in angular frequency space with more samples for shorter periods.\n",
" - Call before `calc_best_period`.\n",
" \n",
" References\n",
" ----------\n",
" .. [1] Ivezic et al, 2014, Statistics, Data Mining, and Machine Learning in Astronomy\n",
" .. [2] http://exoplanetarchive.ipac.caltech.edu/applications/Periodogram/\n",
" \n",
" \"\"\"\n",
" # Check inputs.\n",
" period_min_nyquist = 2.0 * np.median(mjds[1:] - mjds[:-1])\n",
" if period_min < period_min_nyquist:\n",
" warnings.warn(\n",
" (\"`period_min` is set to Nyquist period, 2x the median sampling period.\\n\" +\n",
" \"Original: period_min = {pmin}\\n\" +\n",
" \"New value: period_min = {pmin_nyq}\").format(pmin=period_min, pmin_nyq=period_min_nyquist))\n",
" period_min = period_min_nyquist\n",
" if period_max is None:\n",
" period_max = max(mjds) - min(mjds)\n",
" if num_periods is None:\n",
" num_periods = 10 * len(fluxes_rel)\n",
" # Compute periodogram.\n",
" omega_max = 2.0 * np.pi / period_min\n",
" omega_min = 2.0 * np.pi / period_max\n",
" omegas = np.linspace(start=omega_min, stop=omega_max, num=num_periods, endpoint=True)\n",
" periods = 2.0 * np.pi / omegas\n",
" powers = astroML_ts.lomb_scargle(t=mjds, y=fluxes_rel, dy=fluxes_rel_err, omega=omegas, generalized=True)\n",
" dists = astroML_ts.lomb_scargle_bootstrap(t=mjds, y=fluxes_rel, dy=fluxes_rel_err, omega=omegas, generalized=True,\n",
" N_bootstraps=num_bootstraps, random_state=0)\n",
" sigs_powers = zip(sigs, np.percentile(dists, sigs))\n",
" if show_plot:\n",
" # Plot custom periodogram with delta BIC.\n",
" fig = plt.figure()\n",
" ax0 = fig.add_subplot(111, xscale='log')\n",
" ax0.plot(periods, powers, color='black', linewidth=1)\n",
" xlim = (min(periods), max(periods))\n",
" ax0.set_xlim(xlim)\n",
" for (sig, power) in sigs_powers:\n",
" ax0.plot(xlim, [power, power], color='black', linestyle=':')\n",
" ax0.set_title(\"Generalized Lomb-Scargle periodogram with\\n\" +\n",
" \"relative Bayesian Information Criterion\")\n",
" ax0.set_xlabel(\"Period (days)\")\n",
" ax0.set_ylabel(\"Power (from 2*pi/days)\")\n",
" ax1 = ax0.twinx()\n",
" ax1.set_ylim(tuple(astroML_ts.lomb_scargle_BIC(P=ax0.get_ylim(), y=fluxes_rel, dy=fluxes_rel_err, n_harmonics=1)))\n",
" ax1.set_ylabel(\"delta BIC\")\n",
" plt.show()\n",
" for (sig, power) in sigs_powers:\n",
" print(\"At significance = {sig}%, power = {pwr}\".format(sig=sig, pwr=power))\n",
" return (periods, powers, sigs_powers)\n",
"\n",
"# TODO: Create test."
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 114
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def plot_periodogram(periods, powers, n_terms=1):\n",
" \"\"\"Plot a the periods and powers for a generalized Lomb-Scargle\n",
" periodogram. Convenience function for plot formats from [1]_.\n",
"\n",
" Parameters\n",
" ----------\n",
" periods : numpy.ndarray\n",
" 1D array of periods. Unit is days.\n",
" powers : numpy.ndarray\n",
" 1D array of powers. Unit is power from angular frequency, 2*pi/days.\n",
" n_terms : {1}, int, optional\n",
" Number of Fourier terms used to fit the light curve.\n",
"\n",
" Returns\n",
" -------\n",
" None\n",
"\n",
" References\n",
" ----------\n",
" .. [1] Ivezic et al, 2014, Statistics, Data Mining, and Machine Learning in Astronomy\n",
" \n",
" \"\"\"\n",
" fig = plt.figure()\n",
" ax = fig.add_subplot(111)\n",
" ax.plot(periods, powers, color='black', linewidth=1)\n",
" ax.set_xlim(min(periods), max(periods))\n",
" ax.set_title((\"Generalized Lomb-Scargle periodogram\\n\" +\n",
" \"with {num} Fourier terms fit\").format(num=n_terms))\n",
" ax.set_xlabel(\"Period (days)\")\n",
" ax.set_ylabel(\"Power (from 2*pi/days)\")\n",
" plt.show()\n",
" return None\n",
"\n",
"# TODO: Create test."
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 115
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def plot_phased_light_curve(phases, fits_phased, mjds_phased, fluxes_rel, fluxes_rel_err, n_terms=1):\n",
" \"\"\"Plot a phased light curve. Convenience function for\n",
" plot formats from [1]_.\n",
"\n",
" Parameters\n",
" ----------\n",
" phases : ndarray\n",
" The phase coordinates of the best-fit light curve. Unit is decimal orbital phase.\n",
" fits_phased : ndarray\n",
" The relative fluxes for the `phases` of the best-fit light curve. Unit is relative flux.\n",
" mjds_phased : ndarray\n",
" The phases of the corresponding input `mjds`. Unit is decimal orbital phase.\n",
" fluxes_rel : numpy.ndarray\n",
" 1D array of relative fluxes. Unit is relative integrated flux.\n",
" fluxes_rel_err : numpy.ndarray\n",
" 1D array of errors for relative fluxes. Unit is relative integrated flux.\n",
" n_terms : {1}, int, optional\n",
" Number of Fourier terms used to fit the light curve.\n",
"\n",
" Returns\n",
" -------\n",
" None\n",
"\n",
" References\n",
" ----------\n",
" .. [1] Ivezic et al, 2014, Statistics, Data Mining, and Machine Learning in Astronomy\n",
" \n",
" \"\"\"\n",
" fig = plt.figure()\n",
" ax = fig.add_subplot(111)\n",
" ax.plot(phases, fits_phased, color='black')\n",
" ax.errorbar(mjds_phased, fluxes_rel, fluxes_rel_err, fmt='.k', ecolor='gray', linewidth=1)\n",
" ax.set_title((\"Phased light curve\\n\" +\n",
" \"with {num} Fourier terms fit\").format(num=n_terms))\n",
" ax.set_xlabel(\"Orbital phase\")\n",
" ax.set_ylabel(\"Relative flux\")\n",
" plt.show()\n",
" return None\n",
"\n",
"# TODO: Create test."
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 116
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def calc_best_period(mjds, fluxes_rel, fluxes_rel_err, periods, powers, sig_power,\n",
" n_terms=6, show_plots=True):\n",
" \"\"\"Calculate the period that best represents the data from a multi-term generalized Lomb-Scargle\n",
" periodogram. Convenience function for methods from [1]_.\n",
" \n",
" Parameters\n",
" ----------\n",
" mjds : numpy.ndarray\n",
" 1D array of time coordinates for data. Unit is days in Modified Julian Date.\n",
" fluxes_rel : numpy.ndarray\n",
" 1D array of relative fluxes. Unit is relative integrated flux.\n",
" fluxes_rel_err : numpy.ndarray\n",
" 1D array of errors for relative fluxes. Unit is relative integrated flux.\n",
" periods : numpy.ndarray\n",
" 1D array of periods. Unit is days.\n",
" powers : numpy.ndarray\n",
" 1D array of powers. Unit is power from angular frequency, 2*pi/days.\n",
" sig_power : float\n",
" Power corresponding to a level of statistical significance.\n",
" n_terms : {6}, int, optional\n",
" Number of Fourier terms to fit the light curve. To fit eclipses well often requires ~6 terms,\n",
" from section 10.3.3 of [1]_.\n",
" show_plots : {True, False}, bool, optional\n",
" If `True`, display plots of periodograms, phased light curves, and delta BIC.\n",
" \n",
" Returns\n",
" -------\n",
" best_period : float\n",
" Period with the highest relative Bayesian Information Criterion. Unit is days.\n",
"\n",
" See Also\n",
" --------\n",
" calc_periods_powers, refine_best_period\n",
"\n",
" Notes\n",
" -----\n",
" - Call after `calc_periods_powers`.\n",
" - Call before `refine_best_period`.\n",
" \n",
" References\n",
" ----------\n",
" .. [1] Ivezic et al, 2014, Statistics, Data Mining, and Machine Learning in Astronomy\n",
" \n",
" \"\"\"\n",
" # Select the peak powers above the significance level power.\n",
" sig_periods = \\\n",
" periods[\n",
" np.intersect1d(sci_sig.argrelextrema(powers, np.greater),\n",
" np.where(powers > sig_power))]\n",
" # Calculate the multiterm periodograms using a large range around the trial angular frequency\n",
" # based on the original sampling in angular frequency.\n",
" periods_bics = []\n",
" omegas = 2.0 * np.pi / periods\n",
" range_omega_halfwidth = 5.0 * np.median(omegas[1:] - omegas[:-1])\n",
" for sig_period in sig_periods:\n",
" sig_omega = 2.0 * np.pi / sig_period\n",
" range_omegas = np.linspace(start=sig_omega - range_omega_halfwidth,\n",
" stop=sig_omega + range_omega_halfwidth,\n",
" num=1000, endpoint=True)\n",
" range_periods = 2.0 * np.pi / range_omegas\n",
" range_powers = astroML_ts.multiterm_periodogram(t=mjds, y=fluxes_rel, dy=fluxes_rel_err,\n",
" omega=range_omegas, n_terms=n_terms)\n",
" range_bic_max = max(astroML_ts.lomb_scargle_BIC(P=range_powers, y=fluxes_rel, dy=fluxes_rel_err,\n",
" n_harmonics=n_terms))\n",
" range_omega_best = range_omegas[np.argmax(range_powers)]\n",
" range_period_best = 2.0 * np.pi / range_omega_best\n",
" periods_bics.append((range_period_best, range_bic_max))\n",
" if show_plots:\n",
" print(80*'-')\n",
" plot_periodogram(periods=range_periods, powers=range_powers, n_terms=n_terms)\n",
" print(\"Best period within window: {per} days\".format(per=range_period_best))\n",
" print(\"Relative Bayesian Information Criterion: {bic}\".format(bic=range_bic_max))\n",
" # Choose the best period from the maximum delta BIC.\n",
" best_idx = np.argmax(zip(*periods_bics)[1])\n",
" (best_period, best_bic) = periods_bics[best_idx]\n",
" if show_plots:\n",
" # Plot delta BICs after all periods have been fit.\n",
" print(80*'-')\n",
" periods_bics_t = zip(*periods_bics)\n",
" fig = plt.figure()\n",
" ax = fig.add_subplot(111, xscale='log')\n",
" ax.plot(periods_bics_t[0], periods_bics_t[1], color='black', marker='o')\n",
" ax.set_xlim(min(periods), max(periods))\n",
" ax.set_title(\"Relative Bayesian Information Criterion vs period\")\n",
" ax.set_xlabel(\"Period (days)\")\n",
" ax.set_ylabel(\"delta BIC\")\n",
" plt.show()\n",
" print(\"Best period: {per} days\".format(per=best_period))\n",
" print(\"Relative Bayesian Information Criterion: {bic}\".format(bic=best_bic))\n",
" return best_period\n",
"\n",
"# TODO: Create test."
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 117
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def refine_best_period(mjds, fluxes_rel, fluxes_rel_err, best_period, precision=1.0, period_range=100.0,\n",
" n_terms=6, show_plots=True):\n",
" \"\"\"Refine the best period to a higher precision from a multi-term generalized Lomb-Scargle\n",
" periodogram. Convenience function for methods from [1]_.\n",
" \n",
" Parameters\n",
" ----------\n",
" mjds : numpy.ndarray\n",
" 1D array of time coordinates for data. Unit is days in Modified Julian Date.\n",
" fluxes_rel : numpy.ndarray\n",
" 1D array of relative fluxes. Unit is relative integrated flux.\n",
" fluxes_rel_err : numpy.ndarray\n",
" 1D array of errors for relative fluxes. Unit is relative integrated flux.\n",
" best_period : float\n",
" Period that best represents the data. Unit is days.\n",
" precision : {1.0}, float, optional\n",
" Minimal precision of period to be calculated. Unit is seconds.\n",
" Example: For `precision = 1.0` seconds, periods are sampled with a\n",
" resolution of 0.1 seconds.\n",
" period_range : {100.0}, float, optional\n",
" Full range of periods around `best_period` to be sampled. Unit is seconds.\n",
" n_terms : {6}, int, optional\n",
" Number of Fourier terms to fit the light curve. To fit eclipses well often requires ~6 terms,\n",
" from section 10.3.3 of [1]_.\n",
" show_plots : {True, False}, bool, optional\n",
" If `True`, display plots of periodograms and phased light curves.\n",
" \n",
" Returns\n",
" -------\n",
" refined_period : float\n",
" Refined period with given `precision`. Unit is days.\n",
" phases : ndarray\n",
" The phase coordinates of the best-fit light curve. Unit is decimal orbital phase.\n",
" fits_phased : ndarray\n",
" The relative fluxes for the `phases` of the best-fit light curve. Unit is relative flux.\n",
" mjds_phased : ndarray\n",
" The phases of the corresponding input `mjds`. Unit is decimal orbital phase.\n",
"\n",
" See Also\n",
" --------\n",
" calc_best_period, calc_num_terms\n",
"\n",
" Notes\n",
" -----\n",
" - Call after `calc_best_period`.\n",
" \n",
" References\n",
" ----------\n",
" .. [1] Ivezic et al, 2014, Statistics, Data Mining, and Machine Learning in Astronomy\n",
" \n",
" \"\"\"\n",
" # Calculate the multiterm periodograms. Choose the best period from the maximal power.\n",
" resolution = 0.1 * precision # internal calculations have 10x better resolution than required precision\n",
" num = int(period_range / resolution) # number of samples periods centered on the best period\n",
" period_range_halfwidth = (period_range / 2.0) / sci_con.day # convert from seconds to days\n",
" range_periods = np.linspace(start=best_period - period_range_halfwidth,\n",
" stop=best_period + period_range_halfwidth,\n",
" num=num, endpoint=True)\n",
" range_omegas = 2.0 * np.pi / range_periods\n",
" range_powers = astroML_ts.multiterm_periodogram(t=mjds, y=fluxes_rel, dy=fluxes_rel_err,\n",
" omega=range_omegas, n_terms=n_terms)\n",
" refined_omega = range_omegas[np.argmax(range_powers)]\n",
" refined_period = 2.0 * np.pi / refined_omega\n",
" mtf = astroML_ts.MultiTermFit(omega=refined_omega, n_terms=n_terms)\n",
" mtf.fit(t=mjds, y=fluxes_rel, dy=fluxes_rel_err)\n",
" (phases, fits_phased, mjds_phased) = mtf.predict(Nphase=1000, return_phased_times=True, adjust_offset=True)\n",
" if show_plots:\n",
" plot_periodogram(periods=range_periods, powers=range_powers, n_terms=n_terms)\n",
" print(\"Refined period: {per} days\".format(per=refined_period))\n",
" plot_phased_light_curve(phases=phases, fits_phased=fits_phased, mjds_phased=mjds_phased,\n",
" fluxes_rel=fluxes_rel, fluxes_rel_err=fluxes_rel_err, n_terms=n_terms)\n",
" return (refined_period, phases, fits_phased, mjds_phased)\n",
"\n",
"# TODO: Create test."
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 118
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def calc_num_terms(mjds, fluxes_rel, fluxes_rel_err, best_period, precision=1.0, period_range=100.0,\n",
" max_n_terms=20, show_plots=True):\n",
" \"\"\"Calculate the number of Fourier terms that best represent the data's underlying\n",
" variability for representation by a multi-term generalized Lomb-Scargle periodogram.\n",
" Convenience function for methods from [1]_.\n",
" \n",
" Parameters\n",
" ----------\n",
" mjds : numpy.ndarray\n",
" 1D array of time coordinates for data. Unit is days in Modified Julian Date.\n",
" fluxes_rel : numpy.ndarray\n",
" 1D array of relative fluxes. Unit is relative integrated flux.\n",
" fluxes_rel_err : numpy.ndarray\n",
" 1D array of errors for relative fluxes. Unit is relative integrated flux.\n",
" best_period : float\n",
" Period that best represents the data. Unit is days.\n",
" precision : {1.0}, float, optional\n",
" Minimal precision of period to be calculated. Unit is seconds.\n",
" Example: For `precision = 1.0`, internal calculations are performed with\n",
" resolution of 0.1.\n",
" period_range : {100.0}, float, optional\n",
" Full range of periods around `best_period` to be sampled. Unit is seconds.\n",
" max_n_terms : {20}, int, optional\n",
" Maximal number of terms to attempt fitting.\n",
" Example: From 10.3.3 of [1]_, many light curves of eclipses are well represented\n",
" with ~6 terms and are best fit with ~10 terms.\n",
" show_plots : {True, False}, bool, optional\n",
" If `True`, display plots of periodograms and phased light curves.\n",
" \n",
" Returns\n",
" -------\n",
" best_n_terms : int\n",
" Number of Fourier terms that best fit the light curve. The number of terms\n",
" is determined by the maximal relative Bayesian Information Criterion, from\n",
" section 10.3.3 of [1]_.\n",
" phases : ndarray\n",
" The phase coordinates of the best-fit light curve. Unit is decimal orbital phase.\n",
" fits_phased : ndarray\n",
" The relative fluxes for the `phases` of the best-fit light curve. Unit is relative flux.\n",
" mjds_phased : ndarray\n",
" The phases of the corresponding input `mjds`. Unit is decimal orbital phase.\n",
"\n",
" See Also\n",
" --------\n",
" refine_best_period\n",
"\n",
" Notes\n",
" -----\n",
" - Call after `refine_best_period`.\n",
" \n",
" References\n",
" ----------\n",
" .. [1] Ivezic et al, 2014, Statistics, Data Mining, and Machine Learning in Astronomy\n",
" \n",
" \"\"\"\n",
" # Calculate the multiterm periodograms.\n",
" resolution = 0.1 * precision # internal calculations have 10x better resolution than required precision\n",
" num = int(period_range / resolution) # number of samples periods centered on the best period\n",
" period_range_halfwidth = (period_range / 2.0) / sci_con.day # convert from seconds to days\n",
" range_periods = np.linspace(start=best_period - period_range_halfwidth,\n",
" stop=best_period + period_range_halfwidth,\n",
" num=num, endpoint=True)\n",
" range_omegas = 2.0 * np.pi / range_periods\n",
" best_omega = 2.0 * np.pi / best_period\n",
" nterms_bics = []\n",
" for n_terms in range(1, max_n_terms+1):\n",
" range_powers = astroML_ts.multiterm_periodogram(t=mjds, y=fluxes_rel, dy=fluxes_rel_err,\n",
" omega=range_omegas, n_terms=n_terms)\n",
" range_bic_max = max(astroML_ts.lomb_scargle_BIC(P=range_powers, y=fluxes_rel, dy=fluxes_rel_err,\n",
" n_harmonics=n_terms))\n",
"\n",
" nterms_bics.append((n_terms, range_bic_max))\n",
" if show_plots:\n",
" print(80*'-')\n",
" plot_periodogram(periods=range_periods, powers=range_powers, n_terms=n_terms)\n",
" print(\"Number of Fourier terms: {num}\".format(num=n_terms))\n",
" print(\"Relative Bayesian Information Criterion: {bic}\".format(bic=range_bic_max))\n",
" # Choose the best number of Fourier terms from the maximum delta BIC.\n",
" best_idx = np.argmax(zip(*nterms_bics)[1])\n",
" (best_n_terms, best_bic) = nterms_bics[best_idx]\n",
" mtf = astroML_ts.MultiTermFit(omega=best_omega, n_terms=best_n_terms)\n",
" mtf.fit(t=mjds, y=fluxes_rel, dy=fluxes_rel_err)\n",
" (phases, fits_phased, mjds_phased) = mtf.predict(Nphase=1000, return_phased_times=True, adjust_offset=True)\n",
" if show_plots:\n",
" # Plot delta BICs after all terms have been fit.\n",
" print(80*'-')\n",
" nterms_bics_t = zip(*nterms_bics)\n",
" fig = plt.figure()\n",
" ax = fig.add_subplot(111)\n",
" ax.plot(nterms_bics_t[0], nterms_bics_t[1], color='black', marker='o')\n",
" ax.set_xlim(min(nterms_bics_t[0]), max(nterms_bics_t[0]))\n",
" ax.set_title(\"Relative Bayesian Information Criterion vs\\n\" +\n",
" \"number of Fourier terms\")\n",
" ax.set_xlabel(\"number of Fourier terms\")\n",
" ax.set_ylabel(\"delta BIC\")\n",
" plt.show()\n",
" print(\"Best number of Fourier terms: {num}\".format(num=best_n_terms))\n",
" print(\"Relative Bayesian Information Criterion: {bic}\".format(bic=best_bic))\n",
" plot_phased_light_curve(phases=phases, fits_phased=fits_phased, mjds_phased=mjds_phased,\n",
" fluxes_rel=fluxes_rel, fluxes_rel_err=fluxes_rel_err, n_terms=best_n_terms)\n",
" return (best_n_terms, phases, fits_phased, mjds_phased)\n",
"\n",
"# TODO: Create test"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 120
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def calc_residuals(phases, fits_phased, mjds_phased, fluxes_rel):\n",
" \"\"\"Calculate the residuals from a fit to a phased light curve.\n",
" \n",
" Parameters\n",
" ----------\n",
" phases : numpy.ndarray\n",
" The phase coordinates of the best-fit light curve. Unit is decimal orbital phase.\n",
" Required: numpy.shape(phases) == numpy.shape(fits_phased)\n",
" fits_phased : numpy.ndarray\n",
" The relative fluxes for the `phases` of the best-fit light curve. Unit is relative flux.\n",
" Required: numpy.shape(phases) == numpy.shape(fits_phased)\n",
" mjds_phased : numpy.ndarray\n",
" The phases coordinates of the `fluxes_rel`. Unit is decimal orbital phase.\n",
" Required: numpy.shape(mjds_phased) == numpy.shape(fluxes_rel)\n",
" fluxes_rel : numpy.ndarray\n",
" 1D array of relative fluxes. Unit is relative integrated flux.\n",
" Required: numpy.shape(mjds_phased) == numpy.shape(fluxes_rel)\n",
"\n",
" Returns\n",
" -------\n",
" residuals : numpy.ndarray\n",
" 1D array of the differences between `fluxes_rel` and `fits_phased` resampled at `mjds_phased`:\n",
" residuals = fluxes_rel - fits_phased_resampled\n",
" numpy.shape(residuals) == numpy.shape(fluxes_rel)\n",
" \n",
" \"\"\"\n",
" residuals = fluxes_rel - np.interp(x=mjds_phased, xp=phases, fp=fits_phased)\n",
" return residuals\n",
"\n",
"\n",
"# TODO: create test"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 127
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Custom structures"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Units are in astronomical quantities.\n",
"# Use dict of named tuples instead of dict of dicts to prevent accidentally editing keys.\n",
"stars = ['smaller_primary', 'greater_secondary']\n",
"attrs_star = ['velr_kmps', 'axis_AU', 'radius_Rsun', 'mass_Msun', 'logg_dexcmps2', 'teff_K', 'loglum_dexLsun']\n",
"attrs_syst = ['period_day', 'incl_deg', 'sep_AU', 'massfunc_Msun']\n",
"params_star = collections.namedtuple('params_star', attrs_star)\n",
"params_syst = collections.namedtuple('params_syst', attrs_syst)\n",
"params_init = {star: params_star(**{attr: np.NaN for attr in attrs_star}) for star in stars}\n",
"params_init['system'] = params_syst(**{attr: np.NaN for attr in attrs_syst})\n",
"params_init"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"{'greater_secondary': params_star(velr_kmps=nan, axis_AU=nan, radius_Rsun=nan, mass_Msun=nan, logg_dexcmps2=nan, teff_K=nan, loglum_dexLsun=nan),\n",
" 'smaller_primary': params_star(velr_kmps=nan, axis_AU=nan, radius_Rsun=nan, mass_Msun=nan, logg_dexcmps2=nan, teff_K=nan, loglum_dexLsun=nan),\n",
" 'system': params_syst(period_day=nan, incl_deg=nan, sep_AU=nan, massfunc_Msun=nan)}"
]
}
],
"prompt_number": 8
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Solution for SDSS_J160036.83+272117.8"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Initialize from spectra and models from Gianninas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Radial velocities and model are from Alex Gianninas, private communication, 12/2014. \n",
"Source data: 20140630_SDSS_J160036.83+272117.8/20150129_from_Alex_Gianninas/mass_J1600.txt \n",
"Assumed inclination angle was 90 deg for mass_J1600.txt. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"froot_gianninas = os.path.abspath(r'/Users/harrold/Google Drive/ccd.utexas/Projects')\n",
"frel_gianninas = os.path.relpath(r'20140630_SDSS_J160036.83+272117.8/Work_Logs/20150129_from_Alex_Gianninas/mass_J1600.txt')\n",
"fpath_gianninas = os.path.join(froot_gianninas, frel_gianninas)\n",
"with open(fpath_gianninas, 'rb') as fobj:\n",
" params_gianninas = read_params_gianninas(fobj=fobj)\n",
"params_gianninas"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"OrderedDict([('Name', 'J1600+2721'), ('SpT', 'DA6.0'), ('Teff', 8353.0), ('errT', 126.0), ('log g', 5.244), ('errg', 0.118), ('M/Mo', 0.145), ('errM', 0.02), ('g_0', 17.245), ('errg_0', 0.021), ('M_g', 7.4), ('errMg', 0.54), ('D/kpc', 0.931), ('errD', 0.233), ('R/Ro', 0.1506), ('errR', 0.031), ('log L/Lo', -1.002), ('t_cool', 1.107), ('errtc', 0.633), ('Pd', 0.96246), ('erP', 0.04039), ('K', 132.9), ('erK', 5.1), ('aAU', 0.0158), ('errAU', 0.0012), ('aRs', 3.3979), ('errRs', 0.2624), ('M_f', 0.234), ('errMf', 0.037), ('M_2', 0.423), ('errM2', 0.064), ('M2_60', 0.568), ('erM60', 0.086), ('t_merge', 0.0), ('errtm', 0.0), ('logh', -23.26)])"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Use params_dict to track changes in values with iterations.\n",
"params_dict = {}\n",
"inum = 0\n",
"params_dict[inum] = copy.deepcopy(params_init)\n",
"# Given parameters for both stars from Gianninas.\n",
"params_dict[inum]['system'] = \\\n",
" params_dict[inum]['system']._replace(\n",
" incl_deg = 90.0,\n",
" period_day = params_gianninas['Pd'],\n",
" sep_AU = params_gianninas['aAU'],\n",
" massfunc_Msun = params_gianninas['M_f'])\n",
"# Given parameters for only smaller, primary star from Gianninas.\n",
"params_dict[inum]['smaller_primary'] = \\\n",
" params_dict[inum]['smaller_primary']._replace(\n",
" teff_K = params_gianninas['Teff'],\n",
" logg_dexcmps2 = params_gianninas['log g'],\n",
" mass_Msun = params_gianninas['M/Mo'],\n",
" radius_Rsun = params_gianninas['R/Ro'],\n",
" loglum_dexLsun = params_gianninas['log L/Lo'],\n",
" velr_kmps = params_gianninas['K'])\n",
"# Given parameters for only greater, secondary star from Gianninas.\n",
"params_dict[inum]['greater_secondary'] = \\\n",
" params_dict[inum]['greater_secondary']._replace(\n",
" mass_Msun = params_gianninas['M_2'])\n",
"params_dict[inum]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
"{'greater_secondary': params_star(velr_kmps=nan, axis_AU=nan, radius_Rsun=nan, mass_Msun=0.423, logg_dexcmps2=nan, teff_K=nan, loglum_dexLsun=nan),\n",
" 'smaller_primary': params_star(velr_kmps=132.9, axis_AU=nan, radius_Rsun=0.1506, mass_Msun=0.145, logg_dexcmps2=5.244, teff_K=8353.0, loglum_dexLsun=-1.002),\n",
" 'system': params_syst(period_day=0.96246, incl_deg=90.0, sep_AU=0.0158, massfunc_Msun=0.234)}"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# # TODO: Don't compute derived parameters until lightcurve is analyzed.\n",
"# # Compute derived parameters. Check that parameters are consistent.\n",
"# # Derived parameters for only smaller, primary star.\n",
"# params_dict[inum]['smaller_primary'] = \\\n",
"# params_dict[inum]['smaller_primary']._replace(\n",
"# axis_AU = \\\n",
"# bss.utils.calc_semimaj_axis_from_period_velr_incl(\n",
"# period=params_dict[inum]['system'].period_day*sci_con.day,\n",
"# velr=params_dict[inum]['smaller_primary'].velr_kmps*sci_con.kilo,\n",
"# incl=np.deg2rad(params_dict[inum]['system'].incl_deg)) / \\\n",
"# ast_con.au.value)\n",
"# # Derived parameters for only greater, secondary star.\n",
"# params_dict[inum]['greater_secondary'] = \\\n",
"# params_dict[inum]['greater_secondary']._replace(\n",
"# velr_kmps = \\\n",
"# calc_velr2_from_masses_period_incl_velr1(\n",
"# mass1=params_dict[inum]['smaller_primary'].mass_Msun*ast_con.M_sun.value,\n",
"# mass2=params_dict[inum]['greater_secondary'].mass_Msun*ast_con.M_sun.value,\n",
"# velr1=params_dict[inum]['smaller_primary'].velr_kmps*sci_con.kilo,\n",
"# period=params_dict[inum]['system'].period_day*sci_con.day,\n",
"# incl=np.deg2rad(params_dict[inum]['system'].incl_deg)) / \\\n",
"# sci_con.kilo)\n",
"# params_dict[inum]['greater_secondary'] = \\\n",
"# params_dict[inum]['greater_secondary']._replace(\n",
"# axis_AU = \\\n",
"# bss.utils.calc_semimaj_axis_from_period_velr_incl(\n",
"# period=params_dict[inum]['system'].period_day*sci_con.day,\n",
"# velr=params_dict[inum]['greater_secondary'].velr_kmps*sci_con.kilo,\n",
"# incl=np.deg2rad(params_dict[inum]['system'].incl_deg)) / \\\n",
"# ast_con.au.value)\n",
"# assert np.isclose(params_dict[inum]['system'].sep_AU,\n",
"# (params_dict[inum]['smaller_primary'].axis_AU +\n",
"# params_dict[inum]['greater_secondary'].axis_AU),\n",
"# atol=params_dict[inum]['system'].sep_AU*1e-3)\n",
"# params_dict"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Derived parameters for only greater, secondary star.\n",
"# TODO: incorporate photometry to calculate\n",
"# radius_Rsun: from calc_radius_from_velrs_times(velr_1, velr_2, time_1, time_2)\n",
"# logg_dexcmps2: from def calc_logg_from_mass_radius(mass, radius)\n",
"# teff_K: from calc_teff_ratio_from_flux_rad_ratio(flux_rad_ratio), calc_flux_rad_ratio_from_light(light_oc, light_tr, light_ref=1.0)\n",
"# loglum_dexLsun: calc_loglum_from_radius_teff(radius, teff)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Refine with photometry"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fluxes from Catalina Sky Survey. Durations from McDonald 2.1m.\n",
"\n",
"http://nunuku.cacr.caltech.edu/cgi-bin/getcssconedb_release_img.cgi \n",
"input coordinates \n",
"\n",
"\n",
"http://vao-web.ipac.caltech.edu/workspace/TMP_2DFPdx_4770/VAOTS/2014.12.03_18.14.34_004770/Catalina.tbl.html \n",
"http://exoplanetarchive.ipac.caltech.edu/cgi-bin/IcePlotter/nph-icePlotInit?mode=external&form=banner&ws=TMP_LtNmQm_14795/Pgram/10377&prefix=phased_result_0&config=single \n",
"http://nunuku.cacr.caltech.edu/cgi-bin/getcssconedb_release_img.cgi \n",
"\n",
"From nunuku.cacr.caltech and IcePlotter (with period 1.003365 days from JJ's analysis, 6/30/2014): \n",
"- primary: light levels from 17.4 Vmag to 18.1-18.3 Vmag, so depth of primary is 0.7-0.9 Vmags, so brightness ratio = 2.512^-dmag = 0.52, 0.48, 0.44 \n",
"- secondary: light levels from 17.4 Vmag to 17.5-17.7 Vmag, so depth of secondary is 0.1-0.3 Vmags, so brightness ratio = 2.512^-dmag = 0.91, 0.83, 0.76 \n",
"\n",
"Note: \n",
"- CRTS data is Vmag, McDonald is BG40\n",
"- Vmag is 500-600 nm, BG40 is 300-600 nm \n",
"\n",
"Convert between UBVRI and ugriz with `astropysics.phot.ugriz_to_UBVRI`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**TODO:** Correct MJD to barycentric?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Load original data.\n",
"froot_cacr = os.path.abspath(r'/Users/harrold/Google Drive/ccd.utexas/Projects')\n",
"frel_cacr = os.path.relpath(r'20140630_SDSS_J160036.83+272117.8/Work_Logs/20141203_CRTS_data/from_CACR_search_without_phases/result_web_fileovSSl4.csv')\n",
"fpath_cacr = os.path.join(froot_cacr, frel_cacr)\n",
"phot_cacr = (pd.DataFrame.from_csv(path=fpath_cacr)).reset_index()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 104
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Convert time, magnitudes.\n",
"phot_cacr['datetime_UTC'] = (ast_time.Time(phot_cacr['MJD'].values, format='mjd', scale='utc')).datetime\n",
"phot_cacr['unixtime_UTC'] = map(lambda dt: np.datetime64(dt).astype(np.int64) / 1e9,\n",
" phot_cacr['datetime_UTC'].values)\n",
"phot_cacr['flux_rel'] = map(lambda mag_1: \\\n",
" bss.utils.calc_flux_intg_ratio_from_mags(\n",
" mag_1=mag_1,\n",
" mag_2=phot_cacr['Mag'].median()),\n",
" phot_cacr['Mag'].values)\n",
"phot_cacr['flux_rel_err'] = map(lambda mag_1, mag_2: \\\n",
" abs(1.0 - bss.utils.calc_flux_intg_ratio_from_mags(\n",
" mag_1=mag_1,\n",
" mag_2=mag_2)),\n",
" (phot_cacr['Mag'] + phot_cacr['Magerr']).values,\n",
" phot_cacr['Mag'])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 105
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# TODO: DELETE?\n",
"# Remove outliers.\n",
"# TODO: Use Baysian filter instead. Apply after phased data.\n",
"#phot_cacr['within_5sigG'] = (phot_cacr['flux_rel'] - phot_cacr['flux_rel'].median()) < (5.0 * astroML_stats.sigmaG(phot_cacr['flux_rel']))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 106
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"phot_cacr.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>MasterID</th>\n",
" <th>Mag</th>\n",
" <th>Magerr</th>\n",
" <th>RA</th>\n",
" <th>Dec</th>\n",
" <th>MJD</th>\n",
" <th>Blend</th>\n",
" <th>datetime_UTC</th>\n",
" <th>unixtime_UTC</th>\n",
" <th>flux_rel</th>\n",
" <th>flux_rel_err</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 1126078052790</td>\n",
" <td> 17.45</td>\n",
" <td> 0.10</td>\n",
" <td> 240.15338</td>\n",
" <td> 27.35499</td>\n",
" <td> 53470.38932</td>\n",
" <td> 0</td>\n",
" <td>2005-04-10 09:20:37.248000</td>\n",
" <td> 1.113125e+09</td>\n",
" <td> 0.972747</td>\n",
" <td> 0.087989</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 1126078052790</td>\n",
" <td> 17.41</td>\n",
" <td> 0.10</td>\n",
" <td> 240.15344</td>\n",
" <td> 27.35494</td>\n",
" <td> 53470.39657</td>\n",
" <td> 0</td>\n",
" <td>2005-04-10 09:31:03.648000</td>\n",
" <td> 1.113125e+09</td>\n",
" <td> 1.009253</td>\n",
" <td> 0.087989</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 1126078052790</td>\n",
" <td> 17.36</td>\n",
" <td> 0.10</td>\n",
" <td> 240.15339</td>\n",
" <td> 27.35497</td>\n",
" <td> 53470.40384</td>\n",
" <td> 0</td>\n",
" <td>2005-04-10 09:41:31.776000</td>\n",
" <td> 1.113126e+09</td>\n",
" <td> 1.056818</td>\n",
" <td> 0.087989</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 1126078052790</td>\n",
" <td> 17.39</td>\n",
" <td> 0.10</td>\n",
" <td> 240.15336</td>\n",
" <td> 27.35496</td>\n",
" <td> 53470.41115</td>\n",
" <td> 0</td>\n",
" <td>2005-04-10 09:52:03.360000</td>\n",
" <td> 1.113127e+09</td>\n",
" <td> 1.028016</td>\n",
" <td> 0.087989</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 1126078052790</td>\n",
" <td> 17.49</td>\n",
" <td> 0.09</td>\n",
" <td> 240.15342</td>\n",
" <td> 27.35490</td>\n",
" <td> 53479.38284</td>\n",
" <td> 0</td>\n",
" <td>2005-04-19 09:11:17.376000</td>\n",
" <td> 1.113902e+09</td>\n",
" <td> 0.937562</td>\n",
" <td> 0.079550</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 107,
"text": [
" MasterID Mag Magerr RA Dec MJD Blend \\\n",
"0 1126078052790 17.45 0.10 240.15338 27.35499 53470.38932 0 \n",
"1 1126078052790 17.41 0.10 240.15344 27.35494 53470.39657 0 \n",
"2 1126078052790 17.36 0.10 240.15339 27.35497 53470.40384 0 \n",
"3 1126078052790 17.39 0.10 240.15336 27.35496 53470.41115 0 \n",
"4 1126078052790 17.49 0.09 240.15342 27.35490 53479.38284 0 \n",
"\n",
" datetime_UTC unixtime_UTC flux_rel flux_rel_err \n",
"0 2005-04-10 09:20:37.248000 1.113125e+09 0.972747 0.087989 \n",
"1 2005-04-10 09:31:03.648000 1.113125e+09 1.009253 0.087989 \n",
"2 2005-04-10 09:41:31.776000 1.113126e+09 1.056818 0.087989 \n",
"3 2005-04-10 09:52:03.360000 1.113127e+09 1.028016 0.087989 \n",
"4 2005-04-19 09:11:17.376000 1.113902e+09 0.937562 0.079550 "
]
}
],
"prompt_number": 107
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"phot_cacr.describe()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>MasterID</th>\n",
" <th>Mag</th>\n",
" <th>Magerr</th>\n",
" <th>RA</th>\n",
" <th>Dec</th>\n",
" <th>MJD</th>\n",
" <th>Blend</th>\n",
" <th>unixtime_UTC</th>\n",
" <th>flux_rel</th>\n",
" <th>flux_rel_err</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td> 3.880000e+02</td>\n",
" <td> 388.000000</td>\n",
" <td> 388.000000</td>\n",
" <td> 388.000000</td>\n",
" <td> 388.000000</td>\n",
" <td> 388.000000</td>\n",
" <td> 388</td>\n",
" <td> 3.880000e+02</td>\n",
" <td> 388.000000</td>\n",
" <td> 388.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td> 1.126078e+12</td>\n",
" <td> 17.478428</td>\n",
" <td> 0.103067</td>\n",
" <td> 240.153367</td>\n",
" <td> 27.355005</td>\n",
" <td> 55001.961761</td>\n",
" <td> 0</td>\n",
" <td> 1.245453e+09</td>\n",
" <td> 0.965972</td>\n",
" <td> 0.090177</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td> 0.000000e+00</td>\n",
" <td> 0.228205</td>\n",
" <td> 0.032256</td>\n",
" <td> 0.000084</td>\n",
" <td> 0.000071</td>\n",
" <td> 913.646033</td>\n",
" <td> 0</td>\n",
" <td> 7.893902e+07</td>\n",
" <td> 0.169248</td>\n",
" <td> 0.025432</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td> 1.126078e+12</td>\n",
" <td> 16.730000</td>\n",
" <td> 0.080000</td>\n",
" <td> 240.152930</td>\n",
" <td> 27.354740</td>\n",
" <td> 53470.389320</td>\n",
" <td> 0</td>\n",
" <td> 1.113125e+09</td>\n",
" <td> 0.291072</td>\n",
" <td> 0.071034</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td> 1.126078e+12</td>\n",
" <td> 17.370000</td>\n",
" <td> 0.090000</td>\n",
" <td> 240.153320</td>\n",
" <td> 27.354960</td>\n",
" <td> 54257.247355</td>\n",
" <td> 0</td>\n",
" <td> 1.181109e+09</td>\n",
" <td> 0.952804</td>\n",
" <td> 0.079550</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td> 1.126078e+12</td>\n",
" <td> 17.420000</td>\n",
" <td> 0.090000</td>\n",
" <td> 240.153370</td>\n",
" <td> 27.355000</td>\n",
" <td> 54923.401175</td>\n",
" <td> 0</td>\n",
" <td> 1.238665e+09</td>\n",
" <td> 1.000000</td>\n",
" <td> 0.079550</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td> 1.126078e+12</td>\n",
" <td> 17.472500</td>\n",
" <td> 0.100000</td>\n",
" <td> 240.153410</td>\n",
" <td> 27.355040</td>\n",
" <td> 55830.152332</td>\n",
" <td> 0</td>\n",
" <td> 1.317008e+09</td>\n",
" <td> 1.047129</td>\n",
" <td> 0.087989</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td> 1.126078e+12</td>\n",
" <td> 18.760000</td>\n",
" <td> 0.320000</td>\n",
" <td> 240.153650</td>\n",
" <td> 27.355340</td>\n",
" <td> 56568.132160</td>\n",
" <td> 0</td>\n",
" <td> 1.380770e+09</td>\n",
" <td> 1.887991</td>\n",
" <td> 0.255268</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 108,
"text": [
" MasterID Mag Magerr RA Dec \\\n",
"count 3.880000e+02 388.000000 388.000000 388.000000 388.000000 \n",
"mean 1.126078e+12 17.478428 0.103067 240.153367 27.355005 \n",
"std 0.000000e+00 0.228205 0.032256 0.000084 0.000071 \n",
"min 1.126078e+12 16.730000 0.080000 240.152930 27.354740 \n",
"25% 1.126078e+12 17.370000 0.090000 240.153320 27.354960 \n",
"50% 1.126078e+12 17.420000 0.090000 240.153370 27.355000 \n",
"75% 1.126078e+12 17.472500 0.100000 240.153410 27.355040 \n",
"max 1.126078e+12 18.760000 0.320000 240.153650 27.355340 \n",
"\n",
" MJD Blend unixtime_UTC flux_rel flux_rel_err \n",
"count 388.000000 388 3.880000e+02 388.000000 388.000000 \n",
"mean 55001.961761 0 1.245453e+09 0.965972 0.090177 \n",
"std 913.646033 0 7.893902e+07 0.169248 0.025432 \n",
"min 53470.389320 0 1.113125e+09 0.291072 0.071034 \n",
"25% 54257.247355 0 1.181109e+09 0.952804 0.079550 \n",
"50% 54923.401175 0 1.238665e+09 1.000000 0.079550 \n",
"75% 55830.152332 0 1.317008e+09 1.047129 0.087989 \n",
"max 56568.132160 0 1.380770e+09 1.887991 0.255268 "
]
}
],
"prompt_number": 108
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df_to_plot = phot_cacr.set_index(keys='datetime_UTC', inplace=False)\n",
"pd.DataFrame.plot(df_to_plot[['flux_rel', 'flux_rel_err']], yerr='flux_rel_err', marker='o')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 109,
"text": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1163334d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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xFq7lAm4WMLRumL16tRCPPSZEhw6FIZmDDh4U4rrrhLjoImkjNeLrr+2H+Yq/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\n7gW+CoRz4TkOOujrqS5SEVvusTPF+n3rW2t48cUGICw8AFe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"text": [
"<matplotlib.figure.Figure at 0x119c5ecd0>"
]
}
],
"prompt_number": 109
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Search for a single period."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"TODO: \n",
"- apply barycentric correction to timestamps\n",
"- demonstrate with astroML_ts.search_frequencies"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# calc_periods_powers takes ~1 min for num_bootstraps = 100\n",
"mjds = phot_cacr['MJD'].values\n",
"fluxes_rel = phot_cacr['flux_rel'].values\n",
"fluxes_rel_err = phot_cacr['flux_rel_err'].values\n",
"(periods, powers, sigs_powers) = \\\n",
" calc_periods_powers(\n",
" mjds=mjds, fluxes_rel=fluxes_rel, fluxes_rel_err=fluxes_rel_err, period_min=0.04,\n",
" period_max=None, num_periods=None, sigs=(95.0, 99.0, 99.9), num_bootstraps=100, show_plot=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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kbjrHqQySWEY9JP0E2BdoF5aZmR2V7yBJVcBIAn/jXOBNSaPNbFKszhBgdzPb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\nqJcX0Wk+UlVGZrZJUpTvqAq4w8wmSRoW7h9lZo9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M6tWr/coirU9Ze498ufXWW7l48SK7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\n+X8BhwyM4gaTigAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x1180a11d0>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"At significance = 95.0%, power = 0.0499334363954\n",
"At significance = 99.0%, power = 0.0533399934563\n",
"At significance = 99.9%, power = 0.0659569785997\n"
]
}
],
"prompt_number": 121
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(\"sigs_powers = {sp}\".format(sp=sigs_powers))\n",
"best_period = \\\n",
" calc_best_period(\n",
" mjds=mjds, fluxes_rel=fluxes_rel, fluxes_rel_err=fluxes_rel_err, periods=periods,\n",
" powers=powers, sig_power=sigs_powers[0][-1], n_terms=6, show_plots=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"sigs_powers = [(95.0, 0.049933436395447957), (99.0, 0.053339993456284325), (99.9, 0.065956978599744531)]\n",
"--------------------------------------------------------------------------------"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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yycUgycyor69vcj5NwfTp01uEK24UKfRj5l8x818B/A+ANkQ0loi2yJgFW4Ok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\nwUNEQRAEQfD4f1d8tybVtGYdAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x119d48e10>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Best period within window: 5.57277103034 days\n",
"Relative Bayesian Information Criterion: 168.582938167\n",
"--------------------------------------------------------------------------------"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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prKzEtm3bfPcRxeBP2OCzOYltI4AE8/PXAJYHvQARnU1Eq4loLRHd6fL7lUS0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\nAPZGXU8tnZCQgN69e6N169a4/fbbAbgP3507d26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"text": [
"<matplotlib.figure.Figure at 0x1176b5650>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Best period within window: 1.0033582076 days\n",
"Relative Bayesian Information Criterion: 549.924879864\n",
"--------------------------------------------------------------------------------"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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sWIHhw4dHKrermI9kOc6uoClkmy6nKTDzxe1QD4ulw/L5559H3jddQiHb5qP2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\nHI4ApxQcDofDEfD/UG/iEMxElokAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x119c62e90>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Best period within window: 0.250842440432 days\n",
"Relative Bayesian Information Criterion: 191.269355061\n",
"--------------------------------------------------------------------------------"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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TVWfHC/TcQ2R6SqCqi1V1CuDSeMlza4W33367Ky6TCp+mcPfdd3PNNdeUPLc3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\noEGDMGDAgIyFgl5u6ZgWL15sPM/k6WYyGU2ePBnf/va3Q8swYMCAtGu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IFgrtYWSQLdOm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\nEwoOh8Ph8HFCweFwOBw+/x8xbLbc56AhVQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x1173fe1d0>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Best period within window: 0.229305136482 days\n",
"Relative Bayesian Information Criterion: 117.6296499\n",
"--------------------------------------------------------------------------------"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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TaBCR00XkEhH5lHPtR9UvWm3R0xu2XHR39VHSwFJJUkhap2DPdO29m11cffXV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\nAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x114ebae90>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Best period within window: 0.195655915724 days\n",
"Relative Bayesian Information Criterion: 92.6976277165\n",
"--------------------------------------------------------------------------------"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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rbjk6fNcz29vbgTUL/XUHVq1alXeup5kauhvFhoOamkkp0Vz2/ZXEzJkzeeml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"text": [
"<matplotlib.figure.Figure at 0x114e9d510>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Best period within window: 0.173670120684 days\n",
"Relative Bayesian Information Criterion: 84.3629226196\n",
"--------------------------------------------------------------------------------"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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jZUhLRPvBSbHRH0ADgElE9DNmflov++STT6K+vh4AMHfuXBx22GFpimkBFEZI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\n",
"text": [
"<matplotlib.figure.Figure at 0x1194c0410>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Best period within window: 0.153245281118 days\n",
"Relative Bayesian Information Criterion: 70.8816881368\n",
"--------------------------------------------------------------------------------"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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s+0gCzeVWCsU+X6XfN41qDDTL0hZAEGju6upCQ0NDVSiF6dOnJ643VWxIjKvH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\nnFJwOBwOR8D/Apmb8ikM5haqAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x11673a090>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Best period within window: 0.149010504093 days\n",
"Relative Bayesian Information Criterion: 67.1767088466\n",
"--------------------------------------------------------------------------------"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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/88wzeO2119K2yyypekUnrc5CVrJRDYtMFjBSSaGzsxNf+9rXsrS2+7mPuktM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\n4uizFgVbn4K2lER4SxGFrBVWUp+CzmeHDh0yiUJSf5D0Kfz5z39OtBR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cmHk+hXkt5HnO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\nTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x116103750>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Best period within window: 0.145305513333 days\n",
"Relative Bayesian Information Criterion: 62.9831706394\n",
"--------------------------------------------------------------------------------"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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o6EBDQ0NmG90IKSQ1H7mQ1GwQRQqHHnooXnrppVT5JTEfyUIu7xHRsQCWA9gm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\nBPhfw4TxVWBmby4AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x119c48690>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Best period within window: 0.100158591015 days\n",
"Relative Bayesian Information Criterion: 24.8742346003\n",
"--------------------------------------------------------------------------------"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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aP5dX1DxMKo+ZOXZQPm7F3V3lN45Snj9/fqzZCYqVUHqyq7LQg2SjlOL+APoq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"text": [
"<matplotlib.figure.Figure at 0x11809d310>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Best period within window: 0.0637816833182 days\n",
"Relative Bayesian Information Criterion: 8.55161416218\n",
"--------------------------------------------------------------------------------\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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p67xqSNJbZna0pDfNbED4xrJnzezU+gQl6afANuBfgCIz2yipC7DAzI6QNBnA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\nKthEYGbMmzePkSNHMmzYMA4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"text": [
"<matplotlib.figure.Figure at 0x1176be890>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Best period: 1.0033582076 days\n",
"Relative Bayesian Information Criterion: 549.924879864\n"
]
}
],
"prompt_number": 122
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"(best_period, phases, fits_phased, mjds_phased) = \\\n",
" refine_best_period(\n",
" mjds=mjds, fluxes_rel=fluxes_rel, fluxes_rel_err=fluxes_rel_err, best_period=best_period,\n",
" precision=0.1, period_range=50.0, n_terms=6, show_plots=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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j9Fgfv4mmOnXqMGXKFBo0aOB1KNlavHgxN910E8uXh3X/I2NOyGsf/1MiUgl4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\nVqwIWm737t36wgsv6J/+9Cft1auXrl2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"text": [
"<matplotlib.figure.Figure at 0x119f85a10>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Refined period: 1.00336920737 days\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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11+zdu5eamhrA1bBramo4cuQITz75JJdddhnDhw8nNDTU4/fK5mqd8Y8dO5b1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\nQUREYgoKIiISU1AQEZGYgoKIiMT+H89eD4UbMCpIAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x119d6e950>"
]
}
],
"prompt_number": 123
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# calc_num_terms takes ~1 min for precision = 1.0\n",
"(best_n_terms, phases, fits_phased, mjds_phased) = \\\n",
" calc_num_terms(\n",
" mjds=mjds, fluxes_rel=fluxes_rel, fluxes_rel_err=fluxes_rel_err, best_period=best_period,\n",
" precision=1.0, period_range=100.0, max_n_terms=20, show_plots=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"--------------------------------------------------------------------------------\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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Ju7QqJGNyI1BDc3GfKqPaQDucRuZlqronciFmzhqaTVb+9Kc/Ub16dZ555hmv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\n6NAhpk2blmeG1sjr7DoFE1ZvvPEGl19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5DKQ1fIu8R4pKNuScMZI6ImI/sF/S3aR0wsnc\n1tHGH4MZKyJiJ7CzyrYF2FKj7DFgYWG7hzwoqCp3kpRarLavBlaX6Z8jBdPO1Botvwqcm28aHwJe\nKZStLj+8/TYwIad1NgJLc9TxDnCBpG9zPf11+rGbtIAKEXGcdE9hb7Z/Uyj3MrBZUj9pfnmxP5+R\ndPeL89tfy+9jCNiTZx5BmrpYd767MY2wdLYxo4yky4C1EbGwaeH6dVwHvBERt7ZQdgfwYET82qys\nMdU4UjBmlImIH4FTtR5eawVJq4CPgRdaKNtJesDJDsGMCEcKxhhjKjhSMMYYU8FOwRhjTAU7BWOM\nMRXsFIwxxlSwUzDGGFPBTsEYY0yFfwHM09GvmUhEEAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x119d683d0>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Number of Fourier terms: 1\n",
"Relative Bayesian Information Criterion: 59.7554164535\n",
"--------------------------------------------------------------------------------"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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jNIMyJpoWLFhAp06dYu4WmB06dLCkYKIuWJ9CKRG5Q0T+T0S6BSz7W+RDMyY6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\n/wPIPw3I42AyTgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x119379a90>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Number of Fourier terms: 2\n",
"Relative Bayesian Information Criterion: 212.615715089\n",
"--------------------------------------------------------------------------------"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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WiyLyqKqOz2fTpUBTEWkA7ACuBAbntZsc+ywHlFTVAyJSHugDPJLbhoFJwZiC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\nV2v16tU1KSkpavv0y9q1a7VatWpBr6c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lZjfpPNUm/TglNlDB3Z+JZwrnqf06FzcH7JhZ\nlZhfnu/PPrHufn5++3L6HZfAWZp5BDF1sel8d5FWtHS2yDczs15gzd3LHwY3P8cQsOLuw23EHgNj\n7v7yUaxIkTIFkW/m7g9AvdHLa+0ws2lgF5hpI7ZEvOCkAUG+RJmCiIhklCmIiEhGg4KIiGQ0KIiI\nSEaDgoiIZDQoiIhIRoOCiIhk3gAB/4YHSVp23QAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x119a92dd0>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Number of Fourier terms: 3\n",
"Relative Bayesian Information Criterion: 252.971998362\n",
"--------------------------------------------------------------------------------"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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7XjwCMSYRbdiwgQ4dOni+Hbuq2SSKYFc0b3L/5gIHca5s7gkcLLn2wJiqLl5H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\nkiRJkho5KSRJkiQ1clJIkiRJavwDS/0sa+x/wWgAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x119e1c1d0>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Number of Fourier terms: 4\n",
"Relative Bayesian Information Criterion: 415.347390722\n",
"--------------------------------------------------------------------------------"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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qeinQB/iDt2EZkzqs+chUJ9EkhY04w1qX+IVf76VsTJVnScFUJ6Ia/q6ZIvIG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\n",
"text": [
"<matplotlib.figure.Figure at 0x114ed9c10>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Number of Fourier terms: 5\n",
"Relative Bayesian Information Criterion: 461.034513515\n",
"--------------------------------------------------------------------------------"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x114d77b90>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Number of Fourier terms: 6\n",
"Relative Bayesian Information Criterion: 582.221272407\n",
"--------------------------------------------------------------------------------"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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s27cPVaVGjRoxr9ta/CaZhDM6Z1REpCLwAtAX2AT8T0Smqupyv2KfqOqHbvmz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\n+9vfHn/dokULmjZtyieffEL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CBSsJCPbWkt7Cxiew104xlWAhgjY2doJCCgux\n0qAihEAK/2AlpIlgYZMHGItZ4Qi5JEKI4P0+3S2TyybFZHeytzuT17Fx78cvMi7XxJa4GwBmtuju\nTfon8Dui1HJjZvPEDOGNWHq3ltoXgFLO61vEgRttogxzkJJ0h6jFN1PcdIqBOJM164SYXZymGi5m\nNufudaBuZivE1P8rvVer/9cgf8Xda0Ctq60KVHvEtoFy5vqK9MPfFfdFlAG72ytAZdi+acQv/0Gv\nUe8uMJn+qH0GdjKx3fE/10fARCrBnAHrafZwDEyZ2Wu6TyOnH/fEIRu4+wdR439M7S+ZuG3g3Mwa\nxPrrbH8uiX3bs+u/99LneAIe0ooeiGV/uevBRfJoW2aRETGzWeDQ3csDg/PvsQTsu/vyELG3wKq7\nfw6KFcnSiF9kRNz9Hej0eoBrGGa2CVwAW0PEloiHfJT05dc04hcRKRiN+EVECkaJX0SkYJT4RUQK\nRolfRKRglPhFRApGiV9EpGC+AWfBM3/hMOi3AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1172eeb50>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Number of Fourier terms: 7\n",
"Relative Bayesian Information Criterion: 593.998661524\n",
"--------------------------------------------------------------------------------"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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+sYjMFZFfRWSWiJzjZTzGhLJ+/XrS09PjftUuQHp6Ohs3box7vcbEIpLROWMi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\nmTM1PT09pmGZ3wFaA/OBC4C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6OHdumqSq29yf3B/y56+Emu4ZGfOAv+HcTQw3\n7mv+v737B40qCOI4/h1BsRALsRKEYNBKAoLWgmAhqbRQsBFEECFiq5WKKYJgoQFtbOwUhUjkCKk0\naAiEkxT+CxZCUBKwSBPERhiLmcP1uJc7IcSQ9/s0udvMbTaBzO3O29uXj88DrQuD08Sb0AX+PlF3\niTgwTDambqWequ83gf1m1mdm24Az/FlJjhMXb8mvzwFyd0+DOKxyptvAlPhlM/hZzKquFLXNU0V7\nn7vPZ/uPVfqq+mfstV7bivO215SPR4F7ubK4CGwHcPdvxIzuGHCEKOXg7peIFcxe4K2Z7Sr61GFb\nG1enUsxJM/tKnPrZMLOJbN9jZg0Ad/8FDAGTxIXaJ+7+KbsYAY6b2WdiJTuS7UNAP3C9KC3trhrY\nep/HL7JeJokjcS8DmNkhd59j9QT+mii1vDSzA8QKYZ7Yenc22w8CAxWvXyBuuLFIlGHuZpJeIWrx\ncxm3M2Mg7slaekisLh5lDRcz63f3WWDWzE4QS//l/FkLq/8Z5H9x9ylgqq1tDBjrELsIDBbPJ8g3\n/ra4ZaIM2N4+DAz3OjbN+GUz6DTrvQVszQu174GbRWx7fOv5fWBLlmAeA+dy9fAA2GFmH7OfZsU4\n3hA32cDdl4ga/0y2fyjibgBPzaxJ7L8ux/OCOLe93P99O3+Pd8B07uiB2PZXuR9cpIqOZRZZI2a2\nDxh198GuwdV9HAbuuPvRHmJfAafd/Xu3WJGSZvwia8TdvwArnT7A1Qszuwo8A671EDtAfMhHSV/+\nmWb8IiI1oxm/iEjNKPGLiNSMEr+ISM0o8YuI1IwSv4hIzSjxi4jUzG8TnfFLtr8rMgAAAABJRU5E\nrkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x114fd90d0>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Number of Fourier terms: 8\n",
"Relative Bayesian Information Criterion: 623.33173385\n",
"--------------------------------------------------------------------------------"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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OHbiKu2rXmFJr165dVK1alerVq0elPkv8JtEES/zLROQC3wWq+jDwOtDcy6CM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\nR48eHYeI
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