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# directories a and b | |
comm -12 <(ls a) <(ls b) |
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# how to do the same as Michael Betancourt in | |
# https://betanalpha.github.io/assets/case_studies/gp_part3/part3.html | |
import numpy as np | |
from scipy.stats import invgamma | |
from scipy.optimize import minimize | |
f = lambda x, lims: \ | |
(np.array([invgamma(a=x[0], scale=x[1]).cdf(lims[0]) - 0.01, | |
invgamma(a=x[0], scale=x[1]).sf(lims[1]) - 0.01])**2 |
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import astropy.constants as c | |
import astropy.units as u | |
from math import pi | |
C = (2*pi*c.G)**(1/3) | |
# K [m/s] = C1 .... | |
C1 = C.to( (u.meter/u.second) * u.year**(1/3.) * (1/u.M_jup) * u.M_sun**(2/3.) ).value | |
# mp sini [Mjup] = C2 .... |
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import numpy as np | |
import cStringIO | |
filename = 'example.rdb' | |
def read_rdb(filename): | |
""" Reads a .rdb file with possible comments '#' and header | |
col1 col2 col3 | |
---- ---- ---- | |
Returns a numpy record array |
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def massTorres(teff, erteff, logg, erlogg, feh, erfeh, | |
ntrials=10000, corrected=True): | |
randomteff = teff + erteff * np.random.randn(ntrials) | |
randomlogg = logg + erlogg * np.random.randn(ntrials) | |
randomfeh = feh + erfeh * np.random.randn(ntrials) | |
# Parameters for the Torres calibration | |
a1 = 1.5689 | |
a2 = 1.3787 | |
a3 = 0.4243 |
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gs -o - -sDEVICE=inkcov input.pdf | grep -v "^ 0.00000 0.00000 0.00000" | grep "^ " | wc -l |
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# MIT Licensed | |
# Copyright (c) 2009-2010 Peter Shinners <pete@shinners.org> | |
# | |
# Permission is hereby granted, free of charge, to any person | |
# obtaining a copy of this software and associated documentation | |
# files (the "Software"), to deal in the Software without | |
# restriction, including without limitation the rights to use, | |
# copy, modify, merge, publish, distribute, sublicense, and/or sell | |
# copies of the Software, and to permit persons to whom the | |
# Software is furnished to do so, subject to the following |
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import numpy as np | |
from scipy.stats import binned_statistic | |
from astropy.convolution import convolve, Box1DKernel | |
def f8(time, flux): | |
""" | |
Calculate the F8 statistic, as in Bastien et al. | |
Both `time` and `flux` should be numpy arrays. | |
The 8-hour flicker (F8) is determined by performing a 16-point (8 hour) | |
boxcar smoothing of the light curve, subtracting it from the original light curve |
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from IPython import get_ipython | |
timer = lambda code: get_ipython().run_line_magic('timeit', '-o ' + code) | |
out = timer('pass') | |
out = timer('a = 0') |
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# matplotlib parameters for publication plots | |
import matplotlib.pyplot as plt | |
import matplotlib.ticker as ticker | |
from matplotlib import gridspec | |
import re | |
import os | |
this_folder = os.path.dirname(__file__) | |