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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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# 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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gs -o - -sDEVICE=inkcov input.pdf | grep -v "^ 0.00000  0.00000  0.00000" | grep "^ " | wc -l

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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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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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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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# 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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# directories a and b
comm -12 <(ls a) <(ls b)

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import re
import requests
from itertools import product
import numpy as np

url = 'https://www.eso.org/observing/etc/bin/simu/espresso'


form_data = {
    'almanac_time_option': 'almanac_time_option_ut_time',

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from scipy import optimize
gauss = lambda x,p: p[0]*exp(-(x-p[1])**2/(2*p[2]**2)) + p[3]
gaussfit = lambda x,y,p0: optimize.leastsq(lambda p, x, y: gauss(x, p) - y, p0, args=(x, y))[0]