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import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import LSQUnivariateSpline


def continuum(wave, flux, type='ratio', order=1, low_reject=2, high_reject=0,
              niter=10):
    
    m1 = np.ones_like(wave, dtype=np.bool) # use all points at first
    m1 &= flux!=0 # but remove those where flux = 0

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from numpy import sin
from scipy import optimize
sine = lambda t, p: p[0] * sin(1. / p[1] * t + p[2]) + p[3]
sinefit = lambda t, y, ye, p0, **kwargs: optimize.leastsq(lambda p, t, y, ye: (sine(t, p) - y)/ye, p0, args=(t, y, ye), **kwargs)[0]

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months = {
    'Jan': 1,
    'Feb': 2,
    'Mar': 3,
    'Apr': 4,
    'May': 5,
    'Jun': 6,
    'Jul': 7,
    'Aug': 8,
    'Sep': 9,

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import numpy as np

###############################################################################
# the following is mostly a copy of the scipy implementation of
# binned_statistic and binned_statistic_dd
# but allowing for a weights parameter
from scipy._lib.six import callable, xrange
from scipy._lib._numpy_compat import suppress_warnings

## careful here!

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from sympy import symbols, sin, nsolve

E = symbols('E')

def eccentric_anomaly(M, ecc, prec=15):
  return nsolve(E - e*sin(E) - M, M, prec=prec)


import mpmath as mp

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from sympy import *
from astropy.constants import G
import astropy.units as u

G = G.to(u.m**3 / (u.solMass * u.s**2))

f_rhs = lambda P, K, e: (P * K**3 * (1 - e**2)**(3/2)) / (2 * pi * G.value)
f_lhs = lambda m1, m2, i: (m2**3 * sin(i)) / (m1 + m2)**2

# the earth

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sh downloadRequestNNNNNNscript.sh -X "-P 8 -L 1"

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import numpy as np
try: # numba will provide a ~2x speedup
    from numba import jit
except ImportError: # but we can do without it
    jit = lambda fn: fn

@jit
def massTorres(teff, erteff, logg, erlogg, feh, erfeh, 
               ntrials=10000, corrected=True, add_intrinsic=True):
    """ 

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# https://www.aanda.org/67-author-information/frequent-abbreviations

name_abbrv = {
    'Astronomy and Astrophysics': 'A&A',
    'Astronomy & Astrophysics': 'A&A',
    'Monthly Notices of the Royal Astronomical Society': 'MNRAS',
    'The Astrophysical Journal': 'ApJ',
    'The Astronomical Journal': 'AJ',
    'Publications of the Astronomical Society of the Pacific': 'PASP',
    'Annual Review of Astronomy and Astrophysics': 'ARA&A' ,

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#!/usr/env/python
# -*- coding: utf-8 -*-

# Written by Pedro Figueira.
#  Original version can be found at https://pedrofigueira@bitbucket.org/pedrofigueira/bayesiancorrelation
# Modified by João Faria

"""Bayesian Correlation.

Usage: