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

"""
Print the acknowledgements sentence with all the project references!
See acknowledgements.py -h or run acknowledgements.py ? for help or simply use as 
acknowledgements.py Author1 Author2
"""

name = 'acknowledgements'

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def develop():
    import reimport, os
    mod = reimport.modified()
    try:
        mod.remove('__main__')
    except ValueError:
        pass
    reimport.reimport(*mod)
    print 'Done re-importing'

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

# we assume m_planet << m_star

def get_planet_mass(P, K, e, star_mass=1.0):
    """ 
    P in days, K in m/s, star_mass in solar masses 
    output is planet mass in Jupiter masses
    """

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import numpy as np
from scipy.optimize import bisect
from scipy.special import binom as binom_coeff
from scipy.integrate import quad
from functools import partial

def binom_function(N, n, p):
	c = binom_coeff(N, n)
	return c * p**n * (1.-p)**(N-n)

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ssh -N -f -L port:server2:22 user@server1

# mount the directory locally
sshfs -C -p port user@localhost:remote_directory local_directory(must exist)

# unmount
sudo umount local_directory

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def fun(teff=None, mass=None):
	if teff and mass:
		# gave two parameters
		pass  # goto 1
	elif teff:
		# don't have mass, have teff
		mass = f(teff)
	elif mass:
		# don't have teff, have mass
		# teff = f(mass)

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#!/usr/bin/env python
"""
Convert a PDF file to black&white and display it.
Run as: python bw.py input.pdf
"""

viewer = 'evince'

from contextlib import contextmanager
import tempfile

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import sys

from astroquery.eso import Eso
eso = Eso()

# should use the default username
# and the keyring stored password
authentication = eso.login()
if not authentication:
    raise RuntimeError('Something went wrong with authentication!')

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# file configfile.py
# create a custom INI file reader and read a file called 'test.ini'
import ConfigParser

class iniReader(ConfigParser.ConfigParser):

    def as_dict(self):
        d = dict(self._sections)
        for k in d:
            d[k] = dict(self._defaults, **d[k])

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from math import floor
from numpy import arange, exp, pi, flipud, append, conj, real
from numpy.fft import ifft
from numpy.random import randn


def powernoise(alpha, N, randpower=False, normalize=False):
	"""
	Generate samples of power law noise. The power spectrum 
	of the signal scales as f^(-alpha).