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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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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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from re import split | |
from glob import glob | |
natsort = lambda s: [int(t) if t.isdigit() else t.lower() for t in split(r'(\d+)', s)] | |
files = sorted(glob(path), key=natsort) |
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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 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] |
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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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# 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 | |
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): | |
""" |