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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] |
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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 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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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 |